\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from PIL import Image\\n\",\n \"from PIL.ImageFilter import GaussianBlur\\n\",\n \"im4 = Image.open('testim3.png') # RGB 3-channel image\\n\",\n \"im5 = im4.filter(GaussianBlur(radius=2))\\n\",\n \"im4 = np.asarray(im4.getdata()).reshape(im4.size[::-1]+(3,)).astype('uint8')\\n\",\n \"im5 = np.asarray(im5.getdata()).reshape(im5.size[::-1]+(3,)).astype('uint8')\\n\",\n \"print(im4.dtype, im5.dtype)\\n\",\n \"plt.imshow(im5)\\n\",\n \"im6 = im4 - im5\\n\",\n \"rmin, rmax = np.min(im6, axis=(0, 1)), np.max(im6, axis=(0, 1))\\n\",\n \"im6 = (im6 - rmin) / (rmax - rmin)\\n\",\n \"plt.figure()\\n\",\n \"plt.imshow(im6)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.1\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45478,"cells":{"repo_name":{"kind":"string","value":"samuelsinayoko/kaggle-housing-prices"},"path":{"kind":"string","value":"ols/data_exploration_numerical_features_functional.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"30888"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import itertools\\n\",\n \"import os\\n\",\n \"import sys\\n\",\n \"\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import scipy as sp\\n\",\n \"import sklearn as sk\\n\",\n \"import sklearn.preprocessing\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"import statsmodels.formula.api as smapi\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sys.path.insert(1, os.path.join(sys.path[0], '..')) # add parent directory to path\\n\",\n \"import samlib\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use the cleaner chaining method for transforming the data https://tomaugspurger.github.io/method-chaining.html\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Sale price distribution\\n\",\n \"First step is to look at the target sale price for the training data set, i.e. the column we're trying to predict. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"target = pd.read_csv('../data/train_target.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"target.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The sale price is in hte hundreds of thousands, so let's divide the price by 1000 to get more manageable numbers.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"target = target / 1000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sns.distplot(target);\\n\",\n \"plt.title('SalePrice')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import scipy as sp\\n\",\n \"sp.stats.skew(target)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sp.stats.skewtest(target)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The distribution is skewed (as demonstrated by the large z-score (and small pvalue) of teh skewtest). It is right skewed (the skew is positive). Skewed distribution are not ideal for linear models, which often assume a normal distribution. One way to correct for right-skewness is to take the log [1,2,3]\\n\",\n \"\\n\",\n \"- [1] http://fmwww.bc.edu/repec/bocode/t/transint.html \\n\",\n \"- [2] https://www.r-statistics.com/2013/05/log-transformations-for-skewed-and-wide-distributions-from-practical-data-science-with-r/\\n\",\n \"- [3] Alexandru Papiu's notebook https://www.kaggle.com/apapiu/house-prices-advanced-regression-techniques/regularized-linear-models/commentsnotebook \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We apply the function $x \\\\rightarrow \\\\log(1 + x)$ because it is always positive for $x \\\\geq 0$\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"logtarget = np.log1p(target)\\n\",\n \"print('skewness of logtarget = ', sp.stats.skew(logtarget)[0])\\n\",\n \"print('skewness test of logtarget = ', sp.stats.skewtest(logtarget))\\n\",\n \"sns.distplot(logtarget)\\n\",\n \"plt.title(r'log(1 + SalePrice)')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Merge the training and test datasets for data preparation\\n\",\n \"We're going to explore the training dataset and apply some transformations to it (fixing missing values, transforming columns etc). We'll need to apply the same transformations to the test dataset. To make that easy, let's put the training and test datasets into one dataframe. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def read():\\n\",\n \" \\\"\\\"\\\"Read training and test data and return a dataframe with ['Dataset','Id'] multi-index\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" raw_train = pd.read_csv('../data/train_prepared_light.csv')\\n\",\n \" raw_test = pd.read_csv('../data/test_prepared_light.csv')\\n\",\n \" df = pd.concat([raw_train, raw_test], keys=['train', 'test'])\\n\",\n \" df.index.names = 'Dataset', 'Id'\\n\",\n \" return df\\n\",\n \" \\n\",\n \"df = read()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df.tail()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Initialize pipeline with raw data. We can always get the data and apply all the transformations in the pipeline by calling `pp()`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pp = samlib.Pipeline(df.copy()) \\n\",\n \"assert pp == df # the pipeline output equals df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Select Numerical features\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The dataset is wide with 78 features. Create dataframe containing the numerical features only.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df.columns, len(df.columns)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've got 3 data types: int, float and object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df.dtypes.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Split the data between categorical and numerical features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"is_categorical = (df.dtypes == object)\\n\",\n \"is_numerical = ~is_categorical\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum = df.loc[:, is_numerical].copy()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum.columns, len(dfnum.columns)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We've got 36 numerical features. We can use the `describe` method to get some statistics:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"But that's a lot of numbers to digest. Better get started plotting! \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def select_numerical_features(df):\\n\",\n \" return df.loc[:, df.dtypes != object]\\n\",\n \"\\n\",\n \"pp.append(select_numerical_features)\\n\",\n \"# Check the pipline\\n\",\n \"pp == dfnum\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Deal with NaN values \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"cols_with_nulls = dfnum.columns[dfnum.isnull().sum() > 0]\\n\",\n \"cols_with_nulls\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum[cols_with_nulls].isnull().sum().sort_values(ascending=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Based on the description, the null values for the `MasVnrArea` should be 0 (no massonry veneer type)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# We may want to refine this in the future. Perhaps build a model to predict the missing GarageCars from the other features?\\n\",\n \"median_list = 'LotFrontage', 'BsmtFullBath','BsmtHalfBath', 'GarageCars', 'GarageArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', 'BsmtUnfSF'\\n\",\n \"zero_list = []\\n\",\n \"\\n\",\n \"def fillnans(dfnum):\\n\",\n \" return dfnum.pipe(samlib.fillna, 'median', median_list)\\\\\\n\",\n \" .pipe(samlib.fillna, lambda df: 0, zero_list)\\\\\\n\",\n \" .assign(GarageYrBlt=dfnum.GarageYrBlt.fillna(\\n\",\n \" dfnum.YearBuilt[dfnum.GarageYrBlt.isnull()])) # fill with year garage was built\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum = fillnans(dfnum)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Check that we got rid of the nulls\\n\",\n \"assert not samlib.has_nulls(dfnum)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pp.append(fillnans)\\n\",\n \"# Check the pipline\\n\",\n \"pp == dfnum\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Order columns in alphabetical order\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def order_columns(df):\\n\",\n \" return df.reindex_axis(df.columns.sort_values(), 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pp.append(order_columns)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pp().head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum = pp()\\n\",\n \"dfnum.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plot violinplots for each feature \\n\",\n \"The violin plots give us some idea of the distribution of data for each feature. We can look for things like skewness, non-normality, and the presence of outliers. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot(dfnum, ncols=6, nrows=6, figsize=(12, 4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Many of the features are higly skewed and some have very long tails. Some have discrete values (`YrSold`, `Fireplaces`).\\n\",\n \"\\n\",\n \"The features with very long and thin tails, such as `ScreenPorch`, are almost constant (blobs with long tail) as can be seen below\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, ax = plt.subplots(1,1, figsize=(4, 4))\\n\",\n \"sns.distplot(dfnum.ScreenPorch, ax=ax)\\n\",\n \"ax.set_title('Distribution of ScreenPorch')\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Drop nearly constant features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def test_nearly_constant(series):\\n\",\n \" counts = series.value_counts()\\n\",\n \" max_val_count = max(counts)\\n\",\n \" other_val_count = counts.drop(counts.argmax()).sum()\\n\",\n \" return other_val_count / max_val_count < 0.25\\n\",\n \"\\n\",\n \"is_nearly_constant = dfnum.apply(test_nearly_constant)\\n\",\n \"is_nearly_constant.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dropme = dfnum.columns[is_nearly_constant]\\n\",\n \"dropme\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def drop_constant_features(df):\\n\",\n \" return df.drop(df.columns[df.apply(test_nearly_constant)], axis=1)\\n\",\n \"\\n\",\n \"pp.append(drop_constant_features)\\n\",\n \"pp == dfnum.drop(dropme, axis=1) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum = dfnum.drop(dropme, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Log transform the other features if they have a high skewness\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Using a log transformation for some of the skewed features should help, as illustrated below. We use the raw data (not the standardized one) because we need positive values for the log function (we'll standardize the transformed variables later).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"fig, axes = plt.subplots(1,2, figsize=(8, 4))\\n\",\n \"sns.distplot(dfnum['LotArea'], ax=axes[0])\\n\",\n \"sns.distplot(np.log1p(dfnum['LotArea']), ax=axes[1])\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Use dataframe & series whenever possible for maximum flexibility (see below)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def skewtest(train, sort=True, ascending=True):\\n\",\n \" \\\"\\\"\\\"Return dataframe of zfactor and pvalue for skew test\\\"\\\"\\\"\\n\",\n \" test = sp.stats.skewtest(train)\\n\",\n \" zfactor = test[0]\\n\",\n \" pvalue = test[1]\\n\",\n \" df = pd.DataFrame(dict(zfactor=zfactor, pvalue=pvalue), index=train.columns)\\n\",\n \" if sort:\\n\",\n \" return df.sort_values(by='zfactor', ascending=ascending)\\n\",\n \" else:\\n\",\n \" return df\\n\",\n \"\\n\",\n \"skewtest(dfnum).head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def is_skewed(train, min_zfactor=10, plot=False):\\n\",\n \" \\\"\\\"\\\"Return series of booleans indicating whether a column is skewed or not.\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" sk = skewtest(train)\\n\",\n \" if plot:\\n\",\n \" plt.figure(1)\\n\",\n \" plt.title('Z-factor distribution from skewtest')\\n\",\n \" plt.xlabel('Z-factor')\\n\",\n \" sns.distplot(sk.zfactor)\\n\",\n \" plt.figure(2)\\n\",\n \" sk.zfactor.plot(kind='barh')\\n\",\n \" plt.title('Z-factor for skewtest')\\n\",\n \" return sk.zfactor > min_zfactor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"is_skewed(dfnum, min_zfactor=10, plot=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's apply a log1p transform to all these and plot the distributions again\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def transform_skewed_colums(dfnum):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" dfnum: dataframe to transform\\n\",\n \" dropme: columns to drop\\n\",\n \" is_skewed: iterable of length dfnum.columns indicating if a column is skewed\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" dfnum2 = dfnum.copy()\\n\",\n \" skewed_colz = is_skewed(dfnum)\\n\",\n \" dfnum2.loc[:, skewed_colz] = dfnum2.loc[:, skewed_colz].apply(np.log1p)\\n\",\n \" return dfnum2\\n\",\n \"\\n\",\n \"pp.append(transform_skewed_colums)\\n\",\n \"\\n\",\n \"# the transformed dataset has fewer columns and we only want those\\n\",\n \"dfnum2 = pp()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum2.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"is_skewed(dfnum2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"sorted(sp.stats.skewtest(dfnum2)[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"zfactors2 = sp.stats.skewtest(dfnum2)[0]\\n\",\n \"pd.Series(data=zfactors2, index=dfnum2.columns)[is_skewed(dfnum)].sort_values().plot(kind='barh')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now our originally skewed features look more symmetric. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Check that the distributions are less skewed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"skewed = is_skewed(dfnum)\\n\",\n \"skewed.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot(dfnum2.loc[:, skewed], nrows=3, ncols=6, figsize=(10,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot(dfnum2.loc[:, ~skewed], nrows=2, ncols=5, figsize=(10, 3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Save transformed numerical data\\n\",\n \"Use the storage magic to communicate between notebooks. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dfnum2.to_csv('transformed_dataset_dfnum2.csv', index=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Correlations\\n\",\n \"We're now in a good position to identify the key numerical features. Those should be hightly correlated with the sale price.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def correlation(train, target_t):\\n\",\n \" corr = pd.DataFrame(data=train.apply(lambda feature: sp.stats.pearsonr(feature, target_t['SalePrice'])), \\n\",\n \" columns=['pearsonr'])\\n\",\n \" corr = corr.assign(correlation=corr.applymap(lambda x: x[0]),\\n\",\n \" pvalue=corr.applymap(lambda x: x[1]))\\n\",\n \" corr = corr.drop('pearsonr', axis=1)\\n\",\n \" return corr.sort_values('pvalue', ascending=False)['correlation']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"correlation(dfnum2.loc['train', :], logtarget).plot(kind='barh')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Sort columns in dfnum2 by correlation.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def sort_columns_by_correlation(dfnum2, target_t=logtarget):\\n\",\n \" corr = correlation(dfnum2.loc['train',:], target_t)\\n\",\n \" return dfnum2.reindex_axis(corr[::-1].index, axis=1)\\n\",\n \"\\n\",\n \"#sort_columns_by_correlation(dfnum2)\\n\",\n \"pp.append(sort_columns_by_correlation)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pp().head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pp().to_csv('transformed_numerical_dataset.csv', index=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Scatter plots\\n\",\n \"The correlation can be high even if there is no linear relationship between a feature and the target, so we should check the scatter plots.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"train = pp().loc['train'].assign(target=logtarget)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot2(train, ncols=4, size=3, aspect=1.0, plotfunc=sns.regplot, y=\\\"target\\\", data=train)\\n\",\n \"#(train.iloc[:, :-1], ncols=4, nrows=7, plotfunc=scatter, figsize=(12,3))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Some features have some sort of bi-modal distribution with a lots of 0 values.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"cols_with_zeros = ['OpenPorchSF', 'MasVnrArea', 'TotalBsmtSF', 'WoodDeckSF', 'BsmtUnfSF', 'BsmtFinSF1', '2ndFlrSF']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"not_oktrain = train.loc[:, cols_with_zeros + [\\\"target\\\"]]\\n\",\n \"samlib.featureplot2(not_oktrain, ncols=4, size=3, aspect=1.0, \\n\",\n \" plotfunc=sns.regplot, y=\\\"target\\\", data=not_oktrain)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dealing with the zeros\\n\",\n \"Let's recompute the correlations after dropping the zeros.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"notok = not_oktrain[not_oktrain != 0].drop('target', axis=1)\\n\",\n \"#correlation(notok, logtarget)\\n\",\n \"notok.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def correlation2(train, target=logtarget['SalePrice'], ignorena=True):\\n\",\n \" \\n\",\n \" def corr(series):\\n\",\n \" if ignorena:\\n\",\n \" mask = ~series.isnull()\\n\",\n \" return sp.stats.pearsonr(series[mask], target[mask])\\n\",\n \" \\n\",\n \" df = pd.DataFrame(data=train.apply(corr), columns=['pearsonr'])\\n\",\n \" return df.assign(pearson=df.applymap(lambda x: x[0]), pvalue=df.applymap(lambda x: x[1])).drop('pearsonr', axis=1)\\n\",\n \"\\n\",\n \"notok_corrs = correlation2(notok).sort_values('pearson')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"corrs_with_zeros = correlation(not_oktrain, logtarget).reindex(notok_corrs.index)\\n\",\n \"corrs_without_zeros = notok_corrs['pearson']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"pd.DataFrame(dict(with_zeros=corrs_with_zeros, no_zeros=corrs_without_zeros)).plot(kind='barh')\\n\",\n \"plt.title('Effect of removing zeros on correlation')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"not_oktrain2 = not_oktrain.reindex_axis(notok_corrs.index[::-1], axis=1);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"def regplot_dropzeros(data=not_oktrain, drop_zeros=False, **kwargs):\\n\",\n \" col = data.columns[0]\\n\",\n \" if drop_zeros:\\n\",\n \" mask = data[col] != 0\\n\",\n \" xt = data[mask].squeeze()\\n\",\n \" yt = logtarget[mask].squeeze()\\n\",\n \" else:\\n\",\n \" xt = data.squeeze()\\n\",\n \" yt = logtarget.squeeze()\\n\",\n \" sns.regplot(xt, yt, **kwargs)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**With the zeros**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot(not_oktrain2, ncols=4, nrows=2, figsize=(12, 3), plotfunc=regplot_dropzeros, drop_zeros=False)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"** Without the zeros **\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot(not_oktrain2, ncols=4, nrows=2, figsize=(12, 3), plotfunc=regplot_dropzeros, drop_zeros=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Data imputation\\n\",\n \"Impute the zero values from the rest of the dataset for the \\\"not ok\\\" columns.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import statsmodels.api as sm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.imputation import mice\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df = pp()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Replace zeros by NaNs\\n\",\n \"df[df.loc[:, cols_with_zeros] == 0] = np.nan\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df = df.rename_axis({'1stFlrSF':'FrstFlrSF', '2ndFlrSF':'SndFlrSF'}, axis=1)\\n\",\n \"df.loc['train','SalePrice'] = logtarget.values\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.has_nulls(df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp = mice.MICEData(df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp.update_all()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp.data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"cols = ['OpenPorchSF', 'MasVnrArea', 'TotalBsmtSF', 'WoodDeckSF', 'BsmtUnfSF', 'BsmtFinSF1', 'SndFlrSF','SalePrice']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imputed_notok = imp.data.loc[:, cols]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imputed_notok.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"samlib.featureplot2(imputed_notok, ncols=4, size=3, aspect=1.0, \\n\",\n \" plotfunc=sns.regplot, y=\\\"SalePrice\\\", data=imputed_notok)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp.data.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp.data.index=df.index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp.data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"imp.data.reindex_like(df).to_csv('transformed_numerical_dataset_imputed.csv', index=True)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45479,"cells":{"repo_name":{"kind":"string","value":"dividiti/gemmbench"},"path":{"kind":"string","value":"script/SGEMM_NT/explore-n-lws.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"7370"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Import packages\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import ck.kernel as ck\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib as matplotlib\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import json\\n\",\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"print \\\"Collective Knowledge: v%s\\\" % ck.version({})['version_str']\\n\",\n \"print \\\"pandas: v%s\\\" % pd.__version__\\n\",\n \"print \\\"NumPy: v%s\\\" % np.version.version\\n\",\n \"print \\\"Matplotlib: v%s\\\" % matplotlib.__version__\\n\",\n \"print \\\"JSON: v%s\\\" % json.__version__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Find results\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"dataset = 'SGEMM_NT'\\n\",\n \"data_uoa = dataset + '-explore-n-lws'\\n\",\n \"module_uoa = 'experiment'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"r=ck.access({'action':'list_points', 'module_uoa':module_uoa, 'data_uoa':data_uoa})\\n\",\n \"if r['return']>0:\\n\",\n \" print (\\\"Error: %s\\\" % r['error'])\\n\",\n \" exit(1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Show results\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Table\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"data_list = []\\n\",\n \"index_list = []\\n\",\n \"\\n\",\n \"for point in r['points']:\\n\",\n \" with open(os.path.join(r['path'], 'ckp-%s.flat.json' % point)) as point_file:\\n\",\n \" point_data = json.load(point_file)\\n\",\n \" # Data. \\n\",\n \" Gflops_per_s = point_data.get(\\\"##characteristics#run#run_time_state#EXECUTION#Gflops/s#all\\\")\\n\",\n \" #match = point_data.get(\\\"##characteristics#run#run_time_state#RESULTS#match#all\\\")\\n\",\n \" data_list.append(Gflops_per_s) # + match)\\n\",\n \" # Index.\\n\",\n \" cl_file = point_data.get(\\\"##characteristics#run#run_time_state#METADATA#file#all_unique\\\")[0]\\n\",\n \" lws_j = point_data.get(\\\"##characteristics#run#run_time_state#EXECUTION#lws_j#all_unique\\\")[0]\\n\",\n \" lws_i = point_data.get(\\\"##characteristics#run#run_time_state#EXECUTION#lws_i#all_unique\\\")[0]\\n\",\n \" matrix_order = point_data.get(\\\"##characteristics#run#run_time_state#CMD_LINE_ARGS#matrix_order#all_unique\\\")[0]\\n\",\n \" index_list.append((cl_file, lws_j * lws_i, lws_j, lws_i, matrix_order))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"num_repetitions = 4\\n\",\n \"#ci = pd.MultiIndex.from_arrays([range(num_repetitions)*2, ['Gflops/s']*num_repetitions + ['Match?']*num_repetitions])\\n\",\n \"ci = pd.MultiIndex.from_arrays([range(num_repetitions)])\\n\",\n \"mi = pd.MultiIndex.from_tuples(names=['OpenCL program', 'LWS_jxi', 'LWS_j', 'LWS_i', 'Matrix order'], tuples=index_list)\\n\",\n \"df = pd.DataFrame(data=data_list, index=mi, columns=ci)\\n\",\n \"df.index = df.index.droplevel('OpenCL program') # not interested in as it's fixed here\\n\",\n \"df['mean'] = df[range(num_repetitions)].mean(axis=1)\\n\",\n \"df['std'] = df[range(num_repetitions)].std(axis=1)\\n\",\n \"df.sortlevel('LWS_jxi')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# import re\\n\",\n \"# # left paren, whitespace, number, whitespace, comma, whitespace, number, whitespace, right paren\\n\",\n \"# r = '\\\\(\\\\s*(?P\\\\d+)\\\\s*,\\\\s*(?P\\\\d+)\\\\s*\\\\)'\\n\",\n \"# f = df.index.to_series().values\\n\",\n \"# [ m.groups() for t in f for m in [re.match(r, t[0])] ]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"ymax = np.int64(df['mean'].max() + df['std'].max())\\n\",\n \"plot = df['mean'] \\\\\\n\",\n \" .unstack(level='Matrix order') \\\\\\n\",\n \" .plot(yerr=df['std'].unstack(level='Matrix order'),\\n\",\n \" title='Gflops/s vs Local work size',\\n\",\n \" kind='bar', figsize=(12,10), colormap=matplotlib.cm.autumn,\\n\",\n \" ylim=(0, ymax), yticks=range(ymax))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# plot = mean \\\\\\n\",\n \"# .unstack(level='Local work size') \\\\\\n\",\n \"# .plot(yerr=std.unstack(level='Local work size'),\\n\",\n \"# title='Gflops/s vs Matrix order',\\n\",\n \"# kind='bar', figsize=(12,8), colormap=matplotlib.cm.autumn,\\n\",\n \"# ylim=(0, ymax), yticks=range(ymax))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Dump results for paper\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Table\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"num_repetitions = 4\\n\",\n \"mi_tex = pd.MultiIndex.from_tuples(tuples=index_list)\\n\",\n \"df_tex = pd.DataFrame(data=data_list, index=mi_tex)\\n\",\n \"df_tex.index = df_tex.index.droplevel(0) # not interested in as it's fixed here\\n\",\n \"df_tex = df_tex.sortlevel(0) # 'LWS_jxi'\\n\",\n \"df_tex['mean'] = df_tex[range(num_repetitions)].mean(axis=1)\\n\",\n \"df_tex['std'] = df_tex[range(num_repetitions)].std(axis=1)\\n\",\n \"df_tex = df_tex.loc[[16,32,64]]\\n\",\n \"df_tex\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"with open('%s_tmp.tex' % data_uoa, 'w') as tex_file:\\n\",\n \" tex_file.write(df_tex.to_latex())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Plot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"plot.get_figure().savefig('%s_tmp.pdf' % data_uoa)\\n\",\n \"plot.get_figure().savefig('%s_tmp.png' % data_uoa)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.11\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"bsd-3-clause"}}},{"rowIdx":45480,"cells":{"repo_name":{"kind":"string","value":"poppy-project/community-notebooks"},"path":{"kind":"string","value":"tutorials-education/Documentation Snap! & Poppy/Decouvrir Snap! pour Poppy - Ressources.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"2730"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"![\\\"Logo](\\\"../Images/Poppy_Bandeau_Snap.png\\\")
\\n\",\n \"# Découvrir Snap! en vidéos \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"### Voir sur YouTube : \\n\",\n \"\\n\",\n \"* [(YouTube) Vidéo 1 : Découverte de Snap!](https://www.youtube.com/watch?v=MbyYEPeo9mw&feature=youtu.be)\\n\",\n \"* [(YouTube) Vidéo 2 : Snap! pour _Poppy_ ](https://www.youtube.com/watch?v=-SGL4hd_sBo)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Vidéo 1 : Découverte de Snap!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from IPython.display import HTML\\n\",\n \"HTML(\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Vidéo 2 : Snap! pour _Poppy_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from IPython.display import HTML\\n\",\n \"HTML(\\\"\\\"\\\"\\n\",\n \"\\n\",\n \"\\\"\\\"\\\")\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"lgpl-3.0"}}},{"rowIdx":45481,"cells":{"repo_name":{"kind":"string","value":"BinRoot/TensorFlow-Book"},"path":{"kind":"string","value":"ch02_basics/Concept09_queue.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"10062"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Ch `02`: Concept `09`\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Using Queues\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you have a lot of training data, you probably don't want to load it all into memory at once. The QueueRunner in TensorFlow is a tool to efficiently employ a queue data-structure in a multi-threaded way.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We will be running multiple threads, so let's figure out the number of CPUs:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"import multiprocessing\\n\",\n \"NUM_THREADS = multiprocessing.cpu_count()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Generate some fake data to work with:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"xs = np.random.randn(100, 3)\\n\",\n \"ys = np.random.randint(0, 2, size=100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here's a couple concrete examples of our data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Input [ 1.46034759 0.71462742 0.73288402] ---> Output 0\\n\",\n \"Input [ 1.1537654 -0.09128405 0.08036941] ---> Output 1\\n\",\n \"Input [-0.61164559 -0.19188485 0.06064167] ---> Output 0\\n\",\n \"Input [ 0.1007337 0.34815357 0.24346031] ---> Output 0\\n\",\n \"Input [-1.25581117 1.44738085 1.15035257] ---> Output 0\\n\"\n ]\n }\n ],\n \"source\": [\n \"xs_and_ys = zip(xs, ys)\\n\",\n \"for _ in range(5):\\n\",\n \" x, y = next(xs_and_ys)\\n\",\n \" print('Input {} ---> Output {}'.format(x, y))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Define a queue:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"queue = tf.FIFOQueue(capacity=1000, dtypes=[tf.float32, tf.int32])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Set up the enqueue and dequeue ops:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"enqueue_op = queue.enqueue_many([xs, ys])\\n\",\n \"x_op, y_op = queue.dequeue()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Define a QueueRunner:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"qr = tf.train.QueueRunner(queue, [enqueue_op] * 4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now that all variables and ops have been defined, let's get started with a session:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"sess = tf.InteractiveSession()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Create threads for the QueueRunner:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"coord = tf.train.Coordinator()\\n\",\n \"enqueue_threads = qr.create_threads(sess, coord=coord, start=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Test out dequeueing:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 1.46034753 0.71462744 0.73288405] 0\\n\",\n \"[ 1.15376544 -0.09128405 0.08036941] 1\\n\",\n \"[-0.61164558 -0.19188486 0.06064167] 0\\n\",\n \"[ 0.1007337 0.34815356 0.24346031] 0\\n\",\n \"[-1.25581121 1.4473809 1.1503526 ] 0\\n\",\n \"[ 0.60369009 -0.87942719 -1.37121975] 1\\n\",\n \"[ 1.30641925 1.55316997 1.01789773] 0\\n\",\n \"[ 0.0575242 0.59463078 0.47600508] 1\\n\",\n \"[-1.22782397 -0.86792755 1.37459588] 1\\n\",\n \"[-0.27896652 0.51645088 1.36873603] 0\\n\",\n \"[-0.34542757 0.79360306 0.32000065] 0\\n\",\n \"[-0.46792462 -0.31817994 0.91739392] 0\\n\",\n \"[ 0.24787657 0.83848852 1.16125166] 0\\n\",\n \"[-0.46220389 -0.09412029 -0.9981451 ] 1\\n\",\n \"[ 0.06739734 -1.08405316 -0.3582162 ] 1\\n\",\n \"[-1.2644819 -0.27479929 1.15882337] 1\\n\",\n \"[-0.68015367 -0.10199564 1.4274267 ] 0\\n\",\n \"[-0.48884565 -0.39484504 0.1496018 ] 1\\n\",\n \"[ 1.48414564 -0.43943462 -0.12646018] 0\\n\",\n \"[ 0.49450573 0.42091215 -0.17693481] 0\\n\",\n \"[ 0.02265234 0.99832052 0.26808155] 1\\n\",\n \"[-0.94086462 1.67000341 0.92434174] 1\\n\",\n \"[-0.50961769 -0.39044595 -0.5737586 ] 0\\n\",\n \"[-0.95702702 0.61196166 -0.86487901] 1\\n\",\n \"[-0.6125344 -0.30916786 -1.06602347] 1\\n\",\n \"[-1.91383719 0.26860073 0.50380921] 1\\n\",\n \"[-0.14638679 0.11614402 1.36613548] 1\\n\",\n \"[-0.56817967 1.4221288 0.99365205] 0\\n\",\n \"[-0.04597072 0.43875724 -0.4809106 ] 0\\n\",\n \"[-0.2000681 -0.2384561 0.06599616] 0\\n\",\n \"[ 0.5862993 0.85386461 0.82285357] 1\\n\",\n \"[ 1.64371336 -0.46838599 0.22755136] 0\\n\",\n \"[ 0.21683638 -0.96399426 1.78278649] 1\\n\",\n \"[ 0.03778305 2.49208736 0.07467758] 0\\n\",\n \"[-1.48958826 -0.11699235 0.98281074] 1\\n\",\n \"[-0.27623582 -0.41658697 -0.89554274] 0\\n\",\n \"[-1.64742625 1.83507264 -0.76936585] 0\\n\",\n \"[-1.5386405 0.14272654 0.17047048] 1\\n\",\n \"[ 0.63654041 1.75451732 -1.14198494] 0\\n\",\n \"[-0.57061732 0.11121389 1.39394116] 1\\n\",\n \"[ 1.94736981 -0.36588097 0.54801333] 1\\n\",\n \"[-0.56976408 -1.36990237 -0.9922803 ] 1\\n\",\n \"[-2.47653961 1.19603479 -0.3038739 ] 0\\n\",\n \"[-0.76740891 -0.49611184 0.47167206] 0\\n\",\n \"[ 1.62004089 0.13268068 0.28845155] 0\\n\",\n \"[-0.91749012 -0.30151108 -0.08271972] 0\\n\",\n \"[-0.21053326 -0.16114895 -0.52424961] 1\\n\",\n \"[ 0.19968066 0.2387522 2.0314014 ] 0\\n\",\n \"[-0.29072183 0.53720349 -0.38972732] 0\\n\",\n \"[-0.85891634 -0.26684314 -1.91741192] 1\\n\",\n \"[-2.07077003 1.97488022 -0.92741841] 0\\n\",\n \"[ 2.37270904 2.19385314 -0.29643178] 0\\n\",\n \"[-0.18054648 -0.1651988 1.70858753] 1\\n\",\n \"[-0.27851281 -0.13095042 0.30613536] 1\\n\",\n \"[-0.13653868 -0.14431253 1.3018136 ] 1\\n\",\n \"[-1.79938364 0.26698261 -0.3283855 ] 0\\n\",\n \"[-0.43491617 -0.8737886 -0.48871836] 1\\n\",\n \"[-0.27275884 0.08004636 -0.34334385] 0\\n\",\n \"[-0.06538768 -0.47280514 -1.82918119] 0\\n\",\n \"[ 1.72329473 0.6359638 1.53474641] 0\\n\",\n \"[ 0.88200653 0.87051851 0.17676826] 1\\n\",\n \"[-2.22127795 -0.39812142 0.69118947] 0\\n\",\n \"[-0.90146214 0.23153968 -1.07890677] 0\\n\",\n \"[-0.66513097 -0.74897975 -1.9886812 ] 0\\n\",\n \"[ 0.95217085 -0.1361241 -0.81558466] 1\\n\",\n \"[ 0.97319698 0.10349847 1.78010297] 0\\n\",\n \"[ 0.54321396 1.10134006 -1.03641176] 1\\n\",\n \"[ 0.46445891 0.56387979 0.10383373] 0\\n\",\n \"[ 0.22231635 -1.20880091 0.20125042] 1\\n\",\n \"[ 0.56338882 -0.76195502 -0.33035895] 0\\n\",\n \"[ 0.13885871 0.62347603 0.32560909] 0\\n\",\n \"[-0.63413048 0.19185983 1.65251637] 1\\n\",\n \"[ 0.81965917 -0.14427175 -0.9943186 ] 0\\n\",\n \"[ 1.98786604 -1.38118052 -0.34296793] 0\\n\",\n \"[-0.49028778 -0.30242845 0.81718981] 0\\n\",\n \"[ 0.48434621 -1.3200016 -0.32307461] 0\\n\",\n \"[-0.91041267 -0.34315997 0.71205115] 0\\n\",\n \"[ 0.61457998 -0.85814965 0.6939835 ] 0\\n\",\n \"[-0.40195578 -1.11846507 -0.19713871] 1\\n\",\n \"[-0.47889531 -0.75685191 1.68955612] 1\\n\",\n \"[ 1.51117146 -2.23529124 1.13895822] 0\\n\",\n \"[-0.00831293 -0.50950557 0.08648733] 1\\n\",\n \"[-0.47011089 1.04781067 -0.05893843] 1\\n\",\n \"[-0.34855339 -0.5695411 -0.12196264] 1\\n\",\n \"[-0.47251806 -0.49479187 0.27609721] 0\\n\",\n \"[-2.04546118 -0.16185458 1.42348552] 0\\n\",\n \"[-0.67136103 -0.16650072 0.3609505 ] 0\\n\",\n \"[ 1.22566068 1.18665588 -1.87292075] 0\\n\",\n \"[-0.80474126 -0.1114784 0.00531922] 1\\n\",\n \"[ 0.62691861 -3.26328206 -0.39003551] 0\\n\",\n \"[-0.77470082 -1.23692167 -1.55790484] 0\\n\",\n \"[-0.49005547 -0.19645052 -0.21566501] 1\\n\",\n \"[-0.44095206 -0.13273652 -0.59810853] 0\\n\",\n \"[-0.9750855 -0.46043435 0.06064714] 1\\n\",\n \"[-0.181191 -0.12452056 0.23064452] 1\\n\",\n \"[-0.34818363 -1.13179028 1.20628965] 0\\n\",\n \"[-1.58196092 -1.3506341 -2.05767131] 1\\n\",\n \"[-1.66225421 -0.43541616 1.55258 ] 0\\n\",\n \"[-0.12949325 -0.15456693 0.04389611] 0\\n\",\n \"[ 0.24592777 0.11407969 -0.31221709] 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"for _ in range(100):\\n\",\n \" if coord.should_stop():\\n\",\n \" break\\n\",\n \" x, y = sess.run([x_op, y_op])\\n\",\n \" print(x, y)\\n\",\n \"coord.request_stop()\\n\",\n \"coord.join(enqueue_threads)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45482,"cells":{"repo_name":{"kind":"string","value":"luca-heltai/ePICURE"},"path":{"kind":"string","value":"applications/collocation_laplace_using_bsplines.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"17666"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Populating the interactive namespace from numpy and matplotlib\\n\",\n \"The autoreload extension is already loaded. To reload it, use:\\n\",\n \" %reload_ext autoreload\\n\"\n ]\n }\n ],\n \"source\": [\n \"%pylab inline\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\\n\",\n \"\\n\",\n \"from utilities import *\\n\",\n \"from interfaces import *\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Linfty error: \"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1.73333694995e-33\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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i/braRogTy246gA80nfntyf8xkqjanLhctxtuOoZHR6xw9NXbGRzhvq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\\nlAog/w+yF0IYC+p+5gAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"p = 2 # Degree\\n\",\n \"n = 5 # Knots Boundaries\\n\",\n \"# knots = np.r_[p*[0], linspace(0,1,n), p*[1]] # Make an open knot vector\\n\",\n \"knots = np.r_[(p-1)*[0], linspace(0,1,n), (p-1)*[1]] # Make an uniform vector, such that end points are zero\\n\",\n \"vs = BsplineVectorSpace(p, knots)\\n\",\n \"\\n\",\n \"# Collocation points\\n\",\n \"x = linspace(0,1,vs.n_dofs+2)[1:-1]\\n\",\n \"\\n\",\n \"A = -interpolation_matrix(vs, x, 2) # Second derivatives\\n\",\n \"\\n\",\n \"# Laplace equation using Collocation approach...\\n\",\n \"\\n\",\n \"# A*u = -u''(x) = f(x) \\n\",\n \"f = lambda x: 1\\n\",\n \"exact = lambda x: x*(1-x)/2\\n\",\n \"\\n\",\n \"cu = squeeze(np.linalg.solve(A, f(x)))\\n\",\n \"\\n\",\n \"s = linspace(0,1,1000)\\n\",\n \"u = vs.element(cu)\\n\",\n \"\\n\",\n \"plot(s, u(s))\\n\",\n \"plot(s, exact(s))\\n\",\n \"\\n\",\n \"print 'Linfty error: ', max(abs(u(s)-exact(s))**2)\"\n ]\n }\n ],\n \"metadata\": {\n \"name\": \"\",\n \"signature\": \"sha256:12a660f55ad7c81c72f63842b4fc2a3983580d25fe6e5ee8c2f76ab92f18a0ae\"\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-2.0"}}},{"rowIdx":45483,"cells":{"repo_name":{"kind":"string","value":"cbentivoglio/neurolearn_clone"},"path":{"kind":"string","value":"scripts/Neurolearn_Test_Script.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"210484"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Test Neurolearn Functionality\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Couldn't import dot_parser, loading of dot files will not be possible.\\n\"\n ]\n }\n ],\n \"source\": [\n \"from pyneurovault import api\\n\",\n \"import os\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import time\\n\",\n \"import nibabel as nb\\n\",\n \"import numpy as np\\n\",\n \"import pickle\\n\",\n \"from nltools.analysis import Predict, apply_mask, Roc\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"outfolder = \\\"/Users/lukechang/Downloads/nv_tmp\\\"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Download Pain Images\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Extracting NeuroVault collections meta data...\\n\",\n \"Found 238 results.\\n\",\n \"Extracting NeuroVault images meta data...\\n\",\n \"Found 6302 results.\\n\",\n \"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=1000\\n\",\n \"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=2000\\n\",\n \"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=3000\\n\",\n \"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=4000\\n\",\n \"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=5000\\n\",\n \"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=6000\\n\",\n \"NeuroVault Object (nv) Includes \\n\",\n \"Elapsed: 172.62 seconds\\n\"\n ]\n }\n ],\n \"source\": [\n \"tic = time.time() #Start Timer\\n\",\n \"\\n\",\n \"# Pain Collection\\n\",\n \"collection = 504\\n\",\n \"\\n\",\n \"# Will extract all collections and images in one query to work from\\n\",\n \"nv = api.NeuroVault()\\n\",\n \"\\n\",\n \"# Download all images to file\\n\",\n \"standard = os.path.join(os.path.dirname(api.__file__),'data','MNI152_T1_2mm_brain.nii.gz')\\n\",\n \"nv.download_images(dest_dir = outfolder,target=standard, collection_ids=[collection],resample=False)\\n\",\n \"\\n\",\n \"# Create Variables\\n\",\n \"collection_data = nv.get_images_df().ix[nv.get_images_df().collection_id == collection,:].reset_index()\\n\",\n \"img_index = sorted((e,i) for i,e in enumerate(collection_data.file))\\n\",\n \"index = [x[1] for x in img_index]\\n\",\n \"img_file = [x[0] for x in img_index]\\n\",\n \"\\n\",\n \"dat = nb.funcs.concat_images([os.path.join(outfolder,'original',str(x) + '.nii.gz') for x in collection_data.image_id[index]])\\n\",\n \"holdout = [int(x.split('_')[-2]) for x in img_file]\\n\",\n \"heat_level = [x.split('_')[-1].split('.')[0] for x in img_file]\\n\",\n \"Y_dict = {'High':3,'Medium':2,'Low':1}\\n\",\n \"Y = np.array([Y_dict[x] for x in heat_level])\\n\",\n \"\\n\",\n \"# Pickle for later use\\n\",\n \"# Saving the objects:\\n\",\n \"with open(os.path.join(outfolder,'Pain_Data.pkl'), 'w') as f:\\n\",\n \" pickle.dump([dat,holdout,Y], f)\\n\",\n \"\\n\",\n \"print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Load Pickled Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Load Pickled File - Elapsed: 24.17 seconds\\n\"\n ]\n }\n ],\n \"source\": [\n \"tic = time.time() #Start Timer\\n\",\n \"\\n\",\n \"# Getting back the objects:\\n\",\n \"with open(os.path.join(outfolder,'Pain_Data.pkl')) as f:\\n\",\n \" dat, holdout, Y = pickle.load(f)\\n\",\n \"print 'Load Pickled File - Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Run Prediction\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false,\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"overall Root Mean Squared Error: 0.98\\n\",\n \"overall Correlation: 0.55\\n\",\n \"Total Elapsed: 7.30 seconds\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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IEL9LfkrySbgwj1Mr+XOqilQxmJt/pqwNY9BtJ6LXB9JjqAZ2haekWe\\nCnA2FdqdQxcaqTSRZO1MhcFuAvR8z+jGHkxHt3V4oxn7ZYQuy2Jjo5Z+0WOc38foDJ925JFH4oEH\\nHgAA/PrXv8Z73vOe7NwBBxyAjRs3YvPmzQCARx99FHPmzMHhhx+OlStXBtcccsgh+NWvfgUAWLly\\nJY477jjU1dXhF7/4Bcpl0ypr167F7NmzcdVVV2XrdJMnT8Zf/vKXXfPAGCcLq5/61Kdw8MEHB8eH\\nh4dxzTXX4Mtf/jI+8pGPAACuv/56rFq1Kstz/fXXY5999sG1116LmpoaTJ8+HS+++CI+//nPAwA2\\nbtyIH//4x1i7di1mzDAv55IlS/CrX/0Ky5cvx7XXXhvc94QTTsBDDz2Ez3/+8/jlL3+JadOmYcuW\\nLXjkkUdw6qmnYuXKlfjQhz4EAPjXf/1XLFq0KFvo3W+//bB582YsXLgwWFgdHBzEtddei2uuuSbz\\nj3rzzTfjkUceCepw44034u1vfzsAYOHChVi6dCkA4M1vfjP22GMPvOlNb8Jb3vIWrFmzBuVyGRMn\\nTsTb3vY2vO1tb8MDDzyAxsbGoMyEhISEhISEsYRtAJIFSsKuxTYAfx3pSiSMOyRtl5CQkLBrMGHC\\nhGzhFDDEvpqaGgwPD2PChAno73fOHUqlEpqamqLX1NbWeoRGyfu73/0OF154IRoaGrDXXnvhiCOO\\nwL33mtgMw8PDWLVqFQ466CAcd9xxu+BpDcbFwqqYtGu89NJL+POf/4x3v/vd2bG6ujoccsgh2d+/\\n+c1vcOihh3odeuSRR2a/16wxfmcOO+wwMLZs2ZKtoGvMnz8fp5xyCrZu3YqHH34YRx99NEqlEn7x\\ni1/g5JNPxkMPPYQf/vCHWflPP/00vve972XXb9u2DS+//DIKhYJX7nPPPYe//vWvOPzww7Nj9fX1\\nOPTQQ718NTU12aIqADQ3N+Ovf41PcWfNmoWPf/zjmD9/PiZNmoTjjjsOJ554IubPj4V42PnYunUr\\n1q5dm7lnyOVy2Lx5M84++2z85je/2SV1eCMxefJk/O53v8OECRNGuipRyN4c795qbgLz8vS+t+/S\\n2y9L0iIa4fb15KjzHjMSSHKnEdOh0jeNNof5ux28f2uOSTAgDhKgGU2+lzYZbJkZql3RO+nsyepg\\n9rY7PPaA5mzxHrc+J5I7hDizUK6rxMNg7oPej46NBzLY/xTAeJW7xTD94LP8TDuyd0A+1w/HZdLt\\n3YKy0hs6FAGHrgrLboKTNWEyyJXtCDkPXG+RC+2XksEyJnl1bVgu5fcMP51c77KI2uyW9mKvdZqL\\nw6FFAOCv2Lp1zbiTu+Ph+9xlaL+OHIIqlBs54kLtaT9pcv276FyMQagD9pSyEtm3b5O9q9F1zz72\\nGJzNhzkmPcxvj2bAzEAYxMX5oe6Ge2ukNGkV4c50opECsBm4IDKCShzuvwL49bjUdwbCLxrMAu01\\nwll9+H7EmTcqfbRGBSozZRlIW/fai5rBbEB7jq756le/BAC48MJb7FHpc5bKmNdK0WV5mz6TXcf8\\nf76qj0rX/s8H4WRpipUlOSccsjycH9WBsqsJYN5P8aW4TqVPQQIIbsPWrZvHrdw5yNwjFqCMOXSi\\nB8ybzL55dUC0BvW3jNBSEuBGy3r6LSOT06Ds0VQkl/2Ga52ktRsNiXQ/bRHAPlclX586x/xsQU7l\\n9UvVHH3f3+ruPMd7fd+zHCBUt2GbPdpT0VaMofu3F64fRdacpLCOk5E6T+e0RLB3VsknkJGvOZA/\\n9qWubQDY/7SuO1uYyJ17qnrwjTNXR0ruNm/e7BHxamtrM1eW/f393rnGxkb09fVFr9m2bRu2bduW\\nHZswYQL6+vqwfv16XH311XjggQfwwgsvYPXq1XjppZeyfPPmzcO0adNw3333BRbkGtuLLbSjQe3H\\nxcLq3nvvHT0ujaQbs67OvbJ77rkntm7dWrHs+nozbXjyySe9+wwPD2OvvfaKXnP00Udj69atePLJ\\nJ/Gzn/0MS5cuxebNm/GVr3wFq1evRrlcxtFHG1PRvfbaC+eccw5OO+00r4zh4WFMnDgRmzZtyo7t\\nuafpTha+2PPV1oaudasJ1J133onLLrsM9913Hx588EGcdNJJOP3003HTTTdVvGZnYu7cuR6N+9xz\\nz8V3v/tdb4E7IWFnI8ldwkggyV3CSCDJXcJIIMldwkggyV3CSCHJXsJIYCTk7rHHHsOHPvQh3HXX\\nXTjssMPw7LPPZud+//vfY+rUqWhubsbAwADmzJmDb3zjGxgeHo5es2bNGsyZMwe//OUvcfzxx+Ph\\nhx/GW97yFkyYMAH/5//8H0yYMAGPP/44Vq9ejQsuuABdXV247bbbMDAwgFdeeeUNe0aNcbGwWglv\\nfetbMXHiRDz22GM4/vjjAZhFyTVr1uCoo44CAMycORO33nortm3bli1IcmCqAw88EADw4osv4n3v\\ne192fNGiRZgxY0YWtYzxN3/zN3jf+96HO++8E2vXrsXcuXPxv//7vzjjjDNw/fXX4wMf+EC2SHrg\\ngQfi+eef91i399xzD+68807ceuutXrlTp07F3nvvjSeffBIHHHAAAKBcLuM3v/kN5s2bt8Ptwqvy\\n//mf/4nbbrsN3/rWt/COd7wD5513Hq644gpcfvnlu2xhleuzxx57YPLkyfif//mfXXLv8Yo/2lS4\\nKcwn1cyJWKxp7QesDiF71e3ntsKPNSp3MixIFz9w1yLJHeC8kYW9JvuJM5Sv1COgPQ65XddOhPGD\\nWVYG7S5rMdt17aWzstesS2/O7uBkJWQwOAxQqn10NdE57bFJ7su+5WI+6fSxmP8yzeM6DYAJqDj+\\n5O5vYNpBx41mtkDBpjE/oUPq77Lnx41z8B2kBxut/JYyWWhA6JEyFiVWM6Dl2krnmLPAYBnVWrIZ\\nGUM19y6TKuIqBqh6A7asfuZCaF+/zE90bNnxJ3dxtML1iDRrgc4JV8rJlGEMMrdYM16FJzEZ/pgI\\n+ONn6J2ZmTu+H+gSptu/+yB9qDlTXBcBs0nlPjL6Sv0HUUQvHrX3Mbq4UTFgfMnu8e7bj1B78pPI\\nP2D8yd3BAPaGz0QCgByxj6TtuB9F8/Vk1iP727/L+KnlaXbaEVDKFhXRjNDnpMzt1qMRF154jaqN\\nke5GdGR1KGYjPluO6LHYSZf2f8rPIblF2zlZduO8+L7e375fUu8ZADaVXQ34WZ6BYaZyjTZ5f+8P\\n4GUA40/uHN5r00GVAk47sV9539ttj231Z7A2k1PJIXqOmfqSR0pmvp/IhjMGjkn8OroC8LnX4dxK\\nWP4DmbWUK0XrV2ZMC9NZ7lrCLPvLffG0WB0ouk/uOgDn81pYt430PrMtDDCeZS8G0Sux0cscayFf\\n00Mqt/RhL5yMSV+yPZmUOpHym7sXUQrsUNjTqWZOS+k5hMxpKbUp++Wob+7+2ns5W47IdfrdGgIz\\nw0tejXzeqlwp44T7KhoJubv77rtx7LHH4tFHzXxiwYIFOOWUU/CmN70JN954I84991w8+OCDqK2t\\nxYoVK/Diiy9GrwGA8847DzfccAPq6+uxbt063HXXXQCA6dOnY/Xq1di2bRsWL16MUqmEFStW4Ac/\\n+AHOPPNM7LHHHkEA9zj01+lrw7heWAWAL3/5y7jkkkvwjne8A4ceeiiuuuoqvPDCC9n5z372s1i+\\nfDk++9nP4ktf+hKef/55XHrppaipqUFNTQ32339/nHzyyVi4cCGuueYaTJ06FTfddBOuu+46PPTQ\\nQxXvO3/+fHz+85/HwQcfjKamJjQ1NWHatGm49dZb8YMf/CDLd8kll+CEE07AQQcdhI985CPYsGED\\nzjrrLJxwwgkesxYAcrkcFi1ahEsuuQRtbW3Yb7/9cMUVV6Crq2uHKcyAoWOvX78ef/rTn9DS0oJr\\nr70WuVwOZ555JkqlEn7yk59g9uzZr6KVXx9+/vOfY9u2bWhtbcXLL7+Mn/zkJzv4kiS8Gjxq0yPJ\\nb/fARpOyeYJeNhKwIaxebOWwHjKolLIBlT/RfJcAH4Mx2QRcuJ9dhSR3sxCaLZuebcN6L5gE4Ez1\\nZiAM/SNT4zzcREOmJwITvEqgF3LZNF+b7W9CKI1OYt3CGZvVAr6ZGxuoAeYDUxszNtE5gSy98IKf\\nNnCUtAFhmBHW4cYqYfzJ3VoA/w02ozIYhJu4mvadZSXEGAr6H04xvSRXS8n8+R8GHxLwZFp/ivUh\\nvlgPxMOzSJqH/7nJNeWQSHq5heqg7cY4lRm2FN2fowPaPUCsvuNP7vaEP2Zxi2lDRLfg6G+9AL6W\\n0IumsYVWDqDGKCO21MGL+FKKXrIE5P0484tfBAD85DvfyY7KVXmbiqiwhEmJLpiVu67JLibETDCd\\n6wDtBAEoUXgQvk6cfsjHx3iTu0b44eakr2NahxcNStlmowT/yNu0Kcu5Ho/b1ASve6/a9ATcWOzG\\nX17uF5i//ZCQusYFhMtlgvpMvvUG/CDMYq67N9cwDz3Od9sAj6Kvn6C7yD3EAcE6hJqUF7VK6AZg\\nmEvjTe4ELXamX8zGvWaEWokXjJopHyDjSQ+m4j+svA0otw0y4rBTHTmndSLAsyeWeE3hcGQLHarR\\n6Z9c5p5EL9677Xf3dDyqd2Vl6PdLnnsTirYORft+yYIfhzGqU248+FtovOq8GKS1y9n8XJ9xi9M8\\nfup+5fdd+kDrnDq47xTpc6ZUrMm2TvUkqxVOU0pAUNGgHBpSz+u7g/7nwIJyzn5eZ2CXL3xMI5wB\\n8BHtJGE6YANpjZTcffazn/X+3rDBheK67777cN999233GsDENBJr7u3l7e3tzWIN7Th2zsJqaBM+\\nzvDFL34RS5cuxUUXXYRDDjkEAwMDOPHEE7Pz++yzD+6//3489dRTeNe73oWLL74YixYtwvDwcOYG\\n4MYbb8QHP/hBLFiwAO985zvx4IMP4u67744KgOCEE07A0NAQjjnmmOzYvHnzUFtbixNOOCE79v73\\nvx+33nor7rjjDrzzne/EWWedhTPOOAPXXXddlocXTZctW4aPfvSjOP3003HYYYdhr732wuGHH57V\\nVeePHTv33HOxcuVKzJw5E/vttx/uvfderFq1CjNnzsQxxxyDfffdN/MBuyswd+5czJo1CyeccAJy\\nuRyeeOKJcby7l7CrkOQuYSSQ5C5hJJDkLmEkkOQuYSSQ5C5hpJBkL2EkkORue+jfzr8dQw2A6t5a\\nxwCmTp2K559//g0p+7nnnkNfX58XEOrOO+/EggULMDAwEPVXOpK45557MGfOHLS0uF2fd7zjHTj9\\n9NNx8cUXv6H3njZtmrcTsTOwdetWvPWtb838gpx88sm44YYbcNBBB3nM4rGK0Ri8aviTyLYDB1aZ\\n9HF7bh1CB/Dc45UMKgDfHBwA1mc7kzOCXG0wQeGWAPjsR/0ya/59Ow+wEzC+5U7M8ngf3uzgttkg\\nSzNhArEAbq9W9npnwvFQNHtkI5ypnsiBMFjXgVmEwmhhXoNmTggKmGQZE/oMM3X0DncHplBN81Xu\\nJ9CBrvgOstPZCeCRrF7+uTLcvjkbaQJm3/wv2Lr1pnEod4fBtHuryt0HZMwrwwI5wp7hnAWbSv9u\\nQCh3mnnIHFHh6HRljLCYMbjmIAI+u0bqq01ipb/zcJxuzYDmsjaov3NUhn36AxpckYB5OKlCdrnM\\nh56Gaxl587S7ARO8qrrcvRvAf2KsIiZ3cwG8ifKwHtPcdA5sIr+Z7QSYXtWGqSItzMvTgV6knA1w\\nDBbpqZ5MJicizvUSCJvGCMIky/RhTaXfLj6nA2UwX0w7VWE27/qM9SatJ1y1IWjj2oPsOyzSXALw\\n4HbG2cMAOMdbYw87EjSN30ZtotzhMTvz9jeHcQJ8jrX0JHM4JdU9OpOuF2ngMFCAkW4dxofZpdqV\\njvR5AVMUU1mercsLVhpjrEpOaRkz+5Ty9qcayXyhIzN7zUHeSG223WpLHALQOU7nd0dC+Lp+YB/f\\nFQPgB4XSbm1YVkT7mQ+FKdbs+HC6QgcAZB4qB8ICHBvZWJGY+zRath27K9FsQLmey5c8efpbaity\\n809f/SoAWFcYEuRZ7iSaiu0Q9HslA24vWpQ7FK4PG5bvzrK3o9+zk+h37Jux0tjDrgC6rG4UVutk\\nOBnTYxXLjLY3KsB933Zlrs9EFgagdepxZDUlGmp18L0yiOm2Xto2jeVDtKXTjRx0VzsUYvdkeqYR\\nC5LKzhKz8NLqAAAgAElEQVT6sXXrn3dbudtZGB5+sur5mpods9QeXauCoxAvvPACjjnmGPzbv/0b\\nXnjhBTzyyCNYunQpTjnllFG3qAoAV1xxBT71qU/hv/7rv/CHP/wBl156KTo7O/H3f//3I121nYIf\\n/ehHeOKJJ/Dtb397pKuSMI6Q5C5hJJDkLmEkkOQuYSSQ5C5hJJDkLmGkkGQvYSSQ5C6Gvu382zGM\\nex+r28P73/9+fPOb38SSJUvQ2dmJt7zlLTj55JOxbNmyka5aFLfffjvOOecczJkzB1u2bMGsWbPw\\nwAMPYNq0aSNdtdeE4eGQUP25z30Ozz77LObNm4dVq1aNQK12LhoaGrB582bv2OzZs7Fu3boKV7wx\\nGF5Of9iN2IL9k3e35Ri78AbM3lmMSwOYPTPtMUn8ExVRgOYGef475ZSQ/HYBY3W8yt37Zs/GU+te\\ntH/lIL3WaP1yCcflCDiOiQ7XMwDHvJJ9VJHkbrhdXs2DGUS42y8sgDKAUrDnO2jr1hNwWWfS33JM\\n7vfbrJ4d6PECUnHZzOdiD8OAkWC5A7OtAcePANyuMgdb0CFERNJzAF4Zt3I3e/b7sW6dsEGcpjhM\\nMVWZY6VZDeyVd5065gKvOBZBk8rjAqf1w7FW5KxIez8cx1qHT2AODge7Aow8yTnNHeSyNPOsCY4V\\nY/M8Z+v2HHsN016t+V46AJdgA8zbuyNyp0MwjD1oudsDwIWzZ+M3dpxl7SJvrmbGDSEMiCbYBNcL\\nIm9tVt6k9SYiDBTF2kW0Q0/gD7odPveO0wGq/dO2vuuzepfImx3ggqxwCD53X1dv7VlT4HuQ1nw0\\n9mbc6uXk93N/AH/B9sfZllWrxjRjFYjL3eWzZ2ODkrsBsM9IZs8DZpyQsUaz6bkndZqjvL3qnA7B\\nw+dYV1QygeyD9r3O1hkxH8QG7HmzQaXlyDHz3B2WDd2RlQE4uxmWOzOOFK3EtRKTcLLNsT2561y1\\nyrPEGouIjbNfmT0bm6zcSYsNASh5cxvAZ6lqjp/06GS4rwDDUZVQZwNW/0yFm+EImAurdYtj9/Ug\\nZ/tNa5g85RfpY6a39lIu71QOIRP8wgtvoVL1LDLGfxTtrdvLxQdotPUWlICM19+I3f/botr37JRI\\nfs25ZIav5md2AihmpRi9IEFH12IDZK40y/aB2E/UIQwXy35Ypce7sn4VmxGgEWsBODsBkYqJ4O8V\\nM6YWbBqG3/LnqXIX52ua9a5mhvNbor+iWXdrewc3e2lCabeXu52DFLxql2HRokVYtGjRSFdjh7Df\\nfvvhnnvuGelq7DTsuWcoohs2bMDee+89ArXZ+ejs7MQee+wx0tVIUBiPctcAMWFoiVyRsCswHuXO\\nTKn3QHzBMWFXYPtyd8CurdBORkzu5sE3yU7Y9die3H18V1doJyMmdx8CMHocP41PbE/u9GLgWEOl\\n74rTRqAuCT525zle+p4dvdid5W7n4eWdUkpaWE1ISDC4y6Z5ZC7b2G8hYJhg2scc+y/UO42SJwfH\\ndJWdQ9lPa0RX4Iemna7LcKpJhm1aMxkJOxFvgmn/pmzvvYh6y+KTphZ+yFSE8YAlLSD0m8Q8Tc1Y\\n5R1dx04oeeeGACy7ajEA4AtfuMYrdTIcl0d7xWLejfZQWAZQtrvRxcArUw6hhDMXYqZ/Lpc36WAr\\nPZnwJl0EWYkY2xMsXrdiZw3qYxdGMtg/pMid9sTXDsejktZmJqH04gVXXAEA+NpiIzvMGtA+LnNZ\\nWkJXxsSROxds2oyQwaxjDfM5Zoex5AHxeLaaHfYMlSFaOMak0UxrOTdEv4W/0U9/b8KOyV0djC9c\\nYGx7vXT4K8xGkn7LywiZVMwT4UjDgGtN4/PNZ7D3WK5Nj5WnXhSD6NhS9iZwlGDpRymvHk67xRir\\nvkyVAllz+Yv2PSuiO2PUyjOJv7oetKFNMcZ0dGLDyo15FJTU55IzD6wZztfj9rDQptfvYP7RjhYA\\nb4bj+rHfR8es1/2fh+tv6S2WSuklmXnpd57LHFB/N8BxwsoqHUT1t6EvcsxYmrAGAjjyexnhmMoM\\nQD0b1KN6Mxx/DOpcJ5W5yZZo5FjkbkeCiuwJYJb9vWYH8o8V7APgrfa3zE7eBaDVzkvcnMy0WQ+6\\n4UZd6TNtAwS4PpthrzN5BrE66GEZUVlzNKhzvMWqufB8TGRL5qUbqFzNPesDUMy4o5rRzeNzzHc6\\nYN4tbcHCWtE8RSnj1Lr5waBtz+2NtFOBMc+UroaYRaP+VmhAaE3k55V3X3Sj9MXk7A4fuPDdAIBf\\nWR+6TfA58YBvhSJoydjGjuWumdI869JRGJgNq/38C9j7uHtS1m2aox37mtF6l+0dRILME05HCUN4\\nNaGXxjN2TiulhdWEhHGO/7XpgPUcX/eEc9Eec9WuBwyZogwh/KwXsCGpDGRsEsTGRYAbxOr5RlIJ\\nPVIlvC4stmk9zIAgQ3Yv3DCjJ7x1cN2gg5IV6Lo+lacX2kE7IJMD+ciX8vl+fQCWfeELABzLTKYk\\nHBBGLznwVEQvnfLkZ3UwneWPP72A1oxgcWuQg1jpSbvUrhhMFP0P2SYYPpPsIPOnx+3BlWML02E+\\nacMWMO1ak7kG4Wlk3v7WwSrY5E/ARveiKhYvFv8mInM5+1dXYDTLOksWmEpRkyt5Bm1UxtdJqfJW\\nNFM+bZLPWwq9KpVwKzHwxJuDv/A5rq/WvENwb72YHu+tUsFeFeowuvFeANsAbImc6wXwElyv8AKi\\n/BYdIwue7FhBLyn6R7VrEdMfXejGoN3M0UufxgxfNK0Ez+APfy2xrO34zYiVzufcAttgJq++64Ic\\nLYzxOA2wtOci9+EPPvld510vaAZwkf0ti6yDAP5MeZhRvBBjb3H1DPX3KzBvVhlhMEXTWjyqAf47\\nLm0seQo2rUdoWq+3mxoQ9j/3mTYj1Qas/JsXzP0FUlmYiG1L+v0v9+GwcPIs2mWK3lCYiXBbQtAJ\\n93xmUbnH5iljLdph9IFsEYl8/ZXqtydMa4nGG8sLq1+zqbTSW+BGAV4eleX3vE2dQ4AS+qxZvzNw\\nlxKmIhw9/bGthFlYk42BZnyPBRQSKWWnNbpklmStS/RMCwg1kllU1S56eJzUC6na/U0DQnmTcnir\\nVyDvZD9K2X2NNIlE7wn/rdoLu+fC6gz4AX1ii+osC9KqevulhEmoPMvPQST4q1+9FQC8kHYxx01S\\nth41eWlTz6z4u0dvQ/GirXaSImWb75/YYqmG5OGvYh0KkMO3yW9pPfc9tTeMzMmCn5TybJW7j08k\\nVwAJCQkJCa8TzHtKGEnsAWMkqqeauwtq4C9QCrR/0IRdi1q4fpHlhN3HPKwW8af538ixhF2HPREu\\n8Q2g8jbCWMSb1d+xbaWEXYtauEWORjomGnB3GnX/1qb8Tu1O79dYhYyyddg95U5j9IX5Hl+ohZG5\\n0N91go+0sJqhUCiM2eBMO46P2lSmZq/A7LMy5PNhM0JDqz9n6VvtJ4XsUsrkYgKqf05tL2ZQoVDY\\nTo6E0YjTbXpE5Jwwv2TPuRtsriZwprua6cP8FVk+kX1dZj9qowfBAFfCuieQTb7hFUDNP0YqnbDD\\nuIV+aw+XvXB79tJ3MinnXXXhnDBjNQzCwXxAzXLqzP7SnDpmHcSMtwCfPwrKL9B8FwFPanOWTfGM\\nNXM2DvLNUrMEI2BOak/gAIFNv7WTg8m2TDaE0ounfm1CfMmm346cGwuo5j/VX25gtoBuXQH3b/UN\\nAW26Z1AEUGfdXMSCX5UyHSdHtSEZ/5Ygao6n6gLzCVN2A1z/6qCEzXCSrw2vu4NgGI4HY1DCYQj5\\nPWz2W8lMODa9jpnkaszCWOFxSStoQ2cOphhjUmp2C7M1tUG1oA0lazrLd86pFCgqk3xX0kSEDkuk\\nHA6xIeAAZ1orSo27Ue2T/Xu3GmbPl083s4CYcwk5po0PnXE1H2U+kHmuxYsNb/Ne65aDPfho41s2\\nrxzLC0BfqnCcRwcdeCcHFwAnZI4ORa6QkurheifGVOa7M2JsZs0RYynX8scGpuH9tF51dy/Dt/4A\\nfA2f306deDagQxQNoZJlQBENKFrdO10FmSkj5JgLFgC4GWML2r6Fn0n3GDuL0NqqDtyz3epsM1xf\\naX3FrWn6cVC5G2AWoQa7Y9ESzboplkc74XFg1rZ2VDEA3ykHn2MXCJrTyLZS2jcZy63YPBgOZa8N\\nwcacX0mPs+lPg/qPPQhjNNbP2rEE9wi/6YDeetejMvNE5cr66BWcu0DntDaRbwZ2IaDz8PdRbH56\\n1oUXAgC++tWrAfD8vw6hswOeYchvqaG4PJiMkKkvX2J9kVq02JzFirrtaAA/R4JD8rGaoVwuY8OG\\n3ZFAz5CPK1b2munDpolalXVn6WY7qdhmj8grzp9gMezuLZyQkJCQkJCQkJCQkJCQkJCQMB6QfKyO\\nAyyKHJPF016ELBdZaOUAKnrXN4c6u7AqV/PejuZKCQtN74Uk7A6YDwC42/ZuwbL1ODCRLKYXbFr0\\n/MMYiDzFwrPwTplmI7Ize70PLOgGMPyc+V3zuD0om3Zppf9141SE3no40JTsyHbTMYHsfmqvkIMQ\\nFh0Q9myO7iSlG2bJzKPb8fTPf+7llrq1Uh1imz+VvKHG2BjskamSg/kN6Mj0YiygwjPWT+L6rFbi\\njXgqQvYG+0ustOGVQ+ifcfdBIx4FwExQ2c5jrWHAoaC0N1Pp32aEu+/aD6uB9J5m0hTQk/WoZqg0\\nI/SMJQiD8sjf3IMuGFDRnisG9WXeRU/QLubs97+/DMvOPhtA6GtO7l7y+LvasxyzYYfUOcefa7QM\\nGueVqwVONqXMos3L+UYvTqLfMc6m39I+x1Tefd9LqC9bWqIMk12H6tGywgFQYtx70U4xnugmygfK\\ny2+DTifDzeJ8Dd+CrihTVVKpldapxYyH1ErP4vOAGtGTPZ0wVeXvfiozDx/c9gWEkBnxNZFzowWV\\n2KqA6QHRNuwTWs41WhZ9KWh99tGsGZxlVGaXM+NV+4tmVrM2geQRVbOvBXzPnL2qmD2Lfud6PEsn\\nPXvgcVH7h5Xn5DdWWlGHAOuGk2YdBKYvK3O9NX9qznwKO8R4/AtsOlaYqzFuspYanmXob0EdYNQg\\nxh0V6NFJa86wV9mHv4B519yjXF++qw6rxT4x5fPAnw9UCyBp7tSiLJTciNqDnswCS+tw5vbGOPf+\\n+8x9wXNUxnwA/1Hh3FjAFPqtZ705hLYODM0rdnLCo8dTNs3blKMm+DP6MpymkDMu1oNjVc9S+iCP\\nUGLYziTuJxv4+g034KWXXlJPxZxXbQHFX2FSinbSxlpVM/35C8mfc8QY3ozdiSH9+pFcASQkJCQk\\nJCQkJCQkJCQkJCQkJCQkvEokVwDjANoXG4N3LzRrgKN8CszuWYvy18ZgJpiUPJZ3zBKqgXkVBQBu\\n120dXP+HxHjmnpqzsuPagNDD2zNwcD4BDXNhEvGeNAuRfezITmP+EfvD3uQvnc4nmY4zn1Adw7R1\\nKSwFCbTRbgWhF06jiCbqyFgnzFbRuqYdlTnIHM+9no4BP/95NyQ6ebdl7rA8af4JMxc0o55jqs+0\\nv6VG4rFoED7ni8tmhkeMHya/Z1oJXGcZmWuxju7wHnXHGQgtCdinorwh1byGXqbS0Q9mLrRm77xJ\\nO4hPolmBBTgugh4JW1HZI+0QmI0gvSclcURZzdVmudQRgQUN0NGDW8gzoh6JmQGo4yYzV0GYU302\\nlXPfOPvsIH5x6OsyxvngXNqrI7NszO+SbS95lmYUs3e9LcJPlRIqzyZGHjMR54MAfotpvcLRqgUi\\nRZvgeHOa99cHZhxqH37Mwde+brk/QPm4pgNw+kHbCMQ89bFXbKlDwbsuxmThq7WcdQWsar7CsGXE\\nH/D+8JmYla4aUH8zVzyPsYlqxoTcw9qbLnvXWx9okm6EDE7WW9oDYIwfr1l6zGbVYzKz7TS7qp1S\\nn4nn5L4Q+IZ2iPGmDBpRQsljYQFOEl3ZIZj5Ks8u1+s33OXvs5YnbG1QKR0L+LcKx3l2oXuT9UCM\\nqeo4XNr1XD/cW6qtbJiRPOAdYYnWXlv53dE6ydma+BxFPsfee6W3Xa07bFR5vsJ58Zxkxz75nhEp\\n920PjH7vCaxEWa9r79Dcws66Ra4Oub27B/ZHnBcMmJbS83hevdAjlvNdXyLf5AKeN/vjkKCXyuhC\\nm/3lvk3aVH4en7QfcEEn1dl9F5kr/+mfzkV1DaLHd8EQwtkjx3OQZ9W14XmeQRv5kdZ3SUEUKyEx\\nVscBnkZldcsDWcw0sZJpT9xZNOArERkiFkfufEXF+iaMFSzGt/GQ/a0/72JuH0TC2mhKoQ20/FAB\\nftk83ZVfMkSwS3htRA04Sf6LHUsa7ErrRuwsNTgOUWUWV2M7YXDQtW9HtlAli4X1qLwwxcc0eOob\\nM0A150p28lNHZllaRngRVIfz4M8s7QBfzoVLZPFpsg6sNhVOm75L5WlAEauziZ82+uQANLrUPvhu\\n8blWYxuHVzm3P4qBGRWPSwX7WwdPa4BrOVm2ZhcCIfRyZjvCpSP+W3+WcZ64Yxxj6hpbojImsnmV\\nvy7yW+7KBq5aL4fT9Tr4Eg/ENage8d072kiLwwIxixPpFT3fQPeu9CE/kvhG5Jj+jNa/AX/jRG97\\nsEG2bg+edUlflbwrAF+OtPKVu9RHzsWWOmPLxHqZjj9pt78REHduoaGPNkA2IMSVhGxixWanUtt6\\nqpEsYnBbcpBAvo7xbZtWM7sfKcSCbonMcE/pRWXuKQfOpUc5XnSv9OnMfaZd8MQWgvSmHm8jSkgX\\nca/iTGhdnQo2zaEUhHnjxWJtSm3KMeO+HhEE/EzaWQcvEks99QjO+Vu9vzjskF6iZsj30Gj8Brod\\nlRdMeElGL2s3wY03eqOFl/PF5VeP58hDO2HSS7QIasUhomSeF4bXKVMoSHN9Fy3WditzfZ4L6iVP\\nf0Ony+aTDTBH8hBdpMMD8VP0qAU0f5k4tuUvefz3UTZ+p6MYvAmMsWim/XH6HXMJJ2nY4wYDQLC1\\nwq4Bcnas6c/cF8lCaR7u+0H6Kba4GLpHlF/6na9HqI1Ee/YiRjRxy+Qyp5Ln84PXav3MTyjn+lVa\\niFzXTee6vWP8daU3g2Nj6tE2Hd/BrNLCakJCQkJCQkJCQkJCQkJCQkJCQkLCq8SWnVJKWlgd1XAm\\neNPVmQEU7Z4bsv0ahiO2hzs3mgkgu37sRLuSIWTC2Mbw/jbdCHzdbnoV7CaYxIZ6HM7xu2anlhGa\\n4TAfQHMXmVvjdgzNzl1dhDkj0Lw+wO22MSuxYH9LMItYuLeEEJvsJiibKDdYeXjedsYGGFawgfAC\\n81SKdhQgqEe4v8shIXSAF2aWmNo0Yn12BDDywKa6gM9skr1aYV50ZFqxHevtfXqtxmSWlDyfZgox\\nJ0bvnvNuu8i3cGS6vSt0G7BJmEYZca4T4HMExx7eRb8123IDwhBJzH2RFuzJGNOO4TTd6o+CPRI3\\ngtV8iJjZdcxAXIdWYLagX0Yxk8Q6hBwL06cl1GGDHZU1r4VlT4fMakAoFSEXzZlbulbgABqVzIPd\\n3TVjloNL6JQxGoO6TK5yjo2ptckou/zYCB+h+4VQspjpWs7YKiZ18ltGKIMxk8GYMwkNeYJ2yq9H\\n0l7EAwT5fMONdExSzawUBk7JY+H2ZjXQ1+uasHsf0Zcyz5C/mbGqwzGxFhyN2lCYWtszs9Q9xXOl\\n0Ey12rvLJqI6tIruPWbNCZjDqL8G5Lo8Qmc4PAJLmTo0DEOzWjkfc3kFzJfkczwyaENt1tfSLkOU\\nX8759npSs0GqSUzfjUZ503gcrp7aTU4fwnGHWWyaJS7Xx+w6erwxUX8FaMaXC0HZo1zLGKahbm3m\\nrsdcOJh79GRlFuwxI39TUAo4+wzNRm23dWlHaLmn7QPCcEiAH1BQpEneBW7xOP+eLVIq2XiNFZwW\\nOVbJQQnrcs3PBEK7C+Z66j4seUx8X/JLxFf+l385CwBw6aUh31zfj2VH8+1FK5kenazOOgaqdobm\\nWNItkaeQlPnV2kJB/+bauK/hKZbJKzVjLa155TGMb+ZqYqwmJCQkJCQkJCQkJCQkJCQkJCQkJLw6\\nbK0W32LHkRZWxwi0/xVmmoko8J6tnJNgGEU6V8nvCe+FjIUd2oQdx/Cp9ofdEKtZgWxzL7/Kpjea\\ndGon8JTNzr7+OAXCnbwCwp1y2U8bAvOvzQ6jyGQ7fA8zgM9OlN10YVT2ll1dnJwbLLPpxUiIYa1N\\nuR8zfqVtV+mzxwGszpz96zBPdQhDDziGpgRx0dws9jAofV7M2FyOm1eNQSfsJtnbZa6Ae64ZKgXW\\n2Cddk/GkpEZ8R1PSggVH4PGbb/bqwIwryS11khLLcIyMnqx9hNE7GLmSfcPJU2heFu9iC6636cLI\\nudEBeReZcSm/RU9MRMgEYN1R9PxnAezQf739LX54ee9f/EiGHlwFzNIKfY5WDr7GXDytrdoRSrdj\\nVPXYcz3B9QPZ7xbLNoh53GJJ4b/bUMyYkY5VFGNIat5IN1rIkoUR8/ZZiU892qDfIv0b8INnCORv\\nZs5I7zF3JMbcFWg/0F0qKFgRjVRDXYNBuLdAtByHNtGeYmNaVctfDlpe5S9TN5831EhyJPm0T9kS\\nzRRF12nGD/vJi4Xkk3yaXdxM59pVGvN5ucamsyLndjVYw9RXzBUy4aTHezAJrt9jvMn4R18LutBs\\nx1vJ0RMwm1gOYux9zdvmGZi2EcnbtIlq36TONSD0g8pBTysFd+vLZNB5xTZ/d3jB0/RXDNuV6Nbn\\nMd5vQw7Lp+ezumaM0eTjV6wGYoxHnjnIeR1jgxHzeq81iwR56kIvQlax3JF9A/t6y41N7YjLm5Sn\\n+Y6iLQbhekeOmS+VDvI1KfWMBbnUd+VaCuTJfHsWqZPv5/qKK76AxYv/RZUq4JHTXM8+zbVP+Jju\\nOMymqyPnRit4DhGLnaG/3ZjTW6bf+vqY9+jwjnmbOkueSy+9GoAJkMdl9qAcaCqRql6Es0P33TI9\\nWgtB5TMxLnzMwkj7xB5CyDmVv5/J/JzLF4/oT57pylUic/zVsT1Li3GBnbOumhZWExISEhISEhIS\\nEhISEhISEhISEsYRdtLqclpYHSOIcRu0d5tmdR5g/1gGZYTRbHknU3uUSxi7eC+AX8ofQpr7oU0P\\nWADgb83v2V83ad02AMChX0W2paXjlK+D2z8TudH+WIFw199c36bOyt89QaR35ljpnWTeiRPfcAVV\\nh4VwnL4Eh/9X5Zy0+TM2NaxlYdBIypqII78Cbje+y2OTAj4TQu7TmV0nLKlisH/LbHwpU9KJlEfE\\n2zEcj7DpPKp7waayX7sJThZFik1JN9/8UObXWvuC4qjWcl/2quregyGVqx9h3HcuXe/ra1br2IL2\\nU8ngMUiz4td6/lSll/enKwDWCmdeeT4A4KZzz81yuBaTnulWf7PfM+3rN4eQp8MsAu2NTfpU5Iyv\\nk/tuQshhYK6AqUsxY4gbGW1CKXuWz115JQBgqX3OmJdYadcpxPK69trLAQAXfeYz9h7O1121yLya\\nF5nH6MYt6m9+a3pVytwP7R1wEG6M2xA5p71CyvW9CDmBgqKnTTQPljnIlbyI1lE+3Wv8W9cu9Cvp\\neyH071OipykTq8pd7dCCUnCMWf3ylD2ZTLt3t8e26IBlm4tGbkLIjObxfjRCfNJxn8ftIHx/4M5S\\nQ/hRE+m35g7GxoDuLIe0jbz/7bbvpF07otczc1XL3Qz6u5Kvy3qE7CoeufWbxXn1rM75QxWNV1Zs\\n+un2716USE/G2Pj63WH/05rXOQUA0E0tpK/WTz3aEPtWi3kA1T5WYx6dpRekVTcg1Gn+2Kp1kfbI\\nmoOTyhh/sZJH5dZI2ZKnAWEUEDnXlJXZZWcgwuRrRdz2R2rEfGp+Ap+bKsx+wxCX2l+1eDF9zWjw\\nXc31JSu/JfRnViNy/zw9kbSUaPXRyFg9yab6zeK3XFsk8uxLv3c8f9M6P7S/4Cu5nfXXgvPe7Xyy\\nOr66tvplr+SaG+rbAsR43wAwiEFbfugluxdOcgRsy1XJ624/fCsDgO0fRGNr+wL206/55XxM4yA4\\nC8dxg8RYHV/QrxibLuhPvyGEn3zslFwPxOwGvjLNPi22jhW8l37LOuo/yIFs/jIdLqzMkyZ5j3VX\\nfSgw2boH0J9+PLAVbHrQ+94HAHj44fXZ2TY1vfDNaBCc04azbBhUr2eCtgL14aGsTr1w7SADR0f0\\n7uMLj1Q5p5eCujAL7gNLL/rx9MJffi9RWbIsFgsZpBdRexFOF/hvqddElfqfUiI5dmF18lzgPfZQ\\nvb3it/bv54bhlk60JPUCdvKspfa3lEvqKUun3QB6ouEEAfMxEDNJB0wbSptqA6ixiWqyxiFS5Km7\\n7Eeuv5SiJUjgPurOPddsDMnmYR0kQABQ2W1/GeHHmeTNI3R9IXIyBDdZF1nbn/7WhmNsGimfR7Fl\\nTMkny8zm864rew7g3HOvs7/CUJaCUiA7g/jMZy6yv/3gISzX8gZLLTYi3mKjGRuqnNNvXTdC0182\\nXf8t/QaAEg7KcrXasU2b/U9FuBDr5IdnZHq7UmRmAKG8sWmslik2aNX3GaJzggGVh7eB9MfcAPrs\\n4px2QDDJyk8Z/kIq4GSlxwvQoTV9HWQBscu2QZfV7o2RZQleThmN0Eb3MUj7GB0n7TFTpUCcJgEY\\nvaBHaJN3MHOKEm7FSdqCEi3kxiZUFSZZyEXqVBc5J/LWR3k71XW9dJ0eVUWmXUA0vdghMDXTBtPV\\ngr5IXhdISS/GdKANQ1b2tLuayZRba/CRxElVzukWGETljSR2NyP6Tm9DMty3aBlOT+ne4iW12Pa6\\n1ES/1Tze6wVVQR3CjX6pKc+jZBFTAhl1ZwEvYzKlv0H0bKyExsyMXJOTmim/5Cl574leVHb6uWiP\\ntVv5k5yxIIzieuLbkXMjBR2GTsBbiAL5ezJCFwg8Rutlx9iGpbSTC6rI+kjA7pZ8BxAlGvMksFqd\\n7R/5PCIAACAASURBVDt2T1BPJZjrZH7PDsq0rBYykkedmi8UPZckUj/W3L3qmICXowWhIb+enfLC\\nqibTcWl6fjfa53tvCHZSBLm0sJqQkJCQkJCQkJCQkJCQkJCQkJAwfpBcAYxefNymsl+7Dr6T9NeC\\nmOFMpSAKdQh3JkB/t6tzMe6Z3pHtxaujhc+36SMAShkTSbMihiCssITXDzFJk9ZtB+1ISSc/bdMP\\nPAO3Z2gNTGSbmraxYvtjzsTP7Mj1PCwmg/nsip6A+cAuyX1exQDc/h27vA/uby+rt8W0ln0OBOeP\\n6UeRwg60AMrMcbxgNTHgNNzOr7CzZsDte+ZtysxBYbNqrlgDipYNuEGZTPHOvqRFYhd++fIvAwC+\\nuXSpl6eMkJvALimcDpM72brNA/AxVb05Nv33GuDxabYQ0dZiwO4kSHN5+uGY0fLkXZlpYjPlrGQo\\nC4SSGjOA2hEu1OhFh8ed1+fW0V/SZ8LcytuUzVD1nnsZTmGJiRezFXQf6Lbsw2WXnQ0A+P5llwHg\\noBoDCJkAwltgfs+gOjdEvzXPoRk+e4LryMFYtFFvGSGrUHP4mH2rOWvd2X0asR6MBoQ8GjYLK3nB\\nYoDRPlZXe1u004c+uBbqjOTtyZ5deENO7qSthFvF7BrpWacRNdOfTf0ERo5aUEQxYugYQvN3OK/m\\nWQ2gMueYZVLgDGFFL+fsuCAlXvP/jEOZT37yS/AZ97psHf6H20LaQ1oxD8AExnoCj3ol5elJRiNi\\nTB958q5Ajprhm/5zGjNWZl3jzOUNhInXBii2m563+XWMyZaWKSmBA/JJD8Q4xHp07kblcYw1j+bK\\nd2dliSsKfbV53pjTKMC0rw6lxmOHbhn3LF1ZcDdj2yScyG74nGLGv8F97+1qVFsDkDYT3baWAtVN\\nsbIidhbMNJRRRGZBxmRdfxXyKKGdXQhYVrRBODPZtTZl2yYec/le3MdQedj6Q+C0ca9irHLNtIMW\\n+btEc2Y3JvoBkHq9fDFXLdXsS3uzOui6aTuEakHxRgJHo7L7mz6ErNSYBOk3mO1+BOxgRLtqcK1c\\nH6lF7C2JhQI3kBlOv+1fHtt7g/z1cLImtRf9MjHLXQ6sMHiuHxubNS9eh81imPu1oZi9Vfpq5ktr\\nexiuSWx8la+a0T3z24lIrgASEhISEhISEhISEhISEhISEhISEl4lkiuA0QKzg3UYSlGfKCGExSN7\\nCD/foatiTpz13hdzoDTLilPtSr6aX1VBNbbqLXA7nU9UyReH7ImwL7Ht3XF84DD6vToIpWP2/Kaj\\nmLmQFPCOlfT183aLcNqN9kD5h24bXqIV3WWSvzztiK2iZ4RfVgDv+WsJZA+sAnZbrqXY5O1CCxrs\\nTrJIgexozkAE9rZ15XB3mWVZe6TzdabwV+fZ9Ho6vjt7Y60ciqEU7B2XETLguBWlZUXzyfXt2X2K\\ntqyiFwJAytS7vQNYuvQG+9sPdFYE0JQFuZLc7mq3Ay7Sb8vO0+3YsadkCZSeq5PeiWdfjPI+9GTv\\npbQBt6/UpUB/+0zVRssWL3k+CWMBOcYitGZi8NZw3qbi81nakm0vpC2k5Vspn/alF7uPCIGMVGVc\\ndtlyAMh4Jo6x3QwnUZpxyDpO88Nykbow3yrmKV2Oa2Ys+wXcXv8z000zbesgzyKMmguWnQcAWH7x\\nxYG+lTsNIfQVN9ql8Cmb8vinR5xuSkM/vOwnLT67akMxY7LpYBGupbmttPxyK/qMvjoALZbdooOY\\n+U+j+ScFhIxBZubFZEJS7WkWdM5c968ZQ3WZTWWsnEnXaZYg3weRc5p9LXUsoGTrPmjngDzyVPI6\\n/ShQhR//xuCgCsfrYILGGWgpYb3VoM4xS0/rliGEfSR8xMHAT7Aes/yeYH0FGBnQXxEiW8wtk2P2\\nmeoazD8AGNRsxE56Fq33Yl8o3BamDkXya+w/TTvVPY8Q2s8062S5n0wG2A6lzl7dkT0BYKR8NMaX\\nkOm7vMH9CJmqzvrIzb87rAbryK7sRvge520a4+yZvrryypPxFRtIseh5OAecjPfDyQEHmBJovciz\\ndvZPDiqH7Z60RQvoWbQ32QEUrTyL/2h+L7TmLAU+id3754JP8XxTPydzBrW1Cr9vTTa37/u8AaGv\\nUcFIMqUZ1d6LjWBrVf2+O47wFPvc3Gp6LYVndtILBZu6gLV1VL7vh9oPXOf7V2arHmcFYNJesr0M\\nx3Sg+twzjjaUKFyzthDg7wcdaipk3/7zP38KAHD3kiXB7IBL1qOK3KUfTiKFlSpvMucfN0iM1YSE\\nhISEhISEhISEhISEhISEhISEV4nkY3WkIPtEK1/ldY0Vjh8N4Qc6hkgIvTM0FXHmFsBeW+I7DjpG\\nJ++daTZrDMLRurBKntePgzBeWauHRY9WipEacmJkH64fbs9rlVz97ybd52k4MkXBJJueM+kP3aGA\\ngVMG+83RHoDY/6D2esr+3MKoh+L9r9XuJBco559tkfvYooYGXV2kdNmLlPqyZ7nQy2IZ4V7wAqrv\\nTMoHAP+B3QeV9sABx3Pg/qzkk6wJrrW1TMZYduznTEsqX6eZfi5Gb6dirHIkaiFdB/FaCwipCFbO\\n0Q2g/y/2D2Zv+NkFvN/txl/ZW8/blNu3Wx3bSKUYVozzqFlEV8CmkRrsmC3E6EO+yjl26KyZLHId\\ns0OlLURpPY7KMbCZ16Y9p7m8X/vaxQCAKy64AIDvS81B23UMorI9SC/9Lqj7y7WAe06RdZY0OddL\\nf/er/JqRwwyw7eP7F1+c/dYb9My7kNIn2/eu2pwgjOW+6yDzkZgnMpEQ0SIusi/7DmR2FWDkx/dA\\n32jf1zzlllRGi40IvWd2Zf3IDBVfpluIqSQ1KmZ10ToSQd3MleI1baPK20dMbO3rnu+oWYHOf9wn\\nP3mRPaYZ9a0IvegJYv4sm9X52LnW4JyONz9aUN1qUOsG8WjZAPesk1WeZlS2wemE62P2YwpMovjj\\nDd4Z13bmjkX7v9ZzMV+Q7AdaSlEM0HIzkKuxx/SYxTZ1lVjRfG/m3Gp2n7Zm4zbU/qoZ+suIxwXN\\nlGyAPJdE/c5ZrdaP2Kx75KAjwjPH7RGbOl+geZvORMjgZBljiyLA9Sf7w2WGPHDuuVcDAetfWzbx\\nqKFZely2ZqWW4Y+dXNZA5BjonL4uZMpr/73sVd1ZC2ieHxAyuqWdJiJkqvI92Q+7vrPPdoz5xhyt\\nqObX3LxHove0X2bn5bzDzt777Bg7GSHnvGBTtsJ0bcOtpFnDvEqidQ0z4PU8M9RVXRkzlsHxYhjO\\nG6zIU5vlhPaQv2MHrc8YYt3WEcwG7luyJLu60vysTLXUT8eWnbJGVRdZgZK3fLePTJIYqyOB01DN\\n6XB1k/pKRkzhC+y7xzbQk7g+hJ98MSUXm/xJvh51pzbEP040jrKpW/JwiLliro5qFPpZNl2zw6WN\\nZcgHog75UAbQYpWyC25hFHFfZtzinO1z+IqC/d2t0vd0Ag12HU2U7eM2fQLh4MUGU27SxgE5gPhU\\ngCcbPJAxnJnjM0p150FvzqBf3164Z32GjgFmkWSSLUue2X3887sY20rQ0iuh2HaHBdZqb7ae+DbB\\nDwoB+G+4HNNmLA1wkqc/uLgsrRP5I10btgBFuzBQzBYknDlXKdMV6i14fB93axFTsSZ/ZgjOeYmW\\noKbMxKjbSg4v0oQ6nj9v9EjAbW4qI+86t2CbvU9P9gxSpnOEP7ZQbRSQRQL+bNXtxh9eepRrRzgL\\n4oUmZ1rM1zeSzCzLFlQbvTymh9lMVuoiV1YyzWJj8D6VxkykZSMjtqAiKKPNjs8NNpUSi95CWWxR\\nGeCFYHl2aZnPLVmCf/7nq736yeZuE4IwcFU/8lZXOfdGY332IRxikh039ZK0afOp9BuIbzGbfpEP\\nmna4pQodAGUynLTJ5+Qm2+ZdnksJX27kL3+JUlqb50d6sYIXrMydZZ7An5VO84afpOECnsw+eHOs\\nVaWijzbBH/sZg4jPWuX+eg4g93VuMbS8seOC0YDqix6V5vutcM+uFwF64dpI9EjBpp2QgavFbkPH\\nnM8I9PbMAGJfKixbeuuBvwS0OxRZvG8A+uWcSG83XVdp2WUI4UIE0zxin/9yDva8DufDgXF5o4vr\\n7VwUhfOZAtyz93h3izlh2EnEpteESpIFxBabODCkaHI9VjxB+fX2BY+lOk8TpM3c5o0eS2Nm07yI\\npN1SSH+2I9RNvPgaGmi7v/UYzBsR/V5JcrUJMqflXD/3IJz21zpxBsJAh6w/9XwEdK7buyoGvQ0w\\n0guuU6qc68iW4dqBYC7LWxS+rIm7sBx6gi9F5kbIORdslDdO9BinZYeP8ZxSb52EY5Bz0yNPPwAn\\nD3qDsQynhTeq+nKdtR6qQxiS2ejKEhrRqtxFgK7SX2+hW6KQiNQH18Y5W/bE7O9Q7u7Gbo7kYzUh\\nISEhISEhISEhISEhISEhISEh4VUiuQLYlVhoU971qMxYlXSTTY2JYb5C2TvWk3rvlw2m9O4FOyT2\\n+Y2mHLcoL1eaHZge75i+YzEIDiDl8L637JoIKcyY3OkdZ94B1MZ6MYhx/EjyYt5oTEKvYptIazG3\\no5Cx2hpt6gI/aTlgRqdAeGLr4PMkABf8owvT4feRM4XM0f8OsjPXjMosRpZzbXbRnN1PjNoKlqX8\\nOJxkCE9CZNtxOFyeUmaukUdXwP3lt6KS6QfvomrW0Mdh3MaPZVTj/MgbzQwukT4d7IG5UDpIRB+V\\npY0SmYUYM4XWhp/8Nvgm0EWPd5BX9bN1eW4A2Gj7vyz5RTM/AidVIv28l1v2ngp0xulQ7Uqf2RQD\\nKnXQuts3ydHMiWruG0YzClXOscmyaKVDbcqM6RgTAPBHMp+h0oi10ZBTGs5o0ezUd2S6ox5x81EN\\nvb1dj5DtEjNx1RRqNkQUmPegDT2eYwTAadtWq5OZjabTEtrQqAz15Zxhq/qyVbJnc1jvGWePbhxa\\n8YwEZHBmeOySST8h6x5/fse8Ej2n4r+Fn/OMytOVyXE4DgrLqxElckehmSzNCEd4ZuMMRq/aPjj0\\njWMsX/2DH+CMMz5X4ZpN9Fuu71N/DyIcWwWdVMO8OufYwppH3BspaSTRXeG4eTLNduPAOzH2lvwt\\nulAkSMalbrTZeZ7mgMUcpmjtY+ReM5BZSiSn6GXRNt0I3Ot47GY9t2LmqjYLZ1ay1qusGzVrlt0I\\nQZ0TxKyQtKXHAOXTbeDmJdpRAnNvYy4BvmHT/xs590Zgx8LkyPPx/M1KTs6WMDhsz02Fs57Q7zMb\\nEhdUmW6O5KwrtdExs5OlP6q5hJB6FxDKm7YC4XM8Xmu5kWdz1+m30y+DjfF1HeVd0IFbeZSMMVf1\\njCTGGvePlKlGsTFY7DhnRc690eio6NoQ8N0jaOY0v5Mx/iTQhYbMhU63lSw/QFW1UU5/1fL3QCWd\\nwSxnU3ajnS/ENKQf/ZYtqPhcDmEALbZ+0pqE7xRzfWL+7rB1bos4X5JvYe1UILY+wyX7X1Xhm8g4\\nzaa3R85VQk1NDb73ve/h4IMPxpYtW/DpT38aHR0uQPT8+fNx6aWX4pVXXsFNN92EFStWVLzm7W9/\\nO2655RZs27YNa9euxaJFi7z73Hfffbjnnntw/fXX4/zzz8f73/9+AMCb3/xmtLW1ob19O99SO8kk\\npnbnFJOQkJCQkJCQkJCQkJCQkJCQkJAwXnHiiSeivr4eRx55JC644AIsX748O7fnnnviyiuvxLHH\\nHoujjjoKCxcuRGtrK0488UTstddewTVXXnklLrroIhx11FGoqanBhz/84aysr3zlK2hubsbwsNkw\\n+vrXv45jjjkGxxxzDP77v/8bp59++vYrO7idfzuIxFitistsGvrIczvt67IzMe9EBu0IWUfaD4xD\\nbB9Ge4LsRdyrFRDfu5XU+MiU3R8dRIbZVppJWgx2MNhvp3AYpDXWZPu+ExHukzA7VXboYz6TtPPw\\n42z6U+w+OMmmAyja3byi3RmebHelcoh5nHR8hQ7bVx1290x88g2CfR612mNup0j6T/bcujLfMZOh\\ngwiVMj8vzDLWTOQy1VTv0DYgfEME7DG419bF1Lsepewq2esU6emG43UUs33bPN1XMy2kvt0IfZmx\\nj1X9XE3095fs729jbIPZADowmuiqVvWb0Qr37kobCttlE5xUdcIH79SyXzS5TgfqYcai3t2P7ef7\\ngT2AR4CyZk4VbLoueAZh95XQkjGEYl7H3HsoEqhZiAyRH7fb3mFLGCA7gdA/mQ4WM9awquKZ9xLH\\nZZ3lXhQzH3DioZLZJzLCSH91Avit/W3kbpYtM4+QX615NMynC8HyxJ72pMSYzz/AZ3Fr5k9sZhbz\\ndWlw003/DAD4yplnBqG95NmkNGashs/b4+Xjc+Y5p8JHr82zPtCQsTBKowPHVzlXAAC0WgZKOWPA\\nxFhTopv6ITOZNmUNwtqSPQ3KuUrMNheYtJVK8/VuyZMH7TuyGgd1ICtjQAVlMTpFc1FY75p2uO66\\nbwIALjzrLADA4jPOoIAV2v8vj/faH5zAvSfiC9t54yzR2x/aVLUovVsIajsa0IhYkNkQ+s2cjLA/\\npA3XwY0nj9u7GH+q7MFRX81aR38D+FqlEo8/xgSN6TTtqX8Dwu8akY0Cwq8P1rg6uCnX1H8aLT9c\\nS2kDZqK3ZO+4Zi4OIGSPheO15kc2IZwhjSQqjVv8Nkb9I7/VPms2Ja9xBfbmze9BbTHUBzeH0+Ne\\nM52TvvLfiqLHu5QxXNq8CWFP8vui/WOy90i2duNzIdjaTrP5/Ku059VYnQSh1+4wfBCz1XUZ4Tun\\n32u2Uag8VxkZTLL9zLZ/xYDFyuxSPQvhkVSzWgcho2oxiCTDcR/YGggw77k/Vsn4rWsFmHEIkPUQ\\nvywO36u/UpD5Wq1DqE/4OeNa2PSmH1bQ14daptsp75CtQbNNnaWAvHt6JOZVHX4u+VvPlSt5TX+t\\nOPLII/HAAw8AAH7961/jPe95T3bugAMOwMaNG7F582YAwKOPPoo5c+bg8MMPx8qVK4NrDjnkEPzq\\nV78CAKxcuRLHHXcc7r33Xnz0ox/F1q1b8cADD6CmpoZvj5NOOgnFYhEPP/zw9iubGKsJCQkJCQkJ\\nCQkJCQkJCQkJCQkJowETJkzIFk4BYOvWrdni54QJE9Df78gzpVIJTU1N0Wtqa2u9RVPJe+CBB+LU\\nU0/FkiVLgkVVALjgggtw+eWX71hly9v5t4NIjNUohI2md3Rb4XbdxJ+bi9QrzAXH8WS2oN4XcDsr\\nwjDUu6HtANba3+JRQ+LfOu82IZOE94L0zlydVwe9f1EX5HQltGCd3f3h/RPA7B/KnoyLCChMGN5h\\nkvt1Zlc2ZjtKElVyEl3HTEGu0+7g63Kh+rsdmpXytO31Vrj2LeIg+ytvU/blYtDj7YoK+8vspAkr\\nrpf4MW43lBkUmqlVT6lmzzCnVDNQKvkrYvC+WcGmZiewA00YsGwjYUPLnqDxuyNyptmWDQj9TDHv\\nQEoTpc57wpqtnVfPAjg2+2UYG9CemPhZ9O4p79jr6Kf81kvbyfu8gVIjscIAlTe5C21wbS0cHJHu\\nQYTy5vaXw7qzvOoo7pI/p/JJ3U29p1g2g+Z3dBNPS8BemyS/sGlabcq6V+S0hOn2Vzu0/62egPXI\\n0Gx+d91o4m5Vwnst2yoGMeDpg2vzu/G0/cUyJhAZk/f2t1nEd/GwKZpgBkJto2Nsd8PnrwPAzTd/\\nDQCwYMG1kRo3RH7HGC0+89793Q/3TvisHjPm+b4KzzzziwDMW6ttSHQtWhHGwu2nvzVfrBT1j+a/\\nUyW0odu+u5o3NPpQzQusaSXNAS+SL89wnjcAzT5iphPzC/kq0DnhUovUunkNM1ZFF/C9KnHjeG4m\\nkPq3ZmX0BAzXWFR3p2tEP/+LZaqydnLjrID1tCnnnHP+AQDwrW/9gOrCdXbzYIk63ARjZ2PqO6Cu\\n6ww8zIlWGEDocXnkUE3meHau59HdCHmlooHWQZiqbVZ3im7bH5XiVvt9FfYwQ3ORmCHm+0I/77yT\\nAQC3LV8e8PDka6QPq1HMxraYvOp5IrPXeHwGQl+twCQreXmVk5mZTjc6neq+eeSbgrlYoT0h7NPI\\nsdgbJG090vJXzU7KzbqAcHTrBF4Smy/Vo0zlFP+rL7mZUMhl47HC/G5U37Cu50s0k9JzNM6pdQvn\\n1xZH7im/+MUPAADu/M53ABjmstTlm9ddBwBYYnUbl6BnGC0okZ6TXNqKshmVx36OaKEtjfg9kGeR\\ndYQh6PeE36SdFKR8p2F6lXPyDnZ4DGd5Nm0Zw9+tmrWeQ7iqEYu1IG1YR3/H31CO7KC9LPeTv1I9\\n12pHODtw2roLxex+Wqf3Q/uGbsz0UWOkntzTWkol5blDaIdUzMZb7du1kOVvU2s4PGPVI9oQ3SVm\\na7yj2Lx5Mxob3ZyztrY2M9fv7+/3zjU2NqKvry96zbZt27Bt27bs2IQJE9DX14fTTz8dEydOxM9+\\n9jPk83kMDQ3hj3/8Ix566CEccMAB6Ovrwx//+Mcdq+xOeuHSwmqAhQgnt2yE7C9ClMhgSguhT3Fn\\nxcC5NnqGFYBTKf1wYZv0JwArCv6IkpL1ZEiu7/E+4mImD3oy5F639bbOYk7Hn71Sviw0FDOlxx8p\\n/iDagp6ASF/KnmYi3IAWo9vPt7//A2MLC2yqP5L4b2MSK84Xip55gF7WZvXnTzfPPvtkfP/7D9pj\\n/uSmjlpaZKoYXTqS8B8tlLtSMINeuE8zPZkaRLjML+V0Igw44NqjJ3NRoCcqvHiqJ2Fcvv6wrEfo\\nCIGfXZuosPsPHUjhYpsuw+hGtVAb+iNQ+iAP9+7qD59euOUD+fQ1Zoxt6Ag+hlyv9KCrouwPQX/C\\nyCSZtVLMaYhbwJBfPHXQDu1NW0xHMXirJAeb//VY2WezYL3cHAt1JC3+tP1ILiGHMFiL1GATHdMT\\nzU64DRJ26gIAiwBcg9GIeTblyZu0zwdt2gvgfvu7YNtJgjL4AVDMMpYshO8PN1XX6WSE00+9vAGE\\nBo5fWCC6eQrVVOuOmJuAmGGh1KY3yyMbqFKCq1sXBWcwZTaSqV2lBRWewld63jLC5ZSip0f1sqk8\\nUzu5mOEFPMDIsHZQMJKuUcKlHwfzFuptoQH0oCswJ+XgOv4yMmsVvajDLShbIS54p3yKTqWrtElt\\nzBWTNlV25v7uXJ7+1k4IeMFSytDBHLuDZS2WHzknGxiicWRhfhJKuPdb3wIAfPGLZiPgO9+5k0r0\\nJfcjXzDv121XXUVvkSze92S1108gdVqPSai80dcA4GaMDtRlY0VPJgk879efsJKnE5MslUIWVGfa\\ntB1hKBNeqhUJ0hLtRn3u0Vhv+yUsX27aspHKEkctrHd6rc4u2GM9mR5rR7gwwN8bepHOzcPaiFAA\\nuDeHFzi0jPA2Vsxw20EvOfP9e7PyAdd2sZkra/rXstjwWqGNihn+d5+8reJaIofsKV6yLfpS3qR1\\nDa77s4UFXriRMgo25bmzaQnRCe3KHNnvAykztimhHTAAob4y2rWFRrAH7IKq0wZuGV0WVDVFhJ9A\\n8vagJft2FeTs326DKSZRsZmppDyeCKQHpX/WocW+Q+wwSq6WWaFeUg5HqF2Daq4wnKsiWR9Yg45g\\n4TFGtNHbFtymsXUTkRHRIeb7YxKKFQO7xbQfbxfoBVW562SES6ZM15HgWjJ37fJC38mCqr+R3kZ/\\nhyGouF1iW+lyPu/dw0iLtJ/+3slD2mzQynQslKJeZQFCR26CkwDcXeGcxmOPPYYPfehDuOuuu3DY\\nYYfh2Wefzc79/ve/x9SpU9Hc3IyBgQHMmTMH3/jGNzA8PBy9Zs2aNZgzZw5++ctf4vjjj8fDDz+M\\nu+66KytvyZIl+NOf/oSHHnoIADBv3jzcf//92GHspJcqLawmJCQkJCQkJCQkJCQkJCQkJCQkvC7c\\nfffdOPbYY/Hoo48CABYsWIBTTjkFb3rTm3DjjTfi3HPPxYMPPoja2lqsWLECL774YvQaADjvvPNw\\nww03oL6+HuvWrfMWVWOYNm0afvrTVxGTZycZAtYAGN45RY11CCuuCY6xJXu0h1M+WcM3gTkaLWNy\\nBhzrSVgLa7Pd23kAjrK/Za9A2F5PYDpWAwCOsEeY5SV7EJpDB8SZqnw911b2zkxQoHaVk/d4tbNp\\nZhyyk2hXg0baJdSumHupflJf2dmaSOcKWf3EZO5QhNw0ZnVpjJWAVu9Vf/MuvsidNl0HKu/+98Ix\\nBg3zoYWctbsdNDbtMbtmmjcjYHNmPgaIWZ9mzQpa4Zh1eqexDk6m9E4uM2661TlmVWnzkGaEYRuY\\nTyR8D3mPJ1IezbZkfoyW4jw9UzUTwCuqnBtJtFQ804giSpnuk/aaR38z0wpwAarWAXjE/n7E3sXI\\nHYfq04buvXA9rFl6HIZHzGa4x0UK8jblHWgOaMb36yLH9MIi4t7VvcnMACnrvGWGjXzxxSYy5SQK\\n5Cd1YXaNtJSMA8L3eBST4LhImnPD7EzNEGzGjoUOun0H8rzxWFzl3Nk2zdvHGegHZGok+8oiVT1o\\nwUc/ejQAYP2//zsA13qT4WRMJFT+nggnD9psm8OvPKPO+QxlzZNiidY6mPlMWuILNu3EFMtqmKxy\\ncCAtzY7O0531OyXPtgmhBRPrb8kn0uUsAOoQmugxY0jPKESP5lEZ/7fKuTcK1bgTFwAATrJtLz22\\nCkAxG4t1G2yAe3bzFn/mMx83d7r22kDeJO2Gm2+tzlz2SJvFxo0Yk09QjYUrYAcQsfBsgC8Jmu2+\\nDtMrBLxgaK6RgJmDWv+aYEJ6/BRJdDKmAxO1wo3SUnN5P3vwXrhZcjWe4K5gTx9X5dzTmRm7zFzl\\nic2Yp2deptUmoYR32SMiU8wSrSQRfQhnTxvU393QLmkYMQdy4upsfSa5MrNj+z0yNAfArsDYIYuW\\nfeaIaYuh7izYkHx16dlbHd1Xvgj4+UVeOzwrKykhJsWmpBbLzJ6scsScN8TZsAZdVc69XpyE2xfM\\nvAAAIABJREFUUFvweKBd3vRkfT4ZrgXlCfM23R/hfF1a9Bm4EVneRJHmATTap5XZie7pXsgcjM+K\\n1igjdP3E73Vs9ghMIkvHmGG+1oCsJSXImQtwJnUzFpRcS83gW+MFApQ25HmCNqpmWwbNgzbfH21Y\\nk5Ugs2/+0tMzDBcqO8SumP3JiMmyp2dIgn6EFhyuvZsRWskI2DojZl1r2lCYvjx660BTPDPTtkf8\\nDPqLj2WAma2Av/ohsqX1bh/YWsC/H2vbuFum2HcnEA98yN/12tKNV4+MhhbdKuGjZiBubSclPoPK\\nWF3l3FjFcLU4qABqVu5YOYmxmpCQkJCQkJCQkJCQkJCQkJCQkDB+kHys7hy4/aoOOmq8vDn/F8zw\\nLNjfZm9CduqaEO4g+q5/tUdLFzRHjuhQTc0IPaDFfOzoPDGO38Ysdw684+ijH+GeDe8o6n1ayVsK\\n9iKlxWYiDA8U80PYle3cMGtT7566fWm9u1g5TMpowmGo7FuVfCBlLcXM0Epecx3fSRiDk+mM83nr\\n37dc4bdAc1Ld7m8p86tVzNjFXDfNMua9fr2PKJLBvjVFxgKnT/CZjaY2zl+veWc5+MbaQG5EtnJ0\\nzK+L78RewExp7WO1GndhtOAohCFszPPXoYhw97Od/pbnFE6KtMVTcKxfKYv/8kvcsVZyciR86pK9\\n/yTyh6q9OwIhe9/5WhoMfFtyndiDIeC3jsj3xRf/0LuyC2W0Kr9JdZHf4TMz619zX1qpFppv2Uu1\\n1qyvGNtoZHGvTXmfPeDt2cdmfkrYXnXosExVzT1n7rhoCPbmpfWWxgDdW1t8xMOjsI7TTAsegSv5\\nMu4NfEYKeJwXplvML5zmM8ub2Em1LWYsLZO7hSwT3H2lJs1w8xnd+vV0d+39bTLiTJKRAnvs5XQd\\npLV0j5l2Ej3HIyZgnkW4J0aSrr3W8IEmIZQtZsnLVZXZqBPh82j4HM+xNOspBuZUadsmPqd92Jk2\\nmaJ0GNeatYr2hgnKU6lGdShhMGOFITvq7jLglSH3nYqQy+/8vNUhHKNi8rcrGKs3wclZwaZi/dKN\\nhiyooYEwhAYptKiey7P9j+Rhn6l6ZiXg+TSPClxODkAp0Gksd9p6zbRvCa0oEd9Ml62/PfzayNsm\\nNeYROxYIyTyBHsNl1iZXM5NPwDo15DIzb1jmi34QlzyVr7VID+mKUvbE1bydhjPI1wvhR1dzAVgG\\n+7k0aLVz9Wasx6PZN5bkEvuPGXAjrEAk6Rk4vqEZcSZFOLlaJv14I7FAYaZ24fcmS5e2QjN/T0Q4\\nr4xxQ+WqkjcmmvsVsxZy7Hk3dvosenm2g1BCp9VppcBjZyy8HL8Lm7y0zX6vHIEwhKGU0ovwu197\\nCN5VkFgv2sfwIByTPW/T/5+9t42x7CrPRJ+OdOpHlU6qKFHUVXVLXWq5sdRBsdFAUCAawkw8UeYm\\nN+F+TC4ZKRmwARtHEGI+Ag7fOAwYp4GLMQTMR24ii0DGmhku+SAZAiEwxJaMo07ntspTqpaqSzm3\\nrJMqjqqk1FHE/bHWs9eznrXOAeNTDWnW86P2qb3X3nuttdfHu9b7vO+rozxbFfO9EdftlyVsoc+j\\nI6xicokPi6B2uubTfOnxCOXYqOxqXxtoqyzXFOje722NeXkY5fzos36A+5zVsZgjGXOu0QOYG/e8\\nrdB+F97ujPJDlFKselDeir8HXd9Qbi9Dq19DaD5WGxoaGhoaGhoaGhoaGhoaGhoaGhqeIGbEUfmB\\n31gl10EZS8lP1Fb8RR8zpwF8DQBwKmoCqU1dQclJveOOlwEA7rnn60h7//xy4f8+LnXPcK8iyuNz\\nRugBSqaOak9c450wAHm69CmU+5IJGrntQuddiwcYcjAvV6mxoT5wDqWWhPV7EcCFThPinCT1nJLr\\nep4hPnb4XjICvj+8DE7C81EyHlUrW4s5CoQW4Tp65T3kjEHVUyVeXPiu9DE0RL/zM0Rtm/pJmsRA\\nvozUkldi+0maRjJYFeoDphYlU3OuUA9Jro2jti55lVL/inzrQtSO/u933wQAeO1r6c1Re1aARuGm\\nf8/E9ap5+qyBUcW/X6ITv0x+ey8MPqKHGQfL+/g+0ndwj6EX4SPeQNgnh7Hu/UvnNe/1qzF91Utz\\n8NPFaMGuVFxE2auYZhmjonTKjZikNQ/Rjf3OlPPH4vivLZH3ez/c6u5SbiLfpN/Ftc8pV6vmIxKS\\nwltkyUO7WghjOa0HePwKgNfFPkXuy9rj4bgh55JvVfoAPehamPObjpD7s9JrC0jjgTMK1Mskn+m+\\nt4ZZSmcKb6Hkk2rulA0KKGO/Z0+GpHD7EEK9SE9qq5voo+S97MWyfDuOk3N+dF6aVJbxlBx/BaUf\\n8WPEmoYIOJkfd74E7x2JUXoDksxBz5ZkFam/MoLlTL1LGXRAqB22pWHXXpyJtVfkqeSGMg/6FmWy\\n1DzI+XdgWfR9uce+W9/zHvzu64JHZGetaM5rHtGBNPrrk1VuSP0yjNuDzKd2eNrLX/MaAMAfv/e9\\n3f3nuvvyPG3isuSGPbzGSf9/4vF/rlx7klistbfYfvZT39uLbB72SraRGzDZDkslQB/TtBe7P8t9\\neb5Ge0bxvHxOzVnUrEcfR5J0vxlnss2MQeie+WotiVD5oibjhuMwyqhHsa+xJasdzSQv/0DNDyVZ\\nqqOubflqYw1l+/4v8fgIhihtLramlGX2jNWHp1yr2Re5H+g5AGe7CObh+GiU34fYQJLreIfOd8He\\n8RkmWejXd5lH5ZR+x/L0caiHctWrlmo5m1mjqbv/U2V2su/8x3vvBQDcfvvb45laJHq1vAh3TuKd\\n5rFLLsQy6fju7Gtd6+fe3rX98Vu5n+or8tujU1xtOyX2KedXLqFkcuoKnul0zc8j52IfjfYwwM/d\\nFrzx33ffpwEg81ntvvW1xUySdkKskVBjC7LW45FtxsfWNZRyYmnhVHJMr5NnsuycL9//8Y/jJS95\\ngz1NWf2+k6QcZqZfRw61RHY2fcqpS7VqizlvaQ4ADLo1vb8PSHLeVyrX/pmiuQJ4cnhrPPpy4gip\\nGVM4SoYPO2CjZTdQh80+2P/ePffEX89EGlLCl+tH179rKMOSqLjsglWNlu6izQFKB94jCWDTNzN6\\nddbO9Au2CBxjUJ0WgXwi8s1eXWqyu9O06xJWkQLlMCiBikw+9IY715HMDxy/hqtjCPbE8Fb57V9L\\nh+dJX/kAuYgE5ENkuPba974UAPC+uFiZQ/oew84MKJl8DZEHNZoXgZDixrq8hc/k7w27toNtbBZm\\nX4eSalIoNl2GoZLGTaPTNEaXEGyDarTKGvvUa18LAGLos4z69Aj88u234957H7BrKnJ766+ZYHBD\\n83cq164mapt27mZCtwIJ3dSuiUR+n0/2RyICh++jrSEFy3Kjfl0auAiw041AS7GdUoDbhxuLAf/q\\nRS8CAPy3Bx4ohEEt7aSQL7lL+9Isl2X4enzzT/7kvwAAPPgXfyNP903inuTUe5FunOTi4SmMsoAh\\nikeqzu9TLq8ufjseXcDbwJfwBwDKQAeXkYJWbeOZ8RdLe4DNWIen4zfXb+kbExocURUs4UkBqsby\\n+/h/H6NojgaUW7NqJh7wH/7D/wIA+Pu//3v89R8HIXNYmE6lwIyrUaLQrSF3k6MbI75YYE6S+xwN\\n5UXUaopQ6cHHXRXq6QzppKXdRTniKihoX4UNVrX97I66/Az1QFdIeXAdLs3csHQBkxYpS0glXpdz\\nQPiO613KoFbYLkysD1E6fWKr1HmQ13T70rfbdBXg31g3wX27I5T3da97R9HKS2P9+pY7c+MyoBIB\\nSrUU5+pB9x3+U9xQ5UbDOpIk6O99FJu41EmPHP19WX/M+Pkp1z4bFLg4fBSDOJbtRpNfpQlMcp7R\\nQyn5qRrOQ87qZotLVGxhW/EYvkEYd9iikiJ8D+UGl77NDWVrZro1Q1dXzPicru/jtSOwhLtx/qpJ\\njb6R7O03vDWfM9aQPh+JGE/XCSlW6N9GkYdjxjcAbBcuNjgmbuFqYIgXTLkW+sQpbBbhBbm2W0H6\\nap7mCi7gsbjWZSkHsjYgAcPb6yLKzT1v27phvRPHxBRETV2faIAxIA+NNi6e7aMd28gOUoCk22+/\\nz3KzjxLsKQdgW/Rn69qndDOgak4fBZXeFL7R9UZmugGpp+3bkXfq+6YZfh8flgtJkivFJQyLOYDt\\n62lPRZfRp8TPu/534aiOHtSFExDqmRuqLDHLrW4gOMv7LKrX0giV1t2b3bVQqmehlLFUDnOnPjpD\\n1xThfh+/3fmPfQwA4qbqJMXSPkpagOaOtbVl92nOfHQ8AGegYUx/MdbCCnLpQ5+SO+uZkY389zua\\nK4CGhoaGhoaGhoaGhoaGhoaGhoaGhieI5grgu8dd8rtmhEydwHr3P2n/Z4q06rTYuRvUAAxwGR44\\ngJrEs5jkojs391dWKZAb47k5xCFqTMWkjygdnKcUrpFTtqnr4agPOULS3KhLBdhv3n+p40k8F8E8\\nHkh6Lmosr8CZqjRFOYfEWHW+Qu17fu+hAZyoYXLt/wFKg2ZlqLi5oN4fvu1rXvOheE55KM62mxwu\\nJjECR1V2H8Fv7CZtvewqtc1sZVtyzg0H9bfrMXeRa5UB5bKwfe+aeccKSn5PetuwYOuO4nMCW9XZ\\nqFoLzimbFmbme4XX2f/qrIN1qAy33D1Jfp+6MgcSW28HJXtD3cjzO+YuAUJNelAcDXLhxow6koS7\\nL3Va7FH3FLaWzfgdNx/4y3hmGWNjrmhe+HQeX/zKVwIAPvCBv5Q8EGoUlrN4/uIvTsT/ny/pXdur\\n7jC2rJxqyJS7C1BHIKzhnGfo9cjUV9O5/Bflt89WwNc7lkpgrWigkAvdvMp2RD5RMmPeiCxIHWtK\\n5mZ4ah/bXQ58rlI2ivPfeQzmT5NMWne7wCdM/+gnP9mlJDNtK6ZJATDnu2eRDXQY06jc4IwfHXmc\\ntZJQ9pFciphmnpuf63fclFE3JuYGY0AuKbiLkauEp4pJtk9Sj7uli0IZpM6YVymm5gQnD/bgYVdu\\nQMl9HXeBUPV7OCNF53K3UaoxZ53Dp2azzr3XeSlnB/aFb8Wc6Ijn1epSyTxKFx1ao85pViPjRQvu\\npNZTKo/q+24AcKkzWT5td+6j5KUfA35qyrWtePzyjyMEdwQei99/PV5SSc65evMoOXVqV5Pz9/JS\\nTiox37uLkrecc8RYd+zr/Gpp3CqD2CnUGQ5Z/xzz+R7l7erqAch5a+PsiZ5boGT5cfWgQYTdHP4s\\ngH/Li5ymSZU+jc4Y50eiD4BnxQoPjFXmxp9aG/+OY979r/G4i/SNcic629jD2div2DtYL+oeh3XG\\nNBvIrX+ANH8lG82SQa6jpEseOp74/aNsVek2LNor9rNr+1F20Nkud4fDUk3ife9315Y7ziUD7PXB\\nFjbJoH8OpQ3TYbQ8DW3dGdlJjluO6fy7rKEc7/h1tWUllvkqJmMw5dqTweRVtUqtziDFSaT+FRvB\\nQuzE5y7nLg8Ad1SSW3bSzdgChoXNJ5+jlrqs+WHmfiYvB7m2OxhOZL73UFoI+B4JUM66QLmav+WW\\nV8dfOjsSmjfWqAeAVksa2LUeSncafPMaXNrlCn8eF6o7A7yb1s2jwiLqGkVjrDY0NDQ0NDQ0NDQ0\\nNDQ0NDQ0NDQ0PEE0H6vfPWoOzxXUJrkz6RH2kPz/BOg+vnqTBJJuoY9hFwhn1Rybq86Ouni+7wil\\nByLlhLkGUTWDw85/Ep+QtuJdy0ie0DxKrYzy81wro7pFz4Nqjphuu2MJrsfjDaj7AwECQ4RejoK2\\n79nxv+vkrhoH5/fj8d9Xrl1d0Nem8+L0t5bXdbvn5P9JJVadmrIMeM05Jfplck7nqai9VU2p53yM\\n0ssLcx0CY7nuusYackZozWOQlslbnPoBDLm9hBzLwrpVvrA+Jf9PW3qN2wiE0rM8Xq+ant+ILfD3\\ncXVwezy62k3bnfNdtIdeRg5tIxyVtM04T0nropedyT0N5oxM9c06mYOuLDkyWhKDITHz3RPduNNM\\nz0fta82HFe//wAfo20k5z4RytpyvxjKsIbXnrXhkvfY6/5rEIBtpcxZPXzxbTQrGFBgXN6COn8DV\\ncyw/jaWdGNNf7wInhm8RGAUc53jUGSp8LQZT3O74DfoVc97bCEuihyecZ5/6yKjgxi2gZKYntr2z\\n+zS3zllJ33cOPjYmbui4C8zhLW6lKJ3KHvRBOMKRtavEcElMnFSmsg5Snpbl/5pXTSD3MevzmEom\\nx4jH/2HKxa/G4+fxHPPx3Iv1dAkbSOHSaqFwfI4Kktdu56Gt9L2mM4f7vHw0jlWhPfh8N83qgWNH\\n8nfdj3koR9vySbpOSIyx5XhcRT63AcN4x9ve9hp86i1vyZ7N1rMej9p6XKbWunC+tEozzsRfQTlP\\nK0uMzK9hZ7NE6fUq+YCrxb/xo3Db6Md0I7a73AdogNYP+7h75h1j8qxek568zpPn0kk15TIP290+\\nylatTw0y+vWxTWroq63Y5h8pekNNwkwheLlGWre3HkhKX5+ohYL62gYSx/RHnorEVKUjXxpHLCCn\\n9wJ47h+G41eRAj0NiqCTpXXGTJEF5wOyjO6wBfFbzXeMVWdWjpHGBP8K6mfR2ZJfhY8QCUuVa7W1\\ndRn8SecTfiWWgV9rDJ+DR9Fn9wVsyDM82JquyEu2/6pZL4278yO8/SMfAQC84+Uvt/wiu0ffktbn\\nl7DXjfH97I5lDKvWobzfbfVYA+q1NVmP8KrnDgD+tHJuFpjs0XWxcnVef3hjm09P9JU/n3MZwM03\\n/zQA4P77aQkVvtQm+pg3j6+6RmUdvv7jHwcAvOQl75YXu4VI6Mu7KPnSxCFK+5AdOXp6ZanWZvUA\\nXcuwFjR0lI/eaiPiYzHnvx7KcJq6anjU7kvWcH5XfafBrU2vTcxKiviB3FhtaGhoaGhoaGhoaGho\\naGhoaGho+MHErCgBP1Abq9NidNdYJ9RzJQbqsOOburehWhRUfQ69WSVt1qi7liIwut+oOZAdwoiK\\nyjnxfCYPK2dQsshcI10yEZT7WGP3+RPU1457BEs+XpdR8hOIyyj19izNQyDz5BnxjGpWnLmlniC/\\nf+LXkRGjX819XZKVu9WxuJh6u2O8nUPJuFJNr3vnJZZQajb5/qT1JVNV2y+1iM4n2ELSLaeosmxr\\nqrv01qLcbGclqg/QrXhcj8crSF/XoVrt67IrQxxKvMqdeE6HTWdv1dgUh3bUiI3Oo2B+anghgAcn\\nXJslyCRzNq22gZpnN2dKU8c6h5KRps9xDgzb5CHcU5J+8eS3x/u86nZdb1qytpMPtxp3Sse98P22\\nI1tyNb5fWyv7wGHnc0vzlVOSVjHCIPPd5GlcR51Gxdvf/nYAwJvffLelWZFn5Gxf9QXrY28f6Kwh\\n6mW/WozVTyF9ZWd7KsMxlCD5ONb+5vPDCiZHu9cxwRlRayg5Ke53ba+SpjaOlWXpxfbjtT6PUi44\\nE9vTZvasPE/9jidYztorSCOhM/DVo2Rue6CWIyM80skcNaakeyXTtlPrg4CyJ6fz5l4cj5+oXHuy\\n+KMp10I06J/Fhc4lJiWr5JXwi/ha9AucxhFlTLssthfTLmMnjg43ZFfCd9IREMi9+wHAAHsov5Yy\\nAd2yJNlNvfjFgW730CdSRF+i9O2anuI+/Pc6/7+rKGWG0Mre8pb3d63GGdPsQRoV3GfPBUyWw2pc\\nK43qvW5lINaQRoNh9yVdUjkm/BdnDsqr/3M8fo1pvgEfsXVk89lZWX6T6qyHMo4Be9wV+e0joXrn\\n27Vrea5cotbRZcmuBfSx2fUSJ4L2kPraUmR7frH7ysrDz1cOy9ie6K+T0HjxbA9qW0RJkLn+EaVa\\nk/DHrs7/V+SFsRAno/j+3MeTlP65ohZ13j4G/PiUa3+Ysxf72O5qVX14Avls6bnVUcfrXmdSQr03\\nL9k5H9nUxqusJbXS4dUteTNzcxY5tMc4YxVI8kY+U54SidNXYYcA3hCZqv5Elapceq75QT2yeA9a\\nv84k7gF4Clmch3me1qAl9TefRfm1jouxquvP3eyoXsG9LSyo4ErESjlAKXXxeB2Ar99/PwB0K0z6\\n0V/FaKrPX8rlL3nJG+LZ9XicxyRZZYA+9uM3cz66ep+u7TVMsn7W1VF6m3KTp/mr9nWYjuYuQzJX\\nypx3frRya8PqvR/9QK+gbEU6vydMZi1fS2iM1YaGhoaGhoaGhoaGhoaGhoaGhoaGJ4hZhb38gdpY\\ndZ6cQrVp7qGC2r8DACsWzZSshRtRxmbW2GyMYD2I/Mvke03ZEXwzHf8cdrkgK+mf/nXQyOz8+Z9X\\n8q4MQtdwJWaK85p4f00noZpM9/FW40nmTNU8dyVX4QDJHxq1jORDbmE1snQ17isQvIXwSYnvWeIF\\n8fjFyrXjBb+DsxpV48/WGNgnL0DpR/FyZEZtYRCZLgplEYRa/+hHg5bupS/9dbnm+lfW4h6eYz5V\\n2V61n1A7R/bBAGckp64Z0xiRGqtRj0DJ1NGIjdROq+ec3CdO3o5c36ZMAvfeo+wIZ9fRV42mcxat\\ntmXn6vj1q40XoNThQv73KaOWtuY0znWyyq5ivaonL01TfyJrrN/5KwzHEc6gjGip3IJJvt7Ur6N7\\nBU7p+ubfuofSY1XiOKQ43q55Dr6vBzHn7iPpAJMYPkAPvR7L4J6NTsozyLDei08eFLXBvAXfW3yP\\nMzuA5Od5mr3Gk8HPxqPHswbS2L6LvvkATT7D1I8vv6vaS9T6dXgm64ls4+2sTzr71DkmyjUhNK5t\\njfsX7vuVV4forl89fz578kmUbDJiDyPkEYhzf+uTcrmGxMByxiD94QGlF1WOlF9C8Dcd3ut8okO8\\n+92vBwC8/vUfsRwrX8EjeI+RZoRps8dxQllJ+fz6ghiJ/WYAPx+vnHhqOP784+F4E1Lr/GqUM77U\\n+cg7FVMA5RdZwpHx2dUnP0vvdg1MewpDsUQh2MZq/sTT/1/6xCcsJ+ldfF8t1vo3LD1bwaqMceyP\\njJY9Rul5ls9Wd3mTZrwDlExZ7V3eEvmOi/LMFbtPvRSnp27Jk45x/v3SlGsP+I/PguwxyqA6z7Cn\\nrMejzlT+/VRWn+Q/dw4l28hZR8ouTO/T8VJ5iMBy7Au19xI3IjFVyQznemiM0vP3UhyHLmKEPfFV\\nDOQMvkksaM01v37NFy3LzvJ+KxbthD7EpxUgfRC+MBbu3B8mKZr1Mux6/VwlhzPEyWkXcxbajyON\\nRTXWpTLtgLxfwa7pus/blkrReaupS2abcWz56Ed/GwDw0pe+S97oHGuV7T1mhK6QfFxWdqqvQcZd\\n/p0nrdzASfYr2g6dX6j91b09K2e91tz4/1HM+lws7skoSq0jlXi1szKo+ZI9LrxYfrM21uMxzJ4L\\nGBRlolzy9ItIlRIbyFFcsO+ilNS1vpW1G64lFrCzo4m7PvYx3HLLW+N/bhegNsW8k71lp/OPS6mC\\nKfdRrg1UWvXZGvJ/ORsl7/h5SiCXGH0dpdcmMUfVI6/6xwbCF6GP1SC36RzuNlnabkfmL/h7u8Y9\\nfjRXAN8F1DDbtwS02Uxayj0b5XKYpi+nkYYcQp/JDpicn1PUWsNk+5QDuOjw538exOTr5fklffkI\\nqfPm25+rEtTHu8oWUqeqheaZNDkcIXXlFERGjYAXUccOyuE15HMZw2ITgQO2brHwvRwQB5nrAebh\\nwoT3Hxcm6z2Wzbk8v9I6JhunHwE4jItFnmM9n8Goq90PvfSlAJLrhEMAm+bmn2bQ60itjQt3fvNd\\npMUY63fQbRZfJ3d6K1lAErHy7fdTYu6aNt11A4qioNedCv0+BajY58daTy6N4RJqk1jed/oYoRe/\\n37AQ7VG4ctDa8eBas0etTMQ0dwW6+eeKj5prh9rEzjFNv2d4zy/FTajz5z/TvZ+uAMowU5u40LUN\\nf6+KKvweycA65S7fPF1Cvh2kR3WUsWDHNZRG0rW4JWWb2kepVEibc294A10AsK5usP+B1N5DDW0j\\n9dvShbxub3AUnOaWYNa4OPFKXwKaeKvZ6hSNQV0Z4BvR+yi/XjJoZsAnPvvOO28DANx11wPIjWAV\\nqpxxU/ea8FgaQp4//ykAiKE08n7uwilLdha6EMjzvYI0Q/ry5TTSaOubryrwe85Z+nPQFhraEN0R\\nveENv4p3v/718SrHd5ZgHaXCROfq3co5vvE48T77X/tNcA/ATZ6fB3DiZ+I/UaQ6ESvq2V8FnvW1\\n8NtDpn0e27L5uR6PR93RZz0dT7wlueln3hp9hDkoUiSMiwCmuvnOXNZMwLfsLbC0QNp81/GQ7+Ha\\n2AN8qLm/jn5AaB2U19yRT62eOIroM9fjkeXehY5mPpof88bqZ6dcO/xC/BHGhVX8abfh+Ox4dNIF\\nUM6oWr7vxEGRth5/lm90+cgaoGuL8DQqWj4ex4UllBvcOnORAnIjG0tcEM0dAE9/OPz+qZ30FsJd\\n2+gs72VWaY9HXxsQY5Rl5v9Pv4iyQ/JluoFpfIEf6QFnY+a5Mvt65qrjGOdYH2SO5HfXa8KxRtE5\\nET9x77CUhnXm3opHH316KFVmqjTiM7x9pYDPZ7pzL33p++Ovst0lqBrH1cm65ekzJlvCluQqfHmu\\nudR1ic9oA/Q7ecLX1fp271+T1sQsAXPmyil1FdNJiXHw1HZbmpoz5xpU7rgwbfO21/31Ou022x9P\\n5f1WVGiybI+hPvsBoQ37Jn6pFMrDOgLAq2+5BUmOqa0HXXJK6xan7FCS1hrw8u2hviJlWs9BUnDr\\nesydUqjakdAnqcpUoWOQy227YJ84JX2BZWE9en2Os5Q/GGiuABoaGhoaGhoaGhoaGhoaGhoaGhoa\\nniCaK4AngBfGY81ItGbQ5oyEA7lGxSY1g9Rgri8iqdijmuMnt+K1v1MNWdAYXOp0N4vIyeWaQ3Ut\\nnHMeLmEZZ8RsC9kvDWeRm2Ir/08Zijy67pXl/XbM1XowEqYqDSNTfhOjUaEaH2WqAnXWGCrZAAAg\\nAElEQVSD80GnGa1p2cgtuhqBXJxxl9DHsDCvc7cMQPoeSuh3rfG8mPG7Tom1vQHg+pjuwMz+zyFx\\n5fyL7SG1pM0uSA+5F2sotcQ9uTN38b1q7w15D996O2P7eSlqBh+1oFfOUqEmr8b+UU25M+EuS5pc\\nn63MuMREIsOHQY+m6/aeGY+PTEnz3eHfTLmW3Omn306BOEDphEJbZc3lAf93/V7JGDp/npE9EsfE\\njb7WJf1hbBubhYuGwNsOCO2GuT6Nkt1SM3Jkbvmlj5CPKUBuOuk15TUIJPcCo6wPEOFOZTIn1rZz\\nEdTQTOkp4f+B8QL6sd31MegCIx4/g6EGdd3AsoeaUiY8v2adOeXMXq1p5asBaVwZZHYdAPCxu+6K\\nv54hb+RVziLqAmTXztXgvbrkzF2Rq0ztnIMjyZEzsq6THFCuWI9HDWDh5tPaSpzZoe/y3t2Lbefj\\n73qXzNscK1mrz0LZm5QR7YbezpI4LnjNJpAhzZycUBt5NkZmbx04EQeCXhQilKi23Y0SyicMAWLc\\nIsEdWAD10ZLvGEe3GJtdO1fjcOdCEWms9Svqpsmxi1we0Pv1S7HsKm3q+AqkkVhlEb6XfeCi/M8a\\ndMbqruShFmbRx+RaAJ4EtlHlox0DOqtiBqhSrhnNLIOd67ORWNM3xmPN2kEtxfyaO3dZQsleY52r\\nPOxuQtTRTMn/C+1wKDLrRyJTVZmP3raU096Z+7sNugzdZ3fyMgElW00tMVwucJKpmq+6k6cNlKNV\\nxxI8BG50lw7M1A3ysCsoUPYvvmUBx9rufLE1D3FLkdymAXkeT5hHpaf1gL342+WhXXmSy2ZnUXpI\\n0BnZQ0+xOhNTdR6ldEYcoJQI9OnO89dVuvYMIB8d8rWDvvUou5Ly3RcpyrmA6lBMV+0sgeZwEvge\\nD1m0J/d6nurPP5JjLQzgLDHt+aG2VBLYsrt6ABYeT7+B1CeTI6fU3Wps/ppFMevJ3YCE9Jxbaybs\\n7iCDg9blLhDzTrxfmacuf9UsBZwBXTKNFdrCfDbw30Ap3QEo1huHSLNv6byAFm++5xOCgtVClTMf\\nZ+W33nk1rOGuPmZVoh+IjdWGhoaGhoaGhoaGhoaGhoaGhoaGBqAxVp8QHppyjRWpTA/Xpqsm0B1/\\nr/O4hOA9XG+I6tz1OeBnolKbuu2dLmDLorydekPNlevIkt42uX9XPgTvo85FvbnkWqEaq2aSHy7V\\n3LjuYqd7p0I5Y85qST4v3eeo+pZyHtOFLsBC4ocNOm07v8iO/J5VN5kNFlFqNpnTDTg3JmckDDvf\\nMfSVG+rtACWnSrWczstUrb+zZtVfWmoH7tkPKFuJsj4Da4MBZajvWpNUPHcx+r69UA3qReyj1Gmq\\nJzm2+S1Jj3iPB6iqefnKeRzLuFD4x3Ntcy2XK/hetbbLU64pozRoYp///OD97Utf+h/xvLZIbxHA\\nZIbYXOWajjWTtKDAL//arwEAHn5f8Je4UrkrfcctyVMoT7/iD5BtysdudTDv2sj9SgmcUc5nADnT\\nw9mr9BurpXWfhGNoC3Rdt945Wd/tZQ93jeJf+pLmWKFhTWaNF1TOhS/Zj+ECdeRgfR3aMfeYR+gI\\nmHOaaNmwjnws07vSc4GS8VoG2eqbb7XA4HJOlEoB4y53+gb1peZ8gjnUmRn835mq2hN9pCLr47Kc\\nd76oBnA5add438ve/na8+c2/H/9jDyKL4yRSC/ZesielYT2pb+1pnueeLCazw0bx2PX3HWCOhXXa\\nyQbwD8ak85AP+dVQXg2B4X4zNdCFM25UAnLm8E7XfzWjzjNeKGYxnaec96VsZpcBEmu+X/gpJrF3\\nCbmfX6DkiKmsrD7bgMCdYVUn9hpHK20jOWNnWZhjBJ+zg9rX1znuGBn7XZNmCKQ54HAr/s65TCtI\\ndfdsbwh7wEEsYE2qccaT8pcnWcTsoexxbCts0+qvsWT+jjt5zd+xgpK3pu2pYOrp54y/PU1tPVVj\\nQftcrHwzf6aunB6S35D87yGxNp//Z+F4ghm4KA/1MAGC1MKOm5kf8Wjl3NfIms79m+/JmaU4iHE0\\n1/Wby7cbKNl47OtA6ZlS5zZvb6PCx+UCyi+pX4aj7UVLs4cy/JnmwEcJjgOPgTPkapR5tb2xfNsF\\no7HX/V6M9zFntQBVJfs7YbdyzpuUykbuN1/X1dp/86eqNDkrz5COWkBSIvTYPSR5hFA/smxHOoYD\\nOWPV7YZ2UNqdan3zWQt2Tdet998f1hY33/xGueodmk9YBHvKoHtPCrbq9mNqq+NrCt11cbZxfXby\\n0hxWnqpB2HTtq/cfdnO557vGgE4xU25EGYBUVzo1L9hAfWD654/mY7WhoaGhoaGhoaGhoaGhoaGh\\noaGh4QmiMVafALbxs3ZGuZlhxz95+FQmlnsuSt4nfyJqBzo22+UYdRJAFxKUSog14GTc4HfdQNDe\\nu95LOQHuIUv9rxJMo56RXMMQtDU74t3PNShzmOxtTjXZ1J8wJ4PMT4fzw9TXpcfeS3rnsV2pg1H1\\nrkfJ41EuVO6v9eoi53sq5lB6UFPmG79wiqhJ/6YrcE7SIKY6xGbBRtWW7dq2HUm7ZdeIXajmxr3Z\\nau6du3zQpeeXXY/HNZQtJD1tgEc6jTV1wh6fUe9UPgfzlet2V7Fd6PaY25H4FEuM6RQt0Xmu6n9z\\nUv9Q5rC3yOPET+BS905nVhJrcu3vvxQcdLEGhjiDSTGrw109u+bfHCh5C/ocH0N7eN/77gcAPCee\\nUZ1t0lSHvp7YzGlM4/dbELZ7jffJ/535rr7FRrg+uzqM71vFqGBy8/7AkvfxNdT6MoYTxzD10VXW\\n6yFybo2+MTH7/dkLKP0UDot55DhQa91hJBlFNshe/IbaUja7lseSaExz1rRyokK/JqOKo8Np5Iwb\\npk6/PC52uHpG2KnujzSNn0NsxPrm6DLMPJyGO7YLDX9KxRIR+j63L6n55tRZ28dun38XUPfCxXc4\\nC4719eY3fxKJ08QjOSbqZdO9iNYkhZp9z3GAccg5c+4h1UiYL7ci7+QigBu/ES/t5setHeAL8RRd\\nFn4tHjdxCpPKoT5v3ceq1oiPkspDnSTrXMI+UgtX/9h6LOeXiyj5rRo72Dknypp3QqV62nWembfb\\nOTHBWYifQLk1g44V5lLvacnVFp8GABiih7HFDlAGWJJCErcozxVwLDPv4bdqJ+PROYDiQdsNzSCz\\no2VzD0UzzeIh+DfSuc5lHWfezKHsxcSyzHXuDbMmzSqxcyv+fjppoiqERJGOaZRB5flTizXPg3v0\\nVzlaRwHEV160c8zvZZT1ezauy5Yelfo1aviWVECqO46i6u/zGBjTf1Tz+ke2GK+FmrqM7W4sY70w\\nl8q097liCbn1A5C3P/emrYzE0i7JeZ46jjKVrinCU6+P/Xkcj5sZH9bXlGeRWhXTcGR4CIyl4TEk\\n0tv0vpTzZWEpAvW16CT/nsqHdOap9qElu7aLUi5VdqfbsCYcoqyfWaOcMdI3CN93gFUsVay1gNyD\\nrq8id5C+oMd80V0Pvk2tOZ13qq2Dv994883xF+XNaQzzBbke3nQp3tfDsLA+0jFnGOe4wyhXqpWa\\nywcswypGXfpR1pt4Z6il0pJKv3M5G3BGJHO1Zqc56NY77B3nkKRVb/HKnuWX4+iqnPav41rBrOwQ\\nrvGN1RdOOF9bVukQWQucw7Qh3VfitR38aXf3bV8OqU7mfRTYKKeBhJrZrS+sgXJ5r/fqYgjIxXfN\\ne3A9sBFD6FDmoyi/gHK60fxWLGOyZ+d3hDL0MZQNQndPsNcNTKdto7pWgjRI6mZ0buh2CoOp2wkX\\nplx7cliV3/6VQ96OUArMKmDy3KhbkKho2S1nsmsj9HApDnp7VodD2fBe7gSXgMcq79X/800KTbWL\\nsn/o/yG9T0ZrSF+/ZtB/8mdvBAB87nMeTk7FEh5pRHfQnXvTm/4dAOD33/EOALnbhdJgZlgI3bq5\\nUjPf5P2cVNcsjeJqhhLSnjcpiJkaXLlh9DRFQG7QUkvjowTfuITUL1WsRXZuywQANRAt38HnMl/o\\nNr/SVlfdmbyaVgFJWB1lm8q5UDHAPLh4T2YzhI6U5Ta6LhI1T7pNnaCtbEdSMvfBzQDb27rldkdS\\nl2YsPn8dN0Iro0Cob98uxrSaEtCXHWO8971vAQB88jWvAZAvupma81ca07St5uE12Co14FltA4m5\\n4zM3YlvYRR7sRe+8BOAZptTTDa+aea+XxccqdVfB5bRvJGvcTH8vUI5nh1mq6+wOHQn5BtYhR5Fd\\nyVXP0uyh1hJnhxU7AmlrNJx7NH6rhwAgBs/gUZffD8ff3HvdxjPirzWU6oqN7r+acgPIN/5culSV\\noM8PrNVVjDDIXJ8AeUtI7wFq6sw6/PtznpjHcGJ4R5WCfWN1TjcL40Pn4sU96Xpc4A26M7p01m0d\\nILWnRYxiWxyZUe1tt/0i7rvv05YbllzNoo9jc7+2eeHzXjKPZWm+FdvdCRFK/yE+ilW1FY87SG2S\\n1zQWlLsfI3Rm9c0Z4gip9nMJMg/U6I5PFlBuYmr745g0juVcimsgNXHmV9TVjSu7Ce1fkwggu0gS\\nIMdn/Z8tYZAFUAKAHWzF8Vk3HIF8vFw/zN+vWI/H67sgxBdxfBtbAMDgnypNuLF5yP1XMMZGVLQ/\\nll0J2yfeK/S7uLJHv7FL5MQeSmPxfrdhpG5k+NSyBV0fHdqdsyvAAJtdC+IMz3reQLmNyVw+3IUq\\nXo/H2gqbrtQGouj1mczlN1Wc+ex3BfkKSY9jTB6Dx3If2zBrKcink1bkOuIft5ynPZDvZQmWcCl+\\nNSrEdDXobpp4TR32bcdvwMDea5J+PR41JDHBNINs89Rn59rmMHPBdqUrD4644U03vfpX8Jnz57Mn\\nJIUhZadEZuDmZx+bhasgXZ+lEcNbUq8jEfj6dQmjLvWw6I2JYjGo7j+wXLyPe0Y3oAxPp6sLb1u1\\n+rx20FwBNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ8QTRXAN8Rzn37JIVeUgnlriVaQs5fAzajXuEBPNil\\nelG0KWNK1aLyuN0xHJdQGnKpbtd1gkosX4+/VS8J5IaS1PptxeNcp13ZifxNfwpQGhnsI2mY+KQU\\nNkf5pcwfzXb7lWtJF7hsDB/9Gs4KTMwH1UrlbJolefrVxTQNTijjtgSLuBS//003/SgA4Atf+O+d\\ntney1g0obcsSv2qQMYqIo5iDZ2TXehhUuDDpDr4xaXaVH+Ncm8Qa7pvW0hk8/pslePBzZB2dk7NM\\nHd6dtOGqVw/1/r7IVNUAB1pDQK6Fd1MTZ3Hob+Xleh9Qc4+ae4fjRm2Eq4Xscr0j6yY3unRnAmOU\\nvUlL5SZaagSVj5O5WTE1q4vxKY90eXJj+Jw5ljNWySRdwXbB4VCOBK9tFSWYl5R8D2t0V3iitRHF\\nWRjhqUMktxLO/gGUXeR1fhF5/aWjsr2dzaOpy1ADajQ5a9S42uGr+egV8sVxKzEd0KVWDTmg8+5r\\nXvM2ACg4w0D5rdN7DypXl+I9ab6psbP07UApaIWAPc52IXqFUx51zzKJ+32IkuXC2tmSdGS0OGN1\\nCfXxS99fh/Yp51kfoDR+uyJHZ1poqaY79HlyqAXUyM1OWYcPo7QM4d0PIbHt0tyolj9uc9Hr/k4K\\nrrQnOXE+FaHsJVjaBQSLm5AutzDRMcDfq5YwbhivbiLYJl3KBMr60d7tnPyDmGhBPvmVcZ4mDz7i\\nc4Cy1JV3zSfw3Ho8hjHsvvv+O+ryur7Zcz8rTLNNIcIX2kLqt2xja4+lO67YNbJUH0IyQWU59yKj\\nT0ut3HAgn999NtNcO9eN/x9hssysqyGXDg5QsvS0bbudkfZcf5by5J2p6jOlMsO34pFrkW8AGHRO\\nhtim2K93cSGmvBzNV9V017nw6/K/87wSs2m7Y+wdz8rj0I76dr6PMstSJ7MkeT2U9yJGXSpn0SvP\\nz+XELfntjtx0dVpa26njCZ/dKM9f6uqfR74jt77SkQ7Ief/+7KOu3biLnwOktsV8c72yIibfkPRA\\nHgazxuglPNxqYjYuYrNjMubr3eDWymuBTMoeSqlBuZ8u9c4atefWVhKh5jZjTW/GfKlbrMT2XJb7\\n8hUWU/ewXdguETrmTF9fuRXUCkr3FGpV5yEewzc4f/4zuPXWWwEAH/7w5+I1fQ6/VS5nj7CGUWzN\\ny3EM17Em7W3kKzD97S471PlDuqorQF/VaptRhioAPFvuj5LB6RP5ow+fAhz+bfzHWeMr0FHwWsGs\\nVkvX+MZqQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NCY6x+R/hOfC25LyvV/zvvQPWavC9oADYxxgcQNBoa\\nIAgIegNqqZMnKNXeM6V/1jGSHixoPagDG+EUSt0zn3kWeOrTws/H5Vz3voOYzwtZfpXn6rqqfSS/\\nPbkPT16t64+WMequJG2V66tLlp3qxXg/tX2jjFGSM5N2MboqQYNKTNOfkW81xsB4I1/4wv+I/y92\\nfNVXver/BAC8//0frzz7JHKsofSGVmN/5I7ut9HDzW8JDr7/77e9LXvLvDzxX73oRQCABx74S3ln\\n6Ber5gvmCMlfzih+q5onIOczXezKoUf1SrQVn8lzfFKpMVN9mn8R7SXu6yj3xJi/RXXEB5ZeW6GX\\nL6/x44HyJV1Hqz3DeUGpbnYwma+whywCX3bnHMpRQhmszgVWL255DunAfZR5qnJfnMqB4u8rXS4v\\n2pXaOMaSbGdMQ+bvtKVSro763+Yb/anKzR52TwBSmxGXhHjVb/0KAOCNb/yU3Kfct+QXeR4ouLMl\\nS1O1++T2n8HsRAWHMxYTOM6T7R5SeFvR/53jrbySlfgrjDWui6/dlffGfEwki2QFo25GZA0pM8VZ\\nfcp7LTmTIVdnKr5wa0EfnZGgvYXPTtYgSXZw9gbn3/nYc4AU2kkZrDkHQ9nqyrByPpv6SmXvogSw\\nj1WxHwGUnaOjzXEwV8nXrXG3QxnUt+OjyMGSbECZqj7nKHc+Z6Op5Q7rnNBr3k+1zSRuVf6G2n3K\\nJ3bOkj7bRyaWaBFlD6VnOA3m417TlZlbt0UCFg5L34HKuS9HBpWjJ/ES1QNnbV5asHNHktZtTGaJ\\nL9j7dbzOvVyPkViozuA9RGK0sSWzjV7AM+BtkTy6i7g0MbThgeRqnN1dn7EmscH0mq5I6nzDdB1I\\nZSJ/agGTJPTQb1ze0rKwXggvwz5S3+M4yfdvZwxAjoYnJRUt6QIuxTv7GHTPcvbkaaTVE+t5XdIw\\nbAzzl4dAerK4WDnn69oapy3/kpdwGbvGflYvqN4W9Pt4K1dpyMeN5S7AMPOho0xAP9ZQjZ2cy23+\\ndF03Tu7j3qaIBZS2oU/kfmX21qwHmNtBEVNksbs6Kr6d1sI5Sc9rnDk8JNvWlBLMCrV3eK2o9UG+\\nDzHEofBzPQrCEsrxOrSTAQJrVVOXMl7CqfiWfYQYMjl0vFYrKb02DXPCVOW3U0nKPamqHVF43zC2\\n2/k4TpxEOU8r97i2CwSEOki173lfRznqq6dati2zbu6tl0ZkmcmWWz7X5PZrB83HakNDQ0NDQ0ND\\nQ0NDQ0NDQ0NDQ0PDE0RjrH5HmKa5Vp9/QK6PPbBrqrt3hhO1BDd0scY/i6/EMwELSLqfYeZbFah7\\n7VKvVu67owZjlT31aUJsfEo4PM73bSBpw0Ne9qImZR8lV0D997kG+hkxNzvCSnVuV/KQqYzTfvx/\\nVLAanEGhv5/70z8NAPiTP9nCJG7tAGPA/Nh876AsPf0fKD0dJS+d//X97weALqbpZhb31fXNqsNj\\ne+f75pB0+xrTMtz3trd9OP7WqIrMZ8jLpQf+33iOOvs9kD3tXEbGRdRSKaPB48ST/7SJVSSNmvYa\\npmZdkduxHo+7YJlHkRW8GDWdypjxkmuPcv3rXuW3nmN0c0ZuVPaQ+zdz9tJx4EiOzp1UvpjXRfKh\\nO+q4Gv2MYRjqZtS1QtXE8n9nQ6v3I+c7MTenUdYQx9IdlN7aVOvvXIKQZjvqqj13zEn5HZmXNZTj\\nuHLGnLdY8+7krIqcoarHQ8nfh974xviLTL/d7n1kgqvHLWcSaQ78GmeYATblv+NCyYs+E9sRa2SI\\nPl796l8CAJw///l4Vlksk5iNK0j9O4xR+xUOBME6WcYQw4Kbko/BY5S+SvUrc9RknWo8Y7Za+gcf\\nFqNI2fe3Ku/R97mmXCUAZ6iREdwX/1xzkh5wH3UB7nsu59m4v7Yj+e2eN/cyT6yAMpXmUUoKs4Rz\\nq1K/YS1oPTMnXsohloFC+iDmUXqtDWnVnsl9j9Zy6WP/GKVUqWOWf7ez8pu59TTKGVqoHCkKuqSr\\n72Z759yxh1JC9na4giTX8hpZfztQy4CaF+NJjFUdd12aXEE53ipX9rKkA2Cs6ieHHftfGascSQ67\\nvw9Z7nSscl+MFzqLpjWU8dFDPe0hH4P0qPJJ7tu2ZBlreq1V52wr98xnP30fS74ejyzvHMovrH2C\\ntele2nld31Pzsbpr51Ke1O6O4NPXkNsCJIT5hezOcTzuxnekVdeNlqc1HO9oV/LiF5DLL0BuoUa4\\npNfrfFgOYx0cSblcuoP872xBXRNOwqrM04P4dMZI0NyzBLW4AOm7uUyo/r89xyvYiWsAt/JRxp/P\\nhXuoS7GKHqbL8qkNe4vVmai2FoSk05wrX9j54jp3Hxf47jmUrfxQrk2yp9rFZGtcZe4u2XEPh/Eb\\n+hpTZWhCa3Y33jcsrGMP5J21+UXr1fPp/U1ZsM7E1dVm7uN5O75vQfqG12byUZz6S31ccS6vsmd9\\nF6eGWM7xEbA/l2e9NpwUI+0eyrr6549ZjeHX+MZqzdjF4UvxMdKU4VOHTuk6yPMaNyyvBwBs4FL2\\n5Px9+kw3MFYjQeKUpTmNMuzUUsqas90f14E5p3UrIZ+lcvFDnSA41BjdTcLG8vwU4qbcHnYB9BD5\\npAcAf/InFFdrYbaYOzUJq2FzyrXjgrYANzGk0DHoSuBG10cYYrsQT9X8p7Yo5jUfKnTg57LN71N3\\n/b4tuQduidJ5gTo3cLMtLSV/s21tdpPfOaQNVR6JfZTBL2qbMuHp27GfbGMfhzF/3kJ0c8QDM4SN\\nU5+0iIWuZAw+R3Ntba/EtFY4K2g/nSS61ULL6ajHid6HjD0Al7o7fGGpYyFRW8at21PV9MlFdTV9\\n8uXfgpRiT9KHvDB8FYO+qDhXhjepCXXrdk03ljyfiyjrI5XZF4sqqLuJ71xsowfytloIA97n30/v\\n842aQ6CiupoV/LmHRSDC5MRhhE+dPx//o/k1a0eFs9p2YPgeH/jA7QCA33/lKwHkfctDp80DnZJz\\nskOQ9GYuXVUVwFQXK9cmLWf0W/jmiY7Seg5Q9VCCls9NMJetvy4h1ZybnuvovWPXQil98apv81E8\\nOTdh0AW+Nzd/dJcts4SPOeq4JuSdi5PDLnxEysmvfeADAIBXvvJOlHMby7mLfEs8T+F1y1rREcFN\\n5L8TgX0Rqc+7YxygXNTr1zmwNLoUXLe86Ni/FX/7Zq8u233M0tF33c5xoy3MSxwB1uUOIJR0kjmm\\nLnJdKadpa9t9vP6dyP1PFBv2v27y5lKvUjPcyckaUjtJ7cVNTAFvMVpyl7F0PLps53yV4s8Cwtdg\\nO6P0VdvCYg1o8CyVuvW+6+S3K2FUZtmKR93W2LL03s/mkG9t6Dvy2nCF0EU5F0b9X/7lQNb43d99\\nsFKK0H4fwQbm4oaN1+s+jtsg1uWMsyjVRfp1mX5aGJZQznFlHca7uDJQA2pXFl5BqUByB1BhNZbL\\n4Xku8t8q6R/EOt/u+oJuXvkmWXoa88Sa8DERKJUE27KhxZT9KEvy/rNI8x176obckTagczlIN3Sd\\nODTKRm+XOpbkDbwz9J7lSrCtWaqRAnT15jOaOpLweVTVer6JWYM7TzuJcQxo69uVCyhrS1cFSW5y\\npXcP5fY9v37NlaFvmPLtQC59ey60PbKVcIRYiW8dFuXi20YA7rvvPQCA2257HYBEtNCnuzI5V8C4\\nTFcD798CxrE8O7HX72iZ3Q2UOlrysemfP5orgIaGhoaGhoaGhoaGhoaGhoaGhoaGJ4jmCuA7Qs3x\\nN0Gd3DTDWeqnlLHBPe1vxON6PB7AdSnUgq1ihP/1ttsAAPfd9ycxjfKYqNFwfd8CSharGu0wDME5\\nOYfc1uYyn/ln8fgQqBO+3oKsAJMdlQOlyanyvlRzB+S6EtdV9SrXaoZdakqaP72HkruluXQNmuK4\\nGKs1ZlhpjpPOqd4feMXrX4/PvPvdAEpzlYXsLNvNjqRyVqFq0Zzdp6xU15fxvpMo2WMl65ZsO2U4\\nrmUp0huUlZWexFRnkfrjejzyu15EWXZCW67rAE9jM6a/444XAgB+5557AOQlY88bdezZ05jMzVYN\\nXSgNTU6crae5PR4TsYAt+e36VGWfu45W25abemmaU1EPvt0x0rSHTtLW1lxeKO/J36gaYG/DPjIo\\ntO+H0g+6Y+jnavA76srANrIiV31U66F02K55Ul5oDm+JzrxRqK7ea1OZmG4JoLp2H7OZNpT3uFpf\\nzlHpC1/CfeErWx0xYOKwcDGRnpUz24Im/q7IVOUocYByhGKOQj07m2yhyCff7GapykNwDtya5DKx\\nXELq/+NXfxVf+OAHAZSj7hImm00PM7YM8zLq7p8UQg3yP+v6sqXZReIyXJFz6YluncM7axy3Z8fj\\nVhckYlSwuldQckpmCcp0OsM4gytA+bJM8cpXvjX+qo3xml+XgA66/yZxfNWlA9/tY7LOWBwP1EKF\\nuWI7V66mmuArDlAPPsW0ypbUZ2o7J2pShfNH9dlMz7bF/4M1CuVSZyQpx481pGEkfSYi1BrJDXY3\\n5O1fxezh7By1BsqPI5zCKJbRzZ911kx1z/uvoJypD4r7aq4AnLFMOK8PKF08rCC1Nx7JNd5AvuIA\\n8uBrzuBcj8fnAnhazPC3xnmJ1JzX58RDTGbd6mxfK1fAAUoeLP+/3Llw4n3/7UmHgmwAACAASURB\\nVHd/N/66Xt6gzGEAWMDX4x1nzCJG+940juh3D5cvdlH2AS2nr3nZ1xaxamG13OWCntNxyC2xXEKr\\n5VKvTZKmVRJ0iXANqVTb3dNTWSYHz1zo3Fd5sC6Fyk8pl/ncx/807I+HONXn+VivFjS+akss8E0J\\n/KirJd6Z89u5Zj+AryCPA7Unu9QUbN4CfNxWfqnbzCncIuxKNxu4axOFM+H1OzMHZHsGd2c7llIZ\\n6j6CakjT2gjEa3t2TUckX1vsxbyM0Y9t8+4PB3d8b7z1VgDBpdJtt90JILmb01odFXshWp9u7bcj\\naXytpTI2z2noQYLP4n4Xmasb4trx+xcnTpzAhz70Ifzoj/4o/vEf/xG33HILNjcn7wHNagy/xjdW\\nGxoaGhoaGhoaGhoaGhoaGhoaGq5l/MIv/ALm5ubwvOc9Dz/2Yz+Ge+65By984Qsnpm+M1e8I03xM\\nKDMJqPOK3FthTUu9If/n/Mu+7OXfd9/v2XtV7+saH8j/1N3U+GjX5eeeeiIcNQZMF/BnKx4vgzph\\n14CrTt71TFo61yLNY7Jb6B2UfIUav8T5fi94wQvwxS8+DCDV46jqa6vGyDoe3fF01DwtuXcyoGQl\\nB+34u9/9yc6LrnsfzPWG/DLqW9R1yFqzzvZU36nOLdOj88HUt8pSzOegO8Mc8S0nJTXBp291Z9RD\\nF68qJ82Reyp67WtfhLvvfiCeq3lSCvVxzz1kiQfG4gjJN2rOVGXOPTAYv4AGA8s9aAbm6lDeUjIW\\njwPbEnhseYLucBGlPlY98jivusas7Hf1pX3PS6Yekmvaa/7vfCzlQjkDoeZBycdJ5Xrl1gbDLLhN\\njbtQ80qoz1Uod6vmTTr8f/NrXwsAeODuu7MU2iu9dmqjGJk7Or76KKPBdPj85Lh/CcfX+kpmo7NO\\nfPbMEbTGQ5xCKr0HbumBI8kglnJJWDcaPgFIX3ARGliK2M/S1EKTeTn0N0eHmtdq4oMf/DwYCHAc\\n36zeg32UHnYjfsofc8iePKr0iVXz/TaHUiAkb0lbKiWVUfQBH8DSO1NujHIcVOZGzaaFqNmtzAr+\\nTM1LzmLcRaq5QdYngNyP+GJ2X4CzP8oc+Pi+h7I2nB14EiUvmzlaROk1X0cXZ35pQA+vFR3LmSf1\\nhc68EB5schelHZX7aN1BaoPuUzgPUkioLOJykI6SyluD/e/eHJVpPUlCnQX8yx6gHEF0rA1fiz6u\\nV+J4sFCkUH/Q6oE1l6RrrNSa90JloQLpeyyg5GkRGjKLbeRpT433PV6uDzgv7aE+g3d5jJk4EU8+\\nJT7g9Djly0O47lbyB0u7gvJr5L4HQ0vt44sAcn/FLv880o0L80i1QPmEpdnpfm/GsGS0hgqBEvvx\\n93Fwt3z8WUQayd1S7TKutzykFdOomJ+Vke6rA+Xa+Til/kWVa62oSXTTpLCat1h+qxQYUdckbhPn\\nFnnpWcyb8gtZY6NuDtZWt5A9UcOurlfy6TlKvuVTznLPvmlVHt4WyreZ9VZA7TZXhanqzzwOu5CA\\nnQm/gbrXZ10JAqGl1HYSmJYtgbURyrqMC0VkBu37PkKqtYZ7SU5tfijfjFZ4Kn85/1UDVfGLOrNW\\n24yPSDXLnbT3Q+bprbcyiO1yl5ZrU5ejD6EMXB9xk03ocoztk4flDc+szS7Jwo1yIT1t65zKMScw\\nVlcx6kpzAd+/eN7znoc//uM/BgD89V//NZ71rGdNTT+r1dI1vrHa0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ\\ncC3jh3/4h/HNb36z+/+f/umfcOLECXzrW9+qpm+M1e8I06qJe/c1/xmuU6t5Ia151gnn+pFVk2vq\\nnOFT06q7TnEeueZEcbo893h8dm8BGP9tPOl8pvT8HeOJXpbckeFBPxrqW66mSXHmVa3mnaWl+p6U\\nk/DeR7/4xU6HMzRfdh7/L3/zEab7WD0erFa01ePOnxPZfmdQsmQSp4m+hBInSz0QeXup+QZ23ooy\\nQWt6YmeLaLtzHXLyddmPGrEaX8Pv0sjC7qstZ2GXGuf0f/5U9q/7775b2gh5ojXvTsoIAers4lp/\\nXrRzNR9vyjQLeeD3Pj7fR4rE+XAvRjUumfvQ0n5ai2bP9IkZqzo95yIoY8w9Q6pXK/c0pXVa52nn\\n57wUCygZUOQGqJ9h7zM1z3M1r4bOtT+C9of82gHuvvt+AOj0v+oDynX7yuNljV0wP0p9DDrPRu6T\\nLIzTzKf6KeT9LE/uX+3Jg+8MJRihj7UJbBmgZNvx7iH2UbfU8CcEnsxOLMcGEnuEz9Jo7TzXtzlO\\nWZ4++rF1rMn9bomh/IWdWF5+S43Kuxm/IRlENWuQvnyT1CLzOgzjWs4xcq6C+sp0LsWuvHvYtUj1\\n+Di2O527pL95PC33qR8v5u64/Pqim3uIYHGgHg8B1kZibgMl/2gNNV5LwFjO5VzOGjtImdPqBVCh\\nso+/VVk23hN0FHW/v9N8O+roRB6eeyVdRM6o1vt2UHJC3aefMgd37Vrds6Lmyv0J8ngdJvOOVoC1\\naJXFWth9SjiOlS/ssvIs4G16H6mGJvMn+VslCGLX0uRyyX52bYBl7MaxzJnvPZSzn49fCyjbD9vo\\nOZScdEKjnbtst40+9s3PPtvaFoAf0YaC5Gv1EKWErlIFyzfJdkm/ro9WBxhgEM/Snu/GeOwhMWXT\\nF1LvsOvxt3vSVL4v04dcDrHV5SLF+i797X/38Lb1GFy6PhO/wTQfoGr1QrCF7aJcOZCV2kNpyzPJ\\nZyqQS11A3aM+of50dWXnacsxV0tXs7QI5+gDnbEg1Nok+aqs2dXsZWeUqcua17pDPO9P4n1zSN/F\\nV/970PHbv/Xkkh8i9V/WwKBI/eRwSp7oKyZKKX0AP/PvAgvwD/7gj+JZt8rVJ+gISKmWFr6h35xD\\n2Y5VKve5qubL1vnMi3IttZQgfw0wh3o7QJcyv1PXDz77sufU4n9ojkMLoDwzEsaqQy2x0lNtcJX4\\nFKw7jntrKNcNCo63GzEvj8Vj8JMeWvGy+SteRMlh/n7EN7/5TfT7KYbBD/3QD03cVAVmJ7le4xur\\n00jyblKthja+8NYFvS9AJpOHayagJdRZv5tA6eapd42xvHsrHmPexrrF5cPPSvd8Tv/DbEFyVtIB\\no9h9LmGjc9y+JamZExfa9ejGW741BSTzaeYqTY8hF+GKGg5N+rbHt6ibhlpuWM5LXel0o4PQrRV+\\nd186z2Oy4ccO8i/hrhN8+UYsyjU3atQ2TdFnKx6vFM4pfJsMqDuzTxvkblqhghLzwvLqk8IbdJLl\\nO2key03NYRa4x7cdDmUj1kVQNaz0jUP/rblJ18Z2ZVZasDqSUQeFxcX4/XVb2EcR3SosBY6AA0Ac\\n69f6VTjngbvG0KA2vji+glS/HhxQUTPQdqNvXU76NQ0A6MsG3SyxrbP5p6TX7Xv/0M1hN23a6a5x\\nM23a7OPiVw/J2CZv4TTRyb/OqVjnZzCMApBCW94kw8onCx/pF7uNRt9mX0S5iZnnamnCVRV4GSwp\\nmG/tYrt7Zu6UIzcnpYLwN3/zFQCAT77znd09KiRqLg6RZkFXBtbUU6n1Dou+rpKDb7atx6O2E29N\\nuQHgQjy3HPOSrnoP0i2sYWHuqOOub5/pHMCcbdnTV+Qa56xdSevj7ezg6rcehp3bhWTWyVQ1dze6\\n7NVtJ73vClxxwv5W21TgG1QqmWSurQaCviVQMxOHXPPFrc4zvj2pI+xW/D1NHnX16R40OBvvz+cV\\nNb3k/Te9+MUAgE984iJK80rm6rLkhrKAbrV5r4vbdYsnyk6UNTF3WjBL1OgArjzU7aRc9mDKyyg3\\nBhJU7soVjH0ZW1hzta0K32zRnLm6qmbM20E2QVmb3DRL7eeGboPlK9H5CF0e9AAcxoRrl/O87KJU\\n/qrU5wrimkMS39BXSe2UbfaqlMH7kvKSZq8/hSIIcGZm7kpZnT24QcT2N7uN1VOVYLssF+vlrPzv\\nK8na1r/Xua5KavOXtxsdW5wm4i4BdBahfFObHbwtq1ya1g1KgvKBgNAV53wsQwogx7Ink2rthTlx\\nw6X/i5K/WtA+fgdfuazJOVeKHULHdVdgjrs3UY5h/9IN6+NaX6issy45UhwBePQP/gAAutn3UOTA\\nUUF4SVuDrOea2tbnP/6/L09ymVJ/s050c9ul+FQWlaB8ZOF1oB6yzLdyeZ+GX3OJOBngjwrZfVx1\\n8cK3l8q4JD1w3c+1hQYAc6WI9n0+kw4Z2E8fxhBXYnurra5Yt7Pe0J8l/uqv/go/93M/h89+9rN4\\nznOeg7/5m7+Zmr65AmhoaGhoaGhoaGhoaGhoaGhoaGj4gceDDz6Im266CV/5ylcAAC+Oit9JGOF/\\n+jZP/Pvv6L3X+MbqNGNc11uoGZib+xM7KPWppX6BWoieaG8Taiw4wvXNS6hzZICgZyCb0JlfNVNc\\nLYubzqmuiFpbd9S81Dlu75lzZX1LzeSJcD6Sci0npdVnpwA6pyRf0/RWVw8147My30Cpb9PUrhlT\\n/ZubE9DtROmC4JZXvxoAcP78Z1DyEfleDT3hpsTKQd6Q9Ln5amLYBQQWqIbUUKgmmdCgIR6yi/8f\\nwDV/asrovSkxmZJJWNkal1A3oARyTWWNezpJn5j4EN76jrc1pnwsi8mTHoHSpEb5W27GqSPbpIAM\\nqlFmeuU9p7u27I5dJJ2qG44pK9UZkWrM6NydGjOzVus+vl5nzwdwGM1EeifkmhtaHsm5cOxHZoma\\n89acW3iv11qZrAFOfeelL/3XAICvffSj3ZXr4zyTjKR1/DhevrSOK8HVCTCK5zY7du2osFJIvUwN\\nldRQHwg1Vx8vD1Fql5VrOej4E+G+d77zo/H/wH5Rk3t3BrQuz/T+siJ5n2YSSaidiD9L7RPYAzim\\nprKp8Vv+VOUyDQqrBbI4+3L/ejzqGOsWOFq6i/JboXxft9xJLJvjbHs6kyRmpfe8dZQzRM3wnlwj\\nlSnyL8EU+kRny11G4l/W8sl73PC7tMkopbceSmaPcnF91tRZjFKi22+oe4H6HJCPoS5J1mbK//yJ\\nT8Rfz5QUbkB7JOfcamUFqe5pzBjTjqU2uwrSdnec4SL5TLbtRdS5+UAo91p2bhjLOcQeNgsOoDLU\\ncu7wsqwhkiSO7hxQHyG83fVQroZ0FnUm3WL8VI8hjQIs+WY3tpYWdX8WA0apBOlOOJTnBbs2j9Kh\\njffqJZQuT/yoUKb35e4X1znr8XgW5UygZfP2ql9DDbtni5P2vzqjYm6fG4+LKC1hNOTspJVnTTLX\\nHuSMttJoPsGlY2Vk8pmDjsWo4cQCRpkk7+76dIyYJO9pYLxw37CzJFNnDc4UTIbNbpGnYyrLwD6h\\nK3CeU9Nt5iKXFvPV3CizaFQkvjDlS/12C3acNc7Jb58n1D2Mry0gaeejNDsoTN3HhTSu7dNXAToz\\nu8RRkxBrDhR9pyd9cR0dfVbvwefBBOXPMld84y5KN0kB9977SvzG7bcDUMsqd3dY9tOV7FyYF7Zl\\nbco7fRdhHaktrscj6+dED/j/xvmbp3F2tXdO2pn6fsMrXvGKJ5B6Nqv1a3xjtaGhoaGhoaGhoaGh\\noaGhoaGhoaFBMRvXadf4xuo07w/UEOzYUdlsrn9L2guyEGthUGq8qsQgIWphDlTHlb+v5DJsyTPI\\n/FJ/nc5PUJ5BzX8hEMpOn0PugDo1uEv4BoDk/HkFk9k76tfGmRo1PpVqtV0jl9IqQ831XeoV6uqh\\npudwjkwexiT/Ln0MRXPp/pxKn7LLFT+OfN9nzp+Pz1Qn7a7fW0KuzwZyxirbyI6cC8+b73zeutZX\\nWUDOq9AyODNgR97jnnD0Wx5mKYDSo6a69U9sXvqcXZVU7s8Wlf+3LL9j1DyN8VpffDnpkzTs2CzD\\nGgCJ0VLjv6ue1PllZGCpnzzl7PszXCuuPp7I0uKXOkJqn8NC57yCsg61TTpU9+wMKGVhe9tSuC5W\\n3+NeAmOexkdIfAQeOS8swNvry+64AwDw4D33TNTEH6Csz1qODuLcwnFyhFVcH889/NHAXNA2xdyt\\nxrYwyIIdHVcgv5qnNm/xoZTb2MN2Ec5OxwBnBqs1Ce9jmykDzzmDLrBVfYzJUw+wi7GxQPQO78PK\\nPmZuT1oa9Q/o/sSBVCvr8ahjtwfxSKgJes6oKr2sjrLx10PaqGxQ89SYB4lKfsCYy3MovXbVmPyz\\n9+9b+lhFF0BnVDCb1I+q56mHkg9WWr2w37G02u9Yw9oynStZ81vtM4cy2j1EpebW+Y0cd0/0gIX4\\nArKmlFmkPmCBfJZ3bnF+zGfXUfz/oBp0LeUzYEdymsuby9gWv4k13rnPGTspU5eflr8w45NNsjSb\\nBdzHfRqH+p0sxjmvj5L5VPMguGJpFoBoFZYKOOhSuOdZZYI65989wipj0e9PLM6SpbePkp2Xe+rL\\nWd+jWPffwNe7Z6zHI9urMvS9Tdfs6tz/5xZS7T4cj/Ryui3BHOdMRt4FxB85c/OseFxCPm4Aucys\\n/lb1msrKfGYeYO/JoMaAZ/89aWnWUdouKiverSGUVeoSEtNclvvc0gIofToTWudpneAj1xzKEY91\\neRFJSiWUq3/W0u9Lmj27FlrQCPN44QufDQB48MGH7L7kd5X16nWyj9xLdHjm9fF40Fnz8dp6PO6i\\n9ANPrAHYi318u5CHx52/YB8x1H7ruCzipq1ptWf4CK7rCJaI6xSO+7/922/DO3/91wGUkpraL7qt\\n2B7SnFWzzpjEouyhnKOYdpjJmUzFsUBHR7cj0af5OkJzktuBnDhxopg3U1yU8rvq2zycMvdgxijt\\nkrQ+yT5+ijpejUVaMR/YZKYvIY39LLlaJB+HXcj3HrPhf1/jG6sNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0N\\nDYrGWP226E+5Nm/azMQOGhd+K2uxq137UdvnVo0DP9dpY5j1MOq0DpcksnQCNXPuK3NX0jmrVLmg\\n48p96idKc68MD/NvVSnhZqdH2sy4XMwBc+1aJ+cuKVSb5FoSsi8Du9P1OcftS3A6dirnXOO3jIFE\\no9+O5wJCZM7cd+2etFFvb6xL5Ts7c6uHxBxcMjZl4JHl96W8pbpcNnbqO97xOrzpTefjVaq9NFfO\\neVC/if7lqQs/idKTF3N3GR6VWhk+ytnSJx+hHCKT/ybVJrq/s53K01Qv78ysUD99lGPBcWuUFT1o\\n9NQAskVXhTdKsHaXkEpyXSWN185J+b/ms4/HsjfWuNXOGq7pZgnlCjqbGij5PMoTddaa6sh9XN2Q\\n/70tqq/flSz9Pff8HoAQGdW9LdU0u3yilpJ92kd8YFDYMuhYylkg1YbWwXGNi6EukqZ9GSVLnUf1\\no1rz8EaQHVCLvB04SWeEweFtOpV0DvXIzvq+PQxj+xvGb3hK2GHOZVRvb+7LSuH+1XScrjHC+Rzn\\n6iYfsT2U87T7rNQZomZD4x44/TyQvmeQT9ayJ4Vzh/G4iV2k0UJHhNpzjwc1hn76SutyTm1ggLo1\\nkLekxADOvXvnPd/ZqLURzmeSYfRDrE+Yj/Wqbc5HxCW5vs4XkYbSAxbii879XTjyS1+WMrh3yAWU\\nbbhu65Nz77djGZaxWbS2t3zoQwCAV7ziN1GyNve7tEmuILSmfJ4mh2acQs13UHaaf5HZIzGvUhRs\\nIo1+I7GV8767htSCPJ6BWpotxPf04vFy1zddrllAyerkUzbk/0lyyRhlD9AZj6NyaivqkdflvJCT\\nbaxiziwGmafFyjnINbdJcNl6H4klTsbqEM+Mv5JstxlzfhAtE0LdOD9O2WQ8t2VvVIsY1rR+T9by\\n7OdaX3WtIPE9c64wcFLc/n7rMOUOCPXFunafyzpa19qRrwC3JL2PcwS/2SiLu8Ccakt0y8ia9ZJL\\noXMoRyr1b+ys4mS18+CDXGdwNE2jYs/2BNbtDdrakyVgkqRpEUeLo614Re34CF05sTbOxffvSxo+\\nfcHuU7va45LwtO05n1NHWrdRrN1PcN/lnb/+64WEpvsEzt3VnQ5+eY4BzjEFSqugdUmvlnUBtRpU\\neYrXa1bGPnKqdVB95feKV/xm13p8LF/u7EJSfSrT3+tU7dGYjm3kOjn/lKfGf9YtUQ84EYs6bwOt\\nzqj0Y895u5aXawOzsfC7pjdWp21oeKNY7I5pU9U7PFAaMvKaGjS4CK8bFIQb0gHAaZp74nMAKFT5\\nYlM3pdyNs25qcWDIlwanMBRaPYVbNfKIOZqPz2LAgPENKJcJ4f9N7OFMFPpqzdLNUnSQdlNuHVB9\\nAZsW8bqNVRMJr/4ma93EOx+MhuhLIKs8iIo6N/BlhU5evj2hQrW7etd256Yk+owkWA268z6RsoRv\\netMHUS5W1XjG3Wjo9i2f6svVXaRpj2CL2AL7AM1iVA3gS3j2Y50jWGdpUVQT2jRPnr+9+P7hROWA\\nmmo6jssYW5EvUPJWMkAfCxPcFACTxyYVKVbsmj6LaS5Xrg0LoXgNScD2LQldWLr7ehWm/TgtTJe6\\nfPdlnLZ030Q9RBKNfZzVZ53M/j/EoFhsqOrHHXLopoe7ENBQDT7f6CLJAyIkHN/G6ikRsICwOTSQ\\n//JcapAT/wYbKGtK20zI/0c+8l4AwP/18pcDyI0zpyswtN3ps3tItR+ubcdl+mG3JM+FWiDIpW5m\\n66UG0jfRbZVJW0L7SC2tpqCrv0HPqyuAUKZ7730DAOD2299VvPnDH/4tAMAbb721U8a4GxNtj66W\\nWMMIG3gEADDITDCBunHg7OBPHkMXuWr4zLz4hogqoScFQEnLXSobL8a5cQGlotg3rDSfKbCWmsHm\\nQRUX4ry0KKm8J2hJukbJfbkVdBbkC3EBtfR4OKoJuC8PD+Wcu67Yy0rmhtqhdEOcwjA+je3nbV2g\\niH63EehbJSpPD7NtXqbibw+eVlP1ETtIPWpSYMrvHj7enUK52aHKysNOVnUT0X2UwXjU0YSbS3+p\\nSzuKzxjFUWIptsk9lPKIBy/SUfe0XVPJHnZNn83cLkdSwDBTM3jQ0l5VHRnyXW59qQrb1wkuoe0h\\nmf6P8BPxV61/hRwPMsWAS3Dq0sdVEDryeS8idINv9hv6NZWwu5Tp5DddRNi1dZQOxmpm/LWZpibf\\nAdNl3uR2ax3l3Kv1tBWPvgLUjSlXQx+gXGWrJO6UkZqE4AqBBWzHfJ2M4xZbiPbWZNrONV1ZA6zn\\nk/Y/UBIUTqIc8/W7eK/SlnzchI2atFuTaX0vReVXXwXqGOnzps51kxR+u9C+T9VWUjCfit/OZ64D\\nTHbmMYcR7rj3XgDAm2JQqUSb0fbkIbF0e5vQ0dLXJ6kdsn+89yNvBwC8Ocq1up73jeeTKMdSdZdC\\nceCsHZ++huTxhCdZ2QfoGug4HpVist3141zRN9+FhLvW0BirDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ1P\\nEM3H6rfFtB111zMko/blwuRGdZTONFJtk1O4CXVAzs9GxcEaSso38/JVPCjmvc5ZKp1uq6GgMxxV\\n18J62YrHYVaKePXwW3IOCCV25laqYXc2redZV4OCWTKYaD4tOalo5uYrd9QMm5ypNuvQQQk1txPz\\nxnLoYySBQOhQPYWWoA5o2dhDNQP7mk7dTRT3UOpfeF/NHEu1Yc6CTVB3Bq5P16/mZnl7wnQItXXX\\nXb8BAPiPd94prKNkvs78en+qhR9xdoRi21hZoRbn7A59Qv4dvC8B9WAL0zjUxw11+5CCBYSc/OIv\\n3oS/+PSnAZTOQ4B6YCog1LOb5dXM+ZzBdwUMIgGksYL61GchcS1qfBXXVavdQM6xOBMZOwcABllg\\nHYXSOJwxcYAyQJWO8DWOBo+u9w7PHuJUFtxFU2jvYI5Yv9rGnV1+FuWItiXpy3lOU+eh82YNZXPQ\\n3chBPG5nI7ib4ide0oc+9EYAie02yHjSIf3LX/4aAMn8Vkeekh/ZA6pMSiC13CWUTBpq5VcwjAz6\\n1X8ZzJ43v/xlAKFV1OYqIB9r5+zIe4Ey6MJjkmbUmYrr05wtxRKnkX41tjmmfHtkXiizjtc+cuut\\nAIK8cbb7ZgHK9HADTuZbTdaXopntpSwA4vFx9GtjKeWfQcEQ0THDbWO0fmsGdmJnD+ACvhpTDjoT\\nOz6JthZ7UFnHv2ONrTVZQnWbkLHkqPBeot0rWiXz2+0glc5nOnUjwiO/8aBqFbRo/6cRrWeyzipG\\nhe1AzfYgQVlpk1iBF1FyyFmvG8g54LOF52gJZWAhtof87bxT3Xy5dKU8NmezstYuS/pwbQ9lcF7W\\nSgrSpJy3UD8r5nqiJmvriOPsrxSscoDdmIfU99JXppw4tkC/SyjXVjnD2znAIXd9kd/TOOkrnH2k\\nVuz2IUC5ftKW6PKstmBnnRHK+549U9/dM51EGbZXcRCL5bnsoZSMCA26V5tpynVb+sYvvuOFAJIr\\npNJiCCjXYSr31WwQYOf5TM6UpzHZbYOGkPRnLSAPgKXP3u2euSsWBEC+1tqzc8Ns9RTODmK73xD5\\n9PnyDL1frRScu69SjM+oSzh+xqq+89DOqVTlI5aul1zOVbvEXOrKW5evDLSW01pxwY7znczpFnq6\\nCnCu8iGAd0V5iXjrPfcAAO644z3ydpa+top2++bHUI5DRLL7e/nL3x1/hfF6VXZjfCw+qJwjVlCO\\n011/X0NJZ9UpKGbHZ8+w+vGnJglsNtzO7zfMpldd0xurDQ0NDQ0NDQ0NDQ0NDQ0NDQ0NDQ05miuA\\nb4tpe8+ugU5a5nHnu2NUaL4A17X2onbrCCWzjXv86lXJtb6qvT2ya8ELHB1qh6PqcN03i/rmOGnn\\nIGmp976YnU1vDKBbeL7xISSNYaRFdNrCceayGcj1wEmv7hrhZYyNRao5cT3PyJh4Aa4pmuZ95vgY\\nq14yoPSHGMC88QtpaLRQa0PhvQBBZ+SsQNViua9BfZN7jSLmKvmr+XNzrWLIn/cL1wHq75CDPraF\\ngRfa8h/deSeAoEzbq7B0+WQvs2p9nR+rGBhTNbEc1EeTl2Hc+VMjtA+5ePF+CwAAIABJREFUvy/1\\ntzeJf3S1NHusHzI7WIq/+PSnC767O8WvPWcXJUNavdjVvOimo7cgci/WUQatUr6Ee2NSfpV6dvJW\\n5zwOZVn5e5inGrtBv6L3KG+d+nupO16IswmZdD5O+28g9x7o3sNqOVKOL9tism5Qj1wlq2kWWLIj\\nUPrum4vt8EV33oa77nrAnhBKeQZDfDQyValMJ9tgU0Yi9uEaA1SZBwEHKO0//PseQj2r5yXoge31\\ny1+mZPBvAADbeKx78ynLkzI0avMBv+tWPG5341EttJW2VfcLyDQhb9dju3hvrc3UbDyUZ6r3zSPV\\nhvrxAsL3vSH+Zlih3Y4tr0+bPXNVw3WF9/W7tkHWLsvwott/qrvv3nv/KP5Sn7vuqYwlXkTZ90Pd\\nfx2Pdiw9ptZx720f/jAA4NZbPxjPrsejjlY5d1RbpJNRNYcP8XfsYCf1g8QbDqJvVXLtdXwgW0iD\\n1xxIOj4qQLlUhFt5HGDZ2IjEiqT20R6oBQ7Rd+mYr+8DgG/Eo/KmmcbtyWaPWt9hTpjbIwDvfP/7\\nAQCvetVvAkDnWx8ARsUsTMyhZDivx6N63wv3McDdGNtd6s1uDnBqEsA6vhQlf1pULaK0ISGUk+U+\\n/84h9X/6e01z0Wp3X1pPkdGnHFl/k3pgJXbj/Vx3aF0Ql4v0dc+zPiYxzQ0ox+CaDaK3yT3kgVZn\\ni5r/Sh/jt+KxJ1Xqcr/6B11nenmujzvTPbOnlcI993wl/vZcaV0yN11O5VmT5ohaDlT6roXq5f9h\\nFOt3a+eAIB8555RyaQ8c0XejFYZ/fW2ZKXcM6LgMX6WpHMeWsZalyEvp4/MeJocC1tY/e550CeeF\\nq+Way4K1dSTLSblBR+gae93Z//lI6dKK5ip8T1rQ9KJMMId8LFMcIQ+xDADn77gj/lIGPZ/gPHLP\\nPTHJcmIFZSCsg64ENZ+zPHrvqnHy3Z7pKfpQN7PaQ7fVw3bHdhj6S+4jnHFK1Ir22kJzBdDQ0NDQ\\n0NDQ0NDQ0NDQ0NDQ0NDQ8ATRGKvfFuQnLk9Jk6K/rdoZoNQdHMDj8znjEih1aQtIWgQePQa1vlm1\\nD86KUD6Ls86oOb8OSSvpngB35N2JNUCNTA851wFIvNYN9BG0k+57Zix5cQ1V0HpM0hUtYRjrT3PA\\nZxLUIqWo7vOSgqWYpmO9NOXabFArofulOQDE85jzgHpwHRWZoWMMJ9Zg8kJY1zY7q9C/E/Ol+dTc\\n+f09KCPSPb2uoNTpXoxpdzrWNcui/lGdo6CaONfcqWcdpvPh8BCJMVh6AlauTslUdD9ChHp/cwaw\\n8mXUlxzzclzQPNb6DpB7JPa6PEIZ5Vq5GHyWRxeexuEIPl6Zwv1YahtxhtEOyu+hrXonuy+Pcj3J\\nY5PywIiteFQ9tvP51B+XM1VVd+w645WuPGNrfzpqEepvc1/SaYk0d+6d7wqUk+qa/OOHzlnu1ZRf\\n7vN33dX5RiW0j2kUWSC1hlMYdlFJp/kt5gyVosSeQ/ntatz7vD0lR1TKZlQfh0BggoU3bpvP3z4G\\nhQdIvu1AypWYqvo+/7LKWax520pQ6xUfl2o+oPnkdUyOwK2+332+n+Z3cR4j4c/OfuQbWZRaYK+Y\\nF/j2h++9t+tfz+hSB2xnPY4hc4mzKJnOnK12sBnLtWvz2T6AW299pzwDKCWb5OuS53T89DlcLQac\\nh/nsx9IbeB9tipj2IaSWuxWP6k2auRqY7/dQXtYoy8AnJM+sPtexHSin0MeDfaTyJWsS5f3m/eqM\\nxDxI9ZKzk9WbNHM9S6nPVwI6wpLho/PRq171tvhfKH1uA5MzzhML9whpTOCbpnnUDM8eYl9WIWvZ\\ntXx+28nO7cRcnUT6spOs7oD0jdXHsMsHRJBdtTXoU3WUcimrxvX30VTf6DlIM8OqWWv0oOsSzhWa\\nF9bxur1PGbY1NvVefB99Pc8O3ufnUa6xiC2UHDmmVa+kbBEcW86h5H3q/LrXWSNM+1YeXUPnr3y8\\n68e+GywQXRbUnuVcTP0eXvqN+OzBRK/WKzHGOxAiWgDAZmSnAme6PFCOOIz59JYGlL1yFUMZQwMG\\nnewy6PJCO1D1z+yszK0uj6lWziKHtuXjkva0DTD/bsF2FqXMMc1qh3W5hLLFEMpH9rXFdnU3R/m0\\nObeV1mNjWa36joHaCbgMG1qAsxhZ4+uVp6kto68N9EtxnM7b+BBj7MW26ZZrRyhXTCqd5nYwKdfP\\nfxSYYzZvsgfsAkeX8/tYByEmTH7WmcvXHmZjZXVNb6wSyUSyBl+M6pCVTzPL2ZQ5zFIApTCs04mL\\nFjot+KaXLrDdPDQZiacBZtnyoptRNZNTLkS38cz465ykVpEc4FRwCptZKs2LbsBcEhOgMje+PN7t\\nUvNZGmRLF6V6zGvPJ9jj30StodYdfaNgkAkSngooN5U40fdB0dwDMe1W3q0GUJwwXEDTjUBdMPN/\\nF5R12ZOch9OwgwvTf4uyxdJY7FPYwxcBJLGVWEK5zalTkRvv6ORS6708+rkk0PUrV0MJT4nwxX6s\\nm8yTXO3roo+4GqYSWl5vB2qQ6xvzNbN0NzvSzSAX2GumPHUBb8GOKk55YA99oxvD7sIdMQyzABa+\\nIOV75uVZrlLSrXlfkJ5GuVWjefHNrrQxduedtwAIG4pA/l28navQ7gKqtkzmzp2whPs5ivpm0GE3\\nS8zaCYobseoioOYYxMeYmjrJDbs2kExovZbVoDrhrBx1JtE7VbniLZ+lOinpXUzVEALcxkrOL1yE\\nViPI1Ie4NaEbq4TPGrplBLsWcITpY4+HUNDe4JuSOgN5z50WqE/VaWfMLHi2YdPKccRHcv3yLnFw\\nNjqFgTinURU4UFOgpxF0vXvjKNZo2hRcQ6oJN/pk39yD17qadDuGovC/3hZcHDtWJLduTvpnAEad\\nnOfKBZXcCFeEAaVJdfj/elmssnZ0g3/SdtoOylF+1CknNrPNIL1vDWWf1x7sc/MsJUHfVluqnNvu\\nvlUPpYOAmkOEmjq4to3Da5O2UuYrv/lenflZs+sAUpDALQzx7HjFw2npKLlix0OUGz5sf30MMSru\\n4HEdZatQ9T77tkuhzNVjEuB10OUTyDf0ff2gc8awqEMd8TwUr24h8wlpI2UZF7LSzXJj1XO5g3Ij\\nTpWsOmsAqZZPIknrvjkzrqQnjrJzvhW2W7kzlwz62M4c7ADqzmWIYTH7q/KwtnoF8m25MNJR+aIu\\n+XzzZw7l/JxURpvYxvXyfGAzpjoSGWSSTN+DEn/yfI+R+kXNuYmPaTquu+Spdegy2Kyh42k+ciQJ\\nRt0juAONBZT5rs08LpcomIaz5/33342bb36LvUm3GevS5CXsFK65fJzQ38zTMkbims/nRu0Pk6hz\\nQGn2rysCl67mQfeSfNIVSwmUo9GBvCVR4VJObvi78Pv03+XP2kWSiTx872kAOyaTuBrk2kNjrDY0\\nNDQ0NDQ0NDQ0NDQ0NDQ0NDQ0PEHMhg71A7KxSs6E0shdd6X8IJos5I75VRsKST0JGjzHtYx8a2Ak\\nOLsmmbX0jWs06ozakrniMObii5HH9CgG+KrlhU++Aohmjrxw5RlQ35GbY62g1IZe6JiL09hdWmPK\\n2gjPdl7IYSW1l6HOt9vE9xLTNDjp2hh5kC0gNxdw3WUq8chMCkfd+ZqZUnozA7Ctxnak+nZnT9eC\\nNLn+LfxPHTQZV78SDv/bSeD58RQb/H03dg+g6ezJqOH3MENAqaOumUBqy3KWinI63CQlaSNHnRlc\\nYrMn83IP0gBJ4T3UuTwK51scB5TF6z3Qta96rsa0dW7eY0ifcdSNGdpqwpOHBRdhDWlM8xA4Kyj5\\n9/z/IkrGTo3J4tzImpF9jUPO9CGfy9ju7kqsOuXjuKsLnSsm2Rkc4K67PgAgmR/rE93EaSCmzcOY\\nv+WoJSYfcozEUCWoXR7gDMpZKdXTcTsF0Hbv5uTaxrzdQf53RrqGs3ExR9k2iRGpcyKQB0jzkUy5\\n98zNFUtbc5CRO55I6dJRLUxqQYhSPtfjkbwP5Rx6DR2grD2mDfdvAxjH+W/NUijL3hkJQBr/GIiL\\nOTstea8Fl6iN00A+VvM9s7Uh4TwfZDmVj7wXKOfS29hukUKhgc1YIrYDDetVcqYnh/LQGqvziwMD\\nv+YiKNxzScKe6RPVRQ1ZKhc6Nv+PA4hzcJHvGneUUGY223xuUbOGMjSXzkcrdo1YQR4mDAAOZQR2\\ng2xijMSQZG5rzlmOg8k1jO1tWdhEfN8gY6oC+fjjI5jOxrX257nX7+OOOxikRwNjEWwROnb4WLgW\\nnzIsQoJxrNAcugMDZRCztLx/F4nRXbb3PZRMLXWK46NnKOfHPvZaAMCdt9xSjO7qksJ7DvOr7ZAB\\n6JJrrnUk2aNm6Orz/E53nGSPNwts2f8qD7sUdRHAoJtjcvlpVRzXuM3cHsrZUb91Grn86h58duB4\\nrLx9ZzyrZdxwQm9dFt5nsiHT0dttf9I7nJevOfQnqcXbdvdNua4JeOvHguXR+265pZCR65ah+dpi\\ngD7oBs3H6dqswDzpmOarag0eW5uFZg234eX/ahvEutDgW5OeozZfatkLTGdQv/bmm5H2cWpfYdKd\\n8+B30bGCVybJyXkb9VHxNMrRhW3oEL/zO78BAHjZy37D8qT9xuWEOTwS281zYn7VBtQlVr49jLe5\\nm6Rfvf9OAMC7br65mzdV4uSzfaWl/cfHtto3urYwm1H8B2RjtaGhoaGhoaGhoaGhoaGhoaGhoaEB\\naK4AvivUPM1xlz+5NecZ9yehu/vOd51H0h5sxeNyp7lKfkdLv0Pq/lm1tgDwKEYF/0yP7nL5uvi+\\nJfFdQ9T8kJA3oExSZ2IFXJbfqTz0zKQMNddtqb+y0o8O/ZPN27c5RO5rlk/KUwCo+CX7XsCZkzUE\\nZ9CsA+osVa9aMk4DllB6s9KQEP5M9SoVNGgDY/ioRynX0SwgtWG/9kuvfjXOn+d7omb3X0aewhuA\\n5/6L8JP377CJ/fKLAHw+XguMVfXL5kw+bUVet6p59/uUZeU9bUH+Z40tmQfARUz2CKznFu3/MRKr\\nZTpm6+1S88jRw4MT6EjhGucaZ0ZHirIOaq2Gta76/zlLr8F5vL2SX/8YnImQ/FatZswcwAMOTtLb\\n7yHnaQPPMM01ADxSaKNrXB31FOWMILayLZwxX076JNZQ6unaosIz6Ts2acrLNrOZsaTqvAlgTlr3\\nbNud+3ruofRKqm3uwM4p79Pb2GIljXM1gdpMTb7UGhLrZBqn3ZnPfMtjKBmryhFwDuh+9/aaR9b0\\nVrdSqLFn3ZtszXMry5uYZ+wZd37g1wAA73nlK7s7Bh0r32WJxFffju9YiO2EfreAklGi4YW8xenY\\nOpswADk4wi7FfCpbk3BWB5AzZQC2HffKqqHHnKXpgSgUyko8bdecR6l+CZWND4TS0BPikt2XeGX0\\ne7ZUXNFRkO3/RgA/FX+7T9mHJJ/+BPWQx3OhRmn9ojXhvEwd81g65Xcrt1yfdYCchao5A8qAoJC0\\nNQuYWaHfzUOhBY4xFF/zLD35QMrjclZVTc7TMSn3HZkHQ6ENQ/gOjPmgrMCdjolJcI7VwC7My35X\\nphXz36t9SFsnkLOXKJVuxaPKWsmnfc2GgeDTUnk557ts8lu3BN/lZ1HyvWprtBqYP5bl0ciAH2T+\\nkd06QVsXkXqazxSzhEs1yufdtWNgq/KLsP/vxWuP4nOxLaya3/Ia+4zt/A1veDne9a4PSsrU/5kS\\nKKVC5kLl6V27dlGe5atGQPvzIL5J58jDmJdcfleGrXOhV1Cu6fckTQqkx5yGHNxyy+viu0oWPccm\\nnfeIV78j3PeeN72pk5fKuVzRi29NrHi+z7nmQOpzGzgeaNvzb6g92mdKvY/2ry4bqhTl/lfHlXPs\\nrwfQ4Mk+4vdQ2hKG4ykMJq4H9UvUfBSzvwy6O7akNM5kD9eWMcCbXvYyAOi+fJlfoJRe5rt26FKm\\nzhxuuxAsCvNR8eabfy/+/0xc6qIxhLKwDm5AztzVZ16HVO//P3vvHpzZUV+LLvsimUiRJQTKVDRT\\nNaqJB4NDwFAGF9ghN1Q4lXCcm0vFlQSSc+8xDxsMCcQ2jsmNH4wJBmPzujjmYTAcO3FBHtTlQBmH\\nE3IhM3aGuDAmc4caxqVoqjSqCFEfkj+k4owC3D96r92rV/f+PGM0715VM/vT3r179+7d79/6rWau\\n6rc+Gp4hxx9VCqCioqKioqKioqKioqKioqKioqKi4ghRGas/Jch9cMvRJNbNeuu6aUCu1VKy7my/\\n5BIAwBe+8C+IticyEshs2Iz4MVMGTJo+Z5LOIbWCA9FK/ivIdxxWi4rbPfj3fkTbBN8o2Cx6mJbn\\nULOLdo/zkLNodJ9a10xShtBI8yvNc9WMzG17J96edIPsHMrg6rf569xBhvBzQLrbMG1Iqv84SCeN\\nCLlIlsUQcjUu5RN46hjzp9//fgC/mj6GSTkvSqyylN/JYr51GDgwncSpe8I7O0F5W25ddou9nmPY\\nKeT7vJPPpupxuqsiw7g2nLJi/cvwa4Z89R2gVdXr6CjSaB7wfUtsHqKkduYsXL3GVPcy5l9JLzjy\\ngHN+i5Z7/iZj6mEAgZ2qnDFA69ViQRV7sYl5UfQ2nTU8gm0Nc4bf1XepBaLGX9QxnCjcoepk/E2+\\nQOT5LlsIZfB5X/I7bwh9xEc+8o8o84eBWUwg5w3qsaRcymvHpq3Up5RS44wL/ZZeFzX1XR4iS8nZ\\n0juyjJVqMZC2LK7Xpn2Wx3MA3sdtE9auK3MqsyO2/c4cZHr0XUo7x3ruKcMqXPujP/pAc07Z3F6u\\nyKxbgffbqgtNtomzwpTh5PqZR1t3y3mA2pZ7vzKE/KsvYkvza3MhNh/xAWXGqXJINHbti2c67l8B\\nWS1jLbuQuoib5dmuYx5V6cioOijMVb7nYquFPS1H584zTZuRjzn13ZSxl2MN6diG53h07rXCdR5z\\nDvXg/t5Hm9p3HQ0mTeodwf7ee07lRPG7cdSh7GhXIVQt0Tk7p94dKetIfdd8VHiwCRP9udSzIed4\\n82nOoQZUBzcNo7F4qT0IZVO52nwsW9R2budM6O6xlIGoo1+F9hk+hlAN7Fy1emfBm/AC+ZvvwNIc\\nOXQlLfuNQml/AT6HtXM20VZ0xcutci2812Km3DqKfHwRYr/lls+0oeiBqXMd74F9RjqDnF3Mp08j\\n9jElRcx874fc66ZUVrzF1fLA/NxsYdYAXHvzzQCA66//aDHGUeRtmjIrvR+6+/rr2zDXXvtGAMCt\\nt95pces4hE+L+2H4KJN5+BjyWfxGo6T/6irEqnbrYfcibytUHdr568ok7dKW1RFtZMTrqDD9CttE\\n85ejnpI3kb+DxhLbjMBuX26OwbNstUlLuh+PjiDi12V6dbcFf8P1tq85UIjTy3bcIwTIvQTVfyqk\\niLOotSYtqibP/FcfrVLfCwCzmMRGe8GdGNgYv4PTdGF1G/JlBGKl3YyFrgdrTQEaQU6b1gLnFfar\\nX/hC8+tC5JNzNu1bkS/vxAHXJnH3gT1/rbnGzqmfSMc3qRl5Wjiu83hInsfmmjHMQcWXA9gszMg7\\n+KB/GvnSoi6ierOqXUE6yCtNK/3aGHqygFJyvz72FX5nc7y4cE0Hc6OWNp3Sx0aSZWREjt78qfMN\\nnR59mX+tdSsgmG/hWalLtC8E6bXyEkaTehaf/wHcQq9Dfv6vNccD323TVXLgmEMK39pA01UawJY6\\nRsbBWjEuRx9Ya06XlhCBdPskpiu6A2qsg6aUi4VzTx5a67zO6KKHd9/EMnLXS92IJHbkoSPuZRt1\\nMKSe02GxL1qNIBaYMMGkO9h5yKcF2gq5cyxxHrQdjm5U/Jv3+UI7EFuiOIDge6pjt+eaLr8zDGP/\\nZluPh6zuaWjG+NmPfKT5NYl8SwWd0vpiXMlVnG+tObUxri05uMizqTkumnR+Wh59CV4X6bo2eVtG\\nuU3K//Z6txf50otDp0Lebh6Ab5wyJlswdW07oOWqJF0w2tT92caQEHE+8vEIS+YU8l6xtABfElEJ\\nT83fUxfMUjMAJylLYkrxYfo08rZGF9Y2xuZfRunNfdDPknjDDVdgx473N2eZvyoX4S2ftmNurFyR\\nozs98r5h5PXNzQVxKdg3mwjjKk7/XmL37Ze4wreab771cGL2KjnQl9LA86VzYYz1xzfcAADYsYML\\nDeF5lJaYkimtf/MF5BtaeTus57Q+M8e5/HiwEIYjVxVeKJmqNw7cnCe26fH7sRyxBdiOuLBOx2d1\\nhnXTrfaRbmZakPtSQRE1EHoexwVStjlALGcufTJU3OiHIXWDFCAKEviipqZtFDFXXDpioqFpaHq1\\nb3Zjms9IdGHVN/xRV3nmwUwhfU47GQLw960AirfopdIZl23ZM2zBxsPLsi4cxxquZ9wBXgXVfKRP\\nxIWXVAgKAJbbBdXSuF1jAPLvqZJc2qMBaV9R6t9duEZnPuVl4HDe+wg1X3jLrX3byEh44pjJy+ly\\nu6dT2y9/rlILPn3rrQDibLWXlJa0NPPaiGw45samFcQ2sCfO5hsJHVXxW7BUqZmR8EW/OeQLc7qQ\\n7X3GoHmnlpk49wq5uaVp41aQG5i0nXHzoualjyGIccQRAxGFcWazdlN7Ahd2RBu2J7NAL8kAc55z\\nS27etibbBPumy6ksAUtGyNEgQRjy6m1vey0A4CPvfW97l48cVa6L79DLxipqpjq+G4dvLCpjtaKi\\noqKioqKioqKioqKioqKioqLiCFE1Vn8KqJ3bHUfOgZPre43NXGW2J5EL+EcRcVoT1KbsNj3e+Sii\\nzYCWhrnmGfMDN+BR5yIAeBBfaJ4vLNG1X5E70LwbbcBzyXETHslcK+ZbG5uu5Jd4I87hVZYNbdzu\\nQBhtVFe8/e0AgA/fckv7Ts5AYOjUDevEwqBUDSF3Q1aLHhludEvut1bNGeQ0f7WV0qaWcwnIKZkc\\nYM0sMQ8I3+Qo2MTIJXmoeWzDynjD/xq9p1gMHiLf5D7Q5ZtsAbVCOiuq5JJWdgcOeHNTfv57U36U\\nU+gsjHXkVkSN05tWZanmlk21qx9NrlYZyktyHox+Vy89eh9/e61UtgHzfq51MRwrXNW2zfkmDDuH\\n6Aq7L7kyhZRPpsi5dWlue/kpuS3zPsa9itzdmfWzV9x0j6mbQsw13sGYRltWk1vpp5CzOdXNrZex\\nUZWH5PZk5TB0iT9MIHfW3ihEkYiAyIBXh3ogtfgPYp46t3kUuZsb36afMHy9rCn70/PU208g518s\\nIfaJqYxEqZZrKpxtpe197BlDnu1rz8yhe1uMEXmis1r55P3ImarKVBrk6l7m9/WwRTYLSd9qEXHj\\nnEGiBkdDFoBxlr5wZIGE99ux4wFEURpC5T2c6axsNK+9ytVxSQZiFTlvx+MGYp57S7YdOa9G4/bR\\nYEjjbCM0kaK0IamPv9R7KYThxiDTAO7dsaO5xjFg2iofwL72TOltmYKSAAZzwLf6OiApYos6246D\\nhsG832SeH+sol8WNxuCyrW0L38xbC/V7YSwlDy5nUw9LXOFbM883Iy9RnhdjWJRNpNK3+MAHbsBd\\nb31r8gbKLPOWhbE8ithqOTtLubeEtqHOQlQ/Pvfb821xlXlIcES6IPeVWGQ+YiEmAEy2Lr78djpS\\nKHH0gPCdw9vPt6naOK8kT6fOJGPr1WuePwTgm81ZHwXqBok+rlmQJ/H9QpnchH4nm1ljcOa6zhtK\\nJVnvKcWjcTlrUcsIw89JWC27gM6jYh3ipsl33vk2AMC1b3wj7rj66uQ5/nz1w3JPJ4WPN9VBPbpw\\naw75qGGofYZfiSWsNP6OXjUbgS6fXqYNCPnO8uEigioT4G2I9qLEeRLGS2PJW3ESqfenbqHt7Uup\\ntyeUbezPmULKvtY4pxDHuKV1Gi/TZbf6UmooLxDa+Q/dfTcA4LLLrm7f2T1R9dnO2g1zoHDf1xqm\\nqpZNblRHmYBJab8iU9V9IviWwKnFWN2YdaXTdGG1oqKioqKioqKioqKioqKioqKi4vRElQJ4EvhP\\nhXNuD9MtRpbsWrT60QrxmmuuAQB89LbbRHNpxu5TW4qzU5cQbT2pfUetd7TyMZYZpALhQLSRP4y/\\nx87WhksLJu1BQLQ3BYbjFVe8CADw/330kYJVktbQA4gWCmelqqIS4fYr/a3sq5BHt9xyT3NusgnZ\\ny/hIkUlY2l5JEZVsjjUGsXRUC4tgrqklbjGzEilDzm21S8jtZh425tY2yZOurYfUKu56lmvJnSzD\\n94XDwqPA551PGnV8n9NYVF3fVJ/j2kdqJXbuXbBEh7L80YapSkUztXq6FXIBqVWV5xz+LTXOxUzj\\nRr9uyf6+u/CEnx5qhVUrK5DWMudYsQU4hPx7KJuS+eNbUa2jj8XC2fQIuFZl+HshiVPfwTWcSlbm\\nkv4Sc56tnLNWPFVAyAPNB0BbE+XFMPdYz5S5tWJh1lttpNXCZg/O7FAL9+6iHi1Qlq3XTWa6GHRD\\nhXMbhTTeyZYDElPqvSgQU6rtC9/oDTfeCAD4v9/xjjasqwJGblephhNriOXNS3eJ7+591X5sa6z2\\nrmytWl3ObF9FrnFYQnwH1ejsepeJwjm/P7Ic0k3ieI255/VV2bDOeVtHXvui34Lr0JNBqL3xIE+I\\nJ4sSYzD+JjNJ+SPe4yqvv8T+hv32MYuyUkt165AdS65lpXrKZzkDXvl6DDdjadKyrKqRaNLKtsK3\\nVjsAZ0+qQmv8lt7ihit9Yak7H3wBsZxzXLzYPGOLjEGYEmU85pvykPM+0qYzbmU4n6RM33IjwQ3q\\n9Kty88TFNs+1XFidHTqjCRK1UvN6rSMcH8vFVmO+6V9GxTOiy1tFxwD55nkh7uvf+tZMMVxT6C0T\\nw4wj18Yv6VkSysX1elwqyV5zSnqOq3ZtCfnshDm3Ffm0WUt29FZhPrEOamte0rDmqJP3bRxj1Vuv\\nQ8i/dcSiqIOWeJOp+uyYpNPHI8ou9VGJjjacjeq9rCoKuw+A9u92WZj3AAAgAElEQVSxrYh7ZozL\\nxkNA6s3k353XDkr8uYa1+kGGO9/4xjsAhBmj1x2f0Wp5PmjXVDPU694U8lEHdYcXMYGu2tDHJhlV\\nEa4VDhyd7frKKHm8+QiYebMA3SzSN/Rda/sBb+k2I+f0l97Q1wWUkc5zOsfw8YjOMRjOy8A0YrvA\\nunhAwjL8sB11Px6f07z7Yx/D5ZfTG8SxnnljXHfZZQCCT5h77Cgb33WrtS67F5n2833LybApF6/q\\nWFHvPDE9hn96VCmAioqKioqKioqKioqKioqKioqKioojRJUCeBI43NVoZ2hELSRaE3jlv992GwDg\\nj2+4ATt2fKY5y51dZyw+IGenPoZcAytc62Os1dsk1OZIqwUtKqpdOdGwA7/aHPu4RK7SbhGOX/5o\\nYNNdICl2DtQo9mFfZrvUfHIukrKuupRWeC8Q7S3BptfDVGs53mTafakd/eSxnAyyK64AmG1ZkJ6H\\nc4hfRve/BELeuhJhtMVRI9C1Z1R5T3lTQKoH6DGOQPWBFuzqg+3vMdMmVIuaf7kVKBcqvpUeAVV1\\npFU71uc/uinoI91/001wOPfoACLTYrG1ztEip2xE/w6iXVz8msfOckyUHBc8FWo9dSbdOlI1RiAt\\nB651qmzE9eYb9/AcCzWKnAUYuTBbmvpc0jtz/S0tB116XhPILdRbJWyJM8uj84FiG7OCWErYnvPb\\nq6al2ufD35sKTFUg5MRM89v1kAL7hwwoXiUTRnkrzmFZk3RujLX18ECtp4Ch5Mpkc4xnZ1ut51Sb\\nPDC/w5d8xzs+0pydbOLuJcwZQBktE8g1Vkuqu27bJ5YRv3pk1QOBUUdlTmWq8gnOG9Te0MuTMmtc\\n4XKyrT/63VxLt8TVZpi55hiVBT/+8fcAAF7/+j+Re/jurpOl6mL+XL3mz9X0HkiuhO9D9uPG7lLM\\nVOlRUxcxIkfvdbT+lDQugZCfzjFSNpGrN67ZUa85lGld4oJxZ3Kme7+E8bRo6fS4iNK78LiKnJGb\\np76XxUktNue6REwgH0Mw3+YRdjiOMaVjgZxpphzU9IlaHpz9uJFgnSVPbByRzxih6qBWJta7wgFl\\nZVIfla0jVZJMx+jeAjqzaQrK8k3Z1P22x1JoGeOT5pNnTCOyt5yzu458bKJMUu4G4OMLjcPvZ2k/\\ngJyxyJwMY+iUs8zx6qECU1pzl6VtWzNnmk1Ce53nM7SNYQwbq3WpUAapI5RNauCnqp5jmM+0F5Xp\\n18Xk1X0FvBVQPwnCfRzWEOuJj5w17n47kohwNqxqXXpPplr5bM2pFdlLUsXfM8mdPWzCiDEFvS4t\\nSdoXM93pUfSaq2vN93dtTk1vHKNrDjr/cR05/57pVl++o7O3Q2k06Z4J6g/Dnmtf235PIx8l8Ysf\\nBIyxqvOBQT5YjMnLmuZIiem9JOE8bl/pGZXzHhfnFgeRKpnrO6wj74cYd2Crei10X4H8udo7+EhM\\n+wCfWyhKo5m8F9acyffDSZ98qqEyVisqKioqKioqKioqKioqKioqKioqjgxPZKM4TKvtabawOkgB\\n01kAgLNL/+iPLsEDH/pQFgoAPrpjB4DnN3+VtMmcLRA1Vql10xemTvh7E3y31xInxy0b25Fzcfbh\\nwebXVsQdH4MFmkzXGeRWNnKm9gKYaMJHtUhVD3F1E1VHca5OifnCvOIToyVvMWO8TkhcJxZjdSdi\\nKSjpDaUqR9HiFfTEXB1FeVJq6QNy5irgbIdJLCbfFkitWEyLsw2WkVtrU0afW7Yi45qM7pLei7dJ\\nqp3qeaUssO62LNbnDzVMVbV6dmmDHYRqpPIOshLPQ25NLKmzztlblFTFjj5KT3T+eymcfsESEwEI\\nLYXrBKlaVc7PVSYOy2Boy1irp5B/F7XCsnT7c5VB5UqRw8j3/tY8KO2BC6R6bG5xPoQ+5turtMWr\\nddlV4SIzRGutPlf1OX037HXEOhN5E4cshJ7Tds+5v87A23hss7+XoGxS55jEdr5X9HrwkrCQ/AVo\\nWVMlsxm7T5l/XUqoaqtPuczsd89H/D7OphhGzghQ3ghz3r0BVOHOv84Y9qGf8SmUQ+HcHdfZXcLH\\nPnYDAOAnP/mJhVV1MfVXgIUrqZk5d2ykECYgMo5ia9Jr9dXms/BPFnyyatnGL+wsUdWv83cflth8\\nT/MSP175ds5QZWq0J/PnRfVRenOw/KS6iPxW+5FiFDk/TOuSfxMdu/o1ZUo6jz9gCDFX4ng0YFKY\\nRj721JIV24PuftGv3HT77bj66vdbbOpTkX4XbQ1Ku5ZvFHwksIz47rGf0HKhSqRAyhXyMqU+FFoG\\ngfjuUX12ssAv9ZpZ4t3wu/Wy0RmQty0zkg56kXH0t9imKGfgRbg2vva/I3ZNc2DGzvEZWgO9ljHs\\nGProd3AzlSnt7buywZi2TQ3zcDFpZZxjqyX+aKhKd8NH4do6bGlaFeXXOtdWW6hSPwekXF0vreNI\\nZ7h6v5Zw/37a+vTafsM9C5ayOZJ6zXlpVbheZi+Zd2620CwJq1gxb1T3r0xnmnzTGfl7tXkec+wR\\nACGf+XZ5ehez5/QK7N3cl0tr+NFhrHrbDpR3SeEoJL7bTHM8BzkXNLZrfANfxxiFjwRjmDXkszOF\\ntyfEAvJVGW+DNA3ar7hXRmk/DtcRVjXfso+jc645f+hl5W3QXLg0inbt5RJ7ll/h2ptvxvXXf9Te\\nQsfIPvZ0r4dTDE9EWK0LqyV0OVEA6TTJF+tCbn/oQ3fjwuaMN2Uhv73xI/YiVqswYN6EPQC88Uo3\\nWxqSKXapknLozYquzvQ+TaNY9roMq7jYwUWJ8+Q53lDocPBQ02k/0qZuTWLRZQSmdMGusVLqgKU0\\nKPFFVh2q+bYzcdAZ3UyPDx4bcC11mQTSpRZvbPW904XVSXHL8imgDnJeaE/RcuuLvFpqdRADxFwO\\nG2t518Rj7HLcDVAHrqUOo8ul8BBizdQpOwD0kzNjTUriZhq5E7oePe06tNxu5xjTKLonqcvIOyHi\\n6LmGKbwc6CTQF3NK5ggOMXUA4guWOuCOQ6Y9SZgRxOErl6uZo+pIy1zV1oF1xxclh1Auw0AoIwt2\\nTcuRT1W1XHh7p9IDa817xVZZ6yXvSNs2dQz05SzdeMIxCl0WYpniVysN5Qgd4LiD49HDrGwyAXDx\\nxWuxtmddGwWpqzqX1ePXY+he5h78RAY1X+IsyQXwa4cWhm3yqjg/e4us02qfhA6qZ+o2yfZMRyNx\\n0YNPVvd9NzfwztiPxs0QfOK1VX77goO2+F0TICA34sXWdaxpb6P7trb0G29oKi1zdmMaqfM2kE6b\\n3TFOl374fovJc0pvFEcc48h7vmU7LmVLMzRwr2NeFqN1GwzChTF0QdgXeZUowJLqdS8u5G0yt8zS\\n/GKqCVMSzSFYUoagC7J8p/D8MSnlcUE+xHr11bciH+3qMoqPBSOY810iDBsB7Y/iSMCXXQ8i9nw+\\n9tXFUy8b++UajyRB9DtmJWlo71+0JY4lk2lRw1dpWzggFY1ab/5fbFNdcv0GQk1wOae+CMcMNWWj\\nNE91g7/3JgvIF6rYQob3n2+e56nbil4TotfkxpaCkUAXcQBgG+bb58SxgJrXjp5B3VsTIKen6KzK\\n+x8SK0pjbL7vOPLlRpaGc+Q+hveFTkgYX25Rs07ZFO3tciyHXHKcs43jtMy4qIamb6aNKZSHRYwi\\nX1KPhsw+zgUALDTjdTd+612L7VtsLVwNX4RlbBnzmbnUj0B8v4nmfWeT9PELaUxHl1Sk80IfFWgd\\nSd3KgbSf8h47yvuxbPp4WZ/jS30lOgEkTFdZ0/lDqX/o2oSqFF5JJk530hmjjxlie61jJJ+d5LSq\\nQQQZ3q2tEGPaXAjHIxfDw6KqrzfoUzydR8NseQJhg6rUabawWlFRUVFRUVFRUVFRUVFRUVFRUXFa\\n44kYq4eJ02xhteQ4QKh9wUnqtCFMY3ezcn9uY7tkjP1ENN0dFVZBd9JJE7aeQbQP8G6nvwO5y41S\\n8Pm0OQnvlk61B9JSRBaZWoyc66QWH7dc0t1kHqOI/FmPYRW5PVsZgERpCx1n5vA7HGzjHGvseiX2\\nBFNybPiCh4fI3nDnGWVKuq1rL5gXzPPzmysliza5Euq26mzE6OBVdp+glS53W11Ev3UEdueiCSya\\nnW2t+T5qX2UINQ65hZJ/jyA3IkVLfXSgJdtsRcI4g2U2Ycd4OVVLnLeuao93h97w9+QAd9de55Wf\\nHoNaNF6bT9omt78OgXWOmzxsaWqMslyc96UWYOe/TCFvY9Suz/atVMrVTYxxAWXeljI2WIa9LA+j\\nLNrAowvAKGMrumY5t3YCue04imS4Bb7EcvJ6u4po9c7L3ypy1qrap52DcuzRTxiszkLRGjyI3aOc\\npNS9KcrllHgNzjhUOCdZWUz8PZPccUC8Orzt0dHBIKc8L4fK/3Sb/1SS8sBW6SXp9vcrbZbUVZK1\\nVy9tMeN9rNbKLl5oLIspUxU4Wm6JRGlck/eBuqkUy+JMcyz5SfArzTXHA+0Ge+ouS3g7MtJuOqfx\\nO9+EzNDFrH1QBhfllvg15pPN2txBku11X57WS0KE7zPT/OXvu5Aw9mAhncNY+sLMn9JYggzFMesb\\nwxgo5SC94x2vBwDceOPHEXOY35FSHYsSZ/g+7IXn0O0vshHw1jd9RonN42xoQhmr7pFwAGM2P9BN\\nGP3ZyoXld3Cefnl84H4d48gdoEsyNCH8K/7gDwAAX7733owTqvwmbx+5RZb6BCpH3J9G8B00tR73\\nTHOcA3DZzTcDAK6//u7mrM5+0hSTSzgsZZShOY5egrqV++hF/XM2HiUfS+8/fAwMRA+IueZv5e47\\nu3QYypYM0N7EmbHax3W19sor9zFPZP+q/JmPGWJ7t6fJc8o4rMg7+AigNGfmMbBMOWt2XnR8U26+\\nSVZ1HiIy/Bfb+IblHdJap/xE709UkMZ7jkMA5jO/QpUWObqM1UF+UiURmlj+tGVK+xzOLc5Dzh3W\\n0aKXFZUTKW0+RXhZ03am9Bz9GyhvmMbrcxb3HAZ7p3rbyHK0isVmzSSHSnZ4m1oaARCPYAzbjNld\\nEgvcl0lvqNiB13T16XPm6imKDRq+nrkx0VRUVFRUVFRUVFRUVFRUVFRUVFScjrjkkkuwe/du7Nq1\\nC6997Wuz609/+tPxwAMP4Ktf/Sruu+8+PPWpT+2874wzzsCdd96JXbt24Stf+Qq2bUt3enjVq16F\\nXbt2AQCe97zn4Stf+Ur7b21tDS9/+cufOMFDT/DvMHGaMVYHsWWUDeLqHcqiDLyifa09YFWuzVh4\\nWgAOgHYDZwao+lxJ+chtCapPqHYFxsVrrieo+jjOwyWrbL1wH+1xc+hW2Qibb3Qt9a8ht7YRy4Vr\\nao9yTTGVM09tNkfXdndkiFsLRE2p9AjErzfTHMeR27hW5e+Q+660o0xA50hsR5mn5SlxZeCSnSrl\\nM7DEOQ9WZfDD9+k137WHhZZ1VmIgEFqbmCbnWtIitwCtqbrhG8+4ndhjB3JOpDK1XNXpgMQR2gEy\\nVQdxBY8mY5XqptuQbxIR6+kIcnaxlr+0vZpvrm3GbKYzpfZKr7nMyZcgMlap0cr8OYicTaP2ba3h\\nQMqb8TzWL8Y4XE3wkKS5xPksKW8yHqZlsim3vZaVr4zntBUeRc6iYYhvShoYU8qmZsvPY2mjKq/l\\nR5ch2I1BPgKuqzWC7m37Rgrhh9pYohYjmf6DGIf+DKBbZF+5xane8ixWMNUw7dneMuQaYqvnOV/6\\nEhwlqNqY64Vpe5tjtXDVW2fVSHQ+xjS6lTNLfCgd15Q4DwCw1m6gEzVBtYc5epqDvrFHL2GhMQ+U\\n/dTV85Xyld4HkRft/a0ysIiYi2uIZXAuifN977sCAPDJq67KmOzat7rX0g03vBkAsGPHJ9snsT1S\\njyPGybTxzfYUFdpCqDH02mezvVZ/BuYc36RUvp1ZpE9673vDhmpvexv1f7WnT1N8442flhS4Wlz4\\nu4/JVrfVx8zBK8O/58b1vH3TlAaG2vFMvnXUCpAw2RQlDl/oDcawr21bZpojc2kI+XZW7sUG5K2d\\n9oex9pc0k13PVvuetD7fe+//CwDYhLIWOp/r4xFem5Hffm1NUueefMSgHm8CwEevv775a4td1RkV\\nEd5b+X8+ztC/15t2bzHxXim1oxuDQfywuF+D6jJzvB1Svanpx1aQq5gqM1L7KcUC8pGGlimfqaQc\\n83Q03eX/kN6ppVlLPzDbpGAJ8y1z3Tdym0I6gwR0W6we9rQbkZa2D/XRoO95km8TOtowMGcTxWFi\\npQ2riun+ZkyJz6+XAcy378ezJX/Wo4O5Ade0nLCekyW93s7FlrBjx9sAAJ+6IfQFnA9on+X1eQE5\\ni1dbLN9IVxmazKV3fvzjAIDXv/7PAARvB1e75rcYlTjci1drt3+BuKoTxyUsj+vId+ogtgMYbepl\\n9CwZa9Piqx5aX7zdjPrVOXN9pf0Ok/K2PscYkjfzefII4taaIc4thT61y1fzKU95Ct73vvfhggsu\\nwNraGnbt2oXPf/7zWFqKrdoNN9yAe++9F/fccw+uvfZaXHHFFbjjjjuK91188cU466yzcNFFF+FF\\nL3oRbr/9drzyla8EAJx//vl4zWte08b76KOP4mUvexkA4NJLL8X8/Dy+/OUvd6RUsEHVqjJWKyoq\\nKioqKioqKioqKioqKioqKp4Unv3sZ+Oxxx7D448/jv/4j//Azp078dKXvjQJc9FFF+FLX/oSAOD+\\n++/Hr/3ar+FZz3pW8b6LLroI999/PwDg61//Oi644AIAwOTkJP78z/8cb33rW3HGGWck8Y+MjOCm\\nm27CW97ylsNL9MgT/DtMnGaM1UGKhKqr6tzRmeaoGotux1DmBK8dyK5R42KosWzMILewKT/TmVv6\\ndGeslnSKXK1Kw/HtaMObQG4p2i9hunajDNYWt63qLrXOGlELeGkfbyBVAXVVsxhH116mJwbc1q7w\\nfU23IreYKjstajgCaanrYjspj0m5r4zZWcmEWqKd8xm5ZHpniYmbl2oqwfWb1FB7LWjL5THxb7cw\\n6jdn+YxWe2VnqZKRxq5sN1feGUVeCw42x7hbNL+AWiO9DM7i2GHWmFwpSvxk1bYqsy+XMdt+YWfb\\nLaCsigyEPKFlerNl8zOXgO82kTiTdByqM5pC2y9XudXrzudcRyylrj00HpOVlZBhOccwveTtlZ2k\\nqcrViHjcC+CRVp+YCm7a7nlZJJRd5+2sqlwN6t82Gl5ThwvXtLXytKkCsnt4DLd394SBHqD1lHnv\\nDCxlEPGb+ReeQN7qMMzWVkt93BgxpfaWf28HsvqyKseur6ScAWdkBrCGeD5pDM630rbPGZ2lUYG3\\nAVoGI8ORsSy26fTwJe7+RraEJaa068tqb+VfS9nN/FppSzSN1KNIYz6IXPGdT9+CPlaa8hI9J0Je\\n33jVVQBCrS8xltHE66yaHTve36acbBgyYXRXZebKXkvbJHroZTzGkD/qI8OSoLm7aufcR6sE7Tnf\\n9rbbLDUlTqB/nyXk/NvIpmRtjPmrddc5khuJfFYV0+A8v4MSfq45Mp3KZuW1vO/w3RpKGr8+NgPy\\nWqz3M1dnsxlD9IhK00m4DnOeFvU/4PNnLKy2TK7kSq1C7el8zHo4PoU6ZyrDy8ah9n5/jurWMjcY\\nJmpsqu/gxnuP+LgrlH+yp0vcNoY81KQvhH2OjF58bqizWrYfzmtTlEbRPv5S3jO/cT/r27Tuz9m5\\nYeQKqsNNPNvg+wLEmcm+TEl8NDmGHNyT+S91+5zx25fYsCyH001J0Oe6bq2G1/JbagF5H0eLy00f\\nGscHR99TKeUqBqRz/qh1XcLz0cc/NkzV32jO6QjXRyPav7CF5PxOR9sjFl77UZb317/+luYsv/o2\\nMGenmzRrP8bRuM9lYgzxe/L77MMW+CiA+zEMYTFr/9hv664IkZXfT95Ff+f+0rmPROgb0/1b+gkr\\nm0/3tRg9tx8phoGmj6OXjPc9g3D22WdjZSX2I/1+H+Pj451hfvCDH2B8fLzzvrPPPhuPP/54e/5H\\nP/oRhoaG8IlPfAJXXXUVfvjDH2ZpeO1rX4vPfvaz+P73v38YKUbdvOrJwEX0FbF5OIRu5xN1cPFF\\n1GF0uybyOsCua0/zjAOYzyjxpaLvnf4y8iVeHVRFsfWx5sh3WsNyMyhgA1HqPIm55riAfDrXTya9\\n3l2zaZxATvrXgbYvHZeWldkJxkWFMaOkH8slhcPHoFrKfNou57ocpOIQxgcuOin3RdeDcs6HDQvI\\nc1UXzkspiPHQHcoXgFX2wbuOQ/DhcB/Pae//09uCy8i7rrkme57HpI50sUyyrDB0qdzp0N4n4dpB\\n8ulz9n4H4QtbJ0656wOZuyKhk4mSA7+3OKFk7MM2jDQDOh/iqtwIc84ngwDcyxoYB0a/l6eAR1+s\\nIA4hdzfjIGMZ+dRd7yN8I7Z1OXeOnGOcvDcOYXXhjmUp5BWF46eRm4M4XHkEkwB+pfmLS89a5xln\\nKbe7tk8puaBpLhytrftmBlzzpRd1kXaDpDq58j1CDq4DuPrqsFHK7bd/xu7XeupQEQGf8ulmRvzN\\n5zKPzwO/9V48koTQ8uSuXurKzW9eWorPF4tLhg99F3cdLglX+FJuyeTjBkptr91xcgLR5DrShOi1\\nITe1fYC7VB7OkPungfdIujBf6j+7nNUfQ2xBQivASaK647lhckJi9JZ0DbEGH2g3WxtL4tFUElrL\\nfYzFxVTdIscXPbYj/+o6ge1lJrDwxHmMYbtN6LTU+ATWN2ZbQW7UZzz9ZGMaX3g6hLzNYqybkY9o\\nA8bEBThC+y6vF4vYOJSW63xkol/Sr03INTexhKOOhtXsBIS89mVNrQnec5RcRt1lt9eOb/R7lGRc\\n0t54UzOP0hwvufX62FOdUX0bW+3TvZ9mLumXHjTuivXLt/LKY+WccBTp6BBIN8T0YQyfP1boX7uX\\nmo4ceV+h8GV47ffSo5IXvPStIV/g5nhoRu4r0URcos6JLkuIX2GsNTpxnKpL1l5348xmx463AgBu\\nuOEDEtZTE+VC1m1+q2Uz0llCmPmkbKeUIW+1VE7D51PTyI1iWkbdLM7Uby+kU82EPg/jouAy+mKM\\nPbrodc4ruGDJVie0tyyxo8glTbSOuaFO+5L98juk4dz2zn7WSmrL2LWF4Wo7ZlERD4Z007yWCh99\\nxeXHGeQzj0ebvxazWSdHSssSv8ra8bl8nsuaAeWthyNG7aijGH+i1jc3CRCr7YLqkSzj79ixAxdf\\nfDGe+9znYvfu3e35sbGxbIHz8ccfx9lnn43vfe97GBsbw/LyMh5//HGMjY0l95XOn3nmmXje856H\\nc845B3feeSee+tSn4rzzzsPtt9+Oq6++GgDw6le/Gr/92799+InfoOFrlQKoqKioqKioqKioqKio\\nqKioqKioOCLccMMNeNnLXoZNmzbhnHPOwcTEBIaGhvDSl74UDz30UBJ2165deMUrXgEA+I3f+A18\\n7Wtfw7e//W1s3749ue/BBx9Mwl544YX41re+hYcffhi/9Eu/hJe97GX4vd/7Pezdu7ddVD377LNx\\n1llnYWHBSSkDMPoE/w4TpxVjdZAlkZbLYA2h3c4tANPI7ZolZohb3JcR7RWpo0sfM+hnDNdoN4mb\\nREy25yJSq91ics2dNCKvbL6xUMxnTv2r6CZ755Zr/j0p2wRNNBaOaH3rYX/rxnBucl/Iz5SllPIh\\n3JYVMIbF1urZb+IaK1Dpj7Yl74kxyK7O8qNunm5pUmevYGvd13yf9dZymbtEqMXdebFqFXRXZU0t\\nv59zydT9Z1u7GVVkhuSurLr1lDrVpLjmmncBQFbK1TXMGbnLUIt+6e0J2qn1uTN2Tcv2nKVuTY7M\\npaPNzHoyeDJpWkFuhyebcgmPNKVjW6F+OYuv6CpaUPEYbT7RcHNNnZbJyioJi7iQfYnd6py+BeQs\\nHk2fW4WVTzls11S4fdTauegSFrHfjoEJ2eXuX7IJK7fEWaBqWz8eZfFzzfGVcs45SspbSd171YWM\\nbfmYuYyPAPjY7bc3f7Gea574ZkWEOlt52vS8u8jz68X0zjfPnZA2zkcFxPOQsxnpWrkEYL517ONY\\nQPvasouqlvy4MUJaF3sJV9a9ZsaRs4SJOfnNt2E8y3C5GuW9xnaez2HNPdqiPNufOEgL5YCWvCtK\\nm52lIkReorVd8O1W1Hukby6bLD3T7Ygl/yrriHWBrEKVImCpOU/OhTjzfprlLmU5MqXDzTP6BY5K\\nhI8rPC+UhU30ko10vMVVfqJKRcU0hRR7T093zi3Ix0jHCoNknQh1FvU2UMf46XeIbs3AXPPdXaJG\\nYyeUC+vyFF7TRyU845xrSqXOG6KL+1jzd7/9zfZHGahefpQF5qOuzRKOX4/lVMUG3BfQe8Y15EzX\\nkndfZNVHqY9NNguckee6CIJLnym01HrPvJGM1dhylOqV14E1OGdUv5kvLzBXQpuTeh480uTCAmbx\\nwuac+2pOIH93XluWMN4CT4mkTGRW99s3IBg3Nz5i7xk2sVKHeX3iUDZ21G2Rmb5YFsNz57GALl6e\\njrSYJt5fYviX2ncvy8rY9pGJ9j2DRFQcnxtw7afDoKfqjJAbMOU1wFt73fjJ55+6CVVcM2D51Fan\\nJATo7MtYwnxlQxml/uVLI29nJKdjb8Y+1VzZV9zQEUhF6rw3U2a5b8irKyI5Sv4D6rdQ8oJD4beG\\nWc/O6NO6/b8DfvSjH+Gqq67CAw88gDPPPBOf+MQn8O///u942tOeho9//OO49NJL8c53vhOf/vSn\\n8frXvx5LS0t49atf3Xnf5z73Obz85S/Hzp07AQCXXXZZ8rwzzjgDP/nJT9q/n/nMZ+Lf/u3fniCV\\nhg1S2DitFlYrKioqKioqKioqKioqKioqKioqNhZf/OIX8cUvfjE59/3vfx+XXnopAGBpaalloT7R\\nfQBw5ZVXdj7rwIEDuOiii9q/H3744SOTAQA2zF57Wi2sDibS4/0AACAASURBVFKrIQJ71DUW1Qbl\\niqS0mY4jMl2dCTGKXNG0xOZxnsQUepnCkEpiD9KYc8YqVXNUtdLZd0soWzT8WsokHUKvfYOZ5qi5\\nRCPA/kaPKLV0uCKh2vlSm0/UhpqE5hHQZSU+WvqCh4tB1ma3AaldkzYrfie1Jodr6QYEqa19S5Mb\\nw8hLMEOqWqjry0wh3VAISDXU3MqmXI4LzPK8JLo2UVybT8otjq5pV9roQPmjY62WXaoPGlLpnEbl\\n/8zYE7UOMjx1gpXzyPoUwmykittPD6am1NIN0kDu4oac0/6aNRbvpLw5v78yV8lE4WOnvxfvJYOT\\nCoe6BZpbk1lGR5FvsqbPd/6Cfjm3YbvPAQphShxR1b7ybQ3Jo1MuhbegaXvNt2dt2l94otbMcqxh\\ncxqPn8cnsilvNLxNU47MXBJGW0HqZb/puusAAHe/+90AQk2LjOBQ3uImbUtwlk3KQUgZBHne6NZl\\nXtqm4G2w9swl6z0APCQx+eYLC0lanFemOrPKT+CT2V72kxCsb0PoYzFjTijPoku1cL+k3lnrWxFr\\ncdoupF/Q49Ra/Ag2HuTlXCLnurhl6u9A6NhnPTnH/ukA5rNcYUlWthxR2n4zsuVyXr2PzLRkkjHI\\n9oXMqK1ANsZSjerSdpdA+Jrz2Tgv5+C5N4ByvkuKybwnMnTJLCqNb0qeTj4G1bGAMzr1a5Q0do8F\\nWJafPyBMSdvX81p5cwx/fnPc3yrzrcvGnkCZp6W546XMS72O2wj2WdPoZd96RljxMza6Lmm7usqx\\napaW+lsvkTNy334Lr3UPCD2n+9p1jaqBdNM9vnOJg8c0+SY1qp1c2n2Dzz66s43NA67pPCnk1hbz\\n11tHfK/51i8sloC8nww92CJGsB97ktCad15uSrzunFMaz/u1Ug3yWWe6hZGnZD0r+5yVD+Kcb0FP\\n9FaHkufOG2ObKQDSHt3LoJZXn2spJ1C1N/Wa5jN/l7bn+2zxjTYSs4h8YaK09hC+yapsDuV+qBz1\\nriFnUBPL0HGetyKq0OwthLKO85zuCwtf30DHdLyLbZC2md4OlUeDy20Y13Il1JO0pG7qPYbuPuO8\\n0/kknzbbHRqDl8rSk3OfvuPv9XuMcQTu/oNwWi2sVlRUVFRUVFRUVFRUVFRUVFRUVJzmqFIAR45B\\n7DLa8MbQQz/ZwVOhK/+6ez2QMg5LqiGQcEBqL3Fbnu6x6Jahabk2bOGJdaTsHT1uLZwjluQdmGbl\\nldGOk7IGFrHeMor8DYYxyAiwJCFzMUZamqOWHK0zaisfpP9yoqCUA84M1p3qeY424im5ttXCHEC0\\njYXyMN/+1c+0UlXXhnf9l2uuAQB88rbbkpQBZQ6QawzSEqxcaOVV83iwSVnUGlT250SSdjJ3SvpW\\nfIdQHvwNlVvmdxe0qYZ+rknCTyScW/yUFePcINeUBTZaZevIMUjnsMRS9y/Ka5slrlQjs4dpkNUw\\n0nwrsvRUQ4gocRfZmmiZdIus2lfZ8rkW1SpiTfEWW7WE3R67GTm/T9OScpNTjjx1Dsk1mpYwzAfl\\nPwLAYsK5dc+FZeRtoH6jlI2u+TPWYVc+NqWQDMJfRc4tUf5buEa2AL/hLMbad7q3YaqqCq1zEuLO\\nwkDel+qXKu0y7GFdfVf77bQOuZ2fb6XH7RKTsxsCG9LTpKXGS5m2OfQMCX3suIUIz6c2ovenCu0z\\ngqbtW6+/HgBw881/aW+jnjsp43UEUds8olv96+hgbsC1Ulp8rKR6hDwGxkcfU6229CPZd4n5yj5K\\ne4u1LJTzkPIWVUumt8Cqp+p851I9UYYrEHIpevqkfZWOPJ2tp+l0bqnWmuiF4uNN9TjyFnscuSqo\\n13RNjao7+gjD+2rg6DCliUGUFmUBuQIv80VZ1FMWZiv4/r3mfnqszWO9bSe9XxlHtx4uUSrt/L5b\\n5bfPfCaQ8iE17onCOR0TdqnhqrpxiVnbNaJnuQssLdYMf+Mh5Grv4W1GZAZY8oxxLUXVlvUvVsLR\\nZaxyd21lTHvbtNzWdW/thgDsa+s/R0uq2lzqQwHgEF7xxy8HAPzL+98PIK2x3qN5y6vlh/dpuXDG\\nsrYx7tfBuMfQRz/r5+I8POZGCp29u0fAKsJ8SWOKLXbocXuiZk2O92zLEI6jrflkngqoCvJsk95V\\n0dD2sthVh4Fc3/vYYdCKE1MT6p36+qT854hllHpGBcum58q63OG+lkDOg9VcHG/OpCPjKeQjbx3n\\ndY0k02elX6uPbVho9gzwmb4ytbVXANLexUcc+s0ZZ9yDZ1Vi8ZnSqsTyaHMMbxrWu7j65U/WkYXr\\nrp6iXNbKWH0yKC2CEEoPd/ckdQphZV6wMEPIK7UOH7wjKDk9wK4tIe/odAjkFUibgNKyF49Tdk4X\\np3xBVZdIvarPtc/a0z53sY2JqWWuzLeDcKZ7Ank3xuMhqfBMpzZeOW39xMWghS5ODA4g7/Z1CM0h\\nwEzhGrutBwGkG8P4xEmPHDjedts9zVnWj3425Nc3KbmqEOMSjqkDwlvGUs5vtlWupuVA3d506Umv\\npRuzlIYhDjeKAFhv7h9p6kuyHhEHlzkGDTSO98IqOz3vMEtYQO6sp45vL2l+exu1jLjBT3AZHm6W\\nxdWxmSWTJVo3k1IxCwfDqOsTXbpKk5w5u39Fzv/eW94CIIipA8Bff/jD2XOIBTnf5fKtCyAswTpp\\n8OUEXtuEWZnaMVQc0m1prpaEWnySqr2Cp/P4yFM83Hllk5gqfSD5fPQzt7qSazURNwXSlmjQYqIv\\nVKobrkt+8KjD/kPNc1mXetmCE8ulOjh7uRrDPPpZ263u9z6AVee/tG3rMqcCwFq7CafmdCppsqUJ\\nMwVg5803N+cC5lsZkVF5i9RoOgQd0LOtm5Srx6IEDtrplXnYz0RRFts31dbDN/6aQl7jtDQuN3GF\\nSco2ae99GkiwT44mxHzLNHXX7WpfFDoh98m5ygbEUWjaL2m74Yt12oazfvpI8kASi0tJaIpZl9SQ\\n4GNQDRP6kyjzs03CDBpLHQvsbI4XF67pqMkXVFXewMsUx3ZLEodvlTKCFft+uoTtYzGXTwJysRH9\\nQixnfBq3r9Qc1zfg3z4yZxnR1oMY5PbKcrssvw/KOQDY09Zm3QTSF1hLpfrh5lmxXfKvomnxXqQU\\no8ow7MSxxOB2z9uBeAXIWyX9ei5PEZdz3v/+vwIQl3R9dqwxOzVEzXNudi0Z4LW39iVT5nkYA6Tz\\n6EmRe/A+2GfcpedNITXUA7oxrs6vuSid1u/5ouyM1rBUVGZPM2Jdx2Jb10rb6A5a5/nYgGsbD87w\\nz7Xz0UA9VuhfmJel1Q++52K2LnMOcuEbHc+4WEfI001YzAxDvWRcwo2fA0oLlcRBCePliO/SS+at\\nubmz13xRGsf2NT3muTK/9nZbpcTcgKWGiPL83Guavil78XRD0jCWc8Nrad7ry/2n6MLq4SwjHAZO\\ns4XVioqKioqKioqKioqKioqKioqKitMaVQrgyYAWFbWQhCVqWjbC6r3baIkVRG5Uia9X2oAJCBYY\\nF+JX7uBK8+zUzbOPMdx2240AgPc27tqRcaEsl/RdUr6Bs0yVM+HOaEDOmWDYdeRbGdH6Ea11e5rw\\ne5Ltj2iRp21ONx3RTToUajd223fJHq7YM+Da8cBsc9xSuKYS1+4CrEfnc6nliZyp0eSuEeR8YHKX\\nrvyTP8F73vMpeXbEULOFm2Kxtfytt+68B9vwOaba0JoyFP9Kn/9YcmUBeU2L1sUV5FwezafcfbMT\\na7oBipdJ5TKE32MDWFnHm68aMcj8pvXanbXUpYThWGfVaSb9jrNNG7eKWXM6TksrY6AUAC3WW8SS\\n67yJYE1N7d695v02yeYbBN98EWP44Ae/ZLGGsjzaOlyWt4Yi343fWkual0m+3wpyRqEKLWxq2gK+\\n1+++7nUAgC/ddVe2ZV2+tVvO+FTG6vHdqi+yA52PpSwSZzipaI1vC6klM7ZHDLUVyL6e1mFnfvq2\\neMqncJeyBeR9f0w3Y2b5nZHr50g4Te0hAI9kYwfNodImeimcn6vl0XlMB5o+sI/J1mWdT9NtLInY\\nwoWy3sNYOx5xloT62MQRSGQwHJst0+LzOJpztugUcn+f0SZ1s1hBXuI0BjKTvN/V/jaNW3tw5i35\\nzVonupzg15FyOoHyljU+OlW+GZ+rHEiCZaTE0nOOi/pYpY6eXjO8funb+PiSqZmWWJ3LfrC9j3IT\\nY+34SZnjJYfZY7lZ3yDmrLZqXlM0Xwhlx/tXii1lP5mjpCzjksCYxhJcnQMYlmVGe3lvuzV33ZNC\\nW+CSPICzx5Sx6H4EOkJj/Cxvb7rrLgDA61733ubMDGIrVnIwJzj6CLH3sR8L4oLNtDCEO9Lua9q/\\nksv7ICGKo4tFIOtp0f491MEkW4My05hqZ5KnccVrIY9XbMyr8ky+ZRC/5wLy0bdy/NznUp9emgUB\\n6SZSpb6Qv729UvasQz2N8pqttaKLlaqbYTq0hqZxLhS2nRy0ydbxR/cok+nW79s109MVipin6p+x\\n2a7pV2QbGWag22R87pzN6F803Z5db+qBtjPuRceWo5f4vHhfp+cIloFpeR/dUBlYwu5O7vgS8tEh\\nofc4E7iXlC+vMSoTkPqS9qS0R5mtQfPGE2u75g1HlQKoqKioqKioqKioqKioqKioqKioqDhCVCmA\\nJ49NAzhlQ1AuBG1eIfwWqBJNsI6XVE1VFxAAFmQLBMc0cmZStOb28dcNU5X8iYnmucuYx2J7R0nI\\nnRYM2l6UseOWF1qHlOlDKP/R7ba+gRckbqZ4HJHTw3PKWCXvh3HMyN+uyqNWmRJj+MTG5ABGxToW\\nhfO1tzkqU5J5sB05Ur1Z5d24cDZDvuc9H0eZxZXmcj/TXRnKbFbrsiFNSSeIf3dbYpWRSzZi/Oa0\\nwrO0MZ7w1JJ+G/9muXZLcolnoJxF5n+qTRg5usrPU67PicNVDeCX2oT8i5T4LrSssj2ZQKq3Cvlb\\n5ehTvdBFjOBlr/olAMA/3XcfgJRlFfWPUr2meSxhrfnWOYdC1XbZflDrcASbmroVLdX8PlNI2xSA\\nZWQWwKGOsjUv+rSsA1Pyfb1FUobunJ1L1Trj2wDAPzVsnM3o7tNVQ6+kD3Wi8PNLvBeiy8NGa77b\\n2Q8g1sReq/A2I6HcvMy+RMu6cxGUG8D67NwW5Xf5FmbdelxaQvkO2+VvjhkWmmMvYayWWAZ8/mgb\\nSt9gWkLwN2tl5L32Mk6gqoH5WCUyIeL2VK6+pyxIL7PHkjNIqK4jkDItS1tWAWEMuJhtx6TKZ87A\\ndC69Pjky27xsfOQjgWX3tje8IXmSwtmmQK5yNoycw6y99oxdc5V2jVP9Enz0paxo50w6q/FQcqdz\\nX0vbcGgf7Ywkr6fxWupnVhpZAMde8203gAs7rpW40iVVbP+iQ8jv0/tTjUCFl2BXaNUn+XfVcZnz\\nP0uj7EOF3/6Wyt4u+f/5O2h6fQeHyFTluGQrcu1FHXmW9OBDWI4lS/WQMfLacwpp8vbu+PS93ayx\\nLn3YMK/lWN55xgofRy+1v1NFy8gZBnIfL+XExlk0tZIZ976MpakjdW9J9N2c7a+KnOvyG4hjCB2D\\neA4sIb5XHDvOIIX6ALpKZmklQHudstdcH5taL5FSmSzx8gHgs8XYjh1yn998Xw1lCJcUtfl7vs2n\\nmeaoG2xD7gBCq0BN1XQNprTVd+oPFb5LrznXa0ZLq8UVH9b+CeSzWl2xcb6xtqDus7bcPHesnUu4\\ntnmpbffNSvW+uK3oxIDY1J/JPZnH0d1q6PudopqqjspYraioqKioqKioqKioqKioqKioqKg4QlSN\\n1ScPValxrCNqTThHS5WhSmoqXaqf25FroLnFTaHWUQ+nzKjLr/8dAMDNNz8iqeexpKilMfAJeq60\\n/2XJqlnmLqTXlGvhVr0F+xuIb0rOjSq5OWcG6NLAO5HRw+B92jc1lsvFbH9S3X3QtQNVKzDFCnJG\\nUsq7Kuv+BDam74GoYVJuGpk/kwVLesmOS820ftEW57srzmGlsZZ56MA6om6t20aH0c2nWEPk6riK\\n4wFEDhqPkUE8OcByd+La9Abpcin8Wy8g5dwBuTVer8W9he+77wEAUVVYc7nXMhdyruK65WJsZxeF\\nceHWYWAxKUNAyoFw9cXQrkyil5QWIH7xTei12q/sD5Sb5TvIUnVxGbEFe9Cu9TGJ//pf/zcAwNc+\\n9SkAacvo9nC+5TS6+A7I3vp4gr0QuaXKCvH2R/mYzlWjFX4Wk4jl74LmONMcdf9UHpUP4awlpoZ1\\nWa3xzuHSFCK51kvaxvHmCr1I4rcnt0r7b6aOLc/D2A0A6OMQ8i+sHKCQPnL83M9kSt6AtVP1Xw9Z\\n+MPBMHLunNZ4Z1bM4vhh0Hu5N1D6d+4jlP4NlMceadngd11Dzl/504apCjnPMtKlQanp1PuIObtf\\n7/Oebhz5GEBHTq7oOyttnvOInPMdcityZtKnlMaE+lQvVVoH0z5KfWYmO7xCjk/fO0gN0cdPOqZz\\nhc98dBbhWrRAv4lrSGodR2TOrNSSOm/9mbZNg5iqJS8JfVYp5XPIaw6/8EjhHLGE2Hbtab+8c+1V\\n69K/wSEgY6KH4003/R948KabAJS1YX1k3cUWVBxfbxGfTYy0qvG9rGwB+RvpF3WmqnIMqXkcvseq\\n1EFV3FdwzBPKnI/pwzOCumiYC24r1GD3BtIWw/2o2AdMw0d5KXtfZ5ea7gUoo9aVYxVs79w/VedE\\n3t4tIR8xxjmTj05L4Ff83IAwxxL9ATNZetJuQr+46z0QSkJUKnefGvWUc27mCph3rkW6jvg94+iP\\n32UGec0O5X8ec8j5tjOS4u3yG0j59c5g9neKz9ER26r1Y7o+5OtOOr4g+AY33nYbAOCaa/5CnsO3\\nVwVrHwVE/6exjPmb97GnuLJqRGWsHj34YuaM/O1LAaVBdalD5pKDu6bppLMk8u3VlfcvALj55rub\\nv15oT9RuzrslFc//l+bICq8TPK+ApRLHRuWA3MelCf49gTgV0KEVEBrJ0rYxDNPlJjZoOeGRAdeO\\nPzh88GWuYWhO002AYtLLiBt/Eeo6nzodr0gI/5r9dqlrBT4cHmtSd+3NN+P66z8s8SvUBTfddqaH\\nKfSaLoIuVzrccLedoeZbveaaa3DbbX8n8etxNNtEg3+PIy5qzGedCkMAuVOa1kxfWH0UcWj/KIC4\\nYFwa+BwvR8QjB7tG30BNhc0976cQ89Pzchl56eLwYLXdMMddgIYAccF1J95YHt0UMwVgf1MfYiev\\nbdO03aktNFPBtikOzHzLC7aES8g3E4SE7ZpQqJBErHPRqfBTn/pbAGhFEHy5Ws9p7vqCCbEbJx7Y\\nAtNRdh35csuK/Q3EiX/sLbcjtnu+zYW6fJYXztPfaujTsCWMIm/39LnptcWmNZ+QkumxqznKxUv2\\n4TGJf0HuANL6ieY5KaYQa5LX4GXkxuBBBoJBi3S+hHaigHWg5JjdZZBIxWBY67U98pGe5lA6FmKr\\nMoE8/9/9sY8BAC6//OrmfNwaYq/c5/dzKqcLZf6NtNfvciCPQhK5VIcKP8VvG0uuG0HcIXIIYcNB\\nAJhv+0xdunY5BUIXVt2osQ4vaYNq6vHtd/++Of5q4ZovXmmLN9f83mxhdFFZjepAWu5CieHmjW95\\nx6vxVzeGTW7dxMRYNEe5QLazuX9VtjDxcqRmSW9BtfywjOiSgy+da7lVd2G9tgBtvVkfPYZl5Auq\\nzJuDiG/9aHMMy2l33fT3ON9i5l061yqJTXj/9Zc4ETBIfIdvwxQvITfN+RIOkG8npsuYU82VOC5y\\no9CcPC2m0WfU2qqFUvKm228HAFx99U1yDXYfv9YSJoxsod+ztGzMpzLHmM7o+n0u0kU4jVU3pS6J\\nYxDeQuroUOfKwFizCdQU8mW7knnlxANbXl1gTXufNfSz9kjpXsvZffoVPQ/nAABj2N1KUJQ2d8pd\\n+Wnini7cUTKWMqUcg44in42o0dvTqeXDF2LjtpXzWZ8Yy3a/SctwM57UZX7mJ0vkNde8q42zLKdD\\ndBlVVtpF8rhZZFxYPdHE7Y46Bg02jgB1YbWioqKioqKioqKioqKioqKioqLi9EGVAvjp0MXfAqJd\\ngTYLLmLPIOfGqM2jyy6nTAS3So0il/8/ZGE1fEp1d0uKOu24LViZq+N2jtwJ3stwelR6vvOODkoc\\nIc4bbngNAGDHjk9CN/1Jn7Eq6Ss5HnbxskubD53YTFUHy58y19wJh1/1AHrot4wQhlJuQMhzp/Tz\\nagq1FqdXyWS49frrETm13tIoF8EdqSPTa6GxaOqGKwxFDhrL/T/ddlu2FVSvrZnrndxpZWN1s9eA\\nlMPLJzs/Ya45LiCKoy+2aWdIL3knn4vEIHu4M8r3oszYYliWQWedH2ht/m5dDq5aoWw81hz78q2d\\nh6LuPjFVZEnpZlu06+qWPkBas7TVLW8ZpOwc/iaTcrx5rrag7himPK3I2tASnG54M4ghqK2tt47/\\niBMf3uor2Ar1MCnyGu6GtRXpRmpAtxNpem5TxmUj25ms2EFbtmj74Iz2YeQtdLhWYmJp6+n87tQ4\\n7n4rrG9L7aaHjJslnDmj5biUS16DlW3N8uvCQerXMqjFOJ4SAI4uKSYF3y/UaeYk35St1TAGt3sp\\nO+VP/+JPAQDvuPLKbMRy3eWXN78CK6SHTS0/cAG5xA1TwKcql7aLMTyUpShC86Tk5xNZ9WQeTTR/\\nD2HMPDVKrtJxC0Mv3Uw1OlLn9cq3LEL7/FJ9ObE8RI6UY9blbK5v6PxyIPevCGFuvPHL4HhtU8c3\\nS+NMRzELwlh199oJ5H5iejf7uIP29zpSJrY+XTdG8zmP9nXcnCaX+dEUsFawrC0gehyFVu26614J\\nANj57ne3d3k9ncLhyerc/cRBjiEoRKDiOz6S0bGve/VAwpZnmGkJOpSE2I+YZzy3rx3LeUkqIXoG\\nXH31Hc05ZS26wEXcbHe5aQFmCk/x9kLZxt6a99sZ2HbEGT9j9d5cPWGc4ahiEl6fF+A5xbfcLqHc\\nb0tL+YnBkC6hhy6ZsT62YMm2s3QRlABn9atAFHuYUKenkEv/DEmIl73udQCA2bv+vTnL0CUmqa6f\\nuOyg9lmH7JwyQ33Uocz5UitDeG+ubXp4zmxTEsabsl7qPVPPj64t9oB89sx0H8rCx7HAidXLHhNU\\nKYCKioqKioqKioqKioqKioqKioqKiiNElQLYGNCeQkUOVZDkkXaGceQL2q7gBuSW/XHk9ju1fbld\\nTG3/bhGm4PNvvv3t2HPLP1msyk5NrWybhCHVa7WHnKkzgtw6qGpNbsWMtsvJxnpKvZgHduwAEHTP\\nlptcdktcabOHfmv9eiKuxamBEvPcWZEhj1xDNDKcJhvekEvEK8OS+TvWsgTHWg1JfuGU49ylTqcM\\nQNdajaXa7WOqWsc0lbTlGEOvtejFHHIOtp7ju/Ra7tWaXKXVchD7Q9mXS20MkHQPIf9e8zjZQI6t\\nbt/nenfECmLL4+UvMrcmjVl0HiIrmceZ5rgqv+ea40NNLg4h12TVv719fM6vvwgA8KUv/c/CHcQo\\nUkZ+xBLS1g1IdeRYWth26jYirsqo3EXfeq6kU71g7PJD8mxvzbX1+3ucPGDd0JLm/glAYK12o8sK\\nr/pWKS9rEr2i/lYAmatam0uMM29tmA5VcUu/1Mt/93fxnc98Jrlb09HNBZpCzoqIPaMrVZZ4ayVd\\nQF5z1Va2rHNI2asaRlt5xzpOLKYqQf5WVDSOyEuK8nzd82IIqVcNUN70LHyJK6+8rfl7su1f835d\\nyxpZoSPNMXyF973vz/Dhq66SmFM/DFeKVgaL82ZK7aZvoKrqvX/xF+9s3uXP2if6ZpGuIKg1IULr\\nq26/p1hCnkOqHrfapOBkwc7mSJXf0l4FfD9VvvdZgY6/feSm4Tw/oxLqYjtDmE3uGAe1+oGYs8PN\\nPZvAcYEr5fJefRNCmdLklbGM9TGGbeLhwVQyTSWfM41X0x43O9U84ZN8xLCMmNdzAIDPNkxVzTWW\\ne61nzuU6efDCAdeUGc6c9bK1gvgFS4q4zOt0HqZz2PmWqcqR0Vbk8BKkue6+FqWZeOw5Gdq33FI/\\nPE0fY1mx8GmafAzp9WwCud/HIL9S5ulq+5tjZR0/umeIvsvHcDKgNKcI4NhupOkX03EfvYdKjODU\\nC069dnxlQmckd931z81fL2mOrj7vqQDCt/QxAKHtiXtZaHq9vZ9C3jaxHKvXU6l3XUrOPYKvAgC2\\nYbHQK4Tn/ef//GJ8+4tfBJDP5tagWqmlFSiCd558fpgbhspYraioqKioqKioqKioqKioqKioqKg4\\nMoz+L4OvH67RrS6sNiDb4RKUFTiAlKvgfJYJ5DYLfgS1Q7uFX+1dtGHQqrYfuXrhXHO85ZbPIKqs\\nlSwq1InsJ3EHng4ZZuEYdeeUp8K3V75LmeU3idlM4VCtcF1MHdVTiruChjT1sQ35nqYlLao9OJlB\\nZdhz5ZzrAAXLnmsmhVx9DnrZftnKAubvOXvuiOxO7KpMawD++I+DVs36ekjFhz/8V83VkhqVWsGC\\nTa1k+3WbtMK1d8h2mMVQa/Wk4ktk3SrTze29aqGfs3SW9lekLXChtSo7m1rP7Su8w8kFtTKXmARA\\nyvoNdf3c5rvo93Seq6p5kbFK+/G6nHtYwutTgch857XIY4rP/eaXvtT8ugR5y6paSf4lw/suYrLV\\ney3px3lMqv7lrGtyE7S9C22YhpoCOUGuY1RSdTxVsAh0clLH0BONXb75XHPU3tF1CZVNnfYiJZVH\\nL+Hhuc5XhvydtsLUge0lvGqGCWk666yzsv5Mxwlzdu7/fFfY0fVP//TT8mxXaD+UGdFTZdeUo+1e\\nIevIWzhy2iKPJqK0u/IGeUcdM3BEsA1pPugx3e3XmXwTiNy9ODKJ6Kqh022veu3NbwYAXH/9+y2e\\nCeS9Xfj7qqs+3NaEheRKKXQaI7+btpcaBigzWYgryb9o1gAAIABJREFUr3yHhVrJtM1L2oV8XvQY\\n8VTqkwbpn8enccfs0jjhxFZ9097D+baqy+flTvNDd3EHyvxgH6Nr/CkTST0wqDu9mPhJhPgWmxZ6\\ntMAs6yrteSuZavUuy1hBsSpvwPIz1xxXkHtsxLlLYK2lPTZj0pYs7ReYy4eQes6EVB4eTlyNSyDy\\nGi9DNxdYRxiuI7lfrvlcUn+HYzoe75r5ac7mXnYxTd66KJfYNS1DPNuw2HJ0OU7U2ubjUU19ytX2\\n0Lzqfkhk3+5H3vpqK+rjEZ29h9/MHcaofbdrwp4cbFVFN91vxfTE07ZdyyGg+cWj9hzuETbXHOex\\nCfnMQceN3oNpr+mjKR3BMS3slflEbbdZojR17pnA8FuR94laEs6xayEts5gDR2/sb9mmL3zxi5ny\\nv6KfjWM07ZWpSjxRf1AXVp8kvgDgTc1vX6gCYvVz6eElCVdy5e6Sty8tV6oL8oKdi/EMId8eRCtP\\n+kSt2jPN7yjPnG+kcqipZLqEVhoGoonPu1d9F2+qdOjn5+LArJRr+vfJv7Sl2AfIUotvsKKTsZCz\\nW8T9n24lLimhrloMo2XTF5N06fav3x8mhPNtWvQreglQp+V0kUK7DTcu8K5xdC9zhtCp6H4/KV1u\\n4tAhs3eWXl/ihl+lNBM6OTm1Sh3QLfkAhDwN+fr8po2gKWfGQgFxuDEl11nuzmjGCsPTwDObxnN7\\no0jCodBexK9X2jjHpwPziUHIB1SaKp8sxAHOfBY+H9hvawYvKg1TSh+fFoUHOHzmnWtgrdtiSwQ6\\n3S7hZJIAOBxoCzLZTpjZAuoilpdPrZ26gV+MtY9N7UByi03Dp+XOoaYN7bUubLFn4sDVlznT56X4\\nb//tC62BjFMENZayZP1B45p63XWcOo0D2cA3pGWsES0Acte3UbtDr+WCHeXFXm/Jdbmia8xyspgy\\nZ4tbkxIq7OSLCrpIs2pHHfm5STIuul9//Wfsmsvm6P1xUdE3w1OBCF9wUidHbztKRhpfeltN3iY1\\nIOi41FEykPKtekkb66nqctIFdEPUaGjxUW8fJzZoJn8OumRKQr749EypB+ouCpQ3G2WYmea4ii53\\nbR01xrrOTaFy112mhGVrL2If7gadODrQN4rCJRzHrhe+m7vda9/qC2PEa9/2NgDAJ977XukraCaK\\no7TJZAE2jqd7khZf7C0tMxCf7UjPiYe7AfxO89vL3yHkvYOXMSAfI0WSTt5eTSPfcLLLWAXky/Aa\\nzlubgxIupI9ki18DcEFz5bwkRIjtgPxWTCEXPIgkjVV011mtd74IqOjqadfacQhHhIPGeiebITOC\\nAkHarqQjJ5a4IF9EYxyh/QP7o1yuznvi2M+fh1giSOXQPnrJzmlP5oYBHUGl9WZMFiD7hTY0wg1h\\nM5IWLwHeygJ5/Yxm8vXCTHSQdM5Yk499ORNxYpsrjyXO2qB46sJqRUVFRUVFRUVFRUVFRUVFRUVF\\nxWmDw/VgeCLUhdUC7miO1zZHtco6f0avuSU/2uBTFzsgWq7UNlaSw3bnKT7j9X/yv+M97/l4IZYA\\nSgDknIicbUVbjbIi3EFD3V/dMX9EwtEiyPfV8G7D1C0MIuOB6OLLAKcibxDQd+cXUbaLf8kAZSK4\\nA8sBiUm3TuB9Xk51owxaBaOlq+Qk6i6xOW9BGypn1KoMPLmkTEOv5X5NI1ohPSbdwCZ1WQ9Ht55H\\ny98ms4SWJOj9eCJu2PLTo2zDBNLNpGhP5ZdQQQh+M918yZ2o2ojOj/Gf0Zx74YNN2LXYfnicc4iM\\n2MgIfXFzvAC5xVkZPP4lNay36N7KAeNNzeS7n4e0zdSn7QfQa7fP0drGtwolfdRC6JNPRUQZjxR9\\njIH5M2luqKFk+pY76vBUcooGtF3gVxyXK0DKj+m17NZYF7rY/OtYFAmBfBi2z/xO2M6EDQQCy+q6\\n68hm9O2A4n08p+xvhtLNp9Bcd7kd3TbDmTtM9RxyLxm2yYeQO9uefL3uPKLIjrNHdETD4wEJwxzh\\n5hlk14y133SxZc2xldNNKboEHICUXxWfoWW9NMB3nmPJ18DPLcm5lAsd3papeucHbwQAvOstb2mf\\n72+ivHt/XuRJsnSqx4g7Xo8g5yzmvio5B+dEZ6wSe4CWRZWPJtjORI8LfpHxQnhCZxjuyg90Ccno\\n+J3fKM4pFiUdQ0ks/BrK3S55HDH+xcyzKYbk2I5t8Ga5j6n1tkbTyXL7ife+t70WZQ0CyDz83de9\\nDnfd5cxBhulnbMCS5AHT8gWcjCC/9j81R2V9ujCK8uL9CyhLteRLCYTyWNo+Ckj933z8zRKhW4T6\\nt+plTtJ80nbknlMcEy4j3waSZbi0wfRQcsadzF0MZQi5rIGyLH3suNy8y7625nodLOEDA66dHAi1\\nUgXsSl65saVI2/URdG9XvYz4daJnJ2cnM8jFIVR2R9smxSjyET3vn7Onx3Zsrb0XyMu/+rXNWJw6\\n9vA5tK6cwM7Frc767abkcdXA/bf0TXwdaT1jsFYAdWG1oqKioqKioqKioqKioqKioqKiouKI8dQN\\niqcurA7Arc3xsuZ4CNEWpVL0QMp9UuYWkNrsnEF4AcoqTA7n3v3le97Tbo3i9tmSTLNa61xpRBkJ\\nbiVkmGGJwy0jq8gZZrRJKm/DbXxb5b5o0es179QTixRTcbpogZAbdKGcY+kKOaqqpoR/F/3t5Udt\\nd65yVGJfl5kUc3Zn5DC4HtwQYtlwG/VBxHrVa5keZAEpW5Wp5luvo1uvbAo5Fzcy05wxqFqKzuSl\\nctqpCmcREiUNNbXlu1V5RsI6u2lUqfCM9By5AcDEQ7EMlxh4/6P53WvZGNRROh+xvLhgfLTylllr\\nrmbIv+dAHgRjUhVXlhcqbTG9jyV3uHbjAjY1+m9HYhk9lfRV+8k2fSnIUaFG3hb0MobHctMHzCf6\\nmQzFUjoNtii0yI+YB4dim/UrJc4AlfzChmQsT9stlI4QQolYzFgvQF7mImef784N3ibkTk8Xn/Qv\\ncm7Bjv5kxQRy7pKOJQapL588cG8HhTNFw9/aPzjXcqtsAzHUlkXm8Hbkm2Z4GRkC8Kj8BpRbstU2\\n+dCerjTO45O69MsXkLdR2kLy/T7YMFWZeuXbxHcPUJVZjmcZT2Tzqs6/82XUN8qVX7VXYf3IddBP\\nfIQ0s2/VHGB+jprXzEH0Md+yoJ3rpaNzQnnma8UrJe1QHdMDgY3t14hVRDage6qF8Xmqf596W4Vz\\n800JHG7yZB1ljw3e5R51njYdo1G78pdf9SoAwF13/Z3oZlL/MI5ZS+NS2LlBvnInDx4acM25wKqo\\n7DNAZfo5Y3UZ3WVS67q3syHMoHKnrYD3Qxqjf8/HEBmqbGWVp+vc216yrabP6tni6Q4QrlOretFp\\nCz3ZzOO2IvYCJX1q4o7CuZMZfeRjFp1bdc1Ngbxd0PlH1MT3UqO7injPBMRSFVvg+DTtw4FYejbD\\nt/hkO5jqq3p/vxW556nPCzQ2ZYrnGr0BOgqYaK6Etk5T6GWrNI6rTNUyKmO1oqKioqKioqKioqKi\\noqKioqKiouIIUSJVPBnUhdXDwN3N8VeRa5G5GguQq9QAOWcq3Z04jaukTuOswjW5z5VzFkUHbNiu\\nAanqiB4nkCuGKOPUGRNqs3PlGdUhURaEpncYMR9U3YzHkVaL6nTF7ub4HHjJo6bVnNieSo1Cl4bV\\nGnKuiDM1FdwJMVjpXEmXMTzW6oe54tcKymwaNLHNttY/6uUoO1VrhMaq3Gzn8A4h8qZDid/SqKQq\\n98Pt7aootRunB1iCJu18af9iZZh7W7FcCNcyC0ibUkqKEgEsDh5pN94P1XFjGWHrodp0/mWVl+cM\\n0tXCtcjhYxs605whf6HEPWSMQUeTLaxrdq1lvBDX8lScSkzVCLLxnyPnUgXxyFOdzcoYv/wa5iWc\\n67ytyu+Q04vN32QzbUVefkt9eL5/+Qgin9RVKA+hrDLOd/O2SlvH0LZ5zC+UGJxPw7dfQWzpyKwt\\n6bT731q/S/rucTxxMuNzzbGkOZhrgwOpUqoroOm55eQOIFUmpZi07wOtbQ65gGTKzrehnLGzUPjN\\nb74ZuQY/25PHJJ3RG4SMsV7GSmWpnUGeB87zUe027ZHDsdeyaeJzWTemkfvX6MiP507+kd85A645\\nf4mtH6BvruVGfeCAlIuUcvi8TVsvnPPvC6T6/EAo2bPtyMDv2Crp82vKSg5PnG2Oh7CYKSHq/MjZ\\n87zGty4pUt93333Nry3oZztEhKPuYe+sWO1/T41+N4xd1Bsp1rB0jB40dp2Nyq8wjJxfrD6PXfVY\\nFfipU72YxDKFdD4CpOM/j3GvhGW4GbtvDnHMOFvYNSCymSfbcwGlsaDP7lWx2jEHHw2yjE8jZ9Yq\\nPtYR46kAtmNb7HxJ29hLDpCvOQSHN85FJy1U3H0hn7lsRj4S0l7PNdF1/Ji2mP1kPjqd3pb0oL6z\\njvbOPncuMZ+X7G+A5W/MdvxYh+aHt39B4b/iiVEZq8cB/4g4HRzkJueLmcvISeta5B3a6PhmLqXJ\\nXx7XSDuRpLixNlrekOVy8/nUT0novmHLErgJCcAKzGZDh8mlQZR3NNqonnybZRwt7AGHSNxEQ5tO\\nTrAGbQPhLigriI78izbhCgOQ0Hn58sC4OLemA7OQNn+ODph8sYLpXkhS6KGeqJt1t9rSMslqkhYV\\nEPAJ7BBOlYH1kYMO0ZQYCcaNgNKSkC+eu3EEEGeY5uLPLSOOhm3csB/5BivEEHQDGTo/6/Rou/zW\\nVIwiLzduHtPwvDaXLTZoCMbIlKQbBKV/6SYuKjlRSi0Q+plTH3ua4/ORi0qE79oDMNIMIEv1NF8Y\\n057Xe7eQ0//Xu8OWlLded12yPODQLXgALY+jEufzkGIN5SXK+PwUsUfd1pRtxkyRi+2IbzJnd2td\\n8cUSnfq63E6p9BM6ATz5l7UUbNU5gtNeIOQev8EhdPcPy8hrd7o0xnboAotBl4t0azFAt8Fwg4um\\nkNPGuGAQWtd5cQXneI/fOEhXcHmPsTKmXlsmfEF/BFFEwRdUvf/mW+lzAXVPDLUpboQZnTnjor0u\\ndJzcS/kKygg9vzmWNmVxOY4ALrCqEANz3M1wOjpfbe5mDvP+uDT/HJtkqyOsO0EHe+iQndXWSUVL\\n9G0OIpazdEy2hsVs3qOtNGPwJQttQaMhk3XBFzEA5sW50raydjoZZQjRBHMqgV96C3JyRTQ999vv\\nEZdrmENqtvE+rOS2nDu7U96G35PtyhDydg7y92L2bUMpmccadjblmd9W3clzQ4AaMJkmN/oMI2/Z\\nfUalaVm2MHGcybhLc2eXPvhLnB7gWEKd57006RiYeeeUGkC3mUo3sAutglPQVFKF7ddm5PAltTmJ\\ns2ReBUK5YPxMFZ+rEmRuKtLfJaqeL6jqiksI53U4xNK1quSrTxVdqBqrFRUVFRUVFRUVFRUVFRUV\\nFRUVFRVHiMpYPU4gz2abnV9DvlVOSRi9yx0ayDf1mZPfi5kMfkmKW538UnuQMi8Y2jdIUJTYsG4P\\n4fuq/dvdidVORJtOyT3Jn3u6uGEfPlKmqn4zWvPcs3pEwrnNbBnAYuZ+H9miQ+a2o26zzmaeadJW\\ncqZx91X9zbLfL9ox1d3Nnc31bZxTqYyFlDvOzdA2yQYkjtOVraoga+Fi5FtoKNPDHVaUZcr75uzv\\npRVg6tEYB1BmqTpHZkrSMNe0wnvakn4OIg9ixp+IWCOca6UCFQQ3HpjPGDN0sx5H9yZbY+ihn/UA\\nUQqgVEcZ8lTfJK2MmYFX50W+A/ANe+gSRqjTcs5SAYDrrvswgMCc4CCKrQT7w3WkDBjI3z2sImcb\\nlBxKneNd8vmIzASWXjKqnEGoKHkD+MaA2mKWc2Iwj2F2wLWTGxzBbUJ0qwsliF9lBHlfqvkZx2Qc\\n7WyVoznOD/1c+vj1OeQjvfX2ub6lE0Psh8rleFsHsKVdbFumEQmzHSn4prNF5240dzB2Z1mppFO6\\npWZa1pj2MfNimpLfsY2NG8idiu0g3+niwrWSgAnzky6ffawi5hZrsvfO/ltjPwfstZaNHa8u2Tzy\\nCcPJWbaCLH8vBKZnwk+fTDxamnGE54dxWEiD87ymUJ4/AV2bSvHByqsOMYwZ534aZckd4NRkqyrm\\nEbn6pTkXz0VWuTIznalHZuaiMArpYk/kIkc+p5xALMEMlfJB3cdRj6FM7WvavcsuC9Irf3P33fKE\\n1FNpEv1MxuWWW94EAHj72/8SOv9JU0ocQtk9O/xNLz/3DCnF9FmcniC71NcJgJxjqlCBL2e0TwhD\\neM08dBk2MPipR8b2SzcP9TsYdgE531hZ2UxZPofOpVuIVaQrP0A6JvTRB+cr662cRkk2IfagPgs/\\nXTb9/ulRNVYrKioqKioqKioqKioqKioqKioqKo4QVQrgOIOMDjJXS5ZQ1bRx8Wq1ZrmSC/8O2pWt\\nSqHFPiqxuY03WhjdBqfWSef4la6VRKZ1WxhixbRumOop5Na6LsUeANhZOFcRQRv8uXIubk6Rspp7\\nWGs3k3KbWyibJYXHcD9tXK6zNoWU2QOk9jhnIaoaqgvU91q7pZYEVW5jSlm7Ru2olvSSZo3yAfWY\\nC3lXpmqOnQBe2fz2EjKHXB9LVadKFlUg2HVdi9UZ0EDc+kWZYiyLc81xtGmF92IW/bZMsOQpY4ZP\\nDJpvk819I4hlN2q1DbV3O3uHUJ1FZ4SPA+hn225ERaRosY8sb+BUUhU8UnwOwCXN75LyXehhl5pv\\npn0Orcv9rLdS7rNulRj13lQp1be+Ur6r95/hGdwewzkWK8jZPQrXOwyxntsqoudbHZ2HvJV23ms3\\nN4zlalNydUy8H7yenrpMVcditn2Jwnl/2rv0Mg0/DW1jsHWWB2U6+aZ2ofysoN+qUo7YXf/l7W/H\\nLbd8qfmLDFSWFmVDT9jxxYhsmrnmuL99Bss+Y3qhvNlm7zYbfL9J9mbEcuYM21COQrkjk0vHw10e\\nI3s6zp8q2Angwua3t3aq2Ocjl0ksope1BDyqZqCzqojYLjnPfgR5qSGmAMxmd4jPGwsQjyywzxgF\\nvjfT/OGlOgZnyrUkO2uItcsfkV7VTb5SHULVyXbPh1OdqapQVfMuxDwO/WSYB4R6TLaczv88jw80\\n9+lGOtT7nW7u97mIgi1q8JBcbNJCzxTyvVfgLNa776ba/Vg75+E4X7+5f/93vf3tzS/dTLM0jgBC\\na+dswlg3nH1bwulU3gahh/JmuY5B3rT+t4b1bU2XsIh9mfao+u7yDpZOMlZVi103dANSdWr3HlhD\\nropPHJDfjFtnzF2M6YXOfrOMylQ9UmyUFMCZGxRPRUVFRUVFRUVFRUVFRUVFRUVFxWmISy65BLt3\\n78auXbvw2te+Nrv+9Kc/HQ888AC++tWv4r777sNTn/rUzvvOOOMM3Hnnndi1axe+8pWvYNu2QLp4\\n7nOfiwcffBBf+9rX8IlPfAJDQ2Gp/corr8Tu3bvxz//8z7j00ksPK70TT/DvcFEZqz8llOGxqTNU\\nDuW4uCJMtKtsRb43pu6e7mqpxGPyO7VaLCBah916NySx+z7aes3teBOINhy+V9euuo6PPcH1ijL2\\nNcdzoUzV1JY3KVyunJMKxLIx1xz5tVbavTKdG7EfOXOK3/4A4vd32+AytCQyvZEzMdm8UUmLuJ/Z\\nNkv7w7oKqPJiXLksMgQrU3UwaGH/neaoOe/sOGWsOo+EJWsYORuH94/C939N+fquHsgvvwpgT3uV\\nO2BrC3YwOaeMi6iHle+UTKgSE9PrJSu1Vzs3W3Mt3BHrbM6ePv3wheZIRopqlrKlCm3AlOhpRSax\\n8+Rji6G6ykD8XtNQ/hSauANGC9dYnrdgH+aT1lDvXEZa0vXaKpyrrQwgchBdqUv5ErBrmjatC0xJ\\nwDZ5m5BP/SZUv+Ffnq7gm28pXGM+pirdhPvsqJYySyXHYAtyDQh97Zw9LbRys0lcqkQI3HLLVxBL\\nifoDoXnmVruPKr3nyzXtG4OGqqtm8gmjz5CLTZQ/OZA+4SByfeyoHDfWPkdrI1Pv7eepzlRVcP8A\\neh2VWEjKJgVYDrmTuY/3lpDPAZw3twKWQe+TNSb3YhsHsK157mxbbtmfLgBLT0sjUArj9ywJTd3Y\\ngn7G85qQv1N9Vy+1ISzHv7m/yxr4noxbxyWshafzHg7U+1XmqquEM6/DnDb1p9GxnY/3mdf9Ao9w\\nTxPbJtm/QcuZhp6G7v4ecKDxZ9yHbUgV0WMKxtBv+1C+i6rvel/K9IeZku9WQeiuJ6k+OnV8Rwqh\\ntZ/+Aioc7H8HeY44qx/oXoc4VPjN7z0B4NxmDLgvm7EExX4galqzdM1Iujh6m23v/zXkrYyi5EXJ\\nt3G/I51dOEM23kfPujGbN6TM3hNjTvGUpzwF73vf+3DBBRdgbW0Nu3btwuc//3ksLcUvd8MNN+De\\ne+/FPffcg2uvvRZXXHEF7rjjjuJ9F198Mc466yxcdNFFeNGLXoTbb78dr3zlK3HXXXfhD//wD7F7\\n927s2LEDV155Je655x684Q1vwPnnn4+f+Zmfwd69e/E3f/M3T5jmKgVwAoLdDxdYSw2Fn1Np8LhB\\nFat1SZTendQ0Vm1+SiT6FL7AoW77JafrLgq+yzA/ET5whOEruhEGmGxI06V9ddXRc0D4zhzY72sF\\nBsLAYhL9tvS4w4su53MYwk6shzH0Muegkmusl9e1bAuqdGHV3yJ2NGOtaL5vJFJCyK3T1+36yYNi\\n+xcidD4/RPwKpQ2Z2Ca4oWYEh7fdWKlFc4dEPvdAcsewXVXX217yvFG5ywf4uoGRO3wvI53Sapom\\ngNYVjfIoLjOA5myFg0Iwz26O/9FeGW2+3XcBnAG2C+kgUyc3cfKXL6jyemmBAUg34WNpSje6KrmL\\neUy+4cY4Yjncm8R5nqTLDQvDQ8BSE71vEqeT2dKwPsJNYOUtD05XsPejrJPWUl9gn02MfL5d6Rpy\\nR1eXqdARn7do08h5Edr6lKQH0MTnoiRcIj0PGGrOradyKVOIy6+UABh+RvNjxpKMvNQvITp5u1xK\\niD1dpvEFVuD0WlB1cHFQF/afgti+KVTYaL3tV6K7dfwCLsoQnbndpV6Nl11yXRPQJVsaC/jVtwMH\\nmtiWnxZvAJoqsWrhw3EG+ZaFaoJ0yTGXURuBjl1LMmhxuUxTMYzTu7w5uMD6bAD/E+VyN4R8Jsly\\nM2LhAN10je77k3ChicX2uNSO310CZxy5kTHGEpf4F01uRGlIto0gliV9LlUWQwD5nEU3Pw3ljWO8\\nQa7Z83asKKNnx5Iog/7tY25dfPXwDKtO+9ta4zyfGE3lM82Rvec40t4ZAEaab78H/4IoWuYbCyqc\\nYKFLwEyhtt8lcbQUHPtONWnhFocnEp797Gfjsccew+OPPw4A2LlzJ1760pfib//2b9swF110Ed75\\nzncCAO6//368613vwj/8wz8U73vxi1+M+++/HwDw9a9/HRdccAEAYMuWLdi9O5jKHnzwQVx++eX4\\n4Ac/iPPPPx8//vGP8fM///P44Q9/eFhp3qjNq6oUwAD81m/9FpaX45TlzDPPxAc+8AHs3bsX3/nO\\nd3D55Zcfx9RVnGp405vehH/913/Ft771LXzuc5/DM54RZjm13FUcTfz+7/8+HnnkEXzjG9/Azp07\\n8YIXvABAudztRlhUrajYKNx999246qqr2r/PPHMfPvCBK7B37/+D73zni7j88l9tlfN+fNxSWXGy\\n4hWveAW++c1v4tvf/jY+85nP4Gd/9mePd5IqTiPk7Vvar77i8ssxj7AAc2QaehUVOY5kPAcA34aa\\nMCsqnjzqHLaCOPvss7GyEhea+/0+xsfHO8P84Ac/wPj4eOd9Z599drvYCgA/+tGPcOaZZ2J2dha/\\n/Mu/DAD4zd/8TYyOBvPJj3/8Y1x55ZV46KGHcM899xxWmqsUwFHGOeecg9tuuw1nnHFGe+6KK67A\\nL/zCL+AXf/EXcfbZZ+Ohhx7CN77xDTz88MPJvYt2PBfdGLTVxYmOEneW0AFiFex+YrzgBS/A1Vdf\\njec+97n4wQ9+gFtvvRU333wz3vjGNx52uXPOtG5O4lAGMsvnkjC/3JVCWYLq+Ag4c+JwpgYpU2eL\\nMGQJtTJH6/cgsY1Bz608hS4885nPxK233ornP//5+O53v4tf//Vfx9/93d9hZmams9ztfvhhbEN5\\nqyEgMO/peu0uokPIxRvU8aWLm6xup+S/RMfvLYglhqmatr8BFNzvla2vGEHOVFV5C2cIaulTB930\\neWM40azKxxPPetazcMcdd+DCCy/Et771rfb8oPZuBaMI9uDIl+k3duahhhsyhZxp6OxUlbbxPli3\\ny0u3F+LmfdzYw7dvm0bOfXaHRrSx88p25II/LE/fXY/lnK5orD/Klx21MJHdNltgTMeaeqrjGc94\\nBj75yU/iJS95CWZnZ3HLLbfg3e9+N9785jdnYSnrpOM19dSIcIEA7c8OWpjS5hRdrY4O30uCD7Yx\\nVtY7l+IcAta/3/xWrmnqbjvM4qqVomnADjVJJ1+RxwPIHRpjSgZ5qqgr96mLJ9O+hU17cp5z4FZ5\\nPY5CNsSYbTA0/f+3d7+xUVRrHMd/lFLTkkVK0qi0KBEa4/WGaLyJCRITRW8Q/1Uk0YgxVkKkvjBX\\nTcQ3kiqYmBjIJTG2SDBSE2IiiVYuml4wEfnzAoELETEgkEulRFzTpTRt+FPa+2L27Jw5M7vb2W25\\ndvv9vNnu7szuwDx7ZubMc85jLZnZ1+nHeoW5U3nZmf3mmH4u0CKlo6EnnXPYYw+cNQWFTqTXP5XZ\\nJjdn2xzTJytcqtQ91vYHXjPHU3eMQdh4KMxXyPnc/v37Q6Mtjaj/TbP37ZI/7pRIphXqVbf1rtmz\\nJhd1qnrTbdGR9B69Kx3pFQqP2zTxa085VpPecjubOTh9hh9jUVMBXAn85V5DuMO0k5nfQK6rjdI/\\nqkYbmWtY72rtr+GPz8o++mYbKm+fI7nTkPRfhPfAAAANYklEQVQre6ZihcKZz/5YuDM6EzrO2y23\\nO2rFHeFiv2dfXbjrRV3F25mqfy7vvPOO5s2bpzlz5mQySSUpkUgolUoFlr1w4YKmTJmiP/74Q4lE\\nQufPn9eFCxeUSCQC60W9XlZWpsHBQTU2NmrdunVauXKldu3apalT/S7QDz/8UB999JG++eYb7dq1\\nSzt37sy57SNVvKrkOlabmpq0bNmyzPM77rhD+/fvV2VlZWjZN954Qzt27Ai9XllZqU8//VSvvvqq\\nNm/enHm9oaFB69ev19DQkHp6evTZZ5/pueeei2wcML4UG3cHDx7U7NmzNTg4qOuuu051dXU6efKk\\nJOnJJ59Ua2srcYeQYuPu4sWLWrp0qX7//XdJ0oEDB3TjjTeqvLycuENWI3Gcffnll7Vx40adPn06\\ncAOT4yyyGW7cDQ0N6c0331RNTY327dunU6e8S5CWlhYdPnw4smMVyKaQ9o72DcWK096tWLFCx48f\\n53wOI6KQNo9rWKxcuVKSNHHiRB09elRTp05VX1+f7rvvPr3//vuBZffs2aOFCxeqra1NDz/8sL7/\\n/nv9/PPPqq+vD603NDSkxx57TFu2bAncrHz00Ue1ZMkSpVIprVu3Th0dHaqvr9d7772np556SgMD\\nA7p06ZKuXr2ad9vt43QxSq5jtaWlRS0tLZKk5cuX64UXXtD8+fOHPceCJK1fv16tra2Bu8ySNGPG\\nDP3666+Z511dXZozZ07ez4u6O//3YW9NobwLCXd+FzsbI1s240g44jyWupGIu8HBQT3xxBPasGGD\\nLl68qLfeekuSN4dIvLjz7pkeUXCC+jjccjCTIt6LNpyoGo0Bb24m4H8ilyo1xcZdZ2enOjs7M8/X\\nrl2r9vZ2DQwM5Iy7U1KopJivN1TMwORx2YWt3EzQKgXnYpWCWa3mPq/Jbjmc+b5ahXMU7TvBwTu/\\ndnaDW8DPzjd0M2aiStS470VFv58nWzrZqiPR3r3yyiuSpAcffDDweu7jbLmkiQq2Id6eMkVdJln/\\nz1HlhSRv35t4crNTpyr7nKWT5EdadyijxZ591y5WJNnZjIl022TPwuqWvLLXcuPevGfnQLrba2ca\\nmewNP3N17M6rGjfuVqxYoTNn/LOgrq4uTZkyRZMnT1ZfX/R4oWPKN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piUnWOOdsfeBxhH4cK34KeUvMY597VK3lhEZDjVjpI7u3vH4UfHyboV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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tic = time.time() #Start Timer\\n\",\n \"\\n\",\n \"## Test Prediction with kfold xVal\\n\",\n \"# SVR\\n\",\n \"# negvneu = Predict(dat,Y,algorithm='svr',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5}, **{'kernel':\\\"linear\\\"})\\n\",\n \"# negvneu = Predict(dat,Y,algorithm='svr',subject_id = holdout, output_dir=outfolder, cv_dict = {'loso':holdout}, **{'kernel':\\\"linear\\\"})\\n\",\n \"# negvneu.predict()\\n\",\n \"# print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\\n\",\n \"\\n\",\n \"# Ridge\\n\",\n \"negvneu = Predict(dat,Y,algorithm='ridge',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5})\\n\",\n \"negvneu.predict()\\n\",\n \"\\n\",\n \"# # Principal Components Regression\\n\",\n \"# negvneu = Predict(dat,Y,algorithm='pcr',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5})\\n\",\n \"# negvneu.predict()\\n\",\n \"print 'Total Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## Apply Mask\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Elapsed: 44.62 seconds\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/usr/local/lib/python2.7/site-packages/nilearn/signal.py:50: UserWarning: Standardization of 3D signal has been requested but would lead to zero values. Skipping.\\n\",\n \" warnings.warn('Standardization of 3D signal has been requested but '\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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CisK4uWXLQQMzEnuaGMsJiOqp+4LBIjLFaR3FlH0Ly+EyfqTDa3ZSab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\\nCZRFQEBAQEBKAmUREBAQEJCS/wdaR8bG8157kwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tic = time.time() #Start Timer\\n\",\n \"\\n\",\n \"# Load data using nibabel\\n\",\n \"pines = nb.load(os.path.join(outfolder, 'ridge_weightmap.nii.gz'))\\n\",\n \"\\n\",\n \"pexpd = apply_mask(data=dat, weight_map=pines, output_dir=outfolder, method='dot_product', save_output=True)\\n\",\n \"pexpc = apply_mask(data=dat, weight_map=pines, output_dir=outfolder, method='correlation', save_output=True)\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 1)\\n\",\n \"plt.plot(pexpd)\\n\",\n \"plt.title('Pattern Expression')\\n\",\n \"plt.ylabel('Dot Product')\\n\",\n \"\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"plt.plot(pexpc)\\n\",\n \"plt.xlabel('Subject')\\n\",\n \"plt.ylabel('Correlation')\\n\",\n \"\\n\",\n \"print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": false\n },\n \"source\": [\n \"## ROC Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"------------------------\\n\",\n \".:ROC Analysis Summary:.\\n\",\n \"------------------------\\n\",\n \"Accuracy: 0.84\\n\",\n \"Accuracy SE: 0.11\\n\",\n \"Accuracy p-value: 0.00\\n\",\n \"Sensitivity: 0.93\\n\",\n \"Specificity: 0.75\\n\",\n \"AUC: 0.88\\n\",\n \"PPV: 0.79\\n\",\n \"------------------------\\n\",\n \"[ 1. 1. 1. ..., 0.03571429 0.03571429\\n\",\n \" 0.03571429]\\n\",\n \"Total Elapsed: 1.30 seconds\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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V4mJIp/5heUiMjAqc2i1aJoB+AGYJlkz0vA9sTxU/kFJSLSHHWdbaUw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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"tic = time.time() #Start Timer\\n\",\n \"\\n\",\n \"# Create Variables\\n\",\n \"negvneu.yfit\\n\",\n \"include = (negvneu.Y==3) | (negvneu.Y==1)\\n\",\n \"input_values = negvneu.yfit[include]\\n\",\n \"binary_outcome = negvneu.Y[include]\\n\",\n \"binary_outcome = binary_outcome==3\\n\",\n \"\\n\",\n \"# Single-Interval\\n\",\n \"roc = Roc(input_values=input_values, binary_outcome=binary_outcome)\\n\",\n \"roc.plot()\\n\",\n \"roc.summary()\\n\",\n \"\\n\",\n \"print roc.tpr\\n\",\n \"roc.accuracy\\n\",\n \"\\n\",\n \"# # Forced Choice \\n\",\n \"# roc_fc = Roc(input_values=input_values, binary_outcome=binary_outcome, forced_choice=True)\\n\",\n \"# roc_fc.plot()\\n\",\n \"# roc_fc.summary()\\n\",\n \"\\n\",\n \"print 'Total Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from nilearn.input_data import NiftiMasker\\n\",\n \"mask = nb.load('/Users/lukechang/Github/neurolearn/nltools/resources/MNI152_T1_2mm_brain_mask_dil.nii.gz')\\n\",\n \"nifti_masker = NiftiMasker(mask_img=mask)\\n\",\n \"pcr = nb.load(os.path.join(outfolder,'pcr_weightmap.nii.gz'))\\n\",\n \"ridge = nb.load(os.path.join(outfolder,'ridge_weightmap.nii.gz'))\\n\",\n \"pcr_mask = nifti_masker.fit_transform(pcr)\\n\",\n \"ridge_mask = nifti_masker.fit_transform(ridge)\\n\",\n \"np.corrcoef(pcr_mask,ridge_mask)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"## Univariate Regression\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.9\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45484,"cells":{"repo_name":{"kind":"string","value":"tensorflow/docs-l10n"},"path":{"kind":"string","value":"site/ja/tutorials/estimator/boosted_trees.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"21926"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7765UFHoyGx6\"\n },\n \"source\": [\n \"##### Copyright 2019 The TensorFlow Authors.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"cellView\": \"form\",\n \"id\": \"KVtTDrUNyL7x\"\n },\n \"outputs\": [],\n \"source\": [\n \"#@title Licensed under the Apache License, Version 2.0 (the \\\"License\\\");\\n\",\n \"# you may not use this file except in compliance with the License.\\n\",\n \"# You may obtain a copy of the License at\\n\",\n \"#\\n\",\n \"# https://www.apache.org/licenses/LICENSE-2.0\\n\",\n \"#\\n\",\n \"# Unless required by applicable law or agreed to in writing, software\\n\",\n \"# distributed under the License is distributed on an \\\"AS IS\\\" BASIS,\\n\",\n \"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\\n\",\n \"# See the License for the specific language governing permissions and\\n\",\n \"# limitations under the License.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"xPYxZMrWyA0N\"\n },\n \"source\": [\n \"# Estimators を使用するブースティング木\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"p_vOREjRx-Y0\"\n },\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"6gWdn5lrlkhR\"\n },\n \"source\": [\n \"> 警告: 新しいコードには Estimators は推奨されません。Estimators は `v1.Session` スタイルのコードを実行しますが、これは正しく記述するのはより難しく、特に TF 2 コードと組み合わせると予期しない動作をする可能性があります。Estimators は、[互換性保証] (https://tensorflow.org/guide/versions) の対象となりますが、セキュリティの脆弱性以外の修正は行われません。詳細については、[移行ガイド](https://tensorflow.org/guide/migrate)を参照してください。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"qNW3c_rop5J8\"\n },\n \"source\": [\n \"**注意**: 多くの最先端の決定フォレストアルゴリズムの最新の Keras ベースの実装は、[TensorFlow 決定フォレスト](https://tensorflow.org/decision_forests)から利用できます。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"dW3r7qVxzqN5\"\n },\n \"source\": [\n \"このチュートリアルは、`tf.estimator`API で決定木を使用する勾配ブースティングモデルのエンドツーエンドのウォークスルーです。ブースティング木モデルは、回帰と分類の両方のための最も一般的かつ効果的な機械学習アプローチの 1 つです。これは、複数（10 以上、100 以上、あるいは 1000 以上の場合も考えられます）の木モデルからの予測値を結合するアンサンブル手法です。\\n\",\n \"\\n\",\n \"最小限のハイパーパラメータ調整で優れたパフォーマンスを実現できるため、ブースティング木モデルは多くの機械学習実践者に人気があります。\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"eylrTPAN3rJV\"\n },\n \"source\": [\n \"## Titanic データセットを読み込む\\n\",\n \"\\n\",\n \"Titanic データセットを使用します。ここでの目標は、性別、年齢、クラスなど与えられた特徴から（やや悪趣味ではありますが）乗船者の生存を予測することです。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"KuhAiPfZ3rJW\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"from IPython.display import clear_output\\n\",\n \"from matplotlib import pyplot as plt\\n\",\n \"\\n\",\n \"# Load dataset.\\n\",\n \"dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\\n\",\n \"dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\\n\",\n \"y_train = dftrain.pop('survived')\\n\",\n \"y_eval = dfeval.pop('survived')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"NFtnFm1T0kMf\"\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"tf.random.set_seed(123)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"3ioodHdVJVdA\"\n },\n \"source\": [\n \"データセットはトレーニングセットと評価セットで構成されています。\\n\",\n \"\\n\",\n \"- `dftrain`と`y_train`は *トレーニングセット*です — モデルが学習に使用するデータです。\\n\",\n \"- モデルは*評価セット*、`dfeval`、`y_eval`に対してテストされます。\\n\",\n \"\\n\",\n \"トレーニングには以下の特徴を使用します。\\n\",\n \"\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"AoPiWsJALr-k\"\n },\n \"source\": [\n \"## データを検証する\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"slcat1yzmzw5\"\n },\n \"source\": [\n \"まず最初に、データの一部をプレビューして、トレーニングセットの要約統計を作成します。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"15PLelXBlxEW\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"j2hiM4ETmqP0\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-IR0e8V-LyJ4\"\n },\n \"source\": [\n \"トレーニングセットと評価セットには、それぞれ 627 個と 264 個の例があります。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"_1NwYqGwDjFf\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain.shape[0], dfeval.shape[0]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"28UFJ4KSMK3V\"\n },\n \"source\": [\n \"乗船者の大半は 20 代から 30 代です。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"CaVDmZtuDfux\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain.age.hist(bins=20)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"1pifWiCoMbR5\"\n },\n \"source\": [\n \"男性の乗船者数は女性の乗船者数の約 2 倍です。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"-WazAq30MO5J\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain.sex.value_counts().plot(kind='barh')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"7_XkxrpmmVU_\"\n },\n \"source\": [\n \"乗船者の大半は「3 等」の船室クラスを利用していました。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"zZ3PvVy4l4gI\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain['class'].value_counts().plot(kind='barh')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"HM5SlwlxmZMT\"\n },\n \"source\": [\n \"大半の乗船者はサウサンプトンから乗船しています。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"RVTSrdr4mZaC\"\n },\n \"outputs\": [],\n \"source\": [\n \"dftrain['embark_town'].value_counts().plot(kind='barh')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"aTn1niLPob3x\"\n },\n \"source\": [\n \"女性は男性よりも生存する確率がはるかに高く、これは明らかにモデルの予測特徴です。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Eh3KW5oYkaNS\"\n },\n \"outputs\": [],\n \"source\": [\n \"pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% survive')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"krkRHuMp3rJn\"\n },\n \"source\": [\n \"## 特徴量カラムを作成して関数を入力する\\n\",\n \"\\n\",\n \"勾配ブースティング Estimator は数値特徴とカテゴリ特徴の両方を利用します。特徴量カラムは、全ての TensorFlow Estimator と機能し、その目的はモデリングに使用される特徴を定義することにあります。さらに、One-Hot エンコーディング、正規化、バケット化などいくつかの特徴量エンジニアリング機能を提供します。このチュートリアルでは、`CATEGORICAL_COLUMNS`のフィールドはカテゴリカラムから One-Hot エンコーディングされたカラム（[インジケータカラム](https://www.tensorflow.org/api_docs/python/tf/feature_column/indicator_column)）に変換されます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"upaNWxcF3rJn\"\n },\n \"outputs\": [],\n \"source\": [\n \"CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\\n\",\n \" 'embark_town', 'alone']\\n\",\n \"NUMERIC_COLUMNS = ['age', 'fare']\\n\",\n \"\\n\",\n \"def one_hot_cat_column(feature_name, vocab):\\n\",\n \" return tf.feature_column.indicator_column(\\n\",\n \" tf.feature_column.categorical_column_with_vocabulary_list(feature_name,\\n\",\n \" vocab))\\n\",\n \"feature_columns = []\\n\",\n \"for feature_name in CATEGORICAL_COLUMNS:\\n\",\n \" # Need to one-hot encode categorical features.\\n\",\n \" vocabulary = dftrain[feature_name].unique()\\n\",\n \" feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\\n\",\n \"\\n\",\n \"for feature_name in NUMERIC_COLUMNS:\\n\",\n \" feature_columns.append(tf.feature_column.numeric_column(feature_name,\\n\",\n \" dtype=tf.float32))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"74GNtFpStSAz\"\n },\n \"source\": [\n \"特徴量カラムが生成する変換は表示することができます。例えば、`indicator_column`を単一の例で使用した場合の出力は次のようになります。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"Eaq79D9FtmF8\"\n },\n \"outputs\": [],\n \"source\": [\n \"example = dict(dftrain.head(1))\\n\",\n \"class_fc = tf.feature_column.indicator_column(tf.feature_column.categorical_column_with_vocabulary_list('class', ('First', 'Second', 'Third')))\\n\",\n \"print('Feature value: \\\"{}\\\"'.format(example['class'].iloc[0]))\\n\",\n \"print('One-hot encoded: ', tf.keras.layers.DenseFeatures([class_fc])(example).numpy())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"YbCUn3nCusC3\"\n },\n \"source\": [\n \"さらに、特徴量カラムの変換を全てまとめて表示することができます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"omIYcsVws3g0\"\n },\n \"outputs\": [],\n \"source\": [\n \"tf.keras.layers.DenseFeatures(feature_columns)(example).numpy()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"-UOlROp33rJo\"\n },\n \"source\": [\n \"次に、入力関数を作成する必要があります。これらはトレーニングと推論の両方のためにデータをモデルに読み込む方法を指定します。[ `tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API の`from_tensor_slices`メソッドを使用して Pandas から直接データを読み取ります。これは小規模でインメモリのデータセットに適しています。大規模のデータセットの場合は、多様なファイル形式（[csv](https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset)を含む）をサポートする tf.data API を使用すると、メモリに収まりきれないデータセットも処理することができます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"9dquwCQB3rJp\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Use entire batch since this is such a small dataset.\\n\",\n \"NUM_EXAMPLES = len(y_train)\\n\",\n \"\\n\",\n \"def make_input_fn(X, y, n_epochs=None, shuffle=True):\\n\",\n \" def input_fn():\\n\",\n \" dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))\\n\",\n \" if shuffle:\\n\",\n \" dataset = dataset.shuffle(NUM_EXAMPLES)\\n\",\n \" # For training, cycle thru dataset as many times as need (n_epochs=None).\\n\",\n \" dataset = dataset.repeat(n_epochs)\\n\",\n \" # In memory training doesn't use batching.\\n\",\n \" dataset = dataset.batch(NUM_EXAMPLES)\\n\",\n \" return dataset\\n\",\n \" return input_fn\\n\",\n \"\\n\",\n \"# Training and evaluation input functions.\\n\",\n \"train_input_fn = make_input_fn(dftrain, y_train)\\n\",\n \"eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"HttfNNlN3rJr\"\n },\n \"source\": [\n \"## モデルをトレーニングして評価する\\n\",\n \"\\n\",\n \"以下のステップで行います。\\n\",\n \"\\n\",\n \"1. 特徴とハイパーパラメータを指定してモデルを初期化する。\\n\",\n \"2. `train_input_fn`を使用してモデルにトレーニングデータを与え、`train`関数を使用してモデルをトレーニングする。\\n\",\n \"3. 評価セット（この例では`dfeval` DataFrame）を使用してモデルのパフォーマンスを評価する。予測値が`y_eval`配列のラベルと一致することを確認する。\\n\",\n \"\\n\",\n \"ブースティング木モデルをトレーニングする前に、まず線形分類器（ロジスティック回帰モデル）をトレーニングしてみましょう。ベンチマークを確立するには、より単純なモデルから始めるのがベストプラクティスです。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"JPOGpmmq3rJr\"\n },\n \"outputs\": [],\n \"source\": [\n \"linear_est = tf.estimator.LinearClassifier(feature_columns)\\n\",\n \"\\n\",\n \"# Train model.\\n\",\n \"linear_est.train(train_input_fn, max_steps=100)\\n\",\n \"\\n\",\n \"# Evaluation.\\n\",\n \"result = linear_est.evaluate(eval_input_fn)\\n\",\n \"clear_output()\\n\",\n \"print(pd.Series(result))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"BarkNXwA3rJu\"\n },\n \"source\": [\n \"次に、ブースティング木モデルをトレーニングしてみましょう。ブースティング木では、回帰（`BoostedTreesRegressor`）と分類（`BoostedTreesClassifier`）をサポートします。目標は、生存か非生存かのクラスを予測することなので、`BoostedTreesClassifier`を使用します。\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"tgEzMtlw3rJu\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Since data fits into memory, use entire dataset per layer. It will be faster.\\n\",\n \"# Above one batch is defined as the entire dataset.\\n\",\n \"n_batches = 1\\n\",\n \"est = tf.estimator.BoostedTreesClassifier(feature_columns,\\n\",\n \" n_batches_per_layer=n_batches)\\n\",\n \"\\n\",\n \"# The model will stop training once the specified number of trees is built, not\\n\",\n \"# based on the number of steps.\\n\",\n \"est.train(train_input_fn, max_steps=100)\\n\",\n \"\\n\",\n \"# Eval.\\n\",\n \"result = est.evaluate(eval_input_fn)\\n\",\n \"clear_output()\\n\",\n \"print(pd.Series(result))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"hEflwznXvuMP\"\n },\n \"source\": [\n \"このトレーニングモデルを使用して、評価セットからある乗船者に予測を立てることができます。TensorFlow モデルは、バッチ、コレクション、または例に対してまとめて予測を立てられるように最適化されています。以前は、`eval_input_fn` は評価セット全体を使って定義されていました。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"6zmIjTr73rJ4\"\n },\n \"outputs\": [],\n \"source\": [\n \"pred_dicts = list(est.predict(eval_input_fn))\\n\",\n \"probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\\n\",\n \"\\n\",\n \"probs.plot(kind='hist', bins=20, title='predicted probabilities')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"id\": \"mBUaNN1BzJHG\"\n },\n \"source\": [\n \"最後に、結果の受信者操作特性（ROC）を見てみましょう。真陽性率と偽陽性率間のトレードオフに関し、より明確な予想を得ることができます。\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"id\": \"NzxghvVz3rJ6\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import roc_curve\\n\",\n \"\\n\",\n \"fpr, tpr, _ = roc_curve(y_eval, probs)\\n\",\n \"plt.plot(fpr, tpr)\\n\",\n \"plt.title('ROC curve')\\n\",\n \"plt.xlabel('false positive rate')\\n\",\n \"plt.ylabel('true positive rate')\\n\",\n \"plt.xlim(0,)\\n\",\n \"plt.ylim(0,)\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"colab\": {\n \"collapsed_sections\": [],\n \"name\": \"boosted_trees.ipynb\",\n \"toc_visible\": true\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"name\": \"python3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":45485,"cells":{"repo_name":{"kind":"string","value":"sarathid/Learning"},"path":{"kind":"string","value":"Deep_learning_ND/Week 1/dlnd-your-first-network/DLND-your-first-network/dlnd-your-first-neural-network.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"337782"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Your first neural network\\n\",\n \"\\n\",\n \"In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"%config InlineBackend.figure_format = 'retina'\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load and prepare the data\\n\",\n \"\\n\",\n \"A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"data_path = 'Bike-Sharing-Dataset/hour.csv'\\n\",\n \"\\n\",\n \"rides = pd.read_csv(data_path)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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+BLKd8E4E27+Lt/AvAvpvyba6BcKBBCFhNbjnBOFp3Pfe84Xv7urwIA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\\nrkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 263,\n \"width\": 380\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"rides[:24*10].plot(x='dteday', y='cnt')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Dummy variables\\n\",\n \"Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 252,\n \"width\": 373\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(losses['train'], label='Training loss')\\n\",\n \"plt.plot(losses['validation'], label='Validation loss')\\n\",\n \"plt.legend()\\n\",\n \"plt.ylim(ymax=0.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Check out your predictions\\n\",\n \"\\n\",\n \"Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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N0wr1R+BnLQhRAFALsBKAF4EwCklL0oO+2ThBC6xn97VH4GcuRJCKwe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\\nqqqqqqqqjfY/XN8bcsqoAGAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {\n \"image/png\": {\n \"height\": 272,\n \"width\": 502\n }\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(8,4))\\n\",\n \"\\n\",\n \"mean, std = scaled_features['cnt']\\n\",\n \"predictions = network.run(test_features)*std + mean\\n\",\n \"ax.plot(predictions[0], label='Prediction')\\n\",\n \"ax.plot((test_targets['cnt']*std + mean).values, label='Data')\\n\",\n \"ax.set_xlim(right=len(predictions))\\n\",\n \"ax.legend()\\n\",\n \"\\n\",\n \"dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\\n\",\n \"dates = dates.apply(lambda d: d.strftime('%b %d'))\\n\",\n \"ax.set_xticks(np.arange(len(dates))[12::24])\\n\",\n \"_ = ax.set_xticklabels(dates[12::24], rotation=45)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Thinking about your results\\n\",\n \" \\n\",\n \"Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\\n\",\n \"\\n\",\n \"> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\\n\",\n \"\\n\",\n \"#### Your answer below\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Unit tests\\n\",\n \"\\n\",\n \"Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \".....\\n\",\n \"----------------------------------------------------------------------\\n\",\n \"Ran 5 tests in 0.021s\\n\",\n \"\\n\",\n \"OK\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import unittest\\n\",\n \"\\n\",\n \"inputs = [0.5, -0.2, 0.1]\\n\",\n \"targets = [0.4]\\n\",\n \"test_w_i_h = np.array([[0.1, 0.4, -0.3], \\n\",\n \" [-0.2, 0.5, 0.2]])\\n\",\n \"test_w_h_o = np.array([[0.3, -0.1]])\\n\",\n \"\\n\",\n \"class TestMethods(unittest.TestCase):\\n\",\n \" \\n\",\n \" ##########\\n\",\n \" # Unit tests for data loading\\n\",\n \" ##########\\n\",\n \" \\n\",\n \" def test_data_path(self):\\n\",\n \" # Test that file path to dataset has been unaltered\\n\",\n \" self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\\n\",\n \" \\n\",\n \" def test_data_loaded(self):\\n\",\n \" # Test that data frame loaded\\n\",\n \" self.assertTrue(isinstance(rides, pd.DataFrame))\\n\",\n \" \\n\",\n \" ##########\\n\",\n \" # Unit tests for network functionality\\n\",\n \" ##########\\n\",\n \"\\n\",\n \" def test_activation(self):\\n\",\n \" network = NeuralNetwork(3, 2, 1, 0.5)\\n\",\n \" # Test that the activation function is a sigmoid\\n\",\n \" self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\\n\",\n \"\\n\",\n \" def test_train(self):\\n\",\n \" # Test that weights are updated correctly on training\\n\",\n \" network = NeuralNetwork(3, 2, 1, 0.5)\\n\",\n \" network.weights_input_to_hidden = test_w_i_h.copy()\\n\",\n \" network.weights_hidden_to_output = test_w_h_o.copy()\\n\",\n \" \\n\",\n \" network.train(inputs, targets)\\n\",\n \" self.assertTrue(np.allclose(network.weights_hidden_to_output, \\n\",\n \" np.array([[ 0.37275328, -0.03172939]])))\\n\",\n \" self.assertTrue(np.allclose(network.weights_input_to_hidden,\\n\",\n \" np.array([[ 0.10562014, 0.39775194, -0.29887597],\\n\",\n \" [-0.20185996, 0.50074398, 0.19962801]])))\\n\",\n \"\\n\",\n \" def test_run(self):\\n\",\n \" # Test correctness of run method\\n\",\n \" network = NeuralNetwork(3, 2, 1, 0.5)\\n\",\n \" network.weights_input_to_hidden = test_w_i_h.copy()\\n\",\n \" network.weights_hidden_to_output = test_w_h_o.copy()\\n\",\n \"\\n\",\n \" self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\\n\",\n \"\\n\",\n \"suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\\n\",\n \"unittest.TextTestRunner().run(suite)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":45486,"cells":{"repo_name":{"kind":"string","value":"D-X-Y/NATS-Bench"},"path":{"kind":"string","value":"notebooks/create-query-sss.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"7842"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2021-03-29 08:23:08] Try to use the default NATS-Bench (size) path from fast_mode=True and path=None.\\n\",\n \"[2021-03-29 08:23:08] Create NATS-Bench (size) done with 0/32768 architectures avaliable.\\n\",\n \"\\n\",\n \"API create done: NATSsize(0/32768 architectures, fast_mode=True, file=None)\\n\",\n \"\\n\",\n \"[2021-03-29 08:23:08] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=12, and is_random=True.\\n\",\n \"[2021-03-29 08:23:08] Call query_index_by_arch with arch=1234\\n\",\n \"[2021-03-29 08:23:08] Call clear_params with archive_root=/Users/xuanyidong/.torch/NATS-sss-v1_0-50262-simple and index=1234\\n\",\n \"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\\n\",\n \" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\\n\",\n \" 'performance of the CIFAR-10 test set after training on the '\\n\",\n \" 'train+valid sets by 12 epochs. The per-time and total-time '\\n\",\n \" 'indicate the per epoch and total time costs, respectively.',\\n\",\n \" 'test-accuracy': 83.87,\\n\",\n \" 'test-all-time': 8.31445026397705,\\n\",\n \" 'test-loss': 0.4872739363670349,\\n\",\n \" 'test-per-time': 0.6928708553314209,\\n\",\n \" 'train-accuracy': 85.74,\\n\",\n \" 'train-all-time': 69.73253917694092,\\n\",\n \" 'train-loss': 0.4183172229385376,\\n\",\n \" 'train-per-time': 5.811044931411743}\\n\"\n ]\n }\n ],\n \"source\": [\n \"from nats_bench import create\\n\",\n \"from pprint import pprint\\n\",\n \"\\n\",\n \"# Create the API instance for the size search space in NATS\\n\",\n \"api = create(None, 'sss', fast_mode=True, verbose=True)\\n\",\n \"print('\\\\nAPI create done: {:}\\\\n'.format(api))\\n\",\n \"\\n\",\n \"\\n\",\n \"info = api.get_more_info(1234, 'cifar10')\\n\",\n \"pprint(info)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2021-03-29 08:23:12] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=90, and is_random=True.\\n\",\n \"[2021-03-29 08:23:12] Call query_index_by_arch with arch=1234\\n\",\n \"[2021-03-29 08:23:12] Call _prepare_info with index=1234 skip because it is in arch2infos_dict\\n\",\n \"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\\n\",\n \" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\\n\",\n \" 'performance of the CIFAR-10 test set after training on the '\\n\",\n \" 'train+valid sets by 90 epochs. The per-time and total-time '\\n\",\n \" 'indicate the per epoch and total time costs, respectively.',\\n\",\n \" 'test-accuracy': 89.4,\\n\",\n \" 'test-all-time': 62.35837697982788,\\n\",\n \" 'test-loss': 0.3388326271057129,\\n\",\n \" 'test-per-time': 0.6928708553314209,\\n\",\n \" 'train-accuracy': 95.206,\\n\",\n \" 'train-all-time': 522.9940438270569,\\n\",\n \" 'train-loss': 0.14320597895622253,\\n\",\n \" 'train-per-time': 5.811044931411743}\\n\"\n ]\n }\n ],\n \"source\": [\n \"info = api.get_more_info(1234, 'cifar10', hp='90')\\n\",\n \"pprint(info)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2021-03-29 08:23:15] Call the get_cost_info function with index=12, dataset=cifar10, and hp=12.\\n\",\n \"[2021-03-29 08:23:15] Call clear_params with archive_root=/Users/xuanyidong/.torch/NATS-sss-v1_0-50262-simple and index=12\\n\",\n \"Call query_meta_info_by_index with arch_index=12, hp=12\\n\",\n \"[2021-03-29 08:23:15] Call _prepare_info with index=12 skip because it is in arch2infos_dict\\n\",\n \"{'T-ori-test@epoch': 0.6709375381469727,\\n\",\n \" 'T-ori-test@total': 8.051250457763672,\\n\",\n \" 'T-train@epoch': 5.539922475814819,\\n\",\n \" 'T-train@total': 66.47906970977783,\\n\",\n \" 'flops': 7.991706,\\n\",\n \" 'latency': 0.014862352974560795,\\n\",\n \" 'params': 0.067378}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Query the flops, params, latency. info is a dict.\\n\",\n \"info = api.get_cost_info(12, 'cifar10')\\n\",\n \"pprint(info)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2021-03-29 08:23:17] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=12, and is_random=True.\\n\",\n \"[2021-03-29 08:23:17] Call query_index_by_arch with arch=1234\\n\",\n \"[2021-03-29 08:23:17] Call _prepare_info with index=1234 skip because it is in arch2infos_dict\\n\",\n \"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\\n\",\n \" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\\n\",\n \" 'performance of the CIFAR-10 test set after training on the '\\n\",\n \" 'train+valid sets by 12 epochs. The per-time and total-time '\\n\",\n \" 'indicate the per epoch and total time costs, respectively.',\\n\",\n \" 'test-accuracy': 84.28,\\n\",\n \" 'test-all-time': 8.31445026397705,\\n\",\n \" 'test-loss': 0.46498328766822816,\\n\",\n \" 'test-per-time': 0.6928708553314209,\\n\",\n \" 'train-accuracy': 86.004,\\n\",\n \" 'train-all-time': 69.73253917694092,\\n\",\n \" 'train-loss': 0.405061281375885,\\n\",\n \" 'train-per-time': 5.811044931411743}\\n\"\n ]\n }\n ],\n \"source\": [\n \"info = api.get_more_info(1234, 'cifar10', hp='12', is_random=True)\\n\",\n \"pprint(info)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2021-03-29 08:23:20] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=12, and is_random=True.\\n\",\n \"[2021-03-29 08:23:20] Call query_index_by_arch with arch=1234\\n\",\n \"[2021-03-29 08:23:20] Call _prepare_info with index=1234 skip because it is in arch2infos_dict\\n\",\n \"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\\n\",\n \" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\\n\",\n \" 'performance of the CIFAR-10 test set after training on the '\\n\",\n \" 'train+valid sets by 12 epochs. The per-time and total-time '\\n\",\n \" 'indicate the per epoch and total time costs, respectively.',\\n\",\n \" 'test-accuracy': 83.87,\\n\",\n \" 'test-all-time': 8.31445026397705,\\n\",\n \" 'test-loss': 0.4872739363670349,\\n\",\n \" 'test-per-time': 0.6928708553314209,\\n\",\n \" 'train-accuracy': 85.74,\\n\",\n \" 'train-all-time': 69.73253917694092,\\n\",\n \" 'train-loss': 0.4183172229385376,\\n\",\n \" 'train-per-time': 5.811044931411743}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# The same code as above, but return the different performance because we set is_random=True\\n\",\n \"info = api.get_more_info(1234, 'cifar10', hp='12', is_random=True)\\n\",\n \"pprint(info)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.8\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45487,"cells":{"repo_name":{"kind":"string","value":"drphilmarshall/StatisticalMethods"},"path":{"kind":"string","value":"lessons/mcmc_diagnostics.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"13536"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"# MCMC Diagnostics\\n\",\n \"\\n\",\n \"Goals:\\n\",\n \"* Learn how to determine whether a Markov chain can reliably be used for inference\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## References\\n\",\n \"\\n\",\n \"* Gelman 11.4-11.5\\n\",\n \"* [Kravtsov notebook on convergence](http://nbviewer.ipython.org/url/astro.uchicago.edu/%7Eandrey/classes/150404_mayacamas/mcmc_convergence.ipynb)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Diagnostics\\n\",\n \"\\n\",\n \"To be useful to us, a chain must\\n\",\n \"1. have converged to the posterior distribution\\n\",\n \"2. provide enough effectively independent samples to characterize it\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"What would make us confident of convergence?\\n\",\n \"* Is the chain stationary?\\n\",\n \"* Do independent chains started from overdispersed positions find the same solution?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"How do we guess the number of independent samples?\\n\",\n \"* Check how well the chain appears to exploring the distribution.\\n\",\n \"* Compare the autocorrelation length scale with the chain length.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"There are numerical estimates that can help with this, but **they are not a substitute for human visual inspection**.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Common misuses of/misconceptions about convergence\\n\",\n \"\\n\",\n \"Convergence does **not** mean\\n\",\n \"* that parameters are \\\"well constrained\\\" by the data\\n\",\n \"* that the autocorrelation length is small\\n\",\n \"* that there are not occasional excursions beyond a locus in parameter space\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Convergence tests\\n\",\n \"* Inspection! There is no substitute.\\n\",\n \"* Gelman-Rubin statistic\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"* How stationary does each sequence appear? \\n\",\n \"* Are all chains sampling the same PDF?\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"
\\n\",\n \"\\n\",\n \"Conservatively, we might remove the first $\\\\sim500$ steps based on this.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"#### Gelman-Rubin convergence statistic\\n\",\n \"\\n\",\n \"This approach tests the similarlity of independent chains intended to sample the same PDF. To be meaningful, they should start from different locations and burn-in should be removed.\\n\",\n \"\\n\",\n \"For a given parameter, $\\\\theta$, the $R$ statistic compares the variance across chains with the variance within a chain. Intuitively, if the chains are random-walking in very different places, i.e. not sampling the same distribution, $R$ will be large.\\n\",\n \"\\n\",\n \"We'd like to see $R\\\\approx 1$ (e.g. $R<1.1$ is often used).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"In detail, given chains $J=1,\\\\ldots,m$, each of length $n$,\\n\",\n \"\\n\",\n \"* Let $B=\\\\frac{n}{m-1} \\\\sum_j \\\\left(\\\\bar{\\\\theta}_j - \\\\bar{\\\\theta}\\\\right)^2$, where $\\\\bar{\\\\theta_j}$ is the average $\\\\theta$ for chain $j$ and $\\\\bar{\\\\theta}$ is the global average. This is proportional to the variance of the individual-chain averages for $\\\\theta$.\\n\",\n \"\\n\",\n \"* Let $W=\\\\frac{1}{m}\\\\sum_j s_j^2$, where $s_j^2$ is the estimated variance of $\\\\theta$ within chain $j$. This is the average of the individual-chain variances for $\\\\theta$.\\n\",\n \"\\n\",\n \"* Let $V=\\\\frac{n-1}{n}W + \\\\frac{1}{n}B$. This is an estimate for the overall variance of $\\\\theta$.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"Finally, $R=\\\\sqrt{\\\\frac{V}{W}}$.\\n\",\n \"\\n\",\n \"Note that this calculation can also be used to track convergence of combinations of parameters, or anything else derived from them.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Correlation tests\\n\",\n \"* Inspection! Again, no substitute.\\n\",\n \"* Autocorrelation of parameters\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"Do subsequent samples look particularly independent?\\n\",\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"The *autocorrelation* of a sequence (**after** removing burn-in), as a function of lag, $k$, is defined thusly:\\n\",\n \"\\n\",\n \"$\\\\rho_k = \\\\frac{\\\\sum_{i=1}^{n-k}\\\\left(\\\\theta_{i} - \\\\bar{\\\\theta}\\\\right)\\\\left(\\\\theta_{i+k} - \\\\bar{\\\\theta}\\\\right)}{\\\\sum_{i=1}^{n-k}\\\\left(\\\\theta_{i} - \\\\bar{\\\\theta}\\\\right)^2} = \\\\frac{\\\\mathrm{Cov}_i\\\\left(\\\\theta_i,\\\\theta_{i+k}\\\\right)}{\\\\mathrm{Var}(\\\\theta)}$\\n\",\n \"\\n\",\n \"The larger lag one needs to get a small autocorrelation, the less informative individual samples are.\\n\",\n \"\\n\",\n \"The `pandas` function `autocorrelation_plot()` may be useful for this.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"Note that the positive/negative oscillations basically tell us when the lag is so large compared with the chain length\\n\",\n \"that the autocorrelation is too noisy to be meaningful.\\n\",\n \"\\n\",\n \"We would be justified in thinning the chains by a factor of $\\\\sim150$, apparently!\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"### Effective number of samples\\n\",\n \"\\n\",\n \"From $m$ chains of length $n$, we can estimate the effective number of independent samples as\\n\",\n \"\\n\",\n \"$n_{eff} = \\\\frac{mn}{1+2\\\\sum_0^\\\\infty \\\\hat{\\\\rho}_t}$, with\\n\",\n \"\\n\",\n \"$\\\\hat{\\\\rho}_t = 1 - \\\\frac{V_t}{2V}$, ($V$ as in the Gelman-Rubin calculation)\\n\",\n \"\\n\",\n \"$V_t = \\\\frac{1}{m(n-t)} \\\\sum_{j=0}^m \\\\sum_{i=t+1}^n (\\\\theta_{i,j} - \\\\theta_{i-t,j})^2$\\n\",\n \"\\n\",\n \"In practice, the sum in $n_{eff}$ is cut off when the estimates $\\\\hat{\\\\rho}_t$ become too noisy (see references).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"The example shown turns out to have $n_{eff} \\\\sim 600$, compared with the $\\\\sim 250$ samples we would have left if thinning by a factor of 150.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Exercise: can we declare victory?\\n\",\n \"\\n\",\n \"In each of the following trace plots, different colors show independent chains. For each decide by inspection whether the following are plausible claims:\\n\",\n \"1. The chains have converged to the posterior.\\n\",\n \"2. The chains have a reasonable number of independent samples.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"\\n\",\n \"
\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Take-away\\n\",\n \"There are a handful of useful metrics for assessing convergence of chains and independence of samples. None is foolproof, and visual inspection is a critical check.\\n\",\n \"\\n\",\n \"Extra reading: [this short discussion](https://www.jstor.org/stable/2685466) provides some interesting context regarding how experts in the field (and the inventors of said metrics) approach the practical side of MCMC.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Bonus numerical making-your-life-easier exercise: convergence\\n\",\n \"\\n\",\n \"Write some code to perform the Gelman-Rubin convergence test. Try it out on\\n\",\n \"\\n\",\n \"1. multiple chains from the sandbox notebook. Fiddle with the sampler to get chains that do/do not display nice convergence after e.g. 5000 steps.\\n\",\n \"\\n\",\n \"2. multiple \\\"chains\\\" produced from independent sampling, e.g. from the inverse-transform or rejection examples above or one of the examples in previous chunks.\\n\",\n \"\\n\",\n \"You'll be expected to test convergence from now on, so having a function to do so will be helpful.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Bonus numerical making-your-life-easier exercise: effective samples\\n\",\n \"\\n\",\n \"Write code to compute the effective number of samples, as described above. Cut off the sum $n_{eff}$ at the point where 2 successive values $\\\\hat{\\\\rho}_t$ and $\\\\hat{\\\\rho}_{t+1}$ are negative. Try it out on the same two test cases.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"slideshow\": {\n \"slide_type\": \"slide\"\n }\n },\n \"source\": [\n \"## Megabonus numerical exercise\\n\",\n \"\\n\",\n \"Modify your code from the exercises to compute $R$ and $n_{eff}$ for the eigenvectors of the covariance of the posterior, instead of for individual parameters themselves. This can be informative when there are strong parameter degeneracies, as in the example above. The eigenvectors can be estimated efficiently from a chain using singular value decomposition.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"celltoolbar\": \"Slideshow\",\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.6\"\n },\n \"livereveal\": {\n \"scroll\": true,\n \"start_slideshow_at\": \"selected\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"gpl-2.0"}}},{"rowIdx":45488,"cells":{"repo_name":{"kind":"string","value":"CivicKnowledge/metatab-packages"},"path":{"kind":"string","value":"example.com/example-ipynb/metadata.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"3775"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"tags\": [\n \"Title\"\n ]\n },\n \"source\": [\n \"# A Metatab Example Data Package\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"tags\": [\n \"Description\"\n ]\n },\n \"source\": [\n \"An example data package, from the Metatab tutorial at https://github.com/CivicKnowledge/metatab-py/blob/master/README.rst\"\n ]\n },\n {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"tags\": [\n \"metadata\"\n ]\n },\n \"source\": [\n \"Name: example_data_package-2017-us-2\\n\",\n \"Dataset: example-data-package\\n\",\n \"Version: 1\\n\",\n \"Space: US\\n\",\n \"Time: 2017\\n\",\n \"Spatialgrain: None\\n\",\n \"Giturl: https://github.com/Metatab/metatab-packages.git\\n\",\n \"Identifier: e7466d89-9156-4df6-a171-3102b04ae583\\n\",\n \"\\n\",\n \"Section: Documentation|Title|Description\\n\",\n \"Homepage: http://metatab.org\\n\",\n \" .Title: Metatab Home Page\\n\",\n \" .Description: Main Metatab home page\\n\",\n \"Documentation: https://github.com/CivicKnowledge/metatab-py/blob/master/README.rst\\n\",\n \" .Title: Metatab Python Package README\\n\",\n \" .Description: The README in the Metatab Githup repo contains the tutorial for generating this package.\\n\",\n \"\\n\",\n \"Section: Contacts|email\\n\",\n \"Origin: example.com\\n\",\n \"Creator: Eric Busboom\\n\",\n \" .Email: eric@civicknowledge. com\\n\",\n \"Wrangler: Eric Busboom\\n\",\n \" .Email: eric@civicknowledge. com\\n\",\n \"\\n\",\n \"Section: Notes\\n\",\n \"Note: None\"\n ]\n },\n {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"tags\": [\n \"resources\"\n ]\n },\n \"source\": [\n \"Section: Resources|Name|StartLine|HeaderLines|Encoding|Description\\n\",\n \"Datafile: http://public.source.civicknowledge.com/example.com/sources/test_data.zip#test_data%2Fcsv%2Fsimple-example.csv\\n\",\n \" .Name: simple-example\\n\",\n \" .Description: Random UUIDs, integers and numbers\\n\",\n \"Datafile: metadata.ipynb#df\\n\",\n \" .Name: df\\n\",\n \" .Description: Random UUIDs, integers and numbers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"import metapack as mp\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from IPython.display import display \\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"%load_ext metapack.jupyter.magic\\n\",\n \"sns.set_context('notebook')\\n\",\n \"mp.jupyter.init()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Code goes after metadata, before schema\\n\",\n \"\\n\",\n \"df = pd.DataFrame({\\n\",\n \" 'rand': np.random.randint(0,100,1000)\\n\",\n \"})\\n\"\n ]\n },\n {\n \"cell_type\": \"raw\",\n \"metadata\": {\n \"tags\": [\n \"schema\"\n ]\n },\n \"source\": [\n \"Section: Schema|AltName|DataType|Description|Datatype\\n\",\n \"Table: simple-example\\n\",\n \"Table.Column: id\\n\",\n \" .Datatype: integer\\n\",\n \"Table.Column: uuid\\n\",\n \" .Datatype: string\\n\",\n \"Table.Column: int\\n\",\n \" .Datatype: integer\\n\",\n \"Table.Column: float\\n\",\n \" .Datatype: number\\n\",\n \"Table: df\\n\",\n \"Table.Column: rand\\n\",\n \" .Datatype: integer\"\n ]\n }\n ],\n \"metadata\": {\n \"celltoolbar\": \"Tags\",\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45489,"cells":{"repo_name":{"kind":"string","value":"richardotis/matse501"},"path":{"kind":"string","value":"MATSE501-HW2.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"44027"},"content":{"kind":"string","value":"{\n \"metadata\": {\n \"name\": \"\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n {\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"%matplotlib inline\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 1\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"from scipy.integrate import odeint\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib\\n\",\n \"import matplotlib.pyplot as plt\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 2\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"# System parameters (determined by system definition)\\n\",\n \"# NOTE: These are not necessarily correct for your case\\n\",\n \"alpha = 0.5\\n\",\n \"beta = 1.4\\n\",\n \"gamma = 0.1\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"func accepts a vector of variables l_vec and returns a \\n\",\n \"vector of derivatives with respect to t\\n\",\n \"here, v is defined as dv/dt\\n\",\n \"\\\"\\\"\\\"\\n\",\n \"def func(l_vec,t):\\n\",\n \" l,v = l_vec\\n\",\n \" return [v,(alpha/l)-beta-(gamma*v)]\\n\",\n \"\\n\",\n \"# Initial conditions (units?)\\n\",\n \"# NOTE: These are not necessarily correct for your case\\n\",\n \"l_0 = 0.1\\n\",\n \"v_0 = 0.1\\n\",\n \"\\n\",\n \"y0 = [l_0,v_0] # initial conditions vector\\n\",\n \"\\n\",\n \"t_output = np.arange(0,50,0.1) # get vector of t from 0 to 50, with 0.1 step\\n\",\n \"\\n\",\n \"y_result = odeint(func,y0,t_output)\\n\",\n \"# y_result is the vector of values, starting from y0, integrated according to our constitutive function, func\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 3\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"# Plot setup, 10 by 6\\n\",\n \"fig = plt.figure(figsize=(10,6))\\n\",\n \"# 111 is a layout indicator; only need to worry if we have multiple subplots\\n\",\n \"ax = fig.add_subplot(111)\\n\",\n \"# unpack the values from our solution\\n\",\n \"ll,vv = y_result.T\\n\",\n \"# plot the figure and set the title and labels\\n\",\n \"ax.plot(t_output,ll)\\n\",\n \"ax.set_title('Piston Time Evolution')\\n\",\n \"ax.set_xlabel('Time (seconds)')\\n\",\n \"ax.set_ylabel('Length (m)')\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": \"pyout\",\n \"prompt_number\": 4,\n \"text\": [\n \"\"\n ]\n },\n {\n \"metadata\": {},\n \"output_type\": \"display_data\",\n \"png\": 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nYpKwMSEwPfxiJ+QgghhHgFbc6YSuw4Y8uXA08/Dbz7bvPb/vlPGSD+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\\nJQBgwYIFSEtLw+eff47t27fj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\"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 4\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 4\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [],\n \"prompt_number\": 4\n }\n ],\n \"metadata\": {}\n }\n ]\n}"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45490,"cells":{"repo_name":{"kind":"string","value":"oscar6echo/ezvis3d"},"path":{"kind":"string","value":"demo_ezvisd3.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"2316938"},"content":{"kind":"null"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45491,"cells":{"repo_name":{"kind":"string","value":"rdhyee/working-open-data-2014"},"path":{"kind":"string","value":"notebooks/Day_06_D_Assignment.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"2342"},"content":{"kind":"string","value":"{\n \"metadata\": {\n \"name\": \"\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n {\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"import pandas as pd\\n\",\n \"from pandas import DataFrame\\n\",\n \"\\n\",\n \"import census\\n\",\n \"import settings\\n\",\n \"import us\\n\",\n \"\\n\",\n \"from itertools import islice\\n\",\n \"\\n\",\n \"# instantiate the census object\\n\",\n \"\\n\",\n \"c=census.Census(settings.CENSUS_KEY)\\n\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"## EXERCISE\\n\",\n \"## FILL in with your generator for all census places in the 2010 census \\n\",\n \"\\n\",\n \"\\n\",\n \"def places(variables=\\\"NAME\\\"):\\n\",\n \" \\n\",\n \" # placeholder generator\\n\",\n \" # replace with your own code\\n\",\n \" for k in []:\\n\",\n \" yield k\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"# use this code to run your code\\n\",\n \"# I recommend replacing the None in islice to a small number to make sure you're on \\n\",\n \"# the right track\\n\",\n \"\\n\",\n \"r = list(islice(places(\\\"NAME,P0010001\\\"), None))\\n\",\n \"places_df = DataFrame(r)\\n\",\n \"places_df.P0010001 = places_df.P0010001.astype('int')\\n\",\n \"\\n\",\n \"places_df['FIPS'] = places_df.apply(lambda s: s['state']+s['place'], axis=1)\\n\",\n \"\\n\",\n \"print \\\"number of places\\\", len(places_df)\\n\",\n \"print \\\"total pop\\\", places_df.P0010001.sum()\\n\",\n \"places_df.head()\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"# if you've done this correctly, the following asserts should stop complaining\\n\",\n \"\\n\",\n \"assert places_df.P0010001.sum() == 228457238\\n\",\n \"# number of places in 2010 Census\\n\",\n \"assert len(places_df) == 29261\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n }\n ],\n \"metadata\": {}\n }\n ]\n}"},"license":{"kind":"string","value":"apache-2.0"}}},{"rowIdx":45492,"cells":{"repo_name":{"kind":"string","value":"phaustin/pyman"},"path":{"kind":"string","value":"Book/apdx2/Supporting Materials/FirstNotebook.ipynb"},"copies":{"kind":"string","value":"3"},"size":{"kind":"string","value":"14570"},"content":{"kind":"string","value":"{\n \"metadata\": {\n \"name\": \"\",\n \"signature\": \"sha256:75e9265a392bf07942083d1bd9b6ab568e7dc38cde50cac5bf9542751a9dbfeb\"\n },\n \"nbformat\": 3,\n \"nbformat_minor\": 0,\n \"worksheets\": [\n {\n \"cells\": [\n {\n \"cell_type\": \"heading\",\n \"level\": 1,\n \"metadata\": {},\n \"source\": [\n \"Demo of IPython Notebook\"\n ]\n },\n {\n \"cell_type\": \"heading\",\n \"level\": 2,\n \"metadata\": {},\n \"source\": [\n \"David Pine\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"2+3\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"metadata\": {},\n \"output_type\": 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\"text\": [\n \"\"\n ]\n }\n ],\n \"prompt_number\": 4\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"# Calculates time, gallons of gas used, and cost of gasoline for\\n\",\n \"# a trip\\n\",\n \"\\n\",\n \"distance = float(raw_input(\\\"Input distance of trip in miles: \\\"))\\n\",\n \"mpg = 30. # car mileage\\n\",\n \"speed = 60. # average speed\\n\",\n \"costPerGallon = 4.10 # price of gas\\n\",\n \"\\n\",\n \"time = distance/speed\\n\",\n \"gallons = distance/mpg\\n\",\n \"cost = gallons*costPerGallon\\n\",\n \"\\n\",\n \"print(\\\"\\\\nDuration of trip = {0:0.1f} hours\\\".format(time))\\n\",\n \"print(\\\"Gasoline used = {0:0.1f} gallons (@ {1:0.0f} mpg)\\\"\\n\",\n \" .format(gallons, mpg))\\n\",\n \"print(\\\"Cost of gasoline = ${0:0.2f} (@ ${1:0.2f}/gallon)\\\"\\n\",\n \" .format(cost, costPerGallon))\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \"Input distance of trip in miles: 450\\n\"\n ]\n },\n {\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \"\\n\",\n \"Duration of trip = 7.5 hours\\n\",\n \"Gasoline used = 15.0 gallons (@ 30 mpg)\\n\",\n \"Cost of gasoline = $61.50 (@ $4.10/gallon)\\n\"\n ]\n }\n ],\n \"prompt_number\": 5\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The total distance $x$ traveled during a trip can be\\n\",\n \"obtained by integrating the velocity $v(t)$ over the\\n\",\n \"duration $T$ of the trip:\\n\",\n \"\\\\begin{align}\\n\",\n \" x = \\\\int_0^T v(t)\\\\, dt\\n\",\n \"\\\\end{align}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [\n \"!cat LiamSelinaData.txt\"\n ],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"output_type\": \"stream\",\n \"stream\": \"stdout\",\n \"text\": [\n \"Date: 2013-09-16\\r\\n\",\n \"Data taken by Liam and Selena\\r\\n\",\n \"frequency (Hz) amplitude (mm) amp error (mm)\\r\\n\",\n \" 0.7500 13.52 0.32\\r\\n\",\n \" 1.7885 12.11 0.92\\r\\n\",\n \" 2.8269 14.27 0.73\\r\\n\",\n \" 3.8654 16.60 2.06\\r\\n\",\n \" 4.9038 22.91 1.75\\r\\n\",\n \" 5.9423 35.28 0.91\\r\\n\",\n \" 6.9808 60.99 0.99\\r\\n\",\n \" 8.0192 33.38 0.36\\r\\n\",\n \" 9.0577 17.78 2.32\\r\\n\",\n \" 10.0962 10.99 0.21\\r\\n\",\n \" 11.1346 7.47 0.48\\r\\n\",\n \" 12.1731 6.72 0.51\\r\\n\",\n \" 13.2115 4.40 0.58\\r\\n\",\n \" 14.2500 4.07 0.63\"\n ]\n }\n ],\n \"prompt_number\": 6\n },\n {\n \"cell_type\": \"code\",\n \"collapsed\": false,\n \"input\": [],\n \"language\": \"python\",\n \"metadata\": {},\n \"outputs\": []\n }\n ],\n \"metadata\": {}\n }\n ]\n}"},"license":{"kind":"string","value":"cc0-1.0"}}},{"rowIdx":45493,"cells":{"repo_name":{"kind":"string","value":"dataDogma/Computer-Science"},"path":{"kind":"string","value":"Courses/DAT-208x/DAT208x - Introduction to Python for Data Science.ipynb"},"copies":{"kind":"string","value":"2"},"size":{"kind":"string","value":"2172"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"\\n\",\n \"## Course Objective: \\n\",\n \"\\n\",\n \"-- --\\n\",\n \"\\n\",\n \"+ Python language funadmentals: basic syntax, variables, and types.\\n\",\n \"\\n\",\n \"+ Create and manipulate regular Python lists.\\n\",\n \"\\n\",\n \"+ Use functions andimport packages.\\n\",\n \"\\n\",\n \"+ Build Numpy array and perform interesting calculations.\\n\",\n \"\\n\",\n \"+ Create and customize plots on real data.\\n\",\n \"\\n\",\n \"+ Use control flow and get to know the Pandas data frame.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## About:\\n\",\n \"-- --\\n\",\n \"\\n\",\n \"+ Course has 6 modules, each covering Python baiscs.\\n\",\n \"+ Starting with basic arithematic and variables.\\n\",\n \"+ Data structures -- Python lists, Numpy arrays & Pandas dataframes.\\n\",\n \"+ Python functions & Control flow.\\n\",\n \"+ Data Visualization.\\n\",\n \"+ Working on real datasets.\\n\",\n \"\\n\",\n \"### Further,\\n\",\n \"\\n\",\n \"+ Each module contains quiz, lab sessions.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Course Schedule\\n\",\n \"-- --\\n\",\n \"\\n\",\n \"1. It's a self paced course.\\n\",\n \"2. By default a deadline of 4 weeks.\\n\",\n \"3. Quizzes -- 30% of total grade.\\n\",\n \"4. Lab exercise -- 65% of total grade.\\n\",\n \"5. Survey -- 5%\\n\",\n \"6. To pass -- 70% of total grade\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"anaconda-cloud\": {},\n \"kernelspec\": {\n \"display_name\": \"Python [default]\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":45494,"cells":{"repo_name":{"kind":"string","value":"statkclee/ThinkStats2"},"path":{"kind":"string","value":"code/chap09soln-kor.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"8689"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"통계적 사고 (2판) 연습문제 ([thinkstats2.com](thinkstats2.com), [think-stat.xwmooc.org](http://think-stat.xwmooc.org))
\\n\",\n \"Allen Downey / 이광춘(xwMOOC)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import print_function, division\\n\",\n \"\\n\",\n \"import first\\n\",\n \"import hypothesis\\n\",\n \"import scatter\\n\",\n \"import thinkstats2\\n\",\n \"\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 연습문제 9.1\\n\",\n \"\\n\",\n \"표본크기가 증가함에 따라, 가설검정력은 증가하는데, 효과가 실제하면 좀더 양성임을 의미한다. 반대로, 표본크기가 줄어들면, 검정력은 설사 효과가 실제한다고 하더라도 덜 양성일 것 같다.\\n\",\n \"이런 작동방식을 조사하는데, NSFG 데이터에서 다른 일부 데이터를 갖는 검정을 실시한다. `thinkstats2.SampleRows`을 사용해서, 데이터프레임에 임의로 행일부를 선택한다.\\n\",\n \"\\n\",\n \"표본크기가 감소함에 따라 검정 p-값에는 무슨 일이 일어나는가? 양의 검정을 산출하는 최소 표본크기는 얼마인가?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9148\\t0.16\\t0.00\\t0.00\\t0.00\\n\",\n \"4574\\t0.10\\t0.01\\t0.00\\t0.00\\n\",\n \"2287\\t0.25\\t0.06\\t0.00\\t0.00\\n\",\n \"1143\\t0.24\\t0.03\\t0.39\\t0.03\\n\",\n \"571\\t0.81\\t0.00\\t0.04\\t0.04\\n\",\n \"285\\t0.57\\t0.41\\t0.48\\t0.83\\n\",\n \"142\\t0.45\\t0.08\\t0.60\\t0.04\\n\"\n ]\n }\n ],\n \"source\": [\n \"def RunTests(live, iters=1000):\\n\",\n \" \\\"\\\"\\\"Runs the tests from Chapter 9 with a subset of the data.\\n\",\n \"\\n\",\n \" live: DataFrame\\n\",\n \" iters: how many iterations to run\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" n = len(live)\\n\",\n \" firsts = live[live.birthord == 1]\\n\",\n \" others = live[live.birthord != 1]\\n\",\n \"\\n\",\n \" # compare pregnancy lengths\\n\",\n \" data = firsts.prglngth.values, others.prglngth.values\\n\",\n \" ht = hypothesis.DiffMeansPermute(data)\\n\",\n \" p1 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" data = (firsts.totalwgt_lb.dropna().values,\\n\",\n \" others.totalwgt_lb.dropna().values)\\n\",\n \" ht = hypothesis.DiffMeansPermute(data)\\n\",\n \" p2 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" # test correlation\\n\",\n \" live2 = live.dropna(subset=['agepreg', 'totalwgt_lb'])\\n\",\n \" data = live2.agepreg.values, live2.totalwgt_lb.values\\n\",\n \" ht = hypothesis.CorrelationPermute(data)\\n\",\n \" p3 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" # compare pregnancy lengths (chi-squared)\\n\",\n \" data = firsts.prglngth.values, others.prglngth.values\\n\",\n \" ht = hypothesis.PregLengthTest(data)\\n\",\n \" p4 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" print('%d\\\\t%0.2f\\\\t%0.2f\\\\t%0.2f\\\\t%0.2f' % (n, p1, p2, p3, p4))\\n\",\n \" \\n\",\n \"thinkstats2.RandomSeed(18)\\n\",\n \"\\n\",\n \"live, firsts, others = first.MakeFrames()\\n\",\n \"\\n\",\n \"n = len(live)\\n\",\n \"for _ in range(7):\\n\",\n \" sample = thinkstats2.SampleRows(live, n)\\n\",\n \" RunTests(sample)\\n\",\n \" n //= 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"test1: 평균 임신기간 차이\\n\",\n \"test2: 평균 출생체중 차이\\n\",\n \"test3: 산모 연령과 출생체중 상관관계\\n\",\n \"test4: 임신기간 카이제곱 검정\\n\",\n \"\\n\",\n \"n test1 test2 test2 test4 \\n\",\n \"9148 →\\t0.16 →\\t0.00 →\\t0.00 →\\t0.00 \\n\",\n \"4574→\\t0.10→\\t0.01→\\t0.00→\\t0.00 \\n\",\n \"2287→\\t0.25→\\t0.06→\\t0.00→\\t0.00 \\n\",\n \"1143→\\t0.24→\\t0.03→\\t0.39→\\t0.03 \\n\",\n \"571→\\t0.81→\\t0.00→\\t0.04→\\t0.04 \\n\",\n \"285→\\t0.57→\\t0.41→\\t0.48→\\t0.83 \\n\",\n \"142→\\t0.45→\\t0.08→\\t0.60→\\t0.04\\n\",\n \"\\n\",\n \"결론: 예상했듯이, 데이터를 제거함에 따라 더큰 표본크기를 갖는 양의 검정은 음이 된다. 하지만, 작은 표본크기에서 조차도 일부 양의 값을 갖는 등 패턴은 일정치 않다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 연습문제 9.2 \\n\",\n \"9.3절처럼, 순열로 귀무가설을 모의시험했다; 즉, 관측된 값을 마치 전체 모집단을 대표하는 것처럼 다루었고, 무작위로 모집단의 구성원을 두 집단에 배정했다.\\n\",\n \"대안은 표본을 사용해서 모집단 분포를 추정하고 나서, 분포로부터 임의 표본을 추출하는 것이다. 이런 과정을 재표집(resampling)이라고 부른다. 재표집을 구현하는 몇가지 방식이 있지만, 가장 단순한 것중 하나가 9.10 처럼 관측된 값에서 복원방식으로 표본을 추출하는 것이다.\\n\",\n \"\\n\",\n \"DiffMeansPermute에서 상속받고, 순열보다 재표집을 구현하는 RunModel을 치환(override)하는 클래스 DiffMeansResample을 작성하시오.\\n\",\n \"\\n\",\n \"이 모형을 사용해서 임신기간과 출생체중 사이 차이를 검정하시오. 이 모형이 결과에 얼마나 영향을 주는가?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"means permute preglength\\n\",\n \"p-value = 0.1675\\n\",\n \"actual = 0.0780372667775\\n\",\n \"ts max = 0.225502935911\\n\",\n \"\\n\",\n \"means permute birthweight\\n\",\n \"p-value = 0.0\\n\",\n \"actual = 0.124761184535\\n\",\n \"ts max = 0.116868397475\\n\"\n ]\n }\n ],\n \"source\": [\n \"class DiffMeansResample(hypothesis.DiffMeansPermute):\\n\",\n \" \\\"\\\"\\\"Tests a difference in means using resampling.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def RunModel(self):\\n\",\n \" \\\"\\\"\\\"Run the model of the null hypothesis.\\n\",\n \"\\n\",\n \" returns: simulated data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" group1 = np.random.choice(self.pool, self.n, replace=True)\\n\",\n \" group2 = np.random.choice(self.pool, self.m, replace=True)\\n\",\n \" return group1, group2\\n\",\n \" \\n\",\n \"\\n\",\n \"def RunResampleTest(firsts, others):\\n\",\n \" \\\"\\\"\\\"Tests differences in means by resampling.\\n\",\n \"\\n\",\n \" firsts: DataFrame\\n\",\n \" others: DataFrame\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" data = firsts.prglngth.values, others.prglngth.values\\n\",\n \" ht = DiffMeansResample(data)\\n\",\n \" p_value = ht.PValue(iters=10000)\\n\",\n \" print('\\\\nmeans permute preglength')\\n\",\n \" print('p-value =', p_value)\\n\",\n \" print('actual =', ht.actual)\\n\",\n \" print('ts max =', ht.MaxTestStat())\\n\",\n \"\\n\",\n \" data = (firsts.totalwgt_lb.dropna().values,\\n\",\n \" others.totalwgt_lb.dropna().values)\\n\",\n \" ht = hypothesis.DiffMeansPermute(data)\\n\",\n \" p_value = ht.PValue(iters=10000)\\n\",\n \" print('\\\\nmeans permute birthweight')\\n\",\n \" print('p-value =', p_value)\\n\",\n \" print('actual =', ht.actual)\\n\",\n \" print('ts max =', ht.MaxTestStat())\\n\",\n \"\\n\",\n \"RunResampleTest(firsts, others) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"순열대신에 재표추출을 사용하는 것은 결과에 거의 영향이 없다.\\n\",\n \"\\n\",\n \"두 모형은 약간 다른 가정에 기반하고 있고, 상기 예제에서 이것 혹은 저것을 선택할 강력한 사유는 없다. 하지만, 일반적으로 p-값은 귀무가설 선택에 달려있다; 서로 다른 모형은 매우 다른 결과를 이끌어낼 수 있다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":45495,"cells":{"repo_name":{"kind":"string","value":"stephenl6705/fluentPy"},"path":{"kind":"string","value":"8. Object References, Mutability, and Recycling.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"30754"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Variables Are Not Boxes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[1, 2, 3, 4]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = [1, 2, 3]\\n\",\n \"b = a\\n\",\n \"a.append(4)\\n\",\n \"b\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class Gizmo:\\n\",\n \" def __init__(self):\\n\",\n \" print('Gizmo id: %d' % id(self))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Gizmo id: 1095113832488\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = Gizmo()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Gizmo id: 1095113832376\\n\"\n ]\n },\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for *: 'Gizmo' and 'int'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0my\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mGizmo\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m*\\u001b[0m \\u001b[0;36m10\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: unsupported operand type(s) for *: 'Gizmo' and 'int'\"\n ]\n }\n ],\n \"source\": [\n \"y = Gizmo() * 10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Identity, Equality, and Aliases\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"charles = {'name': 'Charles L. Dodgson', 'born': 1832}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"lewis = charles\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lewis is charles\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1095096406280, 1095096406280)\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"id(charles), id(lewis)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"lewis['balance'] = 950\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{'balance': 950, 'born': 1832, 'name': 'Charles L. Dodgson'}\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"charles\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"alex = {'name': 'Charles L. Dodgson', 'born': 1832, 'balance':950}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"alex == charles\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"alex is not charles\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The Relative Immutability of Tuples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t1 = (1, 2, [30, 40])\\n\",\n \"t2 = (1, 2, [30, 40])\\n\",\n \"t1 == t2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1095111699976\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"id(t1[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"t1[-1].append(99)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1, 2, [30, 40, 99])\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1095111699976\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"id(t1[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t1 == t2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Copies Are Shallow by Default\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[3, [55, 44], (7, 8, 9)]\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"l1 = [3, [55, 44], (7, 8, 9)]\\n\",\n \"l2 = list(l1)\\n\",\n \"l2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"l2 == l1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"l2 is l1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"l1: [3, [66, 44], (7, 8, 9), 100]\\n\",\n \"l2: [3, [66, 44], (7, 8, 9)]\\n\",\n \"l1: [3, [66, 44, 33, 22], (7, 8, 9), 100]\\n\",\n \"l2: [3, [66, 44, 33, 22], (7, 8, 9, 10, 11)]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l1 = [3, [66, 55, 44], (7, 8, 9)]\\n\",\n \"l2 = list(l1)\\n\",\n \"l1.append(100)\\n\",\n \"l1[1].remove(55)\\n\",\n \"print('l1:', l1)\\n\",\n \"print('l2:', l2)\\n\",\n \"l2[1] += [33, 22]\\n\",\n \"l2[2] += (10,11)\\n\",\n \"print('l1:', l1)\\n\",\n \"print('l2:', l2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Deep and Shallow Copies of Arbitrary Objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class Bus:\\n\",\n \" \\n\",\n \" def __init__(self, passengers=None):\\n\",\n \" if passengers is None:\\n\",\n \" self.passengers = []\\n\",\n \" else:\\n\",\n \" self.passengers = list(passengers)\\n\",\n \" \\n\",\n \" def pick(self, name):\\n\",\n \" self.passengers.append(name)\\n\",\n \" \\n\",\n \" def drop(self, name):\\n\",\n \" self.passengers.remove(name)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import copy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1095114522184, 1095114522128, 1095114522296)\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus1 = Bus(['Alice','Bill', 'Claire', 'David'])\\n\",\n \"bus2 = copy.copy(bus1)\\n\",\n \"bus3 = copy.deepcopy(bus1)\\n\",\n \"id(bus1), id(bus2), id(bus3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Alice', 'Claire', 'David']\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus1.drop('Bill')\\n\",\n \"bus2.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1095114547784, 1095114547784, 1095114476232)\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"id(bus1.passengers), id(bus2.passengers), id(bus3.passengers)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Alice', 'Bill', 'Claire', 'David']\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus3.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[10, 20, [[...], 30]]\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a = [10, 20]\\n\",\n \"b = [a, 30]\\n\",\n \"a.append(b)\\n\",\n \"a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[10, 20, [[...], 30]]\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from copy import deepcopy\\n\",\n \"c = deepcopy(a)\\n\",\n \"c\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Function Parameters as References\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def f(a, b):\\n\",\n \" a += b\\n\",\n \" return a\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = 1\\n\",\n \"y = 2\\n\",\n \"f(x, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1, 2)\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x, y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[1, 2, 3, 4]\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = [1, 2]\\n\",\n \"y = [3, 4]\\n\",\n \"f(x, y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([1, 2, 3, 4], [3, 4])\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x, y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 20, 30, 40)\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t = (10, 20)\\n\",\n \"u = (30, 40)\\n\",\n \"f(t, u)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"((10, 20), (30, 40))\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t, u\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Mutable Types as Parameter Defaults: Bad Idea\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class HauntedBus(Bus):\\n\",\n \" \\\"\\\"\\\"A bus model haunted by ghost passengers\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, passengers=[]):\\n\",\n \" self.passengers = passengers\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Alice', 'Bill']\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus1 = HauntedBus(['Alice', 'Bill'])\\n\",\n \"bus1.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Bill', 'Charlie']\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus1.pick('Charlie')\\n\",\n \"bus1.drop('Alice')\\n\",\n \"bus1.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Carrie']\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus2 = HauntedBus()\\n\",\n \"bus2.pick('Carrie')\\n\",\n \"bus2.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Carrie']\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus3 = HauntedBus()\\n\",\n \"bus3.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Carrie', 'Dave']\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus3.pick('Dave')\\n\",\n \"bus2.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus2.passengers is bus3.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Bill', 'Charlie']\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"bus1.passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['__annotations__', '__call__', '__class__', '__closure__', '__code__', '__defaults__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__get__', '__getattribute__', '__globals__', '__gt__', '__hash__', '__init__', '__kwdefaults__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__qualname__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(dir(HauntedBus.__init__))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(['Carrie', 'Dave'],)\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"HauntedBus.__init__.__defaults__\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"HauntedBus.__init__.__defaults__[0] is bus2.passengers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Defensive Programming with Mutable Parameters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class Twilightbus(Bus):\\n\",\n \" \\\"\\\"\\\"A bus model that makes passengers vanish\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def __init__(self, passengers=None):\\n\",\n \" if passengers is None:\\n\",\n \" self.passengers = []\\n\",\n \" else:\\n\",\n \" self.passengers = passengers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Sue', 'Maya', 'Diana']\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"basketball_team = ['Sue', 'Tina', 'Maya', 'Diana', 'Pat']\\n\",\n \"bus = Twilightbus(basketball_team)\\n\",\n \"bus.drop('Tina')\\n\",\n \"bus.drop('Pat')\\n\",\n \"basketball_team\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# del and Garbage Collection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import weakref\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"s1 = {1, 2, 3}\\n\",\n \"s2 = s1\\n\",\n \"def bye():\\n\",\n \" print('Gone with the wind...')\\n\",\n \"ender = weakref.finalize(s1, bye)\\n\",\n \"ender.alive\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"del s1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ender.alive\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Gone with the wind...\\n\"\n ]\n }\n ],\n \"source\": [\n \"s2 = 'spam'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ender.alive\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Weak References\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import weakref\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"a_set = {0, 1}\\n\",\n \"wref = weakref.ref(a_set)\\n\",\n \"wref\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{0, 1}\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"a_set = {2, 3, 4}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{0, 1}\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref() is None\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref() is None\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## The WeakValueDictionary Skit\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class Cheese:\\n\",\n \" \\n\",\n \" def __init__(self, kind):\\n\",\n \" self.kind = kind\\n\",\n \" \\n\",\n \" def __repr__(self):\\n\",\n \" return 'Cheese(%r)' % self.kind\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"import weakref\\n\",\n \"stock = weakref.WeakValueDictionary()\\n\",\n \"catalog = [Cheese('Red Leicester'), Cheese('Tilsit'), Cheese('Brie'),\\n\",\n \" Cheese('Parmesan')]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Brie', 'Parmesan', 'Red Leicester', 'Tilsit']\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"for cheese in catalog:\\n\",\n \" stock[cheese.kind] = cheese\\n\",\n \"sorted(stock.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"del catalog\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Parmesan']\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sorted(stock.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"del cheese\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sorted(stock.keys())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Limitations of Weak References\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"class MyList(list):\\n\",\n \" \\\"\\\"\\\"list subclass whose instances may be weakly referenced\\\"\\\"\\\"\\n\",\n \" \\n\",\n \"a_list = MyList(range(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"wref_to_a_list = weakref.ref(a_list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref_to_a_list()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"del a_list\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref_to_a_list()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref_to_a_list is None\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"wref_to_a_list()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Tricks Python Plays with Immutables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t1 = (1, 2, 3)\\n\",\n \"t2 = tuple(t1)\\n\",\n \"t2 is t1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t3 = t1[:]\\n\",\n \"t3 is t1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t1 = (1, 2, 3)\\n\",\n \"t3 = (1, 2, 3)\\n\",\n \"t3 is t1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"s1 = 'ABC'\\n\",\n \"s2 = 'ABC'\\n\",\n \"s2 is s1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.5.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45496,"cells":{"repo_name":{"kind":"string","value":"harishkrao/DSE200x"},"path":{"kind":"string","value":"Final Project/Week-9-ExampleNotebooks/Planck Satellite Data Simulation using pandas.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"2584722"},"content":{"kind":"null"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45497,"cells":{"repo_name":{"kind":"string","value":"bioe-ml-w18/bioe-ml-winter2018"},"path":{"kind":"string","value":"homeworks/Week2-Statistics.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"156805"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Week 2 - Implementation of Shaffer et al\\n\",\n \"\\n\",\n \"**Due January 25 at 8 PM**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# This line tells matplotlib to include plots here\\n\",\n \"% matplotlib inline\\n\",\n \"import numpy as np # We'll need numpy later\\n\",\n \"from scipy.stats import kstest, ttest_ind, ks_2samp, zscore\\n\",\n \"import matplotlib.pyplot as plt # This lets us access the pyplot functions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## (1) Estimation of a sample mean from a normally distributed variable.\\n\",\n \"\\n\",\n \"Let us assume that a true distribution of a process is described by the normal distribution with $\\\\mu=5$ and $\\\\sigma=1$. You have a measurement technique that allows you to sample n points from this distribution. In Matlab this is a random number generator whose numbers will be chosen from the desired normal distribution by using the call `normrnd(mu, sigma, [1, n])`. Sample from this normal distribution from n=1 to 50 (I.e. n=1:50). Create a plot for the standard deviation of the calculated mean from each n when you repeat the sampling 1000 times each. (i.e. You will repeat your n observations 1000 times and will calculate the sample mean for each of the 1000 trials).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Initial code here\\n\",\n \"\\n\",\n \"numRepeats = 1000\\n\",\n \"mu, sigma = 5.0, 1.0\\n\",\n \"n = 50\\n\",\n \"sampleMean = np.empty((n, numRepeats))\\n\",\n \"nVec = np.array(range(1, n+1))\\n\",\n \"\\n\",\n \"for i in range(numRepeats):\\n\",\n \" for j in range(n):\\n\",\n \" sampleMean[j, i] = np.mean(np.random.normal(loc=mu, scale=sigma, size=(j + 1, )))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (1a) Plot the standard deviation of the estimate of the sample mean versus n. Add a second line which is 1/sqrt(n). Describe what this tells you about the relationship between n and your power to estimate the underlying mean.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Answer to 1a here\\n\",\n \"\\n\",\n \"plt.plot(nVec, np.std(sampleMean, axis=1), label='stddev sample mean')\\n\",\n \"plt.plot(nVec, 1./np.sqrt(nVec), 'r', label='1/sqrt(n)');\\n\",\n \"plt.title('Stdev of mean estimate v. n, 100 trials');\\n\",\n \"plt.ylabel('Stdev');\\n\",\n \"plt.xlabel('n');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This shows that the standard deviation of the sample mean (also called the standard error) follows a $1/\\\\sqrt{n}$ relationship.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1b. Plot the boxplot for the sample means for all values n. Using words, interpret what the boxplot view of the 1000 trials for n=1 means and what the trends in the boxplot demonstrate compared to the plot in 1a (I.e. What information do you gain or lose in the two different plotting schemes)?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Answer to 1b here\\n\",\n \"\\n\",\n \"plt.boxplot(np.transpose(sampleMean));\\n\",\n \"plt.ylabel('Values');\\n\",\n \"plt.xlabel('n');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The box plot shows that the values with higher n converge on the true mean (5 in this case). The box plot shows the overall distribution of values in greater detail, while the standard plot is easier to read for a single value plotted over the x-axis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1c. For n=3, plot the histogram of the mean for the 1000 trials. Use the Kolmogorov-Smirnov test to see if this sample distribution is normal (hint you will need to translate this to the standard normal distribution). Report the sample mean and sample standard deviation, the p-value from the test, and whether you would reject the null hypothesis.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.8820188948890058\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Answer to 1c here\\n\",\n \"\\n\",\n \"sampleVec = sampleMean[4, :]\\n\",\n \"plt.hist(sampleVec)\\n\",\n \"plt.xlabel('Value')\\n\",\n \"plt.ylabel('Density')\\n\",\n \"\\n\",\n \"# Normalize sample mean and stdev\\n\",\n \"P = kstest(zscore(sampleVec), 'norm')[1]\\n\",\n \"\\n\",\n \"print(P)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We do not reject the null hypothesis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1d. Repeat 1c but for n=20. What changes when the number of samples increases?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.8582959898044679\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Answer to 1d here\\n\",\n \"\\n\",\n \"sampleVec = sampleMean[21, :]\\n\",\n \"plt.hist(sampleVec)\\n\",\n \"plt.xlabel('Value')\\n\",\n \"plt.ylabel('Density')\\n\",\n \"\\n\",\n \"# Normalize sample mean and stdev\\n\",\n \"P = kstest(zscore(sampleVec), 'norm')[1]\\n\",\n \"\\n\",\n \"print(P)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Nothing changes in this case. For both low and high n the sample mean is normally distributed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## (2) Weibull distribution. Now we will explore sampling from an alternate distribution type.\\n\",\n \"\\n\",\n \"#### (2a) Sample the Weibull distribution with parameters a = 1, 1000 times. Plot the histogram of these values. Describe the shape of this histogram in words. Is it anything like the normal distribution?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Answer 2a here\\n\",\n \"\\n\",\n \"Wvec = np.random.weibull(1, size=(1000, ))\\n\",\n \"\\n\",\n \"plt.hist(Wvec);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Doesn't look anything like a normal distribution. Much heavier right tail.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (2b) As in problem 1, plot a boxplot of the sample distribution of the Weibull with A=1,B=1 from n=1:50. How does this differ from the plot in 1b and why? Plot the standard deviations of the sample means versus n. Is this any different?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Answer 2b here\\n\",\n \"\\n\",\n \"sampleMean = np.empty((1000, n))\\n\",\n \"\\n\",\n \"for i in range(sampleMean.shape[0]):\\n\",\n \" for j in range(n):\\n\",\n \" sampleMean[i, j] = np.mean(np.random.weibull(1.0, size=(j + 1, )))\\n\",\n \"\\n\",\n \"plt.boxplot(sampleMean);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"nVec = np.arange(1, n+1)\\n\",\n \"\\n\",\n \"plt.plot(nVec, np.std(sampleMean, axis=0), label='stddev sample mean')\\n\",\n \"plt.plot(nVec, 1./np.sqrt(nVec), 'r', label='1/sqrt(n)');\\n\",\n \"plt.title('Stdev of mean estimate v. n, 1000 trials');\\n\",\n \"plt.ylabel('Stdev');\\n\",\n \"plt.xlabel('n');\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Doesn't differ from 1b.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (2c) For n=3, plot the histogram of the sample means. What is this distribution, is it Weibull or normal? Report your test results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9306998616154375\\n\",\n \"3.849915719555952e-09\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sampleVec = sampleMean[2, :]\\n\",\n \"plt.hist(sampleVec)\\n\",\n \"plt.xlabel('Value')\\n\",\n \"plt.ylabel('Density')\\n\",\n \"\\n\",\n \"# Normalize sample mean and stdev\\n\",\n \"Pnorm = kstest(zscore(sampleVec), 'norm')[1]\\n\",\n \"Pweib = kstest(sampleVec, 'expon')[1]\\n\",\n \"# Scipy and numpy's weibull distributions are different, but a weibull(a=1)\\n\",\n \"# is the same as an exponential distribution.\\n\",\n \"\\n\",\n \"print(Pnorm)\\n\",\n \"print(Pweib)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This distribution is closer to normal.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2d. Repeat 2c and 2d for n=20 (don’t include the plots, but do include the test result for normality and explain the impact of the number of samples n, on normality).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.9090854097141294\\n\",\n \"4.392930463836819e-12\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sampleVec = sampleMean[19, :]\\n\",\n \"plt.hist(sampleVec)\\n\",\n \"plt.xlabel('Value')\\n\",\n \"plt.ylabel('Density')\\n\",\n \"\\n\",\n \"# Normalize sample mean and stdev\\n\",\n \"Pnorm = kstest(zscore(sampleVec), 'norm')[1]\\n\",\n \"Pweib = kstest(zscore(sampleVec), 'expon')[1]\\n\",\n \"\\n\",\n \"print(Pnorm)\\n\",\n \"print(Pweib)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also looks normally distributed.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2e. Repeat 2c but with A=10 and B=2 (I.e plot the histogram of the calculated sample means for 1000 trials of n=3). What is this distribution, Weibull or normal? Why does it look different than in 1c?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"inputHidden\": false,\n \"outputHidden\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Answer to 2f\\n\",\n \"\\n\",\n \"# The distribution changes shape but the same outcomes, of the sampling distribution\\n\",\n \"# morphing to look normally distributed as N increases, hold.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## (3) Differential expression . In this problem you will use the two-sample t-test to explore what differential hypothesis testing looks like in known standards and how multiple hypothesis correction effects the number of false positives and negatives from these tests.\\n\",\n \"- Distribution 1, normal with mu=1, sigma=1\\n\",\n \"- Distribution 2, normal with mu=3, sigma=1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"dOne = lambda n: np.random.normal(loc=1.0, scale=1.0, size=(n, ))\\n\",\n \"dTwo = lambda n: np.random.normal(loc=3.0, scale=1.0, size=(n, ))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3a. False Negative: Using n=3, perform 100 comparisons of distribution 1 versus distribution 2 with an alpha=0.05. Anytime you fail to reject the hypothesis it is a false negative. Why is this a false negative? Report the number of false negatives from your 100 tests.\\n\",\n \"\\n\",\n \"Hint: It'd be helpful to define a function that does this for you at this point.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"64\\n\"\n ]\n }\n ],\n \"source\": [\n \"def falseNeg(n=3, nTrial=100, p=0.05):\\n\",\n \" compare = np.empty((nTrial, ))\\n\",\n \"\\n\",\n \" for ii, _ in enumerate(compare):\\n\",\n \" compare[ii] = ttest_ind(dOne(n), dTwo(n), equal_var=False)[1]\\n\",\n \" \\n\",\n \" return sum(compare > p)\\n\",\n \"\\n\",\n \"print(falseNeg())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In reality these are two distributions, so the test has missed an occasion when we should have rejected the null hypothesis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3b. False Positives: Using n=3, perform 100 comparisons of distribution 1 versus distribution 1 with an alpha=0.05. Anytime you reject the hypothesis this is a false positive. Why is this a false positive? Report the number of false positives from your 100 tests.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"def falsePos(n=3, nTrial=100, p=0.05):\\n\",\n \" compare = np.empty((nTrial, ))\\n\",\n \"\\n\",\n \" for ii in range(len(compare)):\\n\",\n \" compare[ii] = ttest_ind(dOne(n), dOne(n), equal_var=False)[1]\\n\",\n \" \\n\",\n \" return sum(compare < p)\\n\",\n \"\\n\",\n \"print(falsePos())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"These are the same distribution, so the null hypothesis is true.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3c. Repeat 3b but 1000 times. What is the number of false positives? Predict the number of false positives you would get if you compared samples from the same distribution 10,000 times and explain why.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"41\\n\",\n \"357\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(falsePos(nTrial=1000))\\n\",\n \"print(falsePos(nTrial=10000))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Number of false positives is p-value * trials, so proportional to the number of trials run.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3d. Now sweep n from 3 to 30 and report the number of false positives and false negatives for each n when you run 100 comparisons. (Provide this in a table format). Please explain the trend you see and interpret its meaning.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"nVec = np.array(range(3, 31))\\n\",\n \"fPos = np.empty(nVec.shape)\\n\",\n \"fNeg = np.empty(nVec.shape)\\n\",\n \"\\n\",\n \"for nn, nItem in enumerate(nVec):\\n\",\n \" fPos[nn] = falsePos(n=nItem)\\n\",\n \" fNeg[nn] = falseNeg(n=nItem)\\n\",\n \" \\n\",\n \"plt.plot(nVec, fPos);\\n\",\n \"plt.plot(nVec, fNeg);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Number of false positives is not dependent upon $n$, while the number of false negatives is.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 3e. For n=3, suggest how the number of false negatives changes according to sigma for the two distributions and test this. Report your new values and sigma and the number of false negatives in 100 tests.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"86\\n\"\n ]\n }\n ],\n \"source\": [\n \"dThree = lambda n: np.random.normal(loc=3.0, scale=2.0, size=(n, ))\\n\",\n \"\\n\",\n \"def falseNegB(n=3, nTrial=100):\\n\",\n \" compare = np.empty((nTrial, ))\\n\",\n \"\\n\",\n \" for ii, _ in enumerate(compare):\\n\",\n \" compare[ii] = ttest_ind(dOne(n), dThree(n), equal_var=False)[1]\\n\",\n \" \\n\",\n \" return sum(compare > 0.05)\\n\",\n \"\\n\",\n \"print(falseNegB())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The number of false negatives increases with sigma.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (3f) Lastly, perform 3d for p < 0.01 instead of p < 0.05. How does this influence the rate of false positives and negatives? How might you use this when performing many tests?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"nVec = np.array(range(3, 31))\\n\",\n \"fPos = np.empty(nVec.shape)\\n\",\n \"fNeg = np.empty(nVec.shape)\\n\",\n \"\\n\",\n \"for nn, nItem in enumerate(nVec):\\n\",\n \" fPos[nn] = falsePos(n=nItem, p=0.01)\\n\",\n \" fNeg[nn] = falseNeg(n=nItem, p=0.01)\\n\",\n \" \\n\",\n \"plt.plot(nVec, fPos);\\n\",\n \"plt.plot(nVec, fNeg);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This decreases the number of false positives but increases the number of false negatives.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## (5) Shaffer et al\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this excercise we're going to explore some basic concepts of statistics, and use them to build up to some more advanced ideas. To examine these ideas we're going to consider a classic of molecular biology—the [Luria-Delbrück experiment](https://en.wikipedia.org/wiki/Luria–Delbrück_experiment).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"repOne = np.loadtxt(\\\"data/wk2/expt_rep1.csv\\\")\\n\",\n \"repTwo = np.loadtxt(\\\"data/wk2/expt_rep2.csv\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (5a) First, we need to build up a distribution of outcomes for what an experiment would look like if it followed the Luria-Delbruck process.\\n\",\n \"Fill in the function below keeping track of normal and mutant cells. Then, make a second function, `CVofNRuns`, that runs the experiment 3000 times. You can assume a culture size of 120000 cells, and mutation rate of 0.0001 per cell per generation. What does the distribution of outcomes look like?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Runs the simulation a bunch of times, and looks for how often the fano (cv/mean) comes out to one side\\n\",\n \"\\n\",\n \"def simLuriaDelbruck(cultureSize, mutationRate):\\n\",\n \" nCells, nMuts = 1, 0 # Start with 1 non-resistant cell\\n\",\n \"\\n\",\n \" for _ in range(np.int(np.floor(np.log2(cultureSize)))): # num of gens\\n\",\n \" nCells = 2 * nCells # Double the number of cells, simulating division\\n\",\n \" newMuts = np.random.poisson(nCells * mutationRate) # de novo\\n\",\n \" nMuts = 2 * nMuts + newMuts # Previous mutants divide and add\\n\",\n \" nCells = nCells - newMuts # Non-resistant pop goes down by newMuts\\n\",\n \"\\n\",\n \" return nMuts\\n\",\n \"\\n\",\n \"def CVofNRuns(N, cultureSize, mutationRate):\\n\",\n \" return np.fromiter((simLuriaDelbruck(cultureSize, mutationRate) for x in range(N)), dtype = np.int)\\n\",\n \"\\n\",\n \"cvs = CVofNRuns(3000, 120000, 0.0001)\\n\",\n \"\\n\",\n \"plt.hist(cvs, bins=30);\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (5b) Compare the distribution of outcomes between the two replicates of the experiment using the 2-sample KS test. Are they consistent with one another?\\n\",\n \"Hint: Each experiment varies slightly in the amount of time it was run. The absolute values of the numbers doesn't matter, so much as the variation of them. You'll need to correct for this by dividing by the mean of the results.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ks_2sampResult(statistic=0.25100240577385735, pvalue=0.19275155911638572)\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ks_2samp(repOne/np.mean(repOne), repTwo/np.mean(repTwo))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (5c) Compare the distribution of outcomes between the experiment and model. Are our results consistent with resistance arising through a Luria-Delbruck related process?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Ks_2sampResult(statistic=0.6293875968992249, pvalue=1.244955761027369e-15)\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ks_2samp(repOne/np.mean(repOne), cvs/np.mean(cvs))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### (5d) We assumed a specific mutation rate and final number of cells. How might you show whether or not these parameters influence our results?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"collapsed\": true\n },\n \"source\": [\n \"Could loop across different values of them, to check that you always get the same outcome.\"\n ]\n }\n ],\n \"metadata\": {\n \"kernel_info\": {\n \"name\": \"python3\"\n },\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.3\"\n },\n \"nteract\": {\n \"version\": \"0.3.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45498,"cells":{"repo_name":{"kind":"string","value":"myuuuuun/oyama_seminar2016"},"path":{"kind":"string","value":"quantecon/An Introductory Example.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"94225"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# An Introductory Example - QuantEcon(Julia)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"PyPlot.Figure(PyObject )\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"1-element Array{Any,1}:\\n\",\n \" PyObject \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"using PyPlot\\n\",\n \"ts_length = 100\\n\",\n \"epsilon_values = randn(ts_length)\\n\",\n \"plot(epsilon_values, \\\"blue\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"linspace(0.0,1.0,11)\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#collect(3:10)\\n\",\n \"# ここの処理は〜〜〜\\n\",\n \"#plot(collect(1:100), epsilon_values, \\\"blue\\\")\\n\",\n \"\\n\",\n \"s = linspace(0, 1, 11)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2x2x2x2 Array{Int64,4}:\\n\",\n \"[:, :, 1, 1] =\\n\",\n \" 0 0\\n\",\n \" 0 0\\n\",\n \"\\n\",\n \"[:, :, 2, 1] =\\n\",\n \" 0 0\\n\",\n \" 0 0\\n\",\n \"\\n\",\n \"[:, :, 1, 2] =\\n\",\n \" 0 0\\n\",\n \" 0 0\\n\",\n \"\\n\",\n \"[:, :, 2, 2] =\\n\",\n \" 0 0\\n\",\n \" 0 0\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ts_length = 100\\n\",\n \"array = zeros(Int64, 2, 2, 2, 2)\\n\",\n \"\\n\",\n \"#array[:, :, :] = 0\\n\",\n \"#array[1, 1, 1] = 21\\n\",\n \"\\n\",\n \"array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3-element Array{Any,1}:\\n\",\n \" 10 \\n\",\n \" \\\"にゃん\\\"\\n\",\n \" false \"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = [10, \\\"にゃん\\\", false]\\n\",\n \"\\n\",\n \"array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3-element Array{Any,1}:\\n\",\n \" 10.2 \\n\",\n \" \\\"aiueo\\\"\\n\",\n \" \\\"aaaaa\\\"\"\n ]\n },\n \"execution_count\": 66,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = Array{Any}(3)\\n\",\n \"\\n\",\n \"array[3] = 20\\n\",\n \"array[2] = \\\"aiueo\\\"\\n\",\n \"array[1] = 10.2\\n\",\n \"\\n\",\n \"array[3] = \\\"aaaaa\\\"\\n\",\n \"\\n\",\n \"array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"UTF8String\"\n ]\n },\n \"execution_count\": 72,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = [\\\"2\\\", 30, \\\"🙅\\\"]\\n\",\n \"\\n\",\n \"typeof(array[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5\"\n ]\n },\n \"execution_count\": 73,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = [3, 2, \\\"20\\\", \\\"aiueo\\\", false]\\n\",\n \"\\n\",\n \"length(array)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4\"\n ]\n },\n \"execution_count\": 74,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = [2 3; 2 1]\\n\",\n \"length(array)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"4-element Array{Any,1}:\\n\",\n \" 3 \\n\",\n \" 2 \\n\",\n \" \\\"20\\\" \\n\",\n \" \\\"aiueo\\\"\"\n ]\n },\n \"execution_count\": 79,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = [3, 2, \\\"20\\\", \\\"aiueo\\\", false]\\n\",\n \"pop!(array)\\n\",\n \"array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"6-element Array{Any,1}:\\n\",\n \" 3 \\n\",\n \" 2 \\n\",\n \" \\\"20\\\" \\n\",\n \" \\\"aiueo\\\"\\n\",\n \" false \\n\",\n \" \\\"added\\\"\"\n ]\n },\n \"execution_count\": 81,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"array = [3, 2, \\\"20\\\", \\\"aiueo\\\", false]\\n\",\n \"x = \\\"added\\\"\\n\",\n \"push!(array, x)\\n\",\n \"\\n\",\n \"array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"aiueo\\n\",\n \"3\\n\",\n \"false\\n\",\n \"20.3\\n\"\n ]\n }\n ],\n \"source\": [\n \"array = [\\\"aiueo\\\", 3, false, 20.3]\\n\",\n \"\\n\",\n \"for variable in array\\n\",\n \" println(variable)\\n\",\n \"end\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1: aiueo\\n\",\n \"2: 3\\n\",\n \"3: false\\n\"\n ]\n }\n ],\n \"source\": [\n \"array = [\\\"aiueo\\\", 3, false, 20.3]\\n\",\n \"\\n\",\n \"for i in 1:3\\n\",\n \" println(i, \\\": \\\", array[i])\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"100-element Array{Float64,1}:\\n\",\n \" -1.98243 \\n\",\n \" -0.289045\\n\",\n \" 0.548386\\n\",\n \" 2.68586 \\n\",\n \" -0.826671\\n\",\n \" -0.27946 \\n\",\n \" 0.833183\\n\",\n \" 0.820687\\n\",\n \" 1.68391 \\n\",\n \" 0.848353\\n\",\n \" 1.43303 \\n\",\n \" 1.02952 \\n\",\n \" -0.138658\\n\",\n \" ⋮ \\n\",\n \" 0.193556\\n\",\n \" 0.376007\\n\",\n \" -0.770974\\n\",\n \" -0.935151\\n\",\n \" 0.798305\\n\",\n \" -1.63905 \\n\",\n \" 1.50708 \\n\",\n \" -1.1761 \\n\",\n \" 0.224439\\n\",\n \" -0.517274\\n\",\n \" -0.944976\\n\",\n \" 1.77557 \"\n ]\n },\n \"execution_count\": 105,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ts_length = 100\\n\",\n \"epsilon_values = Array(Float64, ts_length)\\n\",\n \"\\n\",\n \"for i in 1:ts_length\\n\",\n \" epsilon_values[i] = randn()\\n\",\n \"end\\n\",\n \"\\n\",\n \"epsilon_values\\n\",\n \"#plot(epsilon_values, \\\"b-\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello foo\\n\",\n \"Hello bar\\n\"\n ]\n }\n ],\n \"source\": [\n \"words = [\\\"foo\\\", \\\"bar\\\"]\\n\",\n \"for word in words\\n\",\n \" println(\\\"Hello $(word)\\\")\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 112,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello 1.0\\n\",\n \"Hello 6.123233995736766e-17\\n\",\n \"Hello 0.5000000000000001\\n\"\n ]\n }\n ],\n \"source\": [\n \"xs = [2pi, pi/2, pi/3]\\n\",\n \"for x in xs\\n\",\n \" println(\\\"Hello $(cos(x))\\\")\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 119,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"ename\": \"LoadError\",\n \"evalue\": \"LoadError: InterruptException:\\nwhile loading In[119], in expression starting on line 4\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"LoadError: InterruptException:\\nwhile loading In[119], in expression starting on line 4\",\n \"\"\n ]\n }\n ],\n \"source\": [\n \"s = 0\\n\",\n \"i = 1\\n\",\n \"\\n\",\n \"while true\\n\",\n \" s += i\\n\",\n \" i += 1\\n\",\n \"end\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 120,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"55\"\n ]\n }\n ],\n \"source\": [\n \"s = 0\\n\",\n \"i = 1\\n\",\n \"\\n\",\n \"while true\\n\",\n \" s += i\\n\",\n \" i += 1\\n\",\n \" if i > 10\\n\",\n \" break\\n\",\n \" end\\n\",\n \"end\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 121,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"my_sum (generic function with 1 method)\"\n ]\n },\n \"execution_count\": 121,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"function my_sum(from, to)\\n\",\n \" s = 0\\n\",\n \" for i = from:to\\n\",\n \" s += i\\n\",\n \" end\\n\",\n \" return s\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 123,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"54\"\n ]\n },\n \"execution_count\": 123,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"my_sum(2, 10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"using Distributions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Distributions.Normal(μ=1.0, σ=2.0)\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"Normal(1, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"PyPlot.Figure(PyObject )\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": [\n \"([5.0,40.0,191.0,710.0,1340.0,1369.0,901.0,346.0,84.0,14.0],[1.079994632701876,1.8331729634194591,2.5863512941370423,3.3395296248546256,4.092707955572209,4.845886286289792,5.599064617007375,6.3522429477249585,7.105421278442542,7.858599609160125,8.611777939877708],Any[PyObject ,PyObject ,PyObject ,PyObject ,PyObject ,PyObject ,PyObject ,PyObject ,PyObject ,PyObject ])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"function plot_histogram(distribution, n)\\n\",\n \" epsilon_values = rand(distribution, n) # n draws from distribution\\n\",\n \" plt[:hist](epsilon_values)\\n\",\n \"end\\n\",\n \"\\n\",\n \"lp = Normal(5, 1)\\n\",\n \"plot_histogram(lp, 5000)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"100-element Array{Float64,1}:\\n\",\n \" 0.404843\\n\",\n \" 0.68938 \\n\",\n \" 0.143533\\n\",\n \" 0.705652\\n\",\n \" 0.129699\\n\",\n \" 0.321097\\n\",\n \" 0.532875\\n\",\n \" 0.562169\\n\",\n \" 0.199171\\n\",\n \" 0.50925 \\n\",\n \" 0.844162\\n\",\n \" 0.640836\\n\",\n \" 0.444534\\n\",\n \" ⋮ \\n\",\n \" 0.45307 \\n\",\n \" 0.540133\\n\",\n \" 0.399599\\n\",\n \" 0.828188\\n\",\n \" 0.862301\\n\",\n \" 0.907723\\n\",\n \" 0.968492\\n\",\n \" 0.919998\\n\",\n \" 0.90256 \\n\",\n \" 0.867195\\n\",\n \" 0.212816\\n\",\n \" 0.175562\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rand(100)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise1\\n\",\n \"\\n\",\n \"reduceを使ってみる\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"my_factorial (generic function with 1 method)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"my_factorial(n) = reduce(*, collect(1:n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"24\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"my_factorial(4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"非整数に対応してみる\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"my_binomial_rv (generic function with 1 method)\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"my_binomial_rv(n, p) = sum(rand(n) .< p)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"1\\n\",\n \"0\\n\",\n \"2\\n\",\n \"1\\n\",\n \"1\\n\",\n \"0\\n\",\n \"0\\n\",\n \"3\\n\",\n \"0\\n\",\n \"1\\n\",\n \"0\\n\",\n \"0\\n\",\n \"1\\n\",\n \"3\\n\",\n \"1\\n\",\n \"2\\n\",\n \"2\\n\",\n \"1\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i=1:20\\n\",\n \" println(my_binomial_rv(10, 0.1))\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"montecalro_pi (generic function with 1 method)\"\n ]\n },\n \"execution_count\": 89,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"function montecalro_pi(size)\\n\",\n \" sample = rand(size, 2)\\n\",\n \" inner = 0.0\\n\",\n \"\\n\",\n \" for (s1, s2) in zip(sample[:, 1], sample[:, 2])\\n\",\n \" inner += s1^2 + s2^2 <= 1 ? 1 : 0\\n\",\n \" end\\n\",\n \" \\n\",\n \" return inner * 4 / size\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3.141292\"\n ]\n }\n ],\n \"source\": [\n \"print( montecalro_pi(1000000) )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Exercise4\\n\",\n \"\\n\",\n \"n: コインを投げる回数, p: 表が出る確率 のとき, x回以上連続で表が出る確率を求める\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"coin (generic function with 1 method)\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"function coin(n, x, p)\\n\",\n \" total = 2^n\\n\",\n \" sucess = 0\\n\",\n \" \\n\",\n \" for i=x:n\\n\",\n \" sucess += factorial(n) / factorial(i) / factorial(n-i)\\n\",\n \" end\\n\",\n \" \\n\",\n \" return sucess / total\\n\",\n \"end\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9453125\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"coin(10, 3, 0.5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Julia 0.4.3\",\n \"language\": \"julia\",\n \"name\": \"julia-0.4\"\n },\n \"language_info\": {\n \"file_extension\": \".jl\",\n \"mimetype\": \"application/julia\",\n \"name\": \"julia\",\n \"version\": \"0.4.3\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}},{"rowIdx":45499,"cells":{"repo_name":{"kind":"string","value":"JuliaPackageMirrors/KrylovMethods.jl"},"path":{"kind":"string","value":"benchmarks/benchmarkBiCGSTB.ipynb"},"copies":{"kind":"string","value":"1"},"size":{"kind":"string","value":"4173"},"content":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Benchmark for BiCGSTB\\n\",\n \"\\n\",\n \"As an example, we solve the curl-curl system arising in electromagnetics\\n\",\n \"\\n\",\n \"$$\\n\",\n \" \\\\nabla \\\\times \\\\nabla \\\\times u + \\\\imath w u = 0\\n\",\n \"$$\\n\",\n \"\\n\",\n \"with natural boundary conditions. To generate the discrete PDE, we use a regular mesh and the mimetic finite volume discretization provided by [jInv.Mesh](https://github.com/JuliaInv/jInv.jl)\\n\",\n \"\\n\",\n \"A good solver for these kinds of problems is BiCGSTAB and we test the implemenation from `KrylovMethods` for growing mesh sizes. If you have another implementation please add it here and make a PR. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"using jInv.Mesh\\n\",\n \"using KrylovMethods\\n\",\n \"using ParSpMatVec\\n\",\n \"using Base.Test\\n\",\n \"\\n\",\n \"Ns = 16*[1,2,3,4] # number of grid points\\n\",\n \"nTrials = 10 # number of repititions\\n\",\n \"nthreads = 4 # number of threads used to accelerate sparse matvecs\\n\",\n \"\\n\",\n \"# allocate space for results\\n\",\n \"timesKM = zeros(length(Ns),nTrials)\\n\",\n \"timesK = zeros(length(Ns),nTrials)\\n\",\n \"timesKMOpt = zeros(length(Ns),nTrials)\\n\",\n \"timesIS = zeros(length(Ns),nTrials);\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"relresKM = 0\\n\",\n \"iterKM = 0\\n\",\n \"iterKMOpt = 0\\n\",\n \"relresKMOpt = 0\\n\",\n \"for k=1:length(Ns)\\n\",\n \" n = Ns[k]\\n\",\n \" \\n\",\n \" # build Curl Curl system\\n\",\n \" M = getRegularMesh([0 1 0 1 0 1],[n,n,n])\\n\",\n \" CURL = getCurlMatrix(M)\\n\",\n \" w = 1\\n\",\n \" A = CURL'*CURL - 1im*w*speye(size(CURL,2))\\n\",\n \" rhs = randn(size(A,2)) + 1im *randn(size(A,2))\\n\",\n \" \\n\",\n \" if k==1 # warmup\\n\",\n \" resKM = KrylovMethods.bicgstb(A,rhs,tol=1e-4,maxIter=2,out=-1);\\n\",\n \" end\\n\",\n \" for j=1:nTrials\\n\",\n \" tic()\\n\",\n \" resKM = KrylovMethods.bicgstb(A,rhs,tol=1e-12,maxIter=100,out=-1)\\n\",\n \" timesKM[k,j] = toq();\\n\",\n \" iterKM = resKM[4]\\n\",\n \" relresKM = resKM[3]\\n\",\n \"\\n\",\n \" tic()\\n\",\n \" yt = zeros(Complex128,size(A,1))\\n\",\n \" Afun(x) = (yt[:]=Complex128(0.0); ParSpMatVec.Ac_mul_B!( Complex128(1.0), A, x, Complex128(0.0), yt, nthreads); return yt)\\n\",\n \" resKMOpt = KrylovMethods.bicgstb(Afun,rhs,tol=1e-12,maxIter=100,out=-1)\\n\",\n \" timesKMOpt[k,j] = toq();\\n\",\n \" iterKMOpt = resKM[4]\\n\",\n \" relresKMOpt = resKMOpt[3] \\n\",\n \" @test iterKMOpt==iterKM\\n\",\n \"# @test abs(relresKMOpt-relresKM)/relresKM < 1e-1\\n\",\n \" end\\n\",\n \"println(\\\"n=$n, KM.jl: $(mean(timesKM[k,:])) KM.jl+ParSpMat:$(mean(timesKMOpt[k,:]))\\\")\\n\",\n \"end\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"using PyPlot\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"loglog(Ns,mean(timesKM,2),\\\"--b\\\",linewidth=3)\\n\",\n \"hold(true)\\n\",\n \"loglog(Ns,mean(timesKMOpt,2),\\\"-b\\\",linewidth=3)\\n\",\n \"xlabel(\\\"degrees of freedom\\\")\\n\",\n \"ylabel(\\\"runtime\\\")\\n\",\n \"title(\\\"Comparison of PCG runtimes for 3D CurlCurl\\\")\\n\",\n \"legend((\\\"KrylovMethods.jl\\\",\\\"KrylovMethods+ParSpMatVec\\\"),loc=4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Julia 0.4.6-pre\",\n \"language\": \"julia\",\n \"name\": \"julia-0.4\"\n },\n \"language_info\": {\n \"file_extension\": \".jl\",\n \"mimetype\": \"application/julia\",\n \"name\": \"julia\",\n \"version\": \"0.4.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"mit"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":454,"numItemsPerPage":100,"numTotalItems":47214,"offset":45400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc1MDE1MTk0MCwic3ViIjoiL2RhdGFzZXRzL2NvZGVwYXJyb3QvZ2l0aHViLWp1cHl0ZXIiLCJleHAiOjE3NTAxNTU1NDAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.eBQAaMQ835phcY69EiFTPkKwl6r_uu1hI50z_q8K9O6z3srq6Eq3JqyWbIc2fUt6V-Hdm7O7xDKo4GFrNUhtDg","displayUrls":true},"discussionsStats":{"closed":2,"open":0,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
\\n\",\n \"# Découvrir Snap! en vidéos \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"### Voir sur YouTube : \\n\",\n \"\\n\",\n \"* [(YouTube) Vidéo 1 : Découverte de Snap!](https://www.youtube.com/watch?v=MbyYEPeo9mw&feature=youtu.be)\\n\",\n \"* [(YouTube) Vidéo 2 : Snap! pour _Poppy_ ](https://www.youtube.com/watch?v=-SGL4hd_sBo)\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"---\\n\",\n \"# Vidéo 1 : Découverte de Snap!\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \" ![](\\\"https://www.tensorflow.org/images/tf_logo_32px.png\\\"){kind=logo} TensorFlow.org で表示 | \\n\",\n \" ![](\\\"https://www.tensorflow.org/images/colab_logo_32px.png\\\"){kind=logo} Google Colab で実行 | \\n\",\n \" ![](\\\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\\\") GitHubでソースを表示 | \\n\",\n \" ![](\\\"https://www.tensorflow.org/images/download_logo_32px.png\\\"){kind=logo} ノートブックをダウンロード | \\n\",\n \"
| 特徴名 | \\n\",\n \"説明 | \\n\",\n \"
|---|---|
| sex | \\n\",\n \"乗船者の性別 | \\n\",\n \"
| age | \\n\",\n \"乗船者の年齢 | \\n\",\n \"
| n_siblings_spouses | \\n\",\n \"同乗する兄弟姉妹および配偶者 | \\n\",\n \"
| parch | \\n\",\n \"同乗する両親および子供 | \\n\",\n \"
| fare | \\n\",\n \"運賃 | \\n\",\n \"
| class | \\n\",\n \"船室のクラス | \\n\",\n \"
| deck | \\n\",\n \"搭乗デッキ | \\n\",\n \"
| embark_town | \\n\",\n \"乗船者の乗船地 | \\n\",\n \"
| alone | \\n\",\n \"一人旅か否か | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" instant dteday season yr mnth hr holiday weekday workingday \\\\\\n\",\n \"0 1 2011-01-01 1 0 1 0 0 6 0 \\n\",\n \"1 2 2011-01-01 1 0 1 1 0 6 0 \\n\",\n \"2 3 2011-01-01 1 0 1 2 0 6 0 \\n\",\n \"3 4 2011-01-01 1 0 1 3 0 6 0 \\n\",\n \"4 5 2011-01-01 1 0 1 4 0 6 0 \\n\",\n \"\\n\",\n \" weathersit temp atemp hum windspeed casual registered cnt \\n\",\n \"0 1 0.24 0.2879 0.81 0.0 3 13 16 \\n\",\n \"1 1 0.22 0.2727 0.80 0.0 8 32 40 \\n\",\n \"2 1 0.22 0.2727 0.80 0.0 5 27 32 \\n\",\n \"3 1 0.24 0.2879 0.75 0.0 3 10 13 \\n\",\n \"4 1 0.24 0.2879 0.75 0.0 0 1 1 \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"rides.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Checking out the data\\n\",\n \"\\n\",\n \"This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\\n\",\n \"\\n\",\n \"Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"| \\n\",\n \" | instant | \\n\",\n \"dteday | \\n\",\n \"season | \\n\",\n \"yr | \\n\",\n \"mnth | \\n\",\n \"hr | \\n\",\n \"holiday | \\n\",\n \"weekday | \\n\",\n \"workingday | \\n\",\n \"weathersit | \\n\",\n \"temp | \\n\",\n \"atemp | \\n\",\n \"hum | \\n\",\n \"windspeed | \\n\",\n \"casual | \\n\",\n \"registered | \\n\",\n \"cnt | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"1 | \\n\",\n \"2011-01-01 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"6 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0.24 | \\n\",\n \"0.2879 | \\n\",\n \"0.81 | \\n\",\n \"0.0 | \\n\",\n \"3 | \\n\",\n \"13 | \\n\",\n \"16 | \\n\",\n \"
| 1 | \\n\",\n \"2 | \\n\",\n \"2011-01-01 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"6 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0.22 | \\n\",\n \"0.2727 | \\n\",\n \"0.80 | \\n\",\n \"0.0 | \\n\",\n \"8 | \\n\",\n \"32 | \\n\",\n \"40 | \\n\",\n \"
| 2 | \\n\",\n \"3 | \\n\",\n \"2011-01-01 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"2 | \\n\",\n \"0 | \\n\",\n \"6 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0.22 | \\n\",\n \"0.2727 | \\n\",\n \"0.80 | \\n\",\n \"0.0 | \\n\",\n \"5 | \\n\",\n \"27 | \\n\",\n \"32 | \\n\",\n \"
| 3 | \\n\",\n \"4 | \\n\",\n \"2011-01-01 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"3 | \\n\",\n \"0 | \\n\",\n \"6 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0.24 | \\n\",\n \"0.2879 | \\n\",\n \"0.75 | \\n\",\n \"0.0 | \\n\",\n \"3 | \\n\",\n \"10 | \\n\",\n \"13 | \\n\",\n \"
| 4 | \\n\",\n \"5 | \\n\",\n \"2011-01-01 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"4 | \\n\",\n \"0 | \\n\",\n \"6 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"0.24 | \\n\",\n \"0.2879 | \\n\",\n \"0.75 | \\n\",\n \"0.0 | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" yr holiday temp hum windspeed casual registered cnt season_1 \\\\\\n\",\n \"0 0 0 0.24 0.81 0.0 3 13 16 1 \\n\",\n \"1 0 0 0.22 0.80 0.0 8 32 40 1 \\n\",\n \"2 0 0 0.22 0.80 0.0 5 27 32 1 \\n\",\n \"3 0 0 0.24 0.75 0.0 3 10 13 1 \\n\",\n \"4 0 0 0.24 0.75 0.0 0 1 1 1 \\n\",\n \"\\n\",\n \" season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\\\\n\",\n \"0 0 ... 0 0 0 0 0 0 \\n\",\n \"1 0 ... 0 0 0 0 0 0 \\n\",\n \"2 0 ... 0 0 0 0 0 0 \\n\",\n \"3 0 ... 0 0 0 0 0 0 \\n\",\n \"4 0 ... 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" weekday_3 weekday_4 weekday_5 weekday_6 \\n\",\n \"0 0 0 0 1 \\n\",\n \"1 0 0 0 1 \\n\",\n \"2 0 0 0 1 \\n\",\n \"3 0 0 0 1 \\n\",\n \"4 0 0 0 1 \\n\",\n \"\\n\",\n \"[5 rows x 59 columns]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\\n\",\n \"for each in dummy_fields:\\n\",\n \" dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\\n\",\n \" rides = pd.concat([rides, dummies], axis=1)\\n\",\n \"\\n\",\n \"fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \\n\",\n \" 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\\n\",\n \"data = rides.drop(fields_to_drop, axis=1)\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Scaling target variables\\n\",\n \"To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\\n\",\n \"\\n\",\n \"The scaling factors are saved so we can go backwards when we use the network for predictions.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\\n\",\n \"# Store scalings in a dictionary so we can convert back later\\n\",\n \"scaled_features = {}\\n\",\n \"for each in quant_features:\\n\",\n \" mean, std = data[each].mean(), data[each].std()\\n\",\n \" scaled_features[each] = [mean, std]\\n\",\n \" data.loc[:, each] = (data[each] - mean)/std\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Splitting the data into training, testing, and validation sets\\n\",\n \"\\n\",\n \"We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# Save the last 21 days \\n\",\n \"test_data = data[-21*24:]\\n\",\n \"data = data[:-21*24]\\n\",\n \"\\n\",\n \"# Separate the data into features and targets\\n\",\n \"target_fields = ['cnt', 'casual', 'registered']\\n\",\n \"features, targets = data.drop(target_fields, axis=1), data[target_fields]\\n\",\n \"test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# Hold out the last 60 days of the remaining data as a validation set\\n\",\n \"train_features, train_targets = features[:-60*24], targets[:-60*24]\\n\",\n \"val_features, val_targets = features[-60*24:], targets[-60*24:]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Time to build the network\\n\",\n \"\\n\",\n \"Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\\n\",\n \"\\n\",\n \"The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\\n\",\n \"\\n\",\n \"We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\\n\",\n \"\\n\",\n \"> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\\n\",\n \"\\n\",\n \"Below, you have these tasks:\\n\",\n \"1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\\n\",\n \"2. Implement the forward pass in the `train` method.\\n\",\n \"3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\\n\",\n \"4. Implement the forward pass in the `run` method.\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"class NeuralNetwork(object):\\n\",\n \" def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\\n\",\n \" # Set number of nodes in input, hidden and output layers.\\n\",\n \" self.input_nodes = input_nodes\\n\",\n \" self.hidden_nodes = hidden_nodes\\n\",\n \" self.output_nodes = output_nodes\\n\",\n \"\\n\",\n \" # Initialize weights\\n\",\n \" self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, \\n\",\n \" (self.hidden_nodes, self.input_nodes))\\n\",\n \"\\n\",\n \" self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, \\n\",\n \" (self.output_nodes, self.hidden_nodes))\\n\",\n \" self.lr = learning_rate\\n\",\n \" \\n\",\n \" #### Set this to your implemented sigmoid function ####\\n\",\n \" # Activation function is the sigmoid function\\n\",\n \" self.activation_function = lambda x: 1.0/(1+ np.exp(-x))\\n\",\n \" self.activation_function2 = lambda x: x\\n\",\n \" \\n\",\n \" def sigmoid(self, x):\\n\",\n \" return 1 / (1 + np.exp(-x))\\n\",\n \" \\n\",\n \" def train(self, inputs_list, targets_list):\\n\",\n \" # Convert inputs list to 2d array\\n\",\n \" inputs = np.array(inputs_list, ndmin=2).T\\n\",\n \"# print('Inputs')\\n\",\n \"# print(inputs)\\n\",\n \"# L = inputs_list.size\\n\",\n \" targets = np.array(targets_list, ndmin=2).T\\n\",\n \" \\n\",\n \" #### Implement the forward pass here ####\\n\",\n \" ### Forward pass ###\\n\",\n \" # TODO: Hidden layer\\n\",\n \" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer\\n\",\n \" hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\\n\",\n \" \\n\",\n \" # TODO: Output layer\\n\",\n \" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)# signals into final output layer\\n\",\n \" final_outputs = final_inputs # signals from final output layer\\n\",\n \" \\n\",\n \" #### Implement the backward pass here ####\\n\",\n \" ### Backward pass ###\\n\",\n \" \\n\",\n \" # TODO: Output error\\n\",\n \" # Derivative of out layer function is 1.\\n\",\n \" output_errors = (targets - final_outputs) * 1\\n\",\n \" # Output layer error is the difference between desired target and actual output.\\n\",\n \" \\n\",\n \" # TODO: Backpropagated error\\n\",\n \" hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors)\\n\",\n \" # errors propagated to the hidden layer\\n\",\n \" hidden_grad = hidden_outputs * (1 - hidden_outputs)\\n\",\n \" # hidden layer gradients\\n\",\n \" \\n\",\n \" # TODO: Update the weights\\n\",\n \" self.weights_hidden_to_output += self.lr * np.dot(output_errors, hidden_outputs.T)\\n\",\n \" # update hidden-to-output weights with gradient descent step\\n\",\n \" self.weights_input_to_hidden += self.lr * np.dot((hidden_errors * hidden_grad), inputs.T)\\n\",\n \" # update input-to-hidden weights with gradient descent step\\n\",\n \" \\n\",\n \" \\n\",\n \" def run(self, inputs_list):\\n\",\n \" # Run a forward pass through the network\\n\",\n \" inputs = np.array(inputs_list, ndmin=2).T\\n\",\n \" \\n\",\n \" #### Implement the forward pass here ####\\n\",\n \" # TODO: Hidden layer\\n\",\n \" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs)\\n\",\n \" hidden_outputs = self.activation_function(hidden_inputs)# signals from hidden layer\\n\",\n \" \\n\",\n \" # TODO: Output layer\\n\",\n \" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)\\n\",\n \" final_outputs = final_inputs# signals from final output layer \\n\",\n \" \\n\",\n \" return final_outputs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"def MSE(y, Y):\\n\",\n \" return np.mean((y-Y)**2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Training the network\\n\",\n \"\\n\",\n \"Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\\n\",\n \"\\n\",\n \"You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\\n\",\n \"\\n\",\n \"### Choose the number of epochs\\n\",\n \"This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.\\n\",\n \"\\n\",\n \"### Choose the learning rate\\n\",\n \"This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\\n\",\n \"\\n\",\n \"### Choose the number of hidden nodes\\n\",\n \"The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Progress: 99.9% ... Training loss: 0.042 ... Validation loss: 0.130\"\n ]\n }\n ],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"### Set the hyperparameters here ###\\n\",\n \"epochs = 5000 #100\\n\",\n \"learning_rate = 0.1 #0.1\\n\",\n \"hidden_nodes = 50 #2\\n\",\n \"output_nodes = 1\\n\",\n \"\\n\",\n \"N_i = train_features.shape[1]\\n\",\n \"network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\\n\",\n \"\\n\",\n \"losses = {'train':[], 'validation':[]}\\n\",\n \"for e in range(epochs):\\n\",\n \" # Go through a random batch of 128 records from the training data set\\n\",\n \" batch = np.random.choice(train_features.index, size=128)\\n\",\n \" for record, target in zip(train_features.ix[batch].values, \\n\",\n \" train_targets.ix[batch]['cnt']):\\n\",\n \" network.train(record, target)\\n\",\n \" \\n\",\n \" # Printing out the training progress\\n\",\n \" train_loss = MSE(network.run(train_features), train_targets['cnt'].values)\\n\",\n \" val_loss = MSE(network.run(val_features), val_targets['cnt'].values)\\n\",\n \" sys.stdout.write(\\\"\\\\rProgress: \\\" + str(100 * e/float(epochs))[:4] \\\\\\n\",\n \" + \\\"% ... Training loss: \\\" + str(train_loss)[:5] \\\\\\n\",\n \" + \\\" ... Validation loss: \\\" + str(val_loss)[:5])\\n\",\n \" \\n\",\n \" losses['train'].append(train_loss)\\n\",\n \" losses['validation'].append(val_loss)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(-0.021852061436678137, 0.5)\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Xv37s2OHTuYNWsWY8eOZd++fTRt2pTLL7+c+++/v2imm6OOOoqnn36a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5 rows × 59 columns
\\n\",\n \"![](\\\"graphics/mc1_sandbox_ab.png\\\") | \\n\",\n \"
![](\\\"graphics/mc1_sandbox_a.png\\\") | \\n\",\n \"
![](\\\"graphics/mc1_sandbox_b.png\\\") | \\n\",\n \"
| \\n\",\n \"
![](\\\"graphics/mc1_sandbox_acf-a.png\\\") | \\n\",\n \"
![](\\\"graphics/mc1_sandbox_acf-b.png\\\") | \\n\",\n \"
![](\\\"graphics/mc1_convgame_1.png\\\") | \\n\",\n \" ![](\\\"graphics/mc1_convgame_2.png\\\") | \\n\",\n \"
![](\\\"graphics/mc1_convgame_3.png\\\") | \\n\",\n \" ![](\\\"graphics/mc1_convgame_4.png\\\") | \\n\",\n \"
\\n\",\n \"Allen Downey / 이광춘(xwMOOC)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [],\n \"source\": [\n \"from __future__ import print_function, division\\n\",\n \"\\n\",\n \"import first\\n\",\n \"import hypothesis\\n\",\n \"import scatter\\n\",\n \"import thinkstats2\\n\",\n \"\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 연습문제 9.1\\n\",\n \"\\n\",\n \"표본크기가 증가함에 따라, 가설검정력은 증가하는데, 효과가 실제하면 좀더 양성임을 의미한다. 반대로, 표본크기가 줄어들면, 검정력은 설사 효과가 실제한다고 하더라도 덜 양성일 것 같다.\\n\",\n \"이런 작동방식을 조사하는데, NSFG 데이터에서 다른 일부 데이터를 갖는 검정을 실시한다. `thinkstats2.SampleRows`을 사용해서, 데이터프레임에 임의로 행일부를 선택한다.\\n\",\n \"\\n\",\n \"표본크기가 감소함에 따라 검정 p-값에는 무슨 일이 일어나는가? 양의 검정을 산출하는 최소 표본크기는 얼마인가?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9148\\t0.16\\t0.00\\t0.00\\t0.00\\n\",\n \"4574\\t0.10\\t0.01\\t0.00\\t0.00\\n\",\n \"2287\\t0.25\\t0.06\\t0.00\\t0.00\\n\",\n \"1143\\t0.24\\t0.03\\t0.39\\t0.03\\n\",\n \"571\\t0.81\\t0.00\\t0.04\\t0.04\\n\",\n \"285\\t0.57\\t0.41\\t0.48\\t0.83\\n\",\n \"142\\t0.45\\t0.08\\t0.60\\t0.04\\n\"\n ]\n }\n ],\n \"source\": [\n \"def RunTests(live, iters=1000):\\n\",\n \" \\\"\\\"\\\"Runs the tests from Chapter 9 with a subset of the data.\\n\",\n \"\\n\",\n \" live: DataFrame\\n\",\n \" iters: how many iterations to run\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" n = len(live)\\n\",\n \" firsts = live[live.birthord == 1]\\n\",\n \" others = live[live.birthord != 1]\\n\",\n \"\\n\",\n \" # compare pregnancy lengths\\n\",\n \" data = firsts.prglngth.values, others.prglngth.values\\n\",\n \" ht = hypothesis.DiffMeansPermute(data)\\n\",\n \" p1 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" data = (firsts.totalwgt_lb.dropna().values,\\n\",\n \" others.totalwgt_lb.dropna().values)\\n\",\n \" ht = hypothesis.DiffMeansPermute(data)\\n\",\n \" p2 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" # test correlation\\n\",\n \" live2 = live.dropna(subset=['agepreg', 'totalwgt_lb'])\\n\",\n \" data = live2.agepreg.values, live2.totalwgt_lb.values\\n\",\n \" ht = hypothesis.CorrelationPermute(data)\\n\",\n \" p3 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" # compare pregnancy lengths (chi-squared)\\n\",\n \" data = firsts.prglngth.values, others.prglngth.values\\n\",\n \" ht = hypothesis.PregLengthTest(data)\\n\",\n \" p4 = ht.PValue(iters=iters)\\n\",\n \"\\n\",\n \" print('%d\\\\t%0.2f\\\\t%0.2f\\\\t%0.2f\\\\t%0.2f' % (n, p1, p2, p3, p4))\\n\",\n \" \\n\",\n \"thinkstats2.RandomSeed(18)\\n\",\n \"\\n\",\n \"live, firsts, others = first.MakeFrames()\\n\",\n \"\\n\",\n \"n = len(live)\\n\",\n \"for _ in range(7):\\n\",\n \" sample = thinkstats2.SampleRows(live, n)\\n\",\n \" RunTests(sample)\\n\",\n \" n //= 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"test1: 평균 임신기간 차이\\n\",\n \"test2: 평균 출생체중 차이\\n\",\n \"test3: 산모 연령과 출생체중 상관관계\\n\",\n \"test4: 임신기간 카이제곱 검정\\n\",\n \"\\n\",\n \"n test1 test2 test2 test4 \\n\",\n \"9148 →\\t0.16 →\\t0.00 →\\t0.00 →\\t0.00 \\n\",\n \"4574→\\t0.10→\\t0.01→\\t0.00→\\t0.00 \\n\",\n \"2287→\\t0.25→\\t0.06→\\t0.00→\\t0.00 \\n\",\n \"1143→\\t0.24→\\t0.03→\\t0.39→\\t0.03 \\n\",\n \"571→\\t0.81→\\t0.00→\\t0.04→\\t0.04 \\n\",\n \"285→\\t0.57→\\t0.41→\\t0.48→\\t0.83 \\n\",\n \"142→\\t0.45→\\t0.08→\\t0.60→\\t0.04\\n\",\n \"\\n\",\n \"결론: 예상했듯이, 데이터를 제거함에 따라 더큰 표본크기를 갖는 양의 검정은 음이 된다. 하지만, 작은 표본크기에서 조차도 일부 양의 값을 갖는 등 패턴은 일정치 않다.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## 연습문제 9.2 \\n\",\n \"9.3절처럼, 순열로 귀무가설을 모의시험했다; 즉, 관측된 값을 마치 전체 모집단을 대표하는 것처럼 다루었고, 무작위로 모집단의 구성원을 두 집단에 배정했다.\\n\",\n \"대안은 표본을 사용해서 모집단 분포를 추정하고 나서, 분포로부터 임의 표본을 추출하는 것이다. 이런 과정을 재표집(resampling)이라고 부른다. 재표집을 구현하는 몇가지 방식이 있지만, 가장 단순한 것중 하나가 9.10 처럼 관측된 값에서 복원방식으로 표본을 추출하는 것이다.\\n\",\n \"\\n\",\n \"DiffMeansPermute에서 상속받고, 순열보다 재표집을 구현하는 RunModel을 치환(override)하는 클래스 DiffMeansResample을 작성하시오.\\n\",\n \"\\n\",\n \"이 모형을 사용해서 임신기간과 출생체중 사이 차이를 검정하시오. 이 모형이 결과에 얼마나 영향을 주는가?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"collapsed\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"means permute preglength\\n\",\n \"p-value = 0.1675\\n\",\n \"actual = 0.0780372667775\\n\",\n \"ts max = 0.225502935911\\n\",\n \"\\n\",\n \"means permute birthweight\\n\",\n \"p-value = 0.0\\n\",\n \"actual = 0.124761184535\\n\",\n \"ts max = 0.116868397475\\n\"\n ]\n }\n ],\n \"source\": [\n \"class DiffMeansResample(hypothesis.DiffMeansPermute):\\n\",\n \" \\\"\\\"\\\"Tests a difference in means using resampling.\\\"\\\"\\\"\\n\",\n \" \\n\",\n \" def RunModel(self):\\n\",\n \" \\\"\\\"\\\"Run the model of the null hypothesis.\\n\",\n \"\\n\",\n \" returns: simulated data\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" group1 = np.random.choice(self.pool, self.n, replace=True)\\n\",\n \" group2 = np.random.choice(self.pool, self.m, replace=True)\\n\",\n \" return group1, group2\\n\",\n \" \\n\",\n \"\\n\",\n \"def RunResampleTest(firsts, others):\\n\",\n \" \\\"\\\"\\\"Tests differences in means by resampling.\\n\",\n \"\\n\",\n \" firsts: DataFrame\\n\",\n \" others: DataFrame\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" data = firsts.prglngth.values, others.prglngth.values\\n\",\n \" ht = DiffMeansResample(data)\\n\",\n \" p_value = ht.PValue(iters=10000)\\n\",\n \" print('\\\\nmeans permute preglength')\\n\",\n \" print('p-value =', p_value)\\n\",\n \" print('actual =', ht.actual)\\n\",\n \" print('ts max =', ht.MaxTestStat())\\n\",\n \"\\n\",\n \" data = (firsts.totalwgt_lb.dropna().values,\\n\",\n \" others.totalwgt_lb.dropna().values)\\n\",\n \" ht = hypothesis.DiffMeansPermute(data)\\n\",\n \" p_value = ht.PValue(iters=10000)\\n\",\n \" print('\\\\nmeans permute birthweight')\\n\",\n \" print('p-value =', p_value)\\n\",\n \" print('actual =', ht.actual)\\n\",\n \" print('ts max =', ht.MaxTestStat())\\n\",\n \"\\n\",\n \"RunResampleTest(firsts, others) \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"순열대신에 재표추출을 사용하는 것은 결과에 거의 영향이 없다.\\n\",\n \"\\n\",\n \"두 모형은 약간 다른 가정에 기반하고 있고, 상기 예제에서 이것 혹은 저것을 선택할 강력한 사유는 없다. 하지만, 일반적으로 p-값은 귀무가설 선택에 달려있다; 서로 다른 모형은 매우 다른 결과를 이끌어낼 수 있다.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 2\",\n \"language\": \"python\",\n \"name\": \"python2\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 2\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython2\",\n \"version\": \"2.7.10\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"license":{"kind":"string","value":"gpl-3.0"}}},{"rowIdx":45495,"cells":{"repo_name":{"kind":"string","value":"stephenl6705/fluentPy"},"path":{"kind":"string","value":"8. 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dsacademybr/PythonFundamentos | Cap04/Notebooks/DSA-Python-Cap04-08-List Comprehensions.ipynb | 1 | 4357 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>\n",
"\n",
"## Download: http://github.com/dsacademybr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Versão da Linguagem Python\n",
"from platform import python_version\n",
"print('Versão da Linguagem Python Usada Neste Jupyter Notebook:', python_version())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## List Comprehension"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Retornar cada caracter em uma sequência de caracteres\n",
"lst = [x for x in 'python']"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['p', 'y', 't', 'h', 'o', 'n']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check\n",
"lst"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(lst)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Variável x elevada ao quadrado para um range de números e retornar uma lista\n",
"lst = [x**2 for x in range(0, 11)]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lst"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Verificar os números pares em um range de números\n",
"lst = [x for x in range(11) if x % 2 == 0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 2, 4, 6, 8, 10]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lst"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[32.0, 50.0, 68.18, 94.1]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Converter Celsius para Fahrenheit\n",
"celsius = [0,10,20.1,34.5]\n",
"\n",
"fahrenheit = [ ((float(9) / 5) * temp + 32) for temp in celsius ]\n",
"\n",
"fahrenheit"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Operações aninhadas\n",
"lst = [ x**2 for x in [x**2 for x in range(11)]]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 16, 81, 256, 625, 1296, 2401, 4096, 6561, 10000]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lst"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# FIM"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Obrigado\n",
"\n",
"### Visite o Blog da Data Science Academy - <a href=\"http://blog.dsacademy.com.br\">Blog DSA</a>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
robchambers/nbserve | tests/notebooks/testreset.ipynb | 1 | 98090 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import IPython \n",
"import runipy \n",
"my_var = \"old value\" \n",
"runipy.tempvar = \"temp value\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before reset, had 1: <module 'IPython' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/__init__.pyc'>\n",
"Before reset, had 2: <module 'runipy' from '/Users/rob/anaconda/lib/python2.7/site-packages/runipy-0.1.5-py2.7.egg/runipy/__init__.pyc'>\n",
"Before reset, had 3: old value\n",
"Before reset, had 4: temp value\n"
]
}
],
"source": [
"print \"Before reset, had 1: %s\" % IPython \n",
"print \"Before reset, had 2: %s\" % runipy \n",
"print \"Before reset, had 3: %s\" % my_var \n",
"print \"Before reset, had 4: %s\" % runipy.tempvar"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" 'json.encoder': <module 'json.encoder' from '/Users/rob/anaconda/lib/python2.7/json/encoder.pyc'>,\n",
" 'datetime': <module 'datetime' from '/Users/rob/anaconda/lib/python2.7/lib-dynload/datetime.so'>,\n",
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" 'prompt_toolkit.eventloop.abc': None,\n",
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" 'cmd': <module 'cmd' from '/Users/rob/anaconda/lib/python2.7/cmd.pyc'>,\n",
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" 'concurrent.futures.collections': None,\n",
" 'IPython.core.displayhook': <module 'IPython.core.displayhook' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/displayhook.pyc'>,\n",
" 'zmq.eventloop.Queue': None,\n",
" 'IPython.lib.IPython': None,\n",
" 'pygments.lexers.pygments': None,\n",
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" 'pkg_resources._vendor.pprint': None,\n",
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" 'pexpect.spawnbase': <module 'pexpect.spawnbase' from '/Users/rob/anaconda/lib/python2.7/site-packages/pexpect/spawnbase.pyc'>,\n",
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" 'abc': <module 'abc' from '/Users/rob/anaconda/lib/python2.7/abc.pyc'>,\n",
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" 'IPython.utils._tokenize_py2': <module 'IPython.utils._tokenize_py2' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/utils/_tokenize_py2.pyc'>,\n",
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" 'pexpect.stat': None,\n",
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" '_curses': <module '_curses' from '/Users/rob/anaconda/lib/python2.7/lib-dynload/_curses.so'>,\n",
" 'IPython.core.ast': None,\n",
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" 'prompt_toolkit.styles.abc': None,\n",
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" 'jupyter_client.cPickle': None,\n",
" 'pkg_resources._vendor.sys': None,\n",
" 'prompt_toolkit.filters.collections': None,\n",
" 'zmq.sugar.atexit': None,\n",
" 'email.Parser': <email.LazyImporter at 0x108503490>,\n",
" 'ipykernel.comm.comm': <module 'ipykernel.comm.comm' from '/Users/rob/anaconda/lib/python2.7/site-packages/ipykernel/comm/comm.pyc'>,\n",
" 'zmq.utils.sys': None,\n",
" 'IPython.extensions.sys': None,\n",
" 'backports.shutil_get_terminal_size.ctypes': None,\n",
" 'traitlets.utils': <module 'traitlets.utils' from '/Users/rob/anaconda/lib/python2.7/site-packages/traitlets/utils/__init__.pyc'>,\n",
" 'ipython_genutils.encoding': <module 'ipython_genutils.encoding' from '/Users/rob/anaconda/lib/python2.7/site-packages/ipython_genutils/encoding.pyc'>,\n",
" 'zipimport': <module 'zipimport' (built-in)>,\n",
" 'IPython.core.struct': None,\n",
" 'IPython.core.magics.execution': <module 'IPython.core.magics.execution' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/magics/execution.pyc'>,\n",
" 'pkg_resources.extern.sys': None,\n",
" 'pkg_resources.extern.packaging.markers': <module 'pkg_resources._vendor.packaging.markers' from '/Users/rob/anaconda/lib/python2.7/site-packages/setuptools-27.2.0-py2.7.egg/pkg_resources/_vendor/packaging/markers.py'>,\n",
" 'jupyter_client': <module 'jupyter_client' from '/Users/rob/anaconda/lib/python2.7/site-packages/jupyter_client/__init__.pyc'>,\n",
" 'IPython.terminal.magics': <module 'IPython.terminal.magics' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/terminal/magics.pyc'>,\n",
" 'textwrap': <module 'textwrap' from '/Users/rob/anaconda/lib/python2.7/textwrap.pyc'>,\n",
" 'zmq.platform': None,\n",
" 'curses': <module 'curses' from '/Users/rob/anaconda/lib/python2.7/curses/__init__.pyc'>,\n",
" 'IPython.lib.subprocess': None,\n",
" 'pkg_resources._vendor.threading': None,\n",
" 'getopt': <module 'getopt' from '/Users/rob/anaconda/lib/python2.7/getopt.pyc'>,\n",
" 'jupyter_client.channelsabc': <module 'jupyter_client.channelsabc' from '/Users/rob/anaconda/lib/python2.7/site-packages/jupyter_client/channelsabc.pyc'>,\n",
" 'prompt_toolkit.filters.__future__': None,\n",
" 'pexpect.traceback': None,\n",
" 'ipykernel.math': None,\n",
" 'traitlets.config.re': None,\n",
" 'zmq.backend.cython._version': <module 'zmq.backend.cython._version' from '/Users/rob/anaconda/lib/python2.7/site-packages/zmq/backend/cython/_version.so'>,\n",
" 'pexpect.types': None,\n",
" 'IPython.lib.random': None,\n",
" 'ipykernel.codeutil': <module 'ipykernel.codeutil' from '/Users/rob/anaconda/lib/python2.7/site-packages/ipykernel/codeutil.pyc'>,\n",
" 'IPython.utils.frame': <module 'IPython.utils.frame' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/utils/frame.pyc'>,\n",
" 'signal': <module 'signal' (built-in)>,\n",
" 'IPython.core.payloadpage': <module 'IPython.core.payloadpage' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/payloadpage.pyc'>,\n",
" 'xml.parsers': <module 'xml.parsers' from '/Users/rob/anaconda/lib/python2.7/xml/parsers/__init__.pyc'>,\n",
" 'prompt_toolkit.filters.abc': None,\n",
" 'prompt_toolkit.re': None,\n",
" 'IPython.terminal.warnings': None,\n",
" 'prompt_toolkit.layout.containers': <module 'prompt_toolkit.layout.containers' from '/Users/rob/anaconda/lib/python2.7/site-packages/prompt_toolkit/layout/containers.pyc'>,\n",
" 'quopri': <module 'quopri' from '/Users/rob/anaconda/lib/python2.7/quopri.pyc'>,\n",
" 'IPython.core.magics': <module 'IPython.core.magics' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/magics/__init__.pyc'>,\n",
" 'pygments.lexers.types': None,\n",
" 'IPython.core.getopt': None,\n",
" 'jupyter_client.kernelspec': <module 'jupyter_client.kernelspec' from '/Users/rob/anaconda/lib/python2.7/site-packages/jupyter_client/kernelspec.pyc'>,\n",
" 'IPython.utils.string': None,\n",
" 'IPython.lib.display': <module 'IPython.lib.display' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/lib/display.pyc'>,\n",
" 'ipywidgets._version': <module 'ipywidgets._version' from '/Users/rob/anaconda/lib/python2.7/site-packages/ipywidgets/_version.pyc'>,\n",
" 'IPython.terminal.prompts': <module 'IPython.terminal.prompts' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/terminal/prompts.pyc'>,\n",
" 'mimetypes': <module 'mimetypes' from '/Users/rob/anaconda/lib/python2.7/mimetypes.pyc'>,\n",
" 'IPython.core.magics.auto': <module 'IPython.core.magics.auto' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/magics/auto.pyc'>,\n",
" 'xml.parsers.expat': <module 'xml.parsers.expat' from '/Users/rob/anaconda/lib/python2.7/xml/parsers/expat.pyc'>,\n",
" 'ipykernel.io': None,\n",
" 'prompt_toolkit.key_binding.bindings.emacs': <module 'prompt_toolkit.key_binding.bindings.emacs' from '/Users/rob/anaconda/lib/python2.7/site-packages/prompt_toolkit/key_binding/bindings/emacs.pyc'>,\n",
" 'pygments.lexers._mapping': <module 'pygments.lexers._mapping' from '/Users/rob/anaconda/lib/python2.7/site-packages/pygments/lexers/_mapping.pyc'>,\n",
" 'IPython.utils.token': None,\n",
" 'email.Generator': <email.LazyImporter at 0x108503950>,\n",
" 'IPython.utils.module_paths': <module 'IPython.utils.module_paths' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/utils/module_paths.pyc'>,\n",
" 'pygments.lexers.os': None,\n",
" 'logging.re': None,\n",
" 'logging.traceback': None,\n",
" 'IPython.core.magic_arguments': <module 'IPython.core.magic_arguments' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/magic_arguments.pyc'>,\n",
" 'pygments.codecs': None,\n",
" 'traitlets.utils.getargspec': <module 'traitlets.utils.getargspec' from '/Users/rob/anaconda/lib/python2.7/site-packages/traitlets/utils/getargspec.pyc'>,\n",
" 'IPython.core.magics.logging': <module 'IPython.core.magics.logging' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/core/magics/logging.pyc'>,\n",
" 'wcwidth.table_wide': <module 'wcwidth.table_wide' from '/Users/rob/anaconda/lib/python2.7/site-packages/wcwidth/table_wide.pyc'>,\n",
" 'warnings': <module 'warnings' from '/Users/rob/anaconda/lib/python2.7/warnings.pyc'>,\n",
" 'IPython.utils.sysinfo': <module 'IPython.utils.sysinfo' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/utils/sysinfo.pyc'>,\n",
" 'backports': <module 'backports' from '/Users/rob/anaconda/lib/python2.7/site-packages/backports/__init__.pyc'>,\n",
" 'prompt_toolkit.key_binding.bindings.utils': <module 'prompt_toolkit.key_binding.bindings.utils' from '/Users/rob/anaconda/lib/python2.7/site-packages/prompt_toolkit/key_binding/bindings/utils.pyc'>,\n",
" 'IPython.lib.security': <module 'IPython.lib.security' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/lib/security.pyc'>,\n",
" 'prompt_toolkit.key_binding.digraphs': <module 'prompt_toolkit.key_binding.digraphs' from '/Users/rob/anaconda/lib/python2.7/site-packages/prompt_toolkit/key_binding/digraphs.pyc'>,\n",
" 'IPython.core.subprocess': None,\n",
" 'pygments.formatters.re': None,\n",
" 'tornado.escape': <module 'tornado.escape' from '/Users/rob/anaconda/lib/python2.7/site-packages/tornado/escape.pyc'>,\n",
" 'traitlets.config.logging': None,\n",
" 'ipython_genutils.warnings': None,\n",
" 'IPython.lib.backgroundjobs': <module 'IPython.lib.backgroundjobs' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/lib/backgroundjobs.pyc'>,\n",
" 'IPython.utils.subprocess': None,\n",
" 'jupyter_client.abc': None,\n",
" 'prompt_toolkit.key_binding.bindings.itertools': None,\n",
" 'IPython.utils.syspathcontext': <module 'IPython.utils.syspathcontext' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/utils/syspathcontext.pyc'>,\n",
" 'UserDict': <module 'UserDict' from '/Users/rob/anaconda/lib/python2.7/UserDict.pyc'>,\n",
" 'traitlets.config.ipython_genutils': None,\n",
" 'enum': <module 'enum' from '/Users/rob/anaconda/lib/python2.7/site-packages/enum/__init__.pyc'>,\n",
" 'pygments.os': None,\n",
" 'prompt_toolkit.prompt_toolkit': None,\n",
" 'IPython.utils.PyColorize': <module 'IPython.utils.PyColorize' from '/Users/rob/anaconda/lib/python2.7/site-packages/IPython/utils/PyColorize.pyc'>,\n",
" 'ipywidgets.widgets.widget_int': <module 'ipywidgets.widgets.widget_int' from '/Users/rob/anaconda/lib/python2.7/site-packages/ipywidgets/widgets/widget_int.pyc'>,\n",
" 'prompt_toolkit.time': None,\n",
" ...}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%reset -f \n",
"import sys \n",
"sys.modules \n",
"# del sys.modules['ConfigParser']"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ip = get_ipython() "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'IPython' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-7fd4643b4d99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"After reset, had 1: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"After reset, had 2: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mrunipy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0;34m\"After reset, had 3: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mmy_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'IPython' is not defined"
]
}
],
"source": [
"print \"After reset, had 1: %s\" % IPython\n",
"print \"After reset, had 2: %s\" % runipy\n",
"print \"After reset, had 3: %s\" % my_var"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"After reset, had 4: temp value\n"
]
}
],
"source": [
"import runipy \n",
"print \"After reset, had 4: %s\" % runipy.tempvar"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
},
"name": ""
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
phungkh/phys202-2015-work | assignments/assignment06/LaTeXEx01.ipynb | 1 | 14005 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# LaTeX Exercise 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The images of the equations on this page were taken from the Wikipedia pages referenced for each equation."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"from IPython.display import Image\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Typesetting equations"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"In the following cell, use Markdown and LaTeX to typeset the equation for the probability density of the normal distribution $f(x, \\mu, \\sigma)$, which can be found [here](http://en.wikipedia.org/wiki/Normal_distribution). Following the main equation, write a sentence that defines all of the variable in the equation."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAN0AAAAvBAMAAACVsNI7AAAAMFBMVEX///+KiorMzMyenp4WFhZA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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='normaldist.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "19b733e9b9c40a9d640d0ff730227a31",
"grade": true,
"grade_id": "latexex01a",
"points": 2,
"solution": true
}
},
"source": [
"\\begin{equation*}\n",
"f(x,\\mu,\\sigma)=\\frac{1}{\\sigma\\sqrt{2\\pi}}e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"In the following cell, use Markdown and LaTeX to typeset the equation for the time-dependent Schrodinger equation for non-relativistic particles shown [here](http://en.wikipedia.org/wiki/Schr%C3%B6dinger_equation#Time-dependent_equation) (use the version that includes the Laplacian and potential energy). Following the main equation, write a sentence that defines all of the variable in the equation."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVYAAAAwBAMAAAC4W09DAAAAMFBMVEX///8WFhZ0dHSKiooiIiIM\nDAyenp5AQEBQUFAwMDDMzMwEBATm5ua2trZiYmIAAACeyoKRAAAAAXRSTlMAQObYZgAABuFJREFU\naAW9WW2IVFUYfnZmdnZ39t6ZWZLoR9r8Cf9YDhWYIXm1H2UFSh+YP2Tvjyg01Amkgr4mERQM2jT6\nIpchKFCCVfoRiOUE+aMCHQUpCJxr0IdaumBlhru393zdOWfundl7lHph73nPe973eZ8999xz3zkX\nuD4JwxkTwL2w1zSk7Y2GoZ/Wl/udXGblDszsb5gRx/BD07Sk7BX2f+SndOVuxWX7rPyBqxp8YUUd\naKNkCREhDFgF3gYcikJTKTpXlALAxfFGqsi4kx3XecCOOEY/i84188a7O1vAC/38+43Zcd0I3NoP\nLT6mc8ViNp6pxb3SWey4EubFdLjKy+D6FrNuRlMNWrZ2XIe3ndtll8Dg+sW92+D+9iGtg2sSK66F\n+cissEujcy1caZRaU2Foh9DxtuI6WEH2cic2jaZzzcygVE0T1MPHiuuLQLbrNdQDNjJzrvuZfIIR\nD+1GNGKvWHGlOR1eY5eDcS28x2QSowHO20Wb3jZc3Utg+axEXwPtFp7oCj591uJBs+FaoPv/TFey\nubo61/MozBSNACdwDxuGvh0brngUzvq+aPFBnesqZA88Zbi40/jLMPTtWHE90Xqy65adM8CpXOgW\nnesNcCYrpkPL+cc0qF6xoTTecuQurmbuDGf2O3iBRDHO7VWJsGR8HVOzqi/MI0053Gl0rh1r8QKX\nn4GhescKFMdvosc3rAF36maAI0uuybnxAUU4n4MXSGbwFp/192lGdw0KE1pfqMlccyEXuv8LzQgO\nezNtHTqSQlbzGs+NUxEXWSBFsDSdbR93AHsjE01HGXhb6ws1mat7hY9OIB84RshUhYqbBs1jSzMr\nZM41MTdeoxBfhPACqRPtfEZci7QrEL1IBurAhqinlGSuWM24FH2c/kaucU8ElOrAMVIHRFdcFTLn\nmpgbtHTyE8KbF0ha+EPENVc2y7uST2tF8xFqD65tBjyCQhhOCz9PNKNkr5NqLA2FLNZAUm6+m0yj\ncLYFsAJJl1eJ6+AEclXkw6tvPsyGnl79fAsCTffswTVHNwX0aEXiCW2gjKxP6n3AlvDreU1SO8gC\nPSF3fuvll4EbMTxcAy+QWJiSxcR1tIYB+jeOX/5ylpvP0HWkQpehMSb0E4JJD658wdIcRuIJbWQa\nw0ybT890+NItAbcqZME1KXexTI67sWkwQKxA2tJq+6UKRpv00p3Nvs8RH6DrcJ2r2qUHV7Zgabl2\nxBPq8FW8zrTD9HckWNpgOhSy4JqUO1cnt53AVFMUSCWxz9CVVQVtf3mTL89RdjeZsBdQscZV7cK4\nRoFKmaafsxO0XDXxhJ6ZcatMIw8caQlbhCy4JuVmKxq/ANt5wWIWSJkDbX8tPUpNGpMvHpexSuZK\nAzGhBdtZrs+OjR0cG/PIKX9piLsysCMNriJCFlwTcqPdJFfi+ilNQqxAuuvi9jrxbHS4FssOLdUA\n6dYraMFOkG8kntAKs+LNeYi6imuELLginhtn0AReQX46Q1Vnd4GExQGhDdJdUvM6UivSs1UlqyFs\nghJl9Ulft3uy80fAlfV0VVwjZMk1nhvvgML2IOMtgiqQtBOitk9oIxXB1XkEyFU2CfJk16Qn1/Yq\nzQvwZO9x0e6mhnE9WtGQFddYbqzM06yth7PShyyQtBOiJeNraQrpp747/qcH5yCttEmfuFIKUxTX\n77fWzYGcWQx6cnSvaBcCp8LHgG/LGrLkGs+NDWztr5EQvKHXIa0jKhOUOGo4Iy13q5GolVzdACsi\nG1cKNaPvGT0cVd2KVBiymlduiuXO1qUrb2h73wFWJkTynNSysn0wGlGK5DpED2hF2ZLaCdM45Mt+\nVbYM2eCK7ty5hnTlzUZ+QkRlQiRfSe1j0brmXDGj5DpaR6mLjgxNbhxP2Au+aDmyybUrN74Tnp3r\nRbHnK0M+EJr8743dXYxIrgM1lDxhSXfdLNzUDePIJteu3OYWCHZCJMqEKN89kcaURUaPdyRX0qcC\nZD3+Wo57xS3ZhmHjyCZXmLkzTT1AnBAVy7ptTr3DdVcTuRqWzxnR26GLa29H2pD4CVGu3s8nNhZx\ndaluKFWxNeaR3mDBVZwQ8TIhPX7ElSWi1/aC9KExTwuu4oSIlwkxmJ6GiOsecvkJDs3uNUt6rvKE\niJcJ6dMprsUqWrifqov0oTHP9FzlCREvE2IwPQ2KK/3orWMWQ+VKT9c5B9JzlSdEvEyYEzZykFzd\nBRd+Ddy/caIWREPWigVXcULEy4T0aSRX+ugXVovrftw42Ugf2+1pwbVzQtQN0qev1gBzoe31usSC\n6zXl0bnS9npd8n9ytdzuYv/Xf81V/3a8NJbdxmD77dgGW/jGvsnbQ8iIub/J/wtljA9zA0wI2QAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='tdseqn.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "4d858b55aeb9117b8cfa6f706ab5b617",
"grade": true,
"grade_id": "latexex01b",
"points": 4,
"solution": true
}
},
"source": [
"\\begin{equation*}\n",
"i\\bar{h}\\frac{\\partial}{\\partial t}\\psi(r,t)=\\left[\\frac{-\\bar{h}^2}{2\\mu}\\triangledown^2+V(r,t)\\right]\\psi (r,t)\n",
"\\end{equation*}"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"In the following cell, use Markdown and LaTeX to typeset the equation for the Laplacian squared ($\\Delta=\\nabla^2$) acting on a scalar field $f(r,\\theta,\\phi)$ in spherical polar coordinates found [here](http://en.wikipedia.org/wiki/Laplace_operator#Two_dimensions). Following the main equation, write a sentence that defines all of the variable in the equation."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false,
"nbgrader": {}
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAAzBAMAAAATJ63jAAAAMFBMVEX///+enp4WFhYiIiLm5ua2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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(filename='delsquared.png')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": false,
"nbgrader": {
"checksum": "625624933082a6695c8fd5512a808b77",
"grade": true,
"grade_id": "latexex01c",
"points": 4,
"solution": true
}
},
"source": [
"\\begin{equation*}\n",
"\\triangle f=\\frac{1}{r^2}\\frac{\\partial}{\\partial r}\\left(r^2\\frac{\\partial f}{\\partial r}\\right) + \\frac{1}{r^2\\sin^2\\theta}\\frac{\\partial}{\\partial \\theta}\\left(\\sin\\theta\\frac{\\partial f}{\\partial \\theta}\\right)+\\frac{1}{r^2\\sin^2\\theta}\\frac{\\partial^2 f}{\\partial \\psi^2}\n",
"\\end{equation*}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ledeprogram/algorithms | class3/homework/argueso_olaya_3_2.ipynb | 1 | 25362 | {
"cells": [
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sorting(number_list):\n",
" for item in range(1, len(number_list)):\n",
" number = number_list[item]\n",
" index = item - 1\n",
" while index >= 0: \n",
" if number < number_list[index]:\n",
" number_list[index + 1] = number_list[index]\n",
" number_list[index] = number\n",
" index = index - 1\n",
" else:\n",
" break \n",
" return number_list"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"ten_list = random.sample(range(1, 11), 10)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_ten = sorting(ten_list)\n",
"sorted_ten"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hundred_list = random.sample(range(1,101), 100)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1,\n",
" 2,\n",
" 3,\n",
" 4,\n",
" 5,\n",
" 6,\n",
" 7,\n",
" 8,\n",
" 9,\n",
" 10,\n",
" 11,\n",
" 12,\n",
" 13,\n",
" 14,\n",
" 15,\n",
" 16,\n",
" 17,\n",
" 18,\n",
" 19,\n",
" 20,\n",
" 21,\n",
" 22,\n",
" 23,\n",
" 24,\n",
" 25,\n",
" 26,\n",
" 27,\n",
" 28,\n",
" 29,\n",
" 30,\n",
" 31,\n",
" 32,\n",
" 33,\n",
" 34,\n",
" 35,\n",
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" 39,\n",
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" 42,\n",
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" 53,\n",
" 54,\n",
" 55,\n",
" 56,\n",
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" 58,\n",
" 59,\n",
" 60,\n",
" 61,\n",
" 62,\n",
" 63,\n",
" 64,\n",
" 65,\n",
" 66,\n",
" 67,\n",
" 68,\n",
" 69,\n",
" 70,\n",
" 71,\n",
" 72,\n",
" 73,\n",
" 74,\n",
" 75,\n",
" 76,\n",
" 77,\n",
" 78,\n",
" 79,\n",
" 80,\n",
" 81,\n",
" 82,\n",
" 83,\n",
" 84,\n",
" 85,\n",
" 86,\n",
" 87,\n",
" 88,\n",
" 89,\n",
" 90,\n",
" 91,\n",
" 92,\n",
" 93,\n",
" 94,\n",
" 95,\n",
" 96,\n",
" 97,\n",
" 98,\n",
" 99,\n",
" 100]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_hundred = sorting(hundred_list)\n",
"sorted_hundred"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"thousand_list = random.sample(range(1,1001), 1000)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1,\n",
" 2,\n",
" 3,\n",
" 4,\n",
" 5,\n",
" 6,\n",
" 7,\n",
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" 10,\n",
" 11,\n",
" 12,\n",
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" 109,\n",
" 110,\n",
" 111,\n",
" 112,\n",
" 113,\n",
" 114,\n",
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" 978,\n",
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" 989,\n",
" 990,\n",
" 991,\n",
" 992,\n",
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" 994,\n",
" 995,\n",
" 996,\n",
" 997,\n",
" 998,\n",
" 999,\n",
" 1000]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted_thousand = sorting(thousand_list)\n",
"sorted_thousand"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def searching(looked_for_number, numbers_list):\n",
" for item in range(len(numbers_list)):\n",
" if numbers_list[item] == looked_for_number:\n",
" return \"Your number is in the list at index \" + str(item)\n",
" return \"Your number is not in the list\""
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Your number is in the list at index 3'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"searching(6, [3,4,5,6,7,9,8])"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 9 µs, sys: 1 µs, total: 10 µs\n",
"Wall time: 14.1 µs\n"
]
},
{
"data": {
"text/plain": [
"'Your number is not in the list'"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time searching(15, sorted_ten)\n",
"# CPU times: user 8 µs, sys: 1 µs, total: 9 µs\n",
"# Wall time: 13.1 µs"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 12 µs, sys: 1e+03 ns, total: 13 µs\n",
"Wall time: 17.2 µs\n"
]
},
{
"data": {
"text/plain": [
"'Your number is in the list at index 14'"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time searching(15, sorted_hundred)\n",
"# CPU times: user 12 µs, sys: 1e+03 ns, total: 13 µs\n",
"# Wall time: 17.2 µs"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 12 µs, sys: 1e+03 ns, total: 13 µs\n",
"Wall time: 18.1 µs\n"
]
},
{
"data": {
"text/plain": [
"'Your number is in the list at index 24'"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time searching(25, sorted_thousand)\n",
"# CPU times: user 12 µs, sys: 1e+03 ns, total: 13 µs\n",
"# Wall time: 18.1 µs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
google/or-tools | examples/notebook/constraint_solver/vrp_capacity.ipynb | 1 | 11350 | {
"cells": [
{
"cell_type": "markdown",
"id": "google",
"metadata": {},
"source": [
"##### Copyright 2021 Google LLC."
]
},
{
"cell_type": "markdown",
"id": "apache",
"metadata": {},
"source": [
"Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"you may not use this file except in compliance with the License.\n",
"You may obtain a copy of the License at\n",
"\n",
" http://www.apache.org/licenses/LICENSE-2.0\n",
"\n",
"Unless required by applicable law or agreed to in writing, software\n",
"distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"See the License for the specific language governing permissions and\n",
"limitations under the License.\n"
]
},
{
"cell_type": "markdown",
"id": "basename",
"metadata": {},
"source": [
"# vrp_capacity"
]
},
{
"cell_type": "markdown",
"id": "link",
"metadata": {},
"source": [
"<table align=\"left\">\n",
"<td>\n",
"<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/constraint_solver/vrp_capacity.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n",
"</td>\n",
"<td>\n",
"<a href=\"https://github.com/google/or-tools/blob/master/ortools/constraint_solver/samples/vrp_capacity.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n",
"</td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "doc",
"metadata": {},
"source": [
"First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "install",
"metadata": {},
"outputs": [],
"source": [
"!pip install ortools"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "code",
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/env python3\n",
"# Copyright 2010-2021 Google LLC\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# [START program]\n",
"\"\"\"Capacited Vehicles Routing Problem (CVRP).\"\"\"\n",
"\n",
"# [START import]\n",
"from ortools.constraint_solver import routing_enums_pb2\n",
"from ortools.constraint_solver import pywrapcp\n",
"# [END import]\n",
"\n",
"\n",
"# [START data_model]\n",
"def create_data_model():\n",
" \"\"\"Stores the data for the problem.\"\"\"\n",
" data = {}\n",
" data['distance_matrix'] = [\n",
" [\n",
" 0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,\n",
" 468, 776, 662\n",
" ],\n",
" [\n",
" 548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,\n",
" 1016, 868, 1210\n",
" ],\n",
" [\n",
" 776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,\n",
" 1130, 788, 1552, 754\n",
" ],\n",
" [\n",
" 696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,\n",
" 1164, 560, 1358\n",
" ],\n",
" [\n",
" 582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,\n",
" 1050, 674, 1244\n",
" ],\n",
" [\n",
" 274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,\n",
" 514, 1050, 708\n",
" ],\n",
" [\n",
" 502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,\n",
" 514, 1278, 480\n",
" ],\n",
" [\n",
" 194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,\n",
" 662, 742, 856\n",
" ],\n",
" [\n",
" 308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,\n",
" 320, 1084, 514\n",
" ],\n",
" [\n",
" 194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,\n",
" 274, 810, 468\n",
" ],\n",
" [\n",
" 536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,\n",
" 730, 388, 1152, 354\n",
" ],\n",
" [\n",
" 502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,\n",
" 308, 650, 274, 844\n",
" ],\n",
" [\n",
" 388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,\n",
" 536, 388, 730\n",
" ],\n",
" [\n",
" 354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,\n",
" 342, 422, 536\n",
" ],\n",
" [\n",
" 468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,\n",
" 342, 0, 764, 194\n",
" ],\n",
" [\n",
" 776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,\n",
" 388, 422, 764, 0, 798\n",
" ],\n",
" [\n",
" 662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,\n",
" 536, 194, 798, 0\n",
" ],\n",
" ]\n",
" # [START demands_capacities]\n",
" data['demands'] = [0, 1, 1, 2, 4, 2, 4, 8, 8, 1, 2, 1, 2, 4, 4, 8, 8]\n",
" data['vehicle_capacities'] = [15, 15, 15, 15]\n",
" # [END demands_capacities]\n",
" data['num_vehicles'] = 4\n",
" data['depot'] = 0\n",
" return data\n",
" # [END data_model]\n",
"\n",
"\n",
"# [START solution_printer]\n",
"def print_solution(data, manager, routing, solution):\n",
" \"\"\"Prints solution on console.\"\"\"\n",
" print(f'Objective: {solution.ObjectiveValue()}')\n",
" total_distance = 0\n",
" total_load = 0\n",
" for vehicle_id in range(data['num_vehicles']):\n",
" index = routing.Start(vehicle_id)\n",
" plan_output = 'Route for vehicle {}:\\n'.format(vehicle_id)\n",
" route_distance = 0\n",
" route_load = 0\n",
" while not routing.IsEnd(index):\n",
" node_index = manager.IndexToNode(index)\n",
" route_load += data['demands'][node_index]\n",
" plan_output += ' {0} Load({1}) -> '.format(node_index, route_load)\n",
" previous_index = index\n",
" index = solution.Value(routing.NextVar(index))\n",
" route_distance += routing.GetArcCostForVehicle(\n",
" previous_index, index, vehicle_id)\n",
" plan_output += ' {0} Load({1})\\n'.format(manager.IndexToNode(index),\n",
" route_load)\n",
" plan_output += 'Distance of the route: {}m\\n'.format(route_distance)\n",
" plan_output += 'Load of the route: {}\\n'.format(route_load)\n",
" print(plan_output)\n",
" total_distance += route_distance\n",
" total_load += route_load\n",
" print('Total distance of all routes: {}m'.format(total_distance))\n",
" print('Total load of all routes: {}'.format(total_load))\n",
" # [END solution_printer]\n",
"\n",
"\n",
"\"\"\"Solve the CVRP problem.\"\"\"\n",
"# Instantiate the data problem.\n",
"# [START data]\n",
"data = create_data_model()\n",
"# [END data]\n",
"\n",
"# Create the routing index manager.\n",
"# [START index_manager]\n",
"manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),\n",
" data['num_vehicles'], data['depot'])\n",
"# [END index_manager]\n",
"\n",
"# Create Routing Model.\n",
"# [START routing_model]\n",
"routing = pywrapcp.RoutingModel(manager)\n",
"\n",
"# [END routing_model]\n",
"\n",
"# Create and register a transit callback.\n",
"# [START transit_callback]\n",
"def distance_callback(from_index, to_index):\n",
" \"\"\"Returns the distance between the two nodes.\"\"\"\n",
" # Convert from routing variable Index to distance matrix NodeIndex.\n",
" from_node = manager.IndexToNode(from_index)\n",
" to_node = manager.IndexToNode(to_index)\n",
" return data['distance_matrix'][from_node][to_node]\n",
"\n",
"transit_callback_index = routing.RegisterTransitCallback(distance_callback)\n",
"# [END transit_callback]\n",
"\n",
"# Define cost of each arc.\n",
"# [START arc_cost]\n",
"routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)\n",
"\n",
"# [END arc_cost]\n",
"\n",
"# Add Capacity constraint.\n",
"# [START capacity_constraint]\n",
"def demand_callback(from_index):\n",
" \"\"\"Returns the demand of the node.\"\"\"\n",
" # Convert from routing variable Index to demands NodeIndex.\n",
" from_node = manager.IndexToNode(from_index)\n",
" return data['demands'][from_node]\n",
"\n",
"demand_callback_index = routing.RegisterUnaryTransitCallback(\n",
" demand_callback)\n",
"routing.AddDimensionWithVehicleCapacity(\n",
" demand_callback_index,\n",
" 0, # null capacity slack\n",
" data['vehicle_capacities'], # vehicle maximum capacities\n",
" True, # start cumul to zero\n",
" 'Capacity')\n",
"# [END capacity_constraint]\n",
"\n",
"# Setting first solution heuristic.\n",
"# [START parameters]\n",
"search_parameters = pywrapcp.DefaultRoutingSearchParameters()\n",
"search_parameters.first_solution_strategy = (\n",
" routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)\n",
"search_parameters.local_search_metaheuristic = (\n",
" routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)\n",
"search_parameters.time_limit.FromSeconds(1)\n",
"# [END parameters]\n",
"\n",
"# Solve the problem.\n",
"# [START solve]\n",
"solution = routing.SolveWithParameters(search_parameters)\n",
"# [END solve]\n",
"\n",
"# Print solution on console.\n",
"# [START print_solution]\n",
"if solution:\n",
" print_solution(data, manager, routing, solution)\n",
"# [END print_solution]\n",
"\n"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| apache-2.0 |
ecalio07/enron-paper | dev/tree_percentile_parameter.ipynb | 2 | 5082 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tree Classifier - Focus on percentile parameter"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing Modules"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"from sklearn import tree\n",
"# from email_preprocess import preprocess\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run Variables Setup If Necessary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if 'features_train' not in locals() or globals():\n",
" %run ../dev/environment_setup.ipynb"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def preprocess2 (number):\n",
" \n",
"# words_file = \"../data/word_data.pkl\"\n",
"# authors_file=\"../data/email_authors.pkl\"\n",
" \n",
" ### the words (features) and authors (labels), already largely preprocessed\n",
" ### this preprocessing will be repeated in the text learning mini-project\n",
" word_data = pickle.load( open(\"../data/word_data.pkl\", \"r\"))\n",
" authors = pickle.load( open(\"../data/email_authors.pkl\", \"r\") )\n",
"\n",
" ### test_size is the percentage of events assigned to the test set (remainder go into training)\n",
" features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(word_data, authors, test_size=0.1, random_state=42)\n",
"\n",
"\n",
"\n",
" ### text vectorization--go from strings to lists of numbers\n",
" vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5,\n",
" stop_words='english')\n",
" features_train_transformed = vectorizer.fit_transform(features_train)\n",
" features_test_transformed = vectorizer.transform(features_test)\n",
"\n",
"\n",
"\n",
" ### feature selection, because text is super high dimensional and\n",
" ### can be really computationally chewy as a result\n",
" selector = SelectPercentile(f_classif, percentile=number)\n",
" selector.fit(features_train_transformed, labels_train)\n",
" features_train_transformed = selector.transform(features_train_transformed).toarray()\n",
" features_test_transformed = selector.transform(features_test_transformed).toarray()\n",
" \n",
"\n",
" return features_train_transformed, features_test_transformed, labels_train, labels_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Re run Preprocess with percentile parameter = 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features_train, features_test, labels_train, labels_test = preprocess2(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Tree Classifier"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"clf = tree.DecisionTreeClassifier(min_samples_split=40)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Predict Data "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"train_predict_fulldataset(\"Train and Predict Data with percentile = 1\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Re run Preprocess with percentile parameter = 10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"features_train, features_test, labels_train, labels_test = preprocess2(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Predict Data "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"train_predict(\"Train and Predict Data with percentile = 10\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
charlesll/Examples | Plots_example.ipynb | 1 | 40242 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example illustrates a few basic functionalities of Plots.jl for scatter and line plots"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"┌ Info: Recompiling stale cache file /home/charles/.julia/compiled/v1.1/Plots/ld3vC.ji for Plots [91a5bcdd-55d7-5caf-9e0b-520d859cae80]\n",
"└ @ Base loading.jl:1184\n",
"┌ Info: Recompiling stale cache file /home/charles/.julia/compiled/v1.1/PyPlot/oatAj.ji for PyPlot [d330b81b-6aea-500a-939a-2ce795aea3ee]\n",
"└ @ Base loading.jl:1184\n"
]
},
{
"data": {
"text/plain": [
"Plots.PyPlotBackend()"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We first call the libraries\n",
"using Plots\n",
"using LaTeXStrings # to be able to put LaTex string in the labels, you can call this library\n",
"pyplot()# using the pyplot backend"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Creating a fake dataset\n",
"x = -10:1.:10; y = x.^2; ese_y = sqrt.(y); # errors as sqrt(y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create a scatter plot with the scatter command for displaying this dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"ename": "UndefVarError",
"evalue": "UndefVarError: U8 not defined",
"output_type": "error",
"traceback": [
"UndefVarError: U8 not defined",
"",
"Stacktrace:",
" [1] top-level scope at In[4]:10"
]
}
],
"source": [
"plt = scatter(x,y,\n",
"yerr=ese_y, # error bars, can use yerr or xerr keywords\n",
"color = :red, # marker internal color\n",
"markerstrokecolor = :red, # marker stroke and error bar color\n",
"markersize = 10, # marker size\n",
"xlabel = \"X values\", ylabel = L\"$y = X^2$\",# labels, LaTex string for y, need to add L before the text to declare it\n",
"xtickfont = font(14,\"Arial\"), ytickfont = font(14,\"Arial\"), # thicks font\n",
"xticks = -10:4:10, # X axis values, same thing applies to y\n",
"grid = false) # control the display of the internal grid\n",
"\n",
"# you need to set the label font with the `guidefont` argument after the first call, as for this subplot:\n",
"plt.subplots[1][:yaxis][:guidefont] = Plots.Font(\"Arial\",18,:hcenter,:vcenter,0.0,RGB{U8}(1.0,0.0,0.0))\n",
"plt.subplots[1][:xaxis][:guidefont] = Plots.Font(\"Arial\",18,:hcenter,:vcenter,0.0,RGB{U8}(0.0,0.0,0.0))\n",
"\n",
"#savefig(\"./Figures/plt1.png\") # saving the plot\n",
"#plt # to display the plot again"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A concatenated version of the above plot can be made using magic areguments:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Keyword argument markerstrokestyle not supported with Plots.PyPlotBackend(). Choose from: Set(Symbol[:subplot_index,:markerstrokecolor,:yscale,:quiver,:foreground_color_grid,:tickfont,:levels,:xticks,:yticks,:xforeground_color_text,:zticks,:xlink,:bar_width,:fillcolor,:right_margin,:arrow,:background_color_legend,:linewidth,:xguidefont,:foreground_color_legend,:line_z,:primary,:discrete_values,:orientation,:zguidefont,:aspect_ratio,:zguide,:fillrange,:yerror,:linecolor,:guide,:flip,:x,:xforeground_color_border,:group,:grid,:markerstrokewidth,:scale,:background_color_outside,:legend,:foreground_color_subplot,:yforeground_color_border,:zflip,:ytickfont,:ydiscrete_values,:xlims,:seriestype,:lims,:normalize,:size,:zlims,:foreground_color_text,:overwrite_figure,:markercolor,:margin,:zdiscrete_values,:window_title,:foreground_color_border,:bar_edges,:y,:bar_position,:ylims,:contours,:ylink,:xflip,:dpi,:xrotation,:fillalpha,:bins,:zforeground_color_axis,:smooth,:markersize,:zrotation,:background_color_inside,:ribbon,:left_margin,:titlefont,:title,:link,:markerstrokealpha,:weights,:zscale,:inset_subplots,:zforeground_color_guide,:polar,:z,:projection,:match_dimensions,:foreground_color_axis,:xguide,:series_annotations,:foreground_color_title,:xforeground_color_axis,:clims,:yforeground_color_text,:yrotation,:markeralpha,:linealpha,:yguidefont,:markershape,:background_color_subplot,:annotations,:yforeground_color_axis,:html_output_format,:zlink,:seriesalpha,:layout,:zforeground_color_border,:xtickfont,:ticks,:zforeground_color_text,:yflip,:yguide,:xerror,:yforeground_color_guide,:legendfont,:top_margin,:ztickfont,:guidefont,:seriescolor,:foreground_color,:label,:foreground_color_guide,:xforeground_color_guide,:background_color,:xdiscrete_values,:bottom_margin,:show,:subplot,:xscale,:title_location,:color_palette,:colorbar_title,:colorbar,:linestyle,:rotation,:marker_z])\n"
]
},
{
"data": {
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\" />"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt_bis = scatter(x,y, err = ese_y, c = :red, markerstrokecolor = :red, ms = 10, \n",
" xaxis = (\"X values\", font(14, \"Arial\"), -10:4:10), yaxis = (L\"$y = X^2$\", font(14, \"Arial\")), \n",
" grid = false)\n",
"\n",
"# you need to set the label font with the `guidefont` argument after the first call, as for this subplot:\n",
"plt_bis.subplots[1][:yaxis][:guidefont] = Plots.Font(\"Arial\",18,:hcenter,:vcenter,0.0,RGB{U8}(1.0,0.0,0.0))\n",
"plt_bis.subplots[1][:xaxis][:guidefont] = Plots.Font(\"Arial\",18,:hcenter,:vcenter,0.0,RGB{U8}(0.0,0.0,0.0))\n",
"plt_bis\n",
"savefig(\"./Figures/plt1_bis.png\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Now we can even modify the y values\n",
"y2 = y-(x+0.5)\n",
"y3 = y2+y-(x+2)\n",
"\n",
"# adding to the existing plot\n",
"scatter!(x,y2, marker = (:hexagon, 10, 1.0, :green))\n",
"scatter!(x,y3, marker = (:square, 10, 1.0, :purple))\n",
"\n",
"plt\n",
"savefig(\"./Figures/plt2.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to delete the first y1 and y2 for clarity, we can delete the corresponding series_list"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"deleteat!(plt.series_list,1:3) # here we indicate 1 to 3 as we have y1, yerr and y2 as series\n",
"plt\n",
"savefig(\"./Figures/plt3.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can even concatenate that in a GIF for a dynamic view of the plot!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Saved animation to /Users/charles/Documents/Examples/Figures/plt4.gif\n"
]
},
{
"data": {
"text/html": [
"<img src=\"Figures/plt4.gif?0.35265591344386316>\" />"
],
"text/plain": [
"Plots.AnimatedGif(\"/Users/charles/Documents/Examples/Figures/plt4.gif\")"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plt2 = scatter(x,y,\n",
"yerr=ese_y, # to indicate error bars, use the yerr or xerr keywords\n",
"color = :red, # this is the marker internal color color\n",
"markerstrokecolor = :red, # to set the error bar colors to red\n",
"markersize = 10,\n",
"xlabel = \"X values\", \n",
"ylabel = L\"$y = X^2$\",\n",
"xtickfont = font(14,\"Arial\"),\n",
"ytickfont = font(14,\"Arial\"),\n",
"grid = false,\n",
"xticks = -10:4:10,\n",
"leg=false, # no legend\n",
"ylims=(0,200) # fixed limits in y\n",
")\n",
"\n",
"plt2.subplots[1][:yaxis][:guidefont] = Plots.Font(\"Arial\",18,:hcenter,:vcenter,0.0,RGB{U8}(1.0,0.0,0.0))\n",
"plt2.subplots[1][:xaxis][:guidefont] = Plots.Font(\"Arial\",18,:hcenter,:vcenter,0.0,RGB{U8}(0.0,0.0,0.0))\n",
"\n",
"# to create the gif and record the frames\n",
"anim = Animation()\n",
"frame(anim) # we record the first frame\n",
"\n",
"# we delete the first series and add a new one\n",
"deleteat!(plt2.series_list,1:2) # here we indicate 1 to 2 as we have y1, yerr as 2 series\n",
"scatter!(x,y2, marker = (:hexagon, 10, 1.0, :green)) # adding the y2 serie\n",
"frame(anim) # recording a frame\n",
"\n",
"deleteat!(plt2.series_list,1) # we delete the y2 serie\n",
"scatter!(x,y3, marker = (:square, 10, 1.0, :purple)) # adding the y3 serie\n",
"frame(anim) # recording a frame\n",
"\n",
"# note that the last steps can be embedded in a loop easily, look at the documentation for further details\n",
"\n",
"# recording our gif file with 1 frame per second (to make it even slower, the trick is to record multiple time the same frame)\n",
"gif(anim, \"./Figures/plt4.gif\", fps=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.1.0",
"language": "julia",
"name": "julia-1.1"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.1.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
deepmind/dm_pix | examples/image_augmentation.ipynb | 1 | 682778 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "image_augmentation.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyNrBot+Uhycfb5VdzPFTYhy",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/SupreethRao99/dm_pix/blob/master/examples/image_augmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# PIX\n",
"PIX is an image processing library in JAX, for JAX.\n",
"\n",
"## overview\n",
"JAX is a library resulting from the union of Autograd and XLA for high-performance machine learning research. It provides NumPy, SciPy, automatic differentiation and first class GPU/TPU support.\n",
"\n",
"PIX is a library built on top of JAX with the goal of providing image processing functions and tools to JAX in a way that they can be optimized and parallelised through `jax.jit()`, `jax.vmap()`, `jax.pmap()`"
],
"metadata": {
"id": "cD0DILmSxZQ-"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "7_CCCmHTYz1a"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install dm-pix\n",
"!git clone https://github.com/deepmind/dm_pix.git"
]
},
{
"cell_type": "code",
"source": [
"import dm_pix as pix\n",
"import jax.numpy as jnp\n",
"import numpy as np\n",
"import PIL.Image as pil\n",
"from jax import random"
],
"metadata": {
"id": "fNBWNMsKZsL0"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"IMAGE_PATH = '/content/dm_pix/examples/assets/jax_logo.jpg'"
],
"metadata": {
"id": "5sp3mN5Majwe"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Helper functions to read images and display them\n",
"\n",
"def get_image(img_path) -> jnp.ndarray:\n",
" return jnp.array(pil.open(img_path), dtype=jnp.float32) / 255.\n",
"\n",
"\n",
"def imshow(image: jnp.ndarray) -> None:\n",
" \"\"\"Shows the input image using PIL/Pillow backend.\"\"\"\n",
" image = pil.fromarray(np.asarray(image * 255.).astype(np.uint8), \"RGB\")\n",
" display(image)"
],
"metadata": {
"id": "7lYUmPNLZ79Y"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"# adjust_brightness\n",
"Shifts the brightness of an RGB image by a given amount"
],
"metadata": {
"id": "2P17WoUBd_le"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"delta = 0.42 #@param {type: \"slider\", min: 0, max: 1}\n",
"new_image = pix.adjust_brightness(\n",
" image=image, \n",
" delta=delta)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 208
},
"id": "SKUQmZZ0bZ1r",
"outputId": "bcde0ccb-d096-45fe-f9b5-526cf85d77bb"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n"
]
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B4B59FF90>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# adjust_contrast\n",
"Adjusts the contrast of an RGB image by a given multiplicative amount."
],
"metadata": {
"id": "TEnEakalgEqD"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"factor = 0.42 #@param {type: \"slider\", min: 0, max: 1}\n",
"new_image = pix.adjust_contrast(\n",
" image=image, \n",
" factor=factor)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "bAE6EJGMe-aS",
"outputId": "ba6e527e-ee89-451b-e670-928eb8d1e9d0"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B4B545BD0>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# adjust_gamma\n",
"Adjusts the gamma of an RGB image"
],
"metadata": {
"id": "P8z9ljhEhbWF"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"gamma = 3 #@param {type: \"slider\", min: 0, max: 10}\n",
"gain = 4 #@param{type: \"slider\",min:0, max:10}\n",
"new_image = pix.adjust_gamma(\n",
" image=image,\n",
" gain=gain, \n",
" gamma=gamma)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "gkPRBRuihLiZ",
"outputId": "e3e66344-230a-411b-b40c-57383b439039"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B4B59FB50>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# adjust_hue\n",
"Adjust the hue of an RGB image by a given multiplicative amount"
],
"metadata": {
"id": "l9S3abpajp-A"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"delta = 0.7 #@param {type: \"slider\", min: 0, max: 1}\n",
"new_image = pix.adjust_hue(\n",
" image=image, \n",
" delta=delta)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "4wSmkgUIijyb",
"outputId": "fac3dd9e-9e21-44e1-bb92-becaa51d0588"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36CE8F10>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# adjust_saturation\n",
"Adjusts the saturation of an RGB image by a given multiplicative amount"
],
"metadata": {
"id": "wwBee5SVkiR8"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"factor = 0.42 #@param {type: \"slider\", min: 0, max: 1}\n",
"new_image = pix.adjust_saturation(\n",
" image=image, \n",
" factor=factor)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "pdaz0a3dkgXu",
"outputId": "b31b2986-516b-4a99-acc0-25af75541187"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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9OQGHmIqo2TDnPLoc5jpvZJrdcjd1kO4pVnTFlaqRXUBEZEhGrpop7JV7nV7tpD69HTezQdKZajBk7y+XmYXdPiciQRKWPpydno1fY0d/bSK30ACzRBABQK3WSteYDiJDpHGB/6N7tNYNhmXtp8Pvjwt5QCg2aulq9aNbdxwssseQOICeqv3b3o/+zaAoMphYV3QemuyXo2vh0l7FTJeYf1XIt/OGaBCR4VG8JTJsCJXTFym9pQmCUDttMEmILIa5DQR3Pyd0QbTfNgVrGzJdHfH1yMrdJUmUga4RM7822VBpR7sC1DQtW9Faus9JrjJhH+6N2YyWkNBoNo/+vPuScytQ/vvD/eX5hcWpKSt4ngi9NfEz3E+nnzffhJeChnEGz3IVclY55zx2I9yraWa6RERU23rqZbojNW0j85hkeiAAwEScZ7K52kmDiQIAcINKu+XoeliQBMsFekaqX9zxkBbAOVdC8trT5ehUFCxI8DX9imRZ88G9nVK51s63ZA6MJtM8+72ciboISu1vdr4rNepOfoxmp/0iefrJvYcCY2gVlfbYumHo+pe7L4wlkP2enKlAicjBmUBpr+z+tvymmi1muWJM8vCIyPSUEqMqL6d+SVuIc2SMsVahrbX1+HrEvIyd4ZoLOjYxBq5fEQCmbyUWtxYESw+9loR/USKAXk9NlbS2LiF4Bs92Je0lRMEv8nT+9feHB/3VQoCIr5OnPr9/e3mFiFylxDwjZCxTLD7L78Q2o5MwobVrIUxvx+upptrul15hgmBoRupFpoYVFMYcvDvbourrnRwmuyXVnbYQEMv71fBiSAnJnBtE3M4X/vYgTCLunjnnFEj41p+shAIB58trJvzVyWEEIiCeLzUrXZ+TKNoTl1Q/GJwJxHUfq3+180PLXS4PAQB0rn9/ePDk9l2fokze6RA5yu3XOy8rkWYg5ud8TD5EAM55YMYn+oTacX3oETHGGulm6jTds52rV23d4SgQqdgtFF6XgCyPC9lV3Lr1XivXTmzFwOWxvGxMPQDY/CwIbOG92bnlKdd2d62O/uUIEZutTqoKKhfN7f6Sdra3k23EI4CgpL86+v7V6antp7KuMP85Keabmvr+1jZ4Bx+1OrAXWaPZ/PL4eWQrJAjjLzYm4NRWvPymqmuGS9l2JBhmX+SrenkMFKaTvAcZa8ut1OtUr64CEtoE9qG0etRQwlIg4bvSK3JtGcg5Dy0EVt9bUuSBtKjXTPgXI4NrmVK7pvocs96EGFG3kkZEjEASmKFl/7z3wui3bP1r90jP9nfvbWzFwpHJDUJDw7H6Y+znNwdvKBOa9fOrszoRN3Q9thY1VKORaZ1Jl2had6FT6Z7upbrKRdnmR7fPzco6xCU9V8mWD2ruE7UDlENEtatWj+pT2/ErJXx1R/oLirj8ZHF6LuF0bv5zzYR/MapWW7mGxIkJZ3CJY/rlibiFdSZE5IgB1vpx/7vjfM6+YsRd5WbjqJj/+O69MXq8gNy21p6q/fv+L8qmnwnDQmzoM4yYOypBJbIcKu5WEO1oLFegumOrLOyUCo28IeqXfHrcShvFAYAJrI7V5POU2lFxsCC2MyRBEJqZDiFElsNkWAiB/igHU3G7PjMHK5NYj6zcXhCYOGSdu2bCX4mGVoaq9lKlXlP1Bt8MTryPrTsZnCsC1epv/rz3qr8mztnCfzx8szgzszwzC+TKqW/GB459RnV2FgJkeJBK/VjZm7oRN3QdwHUMc30eShjhxBrHN6PdSq9X78fUuk0pzL5L7/LT58m21ByB7xs1C7Nitrln9aRuKp2sp1pmrU/3rO1BIudc07TSbiW6FhJ8IlHfkDbU41DvpmNDCctrT5disah5x+BIrunXIRd0kIgK5Xqx4wf02CnvhA4KjPmExjd731dbrVFjGaiU0u52fjk9/uTefVEQwbWyTGzBeGpqX77Zdomvdl/05rkv5ANuDXVIerhFovk3IvBFZX/CVzqomlzpYj+7cAUAWFofVY7r6VzakLUBfzoi2Lw6mgSo8Ur+ZZEbI7RlR44hoiAInXK319BMd4VzzcjsUkM0e3d6cXMeRqkj10z4K5HlIUQEgG63m6sLPUPyNqm2RUQA6BONVPbVD2/euL52pWDp+yQAAJCx16mUrPhurayOsNCMKQxxwPuPmC2Vv8m+jG1FTQlCQzYi9zj7gpFPbcfqyabW1oYKPzhGLEufZAIicp2nX+SqrApud4Xp6CfOzti9zD2rp3ROD5ItO6k2nC//ze/L+9XQfFAJy9C3aV/ER5zz4HRg/dFyMBCEUerINRP+imQuPm7kyq1yVzFrfXrfCWNAXKLyn17/0FF7DigU8dyCmURgcOPZwe6T23f9iq/PPJO5S2iokAPQNzuvCsFaKBEcYE+wh2f9bn3QNTW8EBIVsXZSZ2yE3dhKrOZiAMZYM9tMHSZVuUtEFm6Wc/NefgZfxjkHCUqdYmGn5PZJnHeqNOVht9FrZdszN6cuiW1gAlv4YHZ2afrcCy7TyjV5QiYgptHqZGqCrgueZ2NwaksEZO3V8Y97mbT5q8X8LhvDEJkjOS0Wyp32h9s3R0uoK9IIYcJYs9X609EvgU0/Y+i+ZgBTYnZOJCpSYiNa3Csb+lDFlT4SelSnLPuyUNSKIIJT6NvVEQ5d35Ibpy+TTq3Pi8/AaKfxLR9WmR/9CYVzfoHLnog459Gl0NqDJVmSz2v2mgl/PUIAXdey5W5D8xngfTA4EwROJDAyuumvdl8YhtulNmz2cD731T/Abw/2bq/fmIpEYFBfHYOG8Kv2ENnzN2/2tVR4IchtAQUWOw3wCRFFV8N6l7cLXbdbwgRvntcXESFCp9pNvk525CbA8FbinJzNz4asZ4uZymHdgrC/jUwXCyLqqlE9rE9tx5nAHNzpyIcg+aTVj5YTM7ELmr1mwl+VKtVmpi7qfPyifxcQNwyGLCR2v9v7LlUqDpz6RgXUmgFufQmAUGk2Dgq5j+7et4SjR55Dd7+arv9x/xdxTZJk0R1QMjgqkgNSdDlc3C2dMXMO8SqHM7NDxMJuKVfLGZIxUvJbX9q1PvWudlnsi21MEgShmWkBYXS5H10xdKXZy9RWbHF77uIwq2sm9JguCMvr9bq5qtHWJMc06Q0+xiZkgizwSvXg2cGubUsctkMM34JDv+JPR29mp6bXZmaByPyzt9EVyNhhJvNDeTd+I+qWhJwbbo9FYivWKrR7Df28zcp9sTN48wNjTO8ayZeZplBDx7OOjmJu3a4raiqVaWW7ZrTEJYkTmQzGORV3y5HVkOgbAU83v/FFlLXHS5FI2IQvmWvj2jr6zukc6wcR8VKtXej43G6JSZCiZwNMAciHtS9ff1ttNswLxkhP1FXVn44PP7n3QBKtzdvTtN9AnAPRl7svm1MdJSg7+qEjZDjn/rjPF/dV3tTHOJiaMfiCINSOa5lsRpdVAkLGbDawSaCyVsq+LGia5oiyy+yJ2AeUsk6l26l2ExuRIROWjTjHuQcz8zdmGWP2FmD5cofomgl/FSJsdzrpCvUMaVKLh22AEVAAx4wBgIR+0TjJvvz5+NACQwKOgZlGxL10CiTx7to6cG46PCYc8FAHgFiqVr9KvoxvRUf9naa247WjuqGOk0q0H4NvUPpFrsrLeAZmRkRduZ16k+6UOoIgOKrjGN1VD+qh2aAcFvsWZWQ24ty/9v5iwB+4sAGAayZ8l2RKKg4AnIx8uVNuywO4inHDCpAxQMZdph1TaxR48U+vf1J13foOx/TwceLfHux/cPN2MPD2BTQu4Q9v9rKBaiDhcwNKOeehhSAyqCUb4+1WTugQY6xd6JwepXpKhwwDXEdHLuqFViH3qkCTlRNFRLWt1ZKNmZtTZmYgJ8CCCbj84bzplnjrNK6Z8N2RqXswAKg3WpmaYIDkVoiQXQkJPEhkpW8ypSIBBiX1+eEPB9lMv/0Jlle6XCw0G4+2b3mYnLtPRIDQbnf+bf+n4GZQtI9kRCTKQmIjVjqojgSvXIZwQPBh7mWh2CmQSIBWpmNEbEmt5Ku01tZHuh/f1v6wk6N6XBcUDMz4XJZbjK1Glu8tSqJ8GXX+mgnfFTlvV9e1XEWtq5LzPsbJ4TuyCwRkjBPJDHrt0z/vvuADwOiJuvnuzf726upsIuF1dIVtDmLs9fHRrpYKL4Ycd0VsPao2e51i16Nku6Q2tOROqqO0iFuwWF1SM4V05bB2GcfgiBb7NljL5WNoRuWwntiKC6IFghUVtvJ4ITEdMy98a5vXTPjOyHq7VKm3Mw2Ru2LdnL1xwtXN7QTBfqH57f732WrFatYLtqm1mrvZ9Cd3H0ze1DA525Nh/HHnF7YiSIrIOZeDUnghWNqvTT5+d1BYab+SLWdIISISRKEh1NMvsoYdlzhGdJVJnBsEVv6LRqbJVSO6GjEMAwkT27GVOyvCpSsLXDPhuyKT0XqqmirrHcOHLp+BYz2bxOJBRAwROCgCL1cOvtnbcdkGPDClIOIvx8exaHRzcemdhBoCAOJJPvttaSeyFgLTLZFrm+CVCbMf2ZEZHBG1rp58ka5jBRG7Uvv06LSVbb8VJnqJLkyIucnDrLRXjSyHZL8kR6W1J8uh0IiiWufRNRN6TO7VSsSL5VaxKQEhARIgICNAACRATmB+Od6P2RQKTGG1P776rtntIHrMLKqu/Xh8+NHd+5IgACfgHDhHsj4Mfb7CD3GwbyTD+OrVi0a8O3UjpoRlR0v0JJOv466oJ1vpdFoNdMtqKfeqSIRuI8pYLQ+dDKFbUzvl7vR2fOHh7MLarGkLuKRIv06D7zGhhRElIGy325WmLomCBF2v2ncb9BBAZrR/+vzF6Yl9whm+ZkJ6k8vcWlj6uw8fp0pFdkZ/HhG5d1kyU64SAnCiCmve/mj96I9JracPyqhLAVku7AYAgBs88yIfW46k93PdqooIgALA29M0nUdOAEcfyMqN8n51/W+WV+4t+X1+1xTeTtdM6DkRAgKgwfVCtdfSRIXpXvvaLGIIQbGhGh2DBrCLg5DRq/Vs8RWRGYpkHpn++dPP/8/vnjW7HRzEiE3AhAOUrZQPgilUrXB1B8M9uU8VrGMbGqrRU3taR+PcEAQBaNy0U9bYOMCAzsyQEUdJEadm4uYll3/n10zoOVmPvt5oJyusoQUmyS8/ugNEE28dkjrf7/wgCLg1v7ibTnrSuMVUplxluBCL//0Hjxamppqd9v/32ddDLO2VyCWCk0Lu/7n9H7qVHpEHhiX3wExZmtiKFvcqgVm/P+3rNVWY7OR8VoRyoPhKcPn2kiiYPHWFxq/PhN4TEemGli51mpoCwBGIESF49gPEibjEeLed/LeXP3+9+/r9G5uyKJ5du2OuM7Kw3Qzw7x68v7WyGovF/uV3XyzMzJgAM2DM/EFBcD5P8oMC7pyevuwcRZZChuFBoLM7bN9Mlyj5xdwvhXa+E9+Mgh2LOB71o/v735DkF9eeLsenIq7vLruVXDOh94QIpUqr2PIZti/CQ/ilBQdBFhDb3+x+W2rWT8vFWqvx3o1NW1F8S2Tq2wmtlBBbi0v/4b0PZFlGwNs3tv7hwyfIGI3lXntrl9ww/rTzHFYExXdu3N0lyZy2M31BZFNb8cph3dB57bjhiyh+G6Yz3iz60f2uL2duJ5ZuzjN0gAdXaPyaCb2nbrebKesdrkyAiDmXEIAAfSLli7vfHuwBABA929/bnl+MmtkTcAAPPTYpsvyHDx4vzMyai1pR5H94+tHmgu2u8BxJg5gqFL7Jv4puhB1/+Bj7iIMdNYlzHl2NGKrRyDQFQVC7WvWoPnUzblp/xtunzuii6Isp649XQsFQPzvVVR7+NRN6TARGvtwqtmXz4P8uYF8MwYfVP77+zkmqXW42DvPZx1vbA1lkxicCgA83tp7euS+K/dRPy3ML//zpZ4okmQb+Cdof2SEAwNevX9bjXV9ENmX5GPuIO4EyEUl+ObIcKu1VkRgiMsbqqQYARJcj4z2hUY4Nvvje7NzqzDjNAcA1E3pAgxFFrVYnU2MG+MxSn2ZAw9k6PpchN7qtbwoH9Anq3unPr5InbmzXj4dvZiLRpampseYw3HMsGPrDk6eJWMz1JUmS9NnD9x9ubHkbBglgK3aI9Vbz349+imyFJ+HxfmAhsamtWKfc7dZUYNbGxDmUdiuR9bCkCDDKRHrx3M6WWAovhlffX/Ipvv5ErkjXTDgxYf/MZxh6ptSpdWWdm9k1zfAGuCjf3nmtIrpPks67FxGYXvjj6x91YyBsvN3r/nx09Hjz5tlUwlcmgs9u3b1/Y3M4URPAzPT0v37+N5FQ2HtAqU0/Hb45ZvnwbHBkgowrbGeEckzyxeXKQc3NWYjQLnW6lU5sPWLCSS0QPFn41cvwkbUnchJlceXRgiup9jh0zYRekB0qX6u3sjXRrPUJjkvNC8XNwmEBhmT1h4Pvjgu5oQsYYzvpJDK8ubg8kaQiWJ6Z+ccPHw8FwllQO8APbt3+7O598N42AwBAiGqv98f9n5UNn+gKeHdmxGw82kWNWNXtDTNdotrpV3FyTsvVw2ZwLuCLKNwuiuykyoZLeCfNLjjx2Hp46c68KEyUxPmaCcem4TelaWq61G3piifZKwblACPinEgRqF5/8+fdl2f3ayIyDOPb/d331m74r1hlye6LAEAQhH94+MGN5eUhR7zTXygY+ufPv1iann4nUU4AALiXTP7cOIishAzDcKrVD1xxvmZholg45+GFkKhI1eOaC4LDnKD4bqPbyLSmtmJnwzUQ2QUZgJxZE5ESVm58tBqLRyb0BF8z4dg0pKtRsdIodgIcBqpDT16GFam/8fuF5tc735ebDfPXYSZHSJaKxUb9/fXNt7frMuHYEgCJ063F5c8fvKfIynk7CCLeXF3/x8cfeaD3jh4SEedf7jw3llAJyJxfrcqvyWmiJMRvREr7ZXDd7SR9NKdcPaqJATEw1Y8qti/gF+yeZiVz88qp2/GFrTmGwoR4qGsm9IY6nW6mQh1dNBGJtqGBXZDt82JyrDK2NotBGdP51z8c7pu/nr3F7OXb/d2NuflEKAxw4cHNQpkOaJUBn+8Pj5/OT8/AoL97iGRF+fsnH20vr440bIxH6OQmRgTEbKn0deZlbCMy9Ogu+SSjaxGjR618h5APi3QLDMSMnlE9aiS2YpIkOWm87QvOl4RWU8yf8G08WQv6A1fwyp9D10zoARE3CtV2qaOY7GdqRO4MmRMen4hzIC5S8cvX37dNt8RgThRX+1TrtPcy6cdbN8EJabwMIRLRo82tx7dum0VRLqbF2bn/8smniuxZQZshPiGib3ZflqNNf2ygHqAgiPAWdZQkvxhZChV2S+SqUeW+AJEBEQqskW4Qh9CSVcDUNp/yt54KGYOF92dml6Zg4pA0uGZCT6jR6iTLoJNkp8Fkpo9rojOhOzEXE0Ky8frox9eplJU532VBcCAyZhgeEf/5+DAeDK1Mvd1zZWZGAQDifCoS/f2HT6PhiKvp0UMDAkEUP3344aObt8ee4GgynatEyFij1f7T4S/hrSBjbgyaAW8zzExtx1v5ttrQAWjkle7qosXdcnQ1IvpdoL+3vTUiii5H195bUWRvKhxfM+GkZBh6ttxpaD6n1EHf2D2hAAQylVIRSe+lv9p9bgxneu6HL5l3EBEAdtXeT0dvnmzdFC/MOQsudZQx9jd37t/d2OibMeA8pcyKQkrEov/6xRfxUBg8gnE7GqkJmgPEX46PjiEfngs4gNJh7fTMdPxxnxwRK4d161yA7AKxiQidSrdb7U1txNDhhQvnYibCWfpw1o6W8ICumXBSqlSbuYZkkMA8iutxyHZAYlhWfzj4IVkqvu0Om38Y7qZTOjduL6+cH7JDdnZfAICV6Zm/f/TY7/Odc/GZsSEyxh5u3fqb9z6ASQE6rgENjkzt9f7b7k/iDUWUhJH7mh30xG1+o6nteD3Z1rqas5tcLDYRsbRXCc4EfFHZDiAZOkMO8gin+EZ0+c6iHS3hAV0z4USkqr1sVW+o4nmq2yRkijVZoHJl7+u9l5e5w/mXE3+2v/tgbT3o98NghKF9MZooUwAQUPjH9z5cnVu4qi034A/84ZNPV+bmgAYDtrzajBDfpNM/Vffi67EBFh2+ihFxw9BDC0EU8LwqTuf0gHqX15KNxFYMBs/wjrHUuZiIlKiy/nTFrvVpfT32/Ey6ZsLxiYhKtXauKTEmee63ZmD6jsmHta93f6g0m3BFgZOplLOV8ocbA+4KJ9LCJLPBOyurnz94X5KvHL6AiJvLK//p8UeiYJnp0RWO6BX9aedFd07zBZWzwtDNmVa6xN3y1dMlUu2kISgYnAtwzl3mtOE8NIg4cyexcGN28PZJJ3vNhONTp9tJlbnKZU8SogwRB46EAZFOc69+PDoYLz/ftwd7q9OzUyHT1jLseTdbCyi+/+7J05lE4vJNk0vuybLyd4+e3l5dM+MqCNFLnYAIAIqVytfpl7Ez6brdGBcAiN+IaS29U+4xdtE58Cw5aQuntuKCOOJex2rqTyjrj1b8gQAMb4gTzfmaCcckzo18uV3pKn1LpffRPSRQ8cvdH7uq+vZrz/AnEdTbrdep5NObt8xL+ldaTkICgCeb2x9u3RIE4eKFNPBHpwUAAFicm/vnTz+3zpNEptnmrQO+FNmTerb7OucvBxN+x5I0qPySHJJC84HiXvlsBqeLXO82Mcbq6YahGrG1mB1q6KrHBgAAgigsPVqYXZq2H7Qb2juRMLxmwjGp0Wynq0znIhKhFxkZhogAQ7Lx4vD7vVT6MgJw1EkJEPH5yVHI51+bmTMbBbCyq5htTkdif3jycSQSAXiLVoWjPqNtyfjo/oPHN2+DHTXirToKiK12+09HzwObQUQyvRToOq0hQWIz1sy2zHSJQ4oJu/DxuZL5CsXdSnjJLwUk4AO+DQQATpGl0NrDZbPWJw2VIb5GzPz6pGlattxt6n5OHBiSGUfrKYyLga52Tr/aeW7wy2zlQ9S/XtW179/sP97clgQ7MtAWgwj4t/ce3FpdG49niMjxKMQi0X/9/G+norF3lTafsReHh/tqMrIYdh/5iIhz7p/2+yJK5ah+HrtdBBzqn/egV9fa5V5iI0qDnExEgk9cfbroZK+wIlw82nh/LSZ8V4EvvxL1zY4EAFStN7NNWefITN/cGQXm8nTOXRiW1O8Ovs9UK7YN80pPcGAtHuazXVW9t7LqMmNwAFifnfuP73/o9/vH28et5UsAAIyx+1tb/+G9Dwb42TvtAIn61UUVySW+EBnGNsKVw5re089jwosn2Pfdc6N6UPMnfL6YPMgamNiKLt2c79f6dG1nk9OvKQlp4J931Me7iXPrHwMQVFXNVHlblRDsMprs7fE159HQXcQ5oqAIvFY//GZvxz0EojHztnHOvznYvbO6HvJZ7gpEJgniP33waGV+YXAwV+/AlqsBf+D3n3y2Njff5z2vlFI0kx3DUTb7fXk3sRF3xmlWhBcEsZFujgcYIFfFeUEUta5RTzantuMADqoG5LC49mQpGh22DHlFvwYT2pGSttx/l12dObR70ahraRLxQqVZaivg2K+dJTdWt/1N3RQpggBAPlb/0+tntVbTfaVdZHIcylUqqWLhw40tsune2vqn9x5K0gD4c2zgi3njjaXl//zRJ6INPUV3SvAJN0fbK//l6xftGdUXUsxZyD4pfiNW2i1zPnbWpiFYKVWPa4LEwgshbtWu4wsP5xZvzE80/gvp12DCoWfjrbAi539WVxdB4McjtzbY7nQyVezqAvF+yTsAQALCcTTSvhsdAQA4J5+gHaWf/3x0OLyqJpgWInx3sLcyPTMXTxBR2B/4z4+eTsfj3m5XkiT97YdP7t/YIMNA6mf3sEYwKSEgluu1L5PPo1th0xgWvxHt1nvtctc7SxByg8oHtcRGTJQEbhihueDq+4t+n//daXB/gTOhtxHZ55yWvH5ihADAuZErtcsdGQDBUkHJTLhgHdTH00h5H6iBwEWj+OXOT5phDG5W5qoefwbNbufFyfHTjW3G2Efbt97f2mZnoyXGbd8Z2ezU1L98+nk4GKLB7z0jxO/3dzNyyT/lFwMsMOsv71cnae/sCBljrXxbbavRtYgoCksfzM0uJoDeoQb37pnwjPne8x1llJbm9RNDAIBGs5OuiQZJZHEOI0BgglOeZczSLkywisMQhhXjxzff7WXShm5YLG5w4pbtx/zmqj/mXUDw/ORIEsVPbt7+/eOPwqHwCJ1h3MfmbKyCIDy5d/+j23eJxqoVc4mfVqv13/Z/9m/65u7NNtLNXlM9L1riSiN3fyai0n41tBiYuTu9cn9ZEhW3X9Rzeudp8Amop/Z0Xb9Kcv4rECISISL5FN9lAuGuTpYlXtfVUrUhMDkstxggIhKpg9fx8QykJpkBUAqrERhPt2+j+xSNSJybXvCxsgBaiGQijoz969PP5uPxZruFIHDiji5xVQusQwyZO/8SIv7hk88AoKeqphXIG3h3f/mQyARhSQy3A9kfC+hFF0ScMdEd/9mtdtuFzubfrMUTfXvMOxKG75wJNU09TNVLbcl5UpOUpDpLiExgelRozc1EE/EoAHjN7ZYlvlJtFdqKwREAuOVnGyI29kogACQelNpvyqcU8M8Gg5OMeKBlAgBu1+ujWCic67T+7bs3TgiF/cE81U7cHUBYUT6Z3fxfnv4PACCgmQzCGxFirhxE5MjrUqXYKjGGVn3qyc44Z9ckE0QmMODemxjOkrdMOLz8iahca+eavo6hAE70Ms7b7RAwKtd3M91amz8K+SVJHu+huTdaWyr0G+r1eumKXu6EPDkanJWZCCgLRqeZ/C592uh2L0g0NFZ3xMyq2oKwFI19lzpN16vedeGSoIjE+ePF1Wl1yt/1bB8Z6AyRc64F1OxOvtdRExux3KviGKXn3Q2evZcIfDE5kPAf/LejYDgwOzdtfv2OGNJbJrQVZ3uo3W7XrFMLwCc92hIJwAzgQMSYYO9bqAgq77VP81qlaSzP1ZcXpsZ7UsNDd31BRIVSs9zxAyBwAxkbT/9xWjdbtQMaLBNGUGx8mz1p9XrCxLmh3IQWqBoRaCM+3VW1XKPuaRd9HyoRzYbDTxJbvp73tagsIgIJir185mWeq3zpybwvpfQa41dZGtLObGFLU9vx6mm9dlw//SUVS4RlSTFtY+8i0eM7MMw4AoUbuVKz1FbwjJoz3r7FTeXAhhQhIhAPiZ2TbMfgWGvT66NapzN+OU57l3CdwREAoNVupWvY1hAdm/tYCY5sXxfZUsg+qgH6RT1XPzqqVMZ3BZ5D1jwYhBXfUjS6U8x7vYpsJCqByNiDxOIizYIHhZUG+3C880RtqZncTfVqWq+lNjKtxFaMWdvi+GccsmOfiTjnPDQfFCRWPakTQfqHfC5VMi97FxwI78g6ajpSm61Opi4YIHMcKIlKnHsyGQIMyFqj0Sw1CBCQ8CirJXNVorcn6rmoVatt619u6Nlyp64GLAMA7zskxiOXCc4S5gxIwsrrYlozzgVeTUhEtD01k2s2a73Ou8AUEREBLcfi70U2pJ7i4SzcMYRExCUjX88VdktEXEBWOazJQdmfUJy0hWOTs0UKkhDbjJYPamQQIrZLneNvT1vt1l+ZnxAROTcy5W5dVThxN+AdYJyc8AAjAoUYGD5qnuY0y3oP1OrSq8NmvdFyhMzVhu38z6Vi1RrtbE1UOQqMcaAJUdoWi1tQDDAz24ak3knlMNtqep4gw+6UpgPBeCBwUC7gBMenCwgZ+iTpg8TKtJaYnB/cxBgDtICByLApNU5fpPSugYjAkOtUOazFN6NMwMlTvJJpu1qLGG2jlW+bcYlElH2RTx/kPITCDpFnTDg0wEqtma0LZuaV/jXmNMaqGIZ2pKa9hjAodUuVTr3Vl3sIkC7xo2TFGEekOODm/lQ0Tc1WtKamIAx4yseOFWA21tQcnMENCbmuZ18Xs+4evJWGArLtqZmDUqGr6cDfyamGc76VmL7tWwWVoacV4UzELHFORIasZ7Lp+knD0T8RoZ6qE4foStgD5idSAlJ4MVjer/WdOoh6m46/SVar1cmnM5I8Y0L3g1dVNVPWWpo8tLIdpWKMRewkojZzs4rMELXWaVF3982Bqxq9Pu2VKvWxZ+DO4VmuNnNNicMZmOi48nBICjFkQal9UD6tdjrujEwebrmc+HI0JggsWauarkdvWdB8pVG//8PEjVAv7GnbZvv2exGwAuX0i5yhD6WfYKW9SnQ1IvlG1Cq+KiW24+1ip1PrDG5VVD2qn77O6IY2YfsjyXt1lHNeKNcLHR+C0Dc1WtkZGJhnwqsvYkK3/KGw2E4VO90uJ+jb4RCQgBdrfP+kptrR6Fd8Mf2Lu91epkJdw7WVTLZ+XaFrpncbZaY1u+m9Un7AYeApl/gleSMxvVPIWyg4spQur9o3d9fbidl1XAAd0dv0FnYHBNSV2snDZDvfYXb9FvPviNCt9rqVXmIzNqZhHBmYVbUTfjkiVt704xK5jRDWenryWaaUr3gxpWHyvpxAt9dNV6Gni4TDEs8SZeOKEWRmbRBURF3rtrNlwxm+ec40n5xu8J1kN1esE/TF72VGbnbiDDVXalR6gVFO+THJHc4nAGMAAbH5unja0TR39kEPFzEBbU5NN9VuodV04we90UgtHZ1mw6EPYxs+NYBumI8XZBotiQgEKqul3MuCBbA9M4XSfiUw7VMiEtHVYPRkL0tBYImtaOOkpXU1pwuntD1jrJlpHf+U6qld163ekMdMyMnIldrVns98F94Gm4PFbEZIaJ/murpOjm5loagBAAAR6m16dVjttDt2Rmfz/xfqwLamDABA0Gg2c3Whq78rtARHDEh6tnZ0Ui2j1bdrLBMwSf9WhIjimwuFX+dzZpuT896gjgYAIAnC+9MrC3yWDHJ97Q057NSWW6c7SbWhOe+Iu/IgI6LW0evJZmI7BsBpAEN30Qp0dlhd08KLIRRYNdlwJSwdxDwbkP2pkEuWPD+5X10tHAiucx9jCABqjVamJuhcfEemdoMbIUmv1VulOncgOEPmViJCwuOcfpqtEpjHSAAABPa2FWItU4MbuUqv3JPHq7B7fut9y6QAHKnwupTRzuTnmxAJ6bbu3JqZyzbqDVUFAm5DwMdu2bwbB/l8NZ64H1qX1AG3xNjjH7jRzPQNRArPVbJD0RImd7mB49WThqQIwbmgoevgGKIvfIM2v6Hsl6M3IuW9Kjec7OlnWINBu9w5enbaGozznJzGj38b/gyo61qu3K2rMkyAar+gX04kIJepeZpVeR98MrJ6K7V79PKoVau3XE3AJXYvAoBKtZGpiwYXvIWPcUNHRCQgwKDUO64c5RoNN1c4rqqxu3AgHQQ0HQyFFeWgXDInYc1lshmZYsi2T6Nflj+Ir8TV2JBlcowt2ASXD60oAkDEOqumXqbNWp+O0dIq08n76fENzSi/qU9vxUVZBMfX9DZJaDaSuBHTm3q71LHsrueUBCbOizvl090UJ+O3qY5SudrItRTCdwIKJyKGLCKr+XK72SE7WfIZ9cj5hJQp88Nk1TD0vgv+7YSq2stWtZYqe2ppB7CVcwNIFkjXczulPCI4BSnNdz+RjoN9KSoJwq3p2f1ioadrduCzGyU3Tutmths32GA7MX3bt4YqG3lOu2LrzE7Vg2Y0MBEBka5omUymdtowQ2TcFj4AYHbSFxMf28w2tZ4eWQ27Sw5eMGWzLykoBu10iZYYHMXDiMgEoddUk99mq5X6X1IdPY96vV6myls9EYhPmPHhHEIRNdCaKdMt4XyLaD+7QYYk0DX++rhdKNWGXfDnExGVqq1CSyH0OCrKeZ1EFBJbu6WTWqdDBE7iGPv1T/Bq7adNRMuRuEGUbtZdDdpmqjF7sM/LtrkyFgg8it8I9ELmnxGAc2Nyu6spzC1kkgAVo5R+keMGuS+wP7izZZu7AyvulGOrESkgcbuGzAVbg9nUzM0pM10inIkUc2fjdtxslaN68qXbXTHplL1hQiIqVlqltoJsYO16ZlgEAKCQ1EnmOprGyAUGN9+Z+exch0NLJSs2+P5po6d2LtlHt9tNV4yuIXltaHeM3UJAokrrdL+UH8kPk0CEHSyEIok3Eom9Yl43+jk4+s168VYQ8P7U4jpbIo3cEX2TGkgt3K7F7qrSTb5JdYpdd1FrF3h4oJK2qSmrDb2Vb09txrCP8h8gZ5xEZBhGYMqvhOXqUd1tC3X3Zd/FnZVmaEbyWbqQLYFlXB17thZ5w4StVitdhY4uWIe0d4CgD0hcbbfyNc7JABi5+1iZJphL5hic7yW7mVzt7Ts0AXEjV26W2oqHmoZD1lGN6z6h9rqU7BkGEJjp2s7svmPGm1hdGHx7aqba6RTbrQHjMJCNVZpYWCEsRKMfRNflruIAMBD7wDE+tgpk+qAQiYgLRqGdz78unn13/QT4dPYsSpU3NV9cUaISjlJnnAEzxiRJSmzHKoc1racDEB/M6AX9M7ClJ3MiRGSMNXKtkx/T3W4HvfD3eMCExI1cuV3v+gAAkHHDsGAUk2NqbVsCAgSweZLrGfoFb3fAXQGWrRMbbXp11Gi1W+fdZr1jhEark6kJBsiTL1NwzjauoiJmUu1M9ThZraANMqCR2bvP7//i3QRNt0Q4slssQH99WFzoVXVrEdl708tzxgw/B+VzNm7makSAiC25mXydNMOURlxyjg0TEfWeUU+2Zm5NOWkLR5pnDMMILwaAUyPVtMpXuI1kzosb/MzJNDqyzM/57GnRNL+f91IuqZxfnQndBwMiAKg2WrmG3CMBEcG0cZkjnthJaPcCQblXqTYrLXLjEi8JBEdkx3n9NF2zi7ye3VYRrFqf3Ybm55f2Mp07bFfiJmtD5ZyIRCTkhdeltM4tiT3GJooj/X22DkgEd+bnk7VqU+2+q4A+oPXE9H3/Guu9K0cUABiyXqjmy2/OTap9ASFi9biKAoTmg6aFxlWa134onGSfFFkLl/erzk5ymb5snqReTT16dtpoNO2T5IjHfcnBX32R2WYjsw9NU7PlXkOVEcgciG0in5TMdjiRyLjEm6cFnQ8q4Jdx4glM4GT0evTyuFWpNpxmz46vUmtmakzndnEvAHibl2kkOaA8FzyFIWMAGJA6h5WjQrtte+fpkjvlcBdnb7Nl6nw47JfkN+XSO2IPZBiQ5UdTK1E1wjn3UG13+ycIqY6V5C9ptTNmtC5xLB/U4htRQRJMC40LeAxEnAPFb0S1hj5GukQnkKC4W0nv5QzLATv+sxgXQWb9S5VaK9eUdVvVcVRknMA06mjtBMCQhcROvthpdvhVfdhEZNWXZpAtG0fpuq7bFq3BJ2Yiztu6ghMLcBwwITAA4IYOgBIzempmp5gDE6XBPEZSE5EkCNvTs/vFvGbontqlTbL21ltTszelVdSEtxZyuhKRU3KDSJPVZDLVSLdE8crC1mY2aOXbeseIr0dMY6HLOs0B0BdRgnOB0n7lbAtvVX8cflabvaNvktVyHWDEcr/8ah1/wRFRt9tNVYyuoTBEtDOd9S3I4y4ypwWGgiIYpDbTJR0BzqphF7qAmLMdAIGq8dcnnXyxOpR2gSzTbrPY8YPrHI9XhCCOnIJlEWUMgEJSe7902lRV6yzIvc7/gLgaS2iGka7X0V4RHgK1zV0x7vc/im/4e4EJYT0XdSNARS/kXhaNK9f6BBhkodJeJbwYkgICDR72EDG+Ga2nGmpDddYUIrNVen6xBuTYVwVRrJ80Tp4nNV09u9wvv31MsutTvtwstxU3MtOr12KHvfKg2DrNdXqqab+gobV1oQuIuxVjxrBc53snjV6369i7AAABOp1OpkpdXXCzBeE4umifbHXddMD7RKPUON4vl+yxefOc3OiSgCSuR+M7xTy3RQF4YbhzkyCwe4n5ZZgjfbKHcz4RUU/pnB6kTfDKGC24mAq79V6r0E5sxVx/ZZzzwIxfDkqVo7rpk7AtZK6EDBfuv46mBgCGzpPfZwuZ8iRvdHwmbLZamRrrGcIg1KhPk2hE5iHKL2qdZitf0Uf6097ayNA1nOAgraZzVbe9i3MjX2mXuwoiQ+8sun0QDAIC+VntdSml2sowDvozxyZum8eIaGtqpthp1bqds3BwT4iAFqKxD2Obcle5aincy3ZBxAWj2C4UdsY/0w4KPay+qSsROZDwkxXDxZmAU1uxymFd72mEowESl+nb3txZK9c6+v602+sM/uUKNOaj5NzIlju1rgJnrBeu4/V4bZvDYgy4H5oneZWgr044i/tKOCk0TZEAjTZ/fdxuNBvOn2qNVqrGdC6aaQiZIDp8SGNJErc9hogAWVBSTyqH6XoNwYEA9DEfV23fmo956mbWp4QvNB0M7ZcK3NKUxmn1YpJF8YOp5Sk1TnxMe9JbCRE7Suv0ZUpr6XDm/Y60Cr+1QbWr1Y8bia2oKAoIQAaPrkZ01WhmmoJoQ97617PLTAxdcXMAAMByz4vpw6zproCrly58KxOOfuLVWjNTE3QYcW6+/Bsa2k0Z9HHxBlBE0UvVZqVhgAtMNNT0JfsiAG4mgkdIFvVj212h61qu0muqPkchMWHE1vAm0xtNjAUDHXnxdSljjHLBj2N/t1+/E8clMHZzduakWmmpqts77wFh/8N6LHHXtyZq8qSGK9cU+kRERIaspwvpymEVkQEN+9+uZEx2gUtZLdVEgQXmfIZhiH4xuhIu7VXcUaLOJyJ+KRk4mG4cEXp17eTbdL3WsJqjq+mBb32gCC4N2CQz80pb90+I7RoqhsotXzJyIgkNptdTBcPzFICdrvHysFWpNsFEnDdlgzyHalvEmBCWevvlo0q7beJjJie3m8ZMvzIfCvtE8bj6DtIlWi8EgrL8aGo90gt7AA11/b9PiMCghpX0i6zW1dCubzVu3VV0OyS4wct71fiNqOSTE5uxbqWn1nXHjz8hmVsDcaO4W07v5Qzu5Fu5wrt4S8SDA2U0TQymoC1VW/mWYhAy+8sxMtsjMuDc3I+skBU0Q7WJIQtKzVy202xzJgrgcQVmylWN/ZOKLEOmyrtGCIFPeDYb7oC4qdhITG/30juFLH83SWOBocyEramZ1/mcauj9wCvyZkIOYulWYnZLXGYdkQMnM8RhgpcygK2xjtCoKr3UcaqRbrprRY2hrruXorllMMZaxXa4EZx/OCX5xeS3uaHKE2OTDYpCJoqGSsffJGdWp6Znp67azsVMSGdTn3S7vWqjHRAVhbXPAl4nJyISkPyi2g7grVWJCQKeEcWTEALjZOiGUSjWOPdFpRpaebu8zHlljjUg6vlG5/b0rOcyyuIzhJCszIcirV5vORIZ8YQG0mZcvmGyjfWEDBVRvJtYIuANoQacM/vgNLaXYuhGRMZBb/JW7lWBCEzAlZ0J+8qb+3nXl/YrN//Teu6XstpSBWE0tvuq5MyCc46ItdPG6etMNBGRROniG4foQiak4cVjZl7JtwIcRACaUKQPVfNxupjydRuqXwxFRSBvypTY5Lx+n9wtdaChKuYrZ4Begc7NSRGBT9SarVwqk/n/f/8deO0tICKGaHCaiUc/vXv/y2+/6arqxYb18XoRBfYvH32+bCwKumhB5z0StIhoGIYgCEDYkdWu2pZDcrvctbsegJtNQqbCqASlcq9JCvWzV3in/5jrSvKJhqYXc6WFpauV9b2QCc+MsdnqZGqsqfthgo3QanvU7QyZT+iqXKj2/ICMeyqgTCIiiRkMsdwLaCR4dDQYJiQu8NL/9tV/vbW4Yhj6YSb9jnBkbzLJRDgSCgR+Odj3JqOPqWARASIR3blx43Zsy9f2cwDG7TrYpjqKOKEJSACJ6cwQ9FK5cvRDMr4RbeXbWldnjJFlGBtzAWA/Bp8QkQno2wz+r9/8/z5duxebDjQKrfGckCO7MImIpm8maieNblWN/iES8Acu39Rb1FE3IxqGnqt0q6rPNiKhiU0bU1LZ9il3C0h6ROpWe2ZFeLNmg8fWcESIKr22KmokIOc0cVLtITJNGWFZ+3nvhxenJzqnT+4/TJWKmuFRfQYarGlG9M2rl//86e92k8lyrQrWXAgAgXMYz99tfwjI8u/f+3Qe5wxX5QLz6I6IE54MrdkAteRG6odMPdXyx33xjWjhVcVliRgLQA+W8co0NXDOZzanfqrt/bK/bxjG/2Pzb1qltmOAHX/k/dhiAsDglF8Ki/lXJUFmmdu5jbvrHiFmbOYgIiCoVBuZmkggMpfHZhItzgLC9u3gLCyrGgldXTTdEu5QIK/IJ+giM8yk2sAQGfP2ZEtEMqNu+/TL3RcAsJdOcYb31ze8ar/vibetJqV6bTd5+tGduy6WQwAYjwP7RPTh9q2PF99nquWIGsq4MzkHAoAha5l8pnpUQ8TKYT04E1Ai0oR6onMzQ0ZEvqBPnTO+2n0BAK9PTnZ6yehy2DD0C1q4EjHGBIaJrVj9uKH3dLWuHn+batTPDZ0b0cJFfzTlEJnREr1MRWuq4qgwSg+IEwmo+cReU/UhEzhxQ9cRkRu6JyYZi5+JR5Res6cYronzsd7HedA5hiwgtp/tfV+oVQGAE//2YO+97Zshv9+OrjX/D/3PV+rXzvTSvxfxh73d6WhsdXbOIxchAVEiEv2nB59E1KjjoBsPvXARMahhNfU8Y+gcEdSWWjutm2kLPWnePFJGN8N/zrzIlcuAyA3jjzs/w4og+ySXKOvDMy7fsmMvNAwjuBAAhFqqKQgiMKwc1JKvU4Y7J8fQ7YO/vm2zRDAzCxYqzWLbB95lXhly5gBAVFY7mqhyAYAYMtNUPZSB6wrtD07UdLEEJZWIOlwxn4OVhmSs9gXB0uQNO7EKJwJAvwSF0t6zg13nykylnK/Xn9y87QwFoI/9GaNrMLcA173tTvv73dcf374nOvZ9l7C8eusIiL+7++Bh9I6VVPsdxCYSkSp3k0fJZtY6oTHGaicN2ScF54K2sB/IajHy8xDxAecz+OO+QrD2bPe1dSNAMp//rrgT24g4ItMds3tJMjGoAIAEkiImNmKVgzo3uMmcalc7/iZZypfPa3Do+7dGbRAAdLvdbA07huThy+ibd821KxoS05u63zHBTbjpDp1UOREDPazodTUAdhwcm6DGPecGDcbmmvJJpvKXO981O233xd8d7m8sL8/F40Dk2h3IM2yLIOycnhDQ3bV1y61qHxsvRa70MObva3Nz/3TnM6Xnm+Rs9hYSoaQWs68K7kXINV7cr0xvxQURYZijaOTnIXLng0ak8GbwT4c/N5tNa0NiDAD+vPOyFmv7QtIYStbA2RiAA8XWI72G2i71E+EIglBPm+m6e5dp8y0PFxFNiHOl53sHzi4A86RBPCJ3G5pCIFpxiYh8eGWMTwTAkIWlXk9nHR3Jo2C7PsDNGiT6BHU/+cvz4+OhYdfbrZ1M+untewyR0InWwDFE1Qg4JREQ6Ybx51fPP7x5O+DzW3++cJZomkDtX8C1siVR/I8PnqzKK2C8kzdusnVXaZ/uJLvVHiI4x3IUWCvXUrtaeCXEOR8DOWgFrgAgh8hCeM9IPT88BDv00dQgqvX6V8mX0e3I0Gu6UlSAafKRQ1JoPljcKw/Dvwye+SmfTxXdt57X5tt3uHqzna4yVWdwJiHc5IQEnCAga0TUViVuaKZ0Itt2OvaB0LL6ADBABBRR84laQ/WBbVGwMEATzMg5KZlqLQOOWv5POz8ZAxmDzMHg85OTeCx2Y2FpQv/UCDglovlzmstlisXHt2/blpuL+JCI6OzcOQfO766v/+36E7mnIEMiDmdSUU1CiMiBDFHPVTPF/YoFFeZu0zEr71Wjy2El6COiq/oSyKqHSaIiCmvin/aea7oOAMjMDQuAODD2w+7OqVgKTfvdaYsvM023UZQxNr0db2bbWkPrw5uJAEAQhE65e/Rd0kluNDqZkDnhi7vUDbVQ6TU0Hz8DqPWEDCARKSz1GpofmeC2ek/44k1zCwJwIOJGVOm1VEnjyBBtCYbm2xp7SohInKPpNwMMy9qPb747LuSHlzcBAKi6+sPRm4/v3ZdFz5IjD2/kAN/svNpcWJqOxvp8eCViLBgM/uP9j2dohnNuJVHm3Nu6n4DYFGqnL1J6x2CMDcGmTTx0u9iJb4TH8EXbQhUja6GfqvuHmYwj58nE0CMDgJ6q/vv+j8qmX5Ku9jr6fgHOA1N+OSSX31TMNHbk0t3MQ1bhRSl9kDNPWBdEgbyFCauVVrYh6RwFr0u7WN0jC4ptzRA6ujBUs2lChu8XFQUMyFwUqKX73NuVoz9MlOueMTIT4YDebB59vfd6aNiOdgQAb/JZlRsPN7e8QsMO98VYpV57dXL08Z17Y3rSiZ5s33o6/x7TBNdBd0zb2EjinBuyms5naicNxiwvlOu9mE/GSVsoc87pKshbk9l8Qbk3a3y58+JcZyBjuycnL1qH4aWgYQzUlnlrZL3VgMDMdImGxp1n1W8HAIB6LfX4m2St0rg4BfxFrNXrdTNVo6mK7CpRJFchFJnul/SG5n/7tVcnIg5EDCgsdWvdfq1PGHxe403N0mltYROSO9/sfFtq1IeQrpz3M8QR588O9h9sboeDQe+jbk1i7Mf9vXg4vDG/cPmb0FzlRNPR+H+6/1lIDZOTKGgwadXkRIyqvJz+OeM24HP7F6fMi9bVayf1qe24IAhjyMPoZvir1ItCpXI+lI840Z9ePzeWwBdQuJ3hhogYEy8wRJE1YB5dCpHBm5kB8M3gyZAjYuWwdvo6bdBANdshOr8z4qVau9BRCIVJs0hyDnYY+NBfwlKno8kqtyoyjPeqbZebG8Fg/wlZQOpywJ6uAPcIswIAA1YZwS/yTGHnh6NDGNRJwFZ5nbvytUq6Wvnozr2h4XsiapAIgLq93rOdVx/duStZtt+3iREiMlc54hf3H96J3AQdHcSJc34ef1QDa5Q0X+/0MNkr64NJtfvRZE7oQ+20wUQMzQc451ahQueai2YDoelgNlD9/mDvQiwUAmKmXHqW34lshN3SmHMd3H6LwblbB04rXWLNLnXlWGsGJ4Woq8bps3QxV76sYcaKbQUAgE6nmylTTxedoEEcKzUR2a+B2QUGnCHKTJUEMsErONYRxmrKwvQMe885kYhGSFLrWoAQmeBlpRpENO0x3NAUrH2188OQW+I8+v7wYG1hYWF6xh7w8HECYBwnPph+fEBgbC952tO0BxtbZBgAOJA9ZahxFwjuxsLiP9z6ROm6lfZLBbmOJPOlo8XGlpABkYqdQv51ydEA+3x1pq4TcXDSFiIy5mJUF8cO642Mgf+G798Pfmo2bczKhc/y652X1WgrGPO7TMVsUCMdfgZElNiIqHWtXe7YMR/nPiezuujpL9lur3feYAaYEO0gFs6NbLlV7iq2PRkBzsZUXIqYS5dw2/SJ61Gl11At8MqkoRI0AAG1zJXIwnKvZ0imadfb3ERExATBTKq9c/LTy5OTS4qLZqf9PHn6yd374hkvJbkWwtgDM6MT/vzy+cPNrUgoRJy7tOZ+41bArv29JIp/f//xqrhCLi2xH7U0Rnlzzok4t/IJWL205VZyJ6XWVGd+fb4a9PdwbhDxdqGjtrX4esQUhmef8JDeyDmPLIZe905eHh+j4PzpIi5sNBp/OvoltBlgDF36y2gNEwCIyBdRgjOB0l4FLuvVwMyPufxp0ZzwiLgFp2mrewAAqNWb2Zqok9jfw8YlclUpcKVvwaCkErCOJpmqzyhl9QpkHV0cZUYQkFARDEXQ6l7X+nSTiGRo2S93fjYu0YUj7l6dngQCgc3FJeD8LRv1Vai/RhlLl0qpQv7R9q3zgCZOfhpzZA9ubPztxsfygO2KnyemLj0eZmtXQERcNnKVbOmgRoMa8tnN0VwwjAlEVN6rhhdDclBy0HMjnXtmRh9JFnFF+Gr/hVkn1GnuoocsCD+/OXhDmdCs3zgfZO/cj4hT2/F6qqm2tUtuu4jQKrWPvj1ttpoAgGd8RyMUX13XCjWjoSnOpVadqst0eHb0rjzkDBkiM8ErEcWoW9ESZiImDw5FDJlz0kABw3KnrfkMEJ3GvU2JS4BBsfvjwfenxcKVbtS58f3h/tM793w+30ROw6HxOME7RET09euX6/MLc/GEo38O8JIrSUYoEPinh58ktJiu95G642XpdzVvFU4yf0WGDVZLvkhpHRWFfuausxj9PlSVCBG79V4733GnLTxrfwbrIIexG5HvyrvHuZxblUB6i2dW1bQ/Hvwi3/AL4vCUHU+y+S3nPDDlE/1C7aRx1YeTf1VK7WU5N+CM3fqsmkGVWitdZwYxZ+jIGAGMZ1h3u/7MZJ5mrc+ujiqXho6Z4waP9ZMr2/uioLAeA2qoojsRjofxwZzIJ2Kt/uabg51LblD2ayMAOC7kWpr63sYmcK8hEKagYazebD4/fPPJvQd9ZdLtqnJ1+vHte49nHoLGHF+WyQeTaEDWS0HrPKLKvUwmU0+2BEFwOuajNUxzAH27f+WwpkTkQMJ3BgLhjrtHX0huTvX+vPMSiK4mMhAPUumfanuxtchZYeh+CEzAxHaselDTVX1QxTh33fbtvR395FmmWnHVYnCaHbqn11PTFb2lmQX6XN3T+PnhB29ESTAUQa33FE4c7VHalt9xDJgjDnukR5ReXVVMxLknQTdDxBAVVvt69/tyvX5FaWYuMnh2sHd/cysWDhMfsRbHJ6clxn4+PAgoyubC0rmWHqK5RPyf7n0a0iIDbaBpCrkysrnfgmmNA+REIEDVKKdeZLnhtuATGzh6jd6OEVHtarWj+vTNKSYMyUzOmOD4NsKbwS+Tzyv1OuDAGXgEMOgMETe+2n2hLYAv6DN9yKZ+676Tcx5djZABjVzfLWHXpTh3gTlHIcaE6lH19EVKPxO1M6wJFCvNUtuH0PcOWf+fZJEM4kfCYqutKbodl2jueWhdOGY37heIKISknm4wB3HubdiuST7RSGZf/Xj45gr39G3PAAClRv2oWPj47j2wHvLkurJ1/HJ+VzXt2c7Lp7fvyJI08njPmPDF3fdvB7e4NrwRuC2E41jFbb8UEfWUTvJNqlvqMdZfssOm/+Ff+6+MMVZPtwgpvBAYwu6YfGLoenDKl1EqPzpuCdc08UJ5bvfLcuXy15kX0c0wIppVuIecyVJAiqyECjsl6sfZvt2t79poSNeM5PeZQqY0fI37l1arlaxQx07bdRYHMCY5BwBAv6iLAjQ1GV1rYnJfsEvDIQG0gKQ2NT94V+5hqB0GKGP1y9c/dC4HkwfzMbrshCb9fHQ0Pz2zMjsL5Mr4NgGQzuzJ3et+OtXu9R6sb5wtTkicNpcW//HWZ4rqR3bGIT7WtjDUCBGBDGZSbQeldJlDx1BuT0M3SruV+I2oIAtnzTOSLPk3/f9+8HOn0yV3xLPZ1IUL2L4eAODZ7utisOaP+Uxj7OCFmNiIdsu9brXnPJcrTYSIM8aa2dbxD+503QBkSTwgIs6NXKVT770T8IoJekAyIlK3ofr4JYDjl255OFtUWOp2dalnMPQu7sPaLZlAnBOgT+q9PPx+7yqZY1z+3P4tzW77RfL047v3RcGVRtnTIyJx+urFL6a7Yoi9FUn8hwdPl4RFQzfM0O3Juzsr31pS4+TlqVpXgWi8JAZEJAhCp9zrNdTEjajJzM7zNAwjshR61T1+fXIydjIBM6dOs9X649Ev4a0g4qC5iFCJSP6Er/ymNvZTskp0ccj8XMgeF/oCAq2k14CItXorUxM1EjwxVA4REQGykKQagB3dy4rwpqXH/g39Ilck3tT7tT7tM8MEhj5bBeLcACaIjHgv+9Xe80Hs/2UFmFtWIOLr1KkkK7dWVsmsL29u8x6C2hBzlfJxPvvR7bsD7M35e1vbv1t5hF3hAmzxVclt9iQirvBsMVM7agBDmCwzGBEv7pXD80ElLDtaAxEpfpmW2R93fjH0MRFRxE3EPQFjz9+82dfTkcWwYeguG5UxdTNeTza1tgaTrSXGsFfXjr9NN+r9WgzW2U/Te5lyt2VmXvGUrA2ASAA9KKu1rgzeGeUdsviEeFjpNlVZ59h3S5gXTAB/deMnESAkdr/d+zZZKAEMKGwXu6P6Qx0cicGN7w73n9y56/P5TKs8XM6WcCV69vrV0szs0tSUtVUQRUKh3z/8LKEnnFF50pHL343AoAal5C9JtatNyOemq0Btao1se3o7AX01D2M3It8WXqfyBRw3kbp9IwKApuv/vvezuCbJimRmEyWi0HxIlIXaSV0QBC92KyrulNL7OWcTtyRhudoptP2Gd1piv0PbQxiWu11d1MHnoXu6T4wRoF9SGUCjJyCMYLnxHh8REedoaWsoC7xSPfhm/zX2X5y7j7c3eHY5nhYLlU7n0fatCYd6fpfQaLV+Ptj76M59Jxrmkzv330/c5Sp5KAZNIrOiAZGuqKlUppXtTp5s1zkOVA6rUlAMTgfMM5QvItcT3T/vvIQJ1QcXVukom/2hvBtZC5tmJUESEhuR0n7F0PnkW5UJKFU72smzTKVUM79kQNDtdjNl3VstcahrmWl+iTc0n5Ws3/MOAATkYVmtqQoyEcCSOOZfJ/N3ITDm2MN8WPtq5/taq+XCeU4ycIu+Pdi7tbaeiETB8s95+yIQGPvl8I0sSdvLK2QYC1NTf7j/eUANmX8+iyu8WutnzoGiIIEAZb2UeZ6zsm14Ew9FumpUj+rxzQgTGCJGtsJ/PP651mjihOqD25DD+Z9ev+jMqHJIJoNiK2GtZ7Ty7bFTlQ6FVgAAMlY9rp4+T2qaCgCMgBcq7WJbfkexNQCACGG529J8BnkizUcQAYYlVTOwp1tQu75jeuKd3nafCH7ROM28/PnkEF25ZQHH9La776o0G4eF/Md37w35uLwiRNR0/ZtXLx7dvB0MBP723gebvnXShy0l4zlUz24ZXNd7Sie5n+pWemijJSfbWqxnwhirpxpAEFkMBqd8p6zw08E+MCQbhj5+Dy4q12t/Tr2IbITkoBRZCZf3qghjpiNCHFHLEZ3qotkyERdrjVamCjrIVjEq9BjbhYz5hB5D3tJEdFXq8JAbkTEJDb+klnshNKPdGQMnrmJiVKp94NQELP77zk89tWfVHrQZ/Eqry5m7+y4i+un4zX95/PHa3PxpIT/JgM8jxthRPndvfeO///yL/3jjM0lVCLnJGu6D3BicYreAwMzFg4ao55u5/OsiDL7rMV65DTnrewU45+X92tyD6bbR+d+ef9XTNAHRjMbyzCHM6YeD/fufbmw+Xmzm2726hvYgnCmcfYnnzeBsHnEiYoy18u2j706jU2GxXm+SYURYD5j9ZzP4xTsHd0g2dAMirE6ge9ism3wiFzn3kx4QkEzov4f9MAAAUaDd5M5BJg2AnHMHUdh/C2+BKFo0cvdBxE63t5dO/k//+Ptqq+XtucDF9hAOBjfnl1iDMpTiyAGAc2NAHo6DjnF6AiISmEASz77KqS1NGCixRIxd+XDIhqsscWSsXe7qXT20HX6/s/3exvbVR/x2IiJ/3KcEldS3OefV4uAFl2xnhCS0X0dxp1K4XxIVWTo+yafzXbRyDSCOGTl4ziA4Lc76w35h97SFgCNDOSbuA8JBcWPR//KwpRkTGQBGkqkcSALMzYamIpFi/RxnEcIkj04QhZuLy797/0PF5/P+EUEfut/utP/4/fevvnpj8h6a+SCvzh4uGtCfiCg2G968uSjJIjfIDs8zU6pfuYshrz0AEDcEnzC1EtMU/svhm1K9/k5OOACA+Pf3H61OTfdOVXvwE9t4B78JzQbCsbA4PRXbWmkcpTqaTv28wt6dS4joMNV+uB0UkGpN8iq5skOIzOBGo2PEw1I4KB6lO++i5CciMwwjHAw92bj1//np2QCTuB7WOBxoM8f6zNx//OCRz28W2wFvT4bmsMwNORQM3Xy4dvBdspytC4JAQGyyI8jQ4QIBOrVefDYUXQ2XDqqm6uOCZU+uouDMzcTi7XlJlO6v3fh//df/y1t4Q584/7fXP/6PH/wnKS9qXW2SLLUmDSFR5ZC09HguPhVhgiBurE3fWApyF27dyzkhaRpPFbT1pSAy78UUEWeIDPAk252bkoO+d6LuEnHGMJlt3ZjeXp+dM78DyzAzYdMAALIo/eHRk8WZWftbT1cVkdvaQ0RL6/O3PlqTZNFMZ2j+GRmaFcWv+oMMrQ8IgGbwNzt+mVfmFckvDoITxirw4kaxEigRafXxUjgc8vv9v//ks82lZTDtuoge/wjCaaHwbWknfiOGzMzE6U0NGQBgjM3dnV7eXGRm9HE0HHp4Mx4JSmcvnZwYCoiYL3aJaD6hvAtFCwCIqNHSSlV1deGdwO7AtGhxyBfw6Y27kiiAK+dAH4E0rivy3srax3cfyLLs2XDddMaFoMjyvadb0ysxGgSUToDJshQo8/1y5M1yO5MsT23Hxx20u3GXgZH43P3EwtqsKdfXFpf+8NGnkii+q3XF+Z93XjYSHV9IHnm6uxI5Qo7remDKv/Z4JRgIgO2sx9Wlqe21K1RUuzyZSRcNguN0Z2nOJ0seSyoCjjad5nrhoBgLSxPG6Z/TESBivtyN+5bvr6z3l69bHT0Dkr4MRQPBf3768VQs5s1AR9OQyxRn56YffL4l+yQHYDsZpqyPjDXZkYDSuyUKoi+m0Pl5by/fuGkXDS2E1j9c8/v8pu1KFMUv3n/04MYmcO4l1s8hxEqt9mXyeWgjMPYOO/Crqb2LbP79mbmlKXPpmCZR8Pt997enEzGJW0H0numNJjyVIdaaRqNtLM0pkyDIRrbvtKaqRjrfW13weWiCHRoqJzrNqu+v3A4H7D0LXVeOJUg+vnn7webW5GUrR5LDdeavaOdBFQTh5sP15VuzXvnW+j1aZz/sNLtHO5nEVgxtV+rYFT8BgHNDkITlD+dnFkyonSVVZqYS//r5F+FQ6F35uRn7YW8356uFpocDqS53t3WSdGCPnPPYcmT94bIsKdY1YG1buDgbv78RliURAJh31ZccIoDjdHsmroSDkrcLzvFuIcNcqYdIswmL1b3AGZk7uxUCxxDrLQO0xJOtm/YlLiFw9c1yIZ74/eOPQsEQeeZqHqCzQ3J+j8WiD//2ZiA2qQJ/9iFbdleGxZN6l7TwYpi7spxcvX0r+3tsNbz2YEUSZTdMSRCED2/d+WQgi6SHRADQ7an/bf9H/6afCVd+v0TcKhzEGGMC51xUxJWni4mZmN28rY4CgijJd7bmFmdGSyoPZCNRp2vkSr3V+b4J3hOR2x8tgW7w06y6MueTJHYWKzNegAgRITBE0z2ICHSS796euzUfMw886L7y0o2a42H/4cF7m8sr5kDfkZHvPELEtc2FGw/tNMHj9n6Wz53noGv6m+eZ8FpQkAQatNCcx5Bn9TcriZNfXHu6HI2HAGAoxjwSjvzzZ5/PJxID25hHiHQAAIY7J6cvWofxlegFyaDObcIOADATRyQ2o0s35wUTXGk27x5sIh5+eCuuKIyI2PDyHV82OuASQEzne36fkIhKtnRm0FeVxmrcEUT2+bBU7bW7xvKsz7QKul/qeGdFBxuBVvFgVHu8VlOebNxiyMYUXwhEtL249HfvfagoijMZ8GrxXI5CwdDDz27G5yMM2LvoGBHLmVq50ozfiHLX8h3yVdDgLc4HRHTy2E/fii9uzZnrcMAVRICIt29s/sOHTwa2MU93NEPX/rT3nJaZL2BKKadiytt1OucIQES+iHLj6Uo0GnVfwNyDZUy4sRK/sehHuzKZQ32o5Bhke6EQUdP5SbazNu+TRBeWAoCAj+vmdoSqEx6Jp1ltJiEH/SIfwsdNttmby8LEHGUK3eXYxub8wiXFF9Hw/Hyy8vsPHi/MzPQvwEnGOCYtrs7efrqKosvY6wWZDZla1ZtfUsqM7Iv4zltFQ1gwcN1r2mN8MWX9yXI4HHYu6zeCAACKovz904+3l5a8Gv/wCAUhlS88y7+K3ggPiNtLKdh2yA3i7L2p+Ruzwwra0OXhcOj9O7PhkDhUjH6S14NOYkUARCxVdYNwNjHoQQI23pFo5Dms0eoVK9rKvASIA+nWx13hfQiy/UE3eK6Ij2/cUWTZkZAXzMDU+fufAd5f33h6574oSs6Xzn453iCvSGT+J0vK3Sdbs6sx4l4azMh6NYiInVovdVyMb1rb/4hjqp0uaVBltS4TBLbwcGZ+ZW7wFnRmYf6zMr/wXz75nTzSpT7GvM6OhOjPu6/q8W4gogypkG9riQOAYRj+aWX9w2W/f9gNMawSILDl+djt9TCDgRKWRGQqe1eejOtpmlqubhjH6c7CtOxXRMe7QMBxLLgnmgUI7V+cHk9znVBASkQkwzDANYAxuhjRKSIiFiu9sLjwYGWNG9zmw0vcTEBEsWDoD4+fxqORswNyuPEdUx+VMz0bf/DFthKQvNVICSzpj4ipvSIF8GzaQuvKUblnyLRpcB5aCK19sOzz+axWz8zC/EeSpM/e++C9rW1wQRfxTMqZy5IrVM1aNozV6vU/Hv8c3g7jQO7QSz01QWQrjxbnlmfPDsUtJSzy+fz3tqZmE8pweQCXM+BKNHQqY4i1plZv09KszMnWOoABjmOncZuR3LqBpvHTXG91QRGY9cA81LUcU/VJtvf+6r1YKHiFAQMBwGe37ppuCfeY8MyHd04EACAwcev+2urdKxRyunzjAEBAvbZ6/DoX24oyBk4qzrN3DH2JgKIsrDxamJ4zo+kdn+yg0LZUCJiOJ/71d38TCYcdFhk7yBAH1V2LGPvpYP+Y5cMzAdcA3t6FYRjR1cjyvUXRRN4M/tWdzab/x7mZ2L2tsCQKw9uOd+v4JNtJxORwgPUPnzSZ+WdQliJisawSwfy0z7aPj7nPn90aEK06Qq22obYDj7dumdddhhBxKTH1T4+fBPzvCtxzBbLfZzQSev+LW8G4x0NC25uHAmaPSm1NDS+GDEOHc17HGQsqxdYjS3cXREGCgeXnUu7Bds8iMMY+uHX7dw8e2heNv1ztAEXXF0QA0FPVPx384tsKmNVFL7OoiEgJyKtPFhPTkZGwYJc62scXkihKtzdmF2YkGHRXTMiC7tXc7RqZQvfGYoANKr1jN24KaoJ+ISGD03G6szAjKcpEbs/RejK3EkOkC/r29PZiPHHx03FmJjDhH99/tL6w5AWUeQIyLbH2EReRrWzMbz1a9qp5k0nIRtgiIHJ88zwdWg1Kct8cYEtFSzaa35pJujg3pKC4/nQlFguP7ODsJwAIBUP//Nnni9OzQ0v3yuPHISsZEqJ50t09Pf2pthdZCRmGcTk+x6mb8aVbSwKTRv55hDpqfoxFQ+/fSgR8dsVWAJjYej4kqTKFniThVEwiJ+Btsh4QEYGR69da02i09NX5ALfxCkPXOyMbg8y10lONalV+unVHuDCfn7Wxcrq1uPz5g/cURflVfREjBmSOCsEuz+L3+x58ciuxEAEaSIU+JlxrcHpExIFXc41iqRG7ERk6QTjueLMnJ33ezJ2pxc25K+lHiLi1svZPj58wdEVljmOXGXWPZaChL3dfGEuoBGT3ZSNv4ZzksLj6eCkSPhcWeu5mzJhwY316Yznk7vuyM7gc6QYdZzprC35RsCzO7B0Ih9NsbyomRUMKDPmj3A7MMV6SS9fKFruL4RvbCwvmlj98oavxgOL/w6Mn89Mz1gh+E2TrP4ALS1N3P70hSII7zNfb9378IiNOS3JIutjpRQT+hG/z6XoweIXzttmgoih//+Sjmyurpo8RJ490GRoZQLZU+jrzMrph7SbkQvO7/JxmEBctPJxZWJtFYHaFquEmL1r0YX/gwc14JCSalmO3kd0TQsRSVetpfGFGmcgPeU7j5uNod41ssbsyL7uNtOgOMB3PNeJ63DqnVM54fOOeT5bPCHO0woWIAODR1ubj23esePO/rCQ8QwggifKdDzfnNxJ9JHpfYHpD7Vo3dVhMbMWcJdsXVy67GmOw+P7czGLiqu2bDS7Ozv2XTz4zDaoe54+0W/tm91U12vLHfETQL6SLluPddOhzzsPzofUPVvw+PzgesjPDuVDyIFteSNy9EWZoo0bGihK4gDjRcaazMOPz+TxGq/atpoiZgupXhHhUNB3iFks4tZzGa9+lPiFApa4FcO799U2nd3fTJtsnQuHff/g0GrZLr/xWJKGLCKam4w8/31ZC9p5FAC5r8OSEiMmdvKGQ3zaYuYwtVmkgwzAiS+G195YUWTm/pfO7ABBF8dOH73+4dROI4K0Fw8fpAxvN5h8Pf45shRnr7yMG151KG0QkSMLKh/Mzi1OO0XPkYnuL+qcovns3Z+cSCp+4dvlIYoi1hlauqqvz7zAOUNWM02xnbcEvS+Ig4GnSSXGX8+Mk23u4fCceDvfbdJ+zEb+4c//uxsa7ULk9IwTG2Oa9lbW7886Gix5VjzSJgHTVOHyZTmzGRHG0Y11WpOVH84mZ+CQdJeLxf/ndF/FwxFsQQp8Y++XwzSFlQnMBx5k3VGcqthpevrckChLax5+Rz/HtC2I6Eb6/FZVlAd7N6hEYS+bVaEgIh0TjHdQwAwBELFQ0w4CZ+ACM2AIiTpD6zYQZmJ9bHV3thB/fuAmjGHtlauYfHj/x+3y/KQE4cnWGQ6H3Pr8ZTljbInmuOCOUko1auxVZChkDgFLrSJLYjC3fWRQEcZJOGLKH2ze/ePi+t6NHBCQrkV9P1f5t72dpTRLEYRe6gziPT0X68OZBlE9/qG/tVRSlmxuzi9MSjVU88FxyHTC7PT2d795YDJwt4z5J++7lbnDjMN1enPUpcr8L3i8yMT7z902+iKl8b3vm5sr0zNArEZnwj+9/uDq38Bd2S5yhczzmbHVj4eaTNRPx5/mmgYjc4EfPM/5lv+QTnXJRpgqnhOX1j1ej0ch4/bqffDAQ/MMnn64tzHsYJEZ2DVYAQIaHmcxP9YP4enTQTMoZsJnbicXtBYbCGVPK8MwutSbi0dD7d6aD/ol2pmFyWTAYYr6sM6SZ+GhHynjtm2/XXGcMWaOpV5v66pziwAPcgP2rNu92bzhenJ6qF8vCk407AhuoCH17efXzB+/LkgzvQrC8A/L5fPc+3ppajJgxCu/gTAW1YiufqyU2Y27DCSLO3ZuZX5ueSDexHz0ibK2s/tPjj7zc3E1yik9z/uXOi+6c7gu6U7cwOSatP14JXw5KdRETutYK3liZ2lodbnHyaEDbwguabpxkuyvzfklk5NXuOxg/QUSn6XY8JkdDdv6LCbrpP3FX7gZEzJfV2eDqneW+1zvo8/93T57OJBIWkPI3aZE5Qzi/MHPn0xuSIk2YnGK4XRdLn77MCXFRDotmfCDn3BeX1x8tBwKTZVpxbZCyrPzdo6e3VlbBMgLhiKuuTq6NAwuVyrPM6+hmxNUsX3g4M7c6c8nWLmJC93iDgcD9rUQ8IrkXHMKk6QXNB8GBI2K5pnV7fH5a8tYz5RgqEbGrUqbQXV3wmYXUcfywinNJN4x0zni0di9gAY7hyeb2h9u3GPOkGMOvREQkiuKdDzYXNqc8NI2aLTu7e7vRPd3PT23HAYmIBIEtP14cwy0xRENPeWF29l8++8IECbodSN6sMgQi+GbvdSFYCyb8RGAYRnQpuvbBsk/xXbKNS6mjpqVvcT5xZz0k2nXDHSE2HrnjwZg9jONMZ37aF/D1jUATLtwhhRMZZgo9RWJTMYnsBeEtbwiMVRuaSFMfrG8Rp+lI9A9PPo6EI38tHGgrcggA8anIgy+2AuHLLqbLdmGb9AXG0vtFVTACMz4yKLYSWX2w5GReAY/0dkEQPrr/4NH2Letk6LWlFBm2Wu0/Hv4c3AoxhoIkLD2aNxHnl6Qr2AkURbmzPTObkMmONHfU0YnOVDYbEFGjpRUrvZV5HwxkPh+TLFehC+7DiescD1PNtQW/ZOcL8dZ+beIwk1n1/uKtmVjsizv3b62u/bVwILjgM0DEUNi4vbr+cJGNG0Azon0XPokD6apx/DIb24gqYXnp8XxiOuZc6dmpBCAWif7r53+biESB+DhhTW5yNEH3A2H44vBwr3camg/E1iMrdxZNxPkl6bK2FgJAgpmp6MOb0dr3JU0fNMgi4GgXyIVtDvgGrA/5snFvU0lEpUaLm1uh2bBHMXYMABptMjgszflSuS6zy555G8OnakavE/zXJ59+ePOmz+/3ckG9Y3JMGuYjCYeCDz/byh2WGqW2h8URwOqFEfFqodnsdte/WF7aXhDsSB8PTs7WTAAAGGP3Njf//oNH//tXfzQ7BvBYJBoG/+Zk5+b7KyvL81YinEvT5arwuNZQpVo7PC3pOvHJshGPHg0iEYQDgs6h2+MTJjy2G+xP0Cx+gsh8iiAK1Gi9k2KJJokim44pS4tzkuidyffXoOENo9Pt7L046jR7HjKh+70ggOyX5tdm52amXWkQvNy3TP/jcSr56viQuMPj407HuRXJSTllQuHX5+dXF5cCgeCVhv5/A9hNtuLuTPyrAAAAAElFTkSuQmCC\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36CF9D10>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# flip_left_right\n",
"Flips an image along the horizontal axis. \n",
"Assumes that the image is either ...HWC or ...CHW and flips the W axis"
],
"metadata": {
"id": "iltu7yiEle_f"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.flip_left_right(\n",
" image=image)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "lSITHfnHk2ME",
"outputId": "2bf0f5a3-1ee1-42f2-b343-8a94a77fc34a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36D006D0>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# flip_up_down\n",
"Flips an image along the vertical axis. \n",
"Assumes that the image is either ...HWC or ...CHW and flips the H axis"
],
"metadata": {
"id": "2vMRYZl2mCdo"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.flip_up_down(\n",
" image=image)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "H1qehvuAl_2W",
"outputId": "77f98ff5-008d-4c37-b329-b8351252f96e"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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/I3wo9VZaCDDiSjb4upSCGP3R9Pzj+ZVDqZ31dDr747fnp+dlW+16bSTYWZspgSAEbeuVHDa6/kA2B68AivqckLRyYxAQbXJs77G9d63MiMjkHBf1hn4YZydj17Xb2HP76dfj13OlsuqBUM1CawzUvEcAwJujD35394Ysl4wv0dTX1pOWQV8sF679+uHYlQRyK8NaNT0UiYi8rY6BPZGFd5Pqn1bZynEV+WZcppTF+E8XZh7Mme+oiT5BgMezM39z8d2Cslr4cR1TumIkhPClod3NHzvTM0XkYhNbJTEAYBJPPyg++PlkLl8bnl6FtCGEfPlO/J2reaKV6URrGZTXGYMu2onI6WDHd7reu14omqIlEACI1eMogIqLRt2sAICQTu633XlSWkzIrDrlv27lxGR9JgAUQuwfci44Hl+dndqoCo1Y83zZUuE71z4cX1jU/46axbLOMdZJkw8XLv1wnEr6ucoiMl4LIu58tb14p1RcKDzNLWEsQfPLQkRgqOJf5CaKD/77ZDqdNi6rNEIAAMVi8QeXLlydmrBq/LWkiP7m6OfYruX301WxlRt4+8g0OUBEk79cmr05X7MNMbMwnF/K/PD38VRaMIaiZrtqwGSCemwXCjqywxFPK4+m9LxErChF9foJdA4HAFLzEh2RAL90t0AMacWJtI4u9ClT+RmJyOtnu3cXf/b4hmy2g2+GGAEgXp6e+P71S8WibjRD458/EqWzmQ9/MLLwIAVM1LUHrkNE1DwUaIt5Fy8mEXlFEaGq3Z9V31L5IMg458++HZ+4rLkrqjy+CER07cnDf7x6sUrttNA3CMAk+zcHD0nnRCGpZkvojW/g7RtWX0QszBRHfjiRTCarGlcvAgK5XPzDpbnL99XglRUvpOFHEohNQb6r33H+egFq3ESNEZrsq3Ybe2Gf/eLtQj4vGNXqqrU7y8bbRwFAiAyAFMIXdjivF28/SqjxHKLqyo2RuuJlEv/t5tVbkxPqQP/ISb9E9PD6zI1fz6j9NqgmVJM2D9wu7X61NflBVskpwEy+w4rVccWdZj1Qc28LIx46sZTVb69QMp362w/OjS4vQrVxtc7nqSICABR4qKvz2FLv8tWkOeJ8g4SkAkegmlI8925y/OKsLMp689rBAAHF4+nEj8+mC0UZoDFXwVOIEZzY674/UZxdKpOlQcwV5USh3QMORcHbj4uoU+ONAxibBclEHVEeG1z+9cO7+iWVE3odhquH8aX/cvmDdDaDdZ1c1qWVQzJ+j8eTF/5+JDudb7Df1SZZAQBSqOuQ36/YkrfSaphovXuuZkNfvJx+8vZESS6aGVhRlHP3b//87o26Wl6XEABcDvtfdhzJvZmXS5t+v0iMkAGAEIJAQc7ktPz4+9Px2aTevB5Hk88Xf/ne4sOJMm9oslYdhQAAmai7lbeF+Uc3i5bwRnX7SARE4PHww9sd5z8plK3bQ2qGKiGeOGB/Z+7mUk5zS1QECGJ9wuRfR25/8OiBmrxnOenjr2D2qL8rinLz7OjDd+drc3Aa75EAgAlBrqBzx/HWhbMJIesn87qkk34vV3LKkx/NLozFAUCz7hLNLS3/9fmziUxmXZddfYSCXhocHhgJJB/nkG+aL4Sey6++CAGAnC19knrwu0kDTUdTR28+XPr1B7ktUYeIA4AD4eQ+16U7hUxOWLzfEzP8HMd2OWYW5LHZsrQFMgUBhKBt3U4RmXx34oHBfGZIxfoMvPO5zH+89P58ImHRSFelGvQ6MTu1eOEfHxYzZaoc1OqfNGOrIiJCEAgMcPBMmM1QbqKIvHHUPAAUyFn6TubRr8ZyhbyKjyrL8s8++fi9Jw+I1dq9rCGiaCD4RmB/4g85qAtQqebBmaqXFsXETxZmHy8biSMQT2d+/If49HJZR6Oy9GlQEYJ2DDoR8cajIrN63zW6iYVtg51bmJdIAHYbHDlAvxj7pCCTJluIwOTYrTNEA/HcxJNf37leKm0CJ28TVL16iKhQKn/8i4cz1xNGtgQAINYfYqLeiKQtHqawQIe7b0do4d241nhD+7swJLkQbOLny9N359WE7IfTU//5o/MlubwlqjwAMvbl4T1NH7qy82qYaEPaimFjJ0nKPM4/+peJTC4PAExRypeuL757LcuIqYBgQpACoCp4jf8IBd1u9twO23ufZMtFZlWzph8mE8lIJ/Y6bz4ozSdULWULepHp0A7PJBu5Pq9GlqiafJ0qqOk9CwDIy6X/dOXi2MK8/q2lqikzxQUDIOL4vdmrPxwrFWQQQESoAAgghZBQW/Cb+SGFtA8EIAAVQBQ7Xm0uflIsLctGZEl9z6UqtxVJziE/WX7436fS6Ww+n//uxfduTI0BAAhh/StXaCja8hll+9LFBNOsJA0d06psToqY/u3ixI1phWRpdjH5zrWU14V+L2rJrvWB2j+FhEIHt7uEwoplbG9BZHWG3axBJKT2ZqmvVbp6v9jZYiUQjt4BEoLHzrbtzPzH+zdqLa6mLZjDylSmdRvX3uu1hZnvXbv0f28KO1xOVb42OOoaQh0xOpvP3Xh7rFwqhbo9oKrZRMA41mstQ0bmMAgiCvf5WkL+qV/OI2dqLBQRMUDafHLwKi5xRrPvLE+dns32Ke+NPewMN2/RaZAQ/nLoCP1SLmdk5BxAmECk6iGzRRg5K8yXn/zzXLQvIuUL5X27woODTI/vIa4efyxKpiaA3qiSK8I3wmEJZKuareki6hdBh/jiawFWPyD/Ol14nTReujWVSgEAkIq7UvUsjEBpYPMiWflwevzX168sZDONV8Kpalm3HhNhk8ezP9a5a2fv9v+pV7dtKGgdhBcRIXIWFuNvTZZzMuecUDPQkyAAvmnbjCmeUX0QQi4nxPz1BLQ7Tvb2b1GQHxENNbfslzvuPR7XGa9RPNyVSz91P5eeT0uhJm9GLl0c96ivxGxmsIjYRLo43FL4eNon60ZLWq0UTCNd+BzKicHs7UVvMsf0Hceqdaw1xYGODgwfaL328cwoMslgdUTN4rjxLIpVDjBEHqfjSFvX//LmL+4tzFo08ipiBArC/+HIiZuzY2/Yz3Q8aiNFMzAa7wLr2sI03iBEDkhAhOQvl/eXZ8/H5WUFCIAhkmq12vRLESgq0kNNVxfC2+uU/FL6o8w7CyOXJ8YkZAKtXFQo0Ody/L8OfqGwWAw/55v6bdwwMVcNZgNWADVNrIYBNYAVDq2vNkX7wqzJ5z4xDE3ukhCMCFEgkbU/NBF3FGXW0yTLgimARAjArO0inudjS/btsSwRqd8IAepfBTbYl9ogFoXt8XzkjcHDbruzMvXqnzf17k0r3vzlK91Ds0sLd+amxRYQEclC2d3cFvZ4/+P5d3+aPi/HiuoUmd+FMWmb+lFbAABS1KMZYZxHlZb2lyIAAEwtHqQt4s0SA0AyYR+gYBK1nAksvB/P/iz7re7DTptdJu0ZhaJYMFmKopD8Uv9Qz63A6PfnvMNud7uNZIQVkT0b2bBI4EoRiIikCP+Qe+DLnR6XkyHw3hbXcz2lrUEbBAAQAm7POIdbCm67shW6IgBwpPvzdp+DWgIyaYmk2hyhqMeHbiZ1AXBUZpLcizte6hoEg/EQmQXJJdDq9e+Ptvz6zk0gsw3FMiIiu2T7zPZdb43cTuVy/3T7o/tto+jEFYnF9SyCVfYgLvEJV+eBdlevg5RK+Ht9S4ywUtZHCBHc6wMOy9ezyUeFbY+iZ/oHdWAXy8IdWv2hr/r3LZ/LFJfKySvp6JkgSvW6nlGs3HwEAHeynr+INXc1IXIGCE6n8+ig1B4qK1ujXiPSYto2k2I7YwUmNlG3beMkEAplNjJn39Va5EwGLbVoNZmzSSIiRprZgQjvzQZe7z4a8bgNFrLEPfX5/uFrY49nUnEVHczqRApCQSd7+4tl+erkOHD2ZGnhu9Pnil1axrqpuwac6aZfBZShCIHlcOdrLVwrkdpoKQ4kAIE2N488758/l6KSIKLk2+k3wofCPreAxlDITGNDxr62ba/vA3tpoQQ2XLiStvu4f8itau91kWnzUQOahQgdDPa81GGzOUB31mNbxHFqIO/kWxG0wQBAoLg964wF5LCvsBUnaUaCI40t28sEfWFFWc2KVd8iQEQARtqRgxI5Vsz1f3lgt5afUt3khtaBagE30fZIS5vL9eaDew0O9ek9QpPHe6J/26/uXpd1IJxf3L3+of8O9+EG1aqNEyIiMUTkM/aOrtbgHqeQaw9FmyU9C0GJPB8sLMrpRzk1ACA7U4pdcn5xeC80uBtWcjJoZ6z1ldJw/MO0iu2i5JT586noqSbuZA1ujkiaYuYM2Ab/XWtTNKB+r0Xl2STXvh7nQEtxC4ShatXAbFF6NG/f1VZaOxO5XlLD89jdKedQS8ltJ8NVoCNnNiJeNPue+unevPNo83ND0SDUvHhiG1oHiGatycb453uH3r57J1fIG15Hy2tFvbptx6OFuYdLC6Br0cuZ9N89Orvcu6wPypr3bs6BIAU80+HOVzvsPhs0dihgah5cs61pl3vh7LLuPAREWng/9Vnc2d8crZ8L9cxfALBLtm/2H5b/UC5ly0x1pzFM3c3IWSV00C9kC85TRNj8YrjjcIvEuPF06tZM4YD7xWHZ71Q27enaACEiA/Fw0e5i1Nlkrd7LDHAuRJrNsKU029GSZ7r/jWmaZCMPZcR8cY5KtoxTy7GvDx62sWrL/saM71XLnehYe48oFS9NjKpgIup6sDICi6i7Kbqzuf3X926ZJTAxPPfwwe/xCmtWLBSG5iMAIuIya/G0Ro77UBCDeqyXph2bNZ8Oxe9k83Olyi7FsJgqSefEN4cPM6neWWOkYYUKOtbXd2CyLXErjUxScccRkWQ+dzbedMhra7JBA2uJEIRMrg7b4F+0ef2+Sv+gbc3ImLS9w7WvY0vCvlQTYknhN+ec21sLDslCvVew6nDzW3PO1mA55BZCWLaa1XZUW7MENLbo6PUcONzaCRt+H+boSuPLkNN7uqPnl3dulJWy2eJqzaDVfhl+fuee849GljPpGrtFoSz//d3zE13TTOLUQMyamVaeD+0Tvu4XOh0xO9FKO9CGCBGFrHj6Ha4W+8KFpHpAMOaTSTx+LfP8fPfhrp6662UKBCAKeDzfajmUejMrZDRvqShRfrKcfVJoORloYJYEAHAbdH0p2rojYgJ9rNbUPR7PqWGMeYXQ4iIJrDifGGHNHGk6IeWKMBApKToYHlihCxnMxgFTBdvoon1HLA/cMr2uph0BNDIX/NLAEb/dscEWSIO3qNp9zvT0TizOjczPArKKUbT+2dCEvrnX/W2dAYfz3dEHqyTGMLo+NfOj5AdKex5ErZgirKceU00jiIhpCJWisZfDplzYtWBGtD8Z6JgIIIg5eMuZpoULSSWjaGkfJn6TS+Xcm/lvdR5yu+zaAEyTsPYa0K9nAPCZoZ0dNwLZyQIyhtXzhUjz55OuXoenw2Ha2jcgS0wY+aQI/w5P/xc6XQ63qWmo6Qm7W7zP9ReAV05B2r8NrGbz/kSEN2c8fdGizyEroGpBjIh0UVNnN+aJ5qiMLDi8btHuL+tGIOsNTvNJ7qbtL/cObvwWUe1q6vAF90aiv75zE8CMXtMIaec943eHzf768O7f3btdLJdgNYASIcr/fOfDmy0PmavW0YJUWYh17WUMADggItomXJ272j39Dt3AyFYK3sphUn0IEEY1KCFEaK8HBcZvVJKJSD/qCwDkUupRYfBe04v927WiZabHXFsb0vuVu0LhLzr3Lp9NExGgTDWrkWE5Xo5fyrS8FGaSFq0OwNZN+lPPSqppV3Lx3jdao93hVWbKTA6H7bl+qS9UVjQ/rEYKgwaWsjn3nBJZPpO072otMs2SIaCCtF9f/FTtUxRl6c60Y1drUeIWxN2uSgrQyGzglc6jMb9vkzOjbnD0xf7hjx49nE+nLY6n1ZcFCjrdO5QuFq/NjD9VuiKOL8f/29R7he5UVRtqLLaRu1DP/iAAQEbigFQC33Ko+7UY2k3ofoiVwzzV7g6EQhPFguw+W+h5/9zZJSpXzzMxIkVvUST+kH0jsL/ZH6zxtW5kB0HGvz6033MeCvGCJrFRIFad8pGz5Ssp5oDgdg8pQoflUpXGpy4w46xEpISOBHtebLfxWi/dKjpKS8h9alC4bMJkxhSMoN6wz0qsj5buieLOrD3ipYivvCIJrD4+N+7Sjgoclamko6jgQEQ2G4EstMtywHie0pm+r/Tv3iiTa5zGAGBPtC1sc5x9PELMYg+BhuAqRMjrO9478Ivb18lIFzYbYEyd/vLejfO+G5JfGBYaRGQrGGOTpEkABQgR+YyjtbU1dMAj5Iq7zYhzMOu9+giZcRqKHA8UpkqrwCWaMTIY5eZLTR85vjy8BzarVhDtbm89mRlavJRaiV5hngRRpIWzifDJgOSRqhLE1rDJ6SCr9pDU/+9am8JeWKHxGWxa+VaS7Hu7+faWokkSruIT2zAx419CASA4YL4sjczzXW1FxrSAVUtMsgKF0BeckOn2tKu/ueBxyEbj1vpfONKDeeeB8OGdkeZN3ehk0md7h39390a+ULQwPsY4PqhVLD+3bded2cmx+IIhBtEcFWWsLcRELvv3999d6EvYmGQC7GnQvV4NJSrINRXofqldCtiZUKrjKGrhRhFRk42CnK0O3zbX3LuJmseskMoMJCHS8gep1+Vt21pazVuMQFh7kh02+7d7jihv55WCMBIjjWcgPaUFCZCz9KNCOa6EDvs2Ozltr4a7DsUYk4CgxktXk3CtDTYY8JzaJvudRghY/YtXjwkW5u440pMlu8SE6q5g1GgpGEOn1cNqFWC0mOFzKduO1hKzFHjLTHkZJxdjXx06bN+IIq3bDI51dudyuU+mJ2lVTK16qRJTLqg/HO2Ptrz54F6VW8K8bkTFIAGI7z159GvlQ6W5YLqGmbbdOpUgIjQObxjnzY5Yy4kAipow8drGiYhQECnAMHommLieKS2WjVqftavfgO1jWMrK+I7yjYH9XJLMza0xyUyhU30De8ZaE/fznGsvceVmzUDL8COi2bPxpn1eR9i2wVx7UsDT4x54o9Pn8QIAAdXm31RdrZpqARiTtrV7DnUV0VTzyUhI2xQpVd6hSk6KIqRbM45tsaIaptOgokhUtaGiVhNL3JlzxHzlJq8K1WolK6rR8RxpdJl3Ovce6+reoGuhyeU+3dH18zvXyyvKQVcmqgERRESc8y/s0N0STIP/MF8BYCg2ms+gJJf/4c6FJ50TZm+bUDVbgHpDrxkHVaZprTjGPd3Ptzva7YarwmjXyGbU9WGOxHyDLlfItvhRijH2FAD4ainKWfxm5vBs13PdPVDxhqy1PwZ83m9EDyZ/nxOyGqq/olnTDk4IiFiYKaZGctFTTQDr16IAACZR11dbWoYiAACrKfnV1lEdlRkAXC7H8W3YEqicqaiuLCfDLq/UbgBiLmnPFHCopajo+2V9bj0VWN5Qno1XxQEzBf5wwb6nLceAABqqgV5DpKbuAJCwjcwGvth3tMnlXvcuAHi9d2hkZnZ0cU5XCLUzmHnk9XkpmHq8EnSgo8tpk84/fgCcgYbUaGqwOntD40nEWzOTP4xfKHVVCcMGHK3aQcPIsUBEymFTPtr6SphxU7AIAADU5PsSEdoheiqwcD6p5BRCIVA7aSCZWaR2Y1XKkH+r8K32I16vS3/GtXaQzw/vbrnqyUznkRteGWE2e66ETkDE+fdTrg6bp8elKOu48UgRgd2evs/GXA47wOoi+anCAZF3RlzHeop23kiqaqWXlQonEd6acXWHS0GnUI9t9R07a2qYmncaGxOPFpwOTh2hokLWJt3rlj2mzGaYJA+/1tNf6b1i/2DmRd8TCG8Lhn9z96apndVUrPoGhACALofz9eFdv7l7p6gBwq+XImjYYIh+dOvqzegIc2upR0y1wjc6vCpcVmnS0b29wzsoVcdDV6DmtP1IEaEDPlGk5G3VLcEMiAvCtVQa5JB6ku27FXy9fzusBWBHQNQbav68tDP+btocX44kAYA5k7j2YRgqqXL8w3TLmYAanv6UCHgBgiQP7/1WW6S9aQ1eW0tDczhcz/Xz3pAsWx/XDwCASMmcNBWXdsbyW5O/wQRhScHbs84dbWVzmI55Zut9NC3KTJUVd2dcpzuOdfr8pAsWrXFWUeKRsS/2D33w6F48m6lP1q1LKOjFgaG5dOrG7CYqDaoZsYA4mVz87ti72Z40rxSsr3iMraEyeudDHZ9rZQ6T1bqCJSgAAATZA1LoiH/+nbiiKJvO2ASIv5P+indvW1MTPBWzAzmyb2zb7zoHhWQROTPMWoTyGuo3025mS59kgLPAbk/1bmJmKEZE0WOB3hNtEtoAnmoeWueYFA67Tg+V3XaSLYXrNUiguDvrbvIobV4j0N4IBG3cVCNAC9Nh2TwORCuKlhr6pH5uxDDLSKhWpXRRSqZ6vjy4p9bfRcT0JzrY3OZl/L1HD+tDRlyFau2EosUfPNrV/6u7N1fP399Ag79+cPuc6zo1KaZ4qUazkMzEGKMFe3ukNXzYJ2SVu8zta2k90ROB/FgxN1VijG0q3x8RgWFuqeS/wL82fACe4ldDQQe6uo/He5evZs1uCTV6ZCPmAyqJ+XOJyLEAdzOt6mBFlmq6uDNiH/h2hz/kNYq3rUrrdCZx544ex45YgemFgtcd3KZIIszLeG/esbM1jyro12pek/raBtAAMIn4zVlHf7jsdarZFZq7XD1MNgb/rsUbcKSRBduepsN7ojHEqhppqiB0Sc7XewZ/e/tmQbYO17AmxgXwM8O7PpkZm0ou65bYzdmiUFAmm/v7kXcXuhcYY2rxPULGLM34RkHOiUD3i+22Jk6KAGDmDZ4UcHXYPP2uuXfTK4Pp1iU1PBWRlj7MvFQY3NXaZhx6tfdCAgCcdsc3u46U3syrtT43077OaZxlHueLC6XosSY1Jsw42BMCR0Sk1s+G2/e3cJS075+ypNef3KDXdXqbUA2MFruVAQhBYjC6ZEPEnnBZIZs+iWikINXbtua0EEDEKJG1TSVtO2OVGndqMGcjT6SOjZFmjC3K7MlS89cHD9txBZwR0amu7uVE4sbMBOCmeWNlp8Zn9Syn/gy2xHoikbfu3atw4NNtEqvuPsSQGH44+uQX8sdyWxEEATBsbJZquyCSCDEhRXlr6+kmtWVuMmAiF82ng8vX0qV4ARiKTQOTM+3YlpXFW/I3Bw7aJRsAgNBr1yFDQS8ODe94EE4+yJtBtTfymGbTAyli7mw8sMttb6m4K1R7qZAVd59z8GsdXrdHv/6pG8o6q4GIGJOG2twHOwqNpS4/lRQgIditaee25qLLJquqGmmgb9a8fkYgUNydc0S8otlTl6dldaq2qjOajtub7btPdde6KyJu77HWtl/fu6GQYGSwaG1270bIPCHa5ooIiDYufXH7nj+M3EkX9PJ3a1oFjXhd9RcwLa+SLH/39vknbRPo2JI3LhAQyT7m7jrS4e50kAJkYMXLwrfNY/fZEh9nkDOx+V2Y9BJIig3id9P7x9uP9/YxRSsdqe5ZEX/g60374m+nBJDZ6LiRMGlzNowKW5i8k205FapocAiIiHbW9ZXm5v6I6da6DDNGl06n8/gQmeKhLSDjYTggIs2keTwvbW/Jo9Adpo2jIZhID9OR9nbkkJtTk+ptH7kBGmAcn4SA+7Ohz/Ueq3FXfL53+O7k1Fh8CUwBkwBV2b2NEFPoSGcPA35x7LF24NS88Bubw1onOL83N/fPC+fLHcYp2nqLABUgmI60vxJFE54DuljLyaa59+JyXrWWV8Warvq5tlldVDBiQsbU29m/iB3yez3aRiMEAHxxeHf0Y3d2rswAV67otdd4JdCcCAk44NKFlDNm9w44DQuNkJXwHs/AZ9udG0uyWU8vIs292Bn1vNBX0OOhLSD9BFx5qhuz9vYmOeRWiFCoARMAdVsFaqZS3SCfLNkRsStYVKWAqkbWl+kvhHaW4MC1gFVARFrMABQHP9+/07hyoCnc4w/8duS2MRQAnUnq3WjQ8O8BAIDX5X5xaMcv75kCALTA4rqYHBUg+smda5cjd9GtitutsI2jbcrZOdTh36EZGIUQ4cO+UrqcvpdV0RPNwrxGBXhas1V8yyE7Xui8EfjMkPY6BMBAc+wzsGPpfMrkWhZV/21o7xLqmVlhUM7ICx8kmk/70c6AmBpx3v+XHaHW0NOaqfl+TSZUw30QiMhmcx7st/WGZFF9i1WnBQ6Yydsmlx3bWguICiPGNGxPqYFjYYVUK5wi2O1px3BrsRpNpx7sqVWTdAFAoLg/5z7Rerg7EAQACaXP9287/+BeIp8DHVoCQF9em380Rnoctim85qXB4elk/O78jEWiFQFxJhX/7uP3Mv1J7eQJ0OAReiWRAt7ZYPdrrdzJSAFHk73poH/hbEIoaqRx4x0wAFg+m/6yc29XKAxEjPNvDh2S3lFK6bKpHBWr+m9DXbNKpLvE0zdzoEB4n5eEjIIiJwOdx9olxp/WTM33azJhBTYCASHa5H1xh+y0G3mA6v2NTFVVwBoDcXfWHnAoMb+sGoFWRvc2TrMpKV3gapgOCNX+bmUxRkRMFXEx0fn1oQMAcKit3Sno/OhDq9oXoLOfJlGpzd90oKPnN3dvmfI2VdWrMYZBfPvB3XekTyAkq697hS/HApMpLthaQ63h5wJE1HzCn3mYy02VG6zvYawYgQIYFuIF93n+9aH9AHC4q+fofMfStTQyy1D/hBDlMs2fSzQ955N8kiNmH/xmeyDo2XgLaw+lapY5t+3qdO4bK1wc9QgANEIx6pqxKueP/r+i8Nuzzh2txbm0TQgmUFhb9BgAiOjmtONkf350WUkVuPV4OgQA8GDB9lz/gZOdj4+39fzy2selUslK744pHYkQPrN910fjj2bjy8BQZzzdjL55PkQCgcQICSGdz//t3XOHtw11LHfIpDWrnyMEkahPkzcTI+YY9XWdaC0ny65ux9g/zgEIRCYAOKlpN5teAExfXUw3nyxcip/cP/ThwOg3Wg7m/7kgZEIu6mnaRMYCRkTgkH6cD07JLS8EHDFH256WTb3uNb0OVCs4iZQ7o8v/elXKlSVuQXIeW03QiaM9hdEF23TGzpGQrMw/UidOIOyMFcoy3J112DiCmsJpGTsyACELaA+Wh1snr0w9+tHVy2C1i5WIGKIioK3J96Ude//p8oe5UhGsKylh9CJx/B+PvPTK/FFISoYNc8W6qIfUCHhETgLl7vSSc+nRP0wvX0rVUYhzbSIiEBTc7vb8jwH5neL8v8RVRbRBJqwlQfaQNPh/bu86EWvrjm3q1jUlIVblHwEAIu9v9b28benWlEOAlYVEVFIzqZeybFd7XnkYzxYIGF89gaX+PhiBInLw3JDN7YB4FnWl17I3Ymwccqmlu7Xw2guuhhO1VpJQ4wSidle7L3j66DE1b+hpI9kEEQOUDZsZMRyTxNjO6fbxqJCJqWJSbbze2jvVN5YBGCrMQe7SgimMQXVyrunqXL1x0yOrn9XDVGGpPCS84/GSEIIxDpZyIBEhw3JGZg4p0l6LXrEuraOOElHl/A+AAE6nPRZy/+KmNL4scdTVhc1PFhBD0DACjMky3s2h7sz0fP73V8tgE8zaiDkiuw3+p6+425p9t2eVi0/8m3cHr0taFEjIo+wa9Ly9eCdeLm9F8C0gOZj0v+7s/ySVvJ2Jcz1Ei1noCgUApGwL/J+SLa4HXlHZl62ZNM01J5B1udpfiY783YRmC9AMV5vuhUxjq6wugOippoWP0sGdrqUrdjkhw8qAiroGr65YxpiQKXzE1/dCqxYbsBla30+oHTIq2yxrj7hPDhadNkIkjoRIyBRUP2z8hynAtM/A1FAyQYwQUaC4N+faOeRqjzBOgqmQQxb9AMDBAX5qX5PPGzg6wHrCeaFFM1j4g+rMJHNSdqntdLSNIzCy+AcAmMCCLL+/NHe6Oebhtqo/WdEFCmIEQPiH5ambXZPgk0EL0yFsgDhUPqtLihhJ0/b2vvbgHi8Ik52sDosACnNQCxIIIbwDbleLfexfllIPCy0ngwCqtG+UDP8wKbLNj/1/2RFs8dfRzroPKWrCKQDAZnMc6LUNRPMNY/hWuWKRGCNAJA6YKvDZnP+FPdxyuO4mH//aSU805AFgLSHvqcGS0ya2pNw5AJFyd86xWxrocLuNfIsGqSqmA4kD3sskM3L5QDBkOWqz6vdnBMul0r8UHiT7043L81UhxlWvsGe6qfPVmOSxkQKaNKtLWBGRyr2qgxPtrPl0cPFCUsmWFt9PuHscrg6pAhDVGKlbCRFvORPqOtoiMSNbYhMjX3coq+PvR4Ke08OKzyGvPBVs5j1VDZR0SBwAkABH5qWOZu9AKxemmNUaltxgXyrAlFr66+Qu6dBwgDEJACTJvrvHsb21oONNMbOipYZ3b/hZVhJDxJwsjc9GXgn32FaN1awjbA1AAAIAJ+2DLOj8/NLBYCRkt2vPYdHOVcG5JriUWLjYPEbhEtRfFwVAT/6q2Y/Uqg9sSWr1tUWeDwIIoRdoMK4xS871ycgGVER4nxcUkbpVYBKXE/Lyx+nmF5tU2EJtSKZxbOjZzLn2RKSAu93W/xdtvoAXQD+2bUaG17kfMCbt6HTt7SiubORpDLMpIhRFmT2Ie0/tsdulCi4QaR4yMiXabKQ1UPGAOqLsK8fdfm8FgbzJ535xWATcss5+csV1W5dv2rRQBCJyEo+X7a3l3p3+gFIps2dA+9Rj2WK6Y0j1QDCC0ULiSTb1QrhFcw9vQYBLXhY/Tj2eH4yjtBlm2AwRkX3M03Oq3RGVmKh9v2tkITyVBNl9tqbnfHNnk3JJEQAkseUrKcnF/Tu9pEcXmfQxsSF+QAHm8Bou2r4Qad8VY2oYunaa3cQw6xfKPo/75LCIeqtKZJh3uMZWgkBGE3GbzevZ2yetNJ1sZB3UXCMhfO6QfagrQKaoUTU8/VBnEUDUgPPWR+psGFjGSKAoeHcmcKap160f2U3hAQ1JFabnoSLi+0tzfS5fm9OtKaVWq/Ec8H4y/nv3I6W9KITYimhSRMQsD5ejsZfDdRvDESsnGCIKn/AXpkrZ0TxyzYIoijR/LhF9wc/dNqYgmMJNDVrj2cxAbABAivBt8wx+qdPl0NE06nJs1k2st8VzuK+o55o1Aou4SuOMQCjs7qzn+R2ugAdJs3cAPD1kzExkBBYRAICs0LZu9toRn8PpRJMIIgCXy3V8CGN+xQyCatT9qY9Q38jVlLzZFOeZ7uOhqPZXiySJ8fCMIF5WLiUXz0RaJdVGSpb6dQAAQCj0q+XRR31zzAnW+tgMQkQ+4ere2+bpd9VXD9AIdCUiZ6vDP+Q2wyUCCuQs/SBfXCpHjvq1vDkdeM6kqDy1awM7XH2J3Ml6vxGLdjc1dG6p+05EdDqdxwd4Z1NRATKMWhXbVL0LoMJjXFnM8JTwHtsukWl+V165SiN65LeK3uNxsa8dc3W0BGqR/wEQsbPZe6w/bw5P1wF/GnoEndUFEd6ech/y9EbsNoGAAIKRtdYgRuJKYsnB2XZ/QNG1EEv1RoaIk/n8z2Ck0JOjzUNObLSbAniXou2vRbiN1Rdab3yKnWlKXM+UFhWogjIQIGj+3WTTPp+t2V6BLTTq/dI6mfVoJO8qctMRX+9LHXbJsdKGtPEdsE4mNGRyS8h9cqjkZKBFIWBFH6tbHa2ExpIKW+ja1udqa2LyJhMeNHs4AClwZJAf2+mXJHvNA6hkt9sP99t6QkXRGO8B1NyufpYQKZnn8cXWM5F2QiAAJhoMkKwlRCwqyvnF2eORFheXFOsZRAAAAntnafaTjnH0K4pibTFLzQRIDG2z9s7O9uA+T1lZxfK3bjsAQAr4h9y2Jr74UQpRMcavXcCoMFNM3c01n2xapYF1TbKq0UWQrckx+O2OYMRf+dLczIY3qc0zoe43BFCzKxz7ex3DsZJCKLBizGh8hek515gtssmM99QeSdJZfeODJ1JI8CYffu0FTzhkQiBfMT/NTZ7Tw8IpgQKb62IFsRXgSAJAINLIrGuY9Q+4PYoOc1ifMFl5m3qa5YAj6XSqXDoailiriFa6FpQolX6ce5LsSzFWn6R6Gply1gR6Zpq6X2tz+O1Ql1LNHGTAJer6iDlvjnHAxQ+SznbJ2+vcdAA/CiQAhrGXmzoOt0gG+nC9VIczFKCibiEAhHzu08OliFMhIgFMt3BA42d3tX3G4eG8Ixb1DreiCvOo08baR/HSPmnvkOaWWLnuNW7ntj3dzt2t+Wq47voewQyTo35miJiXcXQ2+lK4x84YAMhUT6okrWYlrBRdQzo7O7M3EIrYXVvk/OSAV5bn34uOYbQOMbVRwiUp4miOnfDX04Wg0MGgKELqThY5I5PDwHj7gmMpXY5/mG4+HTTQujfSl4YGR+hutw9+o9Pn8+hpTauw0gYHb0U2CpO2tbt3dRQAgIFA5HrwvjWWRhSkCLi75D25x+Gwr7GyKnYK43YB1BXFLx3zGVAfq4xfywwCv9d5clgJuEuW5LDWpGIRkQrXHSp17wk2qeWKAABWgx5Zg3/WEZ6Ec8X8SDpxKtIMpo1fbVNYpAHnSfnXxOOpgQVuypiraruxjlTjh2vc2/F8h7PFvqq81XPoqrLhAAAE2fw8dNgz94clYXjkVxNTTOLxazli6N/rJUUACKqaW3Omb+UzRwRgyEXnl5vbhsOqW+JpL2WDmo41Ni6Px3NiGFp9igYaa328MkwnGDg9B/oZCQ6r+z9Qf5xKGqTE2BeOOgc6fetPBwIyaajNe6SnaHmYDhiuC0W6NxU4Hejzca75rFfAZ2G9AHBaFxzPL821u9y9bq8OlqUp9qwSk9AQSYQjyeTvXKNyR9E4wBORMfOs3iOpYdhERMzwSKm5/eUIrIIQKYQWlsLMEdsqCED0RCA3WsxNlJGtomgaAxZClMvl+XOJ6LGAzSuBQF61SLRoAaZ/JiKOSESkiOB2T98X2pxON1lhgrYseKe7xf1cb1E/BOvfW7eYifDOnOvIdlfQJ8CUyKa+fn0idPbTMt9wVxd75aDPYd9ohqXT6Tw+yFsDZWttDqBbSpEpM2mEVOcL4VaiVaCz9NVcXxcIAFxArqxcWl46GYnZeOUgZDQrGg5cBgAF6FdLT0Z6p5hLm3/DZghrSvJ1HkENagPNQsMnnJ272s3wLVCl5Atm2o4REQQ62yXPoHvuvQSisuryNsaJiJzz7KN8fq4UPuYnNfilmkyRJzr8qSDu5t3fbot2hECzrn86mBAAHA7H0QHWG5EFsMrOZN1KRqRE1haXvcd3SjXl7HQLhym6ApAQHE72tRfcbS1+nTnXX3yI2B5xn+gv2q1D09GpIp9vznoOOvtiLicgGVBD6uJoJP5TmJbjtdSSDWG3t0mp1tJX5fyNEVNZBHVAnZlc/qfiQb43Y/SOyBv3W+jbEAMALIJ/Idz5Wks1XLfxuVIxQtsIuIi9GFq+kirHy8DWRwxTm5o7uxTY4XG12KG2XBToMf9Aelw4EUWO+ntOt9q4bmlvmIksdLmylpD31FDRwa2su2IQIqrVRQe73O0RLGuzb56yKlc7CTy2jR3dFeTcZlhHNtCPsNudB3pt/dGSbH1MiAAAiTCVw/nFljPhdiAkYdibCRs7TKmmaXWtFBXlncW5Y5Fmr2TTlNJGAe2FqjlXhAPyd5fnrrSOYUDUCNs629e3CfUsjYjEEOdsra2toQM+ISumLvR3oydDICIpwr/da/NK8UvpSmYwrR78rP2RCBgWF+TUzVzz6ZBRfc0ICCKz1iAIhOKI2vv/XSwY2mx55rVo00xYE85X+QxCkuy7u507YiXL65CpfXHAvMzGEt6Te+0OTQdY/ZAjAJsD+MYJTzhggh40TIhrEQOAaMh3epvscSlW5yVIarwBR3o45xjAvgGfz1z/WHukBoSJoc1ywNFsaqFYfD4UJS12Vt3SG3oibZFqEfEiVSr/JP94eTBRA9FdFzeyGmQ9ARo6s2cq2PVimyNoM0UIaDLQiHxCRO7mzSf8c+/FlYIMFcVjrTduxFQsfJiwNUvePhcpgogYmMpcV6l1rPWVUOeBdo6ShSt80w3VBLZXPgMDgCaf+/Sw4ndvlfGaA38clyJhz44uJoQxgKqJRkRg9PJ+aXdfEIGDvvAIVj8kmEhbBJzxnR2OA9ZXF62kuhUU9mQm+lK427ECkqvRYJRKiT08tzCz3RdsttsFgsYnDaqLqEchAQAAI7gaX3ovOKo0F81vvO7xV60o0qoYwDJrdsSip4KmFC5NsFcOurISOeQrpZX0vSxyCSom8rXeoJbPzaicKcUvpJpPB9GuS84V3IsyeHpcA290ej3e+p7uaWSxvGIoDXW693cU9NOqxSqdQKHI/M68+/hup8tZAYZBdV8kAABFQG8Lfu65oMvt1L2a6r9rpiwQVPK2EPxezwvDSsS7VX4wCWh8WQoUeg4GwytDrhvawkwcMlfM38skzzTHoL4shKc3DvoRtKgoP40/muyfN6qLWhoJpK0fx7in+2iHo80GAhGREKoAdQTZQ1LwoHfhbIIUVsV4qmP9KYSoIwVItsSNDCgist9nGIFqthK0Q+fXorHBkP7tn04dXYcQXA7nqWFoDsggViqlVS6EzZNQs37nk5Jicx8Z5IY1S8tFRAAAO2dfPursb/djNbjwuiM3j01F0znSlUNEq7zeZouCwoRCcGfae8LfF7DZzNAoFjrZOeCHSwtRu6ff4zOnR1qip6hVDTng43TmN/ZH5a5cg6bRVTtRexF5bMqE21+JAqNVz58tJ5vSI4XcVBl5rRBb2zqoplYggVIWc2eTwec8Nr/NqLKkRXiTVuuz/zPtTnv92RJrjcFaYox1Nvtf6CtwSTHeR/V5uh6EEnNaAxHennMfGHSF/AwEmrR2Egrb08vOHAzY7ZrxapOqUWVCnE77sWHsCJaMUOuGa3ob9wtGDJEW07ZCvO1kuNUcCGRtTGmyXLy4PHc62mJDLbgXrQ3sRiIQv1uevNM9w1S4bkudO1p2PyCfdnds6/Bt07MrdBFHCrg7Ha5ux+L7iTq9BWqZCs6yo/n8hBx5QQvTQb3UDxHZvFLPX7aG24IWPVYVWcaE5qdX46F7w6bla3IiIdZjttGiALQjE6VyfK7sOblLIjTxhmA+D75x3BEL+57e0tOoEtqif8NiYc8Lg5XqomZdbPPtA6xY/QLF3Rn3PmdPu8tlLlpo4TKWkN1KLgsF9gWaFEBGlmNgIAKbyxd+Jo9k+tLWtg3m11Em/1yo67UW7jRMJirziOYXm5YvpUpJeTW3/sZ60e9bOhf3DbqcrS7zbkKE4WP+3hfMbgkryTqcv+pfm5s8p4YUt73KwKgtwTqRoYU5MBqRRuacvR2u7mYmjGr1AC/sYId3NnFuqy/6Hqr5REXTGWwuKlQTNV3nvOmzoYXXcuDpEpuZi70U6VB9hsaDWEhFEueWZo+Gm302JpgVJ8MVxBh7f3n+cmycNYl63+/qpJ0AVe/BghSLtDUd8ak2TABGCgR3+5kNlq+mGwcsFQzzy+XE9WzsTECL+iIGgpwtOPitjmAw2PDTrE5bkppJRIxJe7udu1qLjFhNxjrVGbFRO9SCzB8nvCf32mxc7RRbg/jlE96A3222Q25i2AC1bn2CsN91apvsd5BMggE2eBw3SgbpHwQDeLDg6KT+bR6/QhZjBKvEAZ/kMtO53PFQjMSWdIGC0mX5x5lHCwPL1lYUFUJVO7XAafe4r+dMu62JgyCmEHdj5Hhg4d2kKFLjlhIGgIhLl1I8xH3DblIEoACGba83t+9vadSwvGa/1pMawuLzOk8PKQF3iSk1a5dZshlzpNElWyDg3dmNpAAweuWgbVdP0ChaVhfpbn3dDcm4bWeHa29HTrcuVsyw9ZFZ3QUARCop+GC66aVwj4tLW+TaQcT3luaGvP42h9ty5Au1fQ54MxE/638sWosWPkXtQSbFI9jSejoMwGQSkecDpeVS5nHRAjGo/sdQySlL7yWjpwLoRBDk7XP1f73T6/ZsFQtuCRMazhwmDbR7j/QUV5oa6syjI6YnDWuHQyHY3XnXC7tcTgf1N+Pnngu4XBuqCLd6+2YxqA6QAAC8bs/pIWr1kTAkYV1nD6P2sJ5PqPkDOdJ0QnJnew42WQ9bqMXECVwoFm4m42cirVbDQOnvAqEkxM/iT8b65tHqo5OxYDigc8Lbebjd3WlzRuzB3Z6FswlDO62zcQIgXXwTQ85Sd7JyXoQP+JFDz9ebY30h7Y9bs0VaVpsGALTII9MrdjqdLwwV7k6Xx+IOxirPUGVN2RghIgGBQAK1NJ/WzXTS3hlyn9pd3N1n72n1NYIVUI18UXkWROxu9Rzty/zqhlchrl1dh/WkUpCWFDXBHrR2iPDWlO/4QP/tdHKxkOdo5eZIRIyBILoQn/9fdfUP+AJ3UsvWdUHaXkVACI/SmX/1Pfi/dDbZH1rs0dYIETKiKdXc8epiIV1K38tn54qc87ojgbR1VYmDEwAgZFw4G+/4UlTy8u5X2x02JwAACLT0vRhkLROukuHfFna9OJy6PKFU8gPqwMxfg4gxxr74nLOvM2CzOQCgvnolpjtWCTR1OBzP9RfmM/l0vmIEslCgqK01S6E32rpvJZetfNnECGS1QSKSFXqjvfes3V55C8QABQIXYAEmBgEUUZ7fttRCKIRWjN4yAaIOFZEk0XaqeeH+0sj/d5KtXdRo3SZXuxc5ZMdL2fH89v+hJ9IS0r/eEg4Eq5lwFbLZHMd2+o8My9XFSSyrJIOIJGzInC6HU//KqrZVEgQMgDWH3Sf6l98dsedllFRkvdr312BVGfZkwbMr1juVmxpNJhEr+hUioiABjECpQ5M3hkqkJMPKS+HYZw8ed7hcRBxAGFpJAxUsqhy/SMrV0Sf/n8KviiVZVJcYaYTU6jDqRzvj36LDregTuiW2kVg/VGuwkVQtG2QhC7DRViSX1tCWMyECOu0OtDsN3rBWhmw9aec/u+Roi/hyN8s3pt2IhKQIkICRgcLY4H6MiESYLTKOjl88uA1KJXdG+7B5HV4lRM3nZefStqbof7h07v967MWj4aGtMJMCQCKV/PuL733v6kXcmvdMJI72DsBFJZHNRY76587GEdSc+EachLqM1cNxXG1O94Br5G+n/P83X1QXhlu0dLecCQEBAc3mBssfw7RHmr6zVnkgAIQmn/OlHfnxRDmdl4gziYx6CQyAGngwAcgACBk8XuLH+w4daR35eHaahFqcCAkAgQPJVFctIQIEBCA63dVXkpWfjNwO2B3bY61+nx9qXke9q8zYKRRF+cPtm7+6d0u1O9XT1nrkdXr+ffeh3D8UErlEz7fbE7eypcVy/W56feRIYAQyIGLL6WDqem7pg+TowcnAN1x27oIt2lS2Ts2tJdPwrTUxUcWfYCbLY2IFADAmDXe4D3SUEEkt2IaI5tjxevutZKaWZfZgIfKVwSMuXsVvBAoYBcPqopDT/UJXz88e3yEhfvbg7rsP7wtlReJyvavMEKozS0t/deFsIptWt0XrhS3RqwPbe++Ec6P54rxI38s3nw420p6xfVfiYxThH3LbAvblj1JySRn70dz8aKJajlhMfwwmrBm9tS8GAUxPIUwJmZaR2fztcTlPbKMWv0JUZXLQdcXNi6nq0h2c40zSFmS7zvT011j8GoopJfZ6//CdxfnR+DIgLuWzf3Xlwlw8bi2LlMvln1z96IMnD4Ez7TRoFDCwYOclIGr1N33Ntz/+Tlptf/GDpDvmqAe20Gi0egoQiTkweiqw8EFCzisg8eT97KOfT+cK+S0Sg/DHYUKEqoTarTznsq3IJ65OoeTdzZ7neotML+DTIJZHJbNBDZMihQTen/F/vvt41FMVAStWKZqwUeppCgxFWn7/eEQNpADE82NPfnH7WrlcNl9Wt5Ki3nh/auI/f3S+rBdaIaJKlEmDOy9pyZBf2bbXf8GeXygCQ2BYSpcXP0xEzoS4Tav8Ud/I9TGikDF8KCDnRfJ2BjljAKSwyV/OTd2abWj8a9IfSx019bWlVpktMjboL0oAgd1uPzrAesJlhbA6bbIhXdT4FZmynGO5bO8XBneYvhdQb1IVY+xL/TsujD9eLuRAXXakFJTyf7l26fHsjPnKemZPt0tl87nvXnzvztx0hd8s80wQAwUItsdir5S2LV6IG6c45GzpkwyAHNzjXa1e+PqEZlhKRXYEedMh78LZOCkaugxyyE/LD384lUwmrXmcFfQnOBP+OZI+fPUEwaJB7+nhkttOMpmTQupTg1e5C5Huzdufjx4ZDIaBGGw6HKSqzQPN7W67/b2JUZNc4gBwfX7mnz75OJ/P18crZMIyF0JcevjgB9evVDGedRuiQLRLtm/1HRZ/KMv5MjDUPF6CSKaFd9ORYwHJJz0tdny9F2OcyXnoZCD7pJAdLyE38gIAkebPJcc+nJWFrjiQ6d+G6Y8pCf//h2w2294u5/aWPCMGglAD/7fyLJovS9PLnV8bPiCxOlZz5bW6JPvrfUO/eXS/oJTNVXLV6Ln/dvv61fHR+kQWomasIKLlVPI75/8wnVxGS6O3Kz0Jer63d/9MR+KWli2hcQhDxlj+YT43W4w8/1S47jXGVDnVK+DqsPn6HPPnkxyqg4QZipT86HvT8QUtV4s2X4RwDXrGhHWS3+c6vU0E3LIApEZr+q5CKlx3v+fAwfbWjTDJKgMgBkRnuvqWC7kbCzOgB6+qiYvq4ptILf3txx+kUimAdfZ1Wu2zgdX71q3rvx+5DYYlxtoYSyK/2/2t1oO5t7JCRjXRVgAYMBYCYfG9hA5bSDW4MgrRWvUGjXljSuzFpvjH+XK8LDhqBbdVOQ8gOMY/yTz67WSxXAIABEarzkhd9IwJ6yTGpMF294EuM1y3xZMpZHw4F/lq//Me+/oB0asIARQRt/f59u5fjtxV2zOuNIQhAPzy0d0/PLyrKMra+3q1O1HzqKlfTszNfefCuUyhoDYKxCxTRPWH+szQzs4bofRYHrlecch8GcPCfDl5Kxc9EwKGgMLMh3wDQYCkiOAuH3fz5SsJLSHD5A3iapJbURn78cz8o0V9os2Kd2MJbo3c/G+c3E7XiWHWEihtxTQiMWA0m0YvbX+1Z1hHjNvclvu5/m03F2cmMgkAMA9SmNB3EsXCX318cXZ5eeNNI1b81qVS4UdXLn40/kTDoaBGV2RNTwDQ2tT0Jfee+NkVdhGsgkhcvJhwRiRvn0PLA9w4CeJuHj3uX3g3rhQqLi7T7ooAgJxlHhcf/mwin8tBrerR0AJ4xoT1EyJ2Rlwn+8qMly0vgUQoVMCWe7OBVzoPx7xetcuNtzDQFO5rCr/5+GF1u6oIqwIU+2Bi7Kc3L5dLpU0Pkuj25MR/uXyhrMiq8NHmwVJ19BvDB7zv2XLxIpgUaZVMyiTKWWXhg0Tz6RDaq1b1un4LIgwd9skpSt/PIWcVOw1WdAftUkEzv1mevDFf3YAFycTPqH6y250H+6T+sGxx3V0A0HKjKZnHXH7wS4P71r/eCItHISH7wsD2s2OPE/k8mJeRuWoiadnVJSr/zfXLI3PTK4sxrE3ZfO7vLp5/MDdTG81jnTq6u639VH5o8eOEEdpSg00KAOqzIJOSN7MkU+ignxRR44B9Kgmyh1hov2/uXEKISugSAGjcYT5hMizMFB/9YCIeN4vlRpnoGRM2StGQ59RQWUXTsdY8I1AF/KL7s/ZDkYM7wtG1ryfQDegCj7R12Rm/ODn+9EAbZj473V2c+6crH+UKxQ2OjYiEEO8/vPWT61fAIves0QiimjIKdofj3/UfgTeLcl5ZRQySkXzFAAWgEDLOnY2Hjrhtflulhsya+L9EFD3VlBrJ5SZLRnXzqsepAVBkOH8+Of7+tBGT0Dg9Y8JGiXPb7i7XzliBkQHboVd+bhBzXkdzzCk4tdT21aGDNlaTAqYnBGqk9is8ducrPQO/enivKNbTMFGoy52E+N7d65cfPxLCFNj0NK8bASAuJpJ/+957s5kkWJSsRKQZNrVQG6JT3X07x5rj93JMqsDdV67H2uWLHHJjhfyE3HzcT4RaDOPTz4ekgLvb6elyzH+QQGao6Gs+C0M5qzz+wfziTLy+x1ylSasa+rdMfq/zzA4IesqG154IG0SzV2Wg+lkCZWyZd7n3Pd/eiUIBE2AUAIBeMAiIiBRE/mJ3/0w2dXtpbv1uDEsmY9OpxHeuXoinU9oA4Gl+MAYIiiz/9sbl32t2V+sIdUA9IZo87jdiB1NvZoVcQYjTVO41hdvCu3HvsNvVJj01hlGPuWcSxV4MLV9KyYkKXKJY1/3HMHE9+eR3E4XSRhWHddqzpJV/44RMGoi5j3YXdSxDpsO91Zwx6iamCPZgNvKl/uf8Ti8AaGVkDEezGSvV5T7S2vnrh/cB1lmsVUQEiL9/9ODN+3eUldkVK2hsfu47F88XSuV1r9wgVYk4IkD83NCejive7GShGvBeBljLAIuIpWU5/kkmdiaMRnWnGlsOCkIERQR2+ZgNkpfzKoa/OgbG2LonfKUMYz9ZmHu4pI620bfb2O3PSCOXy3FsCNuCqoVGCGNX1SDVNi0SKzFTmk+PZtJkU7a92jukt7yCEAHgcwPbr81NT2WSAGstVk2W6uqoSslC4W8uX5xYXABYq3ZFqVD8wccXr06O1h9RvoIMXRSIgKg7HPmic8/Su6naghDrzaQ6b8sfpSQ/BoZdKDNzxiDoGxYqgnl55Lhv/lyiXC7rcFVa9bU1SiVrTXGRe1wY+ZexdD6HDR+InzFh3VR9XgfWHvGeGMipcN2ml9io/dqAHgeAkXnvS51H2r16dkX122cE28LNnb6mN0fvr9tsZa/XrSAAAhhdnB77yc2rhVLxaUuLiK5PPPnHyx/Iwuo6qtqWwZGxN4YPOM5SPlEC5JucQQEM5byy8EEqcjIEzsqbIGAAwHRokvBzgfKSnH5UMMElaubQtdhKe2oGALO/j89cnlNIfhYx86ei2qmz2RwH+5z94aJRaruhM2HVvQKAccBEDpPpvq8O7V/J24jIue3zA9veHHuYKW7yrGIU/QNQFOW/3rg0MjlRc6Y1llk6m/mv5889XJzfkjBRAABlf0fnqeRA/HKGSRxBrNbNGozJAARylrydUXLl8KFAxUyqAlggkCIcUWdwt2f+XMJciNu4TFlLwdSHw7C0WLz/w/HEcrrBINJnTGgF6dAW4YD79DbZ55BV+FCjrogVJACAIz2Ys+8NHdoTbasdghDPt3WSgEvTo4246QTCyMLC3139KJvPmb9HbRD03v07P7lzHaxGSFCJEdgdjm92Hy6+VZBLCujqpbEdCK0OzNrrlgEAyXz+nUTTIa8jaAMTHpRKLae86Tu53GwROUMttrQSf7NWwLcWjcAAgBFbupAee3e2rGw6zqF2uM+oITIFanFu29Xt2tNe4kICfZkSgqgDGKY2RVV7UwUFRheiXx8+4uBVWlPA4X6xp/dXj+6WGy4FIRB+dP/mhSf3RdWoBQDMLy7+9fmzy5nU1mHCv9g7sPNRc/J+1gwbU2EPYhtMGkSJ8uPl7ONS5GTAnGhICnj6XY421+LFFFcTzrXYUm0+N/Sq1FJ8DOWs/OSHM4tTDbkrnjFhw2Q6swGA1+M4OSxCvoJeQ0Y9ItYzz9WrTedzpMmEPWbfc7yjl0xc8nJv/1gq8WBpoY6OVpCYzab/9uNLS8mE6UtWLpd/eePKu49HtgA5RjPJRDy+b0QOx/+QFg0cpSvZSVzMvZfw9DrdnXaUNXHKbdBypil+MV1KK2JFmWRYlyVMr5IQkLPkrfSTX0/liwWAOtMpnjGhxYRg64u5n+spSdrUWj/DsoB7M8Ev9j0fdHrUb9q9gX3N7b95eN8wtzTGJwwAfvf4/u/u3pDlitXh8dzU31w4ny8XBVpcRNFQJT67bXfzx878dAk5kKgvBsBUC4ihnCzFL2dazgRJEmqUT3CvDwDin6TqO9IiiBrHDyls7Cfzs/fr3/6eMaH15HQ6jw5KXcGibDHiFACAQOBIixnE0tBn+4cAABA/PzD04ezEXC4DoKURElGDUdS5Uvmvr304vjCvHgcLhdI/X/rw2sy4qohSvRCDTyWi/mjLF9iu+Pm0yh7IViJZboSYedaRs+UrKe7mgZ1eISsOry3yvH/ubFwpQ31ZoIQrHD+MchPFhz+eSGczFXDdzUz+Mya0noiwLeo+Plhw2qqQ1CwhVQQJFPfn3Kfanu/0BneGmps9gbNPRioZEqhZM+rvgggQr0xP/OD6x6VSSQB9MvrgH69eJCGYydtmHRHj/C+GD/CzSiFdahA7S13TBoMpBbHwbjx63M+dLPS8LzdbzD0uqKbdep9CVP0HQAgzby1PXJpVSAsoRdyED3/rwX//7REi2rh9f6/r2kTx5pRbiz6zbtGq8c3JAi4kOr694xDjyptPRnKyvArXmfHONkxCi1gTMuB3b155fXBnbzj8n94/O7awoJZPMfaUBkPz1DYAgIgd6u4+ttCzcC3OJG62KtfHh0YLhMAYS40UmvbL7V+MOlttEz9cBAACpQEsX6a1rP+uhuk8+v5k885wpLlJu2bDzT9jQstJi5IJ+V2v7UgFnFkiUsgylaPiWgeQOLzYvv+3Y1c+np20qHkNz5+REMhA4MP40n+79tG/37W/yeX590eOM13CGAUDLRGJiPhibDj7nZxcUohrh7VGKrgZhQNA9dELmj2b3PU/90z+bCE/V2DSagaZzRChoOrizRxw8ePs+IWpwBd8Ni5tCgP+GRNaTowAEARntsE2+3Qi//FY/SUTV5JZ+CBC/nHQKfntaCtA0XxR3RqpylRabCoKtezuX184N7I0L61QqxtgQiZQMAIEEESvDO7Y52of8UwCVIkXvRBAnaQhsqEgISSPw2lz2Hw2HaKG8c1ojCvHzwDIxGkChc1OpYy8NBePtUU3ddB7xoQWE4EOCo7gdrv7m8tv38P5tGOLivtwxOf6dp3uvPO7J/d1fBdhoRPvYEu7B/Fvr14qyYp6CNIZj2D10lQbIqO4DZFo8Tf9b5ufG/3ZTPR4MPOgIGdNqYONTRqhxiVoZ12vNwfGWroPSEuXk7mxEjKFiAMq9ZlF9KqGJncF8eipYOp6dmRx3P9/9LqdLtiwuv7MMGMxmacckXe3ep7rL3OJOBJHYiA4EjLiSBID9cv6ftSmSBEjs4HP9z8XcrotLvwI4Jbsr3UP/ObOzaJSJkbEkBgKBGJIjJk+b/IHOaD2GTj/0va9kcvOmYuJ/FwpeixQKWJpAQkGIGQldMDT1tYmPXEG0+GOVyNMItSgAOrspWbrIQU8HXZPr3P2XHzy5/PTd+cFKLBhR9EzJtwqUt+S0247Nij1hApqQKmWLKMbGBvBxFCBQ5HDUpaVC/1fGNxR4UBLTJdEp7t7lpKJ69PjVsfH6JHfRNua2z+jbF+6kEGC+XMJ3w63BlvY6G6iY6ILsgek7pfb3FNNRCRNuzsHOn273EbIaCPI/wbsInLR/GIwfjlTTJQLk+UHP5zMpAsbb+oZE24Z6Vn2sSbX6QHZZaugIRhKVoMubzXqSqAYmfUejx3pD4b0DizgmajH91ys/Zd3Pmm8qVrSh2fj0l8MHYCzSjkjI2elxXL8ZrblxUDddc5MVMnkjJ5oijlbIY6IKGTFPe3vejXG3VwNKGX1hqEjcAYAKEgRgZ1e5uHLV1JM4sRo8Wxi/MOJjWeZPGPCrSJDFZEk+74e+/aWMpFWTKIRu19VFwQAggOmSzC33PmVof2SCWKzQefBZ/uGb45PTC4vWx8mqlpWBR3u7j083Z64kUE9Dnb5w6Q97PAOOFdJbqiDBDpbbd3Pt9tGPQBa0ClbtMf8bZHnA0Y90E01WZlVVVbrcImL51S4RACGpaR49P2Z5fmEOoh123zGhFtHAgDUs0HA7z61TfE5ytUlXUTd6qgGHKqHrXKkx8v2Yd+BA7F245pGnAeDoeZer//NB3esEEorCBGIedyOb3cczr6dVbMlkCqwhS2ngjWwhZshLewUCYBR26vRpnwUs2odSc2S5JrwdJ9ss4UlMyjbBqn2NEgUOuKXU5QaKQDTcLuB0dKV1Ojb0yW5uBF15xkTbh2pKaQcABhKQ+2ugz2lGsTY+tRRBMFAoMl0IRCEjI/mI18dOuyWVIu3YFSneUNC9oX+obMjd1K53PpX10nKq307+m4F008K5toSyFnyZlaUIbTfB3VuUZp+KYTwDbq6t7dLky5ilWKSiAhpKapE21+JNurnFOQI28L7fbNnl0jRgDM0/NUijf5oduHJEjwNqcc84vpH8Iw2Tig8LueJYWzxl+up31VNqudflbHqQmUEwGgmyf2w48XuflUYCFgLpuHprdOhtk57Wbk49pgYAqKVwT6goVfEgqGvBfcs6aDaZk+EBlt41C/52NOqLK3ZvhY7yhzY+VqLdz5Ecu2UI6I04e7c0+bqdSmKYjgSNs6QxoAjJ/3J+9nctFwBwkEhAJCz9IP8g59P16RlrkrPmNBiEqsvWYbIOyOuF3oLEq+c1xvZhs2GVr01vDcb+EzPsRavX72CNp8R5LHbX+0e+OXt6yVZA3HaaALfxggZA8SvD+0NfOAqLJZUdVfo2bbqBbmxQm6iEHnBX1fBQc0t0XQ40BZrxXk7A0QShECm5A8qYiAR7nwtarPZGGMVIbkeCdVJiCCEcHc73Z3O+fdTbLXUTyKa/OXCzM15IQQQAKnO/T9Cbfd/87RG4L/D4TrUz7tCZUvwL1YSopLIsWKu/4v9O3Q0xOr2V/J8jb5K9FLPwPTSwu2FGSNAzlpscRJiV2vbS6XhxYvJyuMTQ+DarygAYPG9hH/I7WmTDExwY53ryFd6rn9l7HoaoSJsTbznxVbnRMCQpUiABOZULz5j7+hqDex1QnkTYDkcNTBLzrHlTGj5o4ySKq88OavDy88WH/xwKplMAwIgqNGmK6fzGRP+USka8rw4LLvtpGxB1XBELlDcm3ceaTkyHI5UqaJagsUK5DIDbY2IEcS8/iPRtt/cub7q9Q0MrNKOXbJ9q++Q+EO5nKtAfapqc4W7GBaXysvX0s1nwoaaZ5Rh0x9I8wDpxSErvRBR7FS4mbewJF91m9OUTwU8M01dr7Rxnw0E4QbOz4a4FrIS2O1hNlq+lkbOVmo0hoo7/15i/MO5SnXR1egZE/7xiAAkyb6n27kjVuCrwhc12D4pHLBQwqnltq8NHeScV0QfIqgyxbRcyMxpiALo8/3DV0cfT6eSWgRcA1SLI6p+KehYX9++qfbErSya4TmIISMzmyHi8sdpW4D5h6rcFWY0V+1WU1ogIpIC7k5n53Md9jEvrPAG1USf45LU4mmNHPcTEW0gPEBzKgqSPDz8gn/ubJxKgogqVUBWTEI5VX7y/cn4bHKtZtft+BlZRaqJw+91nhpWAl6lEQSHp5CKcUpjS7Ze177DrZ0AoJlJAdQS2asLN2IAsC3S0unyvvXgnqZRNxazslI4oKCAx/PNlkPpN/NCrvbRmfrSPjFUcuX595PNp0PcWcN1qxeZUEUictHxajSUilABVDkJq8jPyiAd476eEx1amM56p3T1rwqJ6HNBeYEyj4vaVvKUuUJE5Gzpk8zD30+p1UVXpWdM+EckVfAwabjdc6izyFQbp/UkFMEezIe+0n/Eb3cAMKNc2VNtpShsjH+xf9ub925livnKIbAxYchqVjywzw7v6rwRSE9mtQ1BTVDWRmcc3jTVFJk9eTsjZ+WaKkumBtWznB6AhkCK8G/3tg932KacBsCPgeBkKhaij5AxyEBTMRJ7OawVIcTKvysJEUGQK+rw73bPnFvayHEZCagkxv9lfv7R4tOuecaEfyQiUEUhAYDT6Tw+pMSCZaLV1ZiGOiJEpLkUd8L2l/v6K39ANJslqhhSiOfbO5Vi4dLE6Cq4FXWacEmYlzJRV7jpC4498XeSKpqvamOEFUtQYwCGCIIUNnc20XTIa2syqiwZ25bBljp4tiDmYl2vtfhnglAmU4MEAIyYwFpOVk9utglX1+6OQL9z3YBS9fvm08HknWxxtmSyTT+95gwCcpZ+lHvwr5OZXBaeWUf/lGQyHiBiR9T3Qk/e7K5okIzTDiIiokxiZNb3SsfzMY9ntbFUxY4HnJ6XOnt+dftGWSlXpB+RVmqmLvNMpUY1aXbarw/t977H8omCinFW8UkYGqMZyIxIQUAOuYlS9kmh5WRQd1cw/ZYqfApEToTNR/xt4TZcsIkq9iciXAN1kkrgXWpqey2yapiOOX1ZURR3n9Pdalu8WMHnRwLEdTZTJJj5zeLU9Vl4Zh39E1LNHmy3Ow8POvoilhVU0ZNxAYARkYQsnsd0pu9rg3sNUfY05MWXe/tHFxbvL8wCGslQWrWmuj2ZaPjZUYDAPe0dp/ND85fiyLWAHqjkv+ufVTDPagReRFp6N+nucbg6bIa7wgAqQNS5S5A9xLpe7HCMe1dm8a2azKmfLQUiSrPOttaOyH6PkJWapzZ2N8aYzWZrOdO0eCEjUjKwClvX9LhK/QyG+bnSg+9P6dVFq3aEZ0z4J6NIk+vUUNlhEzKtgmlfR4OIyAGNOkQS0INFz77I4V3NMfWCVaVBu9e3LxL7zZ2b1V83elg1yyKHXfp276Hy7/OiaCxu88Krtq/UPDujQrIYv5yJvdgEzHAaClCPWwLVnYUQWs6Ew9CMaWmDs6f7JBgAkCLc08HOl9vtAbu5SKh6jQYKLCvBfS5GkPwkTRIzxl3jO4HanUsYsnT+YurJ+RlZVErorfL8z+iPSZw59nS797YVeMXAXUnAqaNBkw1QQUSFQb4kJhdjXxk8YK+426pePyJ+YWDHR49H5tNJq7MlNC0TBZ3qG9g72pq4nzeVXtk4MeTS0uUkd/PgLo/ZXUEo1KMtKeDpcvYcarePuetoX7PcLEvNjlj0RMCk4iIZwlmQ3WdX4RLlzZToNWuqIi2P/mDaBNe9ZdC0z2iDREQ+r/P0EIQ9RnTjpoP6n9KyFtHGAMbiUpfrwPOdXRVARADjve+OxKI2xzsPRyzHETWC3YJe7xuRA4k3M0q5HumqKpJKiRbejUePByUXNwWU6otYorbXmv2JEG0ik1YjQqFvScIx7uk81u5qs5OMpBMAADEiijzvy82UM08K6x0Aq9s39lNGyFnyRu7xb6cKpbz5mmdM+Eeniv8cAXl/h/dQd8HwHlgEXqYtE4aKUKT7M/4v9h0NOl3GBWpvDmb/bO/Q7+/d1CDcLSVDHf388O7YNW9mWivEuVnScGIYS40U5JQSOupfAS0hgnu8XX1tthlXHVuYmpMJqtzLYSgXjb0cRkkvaag2SIo75vDtcM+fS6gPVynrvQEvjr73MABQymLip/OzD5ZNRtItiNt4RhsnBHA4bMeGbN3BskJoXZSmjk5LiEyZzTCbsu313qHK9whAdKyjq5jPX50cx0p8jKXBdEL0RKKfl3bG3002IuG1kSkwezbetM/rCFeqLIFQuJt3vdzqmW5SAwAa6AEQ0Tbp6NnR4R2wa3qvdiak6Jlg4nqmuFAC5AAMqo1JaxFVpe4zCTNPCg9/PJHOVrIrnjHhH5U0byGYVztrj7iPD5QckmBkTbS0+VSjfrg/4z7d8VynLwD65t3kcp/q6PrF7WuyUAQwQDWwxkqllEnSN4YOuM5BIVluXM1GDrmZUmokFz0ZMr4kwuixYIs/hou8ETw7RFTL9EIZvfOhzs+0MYcWNkSK8A647SFp8aMUIhIoK+1bazFiRd1VB0wAMPPW8sTVmTKoh8utqvP4jFYnAv2YUWFFYbPZDvTaBqIFhZCRBWUeauI2OWCyyBOpnq8M7UOm7eKv9257PDvzaGlB9dwjWiiHAQBQ4P62jhcSfctXs0ziSiONk5ZYyJEtfZB2d9k8XU5SAAQ5I1LnqTb7hFd1BtbfA5GqwyMiztvbYy3hwz41u4I5sPl0aOmDtJJTgCEirvT0rMdFhm8TAAA5Ky6UHn1/Mr2YVrt+xoR/VFIRFvTfhIFdGQq6XxwSanVRRkBksROfIz1YsO0OHtoTjTGCLl/TtmDTr+7fAh1nhai22FCD5LTbvt1zpPBWtlwUYDj96lcYVY8ilJKlpQ/TzS82MYkAIPZytLnczDIcEflToqg3TYKc403dL7bzkE3ISuiATxSVxK00cm0vMMs9FXBt3ZM8UsW3qdquFz9KPXl3qiQXTWENz+hPQJU0AonZtnU5dncW9fhGZj6/NSajNJdaUWZji81fHTzksEtfHNx28dGD5UzapAo1gnVtzizWIMjODA5texBNjxQ1I5HuzauHUKj58kiAnC1fSzMbC+z0Onucnfva+IRmcBK4GdfBGr0hsiSP8tbW0yGH3xE64p89F1dkLZgOVro4V0TDraTqWk4MGCo55ckP5xemE/gMgfvTQz6P+/RQ6eFceSElMYYAqJYBZNrmy+oFRNFWPwccizueb9r1P+xPSLJ87vE968aOlbGhAKKwz/v1pn3JH6UEEOo5GZuFkKjuQTDdUoolMXc2HvtM0CE5/EthnmeCEyMdut8iso+5uw612yJS9kkhP66eaRuyWtU8PnKWup0d+9VM8H/js6agxzOyhPLFwsU7ySdLFX4zwo7rfk01xh6/i4ajqU/GJh4tLBopS1YZhAAEIldItDc1dZzzTP1kHqtretfTYiWCgRlxbUjQ97+L9X62Q3rkNI6CSKrTzxpWRAKKlhdzS7f+lyelZXkrUOdIEb4+15H/sP2ZJPw0kLZ0XA77jk77nVkxvuxkK5RDo37DJgkJK8XMBEEyz1+XQ59f8tYwXsOszvTa4FBWSg/aHsw4OFSfbbUKLZtquXKzHuVDwL08uC0oOSS+4MKcxeD/Wi+IMOnxtSvONntxqazK8zrGvyoZsabuHrc77H7GhH9iIiLTyZyFA+7hlvQnk1iWbdqrarjChHnpCIB4UerrzewqCZ60C66GhSi6f78ufzqRAsABBEoq7CfGXZ07OxYOLC9eTDDJVrmy7hWsTYJQw9ObTwZ7T7RKdp4KlfiEt95G1yFGLICh7tdabz94LIpE+JSU6DpaZowU4Qjb+r7VFm1temaY+ROTnnuqsZnN5tjfZxtuLgpgjAEiIVMQSf0BEMbnTfxwMG7kSPkSfpiwZwbzZZuW98SYhA0QY4wxRgwRtQZBkHPS3/1Su8Nvrwe2cOUUaRHbEghyx1yD32wPBP0el9u2X4hQGQCMri0kYgSLtrZILHLEvwaGRR2kahytr4S6DrUwqCeg9hlZTsysdoX8njPbykGnvBIMCuvaiY0kQ/VXCejRgnTDV+aRghC1vTdMTOsrwZsdscjJQONGB70FQSiAYdvnmtr2tCAAInpb3crOgoa7ugXWDSGEfdTX8VK7PWQHUuqb/xoiIhUusf+NNp/HC88iZv70pK8cPVYYkEnbOj17O/Mrr21kEZh8j0JW+Idz7vm+PNgUsxxuhPSx6Zk7BI5xT/fznc52eyNeT9PIGCnC1+/s+0qnx+VW583lcDp2odJaWplDaAkxxiDFm0Us9mIQkFsU2Yuc846vRluHY+pTPGPCPzWZICfUXxHA6/acGIZWn6KYTlFESmOLoHLyRKTZBP8YSe7IG9ZF1hgr1owNkSiHTflo28sRrTZuXWReoGiH7m+0tPSGASrzFgx7aV8eHCtnxpz0VF/nAACIaJ9wdR3scHfZzHWWVDf9uqaylReQIgK7XX2f7XTaHepB9xkTfkqpt8XzXG9RYsJQFxmTLKz2S4RXZ92TXQV0a7wtrHYaI6I06eje3uYZcjQeAkSKiB7w977a4bDZK+oDgCRJniEud+eJ0MRsVTp2g/NGBRFMRjtfi3KpkjbBABDEuuyt1diogNGQzc37vt3e0uHXmqFnsaOfVrLb7UcHWE+4rJBqklGrUlrGhYiUzPILWanYmzUS6qxqHAC0eKAyeufDnZ+JqfHQuAJDYKMkyB6Qev6yPdTsV+PMVaA2lQV8vgDfXyZPWbX0qjdY8AQGMZSm7bG+tsBuDwkjLkcQbjT2gAwoAMLw8aaeF1o5043G+Ewd/fQSawl5Tw0VXTaxMivCEhIo7s457kVKGKStsGpoBqF5e2dze/iwT8gK1RvUQoTNp5t6jsYkxtUpMAO1Mca8PS55W5GpEB8rqI55M9+CxJSy8E4GO1+NSR4OgpBgI4jdAKAnamsBGPYIH/x2mz/kNW6nZ2fCTzNJkn13t3NHrNB4IaeVpGZX5Er8wrIjOZgFhlZLQp0EOcb9XS+220P2+gJzSAF3u23gm+3eoNdkBa0ardvpkvYpSri42m7SoOFUAABjjC/bWn1tkeeDasKGqpVsgL213oUQAND+2UjrgShHqRJ78IwJP+XU5HOfGRQBt2z5m1IdCRzpyaLtukumWNFaRHDdKSIQOSZYM2+NnQrWwecCgDHR/vlI284WtdijnjdYCw/la3GL3XkdZ7zKoFXH+M1gisY3rnFv98k2Z7PN3MUG8+tBkK/PNfi1Dr/LVzP+Z0z4qSbGpMFO9/7OolWOBINQT2MVgn0465zvy6Ddwua1amdcDaRBtI+5u450uDo23QeXhXe7Z+DLne4KPAdbVXt22x2OHVxuKxFZFhFNWHUWEBkWkZtjL0aYUvnSiCV4aiNEAMBtrOOrzc39EfOX2vNYMtZntAWkWWGcTufJIWzxFa2F61b0wwgizaSljxVJ7ipYeDJEAkagAKmeDypAMB1pfyW66XZcrPcbsWh3U9WXqhhaMdhAkxsO5sGhiT4VSmddZN61SFTdi8j5hKtrX5uz36mqlxuYMcGRgSD/flff59qddofxCMYVz5jwU0takgNjrLPZe2ygLHGlgv/VsJHGDIMvEb82ax/tKKCnVnNDDcu0bhICmGqh4dPOzqEO/w6XGbZwLfFOTMhK8LC396UOu+RY5YIV45Iku7fXJvfmTFDIa3axHq2AzBBQBN9SuP0zUUmqhhg3Rl3DliQJIWxeNvjN7nBL1VZi0DMm/NRSZenY7fbnBmy94ZIwgsIAGo/JBACh1RUT8Sy/kOG53kqYjprKSFR/KUWhZ8CqFcWoJLyzwe7XWplLS1FH1KoCimowXO3RSLGH7MPf7gxG/Nr3tH5On9vvxgNl8lmT4LuSEFGatXd1tAf3aSCoaHpT1cgJAADIiEiJnGzqeiFm49Kqw3/GhJ9aqno10aDn1FDZbVcU/cBjWEzXqA28NqlJw2oiv8RgZM45Epapqax+03BGf4W03EjGcMHWGmqNHNUCSg33uh6so+EICr1AUuzlpo6jMYnpOiGun+bBUQp2epTt1oM4GmRUF7V5JRAV07XhyK0yCylCjTgP+n3wlOE/Y8I/D2LctqfbvTNWqHlhAkEAibreI6s4vAUAFMvs/SUp0Z9Gvnrxhjp6qPldCOEY93efbrOFq+oBmg2MiMgASAFXh33wG51e76YzlVwul223UKLlxsCfnkqICIs86mqJvhBcWbUCdG+E9kQM2z4XbtvTAk8tTPeMCT/VVKVx+r3Ok9tF0KOYVxZrOAKYkdGAGF9yXncCxUpCCCuMNHqtBb0lzjlLYoRirWdCWjyXgbFbbWBkEnV+ubl1W7g+Oe+PuZW9OcbWj+2sj4jINebrOt7miK2Sq6U9FCNSRMCIOH86PWPCTzNVYSogk4ZbvYe7c2p1UV1drD5N1UlaNLIs4MMZ+2xvFh2k1pZR/1yfPKlIBu3kyYiImBoP3abBFtYOXgAAKcK/w9P/xXaXww2waWwXAnBITvc2qdyZtTDQTyXUyydSDsPFaPvLkadZyIgI7dD5jZaWXtUe81Ree8aEfwZkCCWn03ligLcGFIU4I8bBSoggFCQBzWdsF2Ve6iroPgwGUKc8WVGoSGfpAqjx0Ewyu931Y6Eg7ua932qLtIe171cs8jWGQ+q5C8HX5MEDJbRbHJGnHpiR1PB0V+eudu+Ak5RVACNJEeGD/t5XOxySc+2t4BkT/hmQsdciYluz54W+nEMSauYeYqOxhyafh6bn3phxPGnNM68gUsHerAkSMMQpIkrT9ra+mH+X21i+xrGWiCJH/T2nW21aJcNVHABrCGbVcAwAEuPuPmdpIAuwEW/epkhFKwcqgX8h3PF6C7dVQZAgaRHnfd9uDzX717UnPWPCPzOy2RwH+u19kaIp1bAxBJqKNsUAgAOmCvyDtC3Tn0NESxwhWkd6S4xAyOSZina9HpM8HEgxUKiJyB6x9f9lWzDkW2+0axADACDwe53SPlkEiGkoO5Vig5tpbZUxCP1Ai3O21tbW8EGvWl1UJbWv5tNNXc/HJGZ7WjvVw31Gf1YUDbpPDykexyr4F5YQR3ww77gbkClc2orsCgUIEdkSa/W2q/HQFVFP0PpKqPNgK8d6kxuN8SIAcn+nR+zMCE5G9SXzE4m64mWr5kSQZ8rf8VKbI1gpU0MKuNqkgW+1+f0bMu0+Y8I/P+Kc7+yy720z4LotJ1GU2fuL0nJ/Vk84tThsFQCIFMe4t/tkmyMqae4KBTw9rqE3unweN9QPE1z1m9vpsu8mapZrC6oZZZU2T6amBDBiS1KzIxY9VdlNkIu2L0TadsTYxrChnjHhnyMxv9dzclhEvIr6Bq2t5QIAHGk6br9qw3J7gUixep2oDjQOGYjIza0vaQGl3Ca6vtrcMhRaqTdunnQjEEAg4lP25cFG5kIuG84GrKVqMBuGxAQy54S3+2iHq8MlZCJF+LZ5Br9kjjhfh54x4Z8jCUTe3+o72pNXvepP9wNvmlSARAVICPbxrHOmJ4dOyxeJlmZuxEO7+5xCpsBuX99n25x2V82VdZHu+iew2WzuIS535BVQoDG8mVUJEUUWgrlI28sh5MCdq0Scb2isz+jPihgAOJ325wdZZ1NZtrSoIJICABwQkZbT/KMiL/bkLFd6K8KkCL6lcOdrrY4g7/1WW7gtWH1hw+sTAQA8ARcekMmpqBD6AFA3e6sjr971hAoG1bGtzTfoCB3wPTXi/Cn0DIH7z5WIMBb2nBhIzX/CZYVZe2wDACSQObs979jZnds+64C0lYlUQFqlMATg84624Sj870vdx9tsTEs4pPrAwDWqrUhh43ZvrzM9nLfd8uqmGyQSajnI+jqoIVLQNx/pfaPD3eUIRjYXavesIMyfGdUAbM4vJ68+KhSVLQOnABbzldvyEqStVJoEQlWkgUeBTmhriXHWKP8BVLdAQFoVD5obWyyPVlWTwAYrLZlIRc8ptuaibSG/d3X/ytPo/wfFKlBDcU0yWAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36C8BF10>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#gaussian_blur"
],
"metadata": {
"id": "2-hKQNNxniYj"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"sigma = 5 #@param {type: \"slider\", min: 0, max: 10}\n",
"kernel_size = 5 #@param{type: \"slider\",min:0, max:10}\n",
"new_image = pix.gaussian_blur(\n",
" image=image,\n",
" sigma=sigma, \n",
" kernel_size=kernel_size)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "I76DOcMXmMsB",
"outputId": "0c2edc65-4284-4927-e9d7-1f267a21820c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36C91D90>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#random_brightness\n",
"adjust_brightness(...) with random delta in [-max_delta, max_delta]"
],
"metadata": {
"id": "8mJkeddAoMEm"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(0) # change to see different brightness\n",
"image = get_image(IMAGE_PATH)\n",
"delta = 0.9\n",
"new_image = pix.random_brightness(\n",
" key=key,\n",
" image=image, \n",
" max_delta=delta)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "4ibVT08Nn-Ws",
"outputId": "af8a762c-01b5-4c43-c2cf-7492db8292ef"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36C8B690>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#random_contrast\n",
"adjust_contrast(...) with random factor in [lower, upper)."
],
"metadata": {
"id": "eveZNTeLp_oL"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(0) # change to see different contrast\n",
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.random_contrast(\n",
" key=key,\n",
" image=image, \n",
" lower=0,\n",
" upper=5)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "3RoXfbJtpfs8",
"outputId": "ee23e353-6db2-40e8-b8fc-669f6fe542a7"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36C91310>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"# random_crop\n",
"Crop images randomly to specified sizes.\n",
"\n",
"Given an input image, it crops the image to the specified _crop_sizes_. If _crop_sizes_ are lesser than the image's size, the offset for cropping is chosen at random"
],
"metadata": {
"id": "pSH5_cl9q2SN"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(5) #change to see different crop\n",
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.random_crop(\n",
" key=key,\n",
" image=image, \n",
" crop_sizes=(128,128,3))\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 145
},
"id": "sK6e7vccqYr4",
"outputId": "89126f9d-f584-4bff-b8a5-643699c353de"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=128x128 at 0x7F0B36CF4A10>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#random_flip_left_right\n",
"50% chance of _flip_up_down(...)_ otherwise returns image unchanged."
],
"metadata": {
"id": "GLXYM9lzs_sL"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(1) #change to see different views\n",
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.random_flip_left_right(\n",
" key=key,\n",
" image=image\n",
" )\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "SuNFlbFgsH3w",
"outputId": "a530a28f-e352-45ce-ff4d-cced272ce9e6"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B365DBD90>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#random_flip_up_down\n",
"50% chance of _flip_up_down(...)_ otherwise returns image unchanged."
],
"metadata": {
"id": "tM_fE_MztDCq"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(0) #change to see different views\n",
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.random_flip_up_down(\n",
" key=key,\n",
" image=image\n",
" )\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "O92Cn7kYtGN8",
"outputId": "810d8482-f390-4066-ef2c-819b86624036"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B365F9550>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#random_hue\n",
"_adjust_hue(...)_ with random delta in [-max_delta, max_delta)."
],
"metadata": {
"id": "QNHX2SZitGkw"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(0) #change to see different views\n",
"image = get_image(IMAGE_PATH)\n",
"delta = 0.7\n",
"new_image = pix.random_hue(\n",
" key=key,\n",
" image=image, \n",
" max_delta=delta)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "GeRfwopitJET",
"outputId": "543be4bd-09e7-437e-d9ee-40f14ce89acb"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B3660B9D0>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#random_saturation\n",
"_adjust_saturation(...)_ with random factor in [lower, upper)"
],
"metadata": {
"id": "tJItyyKvtJfE"
}
},
{
"cell_type": "code",
"source": [
"key = random.PRNGKey(0) # change to see different saturation\n",
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.random_saturation(\n",
" key=key,\n",
" image=image, \n",
" lower=0,\n",
" upper=5)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "_DL_AhxBtKja",
"outputId": "4ef6d7db-ddee-43b1-dfb8-21772b3fb604"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36610790>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#rot90\n",
"Rotate an image counter-clockwise by 90 degrees.\n",
"Assumes that the image is either ...HWC or ...CHW"
],
"metadata": {
"id": "1i6k9mnztPq7"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.rot90(\n",
" k=1,#number of times the rotation is applied\n",
" image=image)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 317
},
"id": "cGSjxJ2btQHF",
"outputId": "6b65e83b-981b-4f71-f21b-f8ddbd1e5d9c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=174x300 at 0x7F0B36626890>"
]
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"#solarize\n",
"Applies solarization to an image.\n",
"All values above a given threshold will be inverted"
],
"metadata": {
"id": "Ea_JKtnitTOx"
}
},
{
"cell_type": "code",
"source": [
"image = get_image(IMAGE_PATH)\n",
"new_image = pix.solarize(\n",
" threshold=0.6,\n",
" image=image)\n",
"imshow(new_image)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 191
},
"id": "4HHn6V_CtWKm",
"outputId": "cd8563a4-21dd-425f-fafb-a2a55d64e897"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<PIL.Image.Image image mode=RGB size=300x174 at 0x7F0B36CF9990>"
]
},
"metadata": {}
}
]
}
]
}
| apache-2.0 |
ProgrammingRobotsStudyGroup/TrafficConeFinderCode | notebook/Finding Cone Test cases.ipynb | 1 | 8848 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"\n",
"def is_cv2():\n",
" # if we are using OpenCV 2, then our cv2.__version__ will start\n",
" # with '2.'\n",
" return check_opencv_version(\"2.\")\n",
"\n",
"def is_cv3():\n",
" # if we are using OpenCV 3.X, then our cv2.__version__ will start\n",
" # with '3.'\n",
" return check_opencv_version(\"3.\")\n",
"\n",
"def check_opencv_version(major, lib=None):\n",
" # if the supplied library is None, import OpenCV\n",
" if lib is None:\n",
" import cv2 as lib\n",
"\n",
" # return whether or not the current OpenCV version matches the\n",
" # major version number\n",
" return lib.__version__.startswith(major)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok we going to define a few function here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def convexHullIsPointingUp(hull):\n",
" x, y, w, h = cv2.boundingRect(hull)\n",
"\n",
" aspectRatio = float(w) / h\n",
" if aspectRatio > 0.9:\n",
" return False\n",
"\n",
" listOfPointsAboveCenter = []\n",
" listOfPointsBelowCenter = []\n",
"\n",
" intYcenter = y + h / 2\n",
"\n",
" # step through all points in convex hull\n",
" for point in hull:\n",
" # and add each point to\n",
" # list of points above or below vertical center as applicable\n",
" if point[0][1] < intYcenter:\n",
" listOfPointsAboveCenter.append(point)\n",
"\n",
" if point[0][1] >= intYcenter:\n",
" listOfPointsBelowCenter.append(point)\n",
"\n",
" intLeftMostPointBelowCenter = listOfPointsBelowCenter[0][0][0]\n",
" intRightMostPointBelowCenter = listOfPointsBelowCenter[0][0][0]\n",
"\n",
" # determine left most point below center\n",
" for point in listOfPointsBelowCenter:\n",
"\n",
" if point[0][0] < intLeftMostPointBelowCenter:\n",
" intLeftMostPointBelowCenter = point[0][0]\n",
"\n",
" # determine right most point below center\n",
" for point in listOfPointsBelowCenter:\n",
" if point[0][0] >= intRightMostPointBelowCenter:\n",
" intRightMostPointBelowCenter = point[0][0]\n",
"\n",
" # step through all points above center\n",
" for point in listOfPointsAboveCenter:\n",
" if point[0][0] < intLeftMostPointBelowCenter or \\\n",
" point[0][0] > intRightMostPointBelowCenter:\n",
" return False\n",
"\n",
" # if we get here, shape has passed pointing up check\n",
" return True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def testcone(img, file=''):\n",
" print ('Image shape ', img.shape)\n",
" #cv2.imshow('image', img)\n",
" #plt.show()\n",
" # convert to HSV color space, this will produce better color filtering\n",
" imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
"\n",
" # Threshold on low range of HSV red\n",
" low_redl = np.array([0, 135, 135])\n",
" low_redh = np.array([15, 255, 255])\n",
" imgThreshLow = cv2.inRange(imgHSV, low_redl, low_redh)\n",
"\n",
" # threshold on high range of HSV red\n",
" high_redl = np.array([159, 135, 135])\n",
" high_redh = np.array([179, 255, 255])\n",
" imgThreshHigh = cv2.inRange(imgHSV, high_redl, high_redh)\n",
"\n",
" # combine low range red thresh and high range red thresh\n",
" imgThresh = cv2.bitwise_or(imgThreshLow, imgThreshHigh)\n",
"\n",
" # clone/copy thresh image before smoothing\n",
" imgThreshSmoothed = imgThresh.copy()\n",
" # open image (erode, then dilate)\n",
" kernel = np.ones((3, 3), np.uint8)\n",
" imgThreshSmoothed = cv2.erode(imgThresh, kernel, iterations=1)\n",
" imgThreshSmoothed = cv2.dilate(imgThreshSmoothed, kernel, iterations=1)\n",
" # Gaussian blur\n",
" imgThreshSmoothed = cv2.GaussianBlur(imgThreshSmoothed, (5, 5), 0)\n",
" #cv2.imshow('imgThreshSmoothed ', imgThreshSmoothed)\n",
" # get Canny edges\n",
"\n",
" imgCanny = cv2.Canny(imgThreshSmoothed, 160, 80)\n",
" #cv2.imshow('imgCanny ', imgCanny)\n",
" if is_cv2():\n",
" contours, hierarchy = cv2.findContours(imgCanny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n",
" else:\n",
" image, contours, hierarchy = cv2.findContours(imgCanny,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n",
"\n",
" listOfContours = []\n",
" if len(contours) != 0:\n",
" for cnt in contours:\n",
"\n",
" # epsilon = 0.1 * cv2.arcLength(cnt, True)\n",
" # print'epsilon',epsilon\n",
" listOfContours.append(cv2.approxPolyDP(cnt, 6.7, True))\n",
"\n",
" # print file + ' listOfContours ' , len(listOfContours)\n",
"\n",
" listOfCones = []\n",
" for contour in listOfContours:\n",
" hull = cv2.convexHull(contour)\n",
" # print 'convexHull',len(temp)\n",
" if (len(hull) >= 3 and convexHullIsPointingUp(hull)):\n",
" listOfCones.append(hull)\n",
" #imghull2 = cv2.drawContours(img.copy(), hull, 1, (0, 0, 255), 5)\n",
" # draw hull on image???\n",
" # print '--hull',len(hull) #hull.append(temp)\n",
" \n",
" #if is_cv2():\n",
" imghull = img.copy()\n",
" cv2.drawContours(imghull, listOfCones, -1, (0, 255, 0), 3)\n",
" #else:\n",
" # imghull = cv2.drawContours(img.copy, listOfCones, -1, (0, 255, 0), 3)\n",
" \n",
" #cv2.imshow('hull ', imghull)\n",
" # cv2.imshow('hull 2',imghull2)\n",
" #show what going on\n",
" #plt.axis(\"off\")\n",
" #show what going on\n",
" plt.figure()\n",
" # Show the first image on the left column\n",
" plt.subplot(1,2,1)\n",
" plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
" plt.subplot(1,2,2)\n",
" plt.imshow(cv2.cvtColor(imghull, cv2.COLOR_BGR2RGB))\n",
" plt.axis(\"off\")\n",
" plt.show()\n",
" # remove any inner overlapping cones\n",
"\n",
" print ('Found ', len(listOfCones), ' Cones')\n",
"\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import glob\n",
"# get the files\n",
"files = glob.glob('../images/*.jpg')\n",
"#files.extend(glob.glob('..\\images\\*.png'))\n",
"#print(files)\n",
"\n",
"for file in files:\n",
" print ('Processing file ' + file)\n",
" testcone(cv2.imread(file, -1), file=file)\n",
" print ('Done Processing file ' + file)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Change the number based on what camera you have. 0 is the default camera\n",
"cap = cv2.VideoCapture(1)\n",
"\n",
"while(True):\n",
" # Capture frame-by-frame\n",
" ret, frame = cap.read()\n",
"\n",
" # Our operations on the frame come here\n",
" #cv2.cvtColor(frame, gray, cv2.COLOR_BGR2GRAY)\n",
"\n",
" # Display the resulting frame\n",
" cv2.imshow('frame',frame)\n",
" testcone(frame)\n",
" if cv2.waitKey(1) != -1:\n",
" break\n",
"\n",
"# When everything done, release the capture\n",
"cap.release()\n",
"cv2.destroyAllWindows()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [py27]",
"language": "python",
"name": "Python [py27]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
daniestevez/jupyter_notebooks | eshail2/Sun outage.ipynb | 1 | 445521 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot\n",
"import astropy.units as u\n",
"from astropy.time import Time\n",
"from astropy.coordinates import SkyCoord, EarthLocation, AltAz\n",
"import astropy\n",
"import matplotlib.pyplot as plt\n",
"import astropy.constants"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Time to sweep\n",
"t_start = Time('2019-10-07 00:00:00')\n",
"t_end = Time('2019-10-13 00:00:00')\n",
"\n",
"# Observer location\n",
"ea4gpz = EarthLocation(lat = 40.595865*u.deg, lon = -3.699069*u.deg, height=800*u.m)\n",
"\n",
"# Antenna pointing\n",
"azimuth = 138.92*u.deg\n",
"elevation = 34.23*u.deg"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t_step = 10*u.s\n",
"t = t_start + np.arange((t_end-t_start)/t_step)*t_step\n",
"\n",
"dish_pointing = SkyCoord(AltAz(az = azimuth * np.ones(t.size), alt = elevation*np.ones(t.size),\\\n",
" location = ea4gpz, obstime = t))\n",
"sun = astropy.coordinates.get_sun(t)\n",
"separation = sun.separation(dish_pointing)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"events = list()\n",
"for s in t[separation < 1*u.deg]:\n",
" if len(events) == 0 or s - events[-1][1] > 10*u.min:\n",
" events.append([s,s])\n",
" else:\n",
" events[-1][1] = s"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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PTk5O3TLhcXFxUKlUsLa2RlBQENLS0pCeno6amhrs2LEDw4cP11TUZuPvZIGvRvnixNXb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\nIjKQ/wf4wdQchy4+AwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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efLAvSqp0+GD7cThZq/FEaDfZWURERK1aaZUW4fHJuFKtw/ppA9Gzo63sJGoA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\n0gELNBERERGRDligiYiIiIh08P8Bb3vQ4yMJklsAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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0qJnENAAAcD/ZuVY9OPePiB56jfdEtERIOxUxDQAA3FV2rlUPzVuvRZsO6WkvjGiJkHa6\ni2P625+POHskAACAYtkieuFPh/S3m2M0zAsjWiKkXULBmB45cy0xDQAAXNbFET28cxNnj+Q0hLSL\nsMV087rBxDQAAHBJ2blWPTxvAxF9ASHtQsIC/TV7WNIfMb2NmAYAAK7BFtFf/HSQiL6AkHYxha5M\nv0dMAwAA58shoi+LkHZB1QOraNawjmpWJ+/K9FJiGgAAOElOrlUPvZ8X0X/tRUQXREi7qOqBVTR7\neEc1qx2sZGIaAAA4QX5E/5gX0SO6ENEFEdIujJgGAADOUjCin+rVkoi+DELaxV0c099tP+rskQAA\ngIe7OKKTuzR19kguiZB2AwVjesR7acQ0AACwm5xcqx4mokuFkHYTtpiOjiCmAQCAfdgi+vMfD+rJ\nnkR0SQhpN3JxTC8jpgEAQCW5OKJHdiWiS0JIu5kaQX/E9HBiGgAAVIKcXKse+WAjEV1GhLQbKhjT\nXJkGAAAVYYvozzb+oieI6DIhpN2ULaabXIjp5enENAAAKJucXKseLRDRo4joMiGk3ViNoCqacyGm\nh88gpgEAQOnZIvpTIrrcCGk3V/DK9PAZaVqRfszZIwEAABdXMKLH3URElxch7QFqXojpxrWCNGzG\nGmIaAAAUKSfXqsc+/COiR19LRJcXIe0hagZV0ZwRScQ0AAAoUq7V6LEPN2rBhl/0l5taENEVREh7\nkItj+vsdxDQAAMiTazV69IMNWrDhFz1+YwuNuTba2SO5PULawxSM6aHTiWkAAHBpRI/tRkRXBkLa\nA3FlGgAA2ORajR4jou2CkPZQtjcgNgrPi+kfiGkAALyOLaI/IaLtgpD2YOHBVfNjeigxDQCAV8m1\nGv35w41EtB0R0h7OFtMNaxLTAAB4i1yr0eMfbtTH6zOIaDsipL1AeHBVzRlRIKZ3EtMAAHgqW0T/\nl4i2O0LaS4QHV9VsW0xPJ6YBAPBEBSP6zz2aE9F2Rkh7kVoXYrpBzUBiGgAAD3NxRN9/XTNnj+Tx\nCGkvUyu4quaMSMqP6ZU7M509EgAAqKBcq9Hj8/Mi+rHuRLSjENJeqGBM3zd9NTENAIAby4/odXkR\n/cD1RLSjENJeyhbT9WsQ0wAAuKtcq9Ff5v+o/67L0KNEtMMR0l6sVnBVzU3Oi2mWeQAA4F5sEf3R\nugN6tHtzPUhEO5zdQnr//v3q1q2bYmNjFRcXp/Hjx1+yjzFGDz74oKKjoxUfH69169bZaxwUwXZl\nOqpGNWIaAAA3UTCiH7mBiHYWu4W0n5+fXnnlFW3ZskWpqal68803tWXLlkL7LFq0SOnp6UpPT1dK\nSopGjx5tr3FQjIiQwjGduouYBgDAVeVajcZ99EdEP3QDEe0sdgvpevXqqW3btpKkkJAQxcTEKCMj\no9A+CxYs0KBBg2SxWJSUlKSTJ0/q4MGD9hoJxbDFdGSNarpvGjENAIArskX0/LUH9PANzYhoJ3PI\nGuk9e/Zo/fr16tixY6GfZ2RkqH79+vnfR0VFXRLbkpSSkqLExEQlJibq6NGjdp/XW0WEVNVcYhoA\nAJdktRo9USCiH76hubNH8np2D+msrCz17dtXr7/+ukJDQ8t1jOTkZKWlpSktLU0RERGVPCEKyrsy\n3ZGYBgDAhVgvXIn+cO0BPXQ9Ee0q7BrS2dnZ6tu3rwYOHKg+ffpcsj0yMlL79+/P//7AgQOKjIy0\n50gohdohAYViehUxDQCA01wc0Y90J6Jdhd1C2hijYcOGKSYmRo8++uhl9+ndu7fee+89GWOUmpqq\nsLAw1atXz14joQxsMX1F9QANIaYBAHAKItq1+dnrwN9//71mzpyp1q1bKyEhQZL0wgsvaN++fZKk\nUaNGqVevXlq4cKGio6MVGBioadOm2WsclEPtkADNTU7SgJRUDZm2RtPva6+OTcKdPRYAAF6hYEQ/\nSES7JIsxxjh7iLJITExUWlqas8fwKkd+O6sBKak6+OtZTRtCTAMAYG9Wq9ET//1RH6RdiOgbmsli\nsTh7LK9TUnfyyYYoke3KdL2wAN03fY1W7z7u7JEAAPBYhSL6umgi2oUR0iiV2iEBmjsiL6aHTFtN\nTAMAYAdWq9GT//3pj4ju3pyIdmGENEqtdigxDQCAvdgi+v20/XqAiHYLhDTKxBbTdS/E9Jo9xDQA\nABV1cUQ/SkS7BUIaZVY7NEDzLsT04HeJaQAAKsJqNXrqYyLaHRHSKJeCMT2EmAYAoFxsET1vzX7d\n342IdjeENMrNFtN1QvNiOo2YBgCg1KxWo79+8kdEP9aDiHY3hDQqpHZogOYl58X0YGIaAIBSsUX0\n3NVEtDsjpFFhtUPz7jNNTAMAULK8iN6kuav3a2y3pkS0GyOkUSnqXBTTa/cS0wAAXOyPiN6nsd2a\n6s89WhDRboyQRqWxxXTt0AANeoeYBgCgIKvV6G8L8iJ6zLVEtCcgpFGp6lxYM01MAwDwB1tEz1mV\nF9GP30hEewJCGpWuzoUPbakdGqDB764hpgEAXs1qNXqaiPZIhDTsom5YXkxHhFQlpgEAXssW0bNX\n7dNoItrjENKwG1tM1wquciGmTzh7JAAAHMZqNXrm0z8i+i9EtMchpGFXdcMCNC+504WYXk1MAwC8\ngi2iZ6Xu06iuRLSnIqRhd3XD8u7mQUwDALzBxRE97iYi2lMR0nCIemHVCsX0un3ENADA8xjzR0SP\n7NqEiPZwhDQcxhbT4cFVNOgdYhoA4FmMyXtjoS2in7ipJRHt4QhpOFS9sGqadyGmBxPTAAAPUSii\nuxDR3oKQhsPZYrrmhZheT0wDANyYMUbPLNj8R0T3JKK9BSENpygY04OIaQCAm7JF9MzUvUomor0O\nIQ2nqRdWTXNHENMAAPd0cUQ/SUR7HUIaTnVF9byYrhFETAMA3IcxRn//lIj2doQ0nO6K6nnLPGwx\nvWH/SWePBABAkWwR/d7KvRrRuTER7cUIabiEgjF979RVxDQAwCUZY/RsgYh+qlcMEe3FCGm4jCuq\n591nmpgGALgiY4ye+2yLZhDRuICQhkuJvBDT1YP8de87xDQAwDXYInr6D3s0/BoiGnkIabicyOrV\nNC+5k6oH5sX0RmIaAOBEF0f0X28mopGHkIZLKhjT9xDTAAAnIaJRHEIaLouYBgA4U8GIHkZE4zII\nabi0yAv3mbbF9I8HiGkAgP1dHNF/I6JxGYQ0XF5UjcD8mB44lZgGANiXMUbPf05Eo2SENNxCwZi+\nh5gGANiJLaKnfb9HQ68molE8QhpuwxbTodWIaQBA5bs4op++hYhG8QhpuJWoGoGal/xHTP904Fdn\njwQA8AAFI/q+qxsR0SgVQhpup2BMD5yaSkwDACrEGKN/fL41P6KfuSWWiEapENJwSwWXeRDTAIDy\nskX0u9/vJqJRZoQ03Fb9mnkxHRJATAMAys4Yo//7Ii+ih1xFRKPsCGm4tfo185Z5ENMAgLKwRfQ7\nK/Ii+u9/IqJRdoQ03F7BmL7nnVXalEFMAwCKRkSjshDS8Ai2mA6u6qeBU4lpAMDlGWP0TyIalaTU\nIX369Gnl5ubacxagQohpAEBxbBE9lYhGJSkypK1Wq+bMmaObb75ZtWvXVsuWLVWvXj3Fxsbq8ccf\n144dOxw5J1AqxDQA4HKMMXphIRGNylVkSHfr1k07d+7Uiy++qEOHDmn//v06cuSIVqxYoaSkJI0b\nN06zZs1y5KxAqRDTAICCbBE9ZTkRjcplMcaYy23Izs6Wv79/sQ8uzT6VLTExUWlpaQ59Trin/cd/\nV/+UVGWdy9Hs4R3VKjLM2SMBABzMGKMXF/2slGW7iGiUWUndWeQVaVsgHz9+/JKv7OzsQvsArogr\n0wDg3QpG9OBODYloVLoS32zYtm1bRUREqHnz5mrWrJkiIiLUqFEjtW3bVmvXrnXEjEC5EdMA4J0u\njuhne8cR0ah0JYZ09+7dtXDhQh07dkyZmZlatGiRbrnlFr311lsaM2aMI2YEKqRgTHOfaQDwfMYY\nvUREwwFKDOnU1FTdeOON+d/36NFDK1euVFJSks6dO2fX4YDKYovpoCp5Mb35F2IaADyRLaLfXrZL\ng4ho2FmJIV2vXj29/PLL2rt3r/bu3at//etfqlOnjnJzc+Xjw+e5wH3UrxmouSPyYnrgVGIaADzN\nxRH9HBENOyuxhOfMmaMDBw7otttu0+233679+/drzpw5ys3N1QcffOCIGYFK0yCcmAYAT2SM0UuL\niWg4VpG3v7vY6dOnFRQUVOoDDx06VJ9//rlq166tTZs2XbJ96dKluvXWW9W4cWNJUp8+ffTMM8+U\neFxuf4fKsC/zd/VPWanfs3M1Z3iSYq8IdfZIAIByyo/o73bp3qSGev5WIhqVo9y3v7P54YcfFBsb\nq5iYGEnSxo0bS/UmwyFDhmjx4sXF7tO5c2dt2LBBGzZsKFVEA5WlQXig5iV3UqC/r+6emqotv5xy\n9kgAgHIgouFMJYb0I488oi+//FLh4eGSpDZt2mjZsmUlHrhLly6qWbNmxScE7ISYBgD3RkTD2Ur1\nbsH69esX+t7X17dSnnzlypVq06aNevbsqc2bN1fKMYGyKBjTA4lpAHAbxhi9vHgbEQ2nKjGk69ev\nrx9++EEWi0XZ2dn6z3/+k7/MoyLatm2rvXv3auPGjXrggQd02223FblvSkqKEhMTlZiYqKNHj1b4\nuYGCGoQHam5ykqoR0wDgFmwRPfm7nbonqQERDacpMaQnT56sN998UxkZGYqMjNSGDRv05ptvVviJ\nQ0NDFRwcLEnq1auXsrOzdezYscvum5ycrLS0NKWlpSkiIqLCzw1crGF4EDENAG7AGKN/fflHRP/j\n1lZENJymxJCuVauWZs+ercOHD+vIkSOaNWtW/nrpijh06JBsNwxZvXq1rFZrpRwXKC9bTAdciOmt\nB4lpAHAltoietJSIhmvwK2rDAw88UOzJOWHChGIPPGDAAC1dulTHjh1TVFSUnnvuOWVnZ0uSRo0a\npfnz52vSpEny8/NTtWrVNG/ePP7HAKdrGB6keclJ6p+SqrunpGrOiCTF1OPWeADgbBdH9PO9iWg4\nX5H3kZ4xY4Yk6fvvv9eWLVt01113SZI+/PBDxcbGavLkyY6bsgDuIw1H2Jt5Wv1TUnU2O5eYBgAn\nu1xE+/gQ0bC/krqzxA9kSUpK0ooVK+Tnl3fxOjs7W507d1ZqamrlTlpKhDQcxRbT53Ksmj28IzEN\nAE5gjNG/v9ymt5bu1MCOecs5iGg4SoU/kOXEiRM6deqPtaJZWVk6ceJE5UwHuLCG4UGaOyJJVf18\nNHDqKtZMA4CDEdFwdSWG9BNPPKErr7xSQ4YM0eDBg9W2bVs99dRTjpgNcLpGtYhpAHAGIhruoMSl\nHVLeHTZWrVolSerYsaPq1q1r98GKwtIOOMOeY3nLPM7nsswDAOzNGKP//G+b3vx2p+7u2ED/R0TD\nScq9tGPPnj35v65bt65uvfVW3XrrrfkRbYzRgQMHKm9SwIU1qpV3N48qvlyZBgB7IqLhTooM6ccf\nf1x9+/bVe++9p82bN+vIkSPat2+fvvnmGz399NO6+uqrtXXrVkfOCjgVMQ0A9kVEw90Uu7Rjy5Yt\nmj17tr7//nsdPHhQgYGBiomJUa9evXTHHXcoICDAkbNKYmkHnI9lHgBQ+QpG9IAODfTP24hoOF+F\nb3/naghpuILdx05rADENAJWCiIarqvDt7wBcqnGtvI8TZ5kHAFQMEQ13RkgD5XRxTP98iJgGgLIw\nxuiV/22/ENH1iWi4HUIaqABbTPv7WnT3FGIaAErLFtETv91xIaJbE9FwO6UK6YyMDP3www9atmxZ\n/heAPI1rBWlecidiGgBKiYiGp/AraYdx48bp/fffV2xsrHx9fSVJFotFXbp0sftwgLuwxXT/lJW6\ne8oqzR2RpBZ1Q5w9FgC4nIIR3b89EQ33VmJIf/LJJ9q2bZuqVq3qiHkAt1UwpgdMSSWmAeAiF0f0\nC7cT0XBvJS7taNKkibKzsx0xC+D2Ci7zGDAlVdsO/ebskQDAJRDR8EQlXpEODAxUQkKCrr/++kJX\npSdMmGDXwQB3xZVpACjMGKNXv8qL6LsSiWh4jhJDunfv3urdu7cjZgE8RuE106maQ0wD8FK2iH7j\nm7yIfrEPEQ3PUapPNjx//ry2b98uSWrRooX8/f3tPlhR+GRDuJNdR7M0YEqqcnINMQ3A6xDRcHcV\n/mTDpUuXqlmzZho7dqzGjCk0xNoAACAASURBVBmj5s2bc/s7oJSaRARr7ogk+fladDdrpgF4EWOM\nXiOi4eFKDOnHHntM//vf//Tdd99p2bJl+vLLL/XII484YjbAI9hi2tcnL6a3HyamAXg2W0RPIKLh\n4UoM6ezsbLVo0SL/++bNm3MXD6CMmkQEa15yXkwPSCGmAXguIhrepMSQTkxM1PDhw7V06VItXbpU\nI0aMUGJioiNmAzwKMQ3A0xlj9NrX6UQ0vEaJIT1p0iTFxsZqwoQJmjBhgmJjYzVp0iRHzAZ4nIIx\nzTIPAJ4kP6KXpKtfYhQRDa9Qqrt2uBLu2gFPsPNolgakpMpq8u7m0bwOd/MA4L4ujuiX+sQT0fAI\n5b5rR79+/SRJrVu3Vnx8/CVfAMqvaUSw5iYnycfClWkA7o2Ihjcr8or0wYMHVa9ePe3du/eyD2zY\nsKFdBysKV6ThSbgyDcCdFYzoO9tF6eW+RDQ8S7mvSNerV0+S9NZbb6lhw4aFvt56663KnxTwQlyZ\nBuDOiGh4uxLfbPjVV19d8rNFixbZZRjAG10c0+nENAA38NpX24loeL0iQ3rSpElq3bq1tm3bVmht\ndOPGjVkjDVSygjE9gJgG4OJe+2q7xhPRQNFrpH/99VedOHFCTz75pF566aX8n4eEhKhmzZoOG/Bi\nrJGGJ9t5NEv9U1JljNHcEUlqxpppAC6GiIY3Kfca6bCwMDVq1Ehz585Vw4YNVa1aNVksFmVlZWnf\nvn12GRbwdk0v3GfawpVpAC6IiAYKK3GN9GeffaZmzZqpcePG6tq1qxo1aqSePXs6YjbAKxHTAFwR\nEQ1cqsSQ/tvf/qbU1FQ1b95cu3fv1pIlS5SUlOSI2QCvRUwDcCVENHB5JYa0v7+/wsPDZbVaZbVa\n1a1bN9YoAw5ATANwBUQ0ULQSQ7p69erKyspSly5dNHDgQD300EMKCgpyxGyA1ysc06uIaQAOZYvo\nO9pF6SUiGrhEiSG9YMECBQYG6rXXXtNNN92kpk2b6rPPPnPEbAB04dZ4I5JksYiYBuAwBSP65b7x\n8iWigUsUG9K5ubm65ZZb5OPjIz8/Pw0ePFgPPvigwsPDHTUfAEnRtYlpAI5DRAOlU2xI+/r6ysfH\nR7/++quj5gFQBGIagCMQ0UDp+ZW0Q3BwsFq3bq3u3bsXWhs9YcIEuw4G4FK2mB4wJVUDpqzS3BEd\n+dAWAJWGiAbKpsSQ7tOnj/r06eOIWQCUwsUxPS+5o6JrE9MAKub1r4looKyK/IhwV8VHhAN5dhzJ\n0oApqTJGxDSACnn96+16/et09W0bpX/dQUQDNuX+iHCb9PR03XHHHYqNjVWTJk3yvwA4l+3KtCT1\nT1mlHUdYMw2g7IhooPxKDOn77rtPo0ePlp+fn7799lsNGjRI99xzjyNmA1CC6Np595mWiGkAZUdE\nAxVTYkifOXNG119/vYwxatiwoZ599ll98cUXjpgNQCkQ0wDKg4gGKq7EkK5ataqsVquaNWumiRMn\n6uOPP1ZWVpYjZgNQSsQ0gLIgooHKUWJIjx8/Xr///rsmTJigtWvXaubMmZoxY4YjZgNQBsQ0gNIY\n/3U6EQ1UklLftePUqVOyWCwKCXHunQG4awdQvB1HstQ/JVUSd/MAUNj4r9P12tfbiWiglCp81460\ntDS1bt1a8fHxat26tdq0aaO1a9dW6pAAKg9XpgFcDhENVL4SQ3ro0KF66623tGfPHu3Zs0dvvvmm\n7rvvPkfMBqCciGkABRHRgH2UGNK+vr7q3Llz/vfXXHON/PxK/EBEAE5GTAOQiGjAnkoM6a5du2rk\nyJFaunSpvvvuO40ZM0bXXnut1q1bp3Xr1jliRgDlREwD3o2IBuyrxDcbduvWregHWyz65ptvKn2o\n4vBmQ6DseAMi4H2IaKDiSurOUt+1w1UQ0kD57DiSpQFTUmUMMQ14OiIaqBwVvmvH4cOHNWzYMPXs\n2VOStGXLFr3zzjslPvHQoUNVu3ZttWrV6rLbjTF68MEHFR0drfj4eJaJAHYWXTtYc0ckyWJhmQfg\nyYhowHFKDOkhQ4boxhtv1C+//CJJat68uV5//fUSDzxkyBAtXry4yO2LFi1Senq60tPTlZKSotGj\nR5dhbADlQUwDno2IBhyrxJA+duyY+vXrJx+fvF39/Pzk6+tb4oG7dOmimjVrFrl9wYIFGjRokCwW\ni5KSknTy5EkdPHiwDKMDKA9iGvBMRDTgeCWGdFBQkDIzM2Wx5P0PMjU1VWFhYRV+4oyMDNWvXz//\n+6ioKGVkZFx235SUFCUmJioxMVFHjx6t8HMD3o6YBjwLEQ04R4kh/eqrr6p3797auXOnrr76ag0a\nNEhvvPGGI2bLl5ycrLS0NKWlpSkiIsKhzw14KmIa8AxENOA8JX6yStu2bfXdd99p27ZtMsaoRYsW\n8vf3r/ATR0ZGav/+/fnfHzhwQJGRkRU+LoDSs8X0gCmp6p+yirt5AG6GiAacq8gr0mvWrNGhQ4ck\n5a2LXrt2rf7617/qscce0/Hjxyv8xL1799Z7770nY0z+cpF69epV+LgAyoYr04B7skX0He2IaMBZ\nigzpkSNHqkqVKpKkZcuW6YknntCgQYMUFham5OTkEg88YMAAderUSdu2bVNUVJTeeecdTZ48WZMn\nT5Yk9erVS02aNFF0dLRGjBiht956q5JeEoCyIqYB91Iwol/uS0QDzlLkB7K0adNGGzdulCSNHTtW\nERERevbZZyVJCQkJ2rBhg8OGLIgPZAHshw9tAVwfEQ04Trk/kCU3N1c5OTmSpCVLlui6667L32b7\nOQDPwpVpwLUR0YBrKTKkBwwYoK5du+rWW29VtWrV1LlzZ0nSjh07KuX2dwBcEzENuCYiGnA9RS7t\nkPLuGX3w4EH16NFDQUFBkqTt27crKytLbdu2ddiQBbG0A3AMlnkAruP1r7fr9a/TiWjAwUrqzmJD\n2hUR0oDjENOA8xHRgPOUe400ALDMA3AuIhpwbYQ0gGIR04BzENGA6yOkAZSImAYci4gG3AMhDaBU\niGnAMYhowH0Q0gBK7eKYTj9MTAOV6bWviGjAnRDSAMqkYEwPmEJMA5Xlta+2a/ySdN3ZLkr/IqIB\nt0BIAyiz6NrBmpdMTAOVpWBEv9w3Xj5ENOAWCGkA5dI0omBMpxLTQDkR0YD7IqQBlNsfMW0hpoFy\nIKIB90ZIA6gQYhooHyIacH+ENIAKuzimtxPTQLGIaMAzENIAKoUtpn0sFt1NTANFskV0v0QiGnB3\nhDSAStM0IlhziWmgSAUj+qU+RDTg7ghpAJWqYEwPSCGmAUkyxuhVIhrwOIQ0gEpni2lfH2IaMMbo\nta/TNWFJuu5KrE9EAx6EkAZgF7Y108Q0vNnFEf1in9ZENOBBCGkAdtPkopjedoiYhvcwxui1r7YT\n0YAHI6QB2JUtpv18896ASEzDG+RH9Dc71L89EQ14KkIagN01iQjW3BHENLyD7Y2Ftoh+4XYiGvBU\nhDQAh8i7Mt1Jfr55H9pCTMMT2SL6DSIa8AqENACHaVwrSPOSO8n/Qkz/fOiUs0cCKo0xRq/8Ly+i\nB3QgogFvQEgDcChbTFfx9dHdU1YR0/AItoie+G1eRP/zNiIa8AaENACHa1wrSHOTk/JjeutBYhru\nyxij//xvGxENeCFCGoBT5F2ZzovpgVOJabgnY4z+/eU2vfntTiIa8EKENACnaVQgpu+ekkpMw63Y\nIvqtpTt1d8cGRDTghQhpAE5li+kAf1/dPSVVW34hpuH6jDH6V4GI/r9bWxHRgBcipAE4XcGYHjiV\nmIZrM8bo5cXbNGnpTg0kogGvRkgDcAkNw4lpuD5bRE/+Li+i/0FEA16NkAbgMmwxXc3fV3dPTdXm\nX3519khAPmOMXlr8syZ/t1P3JBHRAAhpAC4mL6Y7KdDfVwOnriKm4RKMMXpp0c96+7tduiepgZ7v\nTUQDIKQBuKAG4YGFYnpTBjEN5zHG6MVFP+vtZbt0b1JDrkQDyEdIA3BJtpgOquJHTMNpbBGdciGi\nn781ThYLEQ0gDyENwGXlxXSSgqsS03A8Y4xeWLhVKct2aVAnIhrApQhpAC6tfk1iGo5njNE/v9iq\nKct3a3CnhnquNxEN4FKENACXR0zDkWwRPXXFbg25qpGeJaIBFIGQBuAWCsb03VNS9dMBYhqVzxij\nf3z+R0T//U+xRDSAIhHSANxG/ZqBen9kkkKr+Wvg1FT9eOCks0eCBzHG6PnPt+jd73frvquJaAAl\nI6QBuJWoGnlXpsMC/XXP1FXENCqFLaKnfb9HQ69urGduIaIBlIyQBuB28mK6k8IC/TVw6ipt3E9M\no/yMMXrus7yIHnZNYz19SwwRDaBUCGkAbimyejXNS+6kGoFVdM87q7SBmEY52CJ6+g97NPyaxvrb\nzUQ0gNIjpAG4rbyYTlKNwCq6dyoxjbIxxujZTzdr+g97NKJzY/2ViAZQRoQ0ALd2xYWYrhmcF9Pr\n9p1w9khwA8YY/f3TzZqxcq+SuzTRU72IaABlR0gDcHu2mA4PrqJB76wmplEsq9Xo6QWb9N7KvRrZ\ntYme7NmSiAZQLoQ0AI9QLyxvzXStCzG9di8xjUvZInpW6j6N6tpUT9xERAMoP0IagMeoGxagecmd\nFBFSVYPeWaW1e487eyS4EKvV6G8LNmn2qn0afW1TjbupBRENoEIIaQAepW5YgOaOSFLt0AANeme1\n0vYQ08iL6L9+8pPmrNqnsd2a6i83EtEAKo6QBuBx8q5MJ6lOaIAGvbtaa4hpr2a1Gj318U+au3q/\n7u8WrT/3IKIBVA5CGoBHqhOaF9N1wwI0+N3VWrUr09kjwQmsVqMn//uT5q3Zrweui9ZjPZoT0QAq\njV1DevHixWrRooWio6P10ksvXbJ9+vTpioiIUEJCghISEjR16lR7jgPAy9QODdC8EUmqFxag+6av\nUSox7VWsVqNxH/2o99P268Hrm+nR7kQ0gMplt5DOzc3V2LFjtWjRIm3ZskVz587Vli1bLtnvrrvu\n0oYNG7RhwwYNHz7cXuMA8FK1QwM0NzlJV1SvpvumrdHKncS0N8i1Gv3lox/14doDeoiIBmAndgvp\n1atXKzo6Wk2aNFGVKlXUv39/LViwwF5PBwBFqh2S9wbEqBrVNHT6Gv2w85izR4Id5VqNHp+/UfPX\nHtDDNzTTI92bO3skAB7KbiGdkZGh+vXr538fFRWljIyMS/b76KOPFB8frzvuuEP79++/7LFSUlKU\nmJioxMREHT161F4jA/BgESFVNWdEkurXvBDTO4hpT5RrNXr8w43677oMPdq9uR6+gYgGYD9OfbPh\nn/70J+3Zs0c//vijunfvrsGDB192v+TkZKWlpSktLU0REREOnhKAp7DFdMOaQbpv+hqtSCemPUmu\n1ejPH27Uf9dn6M89muvB65s5eyQAHs5uIR0ZGVnoCvOBAwcUGRlZaJ/w8HBVrVpVkjR8+HCtXbvW\nXuMAgCSpVnBVzRnRUY1rBWnYjDVans6/cnmCnFyrHv1ggz5en6HHb2yh+68jogHYn91Cun379kpP\nT9fu3bt1/vx5zZs3T7179y60z8GDB/N//emnnyomJsZe4wBAvvDgvCvTeTGdpu+2E9PuLCfXqkc+\n2KgFG37RuJtaamy3aGePBMBL2C2k/fz8NHHiRN14442KiYlRv379FBcXp2eeeUaffvqpJGnChAmK\ni4tTmzZtNGHCBE2fPt1e4wBAITWDqmjuiCRFRwRrxHtpWrrtiLNHQjlk51r10LwN+mzjL3qyZ0uN\nvraps0cC4EUsxhjj7CHKIjExUWlpac4eA4CHOHH6vO55Z5XSD2fp7XvbqVvL2s4eCaWUF9HrtfCn\nQ/prrxiN6NLE2SMB8DAldSefbAjAq9UIqqLZwzuqRd0QjZy5Vku2Hnb2SCiF8zlWPTAnL6L/djMR\nDcA5CGkAXq96YBXNGtZRLeuFaNSstfp6CzHtys7nWHX/nHVavPmQ/v6nWA3vTEQDcA5CGgAkhQX6\na+awjoqtF6rRs9fqy82HnD0SLuNcTq7GzF6n/205rOd6x+m+qxs7eyQAXoyQBoALwqr5a+bwjoq7\nIkxjZ6/T4k0HS34QHOZsdq5Gz1qnr7ce1j9ujdPgqxo5eyQAXo6QBoACQgP8NXNYB8VHhWnsnPVa\n+BMx7QrOZudq5My1+ubnI/rn7a10b6dGzh4JAAhpALhYSIC/3hvWUW0bVNcDc9frs42/OHskr3Y2\nO1cj3su73/eLfVprYMeGzh4JACQR0gBwWcFV/TT9vg5q17CGHpq3Xgs2ZDh7JK905nyuhs9I04od\nx/SvvvEa0KGBs0cCgHyENAAUIaiqn6bf114dGtfUI+9v0MfrDzh7JK/y+/kcDZuxRt/vPKZ/39FG\n/drXd/ZIAFAIIQ0AxQis4qdpQzooqUm4Hv1go+avJaYd4fS5HA2dvkapuzL1ar82uqNdlLNHAoBL\nENIAUIJqVXz1zuD2uia6lh6fv1Hvr9nn7JE8Wta5HN03bY1W7z6u1+5K0O1XEtEAXBMhDQClUK2K\nr6YMSlSXZhEa99FPmrOKmLaH385ma/C7q7V23wlNGHClbk2IdPZIAFAkQhoASinA31dv39tO17Ws\nrac+/knvrdzj7JE8yqmz2br3ndXauP+kJg64UrfEX+HskQCgWIQ0AJRBgL+vJt3TVjfE1NEzCzbr\n3RW7nT2SR/j192zdM3WVNv/yq94a2FY9W9dz9kgAUCJCGgDKqKqfr94a2FY3xdXV859v0ZRlu5w9\nkls7cfq87p6aqp8P/qbJ97RTj7i6zh4JAEqFkAaAcqji56M37r5SN7eup38u3Kq3lu5w9khu6fjp\n87p76iqlH8nS2/e20/UxdZw9EgCUmp+zBwAAd+Xv66Px/RPk62PRvxZvU06u0YPXN3P2WG7j6G/n\ndM/UVdqTeVpTByWqS/MIZ48EAGVCSANABfj5+ui1uxLk52PRq19tV3auVY92by6LxeLs0VzakVNn\nNWBKqn45eVbThrTXVdG1nD0SAJQZIQ0AFeTrY9G/72wjf18fvfHNDmXnGo27qQUxXYSDv57R3VNW\n6cips5oxtIM6NK7p7JEAoFwIaQCoBL4+Fr3Yp7X8fC2a/N1O5eRa9debY4jpixw48bvunrJKJ06f\n13vDOqhdQyIagPsipAGgkvj4WPR/t7WSv6+Ppq7Yrexcq57tHUdMX7Av83cNmJKq385ma+bwjkqo\nX93ZIwFAhRDSAFCJLBaL/v6nWPn7WjRl+W5lW43+79ZW8vHx7pjefey07p6SqjPZuZozIkmtIsOc\nPRIAVBghDQCVzGKx6KleMari56M3v92p8zlWvdw3Xr5eGtPph3/T3VNXyWo1mjsiSTH1Qp09EgBU\nCkIaAOzAYrHozz1aqKqfr179arvO5Vj1ar+8NyR6ky2/nNI976ySn49F749MUnTtEGePBACVhpAG\nADuxWCx68PpmqurnoxcX/azzObmaMOBKVfXzdfZoDrFx/0kNene1gqr4avaIJDWuFeTskQCgUnnX\npREAcIKRXZvq2T/F6svNhzVq5lqdzc519kh2l7bnuO6Zukqh1fz0/shORDQAj0RIA4ADDLm6sV7s\n01pLtx/V0Olr9Pv5HGePZDcrd2Zq0LurVSukqj4Y2Un1awY6eyQAsAtCGgAcZECHBnrlzjZK3ZWp\nwe+u1m9ns509UqVbuu2Ihkxbrcjq1fR+cpLqhVVz9kgAYDeENAA4UJ+2UZow4Eqt33dSA6fmfTCJ\np1j000GNeC9NTSOCNS85SbVDA5w9EgDYFSENAA52S/wVevvedvr50G+6K2Wljpw66+yRKuyjtQc0\nds46tY4M09zkJIUHV3X2SABgd4Q0ADjB9TF1NH1Iex04cUZ3vr1SB0787uyRym3myj167MON6tQ0\nXDOHdVRYNX9njwQADkFIA4CTXBVdS7OGd9SJ0+d15+SV2nU0y9kjldnk73bq6QWbdUNMbb0zuL2C\nqnJXVQDeg5AGACdq26CG5iV30vkcq/q9vVJbD55y9kilYozRf77cppcW/aw/tblCk+5ppwB/77g/\nNgDYENIA4GSxV4Tqg1Gd5O/ro7veXqm1e487e6Ri5VqNnlmwWRO/3aH+7evr9bsSvO4TGwFAIqQB\nwCU0jQjWByM7qWZQFQ2cukrf/HzY2SNd1rmcXD04d71mpu7VyC5N9GKf1vL1sTh7LABwCkIaAFxE\n/ZqBmj/6KjWrHaIR763V/LUHnD1SIVnncjR0+hp98dNBPdWrpZ7sFSOLhYgG4L0IaQBwIbWCq2pu\ncpKSmtTUnz/cqLe/2+nskSRJx7LOaUBKqlJ3Hdcrd7ZRcpemzh4JAJyOkAYAFxNc1U/vDmmvW+Lr\n6cVFP+ufX2yR1WqcNs/+47/rzskrlX7kN00Z1E5920U5bRYAcCXcpwgAXFBVP19N6H+lwoOqaMry\n3TqWdV7/uiPe4W/q2/zLr7pv2hqdzc7V7OEd1a5hTYc+PwC4MkIaAFyUj49Fz/aOU0RIVf3nf9t1\n8NczemtgO9UMquKQ51+86aAeeX+jwqr568NRV6lF3RCHPC8AuAuWdgCAC7NYLLr/umZ67a42Wrfv\npHpPXGH3e00bYzRhSbpGzVqnFnVD9On9VxPRAHAZhDQAuIHbr4zSByM7KTvXqr6TftDiTQft8jxn\nzufqgbnr9epX29XnykjNS05S7dAAuzwXALg7QhoA3ERC/er69P5r1LxOiEbNWqfXvtpeqW9CPPTr\nWfV7e6W++OmgnujZUq/0a8OnFQJAMQhpAHAjdUIDNC85SX3bRmn8knSNmb1OWedyKnzcH3YeU++J\nK7TraJam3JuoUV2bco9oACgBbzYEADcT4O+r/9wZr5h6IXph4VZd958T+vONLdS3bVSZP2Xwl5Nn\n9M+FW/XFjwfVMDxQM4d1ZD00AJQSIQ0AbshisWh45ya6skEN/ePzLfrL/B81/fs9+tstMbqqaa0S\nH382O1dTl+/Sm9/ulNUYPXxDM43q2pSlHABQBoQ0ALixdg1r6OMxV+mzHw/q5UU/6+4pq3RDTB09\n1aulmkQEX/YxS7Ye1vOfb9HezN91U1xd/fXmGNWvGejgyQHA/RHSAODmLBaLere5Qj1i6+jd73fr\nrW93qsdryxRTL1TZudYLX0bZuVady7Hq+OnzahoRpJnDOqhzswhnjw8AbouQBgAPEeDvqzHXRuvO\ndvX15rc7tDfztPx9feTv56Mqvj7y97XI39dHLeuGqH+HBg7/lEQA8DSENAB4mIiQqnq2d5yzxwAA\nj8flCAAAAKAcCGkAAACgHAhpAAAAoBzsGtKLFy9WixYtFB0drZdeeumS7efOndNdd92l6OhodezY\nUXv27LHnOAAAAEClsVtI5+bmauzYsVq0aJG2bNmiuXPnasuWLYX2eeedd1SjRg3t2LFDjzzyiMaN\nG2evcQAAAIBKZbeQXr16taKjo9WkSRNVqVJF/fv314IFCwrts2DBAg0ePFiSdMcdd2jJkiUyxthr\nJAAAAKDS2C2kMzIyVL9+/fzvo6KilJGRUeQ+fn5+CgsLU2Zmpr1GAgAAACqNW9xHOiUlRSkpKZKk\no0ePOnkaAAAAwI5XpCMjI7V///787w8cOKDIyMgi98nJydGvv/6q8PDwS46VnJystLQ0paWlKSKC\nj7MFAACA89ktpNu3b6/09HTt3r1b58+f17x589S7d+9C+/Tu3VszZsyQJM2fP1/XXXedLBaLvUYC\nAAAAKo3dlnb4+flp4sSJuvHGG5Wbm6uhQ4cqLi5OzzzzjBITE9W7d28NGzZM9957r6Kjo1WzZk3N\nmzfPXuMAAAAAlcpi3Ow2GYmJiUpLS3P2GAAAAPBwJXUnn2wIAAAAlIPbXZGuVauWGjVq5OwxinX0\n6FHeFAm74fyCPXF+wZ44v2BvlX2O7dmzR8eOHStyu9uFtDtg+QnsifML9sT5BXvi/IK9OfocY2kH\nAAAAUA6ENAAAAFAOvs8+++yzzh7CE7Vr187ZI8CDcX7Bnji/YE+cX7A3R55jrJEGAAAAyoGlHQAA\nAEA5eH1IL168WC1atFB0dLReeuml/J9/8803atu2rVq1aqXBgwcrJyfnso9/8cUXFR0drRYtWujL\nL7/M//n48ePVqlUrxcXF6fXXXy92hjVr1sjPz0/z58/P/9mMGTPUrFkzNWvWLP9j1OF+XPX88vX1\nVUJCghISEtS7d+8Kvko4izPPr6VLlyosLCz/PHr++edLnAvuxVXPr0aNGql169ZKSEhQYmJiJb1a\nOFpFzq/MzEx169ZNwcHBuv/++wttW7t2rVq3bq3o6Gg9+OCDutzCC2OMHnzwQUVHRys+Pl7r1q3L\n31bm/jJeLCcnxzRp0sTs3LnTnDt3zsTHx5vNmzeb3NxcExUVZbZt22aMMebpp582U6dOveTxmzdv\nNvHx8ebs2bNm165dpkmTJiYnJ8f89NNPJi4uzpw+fdpkZ2eb66+/3qSnpxc5Q7du3UzPnj3Nhx9+\naIwxJjMz0zRu3NhkZmaa48ePm8aNG5vjx4/b7zcCduGq55cxxgQFBdnnRcNhnH1+ffvtt+bmm28u\n9VxwL656fhljTMOGDc3Ro0cr9wXDoSp6fmVlZZnly5ebSZMmmbFjxxba1r59e7Ny5UpjtVrNTTfd\nZBYuXHjJ47/44gtz0003GavValauXGk6dOhgjClff3n1FenVq1crOjpaTZo0UZUqVdS/f38tWLBA\nmZmZqlKlipo3by5J6t69uz766KNLHr9gwQL1799fVatWVePGjRUdHa3Vq1dr69at6tixowIDA+Xn\n56euXbvqv//972VneOONN9S3b1/Vrl07/2dffvmlunfvrpo1a6pGjRrq3r27Fi9ebJ/fBNiNq55f\n8AyucH6VZS64F1c9v+AZKnp+BQUF6ZprrlFAQEChnx88eFCnTp1SUlKSLBaLBg0apE8++eSSxy9Y\nsECDBg2SxWJRUlKSd9ggWAAABzJJREFUTp48qYMHD5arv7w6pDMyMlS/fv3876OiopSRkaFatWop\nJycn/4be8+fP1/79+0v9+FatWmn58uXKzMzU77//roULF+Y/fvLkyZo8eXL+4z/++GONHj26VMeF\ne3HV80uSzp49q8TERCUlJV32Dxm4PmefX5K0cuVKtWnTRj179tTmzZuLPS7ci6ueX5JksVjUo0cP\ntWvXTikpKZX+2mF/FT2/ijtuVFTUJceVLv3/x8s9f3n+/PIr9XRexGKxaN68eXrkkUd07tw59ejR\nQ76+vqV+fExMjMaNG6cePXooKChICQkJ+Y8fNWpU/n4PP/ywXn75Zfn4ePXfZ7yOK5xfe/fuVWRk\npHbt2qXrrrtOrVu3VtOmTSv+4uB0jjq/2rZtq7179yo4OFgLFy7UbbfdpvT09Ep/PXAtrnB+rVix\nQpGRkTpy5Ii6d++uli1bqkuXLpX7QuEUFT2/ilPw/KpMXl1wkZGRhf6mc+DAAUVGRkqSOnXqpOXL\nl2v16tXq0qVL/j8zlPbxw4YN09q1a7Vs2TLVqFHjso9PS0tT//791ahRI82fP19jxozRJ598Uuxx\n4T5c9fyyHVuSmjRpomuvvVbr16+vvBcOh3D2+RUaGqrg4GBJUq9evZSdna1jx47x55eHcNXzy3Zs\nSapdu7Zuv/12rV69upJeNRyloudXccc9cODAZY9bmucv159f5V0o7gmys7NN48aNza5du/IXu2/a\ntMkYY8zhw4eNMcacPXvWXHfddWbJkiWXPH7Tpk2F3kzRuHFjk5OTU+jxe/fuNS1atDAnTpwodpbB\ngwcXerNho0aNzPHjx83x48dNo0aNTGZmZqW9bjiGq55fx48fN2fPnjXGGHP06FETHR3Nm8HckLPP\nr4MHDxqr1WqMMWbVqlWmfv36xmq1FjsX3Iernl9ZWVnm1KlTxpi8N5x16tTJLFq0qPJ/A2BXFT2/\nbKZNm1bimw2/+OKLSx73+eefF3qzYfv27Y0x5esvr17a4efnp4kTJ+rGG29Ubm6uhg4dqri4OEnS\nv//9b33++eeyWq0aPXq0rrvuukseHxcXp379+ik2NlZ+fn5688038/8Jom/fvsrMzJS/v7/efPNN\nVa9eXZLy1+cU908MNWvW1NNPP6327dtLkp555hnVrFmzUl877M9Vz6+tW7dq5MiR8vHxkdVq1RNP\nPKHY2NjKfvmwM2efX/Pnz9ekSZPk5+enatWqad68ebJYLMXOhf9v7/5BkusCOI7/Cm5hqA3R8LT1\nZwizuERuDRFBf2gJmmrIwaFwKxqCqKYmt+gfFYnQJhgNEQqtgTlIUENBOQUtRWoI/cFnCITopZfu\n+9SrPd/PpOeeezxHzvDjcDindBTr/Lq5udHQ0JAk6fn5WSMjI+rr6/uOvwR/0H+dX9LrMYjpdFqP\nj4/a3d1VNBqVy+XSysqKvF6vcrmc+vv71d/fL+nt/BoYGND+/r6amppUVVWl7e1tSdbyFzcbAgAA\nABb81XukAQAAAKsI0gAAAIAFBGkAAADAAoI0AAAAYAFBGgAAALCAIA0ARSKVSsntdr8pW1hYUCAQ\nkN/vl2macrlcstlsMk1TpmkqHA5LkgKBgJqbm2Wapjwej0Kh0Lv2g8Ggrq+vC999Pp/Ozs6+dlAA\n8IP91edIA0CpWF5elvQatgcHB5VMJgvP1tbWFIvFFI/H5XQ6lU6nFYlE3rURDAbldrtVV1cnSdrc\n3PyezgPAD8WKNACUuMXFRa2ursrpdEp6vV55bGzsTZ1wOKxEIqHR0VGZpqlcLqeuri4lEglJkt1u\n1/T0tFpaWtTT06N4PK6uri41NDRob29PkvTy8qLp6Wl5PB61tbVpfX39ewcKAEWGIA0AJSydTiuT\nyaihoeHDesPDw+ro6NDOzo6SyaRsNtub5w8PD+ru7tbp6akcDodmZ2cVi8UUiUQ0NzcnSdra2lJ1\ndbWOj491fHysjY0NXV1dfdnYAKDYsbUDAIpEWVnZp8r/pIqKisJVy62traqsrJRhGGptbVUqlZIk\nRaNRnZycFPZl39/f6+LiQvX19V/ePwAoRgRpACgSNTU1uru7e1N2e3v7YVB1Op2y2+26vLz811Xp\njxiGUQjs5eXlqqysLHx+fn6WJOXzeS0tLam3t9fy7wDAT8LWDgAoEna7Xb9+/dLh4aGk1xB9cHCg\nzs7OD9+bmZmR3+9XOp2WJGWz2X88tcPhcCiTyVjuX29vr1ZXV/X09CRJOj8/18PDg+X2AKDUsSIN\nAEUkFArJ7/drcnJSkjQ/P6/GxsYP35mYmFA2m5XH45FhGDIMQ1NTU+/qeb1ejY+Py2az6ejo6NN9\n8/l8SqVSam9vVz6fV21trXZ3dz/dDgD8FGX5fD7/f3cCAAAAKDVs7QAAAAAsIEgDAAAAFhCkAQAA\nAAsI0gAAAIAFBGkAAADAAoI0AAAAYAFBGgAAALCAIA0AAABY8BsN0MJuzcp9DQAAAABJRU5ErkJg\ngg==\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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kKlHfqJE6EhERkdHQagVe3JKD30+V4d3xfTG4JxeIMDUs3NQqwju3w5Kn+iHn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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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LcPjiTXy1/yKGBbZFVL+OYschDWFBp0diYmSINeEhMDWWInKjHKVVSrEjERER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\nERERmRAWdCIiIiIiE8KCTkRERERkQv4fuh5oK0jgjIUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for event in events:\n",
" t0 = event[0] + 0.5*(event[1]-event[0]) \n",
" time = t0 + np.linspace(-1,1,100) * 10 * u.min\n",
" dish_pointing = SkyCoord(AltAz(az = azimuth * np.ones(time.size), alt = elevation * np.ones(time.size),\\\n",
" location = ea4gpz, obstime = time))\n",
" plt.figure(figsize = (12,6), facecolor = 'w')\n",
" plt.plot(time.datetime, astropy.coordinates.get_sun(time).separation(dish_pointing))\n",
" plt.title(t0)\n",
" plt.xlabel('UTC time')\n",
" plt.ylabel('Separation (deg)')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"noise_pwr = np.fromfile('power_f32_2019-10-06T18:50:20.697445', dtype = 'float32')\n",
"noise_t = np.datetime64('2019-10-06T18:50:20.697445') + np.arange(noise_pwr.size) * np.timedelta64(1, 's')"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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dG82/9957iz2aL+WewRg6dKjXWbhu3bpp06ZNWrRokbp27SpJat68uZKTk7Vo0SJ16NDB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\n/wM6dGJGkxwOswAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"secs_in_day = 3600 * 24\n",
"plt.figure(figsize = (12,8), facecolor = 'w')\n",
"plt.plot(noise_t[2:secs_in_day * 5], 10*np.log10(noise_pwr[2:secs_in_day * 5]))\n",
"plt.title('Noise power measurements (10488MHz, 5MHz BW)')\n",
"plt.xlabel('UTC time')\n",
"plt.ylabel('Noise power (dB)');"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"pwr_by_day = noise_pwr[:secs_in_day * 5].reshape((-1, secs_in_day))\n",
"t_by_day = noise_t[:secs_in_day * 5].astype('<M8[us]').reshape((-1, secs_in_day))\n",
"peak_loc = np.argmax(pwr_by_day, axis = 1)\n",
"span = peak_loc.reshape((-1,1)) + np.arange(-1000,1000).reshape((1,-1))\n",
"peaks_by_day = pwr_by_day[np.tile(np.reshape(np.arange(pwr_by_day.shape[0]), (-1,1)), (1,span.shape[1])), span]\n",
"t_peaks_by_day = t_by_day[np.tile(np.reshape(np.arange(pwr_by_day.shape[0]), (-1,1)), (1,span.shape[1])), span]\n",
"normalization = np.average(peaks_by_day[:,1750:], axis = 1)\n",
"peaks_by_day /= normalization.reshape((-1,1))"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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yy5XRJUayWoUQQgghRBWaujqehLScCq2zcS1b3uvqc9vr8fHxBAQElDhna2uLh4cHJ0+e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\nEREvNcciIiIiIl7/DwqjBuyjSnmcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (12,8), facecolor = 'w')\n",
"plt.plot(np.arange(-1000,1000), 10*np.log10(peaks_by_day).transpose())\n",
"plt.legend([f'October {n+7}' for n in range(peaks_by_day.shape[0])])\n",
"plt.grid()\n",
"plt.title('Sun noise on different days')\n",
"plt.xlabel('Time from maximum (s)')\n",
"plt.ylabel('Noise power (dB)');"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.6124690771102905"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10*np.log10(np.max(peaks_by_day))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"max_times = Time(t_peaks_by_day[:,peaks_by_day.shape[1]//2])\n",
"trace_times = Time(t_peaks_by_day)\n",
"sun_max = astropy.coordinates.get_sun(max_times).transform_to(AltAz(location = ea4gpz, obstime = max_times))\n",
"sun_trace = astropy.coordinates.get_sun(trace_times).transform_to(AltAz(location = ea4gpz, obstime = trace_times))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Spherical angle between sun_max[2] and sun_max[3]"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$0.37997821 \\; \\mathrm{{}^{\\circ}}$"
],
"text/plain": [
"<Quantity 0.37997821 deg>"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"angle = np.arccos(np.sin(sun_max[2].alt) * np.sin(sun_max[3].alt) + np.cos(sun_max[2].alt) * np.cos(sun_max[3].alt) * np.cos(sun_max[2].az - sun_max[3].az))\n",
"np.rad2deg(angle)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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jvp43/xrWgno+Gp6cbSmHS3srTinG9YOGncG1FpRIoKl/U3o26MlzLZ7D39Vf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f1ehg6jrU5TpuzCbB5Y+QAbjm3g4fYPM6LFCKsjlYspKiL1L0/iU706Me+/j190lNWRyiVp50mS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\nTQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10,10), facecolor = 'w')\n",
"plt.plot(sun_trace.az.T, sun_trace.alt.T, '--')\n",
"plt.plot(sun_max.az, sun_max.alt, 'o', color = 'black')\n",
"plt.plot(azimuth, elevation, 'x', color = 'red')\n",
"plt.title('Location of the sun')\n",
"plt.xlabel('Azimuth (deg)')\n",
"plt.ylabel('Elevation (deg)');"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"measure_sig = peaks_by_day[2,500:1001] - 1\n",
"measure_t = Time(t_peaks_by_day[2,500:1001])\n",
"dish_pointing = SkyCoord(AltAz(az = azimuth * np.ones(measure_t.size), alt = elevation*np.ones(measure_t.size),\\\n",
" location = ea4gpz, obstime = measure_t))\n",
"sun = astropy.coordinates.get_sun(measure_t)\n",
"measure_theta = sun.separation(dish_pointing)\n",
"cut = np.argmin(measure_theta)\n",
"measure_theta = measure_theta[:cut][::-1]\n",
"measure_sig = measure_sig[:cut][::-1]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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9x95jqBZne1uNiG6uEdHNtffUBZ06X6AuwZ5Kz7qowuIy2dkalXOxUN/vOqUVOzK1OOW4\nnOxsFBnkrkdvaKOe/8+e0wAAAPUJpRnXrL2fm9r7uUmSolp4/em+ARH+Kiwp1foDOVp3IEc/7T2t\nO+dvVr+wZrqvZ4hiW3px+gYAAKh3KM2ocQ62NrqhQzPd0KGZnhnQXu+uPaQPNx7W6t2n5e5kpz7t\nmurGsGbqEeotb1cHa8cFAACoEqUZFuVoZ6NHb2yjcdeFau3+bP2457SS9mZpadpJSZK3i71iWnqp\nRytv9Qj1VmtfV3bnAAAAdQ6lGbXCyd5GN4X76aZwP5WWmbT12DltO56r3ZkXtOngGa3ceUqS5Ofm\nqAERfuoe6q3SMpO8XewVG+pt5fQAAKCxozSj1tkYDeoW4qVuIb+fD20ymXTs7GX9cvCMftqbpQWb\njunDDUfKHz8g3E/dQrwUGeSu5l7OkqRmbo7WiA4AABopSjOszmAwqIW3i1p4u2hkTLAKiku16+QF\nOdga9eOe00r8+VD5SvQfugR76Kn+7diVAwAA1ApKM+ocRzsbRbXwlPT7PtGP3tBGOXlFSjlyVmcu\nFeliQYk++/WY7py/WTd19FNC10D1D2vGudAAAMBiKM2o8wwGg5o2cdCACP/y2+7rGaK5SQf0ecpx\nrdp1Sl2CPTS6V0v1be8rVwd+rAEAQM2iXaBecrK30dM3tdeT/dtpydYM/Wf1fk36LFU2RoPCA93V\ns5W3RvcMkS/nPgMAgBpAaUa9ZmM0aER0cw3vGqRfj5zV+gM52nz4jN77+ZA++eWoxvZuqXu6t2A/\naAAAYBZKMxoEo9Gg7qHe6v5/29Mdzrmkfy/fo//8eEBvJR/UgHA/ebnYK9DDSbEtvRUR5G7lxAAA\noD6hNKNBaunjovmjopWelacPNhzW9ztPqbCkTHmFJZKkMH839W7ro9uimqu1r6uV0wIAgLqO0owG\nrbWvq15OiNDLCRGSpOyLhVq5M1PfpJ3UB+sPK/HnQxocGaBH+rZW22ZNrJwWAADUVZRmNCpNmzjo\n3h4hurdHiM5eKtL8dYf08cYjWrb9pAaG+2ti39bq4O9m7ZgAAKCOoTSj0fJysdfTN7XXA71D9cGG\nw/powxEt35GpVk1d1NzLWTEtvdS5uYfCA93l5mhn7bgAAMCKKM1o9Dxd7PVk/3YaGxeqTzYd0e7M\nC0rPytOMVfvKHxPVwlNvjOisYG9nKyYFAADWQmkG/o+7s50m9m1T/v3ZS0XaceK8th3P1fx1hzRw\nzjr1buOju2JbKK4Nl+8GAKAxoTQDFfBysdf1bZvq+rZNldAlUG/8uF/rD+Ro5c5TGto5QE/0a8fK\nMwAAjQSlGaiG5l7Oen1EZxUUl2pu0gHNX3dYy7Zn6rboID3QO9Ta8QAAgIVRmoFr4Ghno6f+1l73\n9gjRW2vS9dmvx/XZr8fVwcuoPK+T6h/mJ3tbo7VjAgCAGkZpBv6CZm6OmnZLuCb0ba0vUjL04c/7\nNXFhqpo2cdB1bZpqcCd/9Wnna+2YAACghlCaATP4NnHUhD6t1UHHZfDvqM9TjmvNviwt2ZqhmzsF\nqH9YM13frilb1gEAUM9RmoEaYDQYFN/eV33a+6qopExzkw7o/fWH9d22k3K0M2pghL9GRDdXbEsv\nGQwGa8cFAADXiNIM1DB7W6Oe7N9Oj97QRtsycrVk6wl9l3ZSX209oRBvZ828rZO6hXhZOyYAALgG\nFvvEUkFBgWJiYtSpUyd17NhRU6dOlSQlJSWpa9euCg8P16hRo1RSUmKpCIBV2doYFdXCSy8nROjX\n527UG7d3ksFg0N3zN+vr1AyZTCZrRwQAANVksdLs4OCgpKQkbdu2TWlpaVq1apU2btyoUaNGadGi\nRdq5c6datGihjz/+2FIRgDrDyd5GCV2C9OX4Hurg76bHF29TwlsbtXx7pp5Zsl1PfJ6mr7ZSpAEA\nqKssVpoNBoNcXV0lScXFxSouLpaNjY3s7e3Vtm1bSVK/fv20ZMkSS0UA6hxvVwcteainZgyLVOb5\nfE1YuFXfbjup9Qdy9MTn2zTtu90qK6M4AwBQ1xhMFlzaKi0tVVRUlNLT0zVhwgS9+uqrCgkJ0ZIl\nSxQdHa1HH31USUlJ2rFjxxXPTUxMVGJioiQpIyNDixYtqvA4eXl55QUdfw0zNM9fmV9hiUm7z5aq\nraeNnGylxfuK9P2REsX42WhshIPsbRrPBwb5+TMP8zMP8zMP8zMP8zOPJeY3efJkpaSkXHG7RUvz\nH3Jzc5WQkKC5c+fq4sWLevrpp1VYWKj+/ftr2bJlSktLq/T50dHRVw3/h+TkZMXHx9dw6saFGZqn\npub33s+H9O8Ve+TbxEH3dG+h+3qFqEkj2K6Onz/zMD/zMD/zMD/zMD/zWGJ+FfXOWtk9w8PDQ336\n9NGqVas0efJkrVu3TpL0ww8/aP/+/bURAagXHrguVGEBbkr8+ZBeW71fbyUfVDM3Bw2I8NfD8a0a\nRYEGAKAuslhpzs7Olp2dnTw8PJSfn6/Vq1drypQpysrKkq+vrwoLCzV9+nQ999xzlooA1Eu9Wvuo\nV2sfbc/I1VdbT+j42ct6O/mgPt54RP3DmunmTgHycrFXSx8XeTjbWzsuAACNgsVKc2ZmpkaNGqXS\n0lKVlZVpxIgRGjx4sJ566iktW7ZMZWVleuihh9S3b19LRQDqtcggD0UGeUiSdp44r4W/HtOKHZla\nmnZSkuTtYq8PR3crfwwAALAci5XmyMhIpaamXnH7zJkzNXPmTEsdFmiQwgPd9XJChF68uaN+O3JW\nF/KL9e8Ve3T7u5v0ws1hGtmtOVcaBADAgrgiIFCP2Nsa1au1jyQpqoWnHlucpme/2qGPNhzRvT1b\nKKFLoJzt+c8aAICaxm9XoJ7ydXPUp2Ni9VXqCX2w/rCe+3qnXl25V8O6Bqm1r6t6tPJWq6ZsYwQA\nQE2gNAP1mNFo0PCoIA3rGqitx87p441H9emmoyopM8nB1qipN3fUHTGcugEAgLkozUADYDAYFNXC\nS1EtvDRjeKROnS/Q80t36u9f79DXqRkaExeq+HZN5WhnY+2oAADUS5RmoIFxtLNRiI+L/nt/jD5P\nOa5ZP+zX+E+3qImjrW7q6KdbOgeqRytv2RhZfQYAoLoozUADZTQaNDImWMOjgrTx4Bl9k3ZSK3ee\n0hdbMuTj6qDBkf4aHhWk8EB3a0cFAKDOozQDDZytjVHXtW2q69o21b+Lw7Vmb5a+3XZSC389po82\nHtFtUUGaOqSjXB345wAAgIrwWxJoRBztbDQgwl8DIvx1Pr9Ybycf1HvrDik9O08f3teNKwwCAFAB\no7UDALAOdyc7PTOgvd68s4t2njivuOlr9MrKPTp/udja0QAAqHMozUAjd1O4v76ZEKc+7X2V+PMh\n9Z6RpJnf79XpCwXWjgYAQJ1BaQagsAA3zb2ji1ZM6q2erXz0VvJBxU1P0hOL07TzxHlrxwMAwOo4\npxlAuQ7+bnrnnigdPXNJH244oi9Sjuur1BPqHuqlp/7WXlEtPK0dEQAAq2ClGcAVWni76MUhHbXx\n2Rv094HtdTjnkoa/s1HTvtulS4Ul1o4HAECtY6UZQIXcnew07rpWujO2hWau2qsPNxzR8u2Zimvt\noztjgxUd4mXtiAAA1ApWmgFUydXBVtNuCdcX43uoc3MPrdmXpeHv/KIHP0nRlqNnZTKZrB0RAACL\nYqUZQLV1C/FStxAvXS4qUeLPh/TB+sP6ftdphTZ10a1dAnVbdHM1c3O0dkwAAGocK80Arpmzva0e\nu7Gtfnn2Br16a4R8XB0064f96jsrWZ9uOsrKMwCgwWGlGcBf5uJgq5ExwRoZE6zDOZf0wjc79fzS\nnTp9oUBP9Gsrg8Fg7YgAANQIVpoB1IiWPi76eHSMbo9urrlJ6er72lr9e/lupR3PtXY0AADMRmkG\nUGOMRoNeuTVCr94aoWAvZ3288aiGvrlB/16+W0UlZdaOBwDAX8bpGQBqlNFoKD9lI6+wRNNX7tV7\n6w7rtyPn9MqtEWrv14TTNgAA9Q6lGYDFuDrY6p9Dw9WjlbemfLldA2avk28TBzVzc1REkLvu6Bas\niCB3a8cEAKBKlGYAFjcwwl/dQry0atcppR3LVdbFAn2bdlJfpmTon0M7qpm1AwIAUAVKM4Ba0bSJ\ng+7p3kL3dG8hSTp3qUiTFqVqypId6tvcVj3jymRvy8csAAB1k8V+QxUUFCgmJkadOnVSx44dNXXq\nVEnSTz/9pK5du6pz586Ki4tTenq6pSIAqMM8Xez14X3d9OB1oUo6XqK75m9S1sUCa8cCAOCqLFaa\nHRwclJSUpG3btiktLU2rVq3Spk2b9NBDD2nBggVKS0vTnXfeqX/961+WigCgjrO1MerZgR00vpOD\ndpw4ryFzN2jToTNcHAUAUOdYrDQbDAa5urpKkoqLi1VcXCyDwSCDwaALFy5Iks6fP6+AgABLRQBQ\nT3T3t9WSh3rK1sagkYmb1OOVJD371XbtOnne2tEAAJBk4XOaS0tLFRUVpfT0dE2YMEGxsbGaP3++\nBg4cKCcnJ7m5uWnTpk2WjACgnugY4K7lk3rr+52ntGZflr7blqnPUzI0tndLTejTWm6OdtaOCABo\nxAymWvg7aG5urhISEjR37ly98MILmjJlimJjYzVz5kzt27dP8+fPv+I5iYmJSkxMlCRlZGRo0aJF\nFb5+Xl5e+ao2/hpmaB7mZ56rzS+vyKTF+4q07kSJnGyl4CZGhfvYqFegrbwc+cDg/+LnzzzMzzzM\nzzzMzzyWmN/kyZOVkpJyxe21Upol6aWXXpKTk5PeeecdHTx4UJJ07Ngx3XTTTdq9e3elz42Ojr5q\n+D8kJycrPj6+JuM2OszQPMzPPJXNb+eJ81qw+ah2n7ygbRnnZTBI17Vpqqf+1k7hgezxLPHzZy7m\nZx7mZx7mZx5LzK+i3mmx5Zrs7Gzl5uZKkvLz87V69Wp16NBB58+f1/79+yWp/DYAqEh4oLteuTVS\n30yM09qn4vVI3zbaeeK8hsxbr+eX7lDu5SJrRwQANAIWO6c5MzNTo0aNUmlpqcrKyjRixAgNHjxY\n7733noYNGyaj0ShPT0998MEHlooAoIFp4e2iJ/q11Zi4lnpj9X7995cj+jbtpMbEhWp0XAjnPQMA\nLMZipTkyMlKpqalX3J6QkKCEhARLHRZAI+DuZKcXh3TUyJjmev2H/Xrjx/16f/0hPT8oTCO6Nbd2\nPABAA8SnaQDUW+393JR4b7SWPRKnsAA3Pb1kuyZ9lqpT57lICgCgZlGaAdR74YHuWjC2ux67sY1W\n7TylPrOSNeenAyooLrV2NABAA0FpBtAg2BgNeuzGtvrxievVp31Tvb56v26Zt0H7T1+0djQAQANA\naQbQoAR7O+utu6L04ehuOnOpUMPe2qhfD5+1diwAQD1n0SsCAoC19Gnnq28nxunu9zfr9sRfFN3C\nU22aNVGPUG8NivCX0WiwdkQAQD3CSjOABivAw0lLxvfUI33bqLCkTCt2ZOqRz1KV8NYGVp8BANeE\nlWYADZqni72e6NdWT/Rrq7Iyk75OPaGZ3+/TiHd/0d86NtNzA8MU7O1s7ZgAgDqOlWYAjYbRaNCw\nqCCtmRyvyf3bav2BHA2au07J+7KsHQ0AUMdRmgE0Ok72NprYt42+f/w6BXk6a8zHKfph1ylrxwIA\n1GGUZgCNVpCns74Y30Phge6auDBV//3liErLTNaOBQCogwwmk6nO/4Zo2bKlpk6dWuH9ubm58vDw\nqMVEDQ8zNA/zM4+151dSZlL66YvKzS+Wq4OtWjZ1kYt9/fnIh7XnV98xP/MwP/MwP/NYYn7z5s1T\nSkrKFbfXn98KAGAhtkaD2vu7KSevUEfPXNaOjPNydbCVt6uDmro6yNaG7ekAoLGrFyvN0dHRV238\nf0hOTlZ8fHztBWqAmKF5mJ956tL8zl0q0pKtGfom7aR2nDgvH1cHzRgeob7tm1k7WoXq0vzqI+Zn\nHuZnHuZnHkvMr6LeyTnNAPA/PF3sNbZ3qL57JE7LHomTj6u97v8oRW8lp6serDEAACyE0gwAFQgP\ndNfSCb00pFOAZqzap9ve+UU7T5y3diwAgBVwTjMAVMLRzkazR3ZWr9bemrFqn26et17dW3rL0c6o\nB64LVc9WPtaOCACoBaw0A0AVDAaDbu8WrKTJ8Rob11KXikq099RF3fneZj3xeZrO5BVaOyIAwMJY\naQaAanJ3stNzg8IkSQXFpYGXSc0AACAASURBVJqbdEDvrj2k1btOa1TPEPVu46OoFp6ytWE9AgAa\nGv5lB4C/wNHORk/9rb1WPtpbvVr7aN6adN2euEkj3v1FWRcKrB0PAFDDKM0AYIY2zZronXui9Otz\nN2j6sAjtybyooW9u0JGcS9aOBgCoQZRmAKgBvk0cdXu3YH0xvofyi0t127u/aMmWDC7LDQANBKUZ\nAGpQeKC7Fj/YQ01dHfTkF9t069sblZ510dqxAABmojQDQA1r26yJlj0Sp//c3llHz1zS0Dc3at2B\nbGvHAgCYgdIMABZgNBo0tEugVj7aW4EeTrrn/V8V/a/V+uSXI9aOBgD4Cyy25VxBQYGuu+46FRYW\nqqSkRMOHD9e0adPUu3dvXbz4+58qs7KyFBMTo6VLl1oqBgBYlb+7k754qIe+TMnQj3tO6x/f7FJG\nbr6m/K29jEaDteMBAKrJYqXZwcFBSUlJcnV1VXFxseLi4jRgwACtW7eu/DHDhg3TLbfcYqkIAFAn\nuDna6f64lhrVM0QvfrtL7649pNPnCzRjeCfZ2/IHPwCoDyxWmg0Gg1xdXSVJxcXFKi4ulsHw/6+q\nXLhwQUlJSfrwww8tFQEA6hQbo0Ev3dJRfu6Omvn9PmVdLNSM4ZEK8nS2djQAQBUsekXA0tJSRUVF\nKT09XRMmTFBsbGz5fUuXLtUNN9wgNzc3S0YAgDrFYDBoQp/W8nNz1JQl2xU3fY18mzjIz91RLwwO\nU3SIl7UjAgCuwmAymSy+iWhubq4SEhI0d+5chYeHS5IGDBigsWPHatiwYVd9TmJiohITEyVJGRkZ\nWrRoUYWvn5eXV76qjb+GGZqH+Zmnsc4v63KZUk6V6NRlk3afKdWZfJMeiHRQz4BrW89orPOrKczP\nPMzPPMzPPJaY3+TJk5WSknLF7bVSmiXppZdekrOzsyZPnqycnBy1a9dOJ06ckKOjY5XPjY6Ovmr4\nPyQnJys+Pr4G0zY+zNA8zM88zE+6WFCsMR+naNvxXC15qKfCA92r/VzmZx7mZx7mZx7mZx5LzK+i\n3mmxT6BkZ2crNzdXkpSfn6/Vq1erffv2kqQvv/xSgwcPrlZhBoDGoImjnd66q6s8ne01/J2NmvLl\ndj3yWapW7TylWlrbAABUwmKlOTMzU3369FFkZKS6deumfv36afDgwZKkRYsW6Y477rDUoQGgXvJx\nddCSh3vqhg7N9O22k9qYnqPxn27R5C+2U5wBwMos9kHAyMhIpaamXvW+5ORkSx0WAOq1QA8nvXln\nV0lSSWmZZv2wX++sPaiYlp66vVuwldMBQOPFBqEAUEfZ2hj19N/aqVdrb039dpeO5FyydiQAaLQo\nzQBQhxmNBr12W2fZ2Rg1Zcl2lZVxmgYAWINF92kGAJjPz91R/xgUpqeXbNfAOevk5WKvs5eKdFO4\nn8b2DrV2PABoFCjNAFAP3BYdJBmkBZuO6nx+sdyd7DT7pwNK2pulB9qy+gwAlkZpBoB6wGAwaER0\nc42Ibl5+24+7T2vCwq16+ZxJUTH5CvBwsmJCAGjYOKcZAOqpG8Oa6b/3xyi30KSb567Xf37cr8tF\nJdaOBQANEqUZAOqx2FBv/T3WSRFB7vrPjwd061sbdfzsZWvHAoAGh9IMAPVc8yZGfTQ6Rh+N7qaT\nufka9vZGpWflWTsWADQolGYAaCDi2/nqi/E9VWaSRib+or2nLlg7EgA0GJRmAGhA2vk10eIHu8vW\naNTIxE1asiVDmefzdamQc50BwBzsngEADUyrpq76/MEemrBwq578Ypskyc3RVon3Rqt7qLeV0wFA\n/cRKMwA0QMHezvpmQi+9PypaLydEyNfNUfe+/6u2HD1n7WgAUC9RmgGggTIaDbqhQzPdGRusL8f3\nUNMmDpr8xTblF5VaOxoA1DuUZgBoBDyc7TVzeKQO51zSxIVbdaGg2NqRAKBeoTQDQCPRs7WPXrql\no9buz9YNr63Vqyv36iLlGQCqhdIMAI3IvT1CtPjBHuoU5K7Enw9q6Jsb2NMZAKqB0gwAjUxUC0/N\nH9VNn46NVe7lYg19c4N+2HXK2rEAoE6jNANAI9WzlY++eyROrZq6aMLCrUo7nmvtSABQZ1GaAaAR\nC/Bw0sf3x6iZm6Me/nSLtlGcAeCqKM0A0Mh5ONvrnbujVFRq0tC3NmjMR79py9Gz1o4FAHUKpRkA\noPBAd62ZfL0m9mmtbRnnNebjFBWW/L6fc3rWRU7dANDocRltAIAkqYmjnZ7s307RIV4a9cGvStqT\nJXtboyYuTFVBSakev7GtJvZpLaPRYO2oAFDrKM0AgD+Ja+2jZm4OmvH9Ph07e1lh/m5q6eOi11fv\nV+qxc3qyfzu192siWxv+WAmg8ahWac7KytKGDRt08uRJOTk5KTw8XNHR0TIa+QcTABoaG6NBt3YN\n0tvJBxXdwlMf3R8jF3sbdWvppZe+26U1+7LlZGej2FAv/WtouII8na0dGQAsrtLSvGbNGr366qs6\ne/asunTpIl9fXxUUFGjp0qU6ePCghg8frieffFJubm61lRcAUAse6B0qZzsb3dcrRK4Ov/+quKd7\nC/UPa6bNh89q69FzWrI1Q0Pf3KD3R3VTp+YeVk4MAJZVaWlesWKF3nvvPQUHB19xX0lJiZYtW6bV\nq1dr2LBhV9xfUFCg6667ToWFhSopKdHw4cM1bdo0mUwmPf/88/riiy9kY2Ojhx56SJMmTaq5dwQA\nMJuXi70euaHNFbc3c3PUkE4BGtIpQHd3b6HRH/2qu+dv1geju6lbiJcVkgJA7ai0NM+cObPiJ9ra\naujQoRXe7+DgoKSkJLm6uqq4uFhxcXEaMGCA9uzZo+PHj2vv3r0yGo3Kysr66+kBAFbT2tdVi8f1\n0J3vbdLIxE0aE9dSo3uFyN/dydrRAKDGVXlS8tq1a7V9+3ZJ0ueff66JEyfqjTfeUGFhYaXPMxgM\ncnV1lSQVFxeruLhYBoNBb7/9tl544YXy86F9fX3NfQ8AACsJ8HDSNxPiNKxroBJ/PqRerybp2a+2\n69ylImtHA4AaVelK84QJE7R9+3YVFhaqbdu2ysvL00033aQNGzbo/vvv14IFCyp98dLSUkVFRSk9\nPV0TJkxQbGysDh48qMWLF+vrr79W06ZNNWfOHLVpc+WfAAEA9YO7s51mDO+kiX3a6IMNh/XJpqPa\nfOisvhjfQ96uDtaOBwA1wmAymUwV3RkWFqbdu3eroKBAgYGBysrKko2NjUwmkyIjI7Vjx45qHSQ3\nN1cJCQmaO3euunfvrmnTpunJJ5/UV199pTfeeEPr1q274jmJiYlKTEyUJGVkZGjRokUVvn5eXl75\nqjb+GmZoHuZnHuZnnro2v31nSzUrpUBBrkY9HeMoJ9u6va9zXZtffcP8zMP8zGOJ+U2ePFkpKSlX\n3F7pSrOjo2P5/7do0UI2NjaSfj/1ws7OrtoH9/DwUJ8+fbRq1SoFBQXp1ltvlSQlJCRo9OjRV33O\nuHHjNG7cOElSdHS04uPjK3z95OTkSu9H1ZiheZifeZifeera/OIlte5wWuM+2aIFR5z17j1RcnGo\nu5cFqGvzq2+Yn3mYn3lqc36V/iuWlZWl119/XSaTqfxrSTKZTMrOzq70hbOzs2VnZycPDw/l5+dr\n9erVmjJlioYOHao1a9aoZcuWWrt2rdq2bVtz7wYAUCfc0KGZpg+L1OQvtin25Z90f1xLPXpDG9lw\nNUEA9VSlpfmBBx7QxYsXr/haksaOHVvpC2dmZmrUqFEqLS1VWVmZRowYocGDBysuLk533XWX3njj\nDbm6umr+/Pk18DYAAHXN8KgghTZ10fvrDmvOTweUeuyc/j6wgzr4s7c/gPqn0tI8derUv/zCkZGR\nSk1NveJ2Dw8PLV++/C+/LgCg/uga7Kmud3mq5+ajemXFXg2YvU5tfF01Irq57o9rycozgHqj0tJc\n1UVH5syZU6NhAAAN012xLTQ4IkBfbDmuH3af1r9X7NGKnZmadVsntWrKh6AA1H2V7tMcFRWlqKgo\nFRQUaOvWrWrTpo3atGmjtLQ0FRWxBycAoPrcne00tneoFo/rrtkjO+tQ9iUNnL1OSXtPWzsaAFSp\n0pXmUaNGSZLefvttrV+/Xra2vz98/Pjx6t27t+XTAQAaHIPBoFs6B6pHqLfGfJyihxds1Qejuqln\nax9rRwOAClV5RUBJOnfunC5cuFD+fV5ens6dO2exUACAhs/XzVEfju6mAHcn3Tl/sx5esEVHci5Z\nOxYAXFW1Ns585pln1KVLF/Xp00cmk0k///yzXnzxRQtHAwA0dD6uDvrukTi9t+6QEn8+pB92ndYz\nA9prTFxLGQx8SBBA3VGt0jx69GgNGDBAmzdvliRNnz5dfn5+Fg0GAGgcXBxs9diNbXVnTLD+8c1O\n/Wv5Hm06dFYvJ4TL183R2vEAQFIVp2ccOXKk/Gs/Pz/dcsstuuWWW8oLs8lkUkZGhkUDAgAaB183\nR71zd5SeH9RBPx/I1t/+87PWH8ixdiwAkFTFSvNTTz2lsrIy3XLLLYqKilLTpk1VUFCg9PR0rVmz\nRj/99JOmTZumoKCg2soLAGjADAaDxvYOVXw7Xz28YIvufn+zfFwddH9ciB6Ob23teAAasUpL8xdf\nfKHdu3drwYIF+uCDD5SZmSlnZ2d16NBBAwcO1HPPPSdHR/50BgCoWa19XfX1w7306aajWp+eoxmr\n9snWaNC461pZOxqARqrKc5rDwsL073//uzayAABQzsXBVg9e30pje4dq0qJUvbxir1wd7HRnbLC1\nowFohKq15RwAANZiYzTojRGdFd+uqf7+9Q4NfXODkvdlWTsWgEaG0gwAqPPsbY165+4oTe7fVhfy\nizXuv1u0dn+2tWMBaEQozQCAesHRzkYT+7bR1w/3UmtfVz34SYo2Hzpj7VgAGolq7dMsSSdOnNDR\no0dVUlJSftt1111nkVAAAFTE3dlOn4yJ0e2Jm3T/R7/p+cFhGhHdXDZGLoYCwHKqVZqnTJmixYsX\nKywsTDY2NpJ+3xaI0gwAsAZvVwctGBurRxam6tmvdmj9gRzNuaMLxRmAxVSrNC9dulT79u2Tg4OD\npfMAAFAtzdwctfjB7npn7SFNX7VXxaVlSugSqL919JOR8gyghlWrNIeGhqq4uJjSDACoUwwGgx6K\nb6WikjK9s/agfth9Wvf2aKFpQzrKYKA4A6g51SrNzs7O6ty5s2644YY/Fec5c+ZYLBgAANX16I1t\nNKFPK834fp8Sfz6k3ScvqE97X3Xwb6I+7Xwp0ADMVq3SPGTIEA0ZMsTSWQAA+MtsbYx6dkD730/b\n+O2YZn6/T5I0fViEbu/GBVEAmKdapXnUqFGWzgEAgNkMBoPGxLXUmLiWyiss0ZiPftO/lu/R9W19\n5efuaO14AOqxSvdpHjFihCQpIiJCkZGRV/wPAIC6ytXBVq8Oi1RxaZkGzP5Zn/xyRCWlZdaOBaCe\nqnSlefbs2ZKkZcuW1UoYAABqUksfF339cC9N+26X/vHNLi3YfEyfjIlV0yZ8sB3Atam0NPv7+0uS\nWrRoUSthAACoaR383fTZA921aucpPbY4TU98nqaPR8ewLR2Aa1Kty2hv2rRJ3bp1k6urq+zt7WVj\nYyM3NzdLZwMAoEYYDAYNiPDXi0M6at2BHN3zwWalHjtn7VgA6pFqleaJEyfqs88+U5s2bZSfn6/5\n8+drwoQJlT6noKBAMTEx6tSpkzp27KipU6dKku677z61bNlSnTt3VufOnZWWlmb+uwAAoBpGdmuu\nfwwO0/7Tebp7/mYdPXPJ2pEA1BPVKs2S1Lp1a5WWlsrGxkajR4/WqlWrKn28g4ODkpKStG3bNqWl\npWnVqlXatGmTJGnmzJlKS0tTWlqaOnfubN47AACgmv7YXWPphF4yGg16bHGaistM1o4FoB6o9sVN\nioqK1LlzZz399NPy9/dXWVnln0A2GAxydXWVJBUXF6u4uJjN5QEAdUKgh5NeuTVCExemavYlG0XH\nFsnTxd7asQDUYdVaaf7kk09UVlamefPmycXFRcePH9eSJUuqfF5paak6d+4sX19f9evXT7GxsZKk\n5557TpGRkXr88cdVWFho3jsAAOAvGBwZoBnDIrXrTKmi/rVaExdu1aXCEmvHAlBHGUwmk8X/LpWb\nm6uEhATNnTtX3t7e8vPzU1FRkcaNG6dWrVrphRdeuOI5iYmJSkxMlCRlZGRo0aJFFb5+Xl5e+ao2\n/hpmaB7mZx7mZx7mZ569p/K07by9vj9SrGbOBsX42apvsJ3cHfjraHXw82ce5mceS8xv8uTJSklJ\nueL2SkvzN998o4yMjPIP/cXGxio7O1uSNGPGDA0fPrzaAV566SU5Oztr8uTJ5bclJydr1qxZVe4D\nHR0dfdXw//s68fHx1c6CKzFD8zA/8zA/8zA/8/wxv7X7szXz+73affKCmns569MxsWru5WzteHUe\nP3/mYX7mscT8KuqdlZ6eMWPGDA0ZMqT8+8LCQv32229KTk7W22+/XekBs7OzlZubK0nKz8/X6tWr\n1b59e2VmZkqSTCaTli5dqvDw8Gt+MwAA1LTr2zbVskd6a8lDPZV7uVi3vfOL0rPyrB0LQB1RaWku\nKipS8+bNy7+Pi4uTt7e3goODdelS5dv0ZGZmqk+fPoqMjFS3bt3Ur18/DR48WHfddZciIiIUERGh\nnJwcPf/88zXzTgAAqAFdgj21aFx3lZSZNOztjfpg/WEVc/ltoNGrdPeMc+f+vPH7vHnzyr/+4zSN\nikRGRio1NfWK25OSkq4lHwAAta6Dv5u+HN9Df/96h15atlubDp3Rm3d1lZ1NtXdqBdDAVPpff2xs\nrN57770rbn/33XcVExNjsVAAAFhbiI+LFj7QXS/eHKYfdp/W44vTVMqezkCjVelK8xtvvKGhQ4dq\n4cKF6tq1qyRpy5YtKiws1NKlS2slIAAA1nRfr5YqKi3Tyyv2yt7GqFeHRcrelhVnoLGptDT7+vpq\n48aNSkpK0q5duyRJgwYNUt++fWslHAAAdcG461qpqKRMs37Yr/TsPL12Wye1adbE2rEA1KJqXRGw\nb9++FGUAQKM2sW8btfZtoilLtmvA7HUaGOGvAeF+GhDhb+1oAGoBf18CAKCabgr3U9KT1+uOmGBt\nPJijhxZsVeLPB60dC0AtoDQDAHANvF0d9M+h4dr89xs1KNJfL6/Yq2XbT1o7FgALozQDAPAX2BgN\nemNEZ3UN9tAzS3bo6JnKr18AoH6jNAMA8BfZ2xo1544uMhqkRz5LVVEJF0EBGipKMwAAZgjydNaM\n4ZHannFe01fttXYcABZCaQYAwEw3hfvr3h4t9P76w5r5/V6VcNltoMGp1pZzAACgci8MDlNxaZne\nXHNQ/914VIGeTgr2ctYrt0bI29XB2vEAmImVZgAAaoCtjVEvJ0TovXujNbhTgII8nbV2f7Zue/cX\n7Tt10drxAJiJlWYAAGqIwWBQv7Bm6hfWTJL025GzGvffFA2cs049W3mrc3MPjb++lVwc+PUL1Des\nNAMAYCHdQryU9GS87uneQucuF2nemnTdNPtntqcD6iFKMwAAFuTpYq8Xh3TUskd66/MHe+hCfokm\nLUpTMR8WBOoVSjMAALWkW4iXXk6I0LbjuZr1wz5rxwFwDTipCgCAWjQo0l8bDwbr3bWH5OfmqNG9\nWlo7EoBqoDQDAFDLpg3pqJy8Qk37brfOXSrS4/3aymAwWDsWgEpwegYAALXM1saouXd01YjoIM1J\nStf8dYetHQlAFVhpBgDACuxtjZo+LFIX8ks0fdVeRQa5KzbU29qxAFSAlWYAAKzEYDBo+rBIBXg4\n6Y73NunlFXtUVMKuGkBdRGkGAMCK3J3t9N3EON3erbkSfz6k4e9s1LEzl60dC8D/g9IMAICVuTvb\n6ZVbI/XO3VE6knNJg+as0/x1h5R7ucja0QD8H0ozAAB1xE3hflo+qbfCAtz0r+V71HvGGn326zGZ\nTCZrRwMaPUozAAB1SHMvZy1+sIeWT4pTxwA3PfvVDk1ftY/iDFiZxUpzQUGBYmJi1KlTJ3Xs2FFT\np0790/2TJk2Sq6urpQ4PAEC91jHAXQvHdtddscF6Z+1Bvb+ebekAa7LYlnMODg5KSkqSq6uriouL\nFRcXpwEDBqh79+5KSUnRuXPnLHVoAAAaBKPRoH/eEq7si4WavmqverbyUViAm7VjAY2SxVaaDQZD\n+UpycXGxiouLZTAYVFpaqqeeekozZsyw1KEBAGgwjEaDXh0WKQ9nez26KFWXCkusHQlolCx6TnNp\naak6d+4sX19f9evXT7GxsZo3b56GDBkif39/Sx4aAIAGw8vFXv+5vbMOZufp2a92cH4zYAUGUy38\nl5ebm6uEhARNmzZNf//735WcnCxbW1u5uroqLy/vqs9JTExUYmKiJCkjI0OLFi2q8PXz8vI4P9pM\nzNA8zM88zM88zM889Wl+yw4W6csDxYr1s9GAlnbycjLKzd5g1Uz1aX51EfMzjyXmN3nyZKWkpFxx\ne62UZkl66aWXZDKZ9Pbbb8vR0VGSdOzYMYWGhio9Pb3S50ZHR181/B+Sk5MVHx9fk3EbHWZoHuZn\nHuZnHuZnnvo0P5PJpHfWHtKM7/fKZJLsbAwa0ilQzw/qIE8Xe6tkqk/zq4uYn3ksMb+KeqfFPgiY\nnZ0tOzs7eXh4KD8/X6tXr9aUKVN06tSp8se4urpWWZgBAMDvDAaDHopvpb7tfXXs7GVtSM/Rws3H\n9NuRs/podDeFNmXFErAUi53TnJmZqT59+igyMlLdunVTv379NHjwYEsdDgCARqOdXxP1C2umF4d0\n1KIHuyuvsESPfJaq4tIya0cDGiyLrTRHRkYqNTW10sdUdD4zAAConq7Bnno5IVzjP92qd9ce1MS+\nbawdCWiQuCIgAAD13E3h/rq5U4Bm/bBfbyWns7sGYAEWW2kGAAC157XbOskgacaqfUrel63XR3RS\nkKeztWMBDQYrzQAANAD2tkb95/bOevXWCO0+eUGPLUpTWRkrzkBNoTQDANBAGI0GjYwJ1tSbw5Ry\n9Jw+3XzU2pGABoPSDABAAzM8Kki92/ho6re79J8f96uwpNTakYB6j9IMAEADYzAY9O49URraOVD/\n+fGArp+RrB92nar6iQAqRGkGAKABcra31esjOmnB2Fj5NLHXuE+26JWVe3S5qMTa0YB6idIMAEAD\nZTAY1Ku1j74c31MjuzXXu2sPqc+sZH2dmsGHBIFrRGkGAKCBc7Sz0avDIrXkoR5q5uaoxxdv0wP/\nTdH5/GJrRwPqDUozAACNRFQLLy19uJem3hymtfuzNWTeeu3JvGDtWEC9QGkGAKARMRoNGt2rpRaN\n6678olINmrNON89dr9Rj56wdDajTKM0AADRC0SFeWjYpTo/0baOzl4o0/tMtyskrtHYsoM6iNAMA\n0Ej5NnHU4/3a6r17o5V7uVhPfL5NJhMfEASuhtIMAEAjFxbgpucGddDP+7P1ecpxa8cB6iRKMwAA\n0N2xLdQ91EtTv92lF77ZqbOXiqwdCahTKM0AAEBGo0FzRnbRwHB/Lfr1uO55f7MuFLAlHfAHSjMA\nAJAk+bo56vXbO+vde6O079RFjf0oRflFpdaOBdQJlGYAAPAnfdr56o3bO+u3o2c1/tMtusiKM0Bp\nBgAAV7q5U4BeSYjQ+vQcDZrDPs4ApRkAAFzVyJhgLR7XXaVlJt32zi/6dNNRa0cCrIbSDAAAKhQd\n4qUVj/ZW7zY++sc3O5W8L8vakQCroDQDAIBKuTvZ6c27uqq9n5seWZiqXSfPWzsSUOsozQAAoErO\n9raaPypaTRxtNeqDX7VmbxZXD0SjQmkGAADVEujhpE/GxsrJ3kajP/pNQ+Zt0MaTJTqUnUeBRoNn\nsdJcUFCgmJgYderUSR07dtTUqVMlSWPGjFGnTp0UGRmp4cOHKy8vz1IRAABADWvV1FVJT8ZrxrBI\nXSgoVuL2QvV9ba1Gf/Sbjp+9bO14gMVYrDQ7ODgoKSlJ27ZtU1pamlatWqVNmzbpjTfe0LZt27R9\n+3YFBwdr3rx5loqA/6+9Ow+oqsz/B/4+rCrIIkKuySrIckF2d7DBFZ1I3EbLMU1LUtPsa79pHEdz\nvqW2oKQmqWmLOE024qTiaIgJuaHghqSCqCgpgldEQC7c5/eH0/3GIIJeDucC79c/yVnuefPxcPr4\n8JxziIiIZGBqbIRxQd3xw/xBWNynDRYO80B63h288kU6qqq1SscjkoVsTbMkSbC0tAQAaDQaaDQa\nSJIEKysrAIAQAuXl5ZAkSa4IREREJCMTYyM4WRvjtTAXfDBWhexf7vGxdNRiyTqnubq6Gn5+fnBw\ncEBERARCQkIAAFOnTkWnTp2QnZ2N2bNnyxmBiIiImsBQr04Y4NYRH/77AqdpUIskiSaYua9WqxEV\nFYW4uDh4e3sDeNhQz549G0FBQZg6dWqtfeLj4xEfHw8AyM/Px7Zt2+r8/NLSUt2oNj0d1lA/rJ9+\nWD/9sH76Yf3089v6FZZp8ZefytHV0ghje5rhWSsjtDXhb5Qfh+effuSo34IFC5Cenl5reZM0zQCw\ndOlStGvXDgsWLNAt+/HHH7FixQp8//33j903MDDwkeF/lZKSgrCwsMaK2iqxhvph/fTD+umH9dMP\n66ef/65fYuZ1zN2WCQDoaGmOJaO9MFLVWaF0ho/nn37kqF9dfads0zMKCwuhVqsBAOXl5di3bx/c\n3d1x6dIlAA/nNO/cuRMeHh5yRSAiIqIm9nu/rvjhzUHY8FIgutq0wesJJ3Ekt0jpWER6M5HrgwsK\nCjBlyhRUV1dDq9Vi3LhxGDlyJAYMGICSkhIIIeDr64t169bJFYGIiIgU4GJvCRd7S/RxsUNkXCrm\n/z0T/3itL7ratFU6GtFTk61pVqlUyMjIqLU8LS1NrkMSERGRAbEwN0HseD9M/OwIhnx0ELETeiPC\n8xmlYxE9Fb4RkIiIeD27pwAAIABJREFUiGTj290Ge98YiO4d2mHRjrOo0FQrHYnoqbBpJiIiIll1\n79AOiyI98UtJBbYdu6p0HKKnwqaZiIiIZNfXxQ7BTh2wOvkScgpLlY5D9MTYNBMREZHsJEnCey/4\nwEgCJsYfwZoDl1BU+kDpWEQNxqaZiIiImoSLvSW+nh6KZ6zaYOXenzHjyxPQapvkdRFEemPTTERE\nRE3GvVN7/Gt2f6yIVuHElTv4x4lrSkciahA2zURERNTkov27IcjRFn/bdR7XisuUjkNULzbNRERE\n1OSMjCR8MNYXQgCvbz2JgrvlSkcieiw2zURERKSIHnYWWDnWF2eu30W/95Ox4VCu0pGI6sSmmYiI\niBQzzLsTUhaEY7CHA97bk41T19RKRyJ6JDbNREREpKhn7drhw3F+cGhvjulfpGNT6mUkZl5H4T0+\nko4Mh4nSAYiIiIis25pi45Qg/HnHGSz9PgsA4NDeHCuiVehhZ4EjuUXo79oR3Tu0UzgptVZsmomI\niMggeHaxwvbX+uLy7fu4XVqJ+d9k4o+fH9et7/mMJRJj+qOtmbGCKam1YtNMREREBkOSJDjbW8LZ\nHtgzdwCO5Bbj1r0KmJsY461vT2HaluOIDuiGqN5dIUmS0nGpFWHTTERERAapfRtTRHg+o/taXVaJ\nTw/mYP43p9DG1BgjfDormI5aG94ISERERM3C9AHOOPqn38GpowXWplyCEHwFNzUdNs1ERETUbBgb\nSZg50Blnr5fg87Q8VGiqlY5ErQSbZiIiImpWovy7QtXNGku/z0L0pz+hskqrdCRqBdg0ExERUbNi\nbmKMHbP6YWW0Cmevl2D9wRwAQP6dMtxQ83XcJA/eCEhERETNjpGRhLGB3ZFyoRCxP1zEgZ9v4VT+\nXTh3tMC++YOUjkctEEeaiYiIqNn63+d98Me+jtBUC6i6WePirVJcKbqvdCxqgTjSTERERM2WdTtT\nLIr0BADk3b6PsA9SkPJzIab0tVA4GbU0HGkmIiKiFsGxowWcOlrgwM+3lI5CLRCbZiIiImoxwtzt\ncTinCIX3HigdhVoYNs1ERETUYkQHdIMkAePWH8bVojKl41ALIlvTXFFRgeDgYPj6+sLLywuLFy8G\nAEyaNAnu7u7w9vbGyy+/DI1GI1cEIiIiamW8uljjq2khKCp9gJGrD2HX6QKlI1ELIVvTbG5ujuTk\nZJw6dQqZmZlISkrCkSNHMGnSJGRnZ+PMmTMoLy/Hhg0b5IpARERErVCgYwfsmjMAbs9YImbrSSz9\nVxbe230eebf5VA16erI1zZIkwdLSEgCg0Wig0WggSRJGjBgBSZIgSRKCg4ORn58vVwQiIiJqpbp3\naIeEGaEY5dsFm9IuY/2PuZidkAFNNd8eSE9HEkIIuT68uroaAQEBuHTpEmJiYrB8+XLdOo1Gg5CQ\nEKxatQoDBgyotW98fDzi4+MBAPn5+di2bVudxyktLdU16PR0WEP9sH76Yf30w/rph/XTj6HXTwiB\nu5UCF+5osTbzAV5wM8VoFzOlY+kYev0MnRz1W7BgAdLT02stl7Vp/pVarUZUVBTi4uLg7e0NAHjl\nlVdgYWGB2NjYevcPDAx8ZPhfpaSkICwsrLHitkqsoX5YP/2wfvph/fTD+umnOdVvdkIGks4WIDGm\nPzy7WCkdB0Dzqp8hkqN+dfWdTfL0DBsbG4SHhyMpKQkAsGTJEhQWFuKjjz5qisMTERERYeloL1i3\nNcP8bzJRfL9S6TjUzMjWNBcWFkKtVgMAysvLsW/fPnh4eGDDhg3Yu3cvEhISYGTEJ94RERFR07C1\nMMPKaBVyC+9j5OpDyP6lROlI1IzI1rUWFBQgPDwcKpUKQUFBiIiIQGRkJF599VXcvHkTffr0gZ+f\nH5YuXSpXBCIiIqIawj0c8N2svqjWCrz65QmUVPDRt9QwJnJ9sEqlQkZGRq3lVVVVch2SiIiIqF7e\nXa2xZpI/JsQfQczXJ7Fmkj+s2pgqHYsMHOdHEBERUasT5NgB773gg8M5RRiz9ifc44gz1YNNMxER\nEbVK4wK7Y9Mfg5BTWIr/3Z2tdBwycGyaiYiIqNUa2NMerwxwRsKxq/j66BU0wZN4qZli00xERESt\n2ryInujv2hHv/PMs5n9zCvcf8P4rqo1NMxEREbVqbUyNseXlYMyP6InEzOuIjEtFcvZNjjpTDbI9\nPYOIiIiouTA2kjDnOTcEOXbAn/55Bi9vTodNO1MM6mmPKX0d4f+srdIRSWEcaSYiIiL6jz4udtj7\nxkB8NM4XEb2eQXL2LUSv+wk/XbqtdDRSGJtmIiIiot8wMzHCC/7dsHKsL356ezCc7S0xOyED+XfK\nlI5GCmLTTERERFSH9m1M8enkAFRWazEh/gj2Z93E2et3Od+5FWLTTERERPQYrg6W2Do9FPcqqjD9\ni3RExqXi+TVpOHVNrXQ0akJsmomIiIjq4dPNGslvDsL21/rg3d974da9B4j+9CdsOJTLUedWgk/P\nICIiImoAO0tz2FmaI6BHB4zy7YL/+fY0lu06jxNX7mDNH/xhZCQpHZFkxJFmIiIioidk084M618M\nwIIhPbHn7C/4Z8Z1pSORzNg0ExERET0FSZIwK8wVvt1t8H5SNkr5JsEWjU0zERER0VMyMpLw11Ge\nKLz3AJ8kX1I6DsmITTMRERGRHno/a4sx/t2wKfUy8m7fVzoOyYRNMxEREZGeFg5zh5mJEZbtylI6\nCsmETTMRERGRnhys2mD2YFfsP38LKT/fUjoOyYBNMxEREVEjmNrPCU4dLRDz9UmM/iQVh3OKlI5E\njYhNMxEREVEjMDMxwpo/+GO4T2fcLdfgpU1HsedMgdKxqJGwaSYiIiJqJJ5drPDBWF/sjOkPVTcb\nzPsmE+cLSpSORY2ATTMRERFRI7NuZ4p1k/1h1cYUMVtPQlOtVToS6YlNMxEREZEMHNq3wXsv+CC3\n8D6+Sb+GexUaVLF5brbYNBMRERHJZLCHA/yftcHyPdkIXLYfkzcexbXiMuw6XYC75RpUVguUVGiU\njkkNIFvTXFFRgeDgYPj6+sLLywuLFy8GAHzyySdwdXWFJEm4ffu2XIcnIiIiUpwkSXh7eC9UaLTo\n42KHY5eLMWDFAcRsPYn+7yfj9eQyhK9MwZUivhTF0JnI9cHm5uZITk6GpaUlNBoN+vfvj+HDh6Nf\nv36IjIxEWFiYXIcmIiIiMhjBTh2QtXQoTIyN8O9zv+BIbjEG9OyIf2XewJ3bN5FRJBD96WFUVWsx\nIfhZhDh1wPKknxE3sTdcHSyVjk//IVvTLEkSLC0f/kVrNBpoNBpIkoTevXvLdUgiIiIig2Ri/PCX\n+0O8OmGIVycAQLi7A1JSUhDjqMKixHNoa2qEdSk52Jh6GZVVWizacRZbXwmBJElKRqf/kHVOc3V1\nNfz8/ODg4ICIiAiEhITIeTgiIiKiZifQsQP2zB2Av8/sgwFuHWFnYYbZg11xOLcIO0/dUDoe/Yck\nhBByH0StViMqKgpxcXHw9vYGADg6OiI9PR0dO3Z85D7x8fGIj48HAOTn52Pbtm11fn5paaluVJue\nDmuoH9ZPP6yfflg//bB++mH99PPf9dMKgSotYGIELD1cAfUDgfcGtEVbE442P4oc59+CBQuQnp5e\na7ls0zN+y8bGBuHh4UhKStI1zfWZMWMGZsyYAQAIDAx87BzolJQUzpHWE2uoH9ZPP6yfflg//bB+\n+mH99PO4+nVwVSNqbRrSK57BokjPpg3WTDTl+Sfb9IzCwkKo1WoAQHl5Ofbt2wcPDw+5DkdERETU\novh1t0FU765IOHYV5ZXVSsdp9WRrmgsKChAeHg6VSoWgoCBEREQgMjISq1evRrdu3ZCfnw+VSoXp\n06fLFYGIiIioWYv274ayymqk/HxL6SitnmzTM1QqFTIyMmotnzNnDubMmSPXYYmIiIhajGCnDuho\naYbvzxRguE9npeO0anwjIBEREZGBMjE2wjDvTkg+fwv3+OZARbFpJiIiIjJgYwO640FVNRZuP40m\neOgZ1YFNMxEREZEB8+1ug7eHe2D3mV+w9dhVpeO0WmyaiYiIiAzcKwOc4f+sDTYcugytlqPNSmDT\nTERERGTgJEnClL6OuHz7Pg5duq10nFaJTTMRERFRMzDcuzM6Wpoj7oeLKH1QpXScVodNMxEREVEz\nYGZihIXD3HHy6h2M/iQVt+5VKB2pVWHTTERERNRMjA3sjq+nh+KGuhyzvjqJyiqt0pFaDTbNRERE\nRM1IHxc7rIz2RfqVO1j6/Tml47Qasr0RkIiIiIjkMcq3C87euIv1B3PRzbYdpvZzhLmJsdKxWjSO\nNBMRERE1Q/8z1AODPRzw/p5sDFxxALmFpUpHatHYNBMRERE1Q8ZGEja8FIjNU4OgqRaY/kU6Sviq\nbdmwaSYiIiJqpoyMJIS5O2DtJH9cLSrDkp1ZSkdqsdg0ExERETVzoc52mDnIGdtP5iP1Il9+Igc2\nzUREREQtwOzBbnDqaIFXvkhH3A8X+brtRsammYiIiKgFaGNqjC+nBSPM3R4f7ruAd3acgRBsnBsL\nm2YiIiKiFqKbbTusmxyAmHAXJBy7huVJPysdqcXgc5qJiIiIWpgFQ9yhLtPg04M5cOrYDuODnlU6\nUrPHppmIiIiohZEkCX8d7YWrxWV4559n0d22Hfq6dlQ6VrPG6RlERERELZCpsRHWTPKHs70Fpm4+\njhc3HsWu0wW8QfApsWkmIiIiaqGs2phiy8vBGBfYHVeLyxCz9SQmbzyK0gdVSkdrdtg0ExEREbVg\nna3b4t3nvZH8ZhiWPe+No5eLMWXTMVRoqpWO1qywaSYiIiJqBYyNJEwO7YHVE3rjxJU7+GAvn6zx\nJNg0ExEREbUiI1Wd8WJoD2xIvYwD2beUjtNssGkmIiIiamX+NKIXPDtbYdbXJ7HrdAGKSh8oHcng\nydY0V1RUIDg4GL6+vvDy8sLixYsBAJcvX0ZISAhcXV0xfvx4VFZWyhWBiIiIiB6hrZkxNr8cBAcr\nc8RsPYn+yw/g7PW7SscyaLI1zebm5khOTsapU6eQmZmJpKQkHDlyBAsXLsS8efNw6dIl2NraYuPG\njXJFICIiIqI6OLRvgz1zB2DrKyGwbWeKV786gStF95WOZbBka5olSYKlpSUAQKPRQKPRQJIkJCcn\nIzo6GgAwZcoU7NixQ64IRERERPQY7cxM0NelI9ZODsDt0gcI+yAFsfsvKB3LIElCCNmecF1dXY2A\ngABcunQJMTExeOuttxAaGopLly4BAK5du4bhw4fj7NmztfaNj49HfHw8ACA/Px/btm2r8zilpaW6\nBp2eDmuoH9ZPP6yfflg//bB++mH99GNI9btTocWXWZU4VViNFQPbwq6t4d/6Jkf9FixYgPT09FrL\nZX2NtrGxMTIzM6FWqxEVFYXs7OwG7ztjxgzMmDEDABAYGIiwsLA6t01JSXnseqofa6gf1k8/rJ9+\nWD/9sH76Yf30Y2j1CwopQ9jKFJyudMCS4d5Kx6lXU9avSf4JYWNjg/DwcBw+fBhqtRpVVQ/fQpOf\nn4+uXbs2RQQiIiIiqkc323YY498NW49dxa7TBUrHMSiyNc2FhYVQq9UAgPLycuzbtw+9evVCeHg4\nvv32WwDAli1b8Pvf/16uCERERET0hP7fCA/4drPB6wknseFQLmScydusyNY0FxQUIDw8HCqVCkFB\nQYiIiEBkZCSWL1+Ojz76CK6urigqKsK0adPkikBERERET8imnRm+mh6C4d6dsGzXebz5zSncLKkA\nAKRevI3zBSUKJ1SGbHOaVSoVMjIyai13dnbGsWPH5DosEREREempjakxPpnoj4/tL+DTgzn4IfsW\n/jfKB3O3ZcC6rSmS3hgI+/bmSsdsUoZ/WyQRERERNTkjIwlvDnFH0hsDYWIkIWbrSVi1NcW9B1VY\nuP00tNrWNW2DTTMRERER1cnF3hLrXwyAQ3tzvPeCD94Z0QvJ2bew6oeLSkdrUrI+co6IiIiImr9A\nxw44+qfnIEkShBA4c/0uVv1wEf49bDGop73S8ZoER5qJiIiIqF6SJOn++7cobzh3tMCSnedQWaVV\nOFnTYNNMRERERE/E3MQYi0Z5Ivf2fWxIzVU6TpNg00xERERETyzc3QFDvZ5B7L6LuHDzntJxZMem\nmYiIiIieyt+ifGDZxgSvbz2JS7dKlY4jKzbNRERERPRUOlqaY9UEP9wseYBhsT9i6Mc/Ys+Zlvn6\nbTbNRERERPTUBrjZY//8QZgx0BkarRZ/+ucZlD6owuXb95WO1qjYNBMRERGRXuzbm+N/hnngw7G+\nuFOmwei4VIR/kIJtx64qHa3RsGkmIiIiokbR+1lbDPZwQO7t++hk1QbLk7KhLqtUOlajYNNMRERE\nRI3m43F+SIzph8+nBuFuuQYf/vuC0pEaBZtmIiIiImo01u1M4dvdBr06W+GlPo74+ugVnL1+V+lY\nemPTTERERESymBfRE7btzLAo8Sw01c37zYFsmomIiIhIFtZtTfHX0V7IuKrG4p3nUHjvAYQQSsd6\nKiZKByAiIiKilmuUbxecvXEX6w/mYuvRqxjg1hGx4/1gZ2mudLQnwpFmIiIiIpLVwqEe+HxqEOZH\n9MTRy8UYs+4nlFVWKR3ribBpJiIiIiJZGRlJCHd3wJzn3LD5j0HIKyrDx/ua11M12DQTERERUZPp\n69oRE4OfxcbUyziSW6R0nAZj00xERERETer/jfCAY0cLvPbVCVwtKlM6ToOwaSYiIiKiJmXVxhQb\nXgqEVgBj1/+EM/mG/xxnNs1ERERE1OSc7S3x95mhMJIkjPokFRPjj+DcDcNtntk0ExEREZEiPDpZ\n4fvZ/bFgSE9cvFWK33+Shu9P31A61iOxaSYiIiIixdhZmuP1wW7YN28gPLtY4d3vs1ChqVY6Vi2y\nNc3Xrl1DeHg4PD094eXlhVWrVgEATp06hT59+sDHxwejRo1CSUmJXBGIiIiIqJmwtTDDn0d64mbJ\nA2z5KU/pOLXI1jSbmJjgww8/RFZWFo4cOYI1a9YgKysL06dPx/vvv48zZ84gKioKK1eulCsCERER\nETUjwU4dEOZuj42pl6Gp1iodpwbZmubOnTvD398fANC+fXv06tUL169fx4ULFzBw4EAAQEREBLZv\n3y5XBCIiIiJqZv46ygvbX+sLU2PDmkXcJGny8vKQkZGBkJAQeHl5ITExEQDwj3/8A9euXWuKCERE\nRETUDDh2tED3Du2UjlGLidwHKC0txZgxYxAbGwsrKyts2rQJc+bMwbvvvovRo0fDzMzskfvFx8cj\nPj4eAJCdnY3AwMA6j1FYWAh7e3tZ8rcWrKF+WD/9sH76Yf30w/rph/XTD+unHznql5eX98jlkhBC\nNOqRfkOj0SAyMhJDhw7F/Pnza62/cOECJk+ejGPHjul1nMDAQKSnp+v1Ga0da6gf1k8/rJ9+WD/9\nsH76Yf30w/rppynrJ9v0DCEEpk2bhl69etVomG/dugUA0Gq1WLZsGV599VW5IhARERERNQrZmua0\ntDR8+eWXSE5Ohp+fH/z8/LB7924kJCSgZ8+e8PDwQJcuXTB16lS5IhARERERNQrZ5jT3798fdc38\nmDt3bqMea8aMGY36ea0Ra6gf1k8/rJ9+WD/9sH76Yf30w/rppynrJ+ucZiIiIiKilsCwHoBHRERE\nRGSADL5pTkpKgru7O1xdXfH+++/XWv/gwQOMHz8erq6uCAkJqfGYkPfeew+urq5wd3fH3r17mzC1\n4aivfh999BE8PT2hUqnw3HPP4cqVK7p1xsbGuvnoo0ePbsrYBqO++m3evBn29va6Om3YsEG3bsuW\nLXBzc4Obmxu2bNnSlLENRn31mzdvnq52PXv2hI2NjW4dzz/g5ZdfhoODA7y9vR+5XgiBOXPmwNXV\nFSqVCidPntSt4/lXf/2+/vprqFQq+Pj4oG/fvjh16pRunaOjI3x8fODn5/fYR562ZPXVLyUlBdbW\n1rqf06VLl+rW1fez3xrUV7+VK1fqauft7Q1jY2MUFxcD4PkHANeuXUN4eDg8PT3h5eWFVatW1dqm\nya+BwoBVVVUJZ2dnkZOTIx48eCBUKpU4d+5cjW3WrFkjZs6cKYQQIiEhQYwbN04IIcS5c+eESqUS\nFRUVIjc3Vzg7O4uqqqom/x6U1JD6JScni/v37wshhFi7dq2ufkIIYWFh0aR5DU1D6vf555+LmJiY\nWvsWFRUJJycnUVRUJIqLi4WTk5MoLi5uqugGoSH1+63Vq1eLqVOn6r5u7eefEEIcPHhQnDhxQnh5\neT1y/a5du8SwYcOEVqsVhw8fFsHBwUIInn+/qq9+aWlpurrs3r1bVz8hhOjRo4coLCxskpyGqr76\nHThwQIwcObLW8if92W+p6qvfb+3cuVOEh4frvub5J8SNGzfEiRMnhBBClJSUCDc3t1rnUVNfAw16\npPnYsWNwdXWFs7MzzMzMMGHCBN3bBH+VmJiIKVOmAACio6Pxww8/QAiBxMRETJgwAebm5nBycoKr\nq6vez4NubhpSv/DwcLRr9/CtO6GhocjPz1ciqkFqSP3qsnfvXkRERKBDhw6wtbVFREQEkpKSZE5s\nWJ60fgkJCZg4cWITJjR8AwcORIcOHepcn5iYiJdeegmSJCE0NBRqtRoFBQU8//6jvvr17dsXtra2\nAHj9e5T66lcXfa6dLcmT1I/Xv9o6d+4Mf39/AED79u3Rq1cvXL9+vcY2TX0NNOim+fr16+jevbvu\n627dutUq2G+3MTExgbW1NYqKihq0b0v3pDXYuHEjhg8frvu6oqICgYGBCA0NxY4dO2TNaogaWr/t\n27dDpVIhOjpa91p4nn9PVoMrV67g8uXLGDx4sG5Zaz//GqKuGvP8e3L/ff2TJAlDhgxBQECA7u20\nVNvhw4fh6+uL4cOH49y5cwB4/XtSZWVlSEpKwpgxY3TLeP7VlJeXh4yMDISEhNRY3tTXQNlfo03N\nw1dffYX09HQcPHhQt+zKlSvo2rUrcnNzMXjwYPj4+MDFxUXBlIZn1KhRmDhxIszNzbF+/XpMmTIF\nycnJSsdqdrZt24bo6GgYGxvrlvH8o6Zy4MABbNy4Eampqbplqamp6Nq1K27duoWIiAh4eHhg4MCB\nCqY0PP7+/rhy5QosLS2xe/duPP/887h48aLSsZqdf/3rX+jXr1+NUWmef/+ntLQUY8aMQWxsLKys\nrBTNYtAjzV27dtWN3AFAfn4+unbtWuc2VVVVuHv3Luzs7Bq0b0vX0Brs378ff/vb37Bz506Ym5vX\n2B8AnJ2dERYWhoyMDPlDG5CG1M/Ozk5Xs+nTp+PEiRMN3rele5IabNu2rdavJlv7+dcQddWY51/D\nnT59GtOnT0diYiLs7Ox0y3+tl4ODA6Kiolrd9L6GsLKygqWlJQBgxIgR0Gg0uH37Ns+/J/S4619r\nP/80Gg3GjBmDSZMm4YUXXqi1vsmvgXrPipaRRqMRTk5OIjc3V3czwdmzZ2ts88knn9S4EXDs2LFC\nCCHOnj1b40ZAJyenVncjYEPqd/LkSeHs7CwuXLhQY3lxcbGoqKgQQghRWFgoXF1dW92NHA2p340b\nN3R//u6770RISIgQ4uFNCI6OjqK4uFgUFxcLR0dHUVRU1KT5ldaQ+gkhxPnz50WPHj2EVqvVLeP5\n938uX75c541E33//fY2bYIKCgoQQPP9+63H1u3LlinBxcRFpaWk1lpeWloqSkhLdn/v06SP27Nkj\ne1ZD9Lj6FRQU6H5ujx49Krp37y60Wm2Df/Zbg8fVTwgh1Gq1sLW1FaWlpbplPP8e0mq14sUXXxRz\n586tc5umvgYadNMsxMM7I93c3ISzs7NYtmyZEEKIRYsWicTERCGEEOXl5SI6Olq4uLiIoKAgkZOT\no9t32bJlwtnZWfTs2VPs3r1bkfxKq69+zz33nHBwcBC+vr7C19dXjBo1Sgjx8K5yb29voVKphLe3\nt9iwYYNi34OS6qvf22+/LTw9PYVKpRJhYWHi/Pnzun03btwoXFxchIuLi9i0aZMi+ZVWX/2EEGLx\n4sVi4cKFNfbj+ffQhAkTRKdOnYSJiYno2rWr2LBhg1i3bp1Yt26dEOLh/1RmzZolnJ2dhbe3tzh+\n/LhuX55/9ddv2rRpwsbGRnf9CwgIEEIIkZOTI1QqlVCpVMLT01N37rY29dUvLi5Od/0LCQmp8Y+P\nR/3stzb11U+Ih09gGj9+fI39eP49dOjQIQFA+Pj46H5Gd+3apeg1kG8EJCIiIiKqh0HPaSYiIiIi\nMgRsmomIiIiI6sGmmYiIiIioHmyaiYiIiIjqwaaZiIiIiKgebJqJqFXYsWMHJElCdna2Xp+zefNm\nvP76642SaeLEiVCpVPj444+RnZ0NPz8/9O7dGzk5OTW2+/UFEk3hxo0biI6Orne7ujLt2LEDWVlZ\nde4XGxuLL774otbyvLw8eHt7Nzzof5kwYQLfRkdEsmLTTEStQkJCAvr374+EhASlowAAfvnlFxw/\nfhynT5/GvHnzsGPHDkRHRyMjI6NRXxdeVVX1RNt36dIF33777VMf73FNc1VVFTZt2oQ//OEPT/35\ndXnttdewYsWKRv9cIqJfsWkmohavtLQUqamp2LhxI7Zt26ZbnpKSgrCwMERHR8PDwwOTJk3Cr4+u\n3717Nzw8PBAQEIA5c+YgMjKy1ucWFhZizJgxCAoKQlBQENLS0mptU1FRgalTp8LHxwe9e/fGgQMH\nAABDhgzB9evX4efnhyVLliA2Nhbr1q1DeHj4I7+HefPmwcvLC8899xwKCwsBAJmZmQgNDYVKpUJU\nVBTu3LkDAAgLC8Mbb7yBwMBArFq1qs6cBw8ehJ+fn26E+969ezVGfMvKyjBu3Dh4enoiKioKISEh\nSE9P12V655134Ovri9DQUNy8eRM//fQTdu7cibfeegt+fn61RsyTk5Ph7+8PExMTAMCJEyfg6+sL\nX19frFmzRre+FgZxAAAF9ElEQVRddXU13nrrLQQFBUGlUmH9+vUAAK1Wi1mzZsHDwwMREREYMWKE\nrsEfMGAA9u/f/8T/SCAiaig2zUTU4iUmJmLYsGHo2bMn7OzscOLECd26jIwMxMbGIisrC7m5uUhL\nS0NFRQVmzpyJPXv24MSJE7om9b/NnTsX8+bNw/Hjx7F9+3ZMnz691jZr1qyBJEk4c+YMEhISMGXK\nFFRUVGDnzp1wcXFBZmYmFi9ejFdffRXz5s3TNdW/df/+fQQGBuLcuXMYNGgQlixZAgB46aWXsHz5\ncpw+fRo+Pj665QBQWVmJ9PR0vPnmm3Xm/OCDD7BmzRpkZmbi0KFDaNu2bY3jrl27Fra2tsjKysK7\n775bo273799HaGgoTp06hYEDB+Kzzz5D3759MXr0aKxcuRKZmZm1RszT0tIQEBCg+3rq1KmIi4vD\nqVOnamy3ceNGWFtb4/jx4zh+/Dg+++wzXL58Gd999x3y8vKQlZWFL7/8EocPH9btY2RkBFdX11qf\nRUTUWNg0E1GLl5CQgAkTJgB4OPf1t1M0goOD0a1bNxgZGcHPzw95eXnIzs6Gs7MznJycADyce/wo\n+/fvx+uvvw4/Pz+MHj0aJSUlKC0trbFNamoqJk+eDADw8PBAjx49cOHChSfKb2RkhPHjxwMAJk+e\njNTUVNy9exdqtRqDBg0CAEyZMgU//vijbp9ft39czn79+mH+/PlYvXo11Gq1bgT4t9l/rZu3tzdU\nKpVunZmZmW70PSAgAHl5efV+HwUFBbC3twcAqNVqqNVqDBw4EADw4osv6rb797//jS+++AJ+fn4I\nCQlBUVERLl68iNTUVIwdOxZGRkbo1KlTrVF5BwcH3Lhxo94cRERPw6T+TYiImq/i4mIkJyfjzJkz\nkCQJ1dXVkCQJK1euBACYm5vrtjU2Nn6iX+9rtVocOXIEbdq0afTcjyNJUr3bWFhY6P5cV863334b\nI0eOxO7du9GvXz/s3bu3wd+LqampLkdD69a2bVtUVFTUu50QAnFxcRg6dGiN5bt3737sfhUVFbVG\ny4mIGgtHmomoRfv222/x4osv4sqVK8jLy8O1a9fg5OSEQ4cO1bmPu7s7cnNzdaOnf//73x+53ZAh\nQxAXF6f7OjMzs9Y2AwYMwNdffw0AuHDhAq5evQp3d/cn+h60Wq1u7u7WrVvRv39/WFtbw9bWVvd9\nfPnll7pR54bmzMnJgY+PDxYuXIigoKBaTxbp168fvvnmGwBAVlYWzpw5U2/W9u3b4969e49c16tX\nL1y6dAkAYGNjAxsbG6SmpgKArkYAMHToUKxbtw4ajQbAw7rdv38f/fr1w/bt26HVanHz5k2kpKTU\n+PwLFy7o9QQOIqLHYdNMRC1aQkICoqKiaiwbM2bMY5+i0bZtW6xduxbDhg1DQEAA2rdvD2tr61rb\nrV69Gunp6VCpVPD09MSnn35aa5tZs2ZBq9XCx8cH48ePx+bNm2uMbjeEhYUFjh07Bm9vbyQnJ+Mv\nf/kLAGDLli146623oFKpkJmZqVve0JyxsbG6aRempqYYPnx4reyFhYXw9PTEn//8Z3h5eT2yDr81\nYcIErFy58pGPzhs+fHiNKSSff/45YmJi4Ofnp7sBEwCmT58OT09P+Pv7w9vbGzNnzkRVVRXGjBmD\nbt26wdPTE5MnT4a/v78uz82bN9G2bVt06tSpgVUlInoykvjtlYqIiAA8fOKGpaUlhBCIiYmBm5sb\n5s2bp3SsJlVdXQ2NRoM2bdogJycHv/vd7/Dzzz/DzMzsqT8zKioKK1asgJub21Pt/+vfS1FREYKD\ng5GWloZOnTrh448/hpWVFaZNm/bU2YiIHodzmomIHuGzzz7Dli1bUFlZid69e2PmzJlKR2pyZWVl\nCA8Ph0ajgRACa9eu1athBoD3338fBQUFT900R0ZGQq1Wo7KyEosWLdKNLNvY2NS4mZCIqLFxpJmI\niIiIqB6c00xEREREVA82zURERERE9WDTTERERERUDzbNRERERET1YNNMRERERFQPNs1ERERERPX4\n/xz0ed7ersHIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (12,8), facecolor = 'w')\n",
"gain_average = np.trapz(measure_sig * 2 * np.pi * np.sin(measure_theta), x = measure_theta.rad)/(4*np.pi)\n",
"plt.plot(measure_theta, 10*np.log10(measure_sig/gain_average))\n",
"plt.grid()\n",
"plt.yticks(np.arange(29,42))\n",
"plt.ylim((29,42))\n",
"plt.axhline(40.5, color = 'grey')\n",
"plt.axhline(40.5-3, color = 'grey')\n",
"plt.ylabel('Gain (dB)')\n",
"plt.xlabel('Angle off boresight (deg)')\n",
"plt.title('Dish gain');"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sfu = 1e4 * u.Jy\n",
"flux = 297.2 * sfu\n",
"freq = 10488e6 * u.Hz\n",
"G = 10**(40.5/10)\n",
"aperture = G * freq.to(u.m, equivalencies = u.spectral())**2/(4*np.pi)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$0.72953509 \\; \\mathrm{m^{2}}$"
],
"text/plain": [
"<Quantity 0.72953509 m2>"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"aperture"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$0.64505064 \\; \\mathrm{}$"
],
"text/plain": [
"<Quantity 0.64505064>"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dish_area = (0.6*u.m)**2*np.pi\n",
"efficiency = aperture/dish_area\n",
"efficiency"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$-0.39109974 \\; \\mathrm{}$"
],
"text/plain": [
"<Quantity -0.39109974>"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gamma_atm = 10**(-0.22/10/(np.sin(elevation)))\n",
"10*np.log10(gamma_atm)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gamma_cover = 10**(-0.1/10)\n",
"gamma_pointing = 10**(-0.4/10)"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.14046492708840014"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"L = 1 + 0.38 * (0.5 / 1.7)**2 # http://www.setileague.org/articles/g-t.htm\n",
"gamma_beam = 1/L\n",
"10*np.log10(gamma_b)"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$-1.0315647 \\; \\mathrm{}$"
],
"text/plain": [
"<Quantity -1.03156466>"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gamma = gamma_atm * gamma_cover * gamma_pointing * gamma_beam\n",
"10*np.log10(gamma)"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$8.5488663 \\times 10^{-21} \\; \\mathrm{\\frac{W}{Hz}}$"
],
"text/plain": [
"<Quantity 8.54886635e-21 W / Hz>"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"psd = (0.5 * gamma * flux * aperture).to(u.W/u.Hz)\n",
"psd"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$619.19208 \\; \\mathrm{K}$"
],
"text/plain": [
"<Quantity 619.19208443 K>"
]
},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(psd / astropy.constants.k_B).to(u.K)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.76995450258255"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10*np.log10(np.max(peaks_by_day-1))"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$327.21256 \\; \\mathrm{K}$"
],
"text/plain": [
"<Quantity 327.21255557 K>"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sky_noise = (psd / astropy.constants.k_B).to(u.K) / np.max(peaks_by_day-1)\n",
"sky_noise"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fbebd524a20>]"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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NtmUyjtyV/uIxhbt4wZ9oPzByj2fquQ9DLSL5UrhLqHwc/FqWI3elu/hM4S6h8vEye8kD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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"t_amb = (21 + 273.15) * u.K\n",
"y_factor = noise_pwr[423000:][:3000]\n",
"plt.plot(y_factor)\n",
"sel = slice(1150,1450)\n",
"plt.plot(np.arange(y_factor.size)[sel], y_factor[sel], color = 'red')"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2.91527658700943,\n",
" <Quantity 294.15 K>,\n",
" <Quantity 276.77956761 K>,\n",
" <Quantity 291.77956761 K>)"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Y = 10*np.log10(np.average(y_factor[sel])/np.average(y_factor[:1000]))\n",
"t_cold = 15 * u.K\n",
"t_hot = t_amb\n",
"t_sys = (t_hot - 10**(Y/10)*t_cold)/(10**(Y/10)-1)\n",
"Y, t_hot, t_sys, t_sys + t_cold"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$346.11184 \\; \\mathrm{K}$"
],
"text/plain": [
"<Quantity 346.11184012 K>"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10**(Y/10)*sky_noise - t_hot"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"additional_losses = 10**(-0.05)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$291.6285 \\; \\mathrm{K}$"
],
"text/plain": [
"<Quantity 291.62849712 K>"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sky_noise*additional_losses"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$276.48397 \\; \\mathrm{K}$"
],
"text/plain": [
"<Quantity 276.48396566 K>"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"system_noise = 10**(Y/10)*additional_losses*sky_noise - t_hot\n",
"system_noise"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$15.144531 \\; \\mathrm{K}$"
],
"text/plain": [
"<Quantity 15.14453146 K>"
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sky_noise*additional_losses - (10**(Y/10)*additional_losses*sky_noise - t_hot)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$2.9078962 \\; \\mathrm{}$"
],
"text/plain": [
"<Quantity 2.90789624>"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10*np.log10(1 + system_noise / (290*u.K))"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fbebc69e550>]"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JbRoE/+XgyteTAziqdRrk5/kMPLsPHQ8Ib84+XhUx+OnvfL7noKemccY/ZgDw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\nlFJeNDgopZTyosFBKaWUl/8HBpFxmp6jXB4AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"moon_sel = (noise_t >= np.datetime64('2019-10-11T20:10')) & (noise_t <= np.datetime64('2019-10-11T21:00'))\n",
"plt.plot(noise_t[moon_sel], 10*np.log10(noise_pwr[moon_sel]))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
kinshuk4/MoocX | misc/deep_learning_notes/Proj_Centroid_Loss_LeNet/LeNet_plus_centerloss/Training Evaluation.ipynb | 1 | 6026 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%javascript\n",
"var kernel = IPython.notebook.kernel;\n",
"var body = document.body, \n",
" attribs = body.attributes;\n",
"var command = \"__filename__ = \" + \"'\" + decodeURIComponent(attribs['data-notebook-name'].value) + \"'\";\n",
"kernel.execute(command);"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training Convergence.ipynb\n"
]
}
],
"source": [
"print(__filename__)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"import os, sys, numpy as np, tensorflow as tf\n",
"from pathlib import Path\n",
"\n",
"import time\n",
"try:\n",
" print(__file__)\n",
" __current_dir__ = str(Path(__file__).resolve().parents[0])\n",
" __filename__ = os.path.basename(__file__)\n",
"\n",
"except NameError:\n",
" # jupyter notebook automatically sets the working \n",
" # directory to where the notebook is.\n",
" __current_dir__ = str(Path(os.getcwd()))\n",
"\n",
"module_parent_dir = str(Path(__current_dir__).resolve().parents[0])\n",
"\n",
"sys.path.append(module_parent_dir)\n",
"import LeNet_plus_centerloss\n",
"\n",
"__package__ = 'LeNet_plus_centerloss'\n",
"from . import network\n",
"\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n",
"\n",
"BATCH_SIZE = 250\n",
"SCRIPT_DIR = __current_dir__\n",
"FILENAME = __filename__\n",
"SUMMARIES_DIR = SCRIPT_DIR\n",
"SAVE_PATH = SCRIPT_DIR + \"/network.ckpt\"\n",
"\n",
"### configure devices for this eval script.\n",
"USE_DEVICE = '/gpu:0'\n",
"session_config = tf.ConfigProto(log_device_placement=True)\n",
"session_config.gpu_options.allow_growth = True\n",
"# this is required if want to use GPU as device.\n",
"# see: https://github.com/tensorflow/tensorflow/issues/2292\n",
"session_config.allow_soft_placement = True"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n",
"- MNIST Test accuracy is 1.0\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" with tf.Graph().as_default() as g, tf.device(USE_DEVICE):\n",
" # inference()\n",
" input, hidden_2d, logits = network.inference()\n",
" labels, loss_op = network.loss(logits)\n",
" train = network.training(loss_op, 1e-1)\n",
" eval = network.evaluation(logits, labels)\n",
"\n",
" init = tf.initialize_all_variables()\n",
"\n",
" with tf.Session(config=session_config) as sess:\n",
" # Merge all the summaries and write them out to /tmp/mnist_logs (by default)\n",
" # to see the tensor graph, fire up the tensorboard with --logdir=\"./train\"\n",
" merged = tf.merge_all_summaries()\n",
" train_writer = tf.train.SummaryWriter(SUMMARIES_DIR + '/summaries/train', sess.graph)\n",
" test_writer = tf.train.SummaryWriter(SUMMARIES_DIR + '/summaries/test')\n",
"\n",
" saver = tf.train.Saver()\n",
"\n",
" sess.run(init)\n",
" saver.restore(sess, SAVE_PATH)\n",
"\n",
" # now let's test!\n",
" TEST_BATCH_SIZE = np.shape(mnist.test.labels)[0]\n",
" \n",
" # while True:\n",
" # saver.restore(sess, SAVE_PATH)\n",
" output, loss_value, accuracy = sess.run([logits, loss_op, eval], feed_dict={\n",
" input: mnist.test.images,\n",
" labels: mnist.test.labels\n",
" })\n",
" print(\"- MNIST Test accuracy is \", accuracy / TEST_BATCH_SIZE)\n",
" # time.sleep(5.0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
""
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
wangyu16/Introduction-to-Polymer-Science | .ipynb_checkpoints/ATRP_Kinetic_Simulator_Lite-checkpoint.ipynb | 1 | 171539 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ATRP Kinetic Simulator Lite"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About this program\n",
"\n",
"This is a lite version of ATRP and conventional radical polymerization kinetic simulator. You can select the type of polymerization you want to simulate. The types of polymerizations supported are conventional radical polymerization, normal ATRP, AGET ATRP, ARGET ATRP, eATRP, ATRP by continuous feeding of activators, and ICAR ATRP. You need to input the reaction time, the initial concentrations of reagents, and the rate coefficients of all reactions involved. The results include the concentration changes of all species with time, the monomer conversion vs. time, the first-order kinetic plot of monomer conversion vs. time, and the mole percent of end group loss vs. time. All reactions, rate coefficients, concentrations and monomer conversion information could be exported to a CSV file. This simulator does not distinguish polymers with different chain lengths. Thus, no molecular weight distribution information is provided. This is why the simulator is called a lite version.\n",
"\n",
"For more information, please visit [https://wangyu16.github.io/macroarchilab/simulation/ATRP-kinetic-lite/](https://wangyu16.github.io/macroarchilab/simulation/ATRP-kinetic-lite/).\n",
"\n",
"To run this program online, please visit [https://gke.mybinder.org/v2/gh/wangyu16/PolymerScienceEducation/master?filepath=ATRP_Kinetic_Simulator_Lite.ipynb](https://gke.mybinder.org/v2/gh/wangyu16/PolymerScienceEducation/master?filepath=ATRP_Kinetic_Simulator_Lite.ipynb). \n",
"\n",
"To download this program, please visit [https://github.com/wangyu16/PolymerScienceEducation](https://github.com/wangyu16/PolymerScienceEducation)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import packages "
]
},
{
"cell_type": "code",
"execution_count": 230,
"metadata": {},
"outputs": [],
"source": [
"from chempy import ReactionSystem, Substance\n",
"from chempy.kinetics.ode import get_odesys\n",
"from collections import defaultdict\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from ipywidgets import interact\n",
"import datetime\n",
"import csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize reaction conditions"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [],
"source": [
"# Please choose the type of polymerization you want to simulate.\n",
"# Set the value of Poly_type as:\n",
"# 'conven' for conventional polymerization;\n",
"# 'normal' or any other value for normal ATRP;\n",
"# 'arget' for ARGET and AGET;\n",
"# 'eatrp' for electrochemical ATRP;\n",
"# 'cfa' for ATRP by continuous feeding of activators;\n",
"# 'icar' for ICAR ATRP.\n",
"\n",
"Poly_type = 'icar'"
]
},
{
"cell_type": "code",
"execution_count": 232,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"chempy_ReactionSystem chempy_2362474110648\"><tr><th style=\"text-align:center;\" colspan=\"5\"></th></tr><tr class=\"chempy_2362474110648_0\"><td style=\"text-align:right;\"><span class=\"chempy_AIBN\" style=\"background-color:#ffb6c1; border: 1px solid #c71585; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBN</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span></td><td style=\"text-align:left;\"> 10<sup>-5</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_1\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span> + <span class=\"chempy_M\" style=\"background-color:#ffb6c1; border: 1px solid #c71585; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">M</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span></td><td style=\"text-align:left;\"> 1300</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_2\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span> + <span class=\"chempy_M\" style=\"background-color:#ffb6c1; border: 1px solid #c71585; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">M</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span></td><td style=\"text-align:left;\"> 1300</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_3\"><td style=\"text-align:right;\">2 <span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_D\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">D</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_4\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span> + <span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_5\"><td style=\"text-align:right;\">2 <span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 5⋅10<sup>7</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_6\"><td style=\"text-align:right;\"><span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span> + <span class=\"chempy_RX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RX</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span> + <span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span></td><td style=\"text-align:left;\"> 1</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_7\"><td style=\"text-align:right;\"><span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span> + <span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span> + <span class=\"chempy_RX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RX</span></td><td style=\"text-align:left;\"> 10<sup>6</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_8\"><td style=\"text-align:right;\"><span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span> + <span class=\"chempy_RPnX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPnX</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span> + <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:left;\"> 1</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_9\"><td style=\"text-align:right;\"><span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span> + <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span> + <span class=\"chempy_RPnX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPnX</span></td><td style=\"text-align:left;\"> 10<sup>6</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_10\"><td style=\"text-align:right;\"><span class=\"chempy_M\" style=\"background-color:#ffb6c1; border: 1px solid #c71585; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">M</span> + <span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:left;\"> 1300</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_11\"><td style=\"text-align:right;\"><span class=\"chempy_M\" style=\"background-color:#ffb6c1; border: 1px solid #c71585; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">M</span> + <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:left;\"> 1300</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_12\"><td style=\"text-align:right;\">2 <span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_D\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">D</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_13\"><td style=\"text-align:right;\"><span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span> + <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_14\"><td style=\"text-align:right;\">2 <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 5⋅10<sup>7</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_15\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span> + <span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_16\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span> + <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 5⋅10<sup>7</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_17\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span> + <span class=\"chempy_R\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">R</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_D\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">D</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_18\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span> + <span class=\"chempy_RPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">RPn</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_DPn\" style=\"background-color:#90ee90; border: 1px solid #008000; border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">DPn</span></td><td style=\"text-align:left;\"> 10<sup>9</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_19\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNRX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRX</span> + <span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span> + <span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span></td><td style=\"text-align:left;\"> 1</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_20\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNR\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNR</span> + <span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNRX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRX</span> + <span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span></td><td style=\"text-align:left;\"> 10<sup>6</sup></td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_21\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNRPnX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPnX</span> + <span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span> + <span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span></td><td style=\"text-align:left;\"> 1</td></tr>\n",
"\n",
"<tr class=\"chempy_2362474110648_22\"><td style=\"text-align:right;\"><span class=\"chempy_AIBNRPn\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPn</span> + <span class=\"chempy_CuII\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuII</span></td><td style=\"text-align:center;\">→</td><td style=\"text-align:left;\"><span class=\"chempy_AIBNRPnX\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">AIBNRPnX</span> + <span class=\"chempy_CuI\" style=\"border-radius: 5pt; padding: 0pt 3pt 0pt 3pt;\">CuI</span></td><td style=\"text-align:left;\"> 10<sup>6</sup></td></tr></table><script type=\"text/javascript\">\n",
"var cls_names_substances = [\"chempy_AIBN\", \"chempy_AIBNR\", \"chempy_AIBNRPn\", \"chempy_D\", \"chempy_DPn\", \"chempy_M\", \"chempy_CuI\", \"chempy_CuII\", \"chempy_R\", \"chempy_RPn\", \"chempy_RPnX\", \"chempy_RX\", \"chempy_AIBNRPnX\", \"chempy_AIBNRX\"];\n",
"var substance_row_cls_irrel = {\"chempy_AIBN\": [\"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_AIBNR\": [\"chempy_2362474110648_2\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_AIBNRPn\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_3\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\"], \"chempy_D\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_DPn\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_17\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_M\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_CuI\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\"], \"chempy_CuII\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\"], \"chempy_R\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_11\", \"chempy_2362474110648_14\", \"chempy_2362474110648_16\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_RPn\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_12\", \"chempy_2362474110648_15\", \"chempy_2362474110648_17\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_RPnX\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_RX\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"], \"chempy_AIBNRPnX\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_19\", \"chempy_2362474110648_20\"], \"chempy_AIBNRX\": [\"chempy_2362474110648_0\", \"chempy_2362474110648_1\", \"chempy_2362474110648_2\", \"chempy_2362474110648_3\", \"chempy_2362474110648_4\", \"chempy_2362474110648_5\", \"chempy_2362474110648_6\", \"chempy_2362474110648_7\", \"chempy_2362474110648_8\", \"chempy_2362474110648_9\", \"chempy_2362474110648_10\", \"chempy_2362474110648_11\", \"chempy_2362474110648_12\", \"chempy_2362474110648_13\", \"chempy_2362474110648_14\", \"chempy_2362474110648_15\", \"chempy_2362474110648_16\", \"chempy_2362474110648_17\", \"chempy_2362474110648_18\", \"chempy_2362474110648_21\", \"chempy_2362474110648_22\"]};\n",
"var elms = {};\n",
"var n = {}, nsubstances = cls_names_substances.length;\n",
"var nirrel = {};\n",
"function changeColor(classname, color) {\n",
" var curN = n[classname];\n",
" for(var i = 0; i < curN; i++) {\n",
" elms[classname][i].style.backgroundColor = color;\n",
" }\n",
"}\n",
"function toggleVisibility(classname_substance) {\n",
" var curN = nirrel[classname_substance];\n",
" for (var i=0; i<curN; i++) {\n",
" var objs = document.getElementsByClassName(substance_row_cls_irrel[classname_substance][i]);\n",
" for (var j=0; j<objs.length; ++j){\n",
" objs[j].style.display = objs[j].style.display == \"none\" ? \"table-row\" : \"none\";\n",
" }\n",
" }\n",
"}\n",
"function resetTab(tab){\n",
" tab.style.border = \"0px\";\n",
" var rows = tab.getElementsByTagName('tr');\n",
" [].forEach.call(rows, function(row){\n",
" row.style.display = \"table-row\";\n",
" });\n",
" tab.getElementsByTagName('th')[0].innerHTML = tab.ori_header +\n",
" \"<br>(click on species to show a subset of reactions)\";\n",
"};\n",
"\n",
"for(var k = 0; k < nsubstances; k++) {\n",
" var curClass = cls_names_substances[k];\n",
" var curIrrel = substance_row_cls_irrel[k];\n",
" elms[curClass] = document.getElementsByClassName(curClass);\n",
" n[curClass] = elms[curClass].length;\n",
" nirrel[curClass] = substance_row_cls_irrel[curClass].length;\n",
" var curN = n[curClass];\n",
" for(var i = 0; i < curN; i++) {\n",
" elms[curClass][i].onmouseover = function() {\n",
" changeColor(this.className, \"LightBlue\");\n",
" };\n",
" elms[curClass][i].onmouseout = function() {\n",
" changeColor(this.className, \"inherit\");\n",
" };\n",
" elms[curClass][i].onclick = function() {\n",
" var tab = this.closest(\"table\");\n",
" resetTab(tab);\n",
" tab.style.border = \"1px dashed #000000\";\n",
" toggleVisibility(this.className);\n",
" tab.getElementsByTagName('th')[0].innerHTML = tab.ori_header +\n",
" \"<br>Only showing reactions involving: \" + this.innerHTML +\n",
" \" (double-click to reset)\";\n",
" };\n",
" }\n",
"};\n",
"var chempy_tabs = document.querySelectorAll('table.chempy_2362474110648');\n",
"[].forEach.call(chempy_tabs, function(tab){\n",
" tab.ori_header = tab.getElementsByTagName('th')[0].innerHTML;\n",
" tab.ondblclick = function(){\n",
" resetTab(this);\n",
" this.scrollIntoView();\n",
" };\n",
"});\n",
"[].forEach.call(chempy_tabs, function(tab){\n",
" resetTab(tab);\n",
"});\n",
"</script>"
],
"text/plain": [
"<chempy.reactionsystem.ReactionSystem at 0x2260e6e1eb8>"
]
},
"execution_count": 232,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"################# \n",
"# Reaction time #\n",
"################# \n",
"# Set reaction time limit in seconds. \n",
"\n",
"react_time = 28800\n",
" \n",
"#########################\n",
"# Monomer concentration #\n",
"#########################\n",
"# First set the initial concentration of monomer. Dead chains, D and DPn, should be 0 at the beginning. \n",
"\n",
"c0 = defaultdict(float, {'M': 5, 'D': 0, 'DPn': 0})\n",
"\n",
"#########################################################################################################################\n",
"# In the following part, always remember to set proper rate coefficients for the reactions involved in your simulation. #\n",
"#########################################################################################################################\n",
"\n",
"# Initiate the reaction system with null value. \n",
"\n",
"rsys = ReactionSystem.from_string(\"\"\"\n",
" \"\"\", substance_factory=Substance)\n",
"\n",
"#########################\n",
"# Conventional and ICAR #\n",
"#########################\n",
"# If you are going to simulate conventional radical polymerization or ICAR ATRP, \n",
"# the following set of reactions will be added. \n",
"# Set the initial concentration of AIBN. AIBNR and AIBNRPn should be 0 at the beginning.\n",
"\n",
"if Poly_type is 'conven' or Poly_type is 'icar':\n",
" rsys = rsys + ReactionSystem.from_string(\"\"\"\n",
" AIBN -> AIBNR; 1e-5 \n",
" AIBNR + M -> AIBNRPn; 1.3e3\n",
" AIBNRPn + M -> AIBNRPn; 1.3e3 \n",
" AIBNR + AIBNR -> D; 1e9\n",
" AIBNRPn + AIBNR -> DPn; 1e9\n",
" AIBNRPn + AIBNRPn -> DPn; 5e7\n",
" \"\"\", substance_factory=Substance)\n",
" c0.update({'AIBN': 0.005, 'AIBNR': 0, 'AIBNRPn':0})\n",
"\n",
"\n",
"##################### \n",
"# All kinds of ATRP #\n",
"#####################\n",
"# If you are going to simulate any type of ATRP, the following set of reactions will be added. \n",
"# Set the initial concentrations of RX, \n",
"# CuI and CuII. Other concentrations should be 0. \n",
"if Poly_type is not 'conven':\n",
" rsys = rsys + ReactionSystem.from_string(\"\"\"\n",
" CuI + RX -> CuII + R; 1 \n",
" CuII + R -> CuI + RX; 1e6\n",
" CuI + RPnX -> CuII + RPn; 1\n",
" CuII + RPn -> CuI + RPnX; 1e6 \n",
" R + M -> RPn; 1.3e3\n",
" RPn + M -> RPn; 1.3e3 \n",
" R + R -> D; 1e9\n",
" RPn + R -> DPn; 1e9\n",
" RPn + RPn -> DPn; 5e7\n",
" \"\"\", substance_factory=Substance)\n",
" c0.update({'CuI': 0, 'RX': 0.05, 'CuII': 0.0005, 'R': 0, 'RPnX':0, 'RPn': 0})\n",
"\n",
"################## \n",
"# AGET and ARGET #\n",
"##################\n",
"# If you are going to simulate AGET or ARGET, the following set of reactions will be added. \n",
"# set the initial concentration of the reducing agent. \n",
"# ReducX should be 0.\n",
"\n",
"if Poly_type is 'arget':\n",
" rsys = rsys + ReactionSystem.from_string(\"\"\"\n",
" Reduc + CuII -> ReducX + CuI; 1e-1\n",
" \"\"\", substance_factory=Substance)\n",
" c0.update({'Reduc': 0.025, 'ReducX':0})\n",
" \n",
"######### \n",
"# eATRP #\n",
"#########\n",
"# If you are going to simulate eATRP, the following set of reactions will be added. \n",
"# set the initial concentration of the electrons as a large number, e.g. > 200 times of RX, \n",
"# which will remain nearly constant.\n",
"# Adjust the rate coefficient to change the polymerization rate. \n",
"\n",
"if Poly_type is 'eatrp':\n",
" rsys = rsys + ReactionSystem.from_string(\"\"\"\n",
" elec + CuII -> CuI; 1e-4\n",
" \"\"\", substance_factory=Substance)\n",
" c0.update({'elec': 10})\n",
"\n",
"\n",
"######################\n",
"# Continuous feeding #\n",
"######################\n",
"# If you are going to simulate ATRP by continuous feeding of activators, the following set of reactions will be added. \n",
"# Set the initial concentration of CuIsour as any large number, e.g. > 200 times of RX, which will remain nearly constant. \n",
"# Adjust the rate coefficient to minic different feeding rate. \n",
"\n",
"if Poly_type is 'cfa':\n",
" rsys = rsys + ReactionSystem.from_string(\"\"\"\n",
" CuIsour -> CuI; 1.4e-8\n",
" \"\"\", substance_factory=Substance)\n",
" c0.update({'CuIsour': 10})\n",
"\n",
"######## \n",
"# ICAR #\n",
"########\n",
"# If you are going to simulate ICAR ATRP, the following set of reactions will be added. \n",
"# Initialize the concentrations of AIBNRX and AIBNRPnX as 0. \n",
"\n",
"if Poly_type is 'icar': \n",
" rsys = rsys + ReactionSystem.from_string(\"\"\"\n",
" AIBNRPn + R -> DPn; 1e9\n",
" AIBNRPn + RPn -> DPn; 5e7 \n",
" AIBNR + R -> D; 1e9\n",
" AIBNR + RPn -> DPn; 1e9\n",
" CuI + AIBNRX -> CuII + AIBNR; 1\n",
" AIBNR + CuII -> AIBNRX + CuI; 1e6\n",
" AIBNRPnX + CuI -> CuII + AIBNRPn; 1\n",
" AIBNRPn + CuII -> CuI + AIBNRPnX; 1e6\n",
" \"\"\", substance_factory=Substance) \n",
" c0.update({'AIBNRX': 0, 'AIBNRPnX': 0}) \n",
"\n",
"rsys\n",
"\n",
"# After execute the the cell, double check the output rate coefficients. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation and results"
]
},
{
"cell_type": "code",
"execution_count": 233,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('AIBN', y_0, -1.0e-5*y_0),\n",
" ('AIBNR',\n",
" y_1,\n",
" 1.0e-5*y_0 - 2000000000.0*y_1**2 - 1000000000.0*y_1*y_2 - 1300.0*y_1*y_5 - 1000000.0*y_1*y_7 - 1000000000.0*y_1*y_8 - 1000000000.0*y_1*y_9 + y_13*y_6),\n",
" ('AIBNRPn',\n",
" y_2,\n",
" -1000000000.0*y_1*y_2 + 1300.0*y_1*y_5 + y_12*y_6 - 100000000.0*y_2**2 - 1000000.0*y_2*y_7 - 1000000000.0*y_2*y_8 - 50000000.0*y_2*y_9),\n",
" ('D', y_3, 1000000000.0*y_1**2 + 1000000000.0*y_1*y_8 + 1000000000.0*y_8**2),\n",
" ('DPn',\n",
" y_4,\n",
" 1000000000.0*y_1*y_2 + 1000000000.0*y_1*y_9 + 50000000.0*y_2**2 + 1000000000.0*y_2*y_8 + 50000000.0*y_2*y_9 + 1000000000.0*y_8*y_9 + 50000000.0*y_9**2),\n",
" ('M',\n",
" y_5,\n",
" -1300.0*y_1*y_5 - 1300.0*y_2*y_5 - 1300.0*y_5*y_8 - 1300.0*y_5*y_9),\n",
" ('CuI',\n",
" y_6,\n",
" 1000000.0*y_1*y_7 - y_10*y_6 - y_11*y_6 - y_12*y_6 - y_13*y_6 + 1000000.0*y_2*y_7 + 1000000.0*y_7*y_8 + 1000000.0*y_7*y_9),\n",
" ('CuII',\n",
" y_7,\n",
" -1000000.0*y_1*y_7 + y_10*y_6 + y_11*y_6 + y_12*y_6 + y_13*y_6 - 1000000.0*y_2*y_7 - 1000000.0*y_7*y_8 - 1000000.0*y_7*y_9),\n",
" ('R',\n",
" y_8,\n",
" -1000000000.0*y_1*y_8 + y_11*y_6 - 1000000000.0*y_2*y_8 - 1300.0*y_5*y_8 - 1000000.0*y_7*y_8 - 2000000000.0*y_8**2 - 1000000000.0*y_8*y_9),\n",
" ('RPn',\n",
" y_9,\n",
" -1000000000.0*y_1*y_9 + y_10*y_6 - 50000000.0*y_2*y_9 + 1300.0*y_5*y_8 - 1000000.0*y_7*y_9 - 1000000000.0*y_8*y_9 - 100000000.0*y_9**2),\n",
" ('RPnX', y_10, -y_10*y_6 + 1000000.0*y_7*y_9),\n",
" ('RX', y_11, -y_11*y_6 + 1000000.0*y_7*y_8),\n",
" ('AIBNRPnX', y_12, -y_12*y_6 + 1000000.0*y_2*y_7),\n",
" ('AIBNRX', y_13, 1000000.0*y_1*y_7 - y_13*y_6)]"
]
},
"execution_count": 233,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Intigrate the differential equations. \n",
"\n",
"odesys, extra = get_odesys(rsys)\n",
"tout = sorted(np.concatenate((np.linspace(0, react_time), np.logspace(0, np.floor(np.log10(react_time))))))\n",
"result = odesys.integrate(tout, c0, atol=1e-12, rtol=1e-14)\n",
"kineticeq = list(zip(odesys.names, odesys.dep, odesys.exprs))\n",
"kineticeq\n",
"\n",
"# Show the differential equations."
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x360 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
"for ax in axes:\n",
" _ = result.plot(names=[k for k in rsys.substances if k != 'CuIsour'and k != 'M' and k!= 'elec'], ax=ax)\n",
" _ = ax.legend(loc='best', prop={'size': 9})\n",
" _ = ax.set_xlabel('Time (s)')\n",
" _ = ax.set_ylabel('Concentration')\n",
"_ = axes[1].set_ylim([1e-10, 1e1])\n",
"_ = axes[1].set_xscale('log')\n",
"_ = axes[1].set_yscale('log')\n",
"_ = fig.tight_layout()\n",
"\n",
"# Plot the concentrations of all species vs time \n",
"# except those of monomer, CuI source in continuous feeding and electrons in eATRP. "
]
},
{
"cell_type": "code",
"execution_count": 235,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Get monomer concentration and calculate conversion.\n",
"\n",
"x=list(rsys.substances.keys()).index('M')\n",
"ConcM=list(result[1][:,x])\n",
"ConvM =list((ConcM[0]-ConcM)/ConcM[0])\n",
"LnM = list(np.log(ConcM[0]/ConcM))\n",
"\n",
"\n",
"# Get mole percent of end group loss, i.e., Tmol%.\n",
"if Poly_type is not 'conven':\n",
" x=list(rsys.substances.keys()).index('RX')\n",
" y=list(rsys.substances.keys()).index('RPnX')\n",
" Tmol = list((result[1][0,x]-result[1][:,x]-result[1][:,y])/result[1][0,x])\n",
"else:\n",
" Tmol = [0]*len(result[0])\n",
"result_cal = [list(result[0]),ConcM,ConvM,LnM,Tmol]"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plot monomer conversion vs. time and first order kinetic plot. \n",
"\n",
"fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
"i=2\n",
"for ax in axes:\n",
" _ = ax.plot(result_cal[0], result_cal[i])\n",
" _ = ax.grid()\n",
" i=i+1\n",
"_ = axes[0].set(xlabel='time (s)', ylabel='Conversion')\n",
"_ = axes[1].set(xlabel='time (s)', ylabel='Ln([M]0/[M])')\n",
"_ = axes[2].set(xlabel='time (s)', ylabel='Tmol%')\n",
"_ = fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Additional reaction time\n",
"\n",
"**If you are not simulating eATRP or ATRP by continuous feeding of activators, ignore the following several cells and jump to \"Export the result\" directly.**"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are not simulating ATRP by CFA or eATRP.\n"
]
}
],
"source": [
"# For ATRP with continuous feeding of activators and eATRP, after the feeding or the electric reduction stops,\n",
"# the polymerization may continue or may stop depends on the activity of the CuI complex.\n",
"\n",
"############################ \n",
"# Additional reaction time #\n",
"############################ \n",
"# Set additional reaction time after the activator feeding or the electric reduction stops. \n",
"\n",
"react_time_add = 28800\n",
"\n",
"# Get the final concentrations of all species in previous step, \n",
"# which are the initial concentrations for the next step simulation.\n",
"c1 = result[1][-1].copy()\n",
"\n",
"# Set the 'CuIsour' or 'e' concentration to 0. \n",
"if Poly_type is 'cfa':\n",
" x=list(rsys.substances.keys()).index('CuIsour')\n",
"elif Poly_type is 'eatrp':\n",
" x=list(rsys.substances.keys()).index('elec')\n",
"else:\n",
" print(\"You are not simulating ATRP by CFA or eATRP.\")\n",
"\n",
"if Poly_type is 'cfa' or Poly_type is 'eatrp':\n",
" c1[x]=0 \n",
" tout = sorted(np.concatenate((np.linspace(0, react_time_add), np.logspace(0, np.floor(np.log10(react_time_add))))))\n",
" result2 = odesys.integrate(tout, c1, atol=1e-10, rtol=1e-12)"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [],
"source": [
"if Poly_type is 'cfa' or Poly_type is 'eatrp':\n",
" fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
" for ax in axes:\n",
" _ = result2.plot(names=[k for k in rsys.substances if k != 'CuIsour'and k != 'M' and k!= 'e'], ax=ax)\n",
" _ = ax.legend(loc='best', prop={'size': 9})\n",
" _ = ax.set_xlabel('Time (s)')\n",
" _ = ax.set_ylabel('Concentration')\n",
" _ = axes[1].set_ylim([1e-10, 1e1])\n",
" _ = axes[1].set_xscale('log')\n",
" _ = axes[1].set_yscale('log')\n",
" _ = fig.tight_layout() # The plot shows the concentration changes during the additional reaction time. "
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {},
"outputs": [],
"source": [
"# Get monomer concentration and calculate conversion.\n",
"# Get mole percent of end group loss, i.e., Tmol%.\n",
"\n",
"if Poly_type is 'cfa' or Poly_type is 'eatrp':\n",
" x=list(rsys.substances.keys()).index('M')\n",
" ConcM2=ConcM+list(result2[1][:,x])\n",
" ConvM2 =list((ConcM2[0]-ConcM2)/ConcM2[0]) \n",
" LnM2 = list(np.log(ConcM2[0]/ConcM2))\n",
" x=list(rsys.substances.keys()).index('RX')\n",
" y=list(rsys.substances.keys()).index('RPnX')\n",
" Tmol2 = Tmol+list((result[1][0,x]-result2[1][:,x]-result2[1][:,y])/result[1][0,x])\n",
" result_cal2 = [list(result[0])+list(result[0][-1]+result2[0]),ConcM2,ConvM2,LnM2,Tmol2] "
]
},
{
"cell_type": "code",
"execution_count": 240,
"metadata": {},
"outputs": [],
"source": [
"# Plot monomer conversion vs. time and first order kinetic plot. \n",
"\n",
"if Poly_type is 'cfa' or Poly_type is 'eatrp':\n",
" fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
" i=2\n",
" for ax in axes:\n",
" _ = ax.plot(result_cal2[0], result_cal2[i])\n",
" _ = ax.grid()\n",
" i=i+1\n",
" _ = axes[0].set(xlabel='time (s)', ylabel='Conversion')\n",
" _ = axes[1].set(xlabel='time (s)', ylabel='Ln([M]0/[M])')\n",
" _ = axes[2].set(xlabel='time (s)', ylabel='Tmol%')\n",
" _ = fig.tight_layout() # Plot the conversion, Ln([M]0/[M]) and Tmol% in the whole time range. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Export the result "
]
},
{
"cell_type": "code",
"execution_count": 203,
"metadata": {},
"outputs": [],
"source": [
"# Export the result to a CSV file.\n",
"# The CSV file is saved in the same folder as this ipynb file. \n",
"\n",
"now = datetime.datetime.now()\n",
"filename = str(now.strftime(\"%Y-%m-%d_%Hh%Mm%Ss\")) + '_ATRP_Simulation_Lite_' + str(Poly_type) + '.csv'\n",
"\n",
"with open(filename, 'w', newline='') as f:\n",
" thewriter = csv.writer(f)\n",
" for rxn in rsys.rxns:\n",
" thewriter.writerow([rxn])\n",
" thewriter.writerow(['time']+[k for k in rsys.substances]+['conversion']+['ln([M]0/[M])']+['Tmol%'])\n",
" i=0\n",
" for concen in result[1]:\n",
" thewriter.writerow([result_cal[0][i]]+[k for k in concen]+[ConvM[i]]+[LnM[i]]+[Tmol[i]])\n",
" i+=1\n",
" if Poly_type is 'cfa' or Poly_type is 'eatrp':\n",
" for concen in result2[1][1:]:\n",
" thewriter.writerow([result_cal2[0][i+1]]+[k for k in concen]+[ConvM2[i+1]]+[LnM2[i+1]]+[Tmol2[i+1]])\n",
" i+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| cc0-1.0 |
adaptive-learning/robomission | backend/monitoring/notebooks/monitoring_template.ipynb | 1 | 545035 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Settings, imports, data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"from collections import OrderedDict\n",
"from IPython.display import display, Markdown\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import learn\n",
"from monitoring.data import get_production_data\n",
"from monitoring.visualization import display_level_overview\n",
"\n",
"sns.set()\n",
"pd.options.display.float_format = '{:.2f}'.format"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data loaded from cache (/home/xeffenb1/projects/robomission/.prodcache/robomission-2018-06-21/).\n"
]
}
],
"source": [
"# Load data from local cache, fetch and store if not available.\n",
"data = get_production_data('2018-06-21')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ts = data['task_sessions']\n",
"ts = ts[ts.time_spent > 0]\n",
"ts = ts.assign(date=ts.end.str[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Metrics"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"grouped_ts = ts.groupby('date')\n",
"metrics = pd.DataFrame(OrderedDict(\n",
" active_students=grouped_ts.student.nunique(),\n",
" solving_hours=grouped_ts.time_spent.sum() / 3600,\n",
" solved_count=grouped_ts.solved.sum(),\n",
" success_rate=grouped_ts.solved.mean(),\n",
")).sort_index()\n",
"metrics.index = pd.to_datetime(metrics.index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Active Students"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d6c1731d0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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7VZWz3je9OHPIGJVKGeQpzgkkvckWtfcylFiLd6BYj38gm90Bg8mGoryUQe9tuPsdnZ2M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\nlMAJISRGUQInhJAYRQmcEEJi1P8Huf2HQkO1QGQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6b133438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metrics.active_students.resample('1W').mean().plot()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d6c118e10>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Fhursd8KQo8KNU8ehtduBS1esYi8pLcJzME0akVeSHG5gNqaFAnhaWZ0+cEDS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6c09b470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metrics.solving_hours.resample('1W').mean().plot()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d6b5d76d8>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pqt0jf6AegDMVvV+9S3IXQogOHE4XbTZnr1buvrRDekoyN4wxdnkfb939fO/r7pLchRCi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\norHH/XJTRmSREKdzz3nvRc/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RlJeX8+67714xCruxsZF//ud/5uzZsyiKgt1u937tpptu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\n0gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6b5f2400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metrics.solved_count.resample('1W').mean().plot()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d6b557748>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PTm/AVwcqIBQweGCWe6cp3oxTCd3fV4KeXr1Du/eYy+be3OViXmDU0tmLNpVr\nFxhV9/XTj3JyCx3oW0U6Lxkhci/sOlI1YMGhX4quQaszYtYd/C4sRVzDvCXciWFuT3e8RAmDkeXc\nYOhA/H0leGTOGGh1RvzTxhpKe0/UoKVTgzl3RrttA53BcCqhy4cxdbGxtRsSsQABsltrRZj70V3Z\nSmdZU2VHRZCPy2aW+HiJ8HReKgDgf3cWo1tzvUyCsa8AklgkGJECSIR/MhNDIRIyw+5HP3KhAQwD\n3MWBuee2mJJmew2lji4t8o9UQ+YtRt60uJEJ0A4cS+h9i4vsTOgsy0LZ1oOwAJ8BF9GYt9lyZT96\nU4cG3b16xCpc+xEzKToAedPi0NLZi0/3XLK0KC5UtqCpXYMpqQrIvMUujYHwk4+XCBnxwaht6kJ9\nc5dD51C2dqOivhOpsYEI9POMrQ5vrKG0teDykPnnm8JKaLQGLJ4+Gj5e3Ps741RCNy8u6rRztWhH\nlxa9OgMUQQMXoIqL8APDuHamy/UFRa6v45B3dxwSo+Q4XtJoaVEUnOrbDYYGQ8kwWBYZOdhK94TB\n0IGEyL2xLCsBXRo9/vVj2YDH1DSq8XNRPSJDfJHlhD1RXYFTCd3S5WLntljmKos3D4iaeUlEiA6V\noapB5bLymbZsCu0sQoEAz+SlwlsqxOa9ZSiqaMH5yhYkRstH5PqEv8YnhkAsEuB4idLuKqUsy+JI\ncQMkYgHuGBPqoghdZ9YdUUiMkuNkaSPOlDX1e8w8TZFlgeXZiRAKOJU6LTgVlb/MsRa6ZUA0cPAS\nsQlRcuj0RtQ0OraIwJqrlimLIzOqHxLgjcfmJaNXZ8B7XxcBAOZQVUUyTN5SEcbFB+NaSzfq7Ox2\nKa/rQFO7BhPHhHJi1aS9BAyDVfPHQiRk8NneS/3GqM6Vt6Ckug3p8UHIiOfuDDJOJXS5j2ODooMt\nKrpRQqR5YNT5/egsy6KqQYWwAO8R7VebkhaOqWnhMLIs5DKJR7aKCPeYFxkdt3NOunkls6d1t9wo\nMsQXedPi0K7W4ssDFQAAnd6ILw6UQ8AwWJ6d5OYIh8aphG5poduZ0BsHWVR0I0vlRRfMdGnp1KBL\nox9WhUVHPTpvDDITQ/DAzIQR37+Q8NP4hBBIxAKcKG20udtFpzfiRGkj5DIJUmODXByha82fEovo\nUF8UnqtHaXUbdh+5AmVrN7ImRCIqhNurrzmVAWTeYggYxqEWulQiHHJ7q/AgH/h6iVwyMOqsCouO\n8JaK8PyycZiWHjHi1yb8JJUIMT4hBMrWbpu7KIsqmtGl0WNKqsLj10CIhAI8sSAFDAN8srsUW/Zc\ngrdUhMX3jHZ3aFZxKqELGAaxK58WAAAa1UlEQVR+vmK7di0ysiwa23qgCPQesu43wzCIj5SjqV0z\nrE00BlLlwhWihLjDZDtru5hnW/GlXPPoCH/MmxSDxvYeqHt0yJsWBz+fkdkPdTg4ldAB00yXzi7b\n9xVtV/VCqzcOOsPlRtcXGDm3H928QtTZNVwIcZeM+GBIJUKbZrtcX+rv65JV0u6yeHo8woN8EKOQ\necw2jhxM6FL06gw27/l3fcm/9U2Qry8wcl63i3mFaIjcixb0EN6QiIWYkBiCpnaNpcEymBN9S/09\neTB0IFKxEK8/MQnvvpgFsYhzqXJAnIvS39eUFG3tRx+sKNdARkf4g4FzFxi1qXqh6tZR65zwjnmR\nkbXZLoeLG8AAmJLKr4QOmJK6J03B5FxCNy//t7Wf+/oMF+sJ3cdLhMgQX1y5prK7/vFgXFlhkRB3\nSo8PgrdUiBMlg892UbZ1o6KuEylxnrPUn884mND75qLbuLjI3EIPs6HLBTBNX+zVGVDr4C4lNzN/\nHHXHDBdCXEksEiIzMRQtnRpUXht43OkIzwZDPR3nErq9m0Ur23rgLRXBz8b+64S+DS+cNTBqnuHi\njjnohLja9ZK6t3a7sCyLo8VKSMQCTEymRW1cwLmEbs9m0bZOWbyRecWos/rRqxtUCPKXwt8DpjQR\nYq+00UHwkYpworQRxpu6XSrqOtHY3oM7PHSpPx9xL6HbsVl0a6cGeoNxyCX/N4sI8YW3VOiUUrrt\n6l50dGlpQJTwlkhoKrTVpupFZV3/v5nDfZUVp1F3C2fYlNALCwuRk5ODuXPnYsOGDQMe8/3332PB\nggXIzc3FSy+95HBA/na00G0pynUzAcMgPsIfDa3dUPfYPt99ILSgiNwOrtd2ub6TkU5vxIkSJeS+\nEqTEcWdPzdud1c9JBoMB69atw8cffwyFQoFly5YhOzsbiYmJlmOqqqqwYcMGbNmyBXK5HC0tLQ4H\n5CMVQSS0bfl/o5WyuYOJj5SjuKoNlfWdw9p786qLNoUmhEtSYgPh6yXCiUuNeGiOqThVUUULujR6\nzJsUw9lSsrcjq3eiqKgIsbGxiImJgUQiQW5uLgoKCvod8+WXX2LFihWQy00DjsHBjidJhmH6Vota\nX/5vbqHbOsPFzFlb0lW5sYYLISNFJDQNenaotbjct5eteSMLT9g39HZiNaErlUqEh1+/aQqFAkpl\n/01kq6qqcOXKFTz00EN48MEHUVhYOKyg/H0l6OjSWl1ybG1ji8HEO2lLumqlCgEyCeQymn9L+G1S\niml/0BOljVB1a3GuvBlRob6ICRuZ+v/ENk4ZmjYYDKiursZnn32GhoYGPProo9i5cyf8/Yfeji00\ndOCWbWigafGPj5/3kMvpmzt74ecjxuhR9pXrDAUQFeqLqmudCA6WWarDDRbPQNpUGrSpejEpVWHX\nz3GJp8Zt5snxe1rs04N8sXHXRZy+3IzC07UwGFnMnRyLsDDXb7nIBZ5yv6wmdIVCgYaG6zthK5VK\nKBSKW44ZP348xGIxYmJiEBcXh6qqKowbN27Iczc1DVwjwkts+uBQWd2CiOCB6w8bjEY0tHQhNtxv\n0PMMJU7hh0NNXSi6pERUiC9CQ+07T1GFaZwgItDboeu7m73Pl2s8OX5PjX1CUigOnqnDv/aUggGQ\nHhvgkc/DXly7X0O9uVjtcsnIyEBVVRVqamqg1WqRn5+P7OzsfsfMmTMHx48fBwC0traiqqoKMTEx\nDgfsb8Nq0ZbOXhiMrF0zXG4Ub15g5OB8dEuFReo/J7eJyX21XVTdOoyNDUSQv5ebIyI3s9pCF4lE\nWLt2LVavXg2DwYClS5ciKSkJ69evR3p6OmbPno3p06fj0KFDWLBgAYRCIV555RUEBjo+lcmyuGiI\nzaIdneFilnDDDkbTx9u/g/f1TS1uj4+chIyJCYC8b3yLBkO5yaY+9KysLGRlZfX7txdeeMHyNcMw\neO211/Daa685JShb6rk4OsPFLCrUF1Kx4wuMqhs64e8rQYCMVoiS24NAwGDepBicLGui/Ws5ipMT\nSG2p5+LoDBczoUCA0RF+qG/qQrfGttrrZqpuLVo6exGr8LO55AAhfDB/Sizee2kWvKW01J+LOJnQ\n5TZsFn19lahjCR0wTV9kAVxpsK+VTv3nhBAu4mZCt6WF3tYNPx8xfLwcbylYFhjZOTBaTStECSEc\nxMmE7iURQSIWDLpZtN5gRHO7BmEOznAxS3BwgVE1rRAlhHAQZzvCTMv/B26ht3RoYGTZYXW3AKa+\n+tAAL1TUdVhdlQqYZt2cr2jBpZp2yLzFCPKnFaKEEO7gcEKXorK+E0aWheCmgcfr+4gOr4UOmFrp\nRy8qca25CzevSWVZFleVahRVNONcRQuu1HfCnPanj4ugAVFCCKdwNqH7+0pgZFmoe3S3bB6hNO8j\nakcd9MHER/rj6EUlSqtbkREbCI1Wj5KqNpyraEZRRQva+6ZOChgGSTEBGJ8QjHGJIYgMHv61CSHE\nmTib0G/cueiWhN42vCmLNzJvSbf7cBX2Hq3Gpatt0BtM7XCZtxhT08IxPjEYaaOD4Otl2zZ3hBDi\nDpxP6B1dWkTftIbBsqjICV0uMWEySMQClFa3AQBGhckwLjEY4xJCEB/hbyncRQghXMfZhG7ZuWiA\n1aLK1m74+0qcsrhBJBTgN0syoGUZxIX6UH0KQojH4mxCH2wuut5gREunBol9XSXOkB4fzLmKaoQQ\nYi9OzkMHAP9BVos2tfeAZZ3Tf04IIXzC2YQu9zG30PsvLro+w2X4/eeEEMInnE3o/r4Dt9CdOcOF\nEEL4hLMJXSIWwlsquqUP3ZkzXAghhE84m9ABUyv9lhb6MMvmEkIIX3E6oct9JVB162AwGi3/1tjW\njQCZBFKJ0I2REUII93A+obMw7WEIADq9Aa2dvdQ6J4SQAXA6od88MNrY1gMWNMOFEEIGwumEfvPi\nImfsUkQIIXzlGQldbU7opgHRMErohBByC04ndEuXS3dfQqdFRYQQMihOJ3TzZtHmFnqjuYUeQAmd\nEEJuxu2E7mva4s28/F/Z1oMgfykkYpqySAghN+N0QvfzMW0o0dmlRa/OgDYVTVkkhJDBcDqhi4QC\nyLzF6OjSotEyw4W6WwghZCCcTuiAaaZLZ5fWsuSfZrgQQsjAOJ/Q/X0l6NLoUd/cBYBmuBBCyGA4\nn9DNc9Ev17YDoEVFhBAyGM4ndPNc9PL6TjAMEEpTFgkhZECcT+jmFnqv1oBgfy+IRZwPmRBC3ILz\n2dHcQgdohgshhAyF8wldfkNCDwui/nNCCBkM5xN6/xY6JXRCCBkM5xO6XCa1fE1dLoQQMjibEnph\nYSFycnIwd+5cbNiwYdDj9uzZg+TkZJw/f95pAfp5i8Ewpq8V1OVCCCGDsprQDQYD1q1bh40bNyI/\nPx+7du1CeXn5Lcep1Wp8+umnGD9+vHMDFDDw85FAwDAIkXs59dyEEMInVhN6UVERYmNjERMTA4lE\ngtzcXBQUFNxy3Pr16/H0009DKpUOcJbhmZgcismpYRAJOd9DRAghbiOydoBSqUR4eLjle4VCgaKi\non7HFBcXo6GhATNnzsSmTZtsvnhoqJ9Nx61ZcafN5xwOW+PhC09/vp4cvyfHDnh+/PbylOdrNaFb\nYzQa8c477+A///M/7f7ZpibVcC/vNKGhfpyKx9U8/fl6cvyeHDvg+fHbi2vPd6g3F6t9GAqFAg0N\nDZbvlUolFAqF5fuuri6UlZVh5cqVyM7OxtmzZ/Hss886dWCUEEKIdVZb6BkZGaiqqkJNTQ0UCgXy\n8/Px17/+1fK4n58fjh07Zvn+sccewyuvvIKMjAzXREwIIWRAVhO6SCTC2rVrsXr1ahgMBixduhRJ\nSUlYv3490tPTMXv27JGIkxBCiBUMy7Ksuy7OtX4pLsXjap7+fD05fk+OHfD8+O3Ftec7rD50Qggh\nnoESOiGE8AQldEII4QlK6IQQwhOU0AkhhCcooRNCCE9QQieEEJ6ghE4IITxBCZ0QQniCEjohhPAE\nJXRCCOEJSuiEEMITlNAJIYQnKKETQghPUEInhBCeoIROCCE8QQmdEEJ4ghI6IYTwBCV0QgjhCUro\nhBDCE5TQCSGEJyihE0IIT1BCJ4QQnqCETgghPEEJnRBCeIISOiGE8AQldEII4QlK6IQQwhOU0Akh\nhCcooRNCCE9QQieEEJ6ghE4IITxBCZ0QQniCEjohhPCETQm9sLAQOTk5mDt3LjZs2HDL4x9//DEW\nLFiAvLw8PP7446irq3N6oIQQQoZmNaEbDAasW7cOGzduRH5+Pnbt2oXy8vJ+x6SkpGDbtm3YuXMn\ncnJy8Oc//9llARNCCBmY1YReVFSE2NhYxMTEQCKRIDc3FwUFBf2OmTJlCry9vQEAmZmZaGhocE20\nhBBCBmU1oSuVSoSHh1u+VygUUCqVgx7/9ddfY8aMGc6JjhBCiM1EzjzZt99+iwsXLmDz5s02HR8a\n6ufMyw8b1+JxNU9/vp4cvyfHDnh+/PbylOdrNaErFIp+XShKpRIKheKW4w4fPowPP/wQmzdvhkQi\nseniTU0qO0J1rdBQP07F42qe/nw9OX5Pjh3w/PjtxbXnO9Sbi9Uul4yMDFRVVaGmpgZarRb5+fnI\nzs7ud8zFixexdu1afPDBBwgODh5+xIQQQuxmtYUuEomwdu1arF69GgaDAUuXLkVSUhLWr1+P9PR0\nzJ49G3/605/Q3d2NF154AQAQERGBDz/80OXBE0IIuY5hWZZ118W59jGGS/G4mqc/X0+O35NjBzw/\nfntx7fkOq8uFEEKIZ6CETgghPEEJnRBCeIISOiGE8AQldEII4QlK6IQQwhOU0AkhhCcooRNCCE9Q\nQieEEJ6ghE4IITxBCZ0QQniCEjohhPAEJXRCCOEJSuiEEMITlNAJIYQnKKETQghPUEInhBCeoIRO\nCCE8QQmdEEJ4ghI6IYTwBCV0QgjhCUrohBDCE5TQCSGEJyihE0IIT1BCJ4QQnqCETgghPEEJnRBC\neIISOiGE8AQldEII4QlK6IQQwhOU0AkhhCcooRNCCE9QQieEEJ6ghE4IITxBCZ0QQnjCpoReWFiI\nnJwczJ07Fxs2bLjlca1WixdffBFz587FAw88gNraWqcHSgghZGhWE7rBYMC6deuwceNG5OfnY9eu\nXSgvL+93zFdffQV/f3/8+OOPWLVqFf7yl7+4LGBCCCEDs5rQi4qKEBsbi5iYGEgkEuTm5qKgoKDf\nMfv378eSJUsAADk5OThy5AhYlnVNxIQQQgYksnaAUqlEeHi45XuFQoGioqJbjomIiDCdUCSCn58f\n2traEBQUNOS5Q0P9HInZZbgWj6t5+vP15Pg9OXbA8+O3l6c8XxoUJYQQnrCa0BUKBRoaGizfK5VK\nKBSKW465du0aAECv10OlUiEwMNDJoRJCCBmK1YSekZGBqqoq1NTUQKvVIj8/H9nZ2f2Oyc7Oxjff\nfAMA2LNnD6ZMmQKGYVwTMSGEkAExrA2jlz/99BPefvttGAwGLF26FM8++yzWr1+P9PR0zJ49G729\nvfiP//gPlJSUQC6X47//+78RExMzEvETQgjpY1NCJ4QQwn00KEoIITxBCZ0QQnjitkjoKSkpWLRo\nEXJzc7Fw4UJ89NFHMBqN7g5rREyYMMHdIdjNfL/M/w1VSuLYsWP4t3/7txGMzrrk5GS8/PLLlu/1\nej2mTJnCuTit2bdvH5KTk1FRUeHuUFyGL/fKzOrCIj7w8vLCt99+CwBoaWnBSy+9BLVajeeff97N\nkZGB3Hi/PJGPjw8uX74MjUYDLy8vHDp06Japvtbo9XqIRO7989y1axcmTpyI/Px8u/5WDAYDhEKh\nCyNzHmfcKy65LVroNwoODsYbb7yBf/3rX2BZFgaDAf/1X/+FpUuXIi8vD1u3brUcu2HDBuTl5WHh\nwoUeXZ+mq6sLjz/+OJYsWYK8vDzs27cPAFBbW4v58+fjD3/4A3Jzc/Hkk09Co9G4OdqBDXWf1Go1\nnnnmGeTk5GDt2rWc+PSVlZWFgwcPAgDy8/ORm5treayoqAjLly/H4sWL8dBDD6GyshIAsH37dvzq\nV7/CypUrsWrVKjdEfV1XVxdOnTqFt956C/n5+QBMn4ZWrFgx4Gs9YcIEvPPOO1i4cCHOnDnjztDt\n5si9WrFiBUpKSizHPfzwwygtLR3RuAfE3gYyMzNv+beJEyeyTU1N7NatW9m///3vLMuybG9vL7tk\nyRL26tWr7MGDB9nly5ez3d3dLMuybFtb24jG7CyZmZmsTqdjVSoVy7Is29LSws6ZM4c1Go1sTU0N\nm5KSwl68eJFlWZZ9/vnn2R07drgzXJZlWXbs2LHswoUL2YULF7K//vWvWZZlB71PR48eZdPT09mr\nV6+yer2eXbVqFbt79253hs9mZmayJSUl7HPPPcdqNBp24cKF7NGjR9lnnnmGZVmWValUrE6nY1mW\nZQ8dOsT+5je/YVmWZbdt28ZOnz6dE79r3377Lfvaa6+xLMuyy5cvZ8+fPz/kaz1mzBg2Pz/fnSE7\nxNF7tX37dvbNN99kWZZlKysr2SVLlrjnCdzktuhyGcqhQ4dw6dIl7NmzBwCgUqlQXV2NI0eO4P77\n74e3tzcAICAgwJ1hDgvLsnj33Xdx4sQJCAQCKJVKNDc3AwCio6ORkpICAEhLS0NdXZ07QwUwcJfL\nYPdJLBZj3LhxlnUPubm5OHXqFO69994Rj/tGY8eORW1tLXbt2oWsrKx+j6lUKvz2t79FdXU1GIaB\nTqezPHb33Xdz4nctPz8fK1euBAAsWLAA+fn5mDlz5qCvtVAoRE5OjjtDdpgj9+ree+/F+++/j1de\neQXbtm3D/fff747Qb3FbJvSamhoIhUIEBweDZVn84Q9/wPTp0/sd88svv7gpOufbuXMnWltbsX37\ndojFYmRnZ6O3txcAIJFILMcJhULLv3PNYPfp2LFjt6xK5soq5ezsbPzpT3/Cp59+ivb2dsu/r1+/\nHnfddRf+/ve/o7a21pI4AVgaEO7U3t6Oo0ePoqysDAzDwGAwgGEYZGVlDfpaS6VSj+k3H4i998rb\n2xvTpk1DQUEBdu/eje3bt7sr9H5uuz701tZWvP7661ixYgUYhsE999yDLVu2WN55r1y5gu7ubkyb\nNg3bt29HT08PAPS7yZ5GpVIhODgYYrEYR48e5UQr3F6D3SfA1M9ZU1MDo9GI3bt3Y+LEie4M1WLZ\nsmX493//dyQnJ/f7d5VKZRl4M5fM4JI9e/Zg0aJFOHDgAPbv34+ffvoJ0dHROHnyJGdf6+Fy5F49\n8MADePPNN5GRkQG5XD5isQ7ltmihazQaLFq0CHq9HkKhEIsWLcITTzwBwHRT6urqcP/994NlWQQG\nBuL999/HjBkzUFpaiqVLl0IsFiMrKwtr1qxx8zOxj16vh0QiQV5eHp599lnk5eUhPT0d8fHx7g7N\nboPdJ8BUb+iNN95AdXU17rrrLsydO9fN0ZqEh4f3a32brV69Gq+++io++OCDWz7ic8GuXbvw9NNP\n9/u3efPmYcuWLZx9rYfLkXuVnp4OmUzGme4WgJb+81ppaSn+8Ic/4Ouvv3Z3KIQHjh07ho8++gj/\n+Mc/3B0KJyiVSqxcuRK7d++GQMCNzg5uREGcbsuWLVizZg1efPFFd4dCCO/s2LEDDz74IF588UXO\nJHOAWuiEEMIb3HlrIcNy7do1PPbYY1iwYAFyc3Pxz3/+E4BpMPeJJ57AvHnz8MQTT6CjowMAUFFR\ngeXLlyM9PR2bNm3qd65PPvkEubm5uO+++7BmzRrOznwhhPRHCZ0nhEIhXn31VXz//ff44osv8Pnn\nn6O8vBwbNmzA1KlTsXfvXkydOhUbNmwAYJpX//vf/x5PPfVUv/MolUp8+umn2LZtG3bt2gWDwWBZ\nKUgI4TZK6DwRFhaGtLQ0AIBMJkN8fDyUSiUKCgqwePFiAMDixYsty/6Dg4Mxbty4AeuFGAwGaDQa\n6PV6aDQahIWFjdwTIYQ47LaYtni7qa2tRUlJCcaPH4+WlhZLQg4NDUVLS8uQP6tQKPDkk09i1qxZ\nkEqluPvuu3HPPfeMRNiEkGGiFjrPdHV14fnnn8fvfvc7yGSyfo8xDGN1FWVHRwcKCgpQUFCAn3/+\nGT09PR5d+ZCQ2wkldB7R6XR4/vnnkZeXh3nz5gEwda00NjYCABobGxEUFDTkOQ4fPozo6GgEBQVB\nLBZj3rx5Hlc9j5DbFSV0nmBZFr///e8RHx9vWQULmGpU7NixA4Bp7uzs2bOHPE9kZCTOnTuHnp4e\nsCyLI0eOICEhwaWxE0Kcg+ah88TJkyexYsUKjBkzxrLQYc2aNRg3bhxefPFFXLt2DZGRkfjb3/6G\ngIAANDU1YenSpVCr1RAIBPDx8cH3338PmUyG9957D99//z1EIhFSUlLw1ltv9SviRQjhJkrohBDC\nE9TlQgghPEEJnRBCeIISOiGE8AQldEII4QlK6IQQwhOU0MltKzk5GV1dXYM+Xltbiy+++GIEIyJk\neCihEzKIuro6SujEo1BxLnLb2Lt3L959911IpVJLaQQAeOmll3DlyhXodDqMGjUKb7/9NuRyOdat\nW4fa2losWrQIsbGxeO+991BZWYm3334bbW1t0Ol0ePzxx7F06VI3PitCbsASchtoampiJ0+ezFZU\nVLAsy7IbNmxgx4wZw6rVaralpcVy3Lvvvsv++c9/ZlmWZY8ePcouWbLE8phOp2OXLFnClpeXsyzL\nsiqVip03b57le0LcjVro5LZw7tw5pKamIj4+HgCwfPly/OUvfwEAfPvtt9i5cyd0Oh26u7sRFxc3\n4DmqqqpQUVGBNWvWWP5Np9OhsrKS6t0QTqCETm5rJSUl2LJlC7Zu3YqgoCDs3LkTX3755YDHsiyL\nwMBAKidMOIsGRcltITMzExcvXkRVVRUA4KuvvgIAdHZ2QiaTISAgAFqtFtu2bbP8jEwmg1qttnw/\nevRoeHl5WapXAqa9WW88hhB3ohY6uS0EBwfjjTfewK9+9St4eXlZBkUnT56MUaNGIScnB4GBgbjz\nzjtx/vx5AKZpjaNHj8Z9992H+Ph4vPfee/jwww/x9ttvY9OmTTAajQgODsbf/vY3dz41Qiyo2iIh\nhPAEdbkQQghPUEInhBCeoIROCCE8QQmdEEJ4ghI6IYTwBCV0QgjhCUrohBDCE5TQCSGEJ/4/ZcFZ\nojkHLr4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6b4fd8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"metrics.success_rate.resample('1W').mean().plot(ylim=[0,1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# All task sessions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Histogram of log-times"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d6b48f630>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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UBADAiIIAABhREAAAIwoCAGBEQQAAjCgIAIARBQEAMOJprg/Qo/ilPQCciysIAIARBQEA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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6b49c518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ts.time_spent.apply(np.log).hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# All tasks"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"grouped_ts = ts.groupby('task')\n",
"metrics = pd.DataFrame(OrderedDict(\n",
" time=grouped_ts.time_spent.median(),\n",
" success=grouped_ts.solved.mean(),\n",
" n_attempts=grouped_ts.task.count(),\n",
" n_solved=grouped_ts.solved.sum(),\n",
"))\n",
"metrics['n_unsolved'] = metrics.n_attempts - metrics.n_solved\n",
"tasks = data['tasks'].join(metrics).fillna(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solved and Unsolved Attempts"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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wLrvsMkVFXS6Hw6FBgwbX/0MRl2wAAGhQTZs29bxu0iRAZWVnqt0uIMCp8vJySVJJScl5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\ndMog9AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3918682208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ordered_tasks = tasks.sort_values('n_attempts', ascending=False)\n",
"ax = ordered_tasks[['n_solved', 'n_unsolved']].plot.bar(\n",
" title='Attempts',\n",
" stacked=True,\n",
" color=[sns.xkcd_rgb['medium green'], sns.xkcd_rgb['pale red']],\n",
" figsize=(9,6))\n",
"ax.set_xticklabels([])\n",
"ax.set_xlabel('task');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Success rate distribution"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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iIsn8Hz7QR9O3IBIMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6b4fd748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = tasks.success.plot.hist(title='success rate')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Median time distribution"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6af4e668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = tasks.time.plot.hist(title='median time')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Median Time vs Success Rate"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0d6b41e828>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1115TYmKi4uLitHHjRknS5s2bderUKcXHx+uqq67S008/bVv9IHZwt1MAgCSm\njAAAQQQCAEASgQAACCIQAACSCAQAQBCBAACQRCAAAIL+Dy3S0Hjp6zMdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0d6b066390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tasks.plot.scatter(x='success', y='time', logy=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Missions Overview"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
" #T_13756cb2_7562_11e8_881b_d8cb8a9c1896 tr {\n",
" background-color: white;\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896 .row_heading, .blank {\n",
" display: none;;\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(218, 226, 24, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(202, 224, 30, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(165, 218, 53, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(61, 187, 116, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(136, 213, 71, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(71, 192, 110, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(141, 214, 68, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(91, 200, 98, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(75, 194, 108, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(37, 171, 129, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(30, 156, 137, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(35, 135, 141, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(41, 120, 142, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(38, 127, 142, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(30, 159, 136, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(30, 153, 138, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(39, 126, 142, 0.6);\n",
" } #T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(69, 53, 128, 0.6);\n",
" }</style> \n",
"<table id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >level</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >n_tasks</th> \n",
" <th class=\"col_heading level0 col3\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col4\" >success</th> \n",
" <th class=\"col_heading level0 col5\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >1</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >commands</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >10</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >9382</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >95%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >24s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >2</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >commands-2</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >7</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >5895</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >87%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >94s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >7</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >3</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >repeat</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >11</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >5470</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >83%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >86s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >8</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >4</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >while</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >7</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >2601</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >83%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >74s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >5</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >5</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >loops</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >11</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >2507</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >72%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >115s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >3</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >6</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >if</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >7</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >932</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >55%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >160s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >2</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >7</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >comparing</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >9</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >506</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >40%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >170s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row7\" class=\"row_heading level0 row7\" >4</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col0\" class=\"data row7 col0\" >8</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col1\" class=\"data row7 col1\" >if-else</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col2\" class=\"data row7 col2\" >7</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col3\" class=\"data row7 col3\" >217</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col4\" class=\"data row7 col4\" >57%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row7_col5\" class=\"data row7 col5\" >138s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896level0_row8\" class=\"row_heading level0 row8\" >6</th> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col0\" class=\"data row8 col0\" >9</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col1\" class=\"data row8 col1\" >loops-if</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col2\" class=\"data row8 col2\" >16</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col3\" class=\"data row8 col3\" >572</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col4\" class=\"data row8 col4\" >43%</td> \n",
" <td id=\"T_13756cb2_7562_11e8_881b_d8cb8a9c1896row8_col5\" class=\"data row8 col5\" >253s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3912ae9358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"grouped_tasks = tasks.groupby('mission')\n",
"missions = pd.DataFrame(OrderedDict(\n",
" level=grouped_tasks['level'].min(),\n",
" time=grouped_tasks.time.median(), # median of medians\n",
" success=grouped_tasks.success.mean(),\n",
" n_attempts=grouped_tasks.n_attempts.sum(),\n",
" n_solved=grouped_tasks.n_solved.sum(),\n",
" n_tasks=grouped_tasks.name.count(),\n",
")).reset_index()\n",
"missions = missions.rename(columns={'mission': 'name'})\n",
"missions = missions.sort_values(by='level')\n",
"missions = missions[\n",
" ['level', 'name', 'n_tasks', 'n_attempts', 'success', 'time']]\n",
"display_level_overview(missions, order_by='level')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tasks in Levels"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/markdown": [
"# commands"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896 .row_heading, .blank {\n",
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" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(233, 228, 25, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(212, 225, 26, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(231, 228, 25, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(210, 225, 27, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(218, 226, 24, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(210, 225, 27, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(212, 225, 26, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(154, 216, 60, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(223, 227, 24, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(194, 223, 34, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(205, 224, 29, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(186, 222, 39, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(223, 227, 24, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(207, 225, 28, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(218, 226, 24, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(191, 223, 36, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(178, 221, 44, 0.6);\n",
" } #T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(96, 201, 96, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >51</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >three-steps-forward</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >979</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >97%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >8s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >49</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >turning-right</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >1</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >898</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >97%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >18s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >44</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >turning-left</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >911</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >97%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >20s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >26</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >turning-right-and-left</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >894</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >95%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >19s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >turning-left-and-right</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >4</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >1219</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >94%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >44s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >31</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >diamond-on-right</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >1</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >929</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >95%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >26s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >14</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >beware-of-asteroid</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >2</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >1028</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >93%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >30s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col0\" class=\"data row7 col0\" >67</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col1\" class=\"data row7 col1\" >plus</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col2\" class=\"data row7 col2\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col3\" class=\"data row7 col3\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col4\" class=\"data row7 col4\" >835</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col5\" class=\"data row7 col5\" >96%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row7_col6\" class=\"data row7 col6\" >21s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col0\" class=\"data row8 col0\" >86</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col1\" class=\"data row8 col1\" >diamond-path</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col2\" class=\"data row8 col2\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col3\" class=\"data row8 col3\" >4</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col4\" class=\"data row8 col4\" >855</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col5\" class=\"data row8 col5\" >95%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row8_col6\" class=\"data row8 col6\" >28s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896level0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col0\" class=\"data row9 col0\" >66</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col1\" class=\"data row9 col1\" >surrounded-diamond</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col2\" class=\"data row9 col2\" >3</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col3\" class=\"data row9 col3\" >5</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col4\" class=\"data row9 col4\" >834</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col5\" class=\"data row9 col5\" >89%</td> \n",
" <td id=\"T_29d3b6b2_7562_11e8_881b_d8cb8a9c1896row9_col6\" class=\"data row9 col6\" >72s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3912c9e828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912297b00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
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{
"data": {
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{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39177e8160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# commands-2"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(238, 229, 27, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(197, 223, 33, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(100, 203, 93, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(41, 121, 142, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(112, 206, 86, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(57, 185, 118, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(85, 198, 102, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(40, 122, 142, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(144, 214, 67, 0.6);\n",
" } #T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(61, 187, 116, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >10</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >shot</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >906</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >99%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >17s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >8</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >shooting</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >852</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >97%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >32s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >12</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >wormhole-demo</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >2</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >1</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >873</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >98%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >25s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >6</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >tunnel</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >3</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >1</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >960</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >77%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >178s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >23</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >dont-forget-shot</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >3</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >2</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >784</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >79%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >96s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >19</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >2diamonds-2meteorids</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >3</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >3</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >791</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >74%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >176s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >79</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >last-shot</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >3</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >4</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >729</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >84%</td> \n",
" <td id=\"T_29d3b6b3_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >94s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f391861cc88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f39123303c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3917a55c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912eb9908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912bff7b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# repeat"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<style type=\"text/css\" >\n",
" #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896 tr {\n",
" background-color: white;\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896 .row_heading, .blank {\n",
" display: none;;\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(186, 222, 39, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(129, 211, 76, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(159, 217, 56, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(116, 208, 84, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(238, 229, 27, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(131, 211, 75, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(207, 225, 28, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(146, 215, 65, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(194, 223, 34, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(96, 201, 96, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(149, 215, 63, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(71, 192, 110, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(144, 214, 67, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(66, 190, 113, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(103, 204, 92, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(53, 183, 120, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(64, 189, 114, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(38, 172, 129, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(77, 194, 107, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(33, 166, 133, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(43, 177, 125, 0.6);\n",
" } #T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(41, 120, 142, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >11</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >ladder</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >671</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >90%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >56s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >1</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >diamonds-in-meteoroid-cloud</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >490</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >86%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >62s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >71</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >big-left-turn</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >1</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >401</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >98%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >55s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >70</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >big-right-turn</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >416</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >93%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >48s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >76</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >blocked-wormhole</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >3</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >402</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >91%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >72s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >84</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >triangle</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >4</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >432</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >84%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >86s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >13</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >n</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >5</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >592</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >84%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >90s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col0\" class=\"data row7 col0\" >21</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col1\" class=\"data row7 col1\" >steal-the-nose</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col2\" class=\"data row7 col2\" >3</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col3\" class=\"data row7 col3\" >1</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col4\" class=\"data row7 col4\" >591</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col5\" class=\"data row7 col5\" >77%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row7_col6\" class=\"data row7 col6\" >100s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col0\" class=\"data row8 col0\" >46</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col1\" class=\"data row8 col1\" >find-the-path</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col2\" class=\"data row8 col2\" >3</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col3\" class=\"data row8 col3\" >2</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col4\" class=\"data row8 col4\" >491</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col5\" class=\"data row8 col5\" >69%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row8_col6\" class=\"data row8 col6\" >113s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col0\" class=\"data row9 col0\" >57</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col1\" class=\"data row9 col1\" >stairs</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col2\" class=\"data row9 col2\" >3</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col3\" class=\"data row9 col3\" >3</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col4\" class=\"data row9 col4\" >499</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col5\" class=\"data row9 col5\" >72%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row9_col6\" class=\"data row9 col6\" >122s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896level0_row10\" class=\"row_heading level0 row10\" >10</th> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col0\" class=\"data row10 col0\" >18</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col1\" class=\"data row10 col1\" >clean-your-path</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col2\" class=\"data row10 col2\" >3</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col3\" class=\"data row10 col3\" >4</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col4\" class=\"data row10 col4\" >485</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col5\" class=\"data row10 col5\" >64%</td> \n",
" <td id=\"T_29d3b6b4_7562_11e8_881b_d8cb8a9c1896row10_col6\" class=\"data row10 col6\" >179s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f39129c89e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912c9e128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3917805278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912c71748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912c7ecc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# while"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(210, 225, 27, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(170, 219, 50, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(103, 204, 92, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(53, 183, 120, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(157, 217, 58, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(116, 208, 84, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(119, 208, 82, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(94, 201, 97, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(114, 207, 85, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(59, 186, 117, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(186, 222, 39, 0.6);\n",
" } #T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(35, 168, 131, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >3</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >zig-zag</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >460</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >79%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >74s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >53</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >direct-flight-ahead</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >355</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >94%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >37s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >17</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >yellow-is-not-red</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >2</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >1</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >395</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >77%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >100s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >41</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >yellow-hint</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >2</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >2</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >354</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >86%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >62s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >73</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >stop-on-red</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >3</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >335</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >80%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >74s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >38</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >direction-change</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >2</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >4</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >387</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >79%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >95s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >69</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >diamond-in-house</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >3</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >1</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >315</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >90%</td> \n",
" <td id=\"T_29d3b6b5_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >118s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f39122a1208>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912dfa7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39122a1a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912dfae10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912b2a668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# loops"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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" #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896 tr {\n",
" background-color: white;\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896 .row_heading, .blank {\n",
" display: none;;\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(207, 225, 28, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(141, 214, 68, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(85, 198, 102, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(85, 198, 102, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(105, 204, 91, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(59, 186, 117, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(121, 209, 81, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(66, 190, 113, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(91, 200, 98, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(47, 179, 123, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(35, 169, 130, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(32, 165, 133, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(126, 210, 78, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(34, 167, 132, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(69, 191, 111, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(37, 171, 129, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(31, 163, 134, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(39, 126, 142, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(144, 214, 67, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(48, 103, 141, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(42, 118, 142, 0.6);\n",
" } #T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(71, 40, 120, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >56</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >diamond-cross</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >246</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >93%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >50s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >68</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >stripes</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >198</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >74%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >78s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >37</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >big-slalom</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >3</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >290</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >78%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >94s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >50</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >double-bend</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >4</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >230</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >80%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >90s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >58</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >collect-diamonds</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >233</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >75%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >104s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >55</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >color-slalom</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >225</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >61%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >123s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >52</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >zig-zag-plus</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >3</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >241</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >81%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >119s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col0\" class=\"data row7 col0\" >72</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col1\" class=\"data row7 col1\" >four-vs</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col2\" class=\"data row7 col2\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col3\" class=\"data row7 col3\" >4</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col4\" class=\"data row7 col4\" >165</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col5\" class=\"data row7 col5\" >71%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row7_col6\" class=\"data row7 col6\" >115s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col0\" class=\"data row8 col0\" >63</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col1\" class=\"data row8 col1\" >rectangle</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col2\" class=\"data row8 col2\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col3\" class=\"data row8 col3\" >5</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col4\" class=\"data row8 col4\" >217</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col5\" class=\"data row8 col5\" >58%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row8_col6\" class=\"data row8 col6\" >171s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col0\" class=\"data row9 col0\" >47</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col1\" class=\"data row9 col1\" >arrow</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col2\" class=\"data row9 col2\" >3</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col3\" class=\"data row9 col3\" >1</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col4\" class=\"data row9 col4\" >239</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col5\" class=\"data row9 col5\" >84%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row9_col6\" class=\"data row9 col6\" >200s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896level0_row10\" class=\"row_heading level0 row10\" >10</th> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col0\" class=\"data row10 col0\" >32</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col1\" class=\"data row10 col1\" >double-track</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col2\" class=\"data row10 col2\" >3</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col3\" class=\"data row10 col3\" >2</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col4\" class=\"data row10 col4\" >223</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col5\" class=\"data row10 col5\" >39%</td> \n",
" <td id=\"T_29d3b6b6_7562_11e8_881b_d8cb8a9c1896row10_col6\" class=\"data row10 col6\" >265s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f39123284a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912af1fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Fi4+6FgBACfDAawwFBQVKT0/XsGHD/FoYAMAdD7zGEBQUpFq1arHGAABPqGKtMQAAnlyO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\nACwEAwDAQjAAACwEAwDA8v+cO+aHaEGoaAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39129f5860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f39123885c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39124a5b00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# if"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(154, 216, 60, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(41, 175, 127, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(36, 134, 141, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(39, 126, 142, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(30, 152, 138, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(38, 127, 142, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(30, 154, 137, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(35, 135, 141, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(46, 108, 142, 0.6);\n",
" } #T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(68, 1, 84, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >27</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >on-yellow-to-left</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >157</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >58%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >105s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >5</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >diamonds-with-signals</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >176</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >55%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >138s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >34</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >follow-colors</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >3</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >122</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >85%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >110s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >59</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >two-diamonds</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >2</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >1</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >143</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >46%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >171s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >60</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >red-shooting</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >2</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >108</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >54%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >170s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >78</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >yellow-squares</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >2</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >3</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >110</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >55%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >160s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >81</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >belgian-flag</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >3</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >1</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >116</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >35%</td> \n",
" <td id=\"T_29d3b6b7_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >322s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3912aea7f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912abdcf8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912aeaf98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912f3f588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f391248cdd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# comparing"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(48, 103, 141, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(35, 168, 131, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(46, 107, 142, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(48, 103, 141, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(30, 151, 138, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(42, 117, 142, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(34, 138, 141, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(53, 93, 140, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(38, 127, 142, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(57, 85, 139, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(72, 26, 108, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(61, 76, 137, 0.6);\n",
" } #T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(70, 45, 124, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >85</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >chessboard</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >56</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >43%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >142s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >48</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >edge-to-edge</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >44</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >45%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >150s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >7</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >meteoroids-and-wormholes</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >3</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >36</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >61%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >200s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >83</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >maneuvers-on-left</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >4</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >43</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >60%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >195s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >82</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >free-column</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >42</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >33%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >140s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >9</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >diamond-lines</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >2</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >2</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >90</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >39%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >156s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >65</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >letter-h</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >2</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >3</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >61</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >30%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >170s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col0\" class=\"data row7 col0\" >64</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col1\" class=\"data row7 col1\" >wave</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col2\" class=\"data row7 col2\" >3</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col3\" class=\"data row7 col3\" >1</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col4\" class=\"data row7 col4\" >57</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col5\" class=\"data row7 col5\" >26%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row7_col6\" class=\"data row7 col6\" >278s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col0\" class=\"data row8 col0\" >28</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col1\" class=\"data row8 col1\" >slalom-position-testing</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col2\" class=\"data row8 col2\" >3</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col3\" class=\"data row8 col3\" >2</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col4\" class=\"data row8 col4\" >77</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col5\" class=\"data row8 col5\" >23%</td> \n",
" <td id=\"T_29d3b6b8_7562_11e8_881b_d8cb8a9c1896row8_col6\" class=\"data row8 col6\" >260s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3912e3d4a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f39124e4400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39121ab748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912a972e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912e74668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# if-else"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" } #T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(39, 124, 142, 0.6);\n",
" } #T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(68, 3, 87, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >80</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >letter-e</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >45</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >42%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >66s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >24</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >narrow-passage</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >26</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >65%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >84s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >29</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >color-navigation</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >3</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >22</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >64%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >129s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >75</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >bouncing-from-edge</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >2</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >1</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >34</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >68%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >153s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >16</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >diamonds-on-yellow</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >2</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >2</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >26</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >54%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >138s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >22</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >triple-steps</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >2</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >3</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >26</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >62%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >160s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >43</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >colorful-flowers</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >3</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >1</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >38</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >42%</td> \n",
" <td id=\"T_29d3b6b9_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >296s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3912dee710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912b58a20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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06VFnAQCUAQ98jaGoqEjp6ekaOXKkV4MBAPzjga8xBAQEqF69elxjAIAnVKmuMQAAnlyO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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912dee9b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912bf6518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912e73c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"# loops-if"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(60, 77, 138, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(56, 86, 139, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(30, 159, 136, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(72, 30, 112, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(39, 124, 142, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(46, 107, 142, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col0 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col1 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col2 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col3 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col4 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col5 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(53, 183, 120, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col6 {\n",
" background-color: rgba(0, 0, 0, 0.1);\n",
" background-color: rgba(63, 69, 135, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(49, 100, 141, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(68, 55, 129, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(41, 120, 142, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(69, 52, 127, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(57, 185, 118, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(46, 108, 142, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(31, 149, 139, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(60, 78, 138, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col0 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col1 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col2 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col3 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col4 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col5 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(34, 138, 141, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col6 {\n",
" background-color: rgba(0, 0, 0, 0.2);\n",
" background-color: rgba(68, 1, 84, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(49, 100, 141, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(71, 24, 106, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(59, 81, 138, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(68, 1, 84, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(48, 103, 141, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(68, 1, 84, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(30, 153, 138, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(68, 1, 84, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col0 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col1 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col2 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col3 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col4 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col5 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(47, 106, 141, 0.6);\n",
" } #T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col6 {\n",
" background-color: rgba(0, 0, 0, 0.30000000000000004);\n",
" background-color: rgba(68, 1, 84, 0.6);\n",
" }</style> \n",
"<table id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896\" > \n",
"<thead> <tr> \n",
" <th class=\"blank level0\" ></th> \n",
" <th class=\"col_heading level0 col0\" >id</th> \n",
" <th class=\"col_heading level0 col1\" >name</th> \n",
" <th class=\"col_heading level0 col2\" >level2</th> \n",
" <th class=\"col_heading level0 col3\" >order</th> \n",
" <th class=\"col_heading level0 col4\" >n_attempts</th> \n",
" <th class=\"col_heading level0 col5\" >success</th> \n",
" <th class=\"col_heading level0 col6\" >time</th> \n",
" </tr></thead> \n",
"<tbody> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row0\" class=\"row_heading level0 row0\" >0</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col0\" class=\"data row0 col0\" >20</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col1\" class=\"data row0 col1\" >five-diamonds</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col2\" class=\"data row0 col2\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col3\" class=\"data row0 col3\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col4\" class=\"data row0 col4\" >25</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col5\" class=\"data row0 col5\" >64%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row0_col6\" class=\"data row0 col6\" >120s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row1\" class=\"row_heading level0 row1\" >1</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col0\" class=\"data row1 col0\" >61</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col1\" class=\"data row1 col1\" >mirror</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col2\" class=\"data row1 col2\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col3\" class=\"data row1 col3\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col4\" class=\"data row1 col4\" >141</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col5\" class=\"data row1 col5\" >14%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row1_col6\" class=\"data row1 col6\" >192s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row2\" class=\"row_heading level0 row2\" >2</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col0\" class=\"data row2 col0\" >40</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col1\" class=\"data row2 col1\" >plan-your-shooting</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col2\" class=\"data row2 col2\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col3\" class=\"data row2 col3\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col4\" class=\"data row2 col4\" >38</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col5\" class=\"data row2 col5\" >24%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row2_col6\" class=\"data row2 col6\" >220s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col0\" class=\"data row3 col0\" >45</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col1\" class=\"data row3 col1\" >diagonal-lines</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col2\" class=\"data row3 col2\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col3\" class=\"data row3 col3\" >4</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col4\" class=\"data row3 col4\" >23</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col5\" class=\"data row3 col5\" >57%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row3_col6\" class=\"data row3 col6\" >275s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col0\" class=\"data row4 col0\" >15</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col1\" class=\"data row4 col1\" >wormhole-cloud</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col2\" class=\"data row4 col2\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col3\" class=\"data row4 col3\" >5</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col4\" class=\"data row4 col4\" >45</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col5\" class=\"data row4 col5\" >42%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row4_col6\" class=\"data row4 col6\" >195s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col0\" class=\"data row5 col0\" >30</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col1\" class=\"data row5 col1\" >edge-wormholes</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col2\" class=\"data row5 col2\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col3\" class=\"data row5 col3\" >6</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col4\" class=\"data row5 col4\" >24</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col5\" class=\"data row5 col5\" >67%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row5_col6\" class=\"data row5 col6\" >238s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col0\" class=\"data row6 col0\" >42</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col1\" class=\"data row6 col1\" >two-color-tracks</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col2\" class=\"data row6 col2\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col3\" class=\"data row6 col3\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col4\" class=\"data row6 col4\" >62</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col5\" class=\"data row6 col5\" >32%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row6_col6\" class=\"data row6 col6\" >252s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col0\" class=\"data row7 col0\" >33</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col1\" class=\"data row7 col1\" >diamond-ring</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col2\" class=\"data row7 col2\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col3\" class=\"data row7 col3\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col4\" class=\"data row7 col4\" >25</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col5\" class=\"data row7 col5\" >40%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row7_col6\" class=\"data row7 col6\" >255s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col0\" class=\"data row8 col0\" >36</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col1\" class=\"data row8 col1\" >cross-2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col2\" class=\"data row8 col2\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col3\" class=\"data row8 col3\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col4\" class=\"data row8 col4\" >22</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col5\" class=\"data row8 col5\" >68%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row8_col6\" class=\"data row8 col6\" >193s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col0\" class=\"data row9 col0\" >54</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col1\" class=\"data row9 col1\" >diagonal-diamonds</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col2\" class=\"data row9 col2\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col3\" class=\"data row9 col3\" >4</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col4\" class=\"data row9 col4\" >21</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col5\" class=\"data row9 col5\" >52%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row9_col6\" class=\"data row9 col6\" >228s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row10\" class=\"row_heading level0 row10\" >10</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col0\" class=\"data row10 col0\" >35</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col1\" class=\"data row10 col1\" >six-diamonds</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col2\" class=\"data row10 col2\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col3\" class=\"data row10 col3\" >5</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col4\" class=\"data row10 col4\" >25</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col5\" class=\"data row10 col5\" >48%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row10_col6\" class=\"data row10 col6\" >301s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row11\" class=\"row_heading level0 row11\" >11</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col0\" class=\"data row11 col0\" >4</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col1\" class=\"data row11 col1\" >triple-slalom</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col2\" class=\"data row11 col2\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col3\" class=\"data row11 col3\" >1</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col4\" class=\"data row11 col4\" >31</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col5\" class=\"data row11 col5\" >32%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row11_col6\" class=\"data row11 col6\" >281s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row12\" class=\"row_heading level0 row12\" >12</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col0\" class=\"data row12 col0\" >74</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col1\" class=\"data row12 col1\" >turning-in-square</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col2\" class=\"data row12 col2\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col3\" class=\"data row12 col3\" >2</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col4\" class=\"data row12 col4\" >20</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col5\" class=\"data row12 col5\" >25%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row12_col6\" class=\"data row12 col6\" >417s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row13\" class=\"row_heading level0 row13\" >13</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col0\" class=\"data row13 col0\" >77</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col1\" class=\"data row13 col1\" >letter-d</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col2\" class=\"data row13 col2\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col3\" class=\"data row13 col3\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col4\" class=\"data row13 col4\" >21</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col5\" class=\"data row13 col5\" >33%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row13_col6\" class=\"data row13 col6\" >357s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row14\" class=\"row_heading level0 row14\" >14</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col0\" class=\"data row14 col0\" >39</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col1\" class=\"data row14 col1\" >meteoroids-on-left</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col2\" class=\"data row14 col2\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col3\" class=\"data row14 col3\" >4</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col4\" class=\"data row14 col4\" >26</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col5\" class=\"data row14 col5\" >54%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row14_col6\" class=\"data row14 col6\" >320s</td> \n",
" </tr> <tr> \n",
" <th id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896level0_row15\" class=\"row_heading level0 row15\" >15</th> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col0\" class=\"data row15 col0\" >62</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col1\" class=\"data row15 col1\" >two-bit-instructions</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col2\" class=\"data row15 col2\" >3</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col3\" class=\"data row15 col3\" >5</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col4\" class=\"data row15 col4\" >23</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col5\" class=\"data row15 col5\" >35%</td> \n",
" <td id=\"T_29d3b6ba_7562_11e8_881b_d8cb8a9c1896row15_col6\" class=\"data row15 col6\" >1233s</td> \n",
" </tr></tbody> \n",
"</table> "
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f3912d2c128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f3912cf25c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f39124332b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f391297df98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ERMQ0hYmIiJimMBEREdMUJiIiYprCRERETFOYiIiIaaGBLkB6rqPHm3l+82dU1zVi7RfB\n1JRhREaEB7osEemBFCZySs9v/ox391YBUOlqAODu9EsDWZKI9FABm+ZqbW0lPT2du+66C4ADBw6Q\nlZWF0+kkJyeH5uZmAJqbm8nJycHpdJKVlcXBgwcDVfIZp7qu8bS3RUROCliYrFu3josuuqjj9tKl\nS5k2bRpbtmwhOjqa/Px8ADZs2EB0dDRbtmxh2rRpLF26NFAln3Gs/SJOe1tE5KSAhInL5aKsrIzM\nzEwADMPg7bffJiUlBYCMjAxKSkoAKC0tJSMjA4CUlBQqKiowDCMQZZ9xpqYMY9QlsQy2RzHqklim\npgwLdEki0kMFZM1kyZIlzJ49m2PHjgFQW1tLdHQ0oaHt5djtdtxuNwBut5uBAwe2FxsaSlRUFLW1\ntcTExJx2H1ZrlA9/guDya3thBRbcmdi9xQSYfi881AsP9cI8v4fJm2++SUxMDJdeeinvvPOOz/ZT\nXd3gs9cOJlZrlHrxA/XCQ73wUC88zISq38Pk/fffp7S0lPLycpqamjh69CiLFy+mvr6elpYWQkND\ncblc2Gw2AGw2G4cOHcJut9PS0kJDQwP9+/f3d9kiInIafl8zuf/++ykvL6e0tJS//e1vjBkzhmXL\nljF69Gg2bdoEQEFBAQ6HAwCHw0FBQQEAmzZtYsyYMVgsFn+XLSIip9FjvgE/e/Zs1qxZg9PppK6u\njqysLAAyMzOpq6vD6XSyZs0aHnjggQBXKiIiP2UxeumhUZoDbaf5YA/1wkO98FAvPIJqzUTkVHT6\nFpHgpTCRHkOnbxEJXj1mzUREp28RCV4KE+kxdPoWkeClaS7pMU6eruXHayYiEhwUJtJjREaEa41E\nJEgpTER6MB3hJsFCYSLSg+kINwkWWoAX6cF0hJsEC4WJSA+mI9wkWGiaS6Qbdfcah45wk2ChMBHp\nRt29xqEj3CRYaJpLpBtpjUPOVBqZSMD0xsNerf0iOkYkJ2+LnAkUJhIwvfGw10CscfTGUJbgozCR\ngOmNU0KBWOPojaEswUdrJhIwOuy1e/TGUJbgo5GJBIwOe+0eWqeRnkBhIgHjrymhk2sKdcea6Xdu\neK9bU1AoS0+gMJGA8dfC8Y/XFE7qTWsK+i6K9AQKEwkYfy0ca01BxPcUJhIw/nqT15qCf+gQ5TOb\nwkQCxl9v8ifXEH68ZiLdT4con9kUJhIw/lo4PrmmYLVGUV3d4P0JARLsn+w1nXhmU5hIB3+/mWnh\nuLNg/2Sv6cQzm8JEOgT7m1mwC/ZP9jpE+cymMJEOwf5mFuyC/ZO9RppnNr+HyaFDh5gzZw41NTVY\nLBZuuukmbrvtNurq6vjLX/7CN998w29+8xuWL19O3759MQyDxYsXs23bNs4++2yeeOIJhg8f7u+y\nzwjB/mYW7PTJXoKZ38MkJCSEhx56iOHDh3P06FFuvPFGkpKSePXVV0lMTCQ7O5u8vDzy8vKYPXs2\n5eXlVFZWsnnzZj788EMeeeQRNmzY4O+yzwh6MwssfbKXYOb3MImNjSU2NhaAyMhIhgwZgtvtpqSk\nhOeffx6A9PR0pk6dyuzZsykpKSE9PR2LxUJCQgL19fVUVVV1vEZvEuijec7UN7NA9703U2/PHAFd\nMzl48CCffPIJ8fHx1NTUdASE1WqlpqYGALfbjd1u73iO3W7H7XZ7DROrNcp3hfvIv9e922kB/Kyz\nQnnwD6NMv24w9sJXfqkXvup7T+eP34tg6a3+RswLWJgcO3aMGTNmMHfuXCIjIzvdZ7FYsFgspl6/\nJ3+f4FQOuht+dtvsz9HTv1vhT6fqhS/63tP56/ciGHqrvxEPM6EakOuZfP/998yYMYO0tDSSk5MB\nGDBgAFVV7Z9gqqqqiImJAcBms+FyuTqe63K5sNls/i/aD3R9j8BQ331HvT1z+H1kYhgGDz/8MEOG\nDGH69Okd2x0OB4WFhWRnZ1NYWMiECRM6tr/wwgukpqby4YcfEhUV1SvXS0AL4IGivvuOenvmsBiG\nYfhzh++99x5Tpkxh2LBh9OnTPjCaNWsWl112GTk5ORw6dIhBgwaxfPly+vXrh2EYLFy4kO3btxMR\nEcGSJUsYMWKE1/1o2NpOQ3gP9cJDvfBQLzzMTHP5PUz8Rb8c7fSH4qFeeKgXHuqFR9CtmYiISO+i\nMBEREdMUJiIiYprCRERETFOYiIiIaQoTERExTdczERGvdMJG8UZhIiJe6Sqc4o2muUTEK12FU7xR\nmIiIVzpho3ijaS4R8UonbBRvFCYi4tWZehVO6TpNc4mIiGkKExERMU1hIiIipilMRETENIWJiIiY\npjARERHTeu1le0VExH80MhEREdMUJiIiYprCRERETFOYiIiIaQoTERExTWEiIiKmKUxERMS0oA6T\n8vJyUlJScDqd5OXl/ez+5uZmcnJycDqdZGVlcfDgwQBU6R/eerFmzRomTZpEWloat912G998800A\nqvQPb704adOmTVx88cX873//82N1/tWVXrz++utMmjSJ1NRU7r//fj9X6D/eevHtt98ydepU0tPT\nSUtLY9u2bQGo0vdyc3NJTEzkuuuu+8X7DcNg0aJFOJ1O0tLS2LNnT9de2AhSLS0txoQJE4z9+/cb\nTU1NRlpamrFv375Oj3nhhReM+fPnG4ZhGEVFRcbMmTMDUarPdaUXFRUVxvHjxw3DMIz169ef0b0w\nDMNoaGgwbr31ViMrK8vYvXt3ACr1va704quvvjJuuOEGo66uzjAMwzh8+HAgSvW5rvRi3rx5xvr1\n6w3DMIx9+/YZV199dSBK9bmdO3caH330kZGamvqL95eVlRm333670dbWZnzwwQdGZmZml143aEcm\nu3fv5oILLiAuLo7w8HBSU1MpKSnp9JjS0lIyMjIASElJoaKiAqMXfuG/K70YM2YMERHtl1pNSEjA\n5XIFolSf60ovAJ5++mnuvPNOzjrrrABU6R9d6cXLL7/MlClT6Nu3LwADBgwIRKk+15VeWCwWjh49\nCkBDQwOxsbGBKNXnRo0a1fH//UtKSkpIT0/HYrGQkJBAfX09VVVVXl83aMPE7XZjt9s7bttsNtxu\n988eM3DgQABCQ0OJioqitrbWr3X6Q1d68WP5+fmMGzfOH6X5XVd6sWfPHlwuF1dddZWfq/OvrvSi\nsrKSr776it///vfcdNNNlJeX+7tMv+hKL+69915ee+01xo0bR3Z2NvPmzfN3mT3CT3tlt9tP+35y\nUtCGifw6Gzdu5KOPPuKOO+4IdCkB0dbWxhNPPMGDDz4Y6FJ6hNbWVr7++muef/55li1bxvz586mv\nrw90WQFRXFxMRkYG5eXl5OXlMWfOHNra2gJdVtAI2jCx2Wydpmrcbjc2m+1njzl06BAALS0tNDQ0\n0L9/f7/W6Q9d6QXAW2+9xcqVK1mxYgXh4eH+LNFvvPXi2LFjfPbZZ/zhD3/A4XCwa9cu7r777l65\nCN/VvxGHw0FYWBhxcXEMHjyYyspKP1fqe13pRX5+PhMnTgRg5MiRNDU19cqZDG9+2iuXy/WL7yc/\nFbRhMmLECCorKzlw4ADNzc0UFxfjcDg6PcbhcFBQUAC0H7kzZswYLBZLIMr1qa704uOPP2bBggWs\nWLGi186Lg/deREVF8c4771BaWkppaSkJCQmsWLGCESNGBLBq3+jK78U111zDzp07Afjuu++orKwk\nLi4uEOX6VFd6MXDgQCoqKgD44osvaGpqIiYmJhDlBpTD4aCwsBDDMNi1axdRUVFdWj8K9UNtPhEa\nGsqCBQu44447aG1t5cYbb2To0KE8/fTTXHrppUyYMIHMzExmz56N0+mkb9++/P3vfw902T7RlV48\n+eSTHD9+nJkzZwLtfzgrV64McOXdryu9OFN0pRdjx47lv//9L5MmTSIkJIQ5c+b0ytF7V3rx0EMP\nMW/ePNauXYvFYuGJJ57olR8+Z82axc6dO6mtrWXcuHHcd999tLS0AHDLLbcwfvx4tm3bhtPpJCIi\ngiVLlnTpdXU9ExERMS1op7lERKTnUJiIiIhpChMRETFNYSIiIqYpTERExDSFiYiImKYwERER04L2\nS4si/tDY2MiDDz7I559/TmhoKBdeeCHjx4+nrKyMZ555BoBXX3210+1Vq1ZRVFSExWLhnHPO4cUX\nX6RPnz7k5+ezbt06AMLCwli1ahXnnXce27ZtY8WKFTQ3NxMWFkZubi4JCQl8+eWX5Obm0tjYSFtb\nGxkZGdx+++1s3bqVp59+mj59+tDa2sr8+fMZPXp0wHokAgoTkdPasWMHx44d4/XXXwfgyJEjv3hK\n+5MKCgooLS3lpZdeIjIyktraWvr06cM777zDqlWrePHFF7FarRw7dozQ0FD279/Pc889x7/+9S8i\nIyPZt28fd955J2VlZbz44os4HA7uuuuujn0DPPPMMyxcuJCRI0fS2tpKY2Oj7xsh4oXCROQ0Lrnk\nEr744gseffRRrrjiCq+nrX/zzTe55ZZbiIyMBOg4NUlZWRk33HADVqsVgHPPPReA7du3s3//fqZM\nmdLxGi0tLRw+fJhRo0bx1FNP0djYyOjRoxkzZgzQfm2axx9/nOTkZMaNG8ewYcO6+8cW+X/TmonI\nacTFxVFUVERSUhIVFRXccMMNhISEdDo1eVNTk6l9jB07lo0bN3b827FjB+eddx4pKSmsX7+e888/\nn3/+85/Mnj0bgLlz5/LYY48RFhbGzJkzefnll03tX6Q7KExETsPlchESEsI111xDbm4u3333HXFx\ncXz66ac0NzfT3NzMpk2bOh5/9dVX89JLL3Vcse/kKcyvuuoqNm7cyOHDh4H2U+E3NTWRlJTE9u3b\n2bdvX8dr7N69G4Cvv/4aq9XK5MmTueeeezpOk//ll19y8cUXc9ttt3H99df3ytPnS/DRNJfIaXz6\n6acsW7YMaL+wVnZ2NpdffjmJiYmkpqYSGxvLJZdcQnV1NQDp6em43W5uvvlmQkNDOeecc1i/fj2j\nR48mOzub6dOnY7FYCA8PZ+XKlQwePJinnnqKhx9+mBMnTvD9999z+eWXc9lll/HGG2/w2muvERYW\nhsViYe7cuQAsW7aMr7/+mpCQEKKjo1m8eHHA+iNyks4aLCIipmmaS0RETFOYiIiIaQoTERExTWEi\nIiKmKUxERMQ0hYmIiJimMBEREdP+D6alumWC3LgsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3912991da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/markdown": [
"-----"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def display_plot(ax):\n",
" display(ax)\n",
" plt.show() # forcing to display here\n",
"\n",
"def display_success_rate_hist(tasks):\n",
" ax = tasks.success.plot.hist(\n",
" bins=np.linspace(0, 1, 11),\n",
" title='success rate')\n",
" ax.set_xlim(0, 1)\n",
" ax.set_ylim(0, 10)\n",
" display_plot(ax)\n",
" \n",
"def display_time_vs_success(tasks):\n",
" ax = tasks.plot.scatter(x='success', y='time')\n",
" ax.set_xlim(0, 1)\n",
" display_plot(ax)\n",
" \n",
"def analyze_level(name):\n",
" ts = tasks[tasks.mission == name]\n",
" ts = ts[['name', 'level2', 'order',\n",
" 'n_attempts', 'success', 'time']]\n",
" display(Markdown('# {name}'.format(name=name)))\n",
" display_level_overview(ts.reset_index(), order_by=['level2', 'order'])\n",
" display_success_rate_hist(ts)\n",
" display_time_vs_success(ts)\n",
" display(Markdown('-----'))\n",
"\n",
"for name in missions.name:\n",
" analyze_level(name)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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"metadata": {
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"name": "python3"
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| gpl-3.0 |
H-Liu/spectral-element-methods | Couette-Gradient.ipynb | 1 | 11578 | {
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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Test 2: Caso 1D - Couette Generalizado"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En el flujo de Couette generalizado, el fluido se encuentra confinado entre placas planas paralelas infinitas separadas por una distancia constante $L$ y se encuentra sometido a un gradiente de presi\u00f3n en direcci\u00f3n paralela a las placas. Adicionalmente, las velocidades de las placas son de $U_0$ y $U_1$ (en direcci\u00f3n x) respectivamente. De acuerdo con esto, se tienen las siguientes consideraciones:\n",
"\n",
"- El flujo se presenta \u00fanicamente en la direcci\u00f3n $x$ ($v=w=0$).\n",
"- El gradiente de presi\u00f3n al que est\u00e1 sometido el fluido es constante ($\\frac{\\partial p}{\\partial x} = \\frac{\\Delta p}{\\ell}$).\n",
"- El flujo no depende de la coordenada $z$ ($\\frac{\\partial}{\\partial z}=0$).\n",
"- El fluido se encuentra en estado estacionario ($\\frac{\\partial}{\\partial t}=0$).\n",
"- No hay fuerzas externas afectando el flujo ($f_x=f_y=f_z=0$).\n",
"\n",
"De aqu\u00ed, las ecuaciones de continuidad y de Navier-Stokes se reducen a:\n",
"\n",
"\\begin{align}\n",
"\\frac{\\partial \\rho}{\\partial t} + \\nabla\\cdot(\\rho \\vec{u})=0 \\quad &\\to\\quad \\frac{\\partial u}{\\partial x}=0 \\\\\n",
"\\rho\\left(\\frac{\\partial \\vec{u}}{\\partial t}+\\vec{u}\\cdot\\nabla\\vec{u}\\right)=\\rho\\vec{f}-\\nabla p+\\mu\\nabla^2\\vec{u}\\quad &\\to\\quad 0=-\\frac{\\Delta p}{\\ell}+\\mu\\frac{\\partial^2u}{\\partial y^2}\n",
"\\end{align}\n",
"\n",
"Se puede ver entonces que la ecuaci\u00f3n resultante es un caso particular del problema de difusi\u00f3n estable:\n",
"\n",
"$$k\\frac{d^2 f}{d x^2}+s(x)=0$$\n",
"\n",
"Donde $k = \\mu$, y $s(x)=-\\frac{\\Delta p}{\\ell}$, y las condiciones de frontera corresponden a valores conocidos (tipo Dirichlet):\n",
"\n",
"$$u(0)=U_0\\quad u(L)=U_1$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora, para aplicar el m\u00e9todo de elementos espectrales, es necesario realizar los siguientes pasos:\n",
"\n",
"- Introducci\u00f3n de la informaci\u00f3n del problema\n",
"- Definici\u00f3n de los elementos a usar (cantidad $N_E$ y distribuci\u00f3n, as\u00ed como sus \u00f3rdenes $N_P(i)$)\n",
" - Selecci\u00f3n de los nodos de interpolaci\u00f3n a usar\n",
" - Generaci\u00f3n de la matriz de conectividad\n",
" - Definici\u00f3n de la funci\u00f3n de entrada\n",
"- Generaci\u00f3n de la matriz del problema\n",
" - Generaci\u00f3n de la matriz de difusi\u00f3n y el vector b\n",
"- Soluci\u00f3n del sistema resultante\n",
"- Gr\u00e1fica de la soluci\u00f3n obtenida\n",
"\n",
"Cada uno de los pasos se tratar\u00e1 en detalle a continuaci\u00f3n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Prepara el ambiente de trabajo con las librer\u00edas a utilizar\n",
"\n",
"import numpy as np # Ofrece funciones para operaciones b\u00e1sicas con arreglos\n",
"import scipy.linalg as lin # Ofrece funciones para operaciones de \u00e1lgebra lineal\n",
"import matplotlib.pyplot as plt # Permite incluir un ambiente de visualizaci\u00f3n\n",
"import timeit # Permite la medici\u00f3n de tiempos para los distintos algoritmos\n",
"from sem1D import * # Agrupa las funciones externas en un archivo\n",
"#%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introducci\u00f3n de la informaci\u00f3n del problema\n",
"Se introducen los par\u00e1metros del problema. En este caso se tienen los par\u00e1metros $\\mu$, $\\nabla p$, $L$ y $U_0$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## Valores de prueba correspondientes al ejemplo del libro de Pozrikidis\n",
"# L = 1.\n",
"# mu = 1.\n",
"# U0 = 1.9639\n",
"# U1 = 0.\n",
"\n",
"L = 2.\n",
"mu = 1.\n",
"U0 = 0.\n",
"U1 = 50.\n",
"p = np.array([-40,-20,0,20,40])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definici\u00f3n de los elementos a usar\n",
"\n",
"Se define primero el n\u00famero de elementos y su distribuci\u00f3n, as\u00ed como los \u00f3rdenes de cada uno de los mismos. Por simplicidad, inicialmente se toman elementos uniformemente espaciados."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Ne = 6\n",
"Np = np.array((1,3,5,6,4,2))\n",
"\n",
"# Genera los puntos correspondientes a los nodos extremos de cada elemento\n",
"(xe, h) = np.linspace(0,L,Ne+1,retstep=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selecci\u00f3n de los nodos de interpolaci\u00f3n a usar\n",
"\n",
"Conociendo el n\u00famero de elementos y su distribuci\u00f3n, es posible generar los nodos correspondientes a los extremos de cada elemento. Sin embargo, como no se trabaja con elementos lineales (cada elemento tiene un orden), es necesario generar los nodos intermedios para cada elemento. Para ello, es necesario definir la familia de polinomios sobre la cual van a estar basados estos nodos. En este caso se utilizan los nodos de interpolaci\u00f3n de Lobatto."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xint = genNodes(Ne,Np,xe)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generaci\u00f3n de la matriz de conectividad\n",
"\n",
"Una vez se definen los nodos, se utiliza la matriz de conectividad para almacenar de manera ordenada la numeraci\u00f3n local y global de los mismos"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(xglob, C) = genConn(Ne,Np,xint)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Permite visualizar todos los nodos a utilizar\n",
"\n",
"plt.plot(xe,np.zeros(xe.size),'bo',xglob,np.zeros(xglob.size),'r.')\n",
"plt.xlabel('Posicion (coord y)')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Definici\u00f3n de la funci\u00f3n de entrada\n",
"\n",
"Especifica $s(x)$ para cada uno de los nodos"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# Construye la funci\u00f3n de entrada\n",
"## s = lambda x: 10.*np.exp(-5.*(x**2)/L**2) # Funci\u00f3n de prueba correspondiente al ejemplo del libro de Pozrikidiz\n",
"s = lambda x,y: -p[x] # Para x en 0,1,2,3,4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generaci\u00f3n de la matriz del problema\n",
"\n",
"Para la generaci\u00f3n de la matriz del problema es necesario definir cada una de las matrices de difusi\u00f3n y de masa por cada elemento:\n",
"\n",
"$$\\Theta_{ij}=\\int_{-1}^1 \\psi_i(\\xi)\\psi_j(\\xi)d\\xi \\quad \\Psi_{ij}=\\int_{-1}^1 \\frac{d\\psi_i}{d\\xi}\\frac{d\\psi_j}{d\\xi}d\\xi$$\n",
"\n",
"En este caso se aplica la cuadratura de Lobatto para la construcci\u00f3n de dichas matrices, con lo que se obtienen los siguientes resultados:\n",
"\n",
"$$\\Theta=\\sum_{p=1}^{N_p+1} m_{ip}m_{jp}w_p \\quad \\Psi_{ij}=\\sum_{p=1}^{N_p+1} d_{ip}d_{jp}w_p$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generaci\u00f3n de la matriz de difusi\u00f3n y el vector b\n",
"\n",
"Se recorre sobre los elementos para acoplar las matrices de cada elemento en un sistema global, aprovechando la matriz de conectividad"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Soluci\u00f3n del sistema resultante\n",
"\n",
"Teniendo ya construidos los componentes del sistema a resolver, basta aplicar un esquema de soluci\u00f3n razonable para el tipo de matriz obtenida. En este caso se utiliza el solucionador por defecto del m\u00f3dulo _linalg_. Dado que los valores para el primer y \u00faltimo nodo son conocidos, se omiten los valores correspondientes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Gr\u00e1fica de la soluci\u00f3n obtenida\n",
"\n",
"Una vez resuelto el sistema, es posible visualizar el resultado obtenido por medio de una gr\u00e1fica. En este caso se conoce que la soluci\u00f3n anal\u00edtica corresponde a la linea recta entre las dos condiciones extremas, y se incluye tambi\u00e9n su gr\u00e1fica."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(np.linspace(U0,U1,Ne+1),xe,'-.or',ms=8)\n",
"plt.hold(True)\n",
"\n",
"for it in range(5):\n",
" (Mat_dif, vec_b) = matDiff(Ne,Np,xe,xglob,C,U0,U1,mu,s= lambda x: -p[it])\n",
" sol = np.zeros(xglob.size-2)\n",
" A = Mat_dif[1:-1,1:-1] \n",
" sol = lin.solve(A,vec_b[1:-1])\n",
" solG = np.concatenate([[U0],sol,[U1]])\n",
" \n",
" if(it == 0):\n",
" plt.plot(solG,xglob,'-ob',label='p = -40')\n",
" elif(it == 1):\n",
" plt.plot(solG,xglob,'--*g',label='p = -20')\n",
" elif(it == 2):\n",
" plt.plot(solG,xglob,'-.+c',label='p = 0')\n",
" elif(it == 3):\n",
" plt.plot(solG,xglob,':xm',label='p = 20')\n",
" else:\n",
" plt.plot(solG,xglob,'--vk',label='p = 40')\n",
"\n",
"valy = np.linspace(0,L,100)\n",
"for num in range(5):\n",
" vec = np.zeros(xglob.size)\n",
" vec = p[num]/(2.*mu)*(valy**2-L*valy)+(U1-U0)/L*valy+U0\n",
" plt.plot(vec,valy,':r')\n",
"plt.legend(loc='upper left')\n",
"plt.title('Velocidades para Distintos Valores del Gradiente')\n",
"plt.ylabel('Posicion (coord y)')\n",
"plt.xlabel('Velocidad (U)')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
tuxfux-hlp-notes/python-batches | archieves/batch-57/functions/Learning_Functions_day2.ipynb | 2 | 24268 | {
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# *,**,*args,**kwargs"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func(a,b):\n",
" return a + b\n",
" \n",
"print my_func(\"linux\",\"rocks\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"linuxrocks\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [\"linux\",\" rocks\"]\n",
"my_list1 = [\"linux\",\"rocks\",\"fine\"]\n",
"my_list2 = [\"linux\"]\n",
"\n",
"# please unpack the elements \"linux\" and \"rocks\" into both a and b.\n",
"print my_func(*my_list) # unpacking your list to a funtion.\n",
"print my_func(*my_list1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"linux rocks\n"
]
},
{
"ename": "TypeError",
"evalue": "my_func() takes exactly 2 arguments (3 given)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-d695b25770df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# please unpack the elements \"linux\" and \"rocks\" into both a and b.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmy_list1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: my_func() takes exactly 2 arguments (3 given)"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_func(*my_list2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "my_func() takes exactly 2 arguments (1 given)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-ffe05c32b4ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mmy_list2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: my_func() takes exactly 2 arguments (1 given)"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# **"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func(a,b):\n",
" return a + b\n",
"\n",
"print my_func(a=\"linux\",b=\" rocks\") # keyword arguments"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"linux rocks\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# dictionary\n",
"my_values = {'a':\"linux\",'b':\" rocks\"}\n",
"print my_func(**my_values)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"linuxrocks\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_values1 = {'a':\"linux\",'b':\" rocks\",'c':'choco'}\n",
"print my_func(**my_values1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "my_func() got an unexpected keyword argument 'c'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-4a61bc41e41c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmy_values1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'a'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\"linux\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\" rocks\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m'choco'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mmy_values1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: my_func() got an unexpected keyword argument 'c'"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# *args\n",
"# More than one argument.\n",
"\n",
"print help(max)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on built-in function max in module __builtin__:\n",
"\n",
"max(...)\n",
" max(iterable[, key=func]) -> value\n",
" max(a, b, c, ...[, key=func]) -> value\n",
" \n",
" With a single iterable argument, return its largest item.\n",
" With two or more arguments, return the largest argument.\n",
"\n",
"None\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print max(23,24,25,61)\n",
"print max(1,61)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"61\n",
"61\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# gmax\n",
"# if a function defination has *args and if you return args, you get a tuple of values.\n",
"def gmax(*args):\n",
" return args"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print gmax(21,31,51,63,66)\n",
"print gmax(31,21)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(21, 31, 51, 63, 66)\n",
"(31, 21)\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# *args basically passes n number of functions.\n",
"def gmax(*args):\n",
" big = 0\n",
" print args\n",
" for value in args:\n",
" print \"following are the values : big {} , value {}\".format(big,value)\n",
" if value > big:\n",
" big = value\n",
" return big"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print gmax(21,31,51,63,66)\n",
"print gmax(31,21)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(21, 31, 51, 63, 66)\n",
"following are the values : big 0 , value 21\n",
"following are the values : big 21 , value 31\n",
"following are the values : big 31 , value 51\n",
"following are the values : big 51 , value 63\n",
"following are the values : big 63 , value 66\n",
"66\n",
"(31, 21)\n",
"following are the values : big 0 , value 31\n",
"following are the values : big 31 , value 21\n",
"31\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# **kwargs\n",
"def callme(**kwargs):\n",
" return kwargs # dictionary of values.\n",
"\n",
"print callme(name=\"kumar\",age=\"45\")\n",
"print callme(name=\"kumar\",maiden=\"vijaya\")\n",
"print callme(gender='m',location=\"hyd\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'age': '45', 'name': 'kumar'}\n",
"{'name': 'kumar', 'maiden': 'vijaya'}\n",
"{'gender': 'm', 'location': 'hyd'}\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def callme(**kwargs):\n",
" if 'name' in kwargs:\n",
" print \"my name is {}\".format(kwargs['name'])\n",
" if 'age' in kwargs:\n",
" print \"my age is {}\".format(kwargs['age'])\n",
" if 'maiden' in kwargs:\n",
" print \"my mother name is {}\".format(kwargs['maiden'])\n",
" if 'location' in kwargs:\n",
" print \"The location is {}\".format(kwargs['location'])\n",
" if 'gender' in kwargs:\n",
" print \"the gender is {}\".format(kwargs['gender'])\n",
" \n",
" \n",
"# main\n",
"\n",
"#callme(name=\"kumar\",age=\"45\")\n",
"#callme(name=\"kumar\",maiden=\"vijaya\")\n",
"callme(gender='m',location=\"hyd\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The location is hyd\n",
"the gender is m\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# some defination of functions\n",
"\n",
"def foo():\n",
" pass\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<function __main__.foo>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print type(foo)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<type 'function'>\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print foo"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<function foo at 0x7f3cf8b1bde8>\n"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# function within a functions.\n",
"# the local variables are resticted to function.\n",
"# the lifetime of the local variables is during the runtime of the function.\n",
"\n",
"def upper():\n",
" x = 1 # local variable for upper() function.\n",
" def inner(): # inner() function is a local variable/function within your upper() function.\n",
" return x # x is going to be fetched from global value x of upper() function.\n",
" print locals()\n",
" return inner() # you are returing the value of inner() function"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print upper() # 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'x': 1, 'inner': <function inner at 0x7f3cf8b1bd70>}\n",
"1\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print inner() # Error"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'inner' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-4d12a7e2137b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'inner' is not defined"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# function within a functions.\n",
"# the local variables are resticted to function.\n",
"# the lifetime of the local variables is during the runtime of the function.\n",
"# how did inner() function get the value x.\n",
"# function closures: During the defination of the function , all the global and the local variables available will be present\n",
"# even when we return the address of the function.\n",
"\n",
"def upper():\n",
" x = 1 # local variable for upper() function.\n",
" def inner(): # inner() function is a local variable/function within your upper() function.\n",
" return x # x is going to be fetched from global value x of upper() function.\n",
" print locals()\n",
" return inner # you are returing the address of the inner function."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" new = upper()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{'x': 1, 'inner': <function inner at 0x7f3cf8b1bed8>}\n"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print new"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<function inner at 0x7f3cf8b1bed8>\n"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print new()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# function is a first class object.\n",
"# int,float,str as first class objects.\n",
"\n",
"def add(x,y):\n",
" return x + y\n",
"\n",
"def sub(x,y):\n",
" return x - y\n",
"\n",
"def extra(func,x,y):\n",
" return func(x,y)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print extra(add,22,23)\n",
"print extra(sub,25,23)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"45\n",
"2\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# map,filter and lambda"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print help(map)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on built-in function map in module __builtin__:\n",
"\n",
"map(...)\n",
" map(function, sequence[, sequence, ...]) -> list\n",
" \n",
" Return a list of the results of applying the function to the items of\n",
" the argument sequence(s). If more than one sequence is given, the\n",
" function is called with an argument list consisting of the corresponding\n",
" item of each sequence, substituting None for missing values when not all\n",
" sequences have the same length. If the function is None, return a list of\n",
" the items of the sequence (or a list of tuples if more than one sequence).\n",
"\n",
"None\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_square(a):\n",
" return a * a"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# truth of function: if a function returns a value, its called the truth of a function.\n",
"# if a function return None its called as NOT A TRUE function."
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_square(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_square(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print map(my_square,[22,25,27,29,33])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[484, 625, 729, 841, 1089]\n"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print filter(my_square,[22,25,27,29,33])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[22, 25, 27, 29, 33]\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# filter\n",
"print help(filter)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on built-in function filter in module __builtin__:\n",
"\n",
"filter(...)\n",
" filter(function or None, sequence) -> list, tuple, or string\n",
" \n",
" Return those items of sequence for which function(item) is true. If\n",
" function is None, return the items that are true. If sequence is a tuple\n",
" or string, return the same type, else return a list.\n",
"\n",
"None\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_even(a):\n",
" if a % 2 == 0:\n",
" return 'even'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print my_even(2) # TRUTH OF A FUNCTIONS\n",
"print my_even(3) # FALSE OF A FUNCTION."
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"even\n",
"None\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print filter(my_even,[1,2,3,4,5])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[2, 4]\n"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print map(my_even,[1,2,3,4,5])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[None, 'even', None, 'even', None]\n"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# lambda : writing nameless functions on fly.\n",
"\n",
"print map(my_square,[22,25,27,29,33])\n",
"print map(lambda a:a*a,[22,25,27,29,33])\n",
"\n",
"print filter(my_even,[1,2,3,4,5])\n",
"print filter(lambda a:a % 2 == 0,[1,2,3,4,5])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[484, 625, 729, 841, 1089]\n",
"[484, 625, 729, 841, 1089]\n",
"[2, 4]\n",
"[2, 4]\n"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# https://github.com/zhiwehu/Python-programming-exercises/blob/master/100%2B%20Python%20challenging%20programming%20exercises.txt"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
nickedes/Baby-science | Lessons/Lesson 1.ipynb | 1 | 28579 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import all libraries needed for the tutorial\n",
"\n",
"# General syntax to import specific functions in a library: \n",
"##from (library) import (specific library function)\n",
"from pandas import DataFrame, read_csv, Series\n",
"\n",
"# General syntax to import a library but no functions: \n",
"##import (library) as (give the library a nickname/alias)\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd #this is how I usually import pandas\n",
"import sys #only needed to determine Python version number\n",
"\n",
"# Enable inline plotting\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python version 3.4.0 (default, Apr 11 2014, 13:05:11) \n",
"[GCC 4.8.2]\n",
"Pandas version 0.16.2\n"
]
}
],
"source": [
"print('Python version ' + sys.version)\n",
"print('Pandas version ' + pd.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# The inital set of baby names and bith rates\n",
"names = ['Bob','Jessica','Mary','John','Mel']\n",
"births = [968, 155, 77, 578, 973]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"zip?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[('Bob', 968), ('Jessica', 155), ('Mary', 77), ('John', 578), ('Mel', 973)]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"BabyDataSet = zip(names,births)\n",
"BabyDataSet = list(BabyDataSet)\n",
"BabyDataSet"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Names</th>\n",
" <th>Births</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bob</td>\n",
" <td>968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Jessica</td>\n",
" <td>155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mary</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>John</td>\n",
" <td>578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Mel</td>\n",
" <td>973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Names Births\n",
"0 Bob 968\n",
"1 Jessica 155\n",
"2 Mary 77\n",
"3 John 578\n",
"4 Mel 973"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data = BabyDataSet, columns=['Names', 'Births'])\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.to_csv?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.to_csv('births1880.csv',index=False,header=False)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"read_csv?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# path of csv file\n",
"location = 'births1880.csv'\n",
"df = pd.read_csv(location)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bob</th>\n",
" <th>968</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Jessica</td>\n",
" <td>155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Mary</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>John</td>\n",
" <td>578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Mel</td>\n",
" <td>973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Bob 968\n",
"0 Jessica 155\n",
"1 Mary 77\n",
"2 John 578\n",
"3 Mel 973"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df\n",
"# This brings us the our first problem of the exercise. The read_csv function treated the first\n",
"# record in the csv file as the header names. This is obviously not correct since the text file \n",
"# did not provide us with header names."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bob</td>\n",
" <td>968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Jessica</td>\n",
" <td>155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mary</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>John</td>\n",
" <td>578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Mel</td>\n",
" <td>973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1\n",
"0 Bob 968\n",
"1 Jessica 155\n",
"2 Mary 77\n",
"3 John 578\n",
"4 Mel 973"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To correct this we will pass the header parameter to the read_csv function and set it to None (means null in python).\n",
"df = pd.read_csv(location, header=None)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Names</th>\n",
" <th>Births</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bob</td>\n",
" <td>968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Jessica</td>\n",
" <td>155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mary</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>John</td>\n",
" <td>578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Mel</td>\n",
" <td>973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Names Births\n",
"0 Bob 968\n",
"1 Jessica 155\n",
"2 Mary 77\n",
"3 John 578\n",
"4 Mel 973"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# If we wanted to give the columns specific names, we would have to pass another paramter called names.\n",
"df = pd.read_csv(location, names=['Names','Births'])\n",
"df\n",
"# You can think of the numbers [0,1,2,3,4] as the row numbers in an Excel file. In pandas these are part of \n",
"# the index of the dataframe. You can think of the index as the primary key of a sql table with the exception\n",
"# that an index is allowed to have duplicates."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"os.remove(location)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Names object\n",
"Births int64\n",
"dtype: object"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data type of the columns\n",
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data type of Births column\n",
"df.Births.dtype"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Names</th>\n",
" <th>Births</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Mel</td>\n",
" <td>973</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Names Births\n",
"4 Mel 973"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Analyze Data\n",
"# Method 1:\n",
"Sorted = df.sort(['Births'], ascending=False)\n",
"Sorted.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"973"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Method 2:\n",
"df['Births'].max()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The most popular name\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th>Names</th>\n",
" <th>Births</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Names Births\n",
"4 Mel 973"
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},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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HnUGDdCtT6k8VXjL6UVmkuncPHViGDYsdiTTUww/D669r3E6kseWU8MysrZm9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\nkbKghCciImVBCU9ERMrC/wd1xQp/dCJsGAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02d09aab00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Present Data\n",
"# Create graph\n",
"df['Births'].plot()\n",
"\n",
"# Maximum value in the data set\n",
"MaxValue = df['Births'].max()\n",
"\n",
"# Name associated with the maximum value\n",
"MaxName = df['Names'][df['Births'] == df['Births'].max()].values\n",
"\n",
"# Text to display on graph\n",
"Text = str(MaxValue) + \" - \" + MaxName\n",
"\n",
"# Add text to graph\n",
"plt.annotate(Text, xy=(1, MaxValue), xytext=(8, 0), \n",
" xycoords=('axes fraction', 'data'), textcoords='offset points')\n",
"\n",
"print(\"The most popular name\")\n",
"df[df['Births'] == df['Births'].max()]\n",
"#Sorted.head(1) can also be used"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
phoebe-project/phoebe2-docs | 2.0/tutorials/eclipse.ipynb | 1 | 483197 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Eclipse Detection\n",
"============================\n",
"\n",
"Setup\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first make sure we have the latest version of PHOEBE 2.0 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -I \"phoebe>=2.0,<2.1\""
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As always, let's do imports and initialize a logger and a new Bundle. See [Building a System](building_a_system.html) for more details."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: Constant u'Gravitational constant' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"WARNING: Constant u'Solar mass' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"WARNING: Constant u'Solar radius' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"WARNING: Constant u'Solar luminosity' is already has a definition in the u'si' system [astropy.constants.constant]\n",
"/usr/local/lib/python2.7/dist-packages/astropy/units/quantity.py:782: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
" return super(Quantity, self).__eq__(other)\n"
]
}
],
"source": [
"import phoebe\n",
"from phoebe import u # units\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"logger = phoebe.logger()\n",
"\n",
"b = phoebe.default_binary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's just compute the mesh at a single time-point that we know should be during egress."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 2 parameters | contexts: compute, dataset>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('mesh', times=[0.05])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Native\n",
"-------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The 'native' eclipse method computes what percentage (by area) of each triangle is visible at the current time. It also determines the centroid of the visible portion of each triangle.\n",
"\n",
"Physical quantities (temperatures, intensities, velocities, etc) are computed at the vertices of each triangle, and this centroid is then used to determine the average quantity across the visible portion of the triangle (by assuming a linear gradient across the triangle).\n",
"\n",
"Let's plot the visibilities (ratio of the area that is visible) as the color scale, with red being completely hidden and green being completely visible. We'll also plot the centroids themselves. Here we can see that the centroids for green/visible triangles are exactly in the center, whereas for partially visible triangles the centroid is leaning towards the visible portion of the star."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 62 parameters | components: primary, secondary>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(eclipse_method='native')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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zbWsjJ6JbXUZcW2bP5JuuIhqNPq7zVxNMoDDKGz3935myI8t3t4wsLqeAagLc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\nHvYjAb/85S8T+b8ppCERRAnpoSWE6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ekUcpQwAWJdvr0wCitw0w9L2k4q3E+QP3CEH3OQ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AkaNHot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\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T36519rpNfGx4Ww1v57zTDap4uGCdvSSd3x7W8L51Da533vmu26i/zNnL6Rpcvv481+X0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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc6c42664d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"axs, artists = b.plot(component='primary', facecolor='visibilities', ax=ax)\n",
"\n",
"wcs = b['visible_centroids@primary'].get_value()\n",
"\n",
"ax.plot(wcs[:,0], wcs[:,1], 'b.')\n",
"xlim = ax.set_xlim(-0.5,0.25)\n",
"ylim = ax.set_ylim(-0.4,0.4)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fri, 10 Feb 2017 17:05 BUNDLE WARNING overwriting model: latest\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 62 parameters | components: primary, secondary>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(eclipse_method='visible_partial')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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N/v3T+e77ZzEaDfTpNZfMzAN6R/I6G9bvJiFBtiKfypw5F/PGP+4mM/MQ3brO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\nCKaQwEP0YSH9uIYUx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Ofm4yBKpGr4jRcvFbjJoYUCnJThYjduGnDj9DWa2h9ksqIjCSNZ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sXN+5SSgoHJHBr4rEdiGBaWaByGHazseFiNlR5Eo9fw76YfcUjAQqqZHuBG1EbsAAFvRO05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\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+6H26dElh+fL/uPS2Q0ODGDr0WlZ9+5toz/MA2Vh4274h7xUXb8iry/WEMZdsviGbp7wo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\nIiIiIiIiIiIBoUSUiIiIiIiIiIgExP8Hg2mNlYNxB+gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc6c3ff06d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax = fig.add_subplot(111)\n",
"axs, artists = b.plot(component='primary', facecolor='visibilities', ax=ax)\n",
"\n",
"xlim = ax.set_xlim(-0.5,0.25)\n",
"ylim = ax.set_ylim(-0.4,0.4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
jmarrec/eQSimParsing | eQSimParsing demonstration.ipynb | 1 | 407836 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-size: 157.1%; font-weight: bold; line-height: 1; margin: 1.27em 0 0;\">Table of Contents</p>\n",
"<ol class=\"toc-item\">\n",
"<li>\n",
" <a href=\"#Run-the-file\">Run the file</a>\n",
"</li>\n",
"<li>\n",
" <a href=\"#Example-of-how-to-manipulate-the-objects-in-IPython\">Example of how to manipulate the objects in IPython</a>\n",
"</li>\n",
"<li>\n",
" <a href=\"#Group-by-system-and-apply\">Group by system and apply</a>\n",
" <ol class=\"toc-item\">\n",
" <li>\n",
" <a href=\"#Example-on-aggregating-zones-to-the-system-level\">Example on aggregating zones to the system level</a>\n",
" </li>\n",
" <li>\n",
" <a href=\"#Example-on-adding-all-system-fans'-power\">Example on adding all system fans' power</a>\n",
" </li>\n",
" </ol>\n",
"</li>\n",
"</ol>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run the file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Will only parse out the reports as needed, no weather normalization done, this is an AMY case\n",
"Loading .\\Example.SIM\n",
"Unmet Cooling Hours: 9\n",
"Unmet Heating Hours: 40\n",
"In an interactive prompt, the variables 'usage', 'sv_a_dict', 'pv_a_dict' are initialized\n"
]
}
],
"source": [
"%run eqsimparsing.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example of how to manipulate the objects in IPython\n",
"\n",
"Note that the script does also output PV-A and SV-A to a csv file already"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** I'm importing seaborn because I like it, but this is just plot styling... feel free to delete this following cell**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set(style=\"white\", context=\"talk\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`pv_a_dict` and `sv_a_dict` are dictionaries where the values are Pandas dataframes"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['BOILERS', 'CIRCULATION LOOPS', 'CHILLERS', 'DW-HEATERS', 'PUMPS', 'COOLING TOWERS'])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_a_dict.keys()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(pv_a_dict['CHILLERS'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Attached to</th>\n",
" <th>Flow (GPM)</th>\n",
" <th>Head (ft)</th>\n",
" <th>Head Setpoint (ft)</th>\n",
" <th>Capacity Control</th>\n",
" <th>Power (kW)</th>\n",
" <th>Mech. Eff</th>\n",
" <th>Motor Eff</th>\n",
" <th>W/GPM</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Pump</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>WSHP Pump</th>\n",
" <td>WSHP Loop</td>\n",
" <td>509.8</td>\n",
" <td>79.9</td>\n",
" <td>42.6</td>\n",
" <td>STAGED</td>\n",
" <td>11.200</td>\n",
" <td>0.77</td>\n",
" <td>0.89</td>\n",
" <td>21.969400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CT Pump</th>\n",
" <td>Cooling Tower</td>\n",
" <td>526.6</td>\n",
" <td>38.7</td>\n",
" <td>0.0</td>\n",
" <td>ONE-SPEED</td>\n",
" <td>5.600</td>\n",
" <td>0.77</td>\n",
" <td>0.89</td>\n",
" <td>10.634258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BP-2</th>\n",
" <td>Heating Boiler 2</td>\n",
" <td>83.5</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>ONE-SPEED</td>\n",
" <td>0.191</td>\n",
" <td>0.77</td>\n",
" <td>0.64</td>\n",
" <td>2.287425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BP-1</th>\n",
" <td>Heating Boiler 1</td>\n",
" <td>235.6</td>\n",
" <td>15.5</td>\n",
" <td>0.0</td>\n",
" <td>ONE-SPEED</td>\n",
" <td>1.120</td>\n",
" <td>0.77</td>\n",
" <td>0.80</td>\n",
" <td>4.753820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DHW Pump 1</th>\n",
" <td>DHW Boiler 1</td>\n",
" <td>10.0</td>\n",
" <td>7.7</td>\n",
" <td>0.0</td>\n",
" <td>ONE-SPEED</td>\n",
" <td>0.047</td>\n",
" <td>0.77</td>\n",
" <td>0.40</td>\n",
" <td>4.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DHW Pump 2</th>\n",
" <td>DHW Boiler 2</td>\n",
" <td>10.0</td>\n",
" <td>7.7</td>\n",
" <td>0.0</td>\n",
" <td>ONE-SPEED</td>\n",
" <td>0.047</td>\n",
" <td>0.77</td>\n",
" <td>0.40</td>\n",
" <td>4.700000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Attached to Flow (GPM) Head (ft) Head Setpoint (ft) \\\n",
"Pump \n",
"WSHP Pump WSHP Loop 509.8 79.9 42.6 \n",
"CT Pump Cooling Tower 526.6 38.7 0.0 \n",
"BP-2 Heating Boiler 2 83.5 6.0 0.0 \n",
"BP-1 Heating Boiler 1 235.6 15.5 0.0 \n",
"DHW Pump 1 DHW Boiler 1 10.0 7.7 0.0 \n",
"DHW Pump 2 DHW Boiler 2 10.0 7.7 0.0 \n",
"\n",
" Capacity Control Power (kW) Mech. Eff Motor Eff W/GPM \n",
"Pump \n",
"WSHP Pump STAGED 11.200 0.77 0.89 21.969400 \n",
"CT Pump ONE-SPEED 5.600 0.77 0.89 10.634258 \n",
"BP-2 ONE-SPEED 0.191 0.77 0.64 2.287425 \n",
"BP-1 ONE-SPEED 1.120 0.77 0.80 4.753820 \n",
"DHW Pump 1 ONE-SPEED 0.047 0.77 0.40 4.700000 \n",
"DHW Pump 2 ONE-SPEED 0.047 0.77 0.40 4.700000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_a_dict['PUMPS']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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ApIQoAAAASdUpRCsqKuK0006Lvn37xvDhw+Pee++NiIh169bFmDFjom/fvlFaWhr3339/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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22af7e244a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pv_a_dict['PUMPS'].ix[:, 'W/GPM'].plot(kind='bar', figsize=(16,9), title='Pump W/GPM', color='#EB969C',);\n",
"sns.despine()\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Equipment Type</th>\n",
" <th>Attached to</th>\n",
" <th>Capacity (mmBTU/hr)</th>\n",
" <th>Flow (GPM)</th>\n",
" <th>EIR</th>\n",
" <th>HIR</th>\n",
" <th>Aux. (kW)</th>\n",
" <th>Thermal Eff</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Primary Equipment</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Heating Boiler 1</th>\n",
" <td>HW-BOILER</td>\n",
" <td>WSHP Loop</td>\n",
" <td>-0.327</td>\n",
" <td>91.0</td>\n",
" <td>0</td>\n",
" <td>1.266</td>\n",
" <td>0</td>\n",
" <td>0.789889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Heating Boiler 2</th>\n",
" <td>HW-BOILER</td>\n",
" <td>WSHP Loop</td>\n",
" <td>-0.300</td>\n",
" <td>83.5</td>\n",
" <td>0</td>\n",
" <td>1.266</td>\n",
" <td>0</td>\n",
" <td>0.789889</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Equipment Type Attached to Capacity (mmBTU/hr) Flow (GPM) \\\n",
"Primary Equipment \n",
"Heating Boiler 1 HW-BOILER WSHP Loop -0.327 91.0 \n",
"Heating Boiler 2 HW-BOILER WSHP Loop -0.300 83.5 \n",
"\n",
" EIR HIR Aux. (kW) Thermal Eff \n",
"Primary Equipment \n",
"Heating Boiler 1 0 1.266 0 0.789889 \n",
"Heating Boiler 2 0 1.266 0 0.789889 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pv_a_dict['BOILERS']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Q06dP17vZib+/P1ZWVsoGfm3atMHb25tjx45x4cIFXF1d6dChA5mZmTXKZmRk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wujSZMmDBo0CHNzc5ydncnKyiI8PFwpU7lyZbp168aVK1fo3Lmzsi0sLIwffvgBCwsLevfu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\n1GcdRcIVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22af82b3ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sv_a_dict['Fans'].ix[:,'W/CFM'].plot(kind='barh', figsize=(12,10), color='#EB969C', title='Fan W/CFM')\n",
"sns.despine()\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['Systems', 'Zones', 'Fans'])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sv_a_dict.keys()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>System Type</th>\n",
" <th>Altitude Factor</th>\n",
" <th>Floor Area (sqft)</th>\n",
" <th>Max People</th>\n",
" <th>Outside Air Ratio</th>\n",
" <th>Cooling Capacity (kBTU/hr)</th>\n",
" <th>Sensible (SHR)</th>\n",
" <th>Heating Capacity (kBTU/hr)</th>\n",
" <th>Cooling EIR (BTU/BTU)</th>\n",
" <th>Heating EIR (BTU/BTU)</th>\n",
" <th>Heat Pump Supplemental Heat (kBTU/hr)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>System</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Hallway System - MASTER CA SYS</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>2724.5</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>172.432</td>\n",
" <td>0.709</td>\n",
" <td>-208.729</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP for Office 3rd Floor</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>327.9</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>17.649</td>\n",
" <td>0.729</td>\n",
" <td>-21.365</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Lobby Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>227.3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>25.441</td>\n",
" <td>0.71</td>\n",
" <td>-30.797</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Conf/TV Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>1619.7</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>173.163</td>\n",
" <td>0.704</td>\n",
" <td>-209.614</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Kit Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>1500.8</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>165.764</td>\n",
" <td>0.692</td>\n",
" <td>-200.658</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" System Type Altitude Factor Floor Area (sqft) \\\n",
"System \n",
"Hallway System - MASTER CA SYS PVVT 1 2724.5 \n",
"HP for Office 3rd Floor PVVT 1 327.9 \n",
"HP Above Lobby Sys PVVT 1 227.3 \n",
"HP Conf/TV Sys PVVT 1 1619.7 \n",
"HP Above Kit Sys PVVT 1 1500.8 \n",
"\n",
" Max People Outside Air Ratio \\\n",
"System \n",
"Hallway System - MASTER CA SYS 3 0 \n",
"HP for Office 3rd Floor 2 0 \n",
"HP Above Lobby Sys 1 0 \n",
"HP Conf/TV Sys 81 0 \n",
"HP Above Kit Sys 31 0 \n",
"\n",
" Cooling Capacity (kBTU/hr) Sensible (SHR) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 172.432 0.709 \n",
"HP for Office 3rd Floor 17.649 0.729 \n",
"HP Above Lobby Sys 25.441 0.71 \n",
"HP Conf/TV Sys 173.163 0.704 \n",
"HP Above Kit Sys 165.764 0.692 \n",
"\n",
" Heating Capacity (kBTU/hr) \\\n",
"System \n",
"Hallway System - MASTER CA SYS -208.729 \n",
"HP for Office 3rd Floor -21.365 \n",
"HP Above Lobby Sys -30.797 \n",
"HP Conf/TV Sys -209.614 \n",
"HP Above Kit Sys -200.658 \n",
"\n",
" Cooling EIR (BTU/BTU) Heating EIR (BTU/BTU) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 0.269 0.24 \n",
"HP for Office 3rd Floor 0.269 0.24 \n",
"HP Above Lobby Sys 0.269 0.24 \n",
"HP Conf/TV Sys 0.269 0.24 \n",
"HP Above Kit Sys 0.269 0.24 \n",
"\n",
" Heat Pump Supplemental Heat (kBTU/hr) \n",
"System \n",
"Hallway System - MASTER CA SYS 0 \n",
"HP for Office 3rd Floor 0 \n",
"HP Above Lobby Sys 0 \n",
"HP Conf/TV Sys 0 \n",
"HP Above Kit Sys 0 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sys = sv_a_dict['Systems']\n",
"sys.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Calculating Max People/sqft\n",
"sys['Max People/sqft'] = sys['Max People'] / sys['Floor Area (sqft)']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>System Type</th>\n",
" <th>Altitude Factor</th>\n",
" <th>Floor Area (sqft)</th>\n",
" <th>Max People</th>\n",
" <th>Outside Air Ratio</th>\n",
" <th>Cooling Capacity (kBTU/hr)</th>\n",
" <th>Sensible (SHR)</th>\n",
" <th>Heating Capacity (kBTU/hr)</th>\n",
" <th>Cooling EIR (BTU/BTU)</th>\n",
" <th>Heating EIR (BTU/BTU)</th>\n",
" <th>Heat Pump Supplemental Heat (kBTU/hr)</th>\n",
" <th>Max People/sqft</th>\n",
" </tr>\n",
" <tr>\n",
" <th>System</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>HP Conf/TV Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>1619.7</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>173.163</td>\n",
" <td>0.704</td>\n",
" <td>-209.614</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.0500093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Kit Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>1500.8</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>165.764</td>\n",
" <td>0.692</td>\n",
" <td>-200.658</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.0206557</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-07 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>224.4</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>13.268</td>\n",
" <td>0.718</td>\n",
" <td>-16.06</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.013369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-08 Zn MASTER SYS</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>233.7</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>12.58</td>\n",
" <td>0.707</td>\n",
" <td>-15.228</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.012837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-05 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>249.9</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>20.339</td>\n",
" <td>0.715</td>\n",
" <td>-24.621</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.0120048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-05 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>249.9</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>12.934</td>\n",
" <td>0.729</td>\n",
" <td>-15.656</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.0120048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-05 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>249.9</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>13.111</td>\n",
" <td>0.719</td>\n",
" <td>-15.871</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.0120048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-01 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>275.9</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15.671</td>\n",
" <td>0.725</td>\n",
" <td>-18.969</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.0108735</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-06 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>309.7</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>16.23</td>\n",
" <td>0.712</td>\n",
" <td>-19.646</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.00968679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-03 Zn-Sys</th>\n",
" <td>PVVT</td>\n",
" <td>1</td>\n",
" <td>325.2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>16.65</td>\n",
" <td>0.73</td>\n",
" <td>-20.155</td>\n",
" <td>0.269</td>\n",
" <td>0.24</td>\n",
" <td>0</td>\n",
" <td>0.00922509</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" System Type Altitude Factor Floor Area (sqft) \\\n",
"System \n",
"HP Conf/TV Sys PVVT 1 1619.7 \n",
"HP Above Kit Sys PVVT 1 1500.8 \n",
"B-APT-07 Zn-Sys PVVT 1 224.4 \n",
"B-APT-08 Zn MASTER SYS PVVT 1 233.7 \n",
"2-APT-05 Zn-Sys PVVT 1 249.9 \n",
"4-APT-05 Zn-Sys PVVT 1 249.9 \n",
"3-APT-05 Zn-Sys PVVT 1 249.9 \n",
"3-APT-01 Zn-Sys PVVT 1 275.9 \n",
"B-APT-06 Zn-Sys PVVT 1 309.7 \n",
"4-APT-03 Zn-Sys PVVT 1 325.2 \n",
"\n",
" Max People Outside Air Ratio \\\n",
"System \n",
"HP Conf/TV Sys 81 0 \n",
"HP Above Kit Sys 31 0 \n",
"B-APT-07 Zn-Sys 3 0 \n",
"B-APT-08 Zn MASTER SYS 3 0 \n",
"2-APT-05 Zn-Sys 3 0 \n",
"4-APT-05 Zn-Sys 3 0 \n",
"3-APT-05 Zn-Sys 3 0 \n",
"3-APT-01 Zn-Sys 3 0 \n",
"B-APT-06 Zn-Sys 3 0 \n",
"4-APT-03 Zn-Sys 3 0 \n",
"\n",
" Cooling Capacity (kBTU/hr) Sensible (SHR) \\\n",
"System \n",
"HP Conf/TV Sys 173.163 0.704 \n",
"HP Above Kit Sys 165.764 0.692 \n",
"B-APT-07 Zn-Sys 13.268 0.718 \n",
"B-APT-08 Zn MASTER SYS 12.58 0.707 \n",
"2-APT-05 Zn-Sys 20.339 0.715 \n",
"4-APT-05 Zn-Sys 12.934 0.729 \n",
"3-APT-05 Zn-Sys 13.111 0.719 \n",
"3-APT-01 Zn-Sys 15.671 0.725 \n",
"B-APT-06 Zn-Sys 16.23 0.712 \n",
"4-APT-03 Zn-Sys 16.65 0.73 \n",
"\n",
" Heating Capacity (kBTU/hr) Cooling EIR (BTU/BTU) \\\n",
"System \n",
"HP Conf/TV Sys -209.614 0.269 \n",
"HP Above Kit Sys -200.658 0.269 \n",
"B-APT-07 Zn-Sys -16.06 0.269 \n",
"B-APT-08 Zn MASTER SYS -15.228 0.269 \n",
"2-APT-05 Zn-Sys -24.621 0.269 \n",
"4-APT-05 Zn-Sys -15.656 0.269 \n",
"3-APT-05 Zn-Sys -15.871 0.269 \n",
"3-APT-01 Zn-Sys -18.969 0.269 \n",
"B-APT-06 Zn-Sys -19.646 0.269 \n",
"4-APT-03 Zn-Sys -20.155 0.269 \n",
"\n",
" Heating EIR (BTU/BTU) \\\n",
"System \n",
"HP Conf/TV Sys 0.24 \n",
"HP Above Kit Sys 0.24 \n",
"B-APT-07 Zn-Sys 0.24 \n",
"B-APT-08 Zn MASTER SYS 0.24 \n",
"2-APT-05 Zn-Sys 0.24 \n",
"4-APT-05 Zn-Sys 0.24 \n",
"3-APT-05 Zn-Sys 0.24 \n",
"3-APT-01 Zn-Sys 0.24 \n",
"B-APT-06 Zn-Sys 0.24 \n",
"4-APT-03 Zn-Sys 0.24 \n",
"\n",
" Heat Pump Supplemental Heat (kBTU/hr) Max People/sqft \n",
"System \n",
"HP Conf/TV Sys 0 0.0500093 \n",
"HP Above Kit Sys 0 0.0206557 \n",
"B-APT-07 Zn-Sys 0 0.013369 \n",
"B-APT-08 Zn MASTER SYS 0 0.012837 \n",
"2-APT-05 Zn-Sys 0 0.0120048 \n",
"4-APT-05 Zn-Sys 0 0.0120048 \n",
"3-APT-05 Zn-Sys 0 0.0120048 \n",
"3-APT-01 Zn-Sys 0 0.0108735 \n",
"B-APT-06 Zn-Sys 0 0.00968679 \n",
"4-APT-03 Zn-Sys 0 0.00922509 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Sorting and returning top 10\n",
"sys.sort_values(by='Max People/sqft', ascending=False).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Supply Flow (CFM)</th>\n",
" <th>Exhaust Flow (CFM)</th>\n",
" <th>Fan (kW)</th>\n",
" <th>Minimum Flow (Frac)</th>\n",
" <th>Outside Air Flow (CFM)</th>\n",
" <th>Cooling Capacity (kBTU/hr)</th>\n",
" <th>Sensible (FRAC)</th>\n",
" <th>Extract Rate (kBTU/hr)</th>\n",
" <th>Heating Capacity (kBTU/hr)</th>\n",
" <th>Addition Rate (kBTU/hr)</th>\n",
" <th>Zone Mult</th>\n",
" <th>W/CFM</th>\n",
" </tr>\n",
" <tr>\n",
" <th>System</th>\n",
" <th>Zone Name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"9\" valign=\"top\">Hallway System - MASTER CA SYS</th>\n",
" <th>2-HALLWAY-2 Zn</th>\n",
" <td>400</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9.94</td>\n",
" <td>0</td>\n",
" <td>-13.40</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-HALLWAY Zn</th>\n",
" <td>724</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>17.99</td>\n",
" <td>0</td>\n",
" <td>-24.25</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1-HALLWAY-1 Zn</th>\n",
" <td>270</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6.69</td>\n",
" <td>0</td>\n",
" <td>-9.02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1-HALLWAY-2 Zn</th>\n",
" <td>1185</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29.45</td>\n",
" <td>0</td>\n",
" <td>-39.69</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-HALLWAY-1 Zn</th>\n",
" <td>995</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24.71</td>\n",
" <td>0</td>\n",
" <td>-33.31</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-ELEV-ROOM Zn</th>\n",
" <td>53</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1.31</td>\n",
" <td>0</td>\n",
" <td>-1.77</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-HALLWAY-2 Zn</th>\n",
" <td>680</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16.90</td>\n",
" <td>0</td>\n",
" <td>-22.77</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-HALLWAY-1 Zn</th>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.77</td>\n",
" <td>0</td>\n",
" <td>-1.03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-HALLWAY Zn</th>\n",
" <td>770</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19.13</td>\n",
" <td>0</td>\n",
" <td>-25.78</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP for Office 3rd Floor</th>\n",
" <th>3-OFFICE-Zn</th>\n",
" <td>548</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.61</td>\n",
" <td>0</td>\n",
" <td>-18.34</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Supply Flow (CFM) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 400 \n",
" B-HALLWAY Zn 724 \n",
" 1-HALLWAY-1 Zn 270 \n",
" 1-HALLWAY-2 Zn 1185 \n",
" 2-HALLWAY-1 Zn 995 \n",
" 2-ELEV-ROOM Zn 53 \n",
" 3-HALLWAY-2 Zn 680 \n",
" 3-HALLWAY-1 Zn 31 \n",
" 4-HALLWAY Zn 770 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 548 \n",
"\n",
" Exhaust Flow (CFM) Fan (kW) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 0 0 \n",
" B-HALLWAY Zn 0 0 \n",
" 1-HALLWAY-1 Zn 0 0 \n",
" 1-HALLWAY-2 Zn 0 0 \n",
" 2-HALLWAY-1 Zn 0 0 \n",
" 2-ELEV-ROOM Zn 0 0 \n",
" 3-HALLWAY-2 Zn 0 0 \n",
" 3-HALLWAY-1 Zn 0 0 \n",
" 4-HALLWAY Zn 0 0 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 0 0 \n",
"\n",
" Minimum Flow (Frac) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 1 \n",
" B-HALLWAY Zn 1 \n",
" 1-HALLWAY-1 Zn 1 \n",
" 1-HALLWAY-2 Zn 1 \n",
" 2-HALLWAY-1 Zn 1 \n",
" 2-ELEV-ROOM Zn 1 \n",
" 3-HALLWAY-2 Zn 1 \n",
" 3-HALLWAY-1 Zn 1 \n",
" 4-HALLWAY Zn 1 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 1 \n",
"\n",
" Outside Air Flow (CFM) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 0 \n",
" B-HALLWAY Zn 0 \n",
" 1-HALLWAY-1 Zn 0 \n",
" 1-HALLWAY-2 Zn 0 \n",
" 2-HALLWAY-1 Zn 0 \n",
" 2-ELEV-ROOM Zn 0 \n",
" 3-HALLWAY-2 Zn 0 \n",
" 3-HALLWAY-1 Zn 0 \n",
" 4-HALLWAY Zn 0 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 0 \n",
"\n",
" Cooling Capacity (kBTU/hr) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 0 \n",
" B-HALLWAY Zn 0 \n",
" 1-HALLWAY-1 Zn 0 \n",
" 1-HALLWAY-2 Zn 0 \n",
" 2-HALLWAY-1 Zn 0 \n",
" 2-ELEV-ROOM Zn 0 \n",
" 3-HALLWAY-2 Zn 0 \n",
" 3-HALLWAY-1 Zn 0 \n",
" 4-HALLWAY Zn 0 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 0 \n",
"\n",
" Sensible (FRAC) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 0 \n",
" B-HALLWAY Zn 0 \n",
" 1-HALLWAY-1 Zn 0 \n",
" 1-HALLWAY-2 Zn 0 \n",
" 2-HALLWAY-1 Zn 0 \n",
" 2-ELEV-ROOM Zn 0 \n",
" 3-HALLWAY-2 Zn 0 \n",
" 3-HALLWAY-1 Zn 0 \n",
" 4-HALLWAY Zn 0 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 0 \n",
"\n",
" Extract Rate (kBTU/hr) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 9.94 \n",
" B-HALLWAY Zn 17.99 \n",
" 1-HALLWAY-1 Zn 6.69 \n",
" 1-HALLWAY-2 Zn 29.45 \n",
" 2-HALLWAY-1 Zn 24.71 \n",
" 2-ELEV-ROOM Zn 1.31 \n",
" 3-HALLWAY-2 Zn 16.90 \n",
" 3-HALLWAY-1 Zn 0.77 \n",
" 4-HALLWAY Zn 19.13 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 13.61 \n",
"\n",
" Heating Capacity (kBTU/hr) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 0 \n",
" B-HALLWAY Zn 0 \n",
" 1-HALLWAY-1 Zn 0 \n",
" 1-HALLWAY-2 Zn 0 \n",
" 2-HALLWAY-1 Zn 0 \n",
" 2-ELEV-ROOM Zn 0 \n",
" 3-HALLWAY-2 Zn 0 \n",
" 3-HALLWAY-1 Zn 0 \n",
" 4-HALLWAY Zn 0 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 0 \n",
"\n",
" Addition Rate (kBTU/hr) \\\n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn -13.40 \n",
" B-HALLWAY Zn -24.25 \n",
" 1-HALLWAY-1 Zn -9.02 \n",
" 1-HALLWAY-2 Zn -39.69 \n",
" 2-HALLWAY-1 Zn -33.31 \n",
" 2-ELEV-ROOM Zn -1.77 \n",
" 3-HALLWAY-2 Zn -22.77 \n",
" 3-HALLWAY-1 Zn -1.03 \n",
" 4-HALLWAY Zn -25.78 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn -18.34 \n",
"\n",
" Zone Mult W/CFM \n",
"System Zone Name \n",
"Hallway System - MASTER CA SYS 2-HALLWAY-2 Zn 1 0 \n",
" B-HALLWAY Zn 1 0 \n",
" 1-HALLWAY-1 Zn 1 0 \n",
" 1-HALLWAY-2 Zn 1 0 \n",
" 2-HALLWAY-1 Zn 1 0 \n",
" 2-ELEV-ROOM Zn 1 0 \n",
" 3-HALLWAY-2 Zn 1 0 \n",
" 3-HALLWAY-1 Zn 1 0 \n",
" 4-HALLWAY Zn 1 0 \n",
"HP for Office 3rd Floor 3-OFFICE-Zn 1 0 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zones = sv_a_dict['Zones']\n",
"zones.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Group by system and apply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example on aggregating zones to the system level"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def custom_apply_zones(x):\n",
" \"\"\" Aggregate zone data to the system level\n",
" \n",
" For the zones, some columns should be summed (CFM, Capacity, etc)\n",
" But others should be averaged\n",
" \"\"\"\n",
" # For these three columns, do a mean\n",
" if x.name in ['Minimum Flow (Frac)', 'Sensible (FRAC)', 'W/CFM']:\n",
" return np.mean(x)\n",
" # For the rest, do a sum\n",
" else:\n",
" return np.sum(x)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Supply Flow (CFM)</th>\n",
" <th>Exhaust Flow (CFM)</th>\n",
" <th>Fan (kW)</th>\n",
" <th>Minimum Flow (Frac)</th>\n",
" <th>Outside Air Flow (CFM)</th>\n",
" <th>Cooling Capacity (kBTU/hr)</th>\n",
" <th>Sensible (FRAC)</th>\n",
" <th>Extract Rate (kBTU/hr)</th>\n",
" <th>Heating Capacity (kBTU/hr)</th>\n",
" <th>Addition Rate (kBTU/hr)</th>\n",
" <th>Zone Mult</th>\n",
" <th>W/CFM</th>\n",
" </tr>\n",
" <tr>\n",
" <th>System</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Hallway System - MASTER CA SYS</th>\n",
" <td>5108</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>126.89</td>\n",
" <td>0</td>\n",
" <td>-171.02</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP for Office 3rd Floor</th>\n",
" <td>548</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13.61</td>\n",
" <td>0</td>\n",
" <td>-18.34</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Lobby Sys</th>\n",
" <td>755</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18.76</td>\n",
" <td>0</td>\n",
" <td>-25.29</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Conf/TV Sys</th>\n",
" <td>5069</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>125.91</td>\n",
" <td>0</td>\n",
" <td>-169.70</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Kit Sys</th>\n",
" <td>4708</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>116.94</td>\n",
" <td>0</td>\n",
" <td>-157.62</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Supply Flow (CFM) Exhaust Flow (CFM) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 5108 0 \n",
"HP for Office 3rd Floor 548 0 \n",
"HP Above Lobby Sys 755 0 \n",
"HP Conf/TV Sys 5069 0 \n",
"HP Above Kit Sys 4708 0 \n",
"\n",
" Fan (kW) Minimum Flow (Frac) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 0 1 \n",
"HP for Office 3rd Floor 0 1 \n",
"HP Above Lobby Sys 0 1 \n",
"HP Conf/TV Sys 0 1 \n",
"HP Above Kit Sys 0 1 \n",
"\n",
" Outside Air Flow (CFM) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 0 \n",
"HP for Office 3rd Floor 0 \n",
"HP Above Lobby Sys 0 \n",
"HP Conf/TV Sys 0 \n",
"HP Above Kit Sys 0 \n",
"\n",
" Cooling Capacity (kBTU/hr) Sensible (FRAC) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 0 0 \n",
"HP for Office 3rd Floor 0 0 \n",
"HP Above Lobby Sys 0 0 \n",
"HP Conf/TV Sys 0 0 \n",
"HP Above Kit Sys 0 0 \n",
"\n",
" Extract Rate (kBTU/hr) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 126.89 \n",
"HP for Office 3rd Floor 13.61 \n",
"HP Above Lobby Sys 18.76 \n",
"HP Conf/TV Sys 125.91 \n",
"HP Above Kit Sys 116.94 \n",
"\n",
" Heating Capacity (kBTU/hr) \\\n",
"System \n",
"Hallway System - MASTER CA SYS 0 \n",
"HP for Office 3rd Floor 0 \n",
"HP Above Lobby Sys 0 \n",
"HP Conf/TV Sys 0 \n",
"HP Above Kit Sys 0 \n",
"\n",
" Addition Rate (kBTU/hr) Zone Mult W/CFM \n",
"System \n",
"Hallway System - MASTER CA SYS -171.02 9 0 \n",
"HP for Office 3rd Floor -18.34 1 0 \n",
"HP Above Lobby Sys -25.29 4 0 \n",
"HP Conf/TV Sys -169.70 2 0 \n",
"HP Above Kit Sys -157.62 2 0 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# After the groupby, the apply applies to each group dataframe. So I use a lambda x to apply to each column\n",
"zones_agg_metrics = zones.groupby(level='System').apply(lambda x: x.apply(custom_apply_zones))\n",
"# Recalc a weighted W/CFM\n",
"zones_agg_metrics['W/CFM'] = zones_agg_metrics['Fan (kW)'] * 1000 / zones_agg_metrics['Supply Flow (CFM)']\n",
"\n",
"zones_agg_metrics.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fans = sv_a_dict['Fans']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Capacity (CFM)</th>\n",
" <th>Diversity Factor (FRAC)</th>\n",
" <th>Power Demand (kW)</th>\n",
" <th>Fan deltaT (F)</th>\n",
" <th>Static Pressure (in w.c.)</th>\n",
" <th>Total efficiency</th>\n",
" <th>Mechanical Efficiency</th>\n",
" <th>Fan Placement</th>\n",
" <th>Fan Control</th>\n",
" <th>Max Fan Ratio (Frac)</th>\n",
" <th>Min Fan Ratio (Frac)</th>\n",
" <th>W/CFM</th>\n",
" </tr>\n",
" <tr>\n",
" <th>System</th>\n",
" <th>Fan Type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Hallway System - MASTER CA SYS</th>\n",
" <th>SUPPLY</th>\n",
" <td>5108</td>\n",
" <td>1</td>\n",
" <td>1.533</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.300117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP for Office 3rd Floor</th>\n",
" <th>SUPPLY</th>\n",
" <td>548</td>\n",
" <td>1</td>\n",
" <td>0.164</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.299270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Lobby Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>756</td>\n",
" <td>1</td>\n",
" <td>0.227</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.300265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Conf/TV Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>5069</td>\n",
" <td>1</td>\n",
" <td>1.521</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.300059</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP Above Kit Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>4708</td>\n",
" <td>1</td>\n",
" <td>1.412</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.299915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP 1 Base Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>950</td>\n",
" <td>1</td>\n",
" <td>0.285</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HP 2 Base Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>776</td>\n",
" <td>1</td>\n",
" <td>0.233</td>\n",
" <td>0.93</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.300258</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-08 Zn MASTER SYS</th>\n",
" <th>SUPPLY</th>\n",
" <td>371</td>\n",
" <td>1</td>\n",
" <td>0.100</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-05 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>401</td>\n",
" <td>1</td>\n",
" <td>0.108</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-02 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>628</td>\n",
" <td>1</td>\n",
" <td>0.170</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270701</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-06 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>1006</td>\n",
" <td>1</td>\n",
" <td>0.272</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-01 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>654</td>\n",
" <td>1</td>\n",
" <td>0.177</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-07 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>401</td>\n",
" <td>1</td>\n",
" <td>0.108</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-03 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>1680</td>\n",
" <td>1</td>\n",
" <td>0.454</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270238</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-02 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>585</td>\n",
" <td>1</td>\n",
" <td>0.158</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-03 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>728</td>\n",
" <td>1</td>\n",
" <td>0.197</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-06 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>485</td>\n",
" <td>1</td>\n",
" <td>0.131</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-04 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>698</td>\n",
" <td>1</td>\n",
" <td>0.189</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-05 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>612</td>\n",
" <td>1</td>\n",
" <td>0.165</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-07 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>1298</td>\n",
" <td>1</td>\n",
" <td>0.350</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-02 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>934</td>\n",
" <td>1</td>\n",
" <td>0.252</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-06 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>975</td>\n",
" <td>1</td>\n",
" <td>0.263</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-05 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>398</td>\n",
" <td>1</td>\n",
" <td>0.107</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.268844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-01 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>908</td>\n",
" <td>1</td>\n",
" <td>0.245</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269824</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-06 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>750</td>\n",
" <td>1</td>\n",
" <td>0.203</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-01 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>482</td>\n",
" <td>1</td>\n",
" <td>0.130</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269710</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-01 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>757</td>\n",
" <td>1</td>\n",
" <td>0.204</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-02 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>1031</td>\n",
" <td>1</td>\n",
" <td>0.278</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-07 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>777</td>\n",
" <td>1</td>\n",
" <td>0.210</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-03 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>518</td>\n",
" <td>1</td>\n",
" <td>0.140</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270270</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-04 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>565</td>\n",
" <td>1</td>\n",
" <td>0.153</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4-APT-07 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>690</td>\n",
" <td>1</td>\n",
" <td>0.186</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269565</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3-APT-03 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>556</td>\n",
" <td>1</td>\n",
" <td>0.150</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-08 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>807</td>\n",
" <td>1</td>\n",
" <td>0.218</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>B-APT-04 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>664</td>\n",
" <td>1</td>\n",
" <td>0.179</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.269578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2-APT-04 Zn-Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>1384</td>\n",
" <td>1</td>\n",
" <td>0.374</td>\n",
" <td>0.83</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>DRAW-THRU</td>\n",
" <td>CONSTANT</td>\n",
" <td>1</td>\n",
" <td>0.3</td>\n",
" <td>0.270231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Elev Cooling Sys</th>\n",
" <th>SUPPLY</th>\n",
" <td>586</td>\n",
" <td>0</td>\n",
" <td>0.000</td>\n",
" <td>0.22</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>BLOW-THRU</td>\n",
" <td>2-SPEED</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Capacity (CFM) \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 5108 \n",
"HP for Office 3rd Floor SUPPLY 548 \n",
"HP Above Lobby Sys SUPPLY 756 \n",
"HP Conf/TV Sys SUPPLY 5069 \n",
"HP Above Kit Sys SUPPLY 4708 \n",
"HP 1 Base Sys SUPPLY 950 \n",
"HP 2 Base Sys SUPPLY 776 \n",
"B-APT-08 Zn MASTER SYS SUPPLY 371 \n",
"4-APT-05 Zn-Sys SUPPLY 401 \n",
"4-APT-02 Zn-Sys SUPPLY 628 \n",
"4-APT-06 Zn-Sys SUPPLY 1006 \n",
"B-APT-01 Zn-Sys SUPPLY 654 \n",
"B-APT-07 Zn-Sys SUPPLY 401 \n",
"2-APT-03 Zn-Sys SUPPLY 1680 \n",
"B-APT-02 Zn-Sys SUPPLY 585 \n",
"B-APT-03 Zn-Sys SUPPLY 728 \n",
"B-APT-06 Zn-Sys SUPPLY 485 \n",
"4-APT-04 Zn-Sys SUPPLY 698 \n",
"2-APT-05 Zn-Sys SUPPLY 612 \n",
"2-APT-07 Zn-Sys SUPPLY 1298 \n",
"3-APT-02 Zn-Sys SUPPLY 934 \n",
"2-APT-06 Zn-Sys SUPPLY 975 \n",
"3-APT-05 Zn-Sys SUPPLY 398 \n",
"2-APT-01 Zn-Sys SUPPLY 908 \n",
"3-APT-06 Zn-Sys SUPPLY 750 \n",
"3-APT-01 Zn-Sys SUPPLY 482 \n",
"4-APT-01 Zn-Sys SUPPLY 757 \n",
"2-APT-02 Zn-Sys SUPPLY 1031 \n",
"3-APT-07 Zn-Sys SUPPLY 777 \n",
"4-APT-03 Zn-Sys SUPPLY 518 \n",
"3-APT-04 Zn-Sys SUPPLY 565 \n",
"4-APT-07 Zn-Sys SUPPLY 690 \n",
"3-APT-03 Zn-Sys SUPPLY 556 \n",
"2-APT-08 Zn-Sys SUPPLY 807 \n",
"B-APT-04 Zn-Sys SUPPLY 664 \n",
"2-APT-04 Zn-Sys SUPPLY 1384 \n",
"Elev Cooling Sys SUPPLY 586 \n",
"\n",
" Diversity Factor (FRAC) \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 1 \n",
"HP for Office 3rd Floor SUPPLY 1 \n",
"HP Above Lobby Sys SUPPLY 1 \n",
"HP Conf/TV Sys SUPPLY 1 \n",
"HP Above Kit Sys SUPPLY 1 \n",
"HP 1 Base Sys SUPPLY 1 \n",
"HP 2 Base Sys SUPPLY 1 \n",
"B-APT-08 Zn MASTER SYS SUPPLY 1 \n",
"4-APT-05 Zn-Sys SUPPLY 1 \n",
"4-APT-02 Zn-Sys SUPPLY 1 \n",
"4-APT-06 Zn-Sys SUPPLY 1 \n",
"B-APT-01 Zn-Sys SUPPLY 1 \n",
"B-APT-07 Zn-Sys SUPPLY 1 \n",
"2-APT-03 Zn-Sys SUPPLY 1 \n",
"B-APT-02 Zn-Sys SUPPLY 1 \n",
"B-APT-03 Zn-Sys SUPPLY 1 \n",
"B-APT-06 Zn-Sys SUPPLY 1 \n",
"4-APT-04 Zn-Sys SUPPLY 1 \n",
"2-APT-05 Zn-Sys SUPPLY 1 \n",
"2-APT-07 Zn-Sys SUPPLY 1 \n",
"3-APT-02 Zn-Sys SUPPLY 1 \n",
"2-APT-06 Zn-Sys SUPPLY 1 \n",
"3-APT-05 Zn-Sys SUPPLY 1 \n",
"2-APT-01 Zn-Sys SUPPLY 1 \n",
"3-APT-06 Zn-Sys SUPPLY 1 \n",
"3-APT-01 Zn-Sys SUPPLY 1 \n",
"4-APT-01 Zn-Sys SUPPLY 1 \n",
"2-APT-02 Zn-Sys SUPPLY 1 \n",
"3-APT-07 Zn-Sys SUPPLY 1 \n",
"4-APT-03 Zn-Sys SUPPLY 1 \n",
"3-APT-04 Zn-Sys SUPPLY 1 \n",
"4-APT-07 Zn-Sys SUPPLY 1 \n",
"3-APT-03 Zn-Sys SUPPLY 1 \n",
"2-APT-08 Zn-Sys SUPPLY 1 \n",
"B-APT-04 Zn-Sys SUPPLY 1 \n",
"2-APT-04 Zn-Sys SUPPLY 1 \n",
"Elev Cooling Sys SUPPLY 0 \n",
"\n",
" Power Demand (kW) Fan deltaT (F) \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 1.533 0.93 \n",
"HP for Office 3rd Floor SUPPLY 0.164 0.93 \n",
"HP Above Lobby Sys SUPPLY 0.227 0.93 \n",
"HP Conf/TV Sys SUPPLY 1.521 0.93 \n",
"HP Above Kit Sys SUPPLY 1.412 0.93 \n",
"HP 1 Base Sys SUPPLY 0.285 0.93 \n",
"HP 2 Base Sys SUPPLY 0.233 0.93 \n",
"B-APT-08 Zn MASTER SYS SUPPLY 0.100 0.83 \n",
"4-APT-05 Zn-Sys SUPPLY 0.108 0.83 \n",
"4-APT-02 Zn-Sys SUPPLY 0.170 0.83 \n",
"4-APT-06 Zn-Sys SUPPLY 0.272 0.83 \n",
"B-APT-01 Zn-Sys SUPPLY 0.177 0.83 \n",
"B-APT-07 Zn-Sys SUPPLY 0.108 0.83 \n",
"2-APT-03 Zn-Sys SUPPLY 0.454 0.83 \n",
"B-APT-02 Zn-Sys SUPPLY 0.158 0.83 \n",
"B-APT-03 Zn-Sys SUPPLY 0.197 0.83 \n",
"B-APT-06 Zn-Sys SUPPLY 0.131 0.83 \n",
"4-APT-04 Zn-Sys SUPPLY 0.189 0.83 \n",
"2-APT-05 Zn-Sys SUPPLY 0.165 0.83 \n",
"2-APT-07 Zn-Sys SUPPLY 0.350 0.83 \n",
"3-APT-02 Zn-Sys SUPPLY 0.252 0.83 \n",
"2-APT-06 Zn-Sys SUPPLY 0.263 0.83 \n",
"3-APT-05 Zn-Sys SUPPLY 0.107 0.83 \n",
"2-APT-01 Zn-Sys SUPPLY 0.245 0.83 \n",
"3-APT-06 Zn-Sys SUPPLY 0.203 0.83 \n",
"3-APT-01 Zn-Sys SUPPLY 0.130 0.83 \n",
"4-APT-01 Zn-Sys SUPPLY 0.204 0.83 \n",
"2-APT-02 Zn-Sys SUPPLY 0.278 0.83 \n",
"3-APT-07 Zn-Sys SUPPLY 0.210 0.83 \n",
"4-APT-03 Zn-Sys SUPPLY 0.140 0.83 \n",
"3-APT-04 Zn-Sys SUPPLY 0.153 0.83 \n",
"4-APT-07 Zn-Sys SUPPLY 0.186 0.83 \n",
"3-APT-03 Zn-Sys SUPPLY 0.150 0.83 \n",
"2-APT-08 Zn-Sys SUPPLY 0.218 0.83 \n",
"B-APT-04 Zn-Sys SUPPLY 0.179 0.83 \n",
"2-APT-04 Zn-Sys SUPPLY 0.374 0.83 \n",
"Elev Cooling Sys SUPPLY 0.000 0.22 \n",
"\n",
" Static Pressure (in w.c.) \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 0 \n",
"HP for Office 3rd Floor SUPPLY 0 \n",
"HP Above Lobby Sys SUPPLY 0 \n",
"HP Conf/TV Sys SUPPLY 0 \n",
"HP Above Kit Sys SUPPLY 0 \n",
"HP 1 Base Sys SUPPLY 0 \n",
"HP 2 Base Sys SUPPLY 0 \n",
"B-APT-08 Zn MASTER SYS SUPPLY 0 \n",
"4-APT-05 Zn-Sys SUPPLY 0 \n",
"4-APT-02 Zn-Sys SUPPLY 0 \n",
"4-APT-06 Zn-Sys SUPPLY 0 \n",
"B-APT-01 Zn-Sys SUPPLY 0 \n",
"B-APT-07 Zn-Sys SUPPLY 0 \n",
"2-APT-03 Zn-Sys SUPPLY 0 \n",
"B-APT-02 Zn-Sys SUPPLY 0 \n",
"B-APT-03 Zn-Sys SUPPLY 0 \n",
"B-APT-06 Zn-Sys SUPPLY 0 \n",
"4-APT-04 Zn-Sys SUPPLY 0 \n",
"2-APT-05 Zn-Sys SUPPLY 0 \n",
"2-APT-07 Zn-Sys SUPPLY 0 \n",
"3-APT-02 Zn-Sys SUPPLY 0 \n",
"2-APT-06 Zn-Sys SUPPLY 0 \n",
"3-APT-05 Zn-Sys SUPPLY 0 \n",
"2-APT-01 Zn-Sys SUPPLY 0 \n",
"3-APT-06 Zn-Sys SUPPLY 0 \n",
"3-APT-01 Zn-Sys SUPPLY 0 \n",
"4-APT-01 Zn-Sys SUPPLY 0 \n",
"2-APT-02 Zn-Sys SUPPLY 0 \n",
"3-APT-07 Zn-Sys SUPPLY 0 \n",
"4-APT-03 Zn-Sys SUPPLY 0 \n",
"3-APT-04 Zn-Sys SUPPLY 0 \n",
"4-APT-07 Zn-Sys SUPPLY 0 \n",
"3-APT-03 Zn-Sys SUPPLY 0 \n",
"2-APT-08 Zn-Sys SUPPLY 0 \n",
"B-APT-04 Zn-Sys SUPPLY 0 \n",
"2-APT-04 Zn-Sys SUPPLY 0 \n",
"Elev Cooling Sys SUPPLY 0 \n",
"\n",
" Total efficiency \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 0 \n",
"HP for Office 3rd Floor SUPPLY 0 \n",
"HP Above Lobby Sys SUPPLY 0 \n",
"HP Conf/TV Sys SUPPLY 0 \n",
"HP Above Kit Sys SUPPLY 0 \n",
"HP 1 Base Sys SUPPLY 0 \n",
"HP 2 Base Sys SUPPLY 0 \n",
"B-APT-08 Zn MASTER SYS SUPPLY 0 \n",
"4-APT-05 Zn-Sys SUPPLY 0 \n",
"4-APT-02 Zn-Sys SUPPLY 0 \n",
"4-APT-06 Zn-Sys SUPPLY 0 \n",
"B-APT-01 Zn-Sys SUPPLY 0 \n",
"B-APT-07 Zn-Sys SUPPLY 0 \n",
"2-APT-03 Zn-Sys SUPPLY 0 \n",
"B-APT-02 Zn-Sys SUPPLY 0 \n",
"B-APT-03 Zn-Sys SUPPLY 0 \n",
"B-APT-06 Zn-Sys SUPPLY 0 \n",
"4-APT-04 Zn-Sys SUPPLY 0 \n",
"2-APT-05 Zn-Sys SUPPLY 0 \n",
"2-APT-07 Zn-Sys SUPPLY 0 \n",
"3-APT-02 Zn-Sys SUPPLY 0 \n",
"2-APT-06 Zn-Sys SUPPLY 0 \n",
"3-APT-05 Zn-Sys SUPPLY 0 \n",
"2-APT-01 Zn-Sys SUPPLY 0 \n",
"3-APT-06 Zn-Sys SUPPLY 0 \n",
"3-APT-01 Zn-Sys SUPPLY 0 \n",
"4-APT-01 Zn-Sys SUPPLY 0 \n",
"2-APT-02 Zn-Sys SUPPLY 0 \n",
"3-APT-07 Zn-Sys SUPPLY 0 \n",
"4-APT-03 Zn-Sys SUPPLY 0 \n",
"3-APT-04 Zn-Sys SUPPLY 0 \n",
"4-APT-07 Zn-Sys SUPPLY 0 \n",
"3-APT-03 Zn-Sys SUPPLY 0 \n",
"2-APT-08 Zn-Sys SUPPLY 0 \n",
"B-APT-04 Zn-Sys SUPPLY 0 \n",
"2-APT-04 Zn-Sys SUPPLY 0 \n",
"Elev Cooling Sys SUPPLY 0 \n",
"\n",
" Mechanical Efficiency Fan Placement \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 0 DRAW-THRU \n",
"HP for Office 3rd Floor SUPPLY 0 DRAW-THRU \n",
"HP Above Lobby Sys SUPPLY 0 DRAW-THRU \n",
"HP Conf/TV Sys SUPPLY 0 DRAW-THRU \n",
"HP Above Kit Sys SUPPLY 0 DRAW-THRU \n",
"HP 1 Base Sys SUPPLY 0 DRAW-THRU \n",
"HP 2 Base Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-08 Zn MASTER SYS SUPPLY 0 DRAW-THRU \n",
"4-APT-05 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"4-APT-02 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"4-APT-06 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-01 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-07 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-03 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-02 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-03 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-06 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"4-APT-04 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-05 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-07 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-02 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-06 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-05 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-01 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-06 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-01 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"4-APT-01 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-02 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-07 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"4-APT-03 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-04 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"4-APT-07 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"3-APT-03 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-08 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"B-APT-04 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"2-APT-04 Zn-Sys SUPPLY 0 DRAW-THRU \n",
"Elev Cooling Sys SUPPLY 0 BLOW-THRU \n",
"\n",
" Fan Control Max Fan Ratio (Frac) \\\n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY CONSTANT 1 \n",
"HP for Office 3rd Floor SUPPLY CONSTANT 1 \n",
"HP Above Lobby Sys SUPPLY CONSTANT 1 \n",
"HP Conf/TV Sys SUPPLY CONSTANT 1 \n",
"HP Above Kit Sys SUPPLY CONSTANT 1 \n",
"HP 1 Base Sys SUPPLY CONSTANT 1 \n",
"HP 2 Base Sys SUPPLY CONSTANT 1 \n",
"B-APT-08 Zn MASTER SYS SUPPLY CONSTANT 1 \n",
"4-APT-05 Zn-Sys SUPPLY CONSTANT 1 \n",
"4-APT-02 Zn-Sys SUPPLY CONSTANT 1 \n",
"4-APT-06 Zn-Sys SUPPLY CONSTANT 1 \n",
"B-APT-01 Zn-Sys SUPPLY CONSTANT 1 \n",
"B-APT-07 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-03 Zn-Sys SUPPLY CONSTANT 1 \n",
"B-APT-02 Zn-Sys SUPPLY CONSTANT 1 \n",
"B-APT-03 Zn-Sys SUPPLY CONSTANT 1 \n",
"B-APT-06 Zn-Sys SUPPLY CONSTANT 1 \n",
"4-APT-04 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-05 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-07 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-02 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-06 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-05 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-01 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-06 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-01 Zn-Sys SUPPLY CONSTANT 1 \n",
"4-APT-01 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-02 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-07 Zn-Sys SUPPLY CONSTANT 1 \n",
"4-APT-03 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-04 Zn-Sys SUPPLY CONSTANT 1 \n",
"4-APT-07 Zn-Sys SUPPLY CONSTANT 1 \n",
"3-APT-03 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-08 Zn-Sys SUPPLY CONSTANT 1 \n",
"B-APT-04 Zn-Sys SUPPLY CONSTANT 1 \n",
"2-APT-04 Zn-Sys SUPPLY CONSTANT 1 \n",
"Elev Cooling Sys SUPPLY 2-SPEED 0 \n",
"\n",
" Min Fan Ratio (Frac) W/CFM \n",
"System Fan Type \n",
"Hallway System - MASTER CA SYS SUPPLY 0.3 0.300117 \n",
"HP for Office 3rd Floor SUPPLY 0.3 0.299270 \n",
"HP Above Lobby Sys SUPPLY 0.3 0.300265 \n",
"HP Conf/TV Sys SUPPLY 0.3 0.300059 \n",
"HP Above Kit Sys SUPPLY 0.3 0.299915 \n",
"HP 1 Base Sys SUPPLY 0.3 0.300000 \n",
"HP 2 Base Sys SUPPLY 0.3 0.300258 \n",
"B-APT-08 Zn MASTER SYS SUPPLY 0.3 0.269542 \n",
"4-APT-05 Zn-Sys SUPPLY 0.3 0.269327 \n",
"4-APT-02 Zn-Sys SUPPLY 0.3 0.270701 \n",
"4-APT-06 Zn-Sys SUPPLY 0.3 0.270378 \n",
"B-APT-01 Zn-Sys SUPPLY 0.3 0.270642 \n",
"B-APT-07 Zn-Sys SUPPLY 0.3 0.269327 \n",
"2-APT-03 Zn-Sys SUPPLY 0.3 0.270238 \n",
"B-APT-02 Zn-Sys SUPPLY 0.3 0.270085 \n",
"B-APT-03 Zn-Sys SUPPLY 0.3 0.270604 \n",
"B-APT-06 Zn-Sys SUPPLY 0.3 0.270103 \n",
"4-APT-04 Zn-Sys SUPPLY 0.3 0.270774 \n",
"2-APT-05 Zn-Sys SUPPLY 0.3 0.269608 \n",
"2-APT-07 Zn-Sys SUPPLY 0.3 0.269646 \n",
"3-APT-02 Zn-Sys SUPPLY 0.3 0.269807 \n",
"2-APT-06 Zn-Sys SUPPLY 0.3 0.269744 \n",
"3-APT-05 Zn-Sys SUPPLY 0.3 0.268844 \n",
"2-APT-01 Zn-Sys SUPPLY 0.3 0.269824 \n",
"3-APT-06 Zn-Sys SUPPLY 0.3 0.270667 \n",
"3-APT-01 Zn-Sys SUPPLY 0.3 0.269710 \n",
"4-APT-01 Zn-Sys SUPPLY 0.3 0.269485 \n",
"2-APT-02 Zn-Sys SUPPLY 0.3 0.269641 \n",
"3-APT-07 Zn-Sys SUPPLY 0.3 0.270270 \n",
"4-APT-03 Zn-Sys SUPPLY 0.3 0.270270 \n",
"3-APT-04 Zn-Sys SUPPLY 0.3 0.270796 \n",
"4-APT-07 Zn-Sys SUPPLY 0.3 0.269565 \n",
"3-APT-03 Zn-Sys SUPPLY 0.3 0.269784 \n",
"2-APT-08 Zn-Sys SUPPLY 0.3 0.270136 \n",
"B-APT-04 Zn-Sys SUPPLY 0.3 0.269578 \n",
"2-APT-04 Zn-Sys SUPPLY 0.3 0.270231 \n",
"Elev Cooling Sys SUPPLY 0.0 0.000000 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fans"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example on adding all system fans' power\n",
"\n",
"**The following would add up all system fans (supply, return, relief), then sort it and plot it**"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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MmTNxdnamU6dOxMbG6uQbOXIk+fn5DBs2DD8/P5221KpVi8jISKpXr86yZcvw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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22af82ca470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fans.groupby(level='System').apply(sum)['Power Demand (kW)'].sort_values(inplace=False).plot(kind='barh',\n",
" figsize=(12,10), color='#EB969C',\n",
" title='System wide fan power demand (kW)')\n",
"sns.despine()\n",
"plt.show();"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
keras-team/keras-io | guides/ipynb/keras_tuner/visualize_tuning.ipynb | 1 | 10263 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"# Visualize the hyperparameter tuning process\n",
"\n",
"**Author:** Haifeng Jin<br>\n",
"**Date created:** 2021/06/25<br>\n",
"**Last modified:** 2021/06/05<br>\n",
"**Description:** Using TensorBoard to visualize the hyperparameter tuning process in KerasTuner."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"!pip install keras-tuner -q"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Introduction\n",
"\n",
"KerasTuner prints the logs to screen including the values of the\n",
"hyperparameters in each trial for the user to monitor the progress. However,\n",
"reading the logs is not intuitive enough to sense the influences of\n",
"hyperparameters have on the results, Therefore, we provide a method to\n",
"visualize the hyperparameter values and the corresponding evaluation results\n",
"with interactive figures using TensorBaord.\n",
"\n",
"[TensorBoard](https://www.tensorflow.org/tensorboard) is a useful tool for\n",
"visualizing the machine learning experiments. It can monitor the losses and\n",
"metrics during the model training and visualize the model architectures.\n",
"Running KerasTuner with TensorBoard will give you additional features for\n",
"visualizing hyperparameter tuning results using its HParams plugin."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"We will use a simple example of tuning a model for the MNIST image\n",
"classification dataset to show how to use KerasTuner with TensorBoard.\n",
"\n",
"The first step is to download and format the data."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import keras_tuner\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()\n",
"# Normalize the pixel values to the range of [0, 1].\n",
"x_train = x_train.astype(\"float32\") / 255\n",
"x_test = x_test.astype(\"float32\") / 255\n",
"# Add the channel dimension to the images.\n",
"x_train = np.expand_dims(x_train, -1)\n",
"x_test = np.expand_dims(x_test, -1)\n",
"# Print the shapes of the data.\n",
"print(x_train.shape)\n",
"print(y_train.shape)\n",
"print(x_test.shape)\n",
"print(y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"Then, we write a `build_model` function to build the model with hyperparameters\n",
"and return the model. The hyperparameters include the type of model to use\n",
"(multi-layer perceptron or convolutional neural network), the number of layers,\n",
"the number of units or filters, whether to use dropout."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"def build_model(hp):\n",
" inputs = keras.Input(shape=(28, 28, 1))\n",
" # Model type can be MLP or CNN.\n",
" model_type = hp.Choice(\"model_type\", [\"mlp\", \"cnn\"])\n",
" x = inputs\n",
" if model_type == \"mlp\":\n",
" x = layers.Flatten()(x)\n",
" # Number of layers of the MLP is a hyperparameter.\n",
" for i in range(hp.Int(\"mlp_layers\", 1, 3)):\n",
" # Number of units of each layer are\n",
" # different hyperparameters with different names.\n",
" output_node = layers.Dense(\n",
" units=hp.Int(f\"units_{i}\", 32, 128, step=32), activation=\"relu\",\n",
" )(x)\n",
" else:\n",
" # Number of layers of the CNN is also a hyperparameter.\n",
" for i in range(hp.Int(\"cnn_layers\", 1, 3)):\n",
" x = layers.Conv2D(\n",
" hp.Int(f\"filters_{i}\", 32, 128, step=32),\n",
" kernel_size=(3, 3),\n",
" activation=\"relu\",\n",
" )(x)\n",
" x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
" x = layers.Flatten()(x)\n",
"\n",
" # A hyperparamter for whether to use dropout layer.\n",
" if hp.Boolean(\"dropout\"):\n",
" x = layers.Dropout(0.5)(x)\n",
"\n",
" # The last layer contains 10 units,\n",
" # which is the same as the number of classes.\n",
" outputs = layers.Dense(units=10, activation=\"softmax\")(x)\n",
" model = keras.Model(inputs=inputs, outputs=outputs)\n",
"\n",
" # Compile the model.\n",
" model.compile(\n",
" loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"], optimizer=\"adam\",\n",
" )\n",
" return model\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"We can do a quick test of the models to check if it build successfully for both\n",
"CNN and MLP."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"# Initialize the `HyperParameters` and set the values.\n",
"hp = keras_tuner.HyperParameters()\n",
"hp.values[\"model_type\"] = \"cnn\"\n",
"# Build the model using the `HyperParameters`.\n",
"model = build_model(hp)\n",
"# Test if the model runs with our data.\n",
"model(x_train[:100])\n",
"# Print a summary of the model.\n",
"model.summary()\n",
"\n",
"# Do the same for MLP model.\n",
"hp.values[\"model_type\"] = \"mlp\"\n",
"model = build_model(hp)\n",
"model(x_train[:100])\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"Initialize the `RandomSearch` tuner with 10 trials and using validation\n",
"accuracy as the metric for selecting models."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"tuner = keras_tuner.RandomSearch(\n",
" build_model,\n",
" max_trials=10,\n",
" # Do not resume the previous search in the same directory.\n",
" overwrite=True,\n",
" objective=\"val_accuracy\",\n",
" # Set a directory to store the intermediate results.\n",
" directory=\"/tmp/tb\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"Start the search by calling `tuner.search(...)`. To use TensorBoard, we need\n",
"to pass a `keras.callbacks.TensorBoard` instance to the callbacks."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"tuner.search(\n",
" x_train,\n",
" y_train,\n",
" validation_split=0.2,\n",
" epochs=2,\n",
" # Use the TensorBoard callback.\n",
" # The logs will be write to \"/tmp/tb_logs\".\n",
" callbacks=[keras.callbacks.TensorBoard(\"/tmp/tb_logs\")],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"If running in Colab, the following two commands will show you the TensorBoard\n",
"inside Colab.\n",
"\n",
"`%load_ext tensorboard`\n",
"\n",
"`%tensorboard --logdir /tmp/tb_logs`\n",
"\n",
"You have access to all the common features of the TensorBoard. For example, you\n",
"can view the loss and metrics curves and visualize the computational graph of\n",
"the models in different trials.\n",
"\n",
"\n",
"\n",
"\n",
"In addition to these features, we also have a HParams tab, in which there are\n",
"three views. In the table view, you can view the 10 different trials in a\n",
"table with the different hyperparameter values and evaluation metrics.\n",
"\n",
"\n",
"\n",
"On the left side, you can specify the filters for certain hyperparameters. For\n",
"example, you can specify to only view the MLP models without the dropout layer\n",
"and with 1 to 2 dense layers.\n",
"\n",
"\n",
"\n",
"Besides the table view, it also provides two other views, parallel coordinates\n",
"view and scatter plot matrix view. They are just different visualization\n",
"methods for the same data. You can still use the panel on the left to filter\n",
"the results.\n",
"\n",
"In the parallel coordinates view, each colored line is a trial.\n",
"The axes are the hyperparameters and evaluation metrics.\n",
"\n",
"\n",
"\n",
"In the scatter plot matrix view, each dot is a trial. The plots are projections\n",
"of the trials on planes with different hyperparameter and metrics as the axes.\n",
"\n",
""
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "visualize_tuning",
"private_outputs": false,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | apache-2.0 |
fnielsen/dasem | notebooks/semantic parameter performance.ipynb | 1 | 98044 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parameters that might affect performance\n",
"----------------------------------------\n",
"\n",
"This notebook examines how parameters in the semantic model of the Danish language affects its performance.\n",
"\n",
"- Number of pages read\n",
"- Use of stopwords\n",
"- Exclusion of short pages\n",
"- Scaling of matrix tfidf/count\n",
"- Normalization of document\n",
"- Factorization of matrix"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from everything import *\n",
"from dasem.semantic import Semantic\n",
"from dasem.data import wordsim353 as wordsim353_data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Read datasets\n",
"four_words = read_csv('../dasem/data/four_words.csv', encoding='utf-8')\n",
"wordsim353 = wordsim353_data()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compute_accuracy(semantic, four_words):\n",
" outlier = []\n",
" for idx, words in four_words.iterrows():\n",
" sorted_words = semantic.sort_by_outlierness(words.values[:4])\n",
" outlier.append(sorted_words[0])\n",
"\n",
" accuracy = mean(four_words.word4 == outlier)\n",
" return accuracy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def compute_correlation(semantic, wordsim):\n",
" human = []\n",
" relatednesses = []\n",
" for idx, row in wordsim.iterrows():\n",
" R = semantic.relatedness([row.da1, row.da2])\n",
" relatednesses.append(R[0, 1])\n",
" human.append(row['Human (mean)'])\n",
" human = array(human)\n",
" relatednesses = array(relatednesses)\n",
" indices = (~isnan(relatednesses)).nonzero()[0]\n",
" C = corrcoef(human[indices], relatednesses[indices])\n",
" return C[0, 1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"max_n_pagess = [3000, 30000, None]\n",
"norms = ['l1', 'l2', None]\n",
"stop_wordss = [None, set(nltk.corpus.stopwords.words('danish'))]\n",
"use_idfs = [True, False]\n",
"sublinear_tfs = [True, False]\n",
"\n",
"columns = ['accuracy', 'correlation', 'stop_words', 'use_idf', 'norm', 'sublinear_tf', 'max_n_pages']\n",
"\n",
"n_total = len(max_n_pagess) * len(norms) * len(stop_wordss) * len(use_idfs) * \\\n",
" len(sublinear_tfs)\n",
"results = DataFrame(dtype=float, index=range(n_total), columns=columns)\n",
"\n",
"n = 0\n",
"for stop_words_index, stop_words in (enumerate(stop_wordss)):\n",
" for norm in (norms):\n",
" for use_idf in (use_idfs):\n",
" for sublinear_tf in (sublinear_tfs):\n",
" for max_n_pages in (max_n_pagess):\n",
" results.ix[n, 'max_n_pages'] = max_n_pages\n",
" results.ix[n, 'stop_words'] = stop_words_index\n",
" results.ix[n, 'norm'] = str(norm)\n",
" results.ix[n, 'use_idf'] = use_idf\n",
" results.ix[n, 'sublinear_tf'] = sublinear_tf\n",
" semantic = Semantic(stop_words=stop_words, norm=norm,\n",
" use_idf=use_idf, sublinear_tf=sublinear_tf,\n",
" max_n_pages=max_n_pages)\n",
" results.ix[n, 'accuracy'] = compute_accuracy(semantic, four_words)\n",
" results.ix[n, 'correlation'] = compute_correlation(semantic, wordsim353)\n",
" n += 1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"relatednesses = []\n",
"for idx, row in wordsim353.iterrows():\n",
" R = semantic.relatedness([row.da1, row.da2])\n",
" relatednesses.append(R[0, 1])\n",
"wordsim353['relatedness'] = relatednesses"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Word 1</th>\n",
" <th>da1</th>\n",
" <th>Word 2</th>\n",
" <th>da2</th>\n",
" <th>Human (mean)</th>\n",
" <th>Problem</th>\n",
" <th>relatedness</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>love</td>\n",
" <td>kærlighed</td>\n",
" <td>sex</td>\n",
" <td>sex</td>\n",
" <td>6.77</td>\n",
" <td>NaN</td>\n",
" <td>0.069031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>tiger</td>\n",
" <td>tiger</td>\n",
" <td>cat</td>\n",
" <td>kat</td>\n",
" <td>7.35</td>\n",
" <td>NaN</td>\n",
" <td>0.024325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>tiger</td>\n",
" <td>tiger</td>\n",
" <td>tiger</td>\n",
" <td>tiger</td>\n",
" <td>10.00</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>book</td>\n",
" <td>bog</td>\n",
" <td>paper</td>\n",
" <td>papir</td>\n",
" <td>7.46</td>\n",
" <td>NaN</td>\n",
" <td>0.031266</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>computer</td>\n",
" <td>computer</td>\n",
" <td>keyboard</td>\n",
" <td>tastatur</td>\n",
" <td>7.62</td>\n",
" <td>NaN</td>\n",
" <td>0.117331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>computer</td>\n",
" <td>computer</td>\n",
" <td>internet</td>\n",
" <td>internet</td>\n",
" <td>7.58</td>\n",
" <td>NaN</td>\n",
" <td>0.059367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>plane</td>\n",
" <td>fly</td>\n",
" <td>car</td>\n",
" <td>bil</td>\n",
" <td>5.77</td>\n",
" <td>NaN</td>\n",
" <td>0.013637</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>train</td>\n",
" <td>tog</td>\n",
" <td>car</td>\n",
" <td>bil</td>\n",
" <td>6.31</td>\n",
" <td>NaN</td>\n",
" <td>0.026891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>telephone</td>\n",
" <td>telefon</td>\n",
" <td>communication</td>\n",
" <td>kommunikation</td>\n",
" <td>7.50</td>\n",
" <td>NaN</td>\n",
" <td>0.007303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>television</td>\n",
" <td>tv</td>\n",
" <td>radio</td>\n",
" <td>radio</td>\n",
" <td>6.77</td>\n",
" <td>NaN</td>\n",
" <td>0.164519</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>media</td>\n",
" <td>medie</td>\n",
" <td>radio</td>\n",
" <td>radio</td>\n",
" <td>7.42</td>\n",
" <td>NaN</td>\n",
" <td>0.032748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>drug</td>\n",
" <td>narkotika</td>\n",
" <td>abuse</td>\n",
" <td>misbrug</td>\n",
" <td>6.85</td>\n",
" <td>NaN</td>\n",
" <td>0.074244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>bread</td>\n",
" <td>brød</td>\n",
" <td>butter</td>\n",
" <td>smør</td>\n",
" <td>6.19</td>\n",
" <td>NaN</td>\n",
" <td>0.075498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>cucumber</td>\n",
" <td>agurk</td>\n",
" <td>potato</td>\n",
" <td>kartoffel</td>\n",
" <td>5.92</td>\n",
" <td>NaN</td>\n",
" <td>0.070229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>doctor</td>\n",
" <td>læge</td>\n",
" <td>nurse</td>\n",
" <td>sygeplejerske</td>\n",
" <td>7.00</td>\n",
" <td>NaN</td>\n",
" <td>0.078341</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>professor</td>\n",
" <td>professor</td>\n",
" <td>doctor</td>\n",
" <td>læge</td>\n",
" <td>6.62</td>\n",
" <td>NaN</td>\n",
" <td>0.135296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>student</td>\n",
" <td>studerende</td>\n",
" <td>professor</td>\n",
" <td>professor</td>\n",
" <td>6.81</td>\n",
" <td>NaN</td>\n",
" <td>0.087950</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>smart</td>\n",
" <td>klog</td>\n",
" <td>student</td>\n",
" <td>studerende</td>\n",
" <td>4.62</td>\n",
" <td>NaN</td>\n",
" <td>0.007902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>smart</td>\n",
" <td>klog</td>\n",
" <td>stupid</td>\n",
" <td>dum</td>\n",
" <td>5.81</td>\n",
" <td>NaN</td>\n",
" <td>0.033734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>company</td>\n",
" <td>firma</td>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>7.08</td>\n",
" <td>NaN</td>\n",
" <td>0.023840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>market</td>\n",
" <td>marked</td>\n",
" <td>8.08</td>\n",
" <td>NaN</td>\n",
" <td>0.036268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>phone</td>\n",
" <td>telefon</td>\n",
" <td>1.62</td>\n",
" <td>NaN</td>\n",
" <td>0.012286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>CD</td>\n",
" <td>CD</td>\n",
" <td>1.31</td>\n",
" <td>NaN</td>\n",
" <td>0.000482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>jaguar</td>\n",
" <td>jaguar</td>\n",
" <td>0.92</td>\n",
" <td>NaN</td>\n",
" <td>0.001671</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>egg</td>\n",
" <td>æg</td>\n",
" <td>1.81</td>\n",
" <td>NaN</td>\n",
" <td>0.014525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>fertility</td>\n",
" <td>frugtbar</td>\n",
" <td>egg</td>\n",
" <td>æg</td>\n",
" <td>6.69</td>\n",
" <td>NaN</td>\n",
" <td>0.019678</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>stock</td>\n",
" <td>aktie</td>\n",
" <td>life</td>\n",
" <td>liv</td>\n",
" <td>0.92</td>\n",
" <td>NaN</td>\n",
" <td>0.009396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>book</td>\n",
" <td>bog</td>\n",
" <td>library</td>\n",
" <td>bibliotek</td>\n",
" <td>7.46</td>\n",
" <td>NaN</td>\n",
" <td>0.064770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>bank</td>\n",
" <td>bank</td>\n",
" <td>money</td>\n",
" <td>penge</td>\n",
" <td>8.12</td>\n",
" <td>NaN</td>\n",
" <td>0.121945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>wood</td>\n",
" <td>træ</td>\n",
" <td>forest</td>\n",
" <td>skov</td>\n",
" <td>7.73</td>\n",
" <td>NaN</td>\n",
" <td>0.040285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>322</th>\n",
" <td>gender</td>\n",
" <td>køn</td>\n",
" <td>equality</td>\n",
" <td>lighed</td>\n",
" <td>6.41</td>\n",
" <td>NaN</td>\n",
" <td>0.066694</td>\n",
" </tr>\n",
" <tr>\n",
" <th>323</th>\n",
" <td>change</td>\n",
" <td>ændring</td>\n",
" <td>attitude</td>\n",
" <td>holdning</td>\n",
" <td>5.44</td>\n",
" <td>NaN</td>\n",
" <td>0.086168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td>family</td>\n",
" <td>familie</td>\n",
" <td>planning</td>\n",
" <td>planlægning</td>\n",
" <td>6.25</td>\n",
" <td>NaN</td>\n",
" <td>0.003667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>325</th>\n",
" <td>opera</td>\n",
" <td>opera</td>\n",
" <td>industry</td>\n",
" <td>industri</td>\n",
" <td>2.63</td>\n",
" <td>NaN</td>\n",
" <td>0.007351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>326</th>\n",
" <td>sugar</td>\n",
" <td>sukker</td>\n",
" <td>approach</td>\n",
" <td>tilgang</td>\n",
" <td>0.88</td>\n",
" <td>NaN</td>\n",
" <td>0.012265</td>\n",
" </tr>\n",
" <tr>\n",
" <th>327</th>\n",
" <td>practice</td>\n",
" <td>øvelse</td>\n",
" <td>institution</td>\n",
" <td>institution</td>\n",
" <td>3.19</td>\n",
" <td>NaN</td>\n",
" <td>0.005296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>328</th>\n",
" <td>ministry</td>\n",
" <td>ministerium</td>\n",
" <td>culture</td>\n",
" <td>kultur</td>\n",
" <td>4.69</td>\n",
" <td>NaN</td>\n",
" <td>0.031118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>329</th>\n",
" <td>problem</td>\n",
" <td>problem</td>\n",
" <td>challenge</td>\n",
" <td>udfordring</td>\n",
" <td>6.75</td>\n",
" <td>NaN</td>\n",
" <td>0.055777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>330</th>\n",
" <td>size</td>\n",
" <td>størrelse</td>\n",
" <td>prominence</td>\n",
" <td>fremtrædende</td>\n",
" <td>5.31</td>\n",
" <td>NaN</td>\n",
" <td>0.067736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>331</th>\n",
" <td>country</td>\n",
" <td>land</td>\n",
" <td>citizen</td>\n",
" <td>borger</td>\n",
" <td>7.31</td>\n",
" <td>NaN</td>\n",
" <td>0.018616</td>\n",
" </tr>\n",
" <tr>\n",
" <th>332</th>\n",
" <td>planet</td>\n",
" <td>planet</td>\n",
" <td>people</td>\n",
" <td>folk</td>\n",
" <td>5.75</td>\n",
" <td>NaN</td>\n",
" <td>0.025209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>333</th>\n",
" <td>development</td>\n",
" <td>udvikling</td>\n",
" <td>issue</td>\n",
" <td>spørgsmål</td>\n",
" <td>3.97</td>\n",
" <td>NaN</td>\n",
" <td>0.129536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>334</th>\n",
" <td>experience</td>\n",
" <td>oplevelse</td>\n",
" <td>music</td>\n",
" <td>musik</td>\n",
" <td>3.47</td>\n",
" <td>NaN</td>\n",
" <td>0.045611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>335</th>\n",
" <td>music</td>\n",
" <td>musik</td>\n",
" <td>project</td>\n",
" <td>projekt</td>\n",
" <td>3.63</td>\n",
" <td>NaN</td>\n",
" <td>0.047501</td>\n",
" </tr>\n",
" <tr>\n",
" <th>336</th>\n",
" <td>glass</td>\n",
" <td>glas</td>\n",
" <td>metal</td>\n",
" <td>metal</td>\n",
" <td>5.56</td>\n",
" <td>NaN</td>\n",
" <td>0.012299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>337</th>\n",
" <td>aluminum</td>\n",
" <td>aluminium</td>\n",
" <td>metal</td>\n",
" <td>metal</td>\n",
" <td>7.83</td>\n",
" <td>NaN</td>\n",
" <td>0.019734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>338</th>\n",
" <td>chance</td>\n",
" <td>chance</td>\n",
" <td>credibility</td>\n",
" <td>troværdighed</td>\n",
" <td>3.88</td>\n",
" <td>NaN</td>\n",
" <td>0.032975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>340</th>\n",
" <td>concert</td>\n",
" <td>koncert</td>\n",
" <td>virtuoso</td>\n",
" <td>virtuos</td>\n",
" <td>6.81</td>\n",
" <td>NaN</td>\n",
" <td>0.012196</td>\n",
" </tr>\n",
" <tr>\n",
" <th>341</th>\n",
" <td>rock</td>\n",
" <td>rock</td>\n",
" <td>jazz</td>\n",
" <td>jazz</td>\n",
" <td>7.59</td>\n",
" <td>NaN</td>\n",
" <td>0.080152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>342</th>\n",
" <td>museum</td>\n",
" <td>museum</td>\n",
" <td>theater</td>\n",
" <td>teater</td>\n",
" <td>7.19</td>\n",
" <td>NaN</td>\n",
" <td>0.043194</td>\n",
" </tr>\n",
" <tr>\n",
" <th>343</th>\n",
" <td>observation</td>\n",
" <td>observation</td>\n",
" <td>architecture</td>\n",
" <td>arkitektur</td>\n",
" <td>4.38</td>\n",
" <td>NaN</td>\n",
" <td>0.003176</td>\n",
" </tr>\n",
" <tr>\n",
" <th>344</th>\n",
" <td>space</td>\n",
" <td>rum</td>\n",
" <td>world</td>\n",
" <td>verden</td>\n",
" <td>6.53</td>\n",
" <td>NaN</td>\n",
" <td>0.083000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>345</th>\n",
" <td>preservation</td>\n",
" <td>bevarelse</td>\n",
" <td>world</td>\n",
" <td>verden</td>\n",
" <td>6.19</td>\n",
" <td>NaN</td>\n",
" <td>0.023978</td>\n",
" </tr>\n",
" <tr>\n",
" <th>346</th>\n",
" <td>admission</td>\n",
" <td>adgang</td>\n",
" <td>ticket</td>\n",
" <td>billet</td>\n",
" <td>7.69</td>\n",
" <td>NaN</td>\n",
" <td>0.042346</td>\n",
" </tr>\n",
" <tr>\n",
" <th>347</th>\n",
" <td>shower</td>\n",
" <td>byge</td>\n",
" <td>thunderstorm</td>\n",
" <td>tordenbyge</td>\n",
" <td>6.31</td>\n",
" <td>NaN</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>348</th>\n",
" <td>shower</td>\n",
" <td>byge</td>\n",
" <td>flood</td>\n",
" <td>oversvømmelse</td>\n",
" <td>6.03</td>\n",
" <td>NaN</td>\n",
" <td>0.010859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>349</th>\n",
" <td>weather</td>\n",
" <td>vejr</td>\n",
" <td>forecast</td>\n",
" <td>vejrudsigt</td>\n",
" <td>8.34</td>\n",
" <td>NaN</td>\n",
" <td>0.062763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>350</th>\n",
" <td>disaster</td>\n",
" <td>katastrofe</td>\n",
" <td>area</td>\n",
" <td>område</td>\n",
" <td>6.25</td>\n",
" <td>NaN</td>\n",
" <td>0.086862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>351</th>\n",
" <td>governor</td>\n",
" <td>guvernør</td>\n",
" <td>office</td>\n",
" <td>kontor</td>\n",
" <td>6.34</td>\n",
" <td>NaN</td>\n",
" <td>0.022742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352</th>\n",
" <td>architecture</td>\n",
" <td>arkitektur</td>\n",
" <td>century</td>\n",
" <td>århundrede</td>\n",
" <td>3.78</td>\n",
" <td>NaN</td>\n",
" <td>0.093821</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>319 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Word 1 da1 Word 2 da2 Human (mean) \\\n",
"0 love kærlighed sex sex 6.77 \n",
"1 tiger tiger cat kat 7.35 \n",
"2 tiger tiger tiger tiger 10.00 \n",
"3 book bog paper papir 7.46 \n",
"4 computer computer keyboard tastatur 7.62 \n",
"5 computer computer internet internet 7.58 \n",
"6 plane fly car bil 5.77 \n",
"7 train tog car bil 6.31 \n",
"8 telephone telefon communication kommunikation 7.50 \n",
"9 television tv radio radio 6.77 \n",
"10 media medie radio radio 7.42 \n",
"11 drug narkotika abuse misbrug 6.85 \n",
"12 bread brød butter smør 6.19 \n",
"13 cucumber agurk potato kartoffel 5.92 \n",
"14 doctor læge nurse sygeplejerske 7.00 \n",
"15 professor professor doctor læge 6.62 \n",
"16 student studerende professor professor 6.81 \n",
"17 smart klog student studerende 4.62 \n",
"18 smart klog stupid dum 5.81 \n",
"19 company firma stock aktie 7.08 \n",
"20 stock aktie market marked 8.08 \n",
"21 stock aktie phone telefon 1.62 \n",
"22 stock aktie CD CD 1.31 \n",
"23 stock aktie jaguar jaguar 0.92 \n",
"24 stock aktie egg æg 1.81 \n",
"25 fertility frugtbar egg æg 6.69 \n",
"27 stock aktie life liv 0.92 \n",
"28 book bog library bibliotek 7.46 \n",
"29 bank bank money penge 8.12 \n",
"30 wood træ forest skov 7.73 \n",
".. ... ... ... ... ... \n",
"322 gender køn equality lighed 6.41 \n",
"323 change ændring attitude holdning 5.44 \n",
"324 family familie planning planlægning 6.25 \n",
"325 opera opera industry industri 2.63 \n",
"326 sugar sukker approach tilgang 0.88 \n",
"327 practice øvelse institution institution 3.19 \n",
"328 ministry ministerium culture kultur 4.69 \n",
"329 problem problem challenge udfordring 6.75 \n",
"330 size størrelse prominence fremtrædende 5.31 \n",
"331 country land citizen borger 7.31 \n",
"332 planet planet people folk 5.75 \n",
"333 development udvikling issue spørgsmål 3.97 \n",
"334 experience oplevelse music musik 3.47 \n",
"335 music musik project projekt 3.63 \n",
"336 glass glas metal metal 5.56 \n",
"337 aluminum aluminium metal metal 7.83 \n",
"338 chance chance credibility troværdighed 3.88 \n",
"340 concert koncert virtuoso virtuos 6.81 \n",
"341 rock rock jazz jazz 7.59 \n",
"342 museum museum theater teater 7.19 \n",
"343 observation observation architecture arkitektur 4.38 \n",
"344 space rum world verden 6.53 \n",
"345 preservation bevarelse world verden 6.19 \n",
"346 admission adgang ticket billet 7.69 \n",
"347 shower byge thunderstorm tordenbyge 6.31 \n",
"348 shower byge flood oversvømmelse 6.03 \n",
"349 weather vejr forecast vejrudsigt 8.34 \n",
"350 disaster katastrofe area område 6.25 \n",
"351 governor guvernør office kontor 6.34 \n",
"352 architecture arkitektur century århundrede 3.78 \n",
"\n",
" Problem relatedness \n",
"0 NaN 0.069031 \n",
"1 NaN 0.024325 \n",
"2 NaN 1.000000 \n",
"3 NaN 0.031266 \n",
"4 NaN 0.117331 \n",
"5 NaN 0.059367 \n",
"6 NaN 0.013637 \n",
"7 NaN 0.026891 \n",
"8 NaN 0.007303 \n",
"9 NaN 0.164519 \n",
"10 NaN 0.032748 \n",
"11 NaN 0.074244 \n",
"12 NaN 0.075498 \n",
"13 NaN 0.070229 \n",
"14 NaN 0.078341 \n",
"15 NaN 0.135296 \n",
"16 NaN 0.087950 \n",
"17 NaN 0.007902 \n",
"18 NaN 0.033734 \n",
"19 NaN 0.023840 \n",
"20 NaN 0.036268 \n",
"21 NaN 0.012286 \n",
"22 NaN 0.000482 \n",
"23 NaN 0.001671 \n",
"24 NaN 0.014525 \n",
"25 NaN 0.019678 \n",
"27 NaN 0.009396 \n",
"28 NaN 0.064770 \n",
"29 NaN 0.121945 \n",
"30 NaN 0.040285 \n",
".. ... ... \n",
"322 NaN 0.066694 \n",
"323 NaN 0.086168 \n",
"324 NaN 0.003667 \n",
"325 NaN 0.007351 \n",
"326 NaN 0.012265 \n",
"327 NaN 0.005296 \n",
"328 NaN 0.031118 \n",
"329 NaN 0.055777 \n",
"330 NaN 0.067736 \n",
"331 NaN 0.018616 \n",
"332 NaN 0.025209 \n",
"333 NaN 0.129536 \n",
"334 NaN 0.045611 \n",
"335 NaN 0.047501 \n",
"336 NaN 0.012299 \n",
"337 NaN 0.019734 \n",
"338 NaN 0.032975 \n",
"340 NaN 0.012196 \n",
"341 NaN 0.080152 \n",
"342 NaN 0.043194 \n",
"343 NaN 0.003176 \n",
"344 NaN 0.083000 \n",
"345 NaN 0.023978 \n",
"346 NaN 0.042346 \n",
"347 NaN 0.000000 \n",
"348 NaN 0.010859 \n",
"349 NaN 0.062763 \n",
"350 NaN 0.086862 \n",
"351 NaN 0.022742 \n",
"352 NaN 0.093821 \n",
"\n",
"[319 rows x 7 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wordsim353"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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4I2D/t+CGdp7AOcDdDQz5tq/COU7+FDee/X6cw988sNq337NwDrCHvW2HfNt+Bfdj+zNv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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd69714810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wordsim353.plot(x='Human (mean)', y='relatedness', kind='scatter')\n",
"yscale('log')\n",
"ylim(0.0001, 1)\n",
"title('Scatter plot of Wordsim353 data')\n",
"show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accuracy</th>\n",
" <th>correlation</th>\n",
" <th>stop_words</th>\n",
" <th>use_idf</th>\n",
" <th>norm</th>\n",
" <th>sublinear_tf</th>\n",
" <th>max_n_pages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.36</td>\n",
" <td>0.274049</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.56</td>\n",
" <td>0.210682</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.72</td>\n",
" <td>0.135028</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.34</td>\n",
" <td>0.292945</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.60</td>\n",
" <td>0.216162</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.74</td>\n",
" <td>0.137733</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.38</td>\n",
" <td>0.279397</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.56</td>\n",
" <td>0.214376</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.72</td>\n",
" <td>0.138702</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.36</td>\n",
" <td>0.292561</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.58</td>\n",
" <td>0.216529</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.72</td>\n",
" <td>0.140512</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.38</td>\n",
" <td>0.351491</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.64</td>\n",
" <td>0.328927</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.84</td>\n",
" <td>0.261032</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.38</td>\n",
" <td>0.331091</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.58</td>\n",
" <td>0.321919</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.78</td>\n",
" <td>0.254649</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.38</td>\n",
" <td>0.350975</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.58</td>\n",
" <td>0.334352</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.78</td>\n",
" <td>0.268553</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.40</td>\n",
" <td>0.324016</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.60</td>\n",
" <td>0.299873</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.78</td>\n",
" <td>0.242552</td>\n",
" <td>0.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.40</td>\n",
" <td>0.334992</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.58</td>\n",
" <td>0.378010</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.76</td>\n",
" <td>0.365937</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.44</td>\n",
" <td>0.311267</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.56</td>\n",
" <td>0.353467</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.72</td>\n",
" <td>0.328984</td>\n",
" <td>0.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>0.38</td>\n",
" <td>0.279397</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>0.56</td>\n",
" <td>0.214376</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>0.72</td>\n",
" <td>0.138702</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>0.36</td>\n",
" <td>0.292561</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>0.58</td>\n",
" <td>0.216529</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>0.72</td>\n",
" <td>0.140512</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l1</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>0.38</td>\n",
" <td>0.351491</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>0.64</td>\n",
" <td>0.328927</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>0.84</td>\n",
" <td>0.261032</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>0.38</td>\n",
" <td>0.331091</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>0.58</td>\n",
" <td>0.321919</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>0.78</td>\n",
" <td>0.254649</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>0.38</td>\n",
" <td>0.350975</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>0.58</td>\n",
" <td>0.334352</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>0.78</td>\n",
" <td>0.268553</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>0.40</td>\n",
" <td>0.324016</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>0.60</td>\n",
" <td>0.299873</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>0.78</td>\n",
" <td>0.242552</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>l2</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0.40</td>\n",
" <td>0.334992</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>0.58</td>\n",
" <td>0.378010</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>0.76</td>\n",
" <td>0.365937</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>0.44</td>\n",
" <td>0.311267</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>0.56</td>\n",
" <td>0.353467</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>0.72</td>\n",
" <td>0.328984</td>\n",
" <td>1.0</td>\n",
" <td>True</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>0.38</td>\n",
" <td>0.334995</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>0.60</td>\n",
" <td>0.377975</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68</th>\n",
" <td>0.74</td>\n",
" <td>0.365990</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>0.42</td>\n",
" <td>0.311267</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>3000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>0.56</td>\n",
" <td>0.353424</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>30000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71</th>\n",
" <td>0.72</td>\n",
" <td>0.328968</td>\n",
" <td>1.0</td>\n",
" <td>False</td>\n",
" <td>None</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>72 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" accuracy correlation stop_words use_idf norm sublinear_tf max_n_pages\n",
"0 0.36 0.274049 0.0 True l1 True 3000.0\n",
"1 0.56 0.210682 0.0 True l1 True 30000.0\n",
"2 0.72 0.135028 0.0 True l1 True NaN\n",
"3 0.34 0.292945 0.0 True l1 False 3000.0\n",
"4 0.60 0.216162 0.0 True l1 False 30000.0\n",
"5 0.74 0.137733 0.0 True l1 False NaN\n",
"6 0.38 0.279397 0.0 False l1 True 3000.0\n",
"7 0.56 0.214376 0.0 False l1 True 30000.0\n",
"8 0.72 0.138702 0.0 False l1 True NaN\n",
"9 0.36 0.292561 0.0 False l1 False 3000.0\n",
"10 0.58 0.216529 0.0 False l1 False 30000.0\n",
"11 0.72 0.140512 0.0 False l1 False NaN\n",
"12 0.38 0.351491 0.0 True l2 True 3000.0\n",
"13 0.64 0.328927 0.0 True l2 True 30000.0\n",
"14 0.84 0.261032 0.0 True l2 True NaN\n",
"15 0.38 0.331091 0.0 True l2 False 3000.0\n",
"16 0.58 0.321919 0.0 True l2 False 30000.0\n",
"17 0.78 0.254649 0.0 True l2 False NaN\n",
"18 0.38 0.350975 0.0 False l2 True 3000.0\n",
"19 0.58 0.334352 0.0 False l2 True 30000.0\n",
"20 0.78 0.268553 0.0 False l2 True NaN\n",
"21 0.40 0.324016 0.0 False l2 False 3000.0\n",
"22 0.60 0.299873 0.0 False l2 False 30000.0\n",
"23 0.78 0.242552 0.0 False l2 False NaN\n",
"24 0.40 0.334992 0.0 True None True 3000.0\n",
"25 0.58 0.378010 0.0 True None True 30000.0\n",
"26 0.76 0.365937 0.0 True None True NaN\n",
"27 0.44 0.311267 0.0 True None False 3000.0\n",
"28 0.56 0.353467 0.0 True None False 30000.0\n",
"29 0.72 0.328984 0.0 True None False NaN\n",
".. ... ... ... ... ... ... ...\n",
"42 0.38 0.279397 1.0 False l1 True 3000.0\n",
"43 0.56 0.214376 1.0 False l1 True 30000.0\n",
"44 0.72 0.138702 1.0 False l1 True NaN\n",
"45 0.36 0.292561 1.0 False l1 False 3000.0\n",
"46 0.58 0.216529 1.0 False l1 False 30000.0\n",
"47 0.72 0.140512 1.0 False l1 False NaN\n",
"48 0.38 0.351491 1.0 True l2 True 3000.0\n",
"49 0.64 0.328927 1.0 True l2 True 30000.0\n",
"50 0.84 0.261032 1.0 True l2 True NaN\n",
"51 0.38 0.331091 1.0 True l2 False 3000.0\n",
"52 0.58 0.321919 1.0 True l2 False 30000.0\n",
"53 0.78 0.254649 1.0 True l2 False NaN\n",
"54 0.38 0.350975 1.0 False l2 True 3000.0\n",
"55 0.58 0.334352 1.0 False l2 True 30000.0\n",
"56 0.78 0.268553 1.0 False l2 True NaN\n",
"57 0.40 0.324016 1.0 False l2 False 3000.0\n",
"58 0.60 0.299873 1.0 False l2 False 30000.0\n",
"59 0.78 0.242552 1.0 False l2 False NaN\n",
"60 0.40 0.334992 1.0 True None True 3000.0\n",
"61 0.58 0.378010 1.0 True None True 30000.0\n",
"62 0.76 0.365937 1.0 True None True NaN\n",
"63 0.44 0.311267 1.0 True None False 3000.0\n",
"64 0.56 0.353467 1.0 True None False 30000.0\n",
"65 0.72 0.328984 1.0 True None False NaN\n",
"66 0.38 0.334995 1.0 False None True 3000.0\n",
"67 0.60 0.377975 1.0 False None True 30000.0\n",
"68 0.74 0.365990 1.0 False None True NaN\n",
"69 0.42 0.311267 1.0 False None False 3000.0\n",
"70 0.56 0.353424 1.0 False None False 30000.0\n",
"71 0.72 0.328968 1.0 False None False NaN\n",
"\n",
"[72 rows x 7 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>accuracy</td> <th> No. Observations: </th> <td> 48</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 41</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Gaussian</td> <th> Df Model: </th> <td> 6</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>identity</td> <th> Scale: </th> <td>0.000536585365854</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> 116.40</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Tue, 11 Oct 2016</td> <th> Deviance: </th> <td>0.022000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>01:13:12</td> <th> Pearson chi2: </th> <td>0.0220</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>2</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 0.3713</td> <td> 0.009</td> <td> 40.567</td> <td> 0.000</td> <td> 0.353</td> <td> 0.389</td>\n",
"</tr>\n",
"<tr>\n",
" <th>use_idf[T.True]</th> <td> 0.0017</td> <td> 0.007</td> <td> 0.249</td> <td> 0.803</td> <td> -0.011</td> <td> 0.015</td>\n",
"</tr>\n",
"<tr>\n",
" <th>norm[T.l1]</th> <td> -0.0250</td> <td> 0.008</td> <td> -3.053</td> <td> 0.002</td> <td> -0.041</td> <td> -0.009</td>\n",
"</tr>\n",
"<tr>\n",
" <th>norm[T.l2]</th> <td> 1.509e-16</td> <td> 0.008</td> <td> 1.84e-14</td> <td> 1.000</td> <td> -0.016</td> <td> 0.016</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sublinear_tf[T.True]</th> <td> -0.0017</td> <td> 0.007</td> <td> -0.249</td> <td> 0.803</td> <td> -0.015</td> <td> 0.011</td>\n",
"</tr>\n",
"<tr>\n",
" <th>stop_words</th> <td> 3.816e-17</td> <td> 0.007</td> <td> 5.71e-15</td> <td> 1.000</td> <td> -0.013</td> <td> 0.013</td>\n",
"</tr>\n",
"<tr>\n",
" <th>max_n_pages</th> <td> 7.346e-06</td> <td> 2.48e-07</td> <td> 29.660</td> <td> 0.000</td> <td> 6.86e-06</td> <td> 7.83e-06</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: accuracy No. Observations: 48\n",
"Model: GLM Df Residuals: 41\n",
"Model Family: Gaussian Df Model: 6\n",
"Link Function: identity Scale: 0.000536585365854\n",
"Method: IRLS Log-Likelihood: 116.40\n",
"Date: Tue, 11 Oct 2016 Deviance: 0.022000\n",
"Time: 01:13:12 Pearson chi2: 0.0220\n",
"No. Iterations: 2 \n",
"========================================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"----------------------------------------------------------------------------------------\n",
"Intercept 0.3713 0.009 40.567 0.000 0.353 0.389\n",
"use_idf[T.True] 0.0017 0.007 0.249 0.803 -0.011 0.015\n",
"norm[T.l1] -0.0250 0.008 -3.053 0.002 -0.041 -0.009\n",
"norm[T.l2] 1.509e-16 0.008 1.84e-14 1.000 -0.016 0.016\n",
"sublinear_tf[T.True] -0.0017 0.007 -0.249 0.803 -0.015 0.011\n",
"stop_words 3.816e-17 0.007 5.71e-15 1.000 -0.013 0.013\n",
"max_n_pages 7.346e-06 2.48e-07 29.660 0.000 6.86e-06 7.83e-06\n",
"========================================================================================\n",
"\"\"\""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"formula = 'accuracy ~ stop_words + use_idf + norm + sublinear_tf + max_n_pages'\n",
"model = smf.glm(formula, data=results).fit()\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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}
| apache-2.0 |
bbfamily/abu | ipython/第七章-量化系统——入门.ipynb | 1 | 1155308 | null | gpl-3.0 |
gaufung/ISL | Chapter02.ipynb | 1 | 284611 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 02"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 Esitmate of $f$\n",
"+ Prediction: The input $X$ are readily avaiable, but the $Y$ are not.\n",
"+ Inference: How $Y$ is affected by the $X$ change"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 2 Variance & Bias\n",
"$$E(y_0-\\hat{f}(x_0))^2=Var(\\hat{f}(x_0))+[Bias(\\hat{f}(x_0))]^2+Var(\\eta)$$\n",
"Where \n",
"+ $E(y_0-\\hat{f}(x_0))^2$ : test data's excepted error \n",
"+ $Var(\\hat{f}(x_0))$ : train data's variance \n",
"+ $[Bias(\\hat{f}(x_0))]^2$ : train data's Bias."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 Exerciese"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1 Colleage"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Private</th>\n",
" <th>Apps</th>\n",
" <th>Accept</th>\n",
" <th>Enroll</th>\n",
" <th>Top10perc</th>\n",
" <th>Top25perc</th>\n",
" <th>F.Undergrad</th>\n",
" <th>P.Undergrad</th>\n",
" <th>Outstate</th>\n",
" <th>Room.Board</th>\n",
" <th>Books</th>\n",
" <th>Personal</th>\n",
" <th>PhD</th>\n",
" <th>Terminal</th>\n",
" <th>S.F.Ratio</th>\n",
" <th>perc.alumni</th>\n",
" <th>Expend</th>\n",
" <th>Grad.Rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Abilene Christian University</th>\n",
" <td>Yes</td>\n",
" <td>1660</td>\n",
" <td>1232</td>\n",
" <td>721</td>\n",
" <td>23</td>\n",
" <td>52</td>\n",
" <td>2885</td>\n",
" <td>537</td>\n",
" <td>7440</td>\n",
" <td>3300</td>\n",
" <td>450</td>\n",
" <td>2200</td>\n",
" <td>70</td>\n",
" <td>78</td>\n",
" <td>18.1</td>\n",
" <td>12</td>\n",
" <td>7041</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adelphi University</th>\n",
" <td>Yes</td>\n",
" <td>2186</td>\n",
" <td>1924</td>\n",
" <td>512</td>\n",
" <td>16</td>\n",
" <td>29</td>\n",
" <td>2683</td>\n",
" <td>1227</td>\n",
" <td>12280</td>\n",
" <td>6450</td>\n",
" <td>750</td>\n",
" <td>1500</td>\n",
" <td>29</td>\n",
" <td>30</td>\n",
" <td>12.2</td>\n",
" <td>16</td>\n",
" <td>10527</td>\n",
" <td>56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adrian College</th>\n",
" <td>Yes</td>\n",
" <td>1428</td>\n",
" <td>1097</td>\n",
" <td>336</td>\n",
" <td>22</td>\n",
" <td>50</td>\n",
" <td>1036</td>\n",
" <td>99</td>\n",
" <td>11250</td>\n",
" <td>3750</td>\n",
" <td>400</td>\n",
" <td>1165</td>\n",
" <td>53</td>\n",
" <td>66</td>\n",
" <td>12.9</td>\n",
" <td>30</td>\n",
" <td>8735</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Agnes Scott College</th>\n",
" <td>Yes</td>\n",
" <td>417</td>\n",
" <td>349</td>\n",
" <td>137</td>\n",
" <td>60</td>\n",
" <td>89</td>\n",
" <td>510</td>\n",
" <td>63</td>\n",
" <td>12960</td>\n",
" <td>5450</td>\n",
" <td>450</td>\n",
" <td>875</td>\n",
" <td>92</td>\n",
" <td>97</td>\n",
" <td>7.7</td>\n",
" <td>37</td>\n",
" <td>19016</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alaska Pacific University</th>\n",
" <td>Yes</td>\n",
" <td>193</td>\n",
" <td>146</td>\n",
" <td>55</td>\n",
" <td>16</td>\n",
" <td>44</td>\n",
" <td>249</td>\n",
" <td>869</td>\n",
" <td>7560</td>\n",
" <td>4120</td>\n",
" <td>800</td>\n",
" <td>1500</td>\n",
" <td>76</td>\n",
" <td>72</td>\n",
" <td>11.9</td>\n",
" <td>2</td>\n",
" <td>10922</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albertson College</th>\n",
" <td>Yes</td>\n",
" <td>587</td>\n",
" <td>479</td>\n",
" <td>158</td>\n",
" <td>38</td>\n",
" <td>62</td>\n",
" <td>678</td>\n",
" <td>41</td>\n",
" <td>13500</td>\n",
" <td>3335</td>\n",
" <td>500</td>\n",
" <td>675</td>\n",
" <td>67</td>\n",
" <td>73</td>\n",
" <td>9.4</td>\n",
" <td>11</td>\n",
" <td>9727</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albertus Magnus College</th>\n",
" <td>Yes</td>\n",
" <td>353</td>\n",
" <td>340</td>\n",
" <td>103</td>\n",
" <td>17</td>\n",
" <td>45</td>\n",
" <td>416</td>\n",
" <td>230</td>\n",
" <td>13290</td>\n",
" <td>5720</td>\n",
" <td>500</td>\n",
" <td>1500</td>\n",
" <td>90</td>\n",
" <td>93</td>\n",
" <td>11.5</td>\n",
" <td>26</td>\n",
" <td>8861</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albion College</th>\n",
" <td>Yes</td>\n",
" <td>1899</td>\n",
" <td>1720</td>\n",
" <td>489</td>\n",
" <td>37</td>\n",
" <td>68</td>\n",
" <td>1594</td>\n",
" <td>32</td>\n",
" <td>13868</td>\n",
" <td>4826</td>\n",
" <td>450</td>\n",
" <td>850</td>\n",
" <td>89</td>\n",
" <td>100</td>\n",
" <td>13.7</td>\n",
" <td>37</td>\n",
" <td>11487</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albright College</th>\n",
" <td>Yes</td>\n",
" <td>1038</td>\n",
" <td>839</td>\n",
" <td>227</td>\n",
" <td>30</td>\n",
" <td>63</td>\n",
" <td>973</td>\n",
" <td>306</td>\n",
" <td>15595</td>\n",
" <td>4400</td>\n",
" <td>300</td>\n",
" <td>500</td>\n",
" <td>79</td>\n",
" <td>84</td>\n",
" <td>11.3</td>\n",
" <td>23</td>\n",
" <td>11644</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alderson-Broaddus College</th>\n",
" <td>Yes</td>\n",
" <td>582</td>\n",
" <td>498</td>\n",
" <td>172</td>\n",
" <td>21</td>\n",
" <td>44</td>\n",
" <td>799</td>\n",
" <td>78</td>\n",
" <td>10468</td>\n",
" <td>3380</td>\n",
" <td>660</td>\n",
" <td>1800</td>\n",
" <td>40</td>\n",
" <td>41</td>\n",
" <td>11.5</td>\n",
" <td>15</td>\n",
" <td>8991</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alfred University</th>\n",
" <td>Yes</td>\n",
" <td>1732</td>\n",
" <td>1425</td>\n",
" <td>472</td>\n",
" <td>37</td>\n",
" <td>75</td>\n",
" <td>1830</td>\n",
" <td>110</td>\n",
" <td>16548</td>\n",
" <td>5406</td>\n",
" <td>500</td>\n",
" <td>600</td>\n",
" <td>82</td>\n",
" <td>88</td>\n",
" <td>11.3</td>\n",
" <td>31</td>\n",
" <td>10932</td>\n",
" <td>73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allegheny College</th>\n",
" <td>Yes</td>\n",
" <td>2652</td>\n",
" <td>1900</td>\n",
" <td>484</td>\n",
" <td>44</td>\n",
" <td>77</td>\n",
" <td>1707</td>\n",
" <td>44</td>\n",
" <td>17080</td>\n",
" <td>4440</td>\n",
" <td>400</td>\n",
" <td>600</td>\n",
" <td>73</td>\n",
" <td>91</td>\n",
" <td>9.9</td>\n",
" <td>41</td>\n",
" <td>11711</td>\n",
" <td>76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Allentown Coll. of St. Francis de Sales</th>\n",
" <td>Yes</td>\n",
" <td>1179</td>\n",
" <td>780</td>\n",
" <td>290</td>\n",
" <td>38</td>\n",
" <td>64</td>\n",
" <td>1130</td>\n",
" <td>638</td>\n",
" <td>9690</td>\n",
" <td>4785</td>\n",
" <td>600</td>\n",
" <td>1000</td>\n",
" <td>60</td>\n",
" <td>84</td>\n",
" <td>13.3</td>\n",
" <td>21</td>\n",
" <td>7940</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alma College</th>\n",
" <td>Yes</td>\n",
" <td>1267</td>\n",
" <td>1080</td>\n",
" <td>385</td>\n",
" <td>44</td>\n",
" <td>73</td>\n",
" <td>1306</td>\n",
" <td>28</td>\n",
" <td>12572</td>\n",
" <td>4552</td>\n",
" <td>400</td>\n",
" <td>400</td>\n",
" <td>79</td>\n",
" <td>87</td>\n",
" <td>15.3</td>\n",
" <td>32</td>\n",
" <td>9305</td>\n",
" <td>68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alverno College</th>\n",
" <td>Yes</td>\n",
" <td>494</td>\n",
" <td>313</td>\n",
" <td>157</td>\n",
" <td>23</td>\n",
" <td>46</td>\n",
" <td>1317</td>\n",
" <td>1235</td>\n",
" <td>8352</td>\n",
" <td>3640</td>\n",
" <td>650</td>\n",
" <td>2449</td>\n",
" <td>36</td>\n",
" <td>69</td>\n",
" <td>11.1</td>\n",
" <td>26</td>\n",
" <td>8127</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>American International College</th>\n",
" <td>Yes</td>\n",
" <td>1420</td>\n",
" <td>1093</td>\n",
" <td>220</td>\n",
" <td>9</td>\n",
" <td>22</td>\n",
" <td>1018</td>\n",
" <td>287</td>\n",
" <td>8700</td>\n",
" <td>4780</td>\n",
" <td>450</td>\n",
" <td>1400</td>\n",
" <td>78</td>\n",
" <td>84</td>\n",
" <td>14.7</td>\n",
" <td>19</td>\n",
" <td>7355</td>\n",
" <td>69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Amherst College</th>\n",
" <td>Yes</td>\n",
" <td>4302</td>\n",
" <td>992</td>\n",
" <td>418</td>\n",
" <td>83</td>\n",
" <td>96</td>\n",
" <td>1593</td>\n",
" <td>5</td>\n",
" <td>19760</td>\n",
" <td>5300</td>\n",
" <td>660</td>\n",
" <td>1598</td>\n",
" <td>93</td>\n",
" <td>98</td>\n",
" <td>8.4</td>\n",
" <td>63</td>\n",
" <td>21424</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anderson University</th>\n",
" <td>Yes</td>\n",
" <td>1216</td>\n",
" <td>908</td>\n",
" <td>423</td>\n",
" <td>19</td>\n",
" <td>40</td>\n",
" <td>1819</td>\n",
" <td>281</td>\n",
" <td>10100</td>\n",
" <td>3520</td>\n",
" <td>550</td>\n",
" <td>1100</td>\n",
" <td>48</td>\n",
" <td>61</td>\n",
" <td>12.1</td>\n",
" <td>14</td>\n",
" <td>7994</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Andrews University</th>\n",
" <td>Yes</td>\n",
" <td>1130</td>\n",
" <td>704</td>\n",
" <td>322</td>\n",
" <td>14</td>\n",
" <td>23</td>\n",
" <td>1586</td>\n",
" <td>326</td>\n",
" <td>9996</td>\n",
" <td>3090</td>\n",
" <td>900</td>\n",
" <td>1320</td>\n",
" <td>62</td>\n",
" <td>66</td>\n",
" <td>11.5</td>\n",
" <td>18</td>\n",
" <td>10908</td>\n",
" <td>46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Angelo State University</th>\n",
" <td>No</td>\n",
" <td>3540</td>\n",
" <td>2001</td>\n",
" <td>1016</td>\n",
" <td>24</td>\n",
" <td>54</td>\n",
" <td>4190</td>\n",
" <td>1512</td>\n",
" <td>5130</td>\n",
" <td>3592</td>\n",
" <td>500</td>\n",
" <td>2000</td>\n",
" <td>60</td>\n",
" <td>62</td>\n",
" <td>23.1</td>\n",
" <td>5</td>\n",
" <td>4010</td>\n",
" <td>34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Antioch University</th>\n",
" <td>Yes</td>\n",
" <td>713</td>\n",
" <td>661</td>\n",
" <td>252</td>\n",
" <td>25</td>\n",
" <td>44</td>\n",
" <td>712</td>\n",
" <td>23</td>\n",
" <td>15476</td>\n",
" <td>3336</td>\n",
" <td>400</td>\n",
" <td>1100</td>\n",
" <td>69</td>\n",
" <td>82</td>\n",
" <td>11.3</td>\n",
" <td>35</td>\n",
" <td>42926</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Appalachian State University</th>\n",
" <td>No</td>\n",
" <td>7313</td>\n",
" <td>4664</td>\n",
" <td>1910</td>\n",
" <td>20</td>\n",
" <td>63</td>\n",
" <td>9940</td>\n",
" <td>1035</td>\n",
" <td>6806</td>\n",
" <td>2540</td>\n",
" <td>96</td>\n",
" <td>2000</td>\n",
" <td>83</td>\n",
" <td>96</td>\n",
" <td>18.3</td>\n",
" <td>14</td>\n",
" <td>5854</td>\n",
" <td>70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Aquinas College</th>\n",
" <td>Yes</td>\n",
" <td>619</td>\n",
" <td>516</td>\n",
" <td>219</td>\n",
" <td>20</td>\n",
" <td>51</td>\n",
" <td>1251</td>\n",
" <td>767</td>\n",
" <td>11208</td>\n",
" <td>4124</td>\n",
" <td>350</td>\n",
" <td>1615</td>\n",
" <td>55</td>\n",
" <td>65</td>\n",
" <td>12.7</td>\n",
" <td>25</td>\n",
" <td>6584</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arizona State University Main campus</th>\n",
" <td>No</td>\n",
" <td>12809</td>\n",
" <td>10308</td>\n",
" <td>3761</td>\n",
" <td>24</td>\n",
" <td>49</td>\n",
" <td>22593</td>\n",
" <td>7585</td>\n",
" <td>7434</td>\n",
" <td>4850</td>\n",
" <td>700</td>\n",
" <td>2100</td>\n",
" <td>88</td>\n",
" <td>93</td>\n",
" <td>18.9</td>\n",
" <td>5</td>\n",
" <td>4602</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas College (Lyon College)</th>\n",
" <td>Yes</td>\n",
" <td>708</td>\n",
" <td>334</td>\n",
" <td>166</td>\n",
" <td>46</td>\n",
" <td>74</td>\n",
" <td>530</td>\n",
" <td>182</td>\n",
" <td>8644</td>\n",
" <td>3922</td>\n",
" <td>500</td>\n",
" <td>800</td>\n",
" <td>79</td>\n",
" <td>88</td>\n",
" <td>12.6</td>\n",
" <td>24</td>\n",
" <td>14579</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas Tech University</th>\n",
" <td>No</td>\n",
" <td>1734</td>\n",
" <td>1729</td>\n",
" <td>951</td>\n",
" <td>12</td>\n",
" <td>52</td>\n",
" <td>3602</td>\n",
" <td>939</td>\n",
" <td>3460</td>\n",
" <td>2650</td>\n",
" <td>450</td>\n",
" <td>1000</td>\n",
" <td>57</td>\n",
" <td>60</td>\n",
" <td>19.6</td>\n",
" <td>5</td>\n",
" <td>4739</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Assumption College</th>\n",
" <td>Yes</td>\n",
" <td>2135</td>\n",
" <td>1700</td>\n",
" <td>491</td>\n",
" <td>23</td>\n",
" <td>59</td>\n",
" <td>1708</td>\n",
" <td>689</td>\n",
" <td>12000</td>\n",
" <td>5920</td>\n",
" <td>500</td>\n",
" <td>500</td>\n",
" <td>93</td>\n",
" <td>93</td>\n",
" <td>13.8</td>\n",
" <td>30</td>\n",
" <td>7100</td>\n",
" <td>88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Auburn University-Main Campus</th>\n",
" <td>No</td>\n",
" <td>7548</td>\n",
" <td>6791</td>\n",
" <td>3070</td>\n",
" <td>25</td>\n",
" <td>57</td>\n",
" <td>16262</td>\n",
" <td>1716</td>\n",
" <td>6300</td>\n",
" <td>3933</td>\n",
" <td>600</td>\n",
" <td>1908</td>\n",
" <td>85</td>\n",
" <td>91</td>\n",
" <td>16.7</td>\n",
" <td>18</td>\n",
" <td>6642</td>\n",
" <td>69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Augsburg College</th>\n",
" <td>Yes</td>\n",
" <td>662</td>\n",
" <td>513</td>\n",
" <td>257</td>\n",
" <td>12</td>\n",
" <td>30</td>\n",
" <td>2074</td>\n",
" <td>726</td>\n",
" <td>11902</td>\n",
" <td>4372</td>\n",
" <td>540</td>\n",
" <td>950</td>\n",
" <td>65</td>\n",
" <td>65</td>\n",
" <td>12.8</td>\n",
" <td>31</td>\n",
" <td>7836</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Augustana College IL</th>\n",
" <td>Yes</td>\n",
" <td>1879</td>\n",
" <td>1658</td>\n",
" <td>497</td>\n",
" <td>36</td>\n",
" <td>69</td>\n",
" <td>1950</td>\n",
" <td>38</td>\n",
" <td>13353</td>\n",
" <td>4173</td>\n",
" <td>540</td>\n",
" <td>821</td>\n",
" <td>78</td>\n",
" <td>83</td>\n",
" <td>12.7</td>\n",
" <td>40</td>\n",
" <td>9220</td>\n",
" <td>71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Westfield State College</th>\n",
" <td>No</td>\n",
" <td>3100</td>\n",
" <td>2150</td>\n",
" <td>825</td>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>3234</td>\n",
" <td>941</td>\n",
" <td>5542</td>\n",
" <td>3788</td>\n",
" <td>500</td>\n",
" <td>1300</td>\n",
" <td>75</td>\n",
" <td>79</td>\n",
" <td>15.7</td>\n",
" <td>20</td>\n",
" <td>4222</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Westminster College MO</th>\n",
" <td>Yes</td>\n",
" <td>662</td>\n",
" <td>553</td>\n",
" <td>184</td>\n",
" <td>20</td>\n",
" <td>43</td>\n",
" <td>665</td>\n",
" <td>37</td>\n",
" <td>10720</td>\n",
" <td>4050</td>\n",
" <td>600</td>\n",
" <td>1650</td>\n",
" <td>66</td>\n",
" <td>70</td>\n",
" <td>12.5</td>\n",
" <td>20</td>\n",
" <td>7925</td>\n",
" <td>62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Westminster College</th>\n",
" <td>Yes</td>\n",
" <td>996</td>\n",
" <td>866</td>\n",
" <td>377</td>\n",
" <td>29</td>\n",
" <td>58</td>\n",
" <td>1411</td>\n",
" <td>72</td>\n",
" <td>12065</td>\n",
" <td>3615</td>\n",
" <td>430</td>\n",
" <td>685</td>\n",
" <td>62</td>\n",
" <td>78</td>\n",
" <td>12.5</td>\n",
" <td>41</td>\n",
" <td>8596</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Westminster College of Salt Lake City</th>\n",
" <td>Yes</td>\n",
" <td>917</td>\n",
" <td>720</td>\n",
" <td>213</td>\n",
" <td>21</td>\n",
" <td>60</td>\n",
" <td>979</td>\n",
" <td>743</td>\n",
" <td>8820</td>\n",
" <td>4050</td>\n",
" <td>600</td>\n",
" <td>2025</td>\n",
" <td>68</td>\n",
" <td>83</td>\n",
" <td>10.5</td>\n",
" <td>34</td>\n",
" <td>7170</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Westmont College</th>\n",
" <td>No</td>\n",
" <td>950</td>\n",
" <td>713</td>\n",
" <td>351</td>\n",
" <td>42</td>\n",
" <td>72</td>\n",
" <td>1276</td>\n",
" <td>9</td>\n",
" <td>14320</td>\n",
" <td>5304</td>\n",
" <td>490</td>\n",
" <td>1410</td>\n",
" <td>77</td>\n",
" <td>77</td>\n",
" <td>14.9</td>\n",
" <td>17</td>\n",
" <td>8837</td>\n",
" <td>87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wheaton College IL</th>\n",
" <td>Yes</td>\n",
" <td>1432</td>\n",
" <td>920</td>\n",
" <td>548</td>\n",
" <td>56</td>\n",
" <td>84</td>\n",
" <td>2200</td>\n",
" <td>56</td>\n",
" <td>11480</td>\n",
" <td>4200</td>\n",
" <td>530</td>\n",
" <td>1400</td>\n",
" <td>81</td>\n",
" <td>83</td>\n",
" <td>12.7</td>\n",
" <td>40</td>\n",
" <td>11916</td>\n",
" <td>85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Westminster College PA</th>\n",
" <td>Yes</td>\n",
" <td>1738</td>\n",
" <td>1373</td>\n",
" <td>417</td>\n",
" <td>21</td>\n",
" <td>55</td>\n",
" <td>1335</td>\n",
" <td>30</td>\n",
" <td>18460</td>\n",
" <td>5970</td>\n",
" <td>700</td>\n",
" <td>850</td>\n",
" <td>92</td>\n",
" <td>96</td>\n",
" <td>13.2</td>\n",
" <td>41</td>\n",
" <td>22704</td>\n",
" <td>71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wheeling Jesuit College</th>\n",
" <td>Yes</td>\n",
" <td>903</td>\n",
" <td>755</td>\n",
" <td>213</td>\n",
" <td>15</td>\n",
" <td>49</td>\n",
" <td>971</td>\n",
" <td>305</td>\n",
" <td>10500</td>\n",
" <td>4545</td>\n",
" <td>600</td>\n",
" <td>600</td>\n",
" <td>66</td>\n",
" <td>71</td>\n",
" <td>14.1</td>\n",
" <td>27</td>\n",
" <td>7494</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whitman College</th>\n",
" <td>Yes</td>\n",
" <td>1861</td>\n",
" <td>998</td>\n",
" <td>359</td>\n",
" <td>45</td>\n",
" <td>77</td>\n",
" <td>1220</td>\n",
" <td>46</td>\n",
" <td>16670</td>\n",
" <td>4900</td>\n",
" <td>750</td>\n",
" <td>800</td>\n",
" <td>80</td>\n",
" <td>83</td>\n",
" <td>10.5</td>\n",
" <td>51</td>\n",
" <td>13198</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whittier College</th>\n",
" <td>Yes</td>\n",
" <td>1681</td>\n",
" <td>1069</td>\n",
" <td>344</td>\n",
" <td>35</td>\n",
" <td>63</td>\n",
" <td>1235</td>\n",
" <td>30</td>\n",
" <td>16249</td>\n",
" <td>5699</td>\n",
" <td>500</td>\n",
" <td>1998</td>\n",
" <td>84</td>\n",
" <td>92</td>\n",
" <td>13.6</td>\n",
" <td>29</td>\n",
" <td>11778</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whitworth College</th>\n",
" <td>Yes</td>\n",
" <td>1121</td>\n",
" <td>926</td>\n",
" <td>372</td>\n",
" <td>43</td>\n",
" <td>70</td>\n",
" <td>1270</td>\n",
" <td>160</td>\n",
" <td>12660</td>\n",
" <td>4500</td>\n",
" <td>678</td>\n",
" <td>2424</td>\n",
" <td>80</td>\n",
" <td>80</td>\n",
" <td>16.9</td>\n",
" <td>20</td>\n",
" <td>8328</td>\n",
" <td>80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Widener University</th>\n",
" <td>Yes</td>\n",
" <td>2139</td>\n",
" <td>1492</td>\n",
" <td>502</td>\n",
" <td>24</td>\n",
" <td>64</td>\n",
" <td>2186</td>\n",
" <td>2171</td>\n",
" <td>12350</td>\n",
" <td>5370</td>\n",
" <td>500</td>\n",
" <td>1350</td>\n",
" <td>88</td>\n",
" <td>86</td>\n",
" <td>12.6</td>\n",
" <td>19</td>\n",
" <td>9603</td>\n",
" <td>63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wilkes University</th>\n",
" <td>Yes</td>\n",
" <td>1631</td>\n",
" <td>1431</td>\n",
" <td>434</td>\n",
" <td>15</td>\n",
" <td>36</td>\n",
" <td>1803</td>\n",
" <td>603</td>\n",
" <td>11150</td>\n",
" <td>5130</td>\n",
" <td>550</td>\n",
" <td>1260</td>\n",
" <td>78</td>\n",
" <td>92</td>\n",
" <td>13.3</td>\n",
" <td>24</td>\n",
" <td>8543</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Willamette University</th>\n",
" <td>Yes</td>\n",
" <td>1658</td>\n",
" <td>1327</td>\n",
" <td>395</td>\n",
" <td>49</td>\n",
" <td>80</td>\n",
" <td>1595</td>\n",
" <td>159</td>\n",
" <td>14800</td>\n",
" <td>4620</td>\n",
" <td>400</td>\n",
" <td>790</td>\n",
" <td>91</td>\n",
" <td>94</td>\n",
" <td>13.3</td>\n",
" <td>37</td>\n",
" <td>10779</td>\n",
" <td>68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Jewell College</th>\n",
" <td>Yes</td>\n",
" <td>663</td>\n",
" <td>547</td>\n",
" <td>315</td>\n",
" <td>32</td>\n",
" <td>67</td>\n",
" <td>1279</td>\n",
" <td>75</td>\n",
" <td>10060</td>\n",
" <td>2970</td>\n",
" <td>500</td>\n",
" <td>2600</td>\n",
" <td>74</td>\n",
" <td>80</td>\n",
" <td>11.2</td>\n",
" <td>19</td>\n",
" <td>7885</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Woods University</th>\n",
" <td>Yes</td>\n",
" <td>469</td>\n",
" <td>435</td>\n",
" <td>227</td>\n",
" <td>17</td>\n",
" <td>39</td>\n",
" <td>851</td>\n",
" <td>120</td>\n",
" <td>10535</td>\n",
" <td>4365</td>\n",
" <td>550</td>\n",
" <td>3700</td>\n",
" <td>39</td>\n",
" <td>66</td>\n",
" <td>12.9</td>\n",
" <td>16</td>\n",
" <td>7438</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Williams College</th>\n",
" <td>Yes</td>\n",
" <td>4186</td>\n",
" <td>1245</td>\n",
" <td>526</td>\n",
" <td>81</td>\n",
" <td>96</td>\n",
" <td>1988</td>\n",
" <td>29</td>\n",
" <td>19629</td>\n",
" <td>5790</td>\n",
" <td>500</td>\n",
" <td>1200</td>\n",
" <td>94</td>\n",
" <td>99</td>\n",
" <td>9.0</td>\n",
" <td>64</td>\n",
" <td>22014</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wilson College</th>\n",
" <td>Yes</td>\n",
" <td>167</td>\n",
" <td>130</td>\n",
" <td>46</td>\n",
" <td>16</td>\n",
" <td>50</td>\n",
" <td>199</td>\n",
" <td>676</td>\n",
" <td>11428</td>\n",
" <td>5084</td>\n",
" <td>450</td>\n",
" <td>475</td>\n",
" <td>67</td>\n",
" <td>76</td>\n",
" <td>8.3</td>\n",
" <td>43</td>\n",
" <td>10291</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wingate College</th>\n",
" <td>Yes</td>\n",
" <td>1239</td>\n",
" <td>1017</td>\n",
" <td>383</td>\n",
" <td>10</td>\n",
" <td>34</td>\n",
" <td>1207</td>\n",
" <td>157</td>\n",
" <td>7820</td>\n",
" <td>3400</td>\n",
" <td>550</td>\n",
" <td>1550</td>\n",
" <td>69</td>\n",
" <td>81</td>\n",
" <td>13.9</td>\n",
" <td>8</td>\n",
" <td>7264</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Winona State University</th>\n",
" <td>No</td>\n",
" <td>3325</td>\n",
" <td>2047</td>\n",
" <td>1301</td>\n",
" <td>20</td>\n",
" <td>45</td>\n",
" <td>5800</td>\n",
" <td>872</td>\n",
" <td>4200</td>\n",
" <td>2700</td>\n",
" <td>300</td>\n",
" <td>1200</td>\n",
" <td>53</td>\n",
" <td>60</td>\n",
" <td>20.2</td>\n",
" <td>18</td>\n",
" <td>5318</td>\n",
" <td>58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Winthrop University</th>\n",
" <td>No</td>\n",
" <td>2320</td>\n",
" <td>1805</td>\n",
" <td>769</td>\n",
" <td>24</td>\n",
" <td>61</td>\n",
" <td>3395</td>\n",
" <td>670</td>\n",
" <td>6400</td>\n",
" <td>3392</td>\n",
" <td>580</td>\n",
" <td>2150</td>\n",
" <td>71</td>\n",
" <td>80</td>\n",
" <td>12.8</td>\n",
" <td>26</td>\n",
" <td>6729</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wisconsin Lutheran College</th>\n",
" <td>Yes</td>\n",
" <td>152</td>\n",
" <td>128</td>\n",
" <td>75</td>\n",
" <td>17</td>\n",
" <td>41</td>\n",
" <td>282</td>\n",
" <td>22</td>\n",
" <td>9100</td>\n",
" <td>3700</td>\n",
" <td>500</td>\n",
" <td>1400</td>\n",
" <td>48</td>\n",
" <td>48</td>\n",
" <td>8.5</td>\n",
" <td>26</td>\n",
" <td>8960</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wittenberg University</th>\n",
" <td>Yes</td>\n",
" <td>1979</td>\n",
" <td>1739</td>\n",
" <td>575</td>\n",
" <td>42</td>\n",
" <td>68</td>\n",
" <td>1980</td>\n",
" <td>144</td>\n",
" <td>15948</td>\n",
" <td>4404</td>\n",
" <td>400</td>\n",
" <td>800</td>\n",
" <td>82</td>\n",
" <td>95</td>\n",
" <td>12.8</td>\n",
" <td>29</td>\n",
" <td>10414</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wofford College</th>\n",
" <td>Yes</td>\n",
" <td>1501</td>\n",
" <td>935</td>\n",
" <td>273</td>\n",
" <td>51</td>\n",
" <td>83</td>\n",
" <td>1059</td>\n",
" <td>34</td>\n",
" <td>12680</td>\n",
" <td>4150</td>\n",
" <td>605</td>\n",
" <td>1440</td>\n",
" <td>91</td>\n",
" <td>92</td>\n",
" <td>15.3</td>\n",
" <td>42</td>\n",
" <td>7875</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Worcester Polytechnic Institute</th>\n",
" <td>Yes</td>\n",
" <td>2768</td>\n",
" <td>2314</td>\n",
" <td>682</td>\n",
" <td>49</td>\n",
" <td>86</td>\n",
" <td>2802</td>\n",
" <td>86</td>\n",
" <td>15884</td>\n",
" <td>5370</td>\n",
" <td>530</td>\n",
" <td>730</td>\n",
" <td>92</td>\n",
" <td>94</td>\n",
" <td>15.2</td>\n",
" <td>34</td>\n",
" <td>10774</td>\n",
" <td>82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Worcester State College</th>\n",
" <td>No</td>\n",
" <td>2197</td>\n",
" <td>1515</td>\n",
" <td>543</td>\n",
" <td>4</td>\n",
" <td>26</td>\n",
" <td>3089</td>\n",
" <td>2029</td>\n",
" <td>6797</td>\n",
" <td>3900</td>\n",
" <td>500</td>\n",
" <td>1200</td>\n",
" <td>60</td>\n",
" <td>60</td>\n",
" <td>21.0</td>\n",
" <td>14</td>\n",
" <td>4469</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xavier University</th>\n",
" <td>Yes</td>\n",
" <td>1959</td>\n",
" <td>1805</td>\n",
" <td>695</td>\n",
" <td>24</td>\n",
" <td>47</td>\n",
" <td>2849</td>\n",
" <td>1107</td>\n",
" <td>11520</td>\n",
" <td>4960</td>\n",
" <td>600</td>\n",
" <td>1250</td>\n",
" <td>73</td>\n",
" <td>75</td>\n",
" <td>13.3</td>\n",
" <td>31</td>\n",
" <td>9189</td>\n",
" <td>83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xavier University of Louisiana</th>\n",
" <td>Yes</td>\n",
" <td>2097</td>\n",
" <td>1915</td>\n",
" <td>695</td>\n",
" <td>34</td>\n",
" <td>61</td>\n",
" <td>2793</td>\n",
" <td>166</td>\n",
" <td>6900</td>\n",
" <td>4200</td>\n",
" <td>617</td>\n",
" <td>781</td>\n",
" <td>67</td>\n",
" <td>75</td>\n",
" <td>14.4</td>\n",
" <td>20</td>\n",
" <td>8323</td>\n",
" <td>49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yale University</th>\n",
" <td>Yes</td>\n",
" <td>10705</td>\n",
" <td>2453</td>\n",
" <td>1317</td>\n",
" <td>95</td>\n",
" <td>99</td>\n",
" <td>5217</td>\n",
" <td>83</td>\n",
" <td>19840</td>\n",
" <td>6510</td>\n",
" <td>630</td>\n",
" <td>2115</td>\n",
" <td>96</td>\n",
" <td>96</td>\n",
" <td>5.8</td>\n",
" <td>49</td>\n",
" <td>40386</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>York College of Pennsylvania</th>\n",
" <td>Yes</td>\n",
" <td>2989</td>\n",
" <td>1855</td>\n",
" <td>691</td>\n",
" <td>28</td>\n",
" <td>63</td>\n",
" <td>2988</td>\n",
" <td>1726</td>\n",
" <td>4990</td>\n",
" <td>3560</td>\n",
" <td>500</td>\n",
" <td>1250</td>\n",
" <td>75</td>\n",
" <td>75</td>\n",
" <td>18.1</td>\n",
" <td>28</td>\n",
" <td>4509</td>\n",
" <td>99</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>777 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" Private Apps Accept Enroll \\\n",
"Abilene Christian University Yes 1660 1232 721 \n",
"Adelphi University Yes 2186 1924 512 \n",
"Adrian College Yes 1428 1097 336 \n",
"Agnes Scott College Yes 417 349 137 \n",
"Alaska Pacific University Yes 193 146 55 \n",
"Albertson College Yes 587 479 158 \n",
"Albertus Magnus College Yes 353 340 103 \n",
"Albion College Yes 1899 1720 489 \n",
"Albright College Yes 1038 839 227 \n",
"Alderson-Broaddus College Yes 582 498 172 \n",
"Alfred University Yes 1732 1425 472 \n",
"Allegheny College Yes 2652 1900 484 \n",
"Allentown Coll. of St. Francis de Sales Yes 1179 780 290 \n",
"Alma College Yes 1267 1080 385 \n",
"Alverno College Yes 494 313 157 \n",
"American International College Yes 1420 1093 220 \n",
"Amherst College Yes 4302 992 418 \n",
"Anderson University Yes 1216 908 423 \n",
"Andrews University Yes 1130 704 322 \n",
"Angelo State University No 3540 2001 1016 \n",
"Antioch University Yes 713 661 252 \n",
"Appalachian State University No 7313 4664 1910 \n",
"Aquinas College Yes 619 516 219 \n",
"Arizona State University Main campus No 12809 10308 3761 \n",
"Arkansas College (Lyon College) Yes 708 334 166 \n",
"Arkansas Tech University No 1734 1729 951 \n",
"Assumption College Yes 2135 1700 491 \n",
"Auburn University-Main Campus No 7548 6791 3070 \n",
"Augsburg College Yes 662 513 257 \n",
"Augustana College IL Yes 1879 1658 497 \n",
"... ... ... ... ... \n",
"Westfield State College No 3100 2150 825 \n",
"Westminster College MO Yes 662 553 184 \n",
"Westminster College Yes 996 866 377 \n",
"Westminster College of Salt Lake City Yes 917 720 213 \n",
"Westmont College No 950 713 351 \n",
"Wheaton College IL Yes 1432 920 548 \n",
"Westminster College PA Yes 1738 1373 417 \n",
"Wheeling Jesuit College Yes 903 755 213 \n",
"Whitman College Yes 1861 998 359 \n",
"Whittier College Yes 1681 1069 344 \n",
"Whitworth College Yes 1121 926 372 \n",
"Widener University Yes 2139 1492 502 \n",
"Wilkes University Yes 1631 1431 434 \n",
"Willamette University Yes 1658 1327 395 \n",
"William Jewell College Yes 663 547 315 \n",
"William Woods University Yes 469 435 227 \n",
"Williams College Yes 4186 1245 526 \n",
"Wilson College Yes 167 130 46 \n",
"Wingate College Yes 1239 1017 383 \n",
"Winona State University No 3325 2047 1301 \n",
"Winthrop University No 2320 1805 769 \n",
"Wisconsin Lutheran College Yes 152 128 75 \n",
"Wittenberg University Yes 1979 1739 575 \n",
"Wofford College Yes 1501 935 273 \n",
"Worcester Polytechnic Institute Yes 2768 2314 682 \n",
"Worcester State College No 2197 1515 543 \n",
"Xavier University Yes 1959 1805 695 \n",
"Xavier University of Louisiana Yes 2097 1915 695 \n",
"Yale University Yes 10705 2453 1317 \n",
"York College of Pennsylvania Yes 2989 1855 691 \n",
"\n",
" Top10perc Top25perc F.Undergrad \\\n",
"Abilene Christian University 23 52 2885 \n",
"Adelphi University 16 29 2683 \n",
"Adrian College 22 50 1036 \n",
"Agnes Scott College 60 89 510 \n",
"Alaska Pacific University 16 44 249 \n",
"Albertson College 38 62 678 \n",
"Albertus Magnus College 17 45 416 \n",
"Albion College 37 68 1594 \n",
"Albright College 30 63 973 \n",
"Alderson-Broaddus College 21 44 799 \n",
"Alfred University 37 75 1830 \n",
"Allegheny College 44 77 1707 \n",
"Allentown Coll. of St. Francis de Sales 38 64 1130 \n",
"Alma College 44 73 1306 \n",
"Alverno College 23 46 1317 \n",
"American International College 9 22 1018 \n",
"Amherst College 83 96 1593 \n",
"Anderson University 19 40 1819 \n",
"Andrews University 14 23 1586 \n",
"Angelo State University 24 54 4190 \n",
"Antioch University 25 44 712 \n",
"Appalachian State University 20 63 9940 \n",
"Aquinas College 20 51 1251 \n",
"Arizona State University Main campus 24 49 22593 \n",
"Arkansas College (Lyon College) 46 74 530 \n",
"Arkansas Tech University 12 52 3602 \n",
"Assumption College 23 59 1708 \n",
"Auburn University-Main Campus 25 57 16262 \n",
"Augsburg College 12 30 2074 \n",
"Augustana College IL 36 69 1950 \n",
"... ... ... ... \n",
"Westfield State College 3 20 3234 \n",
"Westminster College MO 20 43 665 \n",
"Westminster College 29 58 1411 \n",
"Westminster College of Salt Lake City 21 60 979 \n",
"Westmont College 42 72 1276 \n",
"Wheaton College IL 56 84 2200 \n",
"Westminster College PA 21 55 1335 \n",
"Wheeling Jesuit College 15 49 971 \n",
"Whitman College 45 77 1220 \n",
"Whittier College 35 63 1235 \n",
"Whitworth College 43 70 1270 \n",
"Widener University 24 64 2186 \n",
"Wilkes University 15 36 1803 \n",
"Willamette University 49 80 1595 \n",
"William Jewell College 32 67 1279 \n",
"William Woods University 17 39 851 \n",
"Williams College 81 96 1988 \n",
"Wilson College 16 50 199 \n",
"Wingate College 10 34 1207 \n",
"Winona State University 20 45 5800 \n",
"Winthrop University 24 61 3395 \n",
"Wisconsin Lutheran College 17 41 282 \n",
"Wittenberg University 42 68 1980 \n",
"Wofford College 51 83 1059 \n",
"Worcester Polytechnic Institute 49 86 2802 \n",
"Worcester State College 4 26 3089 \n",
"Xavier University 24 47 2849 \n",
"Xavier University of Louisiana 34 61 2793 \n",
"Yale University 95 99 5217 \n",
"York College of Pennsylvania 28 63 2988 \n",
"\n",
" P.Undergrad Outstate Room.Board \\\n",
"Abilene Christian University 537 7440 3300 \n",
"Adelphi University 1227 12280 6450 \n",
"Adrian College 99 11250 3750 \n",
"Agnes Scott College 63 12960 5450 \n",
"Alaska Pacific University 869 7560 4120 \n",
"Albertson College 41 13500 3335 \n",
"Albertus Magnus College 230 13290 5720 \n",
"Albion College 32 13868 4826 \n",
"Albright College 306 15595 4400 \n",
"Alderson-Broaddus College 78 10468 3380 \n",
"Alfred University 110 16548 5406 \n",
"Allegheny College 44 17080 4440 \n",
"Allentown Coll. of St. Francis de Sales 638 9690 4785 \n",
"Alma College 28 12572 4552 \n",
"Alverno College 1235 8352 3640 \n",
"American International College 287 8700 4780 \n",
"Amherst College 5 19760 5300 \n",
"Anderson University 281 10100 3520 \n",
"Andrews University 326 9996 3090 \n",
"Angelo State University 1512 5130 3592 \n",
"Antioch University 23 15476 3336 \n",
"Appalachian State University 1035 6806 2540 \n",
"Aquinas College 767 11208 4124 \n",
"Arizona State University Main campus 7585 7434 4850 \n",
"Arkansas College (Lyon College) 182 8644 3922 \n",
"Arkansas Tech University 939 3460 2650 \n",
"Assumption College 689 12000 5920 \n",
"Auburn University-Main Campus 1716 6300 3933 \n",
"Augsburg College 726 11902 4372 \n",
"Augustana College IL 38 13353 4173 \n",
"... ... ... ... \n",
"Westfield State College 941 5542 3788 \n",
"Westminster College MO 37 10720 4050 \n",
"Westminster College 72 12065 3615 \n",
"Westminster College of Salt Lake City 743 8820 4050 \n",
"Westmont College 9 14320 5304 \n",
"Wheaton College IL 56 11480 4200 \n",
"Westminster College PA 30 18460 5970 \n",
"Wheeling Jesuit College 305 10500 4545 \n",
"Whitman College 46 16670 4900 \n",
"Whittier College 30 16249 5699 \n",
"Whitworth College 160 12660 4500 \n",
"Widener University 2171 12350 5370 \n",
"Wilkes University 603 11150 5130 \n",
"Willamette University 159 14800 4620 \n",
"William Jewell College 75 10060 2970 \n",
"William Woods University 120 10535 4365 \n",
"Williams College 29 19629 5790 \n",
"Wilson College 676 11428 5084 \n",
"Wingate College 157 7820 3400 \n",
"Winona State University 872 4200 2700 \n",
"Winthrop University 670 6400 3392 \n",
"Wisconsin Lutheran College 22 9100 3700 \n",
"Wittenberg University 144 15948 4404 \n",
"Wofford College 34 12680 4150 \n",
"Worcester Polytechnic Institute 86 15884 5370 \n",
"Worcester State College 2029 6797 3900 \n",
"Xavier University 1107 11520 4960 \n",
"Xavier University of Louisiana 166 6900 4200 \n",
"Yale University 83 19840 6510 \n",
"York College of Pennsylvania 1726 4990 3560 \n",
"\n",
" Books Personal PhD Terminal \\\n",
"Abilene Christian University 450 2200 70 78 \n",
"Adelphi University 750 1500 29 30 \n",
"Adrian College 400 1165 53 66 \n",
"Agnes Scott College 450 875 92 97 \n",
"Alaska Pacific University 800 1500 76 72 \n",
"Albertson College 500 675 67 73 \n",
"Albertus Magnus College 500 1500 90 93 \n",
"Albion College 450 850 89 100 \n",
"Albright College 300 500 79 84 \n",
"Alderson-Broaddus College 660 1800 40 41 \n",
"Alfred University 500 600 82 88 \n",
"Allegheny College 400 600 73 91 \n",
"Allentown Coll. of St. Francis de Sales 600 1000 60 84 \n",
"Alma College 400 400 79 87 \n",
"Alverno College 650 2449 36 69 \n",
"American International College 450 1400 78 84 \n",
"Amherst College 660 1598 93 98 \n",
"Anderson University 550 1100 48 61 \n",
"Andrews University 900 1320 62 66 \n",
"Angelo State University 500 2000 60 62 \n",
"Antioch University 400 1100 69 82 \n",
"Appalachian State University 96 2000 83 96 \n",
"Aquinas College 350 1615 55 65 \n",
"Arizona State University Main campus 700 2100 88 93 \n",
"Arkansas College (Lyon College) 500 800 79 88 \n",
"Arkansas Tech University 450 1000 57 60 \n",
"Assumption College 500 500 93 93 \n",
"Auburn University-Main Campus 600 1908 85 91 \n",
"Augsburg College 540 950 65 65 \n",
"Augustana College IL 540 821 78 83 \n",
"... ... ... ... ... \n",
"Westfield State College 500 1300 75 79 \n",
"Westminster College MO 600 1650 66 70 \n",
"Westminster College 430 685 62 78 \n",
"Westminster College of Salt Lake City 600 2025 68 83 \n",
"Westmont College 490 1410 77 77 \n",
"Wheaton College IL 530 1400 81 83 \n",
"Westminster College PA 700 850 92 96 \n",
"Wheeling Jesuit College 600 600 66 71 \n",
"Whitman College 750 800 80 83 \n",
"Whittier College 500 1998 84 92 \n",
"Whitworth College 678 2424 80 80 \n",
"Widener University 500 1350 88 86 \n",
"Wilkes University 550 1260 78 92 \n",
"Willamette University 400 790 91 94 \n",
"William Jewell College 500 2600 74 80 \n",
"William Woods University 550 3700 39 66 \n",
"Williams College 500 1200 94 99 \n",
"Wilson College 450 475 67 76 \n",
"Wingate College 550 1550 69 81 \n",
"Winona State University 300 1200 53 60 \n",
"Winthrop University 580 2150 71 80 \n",
"Wisconsin Lutheran College 500 1400 48 48 \n",
"Wittenberg University 400 800 82 95 \n",
"Wofford College 605 1440 91 92 \n",
"Worcester Polytechnic Institute 530 730 92 94 \n",
"Worcester State College 500 1200 60 60 \n",
"Xavier University 600 1250 73 75 \n",
"Xavier University of Louisiana 617 781 67 75 \n",
"Yale University 630 2115 96 96 \n",
"York College of Pennsylvania 500 1250 75 75 \n",
"\n",
" S.F.Ratio perc.alumni Expend \\\n",
"Abilene Christian University 18.1 12 7041 \n",
"Adelphi University 12.2 16 10527 \n",
"Adrian College 12.9 30 8735 \n",
"Agnes Scott College 7.7 37 19016 \n",
"Alaska Pacific University 11.9 2 10922 \n",
"Albertson College 9.4 11 9727 \n",
"Albertus Magnus College 11.5 26 8861 \n",
"Albion College 13.7 37 11487 \n",
"Albright College 11.3 23 11644 \n",
"Alderson-Broaddus College 11.5 15 8991 \n",
"Alfred University 11.3 31 10932 \n",
"Allegheny College 9.9 41 11711 \n",
"Allentown Coll. of St. Francis de Sales 13.3 21 7940 \n",
"Alma College 15.3 32 9305 \n",
"Alverno College 11.1 26 8127 \n",
"American International College 14.7 19 7355 \n",
"Amherst College 8.4 63 21424 \n",
"Anderson University 12.1 14 7994 \n",
"Andrews University 11.5 18 10908 \n",
"Angelo State University 23.1 5 4010 \n",
"Antioch University 11.3 35 42926 \n",
"Appalachian State University 18.3 14 5854 \n",
"Aquinas College 12.7 25 6584 \n",
"Arizona State University Main campus 18.9 5 4602 \n",
"Arkansas College (Lyon College) 12.6 24 14579 \n",
"Arkansas Tech University 19.6 5 4739 \n",
"Assumption College 13.8 30 7100 \n",
"Auburn University-Main Campus 16.7 18 6642 \n",
"Augsburg College 12.8 31 7836 \n",
"Augustana College IL 12.7 40 9220 \n",
"... ... ... ... \n",
"Westfield State College 15.7 20 4222 \n",
"Westminster College MO 12.5 20 7925 \n",
"Westminster College 12.5 41 8596 \n",
"Westminster College of Salt Lake City 10.5 34 7170 \n",
"Westmont College 14.9 17 8837 \n",
"Wheaton College IL 12.7 40 11916 \n",
"Westminster College PA 13.2 41 22704 \n",
"Wheeling Jesuit College 14.1 27 7494 \n",
"Whitman College 10.5 51 13198 \n",
"Whittier College 13.6 29 11778 \n",
"Whitworth College 16.9 20 8328 \n",
"Widener University 12.6 19 9603 \n",
"Wilkes University 13.3 24 8543 \n",
"Willamette University 13.3 37 10779 \n",
"William Jewell College 11.2 19 7885 \n",
"William Woods University 12.9 16 7438 \n",
"Williams College 9.0 64 22014 \n",
"Wilson College 8.3 43 10291 \n",
"Wingate College 13.9 8 7264 \n",
"Winona State University 20.2 18 5318 \n",
"Winthrop University 12.8 26 6729 \n",
"Wisconsin Lutheran College 8.5 26 8960 \n",
"Wittenberg University 12.8 29 10414 \n",
"Wofford College 15.3 42 7875 \n",
"Worcester Polytechnic Institute 15.2 34 10774 \n",
"Worcester State College 21.0 14 4469 \n",
"Xavier University 13.3 31 9189 \n",
"Xavier University of Louisiana 14.4 20 8323 \n",
"Yale University 5.8 49 40386 \n",
"York College of Pennsylvania 18.1 28 4509 \n",
"\n",
" Grad.Rate \n",
"Abilene Christian University 60 \n",
"Adelphi University 56 \n",
"Adrian College 54 \n",
"Agnes Scott College 59 \n",
"Alaska Pacific University 15 \n",
"Albertson College 55 \n",
"Albertus Magnus College 63 \n",
"Albion College 73 \n",
"Albright College 80 \n",
"Alderson-Broaddus College 52 \n",
"Alfred University 73 \n",
"Allegheny College 76 \n",
"Allentown Coll. of St. Francis de Sales 74 \n",
"Alma College 68 \n",
"Alverno College 55 \n",
"American International College 69 \n",
"Amherst College 100 \n",
"Anderson University 59 \n",
"Andrews University 46 \n",
"Angelo State University 34 \n",
"Antioch University 48 \n",
"Appalachian State University 70 \n",
"Aquinas College 65 \n",
"Arizona State University Main campus 48 \n",
"Arkansas College (Lyon College) 54 \n",
"Arkansas Tech University 48 \n",
"Assumption College 88 \n",
"Auburn University-Main Campus 69 \n",
"Augsburg College 58 \n",
"Augustana College IL 71 \n",
"... ... \n",
"Westfield State College 65 \n",
"Westminster College MO 62 \n",
"Westminster College 80 \n",
"Westminster College of Salt Lake City 50 \n",
"Westmont College 87 \n",
"Wheaton College IL 85 \n",
"Westminster College PA 71 \n",
"Wheeling Jesuit College 72 \n",
"Whitman College 72 \n",
"Whittier College 52 \n",
"Whitworth College 80 \n",
"Widener University 63 \n",
"Wilkes University 67 \n",
"Willamette University 68 \n",
"William Jewell College 59 \n",
"William Woods University 52 \n",
"Williams College 99 \n",
"Wilson College 67 \n",
"Wingate College 91 \n",
"Winona State University 58 \n",
"Winthrop University 59 \n",
"Wisconsin Lutheran College 50 \n",
"Wittenberg University 78 \n",
"Wofford College 75 \n",
"Worcester Polytechnic Institute 82 \n",
"Worcester State College 40 \n",
"Xavier University 83 \n",
"Xavier University of Louisiana 49 \n",
"Yale University 99 \n",
"York College of Pennsylvania 99 \n",
"\n",
"[777 rows x 18 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"college_file_name = 'data/College.csv'\n",
"college = pd.read_csv(college_file_name,index_col=0)\n",
"college"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Apps</th>\n",
" <th>Accept</th>\n",
" <th>Enroll</th>\n",
" <th>Top10perc</th>\n",
" <th>Top25perc</th>\n",
" <th>F.Undergrad</th>\n",
" <th>P.Undergrad</th>\n",
" <th>Outstate</th>\n",
" <th>Room.Board</th>\n",
" <th>Books</th>\n",
" <th>Personal</th>\n",
" <th>PhD</th>\n",
" <th>Terminal</th>\n",
" <th>S.F.Ratio</th>\n",
" <th>perc.alumni</th>\n",
" <th>Expend</th>\n",
" <th>Grad.Rate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.000000</td>\n",
" <td>777.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3001.638353</td>\n",
" <td>2018.804376</td>\n",
" <td>779.972973</td>\n",
" <td>27.558559</td>\n",
" <td>55.796654</td>\n",
" <td>3699.907336</td>\n",
" <td>855.298584</td>\n",
" <td>10440.669241</td>\n",
" <td>4357.526384</td>\n",
" <td>549.380952</td>\n",
" <td>1340.642214</td>\n",
" <td>72.660232</td>\n",
" <td>79.702703</td>\n",
" <td>14.089704</td>\n",
" <td>22.743887</td>\n",
" <td>9660.171171</td>\n",
" <td>65.46332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3870.201484</td>\n",
" <td>2451.113971</td>\n",
" <td>929.176190</td>\n",
" <td>17.640364</td>\n",
" <td>19.804778</td>\n",
" <td>4850.420531</td>\n",
" <td>1522.431887</td>\n",
" <td>4023.016484</td>\n",
" <td>1096.696416</td>\n",
" <td>165.105360</td>\n",
" <td>677.071454</td>\n",
" <td>16.328155</td>\n",
" <td>14.722359</td>\n",
" <td>3.958349</td>\n",
" <td>12.391801</td>\n",
" <td>5221.768440</td>\n",
" <td>17.17771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>81.000000</td>\n",
" <td>72.000000</td>\n",
" <td>35.000000</td>\n",
" <td>1.000000</td>\n",
" <td>9.000000</td>\n",
" <td>139.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2340.000000</td>\n",
" <td>1780.000000</td>\n",
" <td>96.000000</td>\n",
" <td>250.000000</td>\n",
" <td>8.000000</td>\n",
" <td>24.000000</td>\n",
" <td>2.500000</td>\n",
" <td>0.000000</td>\n",
" <td>3186.000000</td>\n",
" <td>10.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>776.000000</td>\n",
" <td>604.000000</td>\n",
" <td>242.000000</td>\n",
" <td>15.000000</td>\n",
" <td>41.000000</td>\n",
" <td>992.000000</td>\n",
" <td>95.000000</td>\n",
" <td>7320.000000</td>\n",
" <td>3597.000000</td>\n",
" <td>470.000000</td>\n",
" <td>850.000000</td>\n",
" <td>62.000000</td>\n",
" <td>71.000000</td>\n",
" <td>11.500000</td>\n",
" <td>13.000000</td>\n",
" <td>6751.000000</td>\n",
" <td>53.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1558.000000</td>\n",
" <td>1110.000000</td>\n",
" <td>434.000000</td>\n",
" <td>23.000000</td>\n",
" <td>54.000000</td>\n",
" <td>1707.000000</td>\n",
" <td>353.000000</td>\n",
" <td>9990.000000</td>\n",
" <td>4200.000000</td>\n",
" <td>500.000000</td>\n",
" <td>1200.000000</td>\n",
" <td>75.000000</td>\n",
" <td>82.000000</td>\n",
" <td>13.600000</td>\n",
" <td>21.000000</td>\n",
" <td>8377.000000</td>\n",
" <td>65.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3624.000000</td>\n",
" <td>2424.000000</td>\n",
" <td>902.000000</td>\n",
" <td>35.000000</td>\n",
" <td>69.000000</td>\n",
" <td>4005.000000</td>\n",
" <td>967.000000</td>\n",
" <td>12925.000000</td>\n",
" <td>5050.000000</td>\n",
" <td>600.000000</td>\n",
" <td>1700.000000</td>\n",
" <td>85.000000</td>\n",
" <td>92.000000</td>\n",
" <td>16.500000</td>\n",
" <td>31.000000</td>\n",
" <td>10830.000000</td>\n",
" <td>78.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>48094.000000</td>\n",
" <td>26330.000000</td>\n",
" <td>6392.000000</td>\n",
" <td>96.000000</td>\n",
" <td>100.000000</td>\n",
" <td>31643.000000</td>\n",
" <td>21836.000000</td>\n",
" <td>21700.000000</td>\n",
" <td>8124.000000</td>\n",
" <td>2340.000000</td>\n",
" <td>6800.000000</td>\n",
" <td>103.000000</td>\n",
" <td>100.000000</td>\n",
" <td>39.800000</td>\n",
" <td>64.000000</td>\n",
" <td>56233.000000</td>\n",
" <td>118.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Apps Accept Enroll Top10perc Top25perc \\\n",
"count 777.000000 777.000000 777.000000 777.000000 777.000000 \n",
"mean 3001.638353 2018.804376 779.972973 27.558559 55.796654 \n",
"std 3870.201484 2451.113971 929.176190 17.640364 19.804778 \n",
"min 81.000000 72.000000 35.000000 1.000000 9.000000 \n",
"25% 776.000000 604.000000 242.000000 15.000000 41.000000 \n",
"50% 1558.000000 1110.000000 434.000000 23.000000 54.000000 \n",
"75% 3624.000000 2424.000000 902.000000 35.000000 69.000000 \n",
"max 48094.000000 26330.000000 6392.000000 96.000000 100.000000 \n",
"\n",
" F.Undergrad P.Undergrad Outstate Room.Board Books \\\n",
"count 777.000000 777.000000 777.000000 777.000000 777.000000 \n",
"mean 3699.907336 855.298584 10440.669241 4357.526384 549.380952 \n",
"std 4850.420531 1522.431887 4023.016484 1096.696416 165.105360 \n",
"min 139.000000 1.000000 2340.000000 1780.000000 96.000000 \n",
"25% 992.000000 95.000000 7320.000000 3597.000000 470.000000 \n",
"50% 1707.000000 353.000000 9990.000000 4200.000000 500.000000 \n",
"75% 4005.000000 967.000000 12925.000000 5050.000000 600.000000 \n",
"max 31643.000000 21836.000000 21700.000000 8124.000000 2340.000000 \n",
"\n",
" Personal PhD Terminal S.F.Ratio perc.alumni \\\n",
"count 777.000000 777.000000 777.000000 777.000000 777.000000 \n",
"mean 1340.642214 72.660232 79.702703 14.089704 22.743887 \n",
"std 677.071454 16.328155 14.722359 3.958349 12.391801 \n",
"min 250.000000 8.000000 24.000000 2.500000 0.000000 \n",
"25% 850.000000 62.000000 71.000000 11.500000 13.000000 \n",
"50% 1200.000000 75.000000 82.000000 13.600000 21.000000 \n",
"75% 1700.000000 85.000000 92.000000 16.500000 31.000000 \n",
"max 6800.000000 103.000000 100.000000 39.800000 64.000000 \n",
"\n",
" Expend Grad.Rate \n",
"count 777.000000 777.00000 \n",
"mean 9660.171171 65.46332 \n",
"std 5221.768440 17.17771 \n",
"min 3186.000000 10.00000 \n",
"25% 6751.000000 53.00000 \n",
"50% 8377.000000 65.00000 \n",
"75% 10830.000000 78.00000 \n",
"max 56233.000000 118.00000 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"college.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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PPFwjCiULkFwIvhANMd1MO2CWyp8R10y+HOCaOTz3lDwfDXpGCoDCvRmaDoBv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\nEIQTJEAIgiAIJ/4/CX2P1rhdtZkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10eb56d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"column_10 = college.columns[-10:]\n",
"def getColumns(idx):\n",
" values = college[column_10[idx]]\n",
" return [item for item in values]\n",
"for row in range(10):\n",
" for col in range(10):\n",
" if row!=col:\n",
" plt.subplot(10, 10, row*10+col+1)\n",
" plt.scatter(getColumns(row), getColumns(col))\n",
" plt.axis('off')\n",
"plt.show() "
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"Top10prerc=college['Top10perc'].values\n",
"elite = np.array(range(len(Top10prerc)), dtype=str)\n",
"elite[Top10prerc>50]='Yes'\n",
"elite[Top10prerc<=50]='No'\n",
"colleage['Elite']=elite"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ajhERq4HVAENDQxP+oDBrt3b06P8kIhZExFAaH/Ngllk3iYiVETE7IgYpDrL+NCI+BWwA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\n6M3MMvf/aKBX5PEf31IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e5d3438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def createHist(data, bin_num=20):\n",
" hist, bins = np.histogram(data,bins=bin_num)\n",
" center = (bins[:-1] + bins[1:]) / 2\n",
" width = 0.7 * (bins[1] - bins[0])\n",
" return center, hist, width\n",
"def draw_hist(col_name, pos):\n",
" data = colleage[col_name].values\n",
" center, hist, width = createHist(data)\n",
" plt.subplot(2,2,pos)\n",
" plt.bar(center, hist, align='center', width=width)\n",
"draw_hist('Apps', 1)\n",
"draw_hist('Enroll',2)\n",
"draw_hist('Outstate',3)\n",
"draw_hist('P.Undergrad',4)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 Auto"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'np' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-488606213387>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mauto\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data/Auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'\\s+'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'?'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdelete_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m!=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
]
}
],
"source": [
"auto = pd.read_table('data/Auto',sep='\\s+')\n",
"rows=np.sum(auto.values=='?',axis=1)\n",
"delete_rows = []\n",
"for idx,_ in enumerate(rows):\n",
" if _!=0:\n",
" delete_rows.append(idx)\n",
"auto=auto.drop(auto.index[delete_rows])"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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iIttCmOjKdqQKvbe3F6Ojo1EeUuggiCiMRZq1ENkWwkRXtiNV6EI4jIyVsWHn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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113e65278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mpg = auto['mpg'].values\n",
"displacement = auto['displacement'].values\n",
"horsepower = auto['horsepower'].values\n",
"weight = auto['weight'].values\n",
"acceleration = auto['acceleration'].values\n",
"plt.subplot(2,2,1)\n",
"plt.scatter(displacement, mpg)\n",
"plt.subplot(2,2,2)\n",
"plt.scatter(weight, mpg)\n",
"plt.subplot(2,2,3)\n",
"plt.scatter(acceleration, mpg)\n",
"plt.subplot(2,2,4)\n",
"displacement = [float(item) for item in displacement]\n",
"plt.scatter(displacement, mpg)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.3 Boston"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>crim</th>\n",
" <th>zn</th>\n",
" <th>indus</th>\n",
" <th>chas</th>\n",
" <th>nox</th>\n",
" <th>rm</th>\n",
" <th>age</th>\n",
" <th>dis</th>\n",
" <th>rad</th>\n",
" <th>tax</th>\n",
" <th>ptratio</th>\n",
" <th>black</th>\n",
" <th>lstat</th>\n",
" <th>medv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.00632</td>\n",
" <td>18.0</td>\n",
" <td>2.31</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.575</td>\n",
" <td>65.2</td>\n",
" <td>4.0900</td>\n",
" <td>1</td>\n",
" <td>296</td>\n",
" <td>15.3</td>\n",
" <td>396.90</td>\n",
" <td>4.98</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.02731</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>6.421</td>\n",
" <td>78.9</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>396.90</td>\n",
" <td>9.14</td>\n",
" <td>21.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.02729</td>\n",
" <td>0.0</td>\n",
" <td>7.07</td>\n",
" <td>0</td>\n",
" <td>0.469</td>\n",
" <td>7.185</td>\n",
" <td>61.1</td>\n",
" <td>4.9671</td>\n",
" <td>2</td>\n",
" <td>242</td>\n",
" <td>17.8</td>\n",
" <td>392.83</td>\n",
" <td>4.03</td>\n",
" <td>34.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.03237</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.998</td>\n",
" <td>45.8</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>394.63</td>\n",
" <td>2.94</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.06905</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>7.147</td>\n",
" <td>54.2</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>396.90</td>\n",
" <td>5.33</td>\n",
" <td>36.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.02985</td>\n",
" <td>0.0</td>\n",
" <td>2.18</td>\n",
" <td>0</td>\n",
" <td>0.458</td>\n",
" <td>6.430</td>\n",
" <td>58.7</td>\n",
" <td>6.0622</td>\n",
" <td>3</td>\n",
" <td>222</td>\n",
" <td>18.7</td>\n",
" <td>394.12</td>\n",
" <td>5.21</td>\n",
" <td>28.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.08829</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.012</td>\n",
" <td>66.6</td>\n",
" <td>5.5605</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>395.60</td>\n",
" <td>12.43</td>\n",
" <td>22.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.14455</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.172</td>\n",
" <td>96.1</td>\n",
" <td>5.9505</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>396.90</td>\n",
" <td>19.15</td>\n",
" <td>27.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.21124</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>5.631</td>\n",
" <td>100.0</td>\n",
" <td>6.0821</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>386.63</td>\n",
" <td>29.93</td>\n",
" <td>16.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.17004</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.004</td>\n",
" <td>85.9</td>\n",
" <td>6.5921</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>386.71</td>\n",
" <td>17.10</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.22489</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.377</td>\n",
" <td>94.3</td>\n",
" <td>6.3467</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>392.52</td>\n",
" <td>20.45</td>\n",
" <td>15.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.11747</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>6.009</td>\n",
" <td>82.9</td>\n",
" <td>6.2267</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>396.90</td>\n",
" <td>13.27</td>\n",
" <td>18.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.09378</td>\n",
" <td>12.5</td>\n",
" <td>7.87</td>\n",
" <td>0</td>\n",
" <td>0.524</td>\n",
" <td>5.889</td>\n",
" <td>39.0</td>\n",
" <td>5.4509</td>\n",
" <td>5</td>\n",
" <td>311</td>\n",
" <td>15.2</td>\n",
" <td>390.50</td>\n",
" <td>15.71</td>\n",
" <td>21.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.62976</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.949</td>\n",
" <td>61.8</td>\n",
" <td>4.7075</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>396.90</td>\n",
" <td>8.26</td>\n",
" <td>20.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.63796</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.096</td>\n",
" <td>84.5</td>\n",
" <td>4.4619</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>380.02</td>\n",
" <td>10.26</td>\n",
" <td>18.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.62739</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.834</td>\n",
" <td>56.5</td>\n",
" <td>4.4986</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>395.62</td>\n",
" <td>8.47</td>\n",
" <td>19.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>1.05393</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.935</td>\n",
" <td>29.3</td>\n",
" <td>4.4986</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>386.85</td>\n",
" <td>6.58</td>\n",
" <td>23.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.78420</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.990</td>\n",
" <td>81.7</td>\n",
" <td>4.2579</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>386.75</td>\n",
" <td>14.67</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.80271</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.456</td>\n",
" <td>36.6</td>\n",
" <td>3.7965</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>288.99</td>\n",
" <td>11.69</td>\n",
" <td>20.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.72580</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.727</td>\n",
" <td>69.5</td>\n",
" <td>3.7965</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>390.95</td>\n",
" <td>11.28</td>\n",
" <td>18.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>1.25179</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.570</td>\n",
" <td>98.1</td>\n",
" <td>3.7979</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>376.57</td>\n",
" <td>21.02</td>\n",
" <td>13.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.85204</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.965</td>\n",
" <td>89.2</td>\n",
" <td>4.0123</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>392.53</td>\n",
" <td>13.83</td>\n",
" <td>19.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>1.23247</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.142</td>\n",
" <td>91.7</td>\n",
" <td>3.9769</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>396.90</td>\n",
" <td>18.72</td>\n",
" <td>15.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.98843</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.813</td>\n",
" <td>100.0</td>\n",
" <td>4.0952</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>394.54</td>\n",
" <td>19.88</td>\n",
" <td>14.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.75026</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.924</td>\n",
" <td>94.1</td>\n",
" <td>4.3996</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>394.33</td>\n",
" <td>16.30</td>\n",
" <td>15.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.84054</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.599</td>\n",
" <td>85.7</td>\n",
" <td>4.4546</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>303.42</td>\n",
" <td>16.51</td>\n",
" <td>13.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.67191</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>5.813</td>\n",
" <td>90.3</td>\n",
" <td>4.6820</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>376.88</td>\n",
" <td>14.81</td>\n",
" <td>16.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.95577</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.047</td>\n",
" <td>88.8</td>\n",
" <td>4.4534</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>306.38</td>\n",
" <td>17.28</td>\n",
" <td>14.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.77299</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.495</td>\n",
" <td>94.4</td>\n",
" <td>4.4547</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>387.94</td>\n",
" <td>12.80</td>\n",
" <td>18.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>1.00245</td>\n",
" <td>0.0</td>\n",
" <td>8.14</td>\n",
" <td>0</td>\n",
" <td>0.538</td>\n",
" <td>6.674</td>\n",
" <td>87.3</td>\n",
" <td>4.2390</td>\n",
" <td>4</td>\n",
" <td>307</td>\n",
" <td>21.0</td>\n",
" <td>380.23</td>\n",
" <td>11.98</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>477</th>\n",
" <td>4.87141</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.614</td>\n",
" <td>6.484</td>\n",
" <td>93.6</td>\n",
" <td>2.3053</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>396.21</td>\n",
" <td>18.68</td>\n",
" <td>16.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>478</th>\n",
" <td>15.02340</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.614</td>\n",
" <td>5.304</td>\n",
" <td>97.3</td>\n",
" <td>2.1007</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>349.48</td>\n",
" <td>24.91</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>479</th>\n",
" <td>10.23300</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.614</td>\n",
" <td>6.185</td>\n",
" <td>96.7</td>\n",
" <td>2.1705</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>379.70</td>\n",
" <td>18.03</td>\n",
" <td>14.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480</th>\n",
" <td>14.33370</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.614</td>\n",
" <td>6.229</td>\n",
" <td>88.0</td>\n",
" <td>1.9512</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>383.32</td>\n",
" <td>13.11</td>\n",
" <td>21.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>481</th>\n",
" <td>5.82401</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.532</td>\n",
" <td>6.242</td>\n",
" <td>64.7</td>\n",
" <td>3.4242</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>396.90</td>\n",
" <td>10.74</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>482</th>\n",
" <td>5.70818</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.532</td>\n",
" <td>6.750</td>\n",
" <td>74.9</td>\n",
" <td>3.3317</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>393.07</td>\n",
" <td>7.74</td>\n",
" <td>23.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>5.73116</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.532</td>\n",
" <td>7.061</td>\n",
" <td>77.0</td>\n",
" <td>3.4106</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>395.28</td>\n",
" <td>7.01</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>484</th>\n",
" <td>2.81838</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.532</td>\n",
" <td>5.762</td>\n",
" <td>40.3</td>\n",
" <td>4.0983</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>392.92</td>\n",
" <td>10.42</td>\n",
" <td>21.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>485</th>\n",
" <td>2.37857</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.583</td>\n",
" <td>5.871</td>\n",
" <td>41.9</td>\n",
" <td>3.7240</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>370.73</td>\n",
" <td>13.34</td>\n",
" <td>20.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>486</th>\n",
" <td>3.67367</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.583</td>\n",
" <td>6.312</td>\n",
" <td>51.9</td>\n",
" <td>3.9917</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>388.62</td>\n",
" <td>10.58</td>\n",
" <td>21.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>487</th>\n",
" <td>5.69175</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.583</td>\n",
" <td>6.114</td>\n",
" <td>79.8</td>\n",
" <td>3.5459</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>392.68</td>\n",
" <td>14.98</td>\n",
" <td>19.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>488</th>\n",
" <td>4.83567</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.583</td>\n",
" <td>5.905</td>\n",
" <td>53.2</td>\n",
" <td>3.1523</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>388.22</td>\n",
" <td>11.45</td>\n",
" <td>20.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>489</th>\n",
" <td>0.15086</td>\n",
" <td>0.0</td>\n",
" <td>27.74</td>\n",
" <td>0</td>\n",
" <td>0.609</td>\n",
" <td>5.454</td>\n",
" <td>92.7</td>\n",
" <td>1.8209</td>\n",
" <td>4</td>\n",
" <td>711</td>\n",
" <td>20.1</td>\n",
" <td>395.09</td>\n",
" <td>18.06</td>\n",
" <td>15.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>490</th>\n",
" <td>0.18337</td>\n",
" <td>0.0</td>\n",
" <td>27.74</td>\n",
" <td>0</td>\n",
" <td>0.609</td>\n",
" <td>5.414</td>\n",
" <td>98.3</td>\n",
" <td>1.7554</td>\n",
" <td>4</td>\n",
" <td>711</td>\n",
" <td>20.1</td>\n",
" <td>344.05</td>\n",
" <td>23.97</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>491</th>\n",
" <td>0.20746</td>\n",
" <td>0.0</td>\n",
" <td>27.74</td>\n",
" <td>0</td>\n",
" <td>0.609</td>\n",
" <td>5.093</td>\n",
" <td>98.0</td>\n",
" <td>1.8226</td>\n",
" <td>4</td>\n",
" <td>711</td>\n",
" <td>20.1</td>\n",
" <td>318.43</td>\n",
" <td>29.68</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>492</th>\n",
" <td>0.10574</td>\n",
" <td>0.0</td>\n",
" <td>27.74</td>\n",
" <td>0</td>\n",
" <td>0.609</td>\n",
" <td>5.983</td>\n",
" <td>98.8</td>\n",
" <td>1.8681</td>\n",
" <td>4</td>\n",
" <td>711</td>\n",
" <td>20.1</td>\n",
" <td>390.11</td>\n",
" <td>18.07</td>\n",
" <td>13.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>493</th>\n",
" <td>0.11132</td>\n",
" <td>0.0</td>\n",
" <td>27.74</td>\n",
" <td>0</td>\n",
" <td>0.609</td>\n",
" <td>5.983</td>\n",
" <td>83.5</td>\n",
" <td>2.1099</td>\n",
" <td>4</td>\n",
" <td>711</td>\n",
" <td>20.1</td>\n",
" <td>396.90</td>\n",
" <td>13.35</td>\n",
" <td>20.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>0.17331</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.707</td>\n",
" <td>54.0</td>\n",
" <td>2.3817</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>396.90</td>\n",
" <td>12.01</td>\n",
" <td>21.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>495</th>\n",
" <td>0.27957</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.926</td>\n",
" <td>42.6</td>\n",
" <td>2.3817</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>396.90</td>\n",
" <td>13.59</td>\n",
" <td>24.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>496</th>\n",
" <td>0.17899</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.670</td>\n",
" <td>28.8</td>\n",
" <td>2.7986</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>393.29</td>\n",
" <td>17.60</td>\n",
" <td>23.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>497</th>\n",
" <td>0.28960</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.390</td>\n",
" <td>72.9</td>\n",
" <td>2.7986</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>396.90</td>\n",
" <td>21.14</td>\n",
" <td>19.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>498</th>\n",
" <td>0.26838</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.794</td>\n",
" <td>70.6</td>\n",
" <td>2.8927</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>396.90</td>\n",
" <td>14.10</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>499</th>\n",
" <td>0.23912</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>6.019</td>\n",
" <td>65.3</td>\n",
" <td>2.4091</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>396.90</td>\n",
" <td>12.92</td>\n",
" <td>21.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>500</th>\n",
" <td>0.17783</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.569</td>\n",
" <td>73.5</td>\n",
" <td>2.3999</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>395.77</td>\n",
" <td>15.10</td>\n",
" <td>17.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>501</th>\n",
" <td>0.22438</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>6.027</td>\n",
" <td>79.7</td>\n",
" <td>2.4982</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>396.90</td>\n",
" <td>14.33</td>\n",
" <td>16.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>502</th>\n",
" <td>0.06263</td>\n",
" <td>0.0</td>\n",
" <td>11.93</td>\n",
" <td>0</td>\n",
" <td>0.573</td>\n",
" <td>6.593</td>\n",
" <td>69.1</td>\n",
" <td>2.4786</td>\n",
" <td>1</td>\n",
" <td>273</td>\n",
" <td>21.0</td>\n",
" <td>391.99</td>\n",
" <td>9.67</td>\n",
" <td>22.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>503</th>\n",
" <td>0.04527</td>\n",
" <td>0.0</td>\n",
" <td>11.93</td>\n",
" <td>0</td>\n",
" <td>0.573</td>\n",
" <td>6.120</td>\n",
" <td>76.7</td>\n",
" <td>2.2875</td>\n",
" <td>1</td>\n",
" <td>273</td>\n",
" <td>21.0</td>\n",
" <td>396.90</td>\n",
" <td>9.08</td>\n",
" <td>20.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>504</th>\n",
" <td>0.06076</td>\n",
" <td>0.0</td>\n",
" <td>11.93</td>\n",
" <td>0</td>\n",
" <td>0.573</td>\n",
" <td>6.976</td>\n",
" <td>91.0</td>\n",
" <td>2.1675</td>\n",
" <td>1</td>\n",
" <td>273</td>\n",
" <td>21.0</td>\n",
" <td>396.90</td>\n",
" <td>5.64</td>\n",
" <td>23.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>505</th>\n",
" <td>0.10959</td>\n",
" <td>0.0</td>\n",
" <td>11.93</td>\n",
" <td>0</td>\n",
" <td>0.573</td>\n",
" <td>6.794</td>\n",
" <td>89.3</td>\n",
" <td>2.3889</td>\n",
" <td>1</td>\n",
" <td>273</td>\n",
" <td>21.0</td>\n",
" <td>393.45</td>\n",
" <td>6.48</td>\n",
" <td>22.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>506</th>\n",
" <td>0.04741</td>\n",
" <td>0.0</td>\n",
" <td>11.93</td>\n",
" <td>0</td>\n",
" <td>0.573</td>\n",
" <td>6.030</td>\n",
" <td>80.8</td>\n",
" <td>2.5050</td>\n",
" <td>1</td>\n",
" <td>273</td>\n",
" <td>21.0</td>\n",
" <td>396.90</td>\n",
" <td>7.88</td>\n",
" <td>11.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>506 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" crim zn indus chas nox rm age dis rad tax \\\n",
"1 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 \n",
"2 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 \n",
"3 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 \n",
"4 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 \n",
"5 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 \n",
"6 0.02985 0.0 2.18 0 0.458 6.430 58.7 6.0622 3 222 \n",
"7 0.08829 12.5 7.87 0 0.524 6.012 66.6 5.5605 5 311 \n",
"8 0.14455 12.5 7.87 0 0.524 6.172 96.1 5.9505 5 311 \n",
"9 0.21124 12.5 7.87 0 0.524 5.631 100.0 6.0821 5 311 \n",
"10 0.17004 12.5 7.87 0 0.524 6.004 85.9 6.5921 5 311 \n",
"11 0.22489 12.5 7.87 0 0.524 6.377 94.3 6.3467 5 311 \n",
"12 0.11747 12.5 7.87 0 0.524 6.009 82.9 6.2267 5 311 \n",
"13 0.09378 12.5 7.87 0 0.524 5.889 39.0 5.4509 5 311 \n",
"14 0.62976 0.0 8.14 0 0.538 5.949 61.8 4.7075 4 307 \n",
"15 0.63796 0.0 8.14 0 0.538 6.096 84.5 4.4619 4 307 \n",
"16 0.62739 0.0 8.14 0 0.538 5.834 56.5 4.4986 4 307 \n",
"17 1.05393 0.0 8.14 0 0.538 5.935 29.3 4.4986 4 307 \n",
"18 0.78420 0.0 8.14 0 0.538 5.990 81.7 4.2579 4 307 \n",
"19 0.80271 0.0 8.14 0 0.538 5.456 36.6 3.7965 4 307 \n",
"20 0.72580 0.0 8.14 0 0.538 5.727 69.5 3.7965 4 307 \n",
"21 1.25179 0.0 8.14 0 0.538 5.570 98.1 3.7979 4 307 \n",
"22 0.85204 0.0 8.14 0 0.538 5.965 89.2 4.0123 4 307 \n",
"23 1.23247 0.0 8.14 0 0.538 6.142 91.7 3.9769 4 307 \n",
"24 0.98843 0.0 8.14 0 0.538 5.813 100.0 4.0952 4 307 \n",
"25 0.75026 0.0 8.14 0 0.538 5.924 94.1 4.3996 4 307 \n",
"26 0.84054 0.0 8.14 0 0.538 5.599 85.7 4.4546 4 307 \n",
"27 0.67191 0.0 8.14 0 0.538 5.813 90.3 4.6820 4 307 \n",
"28 0.95577 0.0 8.14 0 0.538 6.047 88.8 4.4534 4 307 \n",
"29 0.77299 0.0 8.14 0 0.538 6.495 94.4 4.4547 4 307 \n",
"30 1.00245 0.0 8.14 0 0.538 6.674 87.3 4.2390 4 307 \n",
".. ... ... ... ... ... ... ... ... ... ... \n",
"477 4.87141 0.0 18.10 0 0.614 6.484 93.6 2.3053 24 666 \n",
"478 15.02340 0.0 18.10 0 0.614 5.304 97.3 2.1007 24 666 \n",
"479 10.23300 0.0 18.10 0 0.614 6.185 96.7 2.1705 24 666 \n",
"480 14.33370 0.0 18.10 0 0.614 6.229 88.0 1.9512 24 666 \n",
"481 5.82401 0.0 18.10 0 0.532 6.242 64.7 3.4242 24 666 \n",
"482 5.70818 0.0 18.10 0 0.532 6.750 74.9 3.3317 24 666 \n",
"483 5.73116 0.0 18.10 0 0.532 7.061 77.0 3.4106 24 666 \n",
"484 2.81838 0.0 18.10 0 0.532 5.762 40.3 4.0983 24 666 \n",
"485 2.37857 0.0 18.10 0 0.583 5.871 41.9 3.7240 24 666 \n",
"486 3.67367 0.0 18.10 0 0.583 6.312 51.9 3.9917 24 666 \n",
"487 5.69175 0.0 18.10 0 0.583 6.114 79.8 3.5459 24 666 \n",
"488 4.83567 0.0 18.10 0 0.583 5.905 53.2 3.1523 24 666 \n",
"489 0.15086 0.0 27.74 0 0.609 5.454 92.7 1.8209 4 711 \n",
"490 0.18337 0.0 27.74 0 0.609 5.414 98.3 1.7554 4 711 \n",
"491 0.20746 0.0 27.74 0 0.609 5.093 98.0 1.8226 4 711 \n",
"492 0.10574 0.0 27.74 0 0.609 5.983 98.8 1.8681 4 711 \n",
"493 0.11132 0.0 27.74 0 0.609 5.983 83.5 2.1099 4 711 \n",
"494 0.17331 0.0 9.69 0 0.585 5.707 54.0 2.3817 6 391 \n",
"495 0.27957 0.0 9.69 0 0.585 5.926 42.6 2.3817 6 391 \n",
"496 0.17899 0.0 9.69 0 0.585 5.670 28.8 2.7986 6 391 \n",
"497 0.28960 0.0 9.69 0 0.585 5.390 72.9 2.7986 6 391 \n",
"498 0.26838 0.0 9.69 0 0.585 5.794 70.6 2.8927 6 391 \n",
"499 0.23912 0.0 9.69 0 0.585 6.019 65.3 2.4091 6 391 \n",
"500 0.17783 0.0 9.69 0 0.585 5.569 73.5 2.3999 6 391 \n",
"501 0.22438 0.0 9.69 0 0.585 6.027 79.7 2.4982 6 391 \n",
"502 0.06263 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 \n",
"503 0.04527 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 \n",
"504 0.06076 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 \n",
"505 0.10959 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 \n",
"506 0.04741 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 \n",
"\n",
" ptratio black lstat medv \n",
"1 15.3 396.90 4.98 24.0 \n",
"2 17.8 396.90 9.14 21.6 \n",
"3 17.8 392.83 4.03 34.7 \n",
"4 18.7 394.63 2.94 33.4 \n",
"5 18.7 396.90 5.33 36.2 \n",
"6 18.7 394.12 5.21 28.7 \n",
"7 15.2 395.60 12.43 22.9 \n",
"8 15.2 396.90 19.15 27.1 \n",
"9 15.2 386.63 29.93 16.5 \n",
"10 15.2 386.71 17.10 18.9 \n",
"11 15.2 392.52 20.45 15.0 \n",
"12 15.2 396.90 13.27 18.9 \n",
"13 15.2 390.50 15.71 21.7 \n",
"14 21.0 396.90 8.26 20.4 \n",
"15 21.0 380.02 10.26 18.2 \n",
"16 21.0 395.62 8.47 19.9 \n",
"17 21.0 386.85 6.58 23.1 \n",
"18 21.0 386.75 14.67 17.5 \n",
"19 21.0 288.99 11.69 20.2 \n",
"20 21.0 390.95 11.28 18.2 \n",
"21 21.0 376.57 21.02 13.6 \n",
"22 21.0 392.53 13.83 19.6 \n",
"23 21.0 396.90 18.72 15.2 \n",
"24 21.0 394.54 19.88 14.5 \n",
"25 21.0 394.33 16.30 15.6 \n",
"26 21.0 303.42 16.51 13.9 \n",
"27 21.0 376.88 14.81 16.6 \n",
"28 21.0 306.38 17.28 14.8 \n",
"29 21.0 387.94 12.80 18.4 \n",
"30 21.0 380.23 11.98 21.0 \n",
".. ... ... ... ... \n",
"477 20.2 396.21 18.68 16.7 \n",
"478 20.2 349.48 24.91 12.0 \n",
"479 20.2 379.70 18.03 14.6 \n",
"480 20.2 383.32 13.11 21.4 \n",
"481 20.2 396.90 10.74 23.0 \n",
"482 20.2 393.07 7.74 23.7 \n",
"483 20.2 395.28 7.01 25.0 \n",
"484 20.2 392.92 10.42 21.8 \n",
"485 20.2 370.73 13.34 20.6 \n",
"486 20.2 388.62 10.58 21.2 \n",
"487 20.2 392.68 14.98 19.1 \n",
"488 20.2 388.22 11.45 20.6 \n",
"489 20.1 395.09 18.06 15.2 \n",
"490 20.1 344.05 23.97 7.0 \n",
"491 20.1 318.43 29.68 8.1 \n",
"492 20.1 390.11 18.07 13.6 \n",
"493 20.1 396.90 13.35 20.1 \n",
"494 19.2 396.90 12.01 21.8 \n",
"495 19.2 396.90 13.59 24.5 \n",
"496 19.2 393.29 17.60 23.1 \n",
"497 19.2 396.90 21.14 19.7 \n",
"498 19.2 396.90 14.10 18.3 \n",
"499 19.2 396.90 12.92 21.2 \n",
"500 19.2 395.77 15.10 17.5 \n",
"501 19.2 396.90 14.33 16.8 \n",
"502 21.0 391.99 9.67 22.4 \n",
"503 21.0 396.90 9.08 20.6 \n",
"504 21.0 396.90 5.64 23.9 \n",
"505 21.0 393.45 6.48 22.0 \n",
"506 21.0 396.90 7.88 11.9 \n",
"\n",
"[506 rows x 14 columns]"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"boston = pd.read_csv('Boston.csv', index_col=0)\n",
"boston"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9rOKYTec+RY4IjJZ8acTVwL9m5vKivQYgM+8Za/2+vr7s7++f0DY6JeRbofuMgIDjQ839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\nDHpJqrj/B0QhvkwYOEh1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ecb4240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"crim = boston['crim'].values\n",
"zn = boston['zn'].values\n",
"plt.scatter(crim, zn)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10f979588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"indus=boston['indus'].values\n",
"plt.scatter(crim, indus)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10ea3b0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nox = boston['nox'].values\n",
"plt.scatter(nox,crim,)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10f3738d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rm = boston['rm'].values\n",
"plt.scatter(rm,crim, )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10f1c4668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"age = boston['age'].values\n",
"plt.scatter(age,crim, )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11339f5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dis = boston['dis'].values\n",
"plt.scatter(dis,crim)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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UhisNxJAl27dLOh0Rk/W2GaR+ZJZL+m1Jj0fEJkm/1JzLCYPUk+za+B2q/EO3VtJK25+t\n3ibFfvRDoC/5M2NSZHtYlTB/KiJ2Z4tP2R7L1o9JOt2r+rrsJkmftH1clUtwv2/77zW4/ZAq/3N9\nIyL2Z4+/q0rAD2pPPibpvyOiHBEzknZL+rAS70c/BPpPJF1n+/22V6jyxsbzPa6pq2xblWujRyPi\nsapVz0valt3fJum5btfWCxHxQERcExHjqvx9+LeI+KwGtB+SFBFvSvq57Yvfnr5Z0ssa3J78TNKN\nti/Pfn82q/LeU9L96IuJRba3qHLN9OJnxjzc45K6yvZHJP27pEO6dM34QVWuo39H0q9Lel3SnRHx\ndk+K7BHbN0v6y4i43favaYD7YXujKm8Sr5D0mqQ/UeWkbSB7YvvLkj6jyiixA5L+VNIVSrgffRHo\nAIDG+uGSCwCgCQQ6ACSCQAeARBDoAJAIAh0AEkGgA0AiCHQASASBDgCJ+H9YV5h66TvI8gAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11422bb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"rad = boston['rad'].values\n",
"plt.scatter(crim, rad)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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fc1orVhLo3wMujIgLIuI04Bpgb3O6VTO0feuy5u/p7nreMhe+9MxmdqntXHtJ7T23VdF6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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e587ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tax = boston['tax'].values\n",
"plt.scatter(tax,crim, )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10fc93c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ptratio=boston['ptratio'].values\n",
"plt.scatter(ptratio,crim, )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x113384f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"b = boston['black'].values\n",
"plt.scatter(b,crim, )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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btGZM1lcLvbjmPMAces8Ly28H1Rs3OlKpBj4/TiqjTwTrx3bNPydtD66VOvCo99f8s9o1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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113b4bfd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lstat = boston['lstat'].values\n",
"plt.scatter(lstat,crim)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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IUe7VCVBrbXJpHtVy/x0TK4ZdZr2wV7PFa66RDes1929/te19e7W2TP01svvQyVV/E2Pu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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10fc23be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"medv = boston['medv'].values\n",
"plt.scatter(medv,crim, )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Upward-Spiral-Science/team1 | code/Assignment11_Akash.ipynb | 1 | 48040 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assignment 11: Akash"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"end setup\n"
]
}
],
"source": [
"from mpl_toolkits.mplot3d import axes3d\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline \n",
"import numpy as np\n",
"import urllib2\n",
"import scipy.stats as stats\n",
"\n",
"np.set_printoptions(precision=3, suppress=True)\n",
"url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'\n",
" '/data/master/syn-density/output.csv')\n",
"data = urllib2.urlopen(url)\n",
"csv = np.genfromtxt(data, delimiter=\",\")[1:] # don't want first row (labels)\n",
"\n",
"# chopping data based on thresholds on x and y coordinates\n",
"x_bounds = (409, 3529)\n",
"y_bounds = (1564, 3124)\n",
"\n",
"def check_in_bounds(row, x_bounds, y_bounds):\n",
" if row[0] < x_bounds[0] or row[0] > x_bounds[1]:\n",
" return False\n",
" if row[1] < y_bounds[0] or row[1] > y_bounds[1]:\n",
" return False\n",
" if row[3] == 0:\n",
" return False\n",
" \n",
" return True\n",
"\n",
"indices_in_bound, = np.where(np.apply_along_axis(check_in_bounds, 1, csv,\n",
" x_bounds, y_bounds))\n",
"data_thresholded = csv[indices_in_bound]\n",
"n = data_thresholded.shape[0]\n",
"\n",
"\n",
"def synapses_over_unmasked(row):\n",
" s = (row[4]/row[3])*(64**3)\n",
" return [row[0], row[1], row[2], s]\n",
"\n",
"syn_unmasked = np.apply_along_axis(synapses_over_unmasked, 1, data_thresholded)\n",
"syn_normalized = syn_unmasked\n",
"print 'end setup'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Label Data by It's Associated Cluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cluster number from previous homeworks where optimal cluster number was 4 (jay helped adapt)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4\n",
"(1L, 6937L, 4L)\n"
]
}
],
"source": [
"import sklearn.mixture as mixture\n",
"\n",
"n_clusters = 4\n",
"gmm = mixture.GMM(n_components=n_clusters, n_iter=1000, covariance_type='diag')\n",
"labels = gmm.fit_predict(syn_unmasked)\n",
"clusters = []\n",
"for l in range(n_clusters):\n",
" a = np.where(labels == l)\n",
" clusters.append(syn_unmasked[a,:])\n",
"\n",
"print len(clusters)\n",
"print clusters[0].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1) Run general regressions on data associated with actual cluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Running our previous regressions on actual clusters (before they were split by visual estimates), we find the following:\n",
"* Cluster 1: No regression fits\n",
"* * asdf\n",
"* Cluster 2: Knn is the most promising at R^2: 0.19 (+/- 0.11) \n",
"* Cluster 3: The following regressions are the most promising: \n",
" R^2 of KNN Regression: 0.27 (+/- 0.16)\n",
" R^2 of Random Forest Regression: 0.34 (+/- 0.10)\n",
" R^2 of Polynomial Regression: 0.22 (+/- 0.17)\n",
"* Cluster 4: R^2 of Random Forest Regression: 0.14 (+/- 0.07)\n",
"\n",
"As we can see, nothing is extrodinary, but Cluster 3 with a RFR is the closest one with the original parameters."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Regression (x,y,z,syn/unmasked) on cleaned data ##################################\n",
"# Load regressions\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.svm import LinearSVR\n",
"from sklearn.neighbors import KNeighborsRegressor as KNN\n",
"from sklearn.ensemble import RandomForestRegressor as RF\n",
"from sklearn.preprocessing import PolynomialFeatures as PF\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn import cross_validation\n",
"names = ['Linear Regression','SVR','KNN Regression','Random Forest Regression','Polynomial Regression']\n",
"regressions = [LinearRegression(),\n",
" LinearSVR(C=1.0),\n",
" KNN(n_neighbors=10, algorithm='auto'),\n",
" RF(max_depth=5, max_features=1),\n",
" Pipeline([('poly', PF(degree=2)),('linear', LinearRegression(fit_intercept=False))])]\n",
"k_fold = 10"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Regression on cluster: 1\n",
"R^2 of Linear Regression: -0.30 (+/- 0.68)\n",
"R^2 of SVR: -1.35 (+/- 2.00)\n",
"R^2 of KNN Regression: 0.08 (+/- 0.20)\n",
"R^2 of Random Forest Regression: 0.03 (+/- 0.30)\n",
"R^2 of Polynomial Regression: -0.02 (+/- 0.29)\n",
"Regression done\n",
"\n",
"Regression on cluster: 2\n",
"R^2 of Linear Regression: 0.00 (+/- 0.07)\n",
"R^2 of SVR: -0.94 (+/- 1.87)\n",
"R^2 of KNN Regression: 0.19 (+/- 0.11)\n",
"R^2 of Random Forest Regression: 0.14 (+/- 0.07)\n",
"R^2 of Polynomial Regression: 0.09 (+/- 0.08)\n",
"Regression done\n",
"\n",
"Regression on cluster: 3\n",
"R^2 of Linear Regression: 0.05 (+/- 0.11)\n",
"R^2 of SVR: -0.86 (+/- 2.16)\n",
"R^2 of KNN Regression: 0.27 (+/- 0.16)\n",
"R^2 of Random Forest Regression: 0.34 (+/- 0.10)\n",
"R^2 of Polynomial Regression: 0.22 (+/- 0.17)\n",
"Regression done\n",
"\n",
"Regression on cluster: 4\n",
"R^2 of Linear Regression: 0.00 (+/- 0.04)\n",
"R^2 of SVR: -1.25 (+/- 2.81)\n",
"R^2 of KNN Regression: 0.16 (+/- 0.21)\n",
"R^2 of Random Forest Regression: 0.14 (+/- 0.07)\n",
"R^2 of Polynomial Regression: 0.09 (+/- 0.07)\n",
"Regression done\n"
]
}
],
"source": [
"counter = 0\n",
"for cluster in clusters:\n",
" s = cluster.shape\n",
" cluster = cluster.reshape((s[1], s[2]))\n",
" counter += 1\n",
" print \n",
" print'Regression on cluster: ' + str(counter)\n",
" X = cluster[:, (0,1,2)] # x,y,z\n",
" Y = cluster[:,-1] # syn/unmasked from spike\n",
" for idx2, reg in enumerate(regressions):\n",
" scores = cross_validation.cross_val_score(reg, X, Y, scoring='r2', cv=k_fold)\n",
" print(\"R^2 of %s: %0.2f (+/- %0.2f)\" % (names[idx2], scores.mean(), scores.std() * 2))\n",
" print \"Regression done\"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## 2) Change polynomial degree for regression on all data, by cluster"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Regression on cluster: 1\n",
"R^2 of Polynomial Regression of power of 2: -0.02 (+/- 0.29)\n",
"R^2 of Polynomial Regression of power of 3: -0.12 (+/- 0.89)\n",
"R^2 of Polynomial Regression of power of 4: -0.19 (+/- 1.11)\n",
"R^2 of Polynomial Regression of power of 5: -0.08 (+/- 1.16)\n",
"R^2 of Polynomial Regression of power of 6: -1.17 (+/- 6.05)\n",
"R^2 of Polynomial Regression of power of 7: -0.65 (+/- 3.15)\n",
"R^2 of Polynomial Regression of power of 8: -1.61 (+/- 8.05)\n",
"R^2 of Polynomial Regression of power of 9: -23.93 (+/- 101.98)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 2\n",
"R^2 of Polynomial Regression of power of 2: 0.09 (+/- 0.08)\n",
"R^2 of Polynomial Regression of power of 3: 0.13 (+/- 0.09)\n",
"R^2 of Polynomial Regression of power of 4: 0.14 (+/- 0.10)\n",
"R^2 of Polynomial Regression of power of 5: 0.13 (+/- 0.14)\n",
"R^2 of Polynomial Regression of power of 6: 0.10 (+/- 0.16)\n",
"R^2 of Polynomial Regression of power of 7: -0.14 (+/- 1.02)\n",
"R^2 of Polynomial Regression of power of 8: -1.08 (+/- 3.49)\n",
"R^2 of Polynomial Regression of power of 9: -6.17 (+/- 36.31)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 3\n",
"R^2 of Polynomial Regression of power of 2: 0.22 (+/- 0.17)\n",
"R^2 of Polynomial Regression of power of 3: 0.29 (+/- 0.17)\n",
"R^2 of Polynomial Regression of power of 4: 0.33 (+/- 0.27)\n",
"R^2 of Polynomial Regression of power of 5: 0.37 (+/- 0.35)\n",
"R^2 of Polynomial Regression of power of 6: -0.05 (+/- 1.97)\n",
"R^2 of Polynomial Regression of power of 7: -0.17 (+/- 2.93)\n",
"R^2 of Polynomial Regression of power of 8: -5.76 (+/- 25.96)\n",
"R^2 of Polynomial Regression of power of 9: -10.13 (+/- 43.37)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 4\n",
"R^2 of Polynomial Regression of power of 2: 0.09 (+/- 0.07)\n",
"R^2 of Polynomial Regression of power of 3: 0.11 (+/- 0.13)\n",
"R^2 of Polynomial Regression of power of 4: 0.03 (+/- 0.46)\n",
"R^2 of Polynomial Regression of power of 5: 0.08 (+/- 0.28)\n",
"R^2 of Polynomial Regression of power of 6: -0.10 (+/- 1.01)\n",
"R^2 of Polynomial Regression of power of 7: -0.06 (+/- 0.66)\n",
"R^2 of Polynomial Regression of power of 8: -5.96 (+/- 28.22)\n",
"R^2 of Polynomial Regression of power of 9: -10.16 (+/- 41.10)\n",
"Regression on section done\n"
]
}
],
"source": [
"counter = 0\n",
"for cluster in clusters:\n",
" s = cluster.shape\n",
" cluster = cluster.reshape((s[1], s[2]))\n",
" counter += 1\n",
" print \n",
" print'Regression on cluster: ' + str(counter)\n",
" X = cluster[:, (0,1,2)] # x,y,z\n",
" Y = cluster[:,-1] # syn/unmasked from spike\n",
" for power in range(2,10):\n",
" a = Pipeline([('poly', PF(degree=power)),('linear', LinearRegression(fit_intercept=False))])\n",
" scores = cross_validation.cross_val_score(a, X, Y, scoring='r2', cv=k_fold)\n",
" print(\"R^2 of Polynomial Regression of power of %i: %0.2f (+/- %0.2f)\" % (power, scores.mean(), scores.std() * 2))\n",
" print \"Regression on section done\"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3) Change Random Forest Regression Parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Only on cluster 3 does there seem to be any relation, even if it is weak. Why is it all always the same?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Regression on cluster: 1\n",
"R^2 of Random Forrest Regression of Depth 3: 0.03 (+/- 0.29)\n",
"R^2 of Random Forrest Regression of Depth 4: 0.04 (+/- 0.23)\n",
"R^2 of Random Forrest Regression of Depth 5: 0.03 (+/- 0.28)\n",
"R^2 of Random Forrest Regression of Depth 6: 0.02 (+/- 0.29)\n",
"R^2 of Random Forrest Regression of Depth 7: 0.01 (+/- 0.29)\n",
"R^2 of Random Forrest Regression of Depth 8: 0.03 (+/- 0.30)\n",
"R^2 of Random Forrest Regression of Depth 9: 0.03 (+/- 0.24)\n",
"R^2 of Random Forrest Regression of Depth 10: 0.02 (+/- 0.29)\n",
"R^2 of Random Forrest Regression of Depth 11: 0.02 (+/- 0.25)\n",
"R^2 of Random Forrest Regression of no set depth: -0.03 (+/- 0.20)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 2\n",
"R^2 of Random Forrest Regression of Depth 3: 0.15 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 4: 0.14 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 5: 0.14 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 6: 0.14 (+/- 0.06)\n",
"R^2 of Random Forrest Regression of Depth 7: 0.15 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of Depth 8: 0.14 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 9: 0.14 (+/- 0.05)\n",
"R^2 of Random Forrest Regression of Depth 10: 0.14 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of Depth 11: 0.14 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of no set depth: 0.11 (+/- 0.29)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 3\n",
"R^2 of Random Forrest Regression of Depth 3: 0.33 (+/- 0.16)\n",
"R^2 of Random Forrest Regression of Depth 4: 0.33 (+/- 0.11)\n",
"R^2 of Random Forrest Regression of Depth 5: 0.32 (+/- 0.09)\n",
"R^2 of Random Forrest Regression of Depth 6: 0.35 (+/- 0.09)\n",
"R^2 of Random Forrest Regression of Depth 7: 0.33 (+/- 0.15)\n",
"R^2 of Random Forrest Regression of Depth 8: 0.34 (+/- 0.11)\n",
"R^2 of Random Forrest Regression of Depth 9: 0.35 (+/- 0.12)\n",
"R^2 of Random Forrest Regression of Depth 10: 0.35 (+/- 0.11)\n",
"R^2 of Random Forrest Regression of Depth 11: 0.32 (+/- 0.12)\n",
"R^2 of Random Forrest Regression of no set depth: 0.31 (+/- 0.24)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 4\n",
"R^2 of Random Forrest Regression of Depth 3: 0.14 (+/- 0.09)\n",
"R^2 of Random Forrest Regression of Depth 4: 0.14 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 5: 0.14 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of Depth 6: 0.14 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 7: 0.14 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of Depth 8: 0.14 (+/- 0.07)\n",
"R^2 of Random Forrest Regression of Depth 9: 0.13 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of Depth 10: 0.14 (+/- 0.05)\n",
"R^2 of Random Forrest Regression of Depth 11: 0.14 (+/- 0.08)\n",
"R^2 of Random Forrest Regression of no set depth: 0.08 (+/- 0.32)\n",
"Regression on section done\n"
]
}
],
"source": [
"counter = 0\n",
"for cluster in clusters:\n",
" s = cluster.shape\n",
" cluster = cluster.reshape((s[1], s[2]))\n",
" counter += 1\n",
" print \n",
" print'Regression on cluster: ' + str(counter)\n",
" X = cluster[:, (0,1,2)] # x,y,z\n",
" Y = cluster[:,-1] # syn/unmasked from spike\n",
" for depth in range(3,12):\n",
" a = RF(max_depth=5, max_features=1)\n",
" scores = cross_validation.cross_val_score(a, X, Y, scoring='r2', cv=k_fold)\n",
" print(\"R^2 of Random Forrest Regression of Depth %i: %0.2f (+/- %0.2f)\" % (depth, scores.mean(), scores.std() * 2))\n",
" \n",
" a = RF(max_features=1)\n",
" scores = cross_validation.cross_val_score(a, X, Y, scoring='r2', cv=k_fold)\n",
" print(\"R^2 of Random Forrest Regression of no set depth: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n",
" print \"Regression on section done\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4) Changing known nearest neighbor parameters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In cluster 4, the more neighbors there are, the higher the relationship\n",
"\n",
"How do i get into cluster 4 to go deeper, if statement logic not working as == 4?"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Regression on cluster: 1\n",
"R^2 of KNN w/ 5 neighbors: 0.03 (+/- 0.21)\n",
"R^2 of KNN w/ 6 neighbors: 0.05 (+/- 0.21)\n",
"R^2 of KNN w/ 7 neighbors: 0.06 (+/- 0.21)\n",
"R^2 of KNN w/ 8 neighbors: 0.07 (+/- 0.20)\n",
"R^2 of KNN w/ 9 neighbors: 0.07 (+/- 0.20)\n",
"R^2 of KNN w/ 10 neighbors: 0.08 (+/- 0.20)\n",
"R^2 of KNN w/ 11 neighbors: 0.08 (+/- 0.20)\n",
"R^2 of KNN w/ 12 neighbors: 0.08 (+/- 0.20)\n",
"R^2 of KNN w/ 13 neighbors: 0.09 (+/- 0.21)\n",
"R^2 of KNN w/ 14 neighbors: 0.09 (+/- 0.21)\n",
"R^2 of KNN w/ 15 neighbors: 0.09 (+/- 0.21)\n",
"R^2 of KNN w/ 16 neighbors: 0.09 (+/- 0.21)\n",
"R^2 of KNN w/ 17 neighbors: 0.10 (+/- 0.21)\n",
"R^2 of KNN w/ 18 neighbors: 0.10 (+/- 0.20)\n",
"R^2 of KNN w/ 19 neighbors: 0.10 (+/- 0.20)\n",
"R^2 of KNN w/ 20 neighbors: 0.10 (+/- 0.20)\n",
"R^2 of KNN w/ 21 neighbors: 0.10 (+/- 0.19)\n",
"R^2 of KNN w/ 22 neighbors: 0.10 (+/- 0.19)\n",
"R^2 of KNN w/ 23 neighbors: 0.10 (+/- 0.19)\n",
"R^2 of KNN w/ 24 neighbors: 0.10 (+/- 0.19)\n",
"R^2 of KNN w/ 25 neighbors: 0.10 (+/- 0.19)\n",
"R^2 of KNN w/ 26 neighbors: 0.10 (+/- 0.18)\n",
"R^2 of KNN w/ 27 neighbors: 0.10 (+/- 0.18)\n",
"R^2 of KNN w/ 28 neighbors: 0.10 (+/- 0.18)\n",
"R^2 of KNN w/ 29 neighbors: 0.10 (+/- 0.18)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 2\n",
"R^2 of KNN w/ 5 neighbors: 0.16 (+/- 0.12)\n",
"R^2 of KNN w/ 6 neighbors: 0.17 (+/- 0.12)\n",
"R^2 of KNN w/ 7 neighbors: 0.18 (+/- 0.12)\n",
"R^2 of KNN w/ 8 neighbors: 0.18 (+/- 0.12)\n",
"R^2 of KNN w/ 9 neighbors: 0.19 (+/- 0.12)\n",
"R^2 of KNN w/ 10 neighbors: 0.19 (+/- 0.11)\n",
"R^2 of KNN w/ 11 neighbors: 0.19 (+/- 0.11)\n",
"R^2 of KNN w/ 12 neighbors: 0.20 (+/- 0.10)\n",
"R^2 of KNN w/ 13 neighbors: 0.21 (+/- 0.10)\n",
"R^2 of KNN w/ 14 neighbors: 0.21 (+/- 0.10)\n",
"R^2 of KNN w/ 15 neighbors: 0.21 (+/- 0.10)\n",
"R^2 of KNN w/ 16 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 17 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 18 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 19 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 20 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 21 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 22 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 23 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 24 neighbors: 0.21 (+/- 0.09)\n",
"R^2 of KNN w/ 25 neighbors: 0.21 (+/- 0.08)\n",
"R^2 of KNN w/ 26 neighbors: 0.21 (+/- 0.08)\n",
"R^2 of KNN w/ 27 neighbors: 0.21 (+/- 0.08)\n",
"R^2 of KNN w/ 28 neighbors: 0.20 (+/- 0.08)\n",
"R^2 of KNN w/ 29 neighbors: 0.20 (+/- 0.08)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 3\n",
"R^2 of KNN w/ 5 neighbors: 0.27 (+/- 0.19)\n",
"R^2 of KNN w/ 6 neighbors: 0.27 (+/- 0.19)\n",
"R^2 of KNN w/ 7 neighbors: 0.27 (+/- 0.18)\n",
"R^2 of KNN w/ 8 neighbors: 0.27 (+/- 0.17)\n",
"R^2 of KNN w/ 9 neighbors: 0.27 (+/- 0.17)\n",
"R^2 of KNN w/ 10 neighbors: 0.27 (+/- 0.16)\n",
"R^2 of KNN w/ 11 neighbors: 0.27 (+/- 0.16)\n",
"R^2 of KNN w/ 12 neighbors: 0.27 (+/- 0.16)\n",
"R^2 of KNN w/ 13 neighbors: 0.26 (+/- 0.15)\n",
"R^2 of KNN w/ 14 neighbors: 0.26 (+/- 0.15)\n",
"R^2 of KNN w/ 15 neighbors: 0.26 (+/- 0.14)\n",
"R^2 of KNN w/ 16 neighbors: 0.26 (+/- 0.14)\n",
"R^2 of KNN w/ 17 neighbors: 0.26 (+/- 0.14)\n",
"R^2 of KNN w/ 18 neighbors: 0.26 (+/- 0.14)\n",
"R^2 of KNN w/ 19 neighbors: 0.26 (+/- 0.13)\n",
"R^2 of KNN w/ 20 neighbors: 0.25 (+/- 0.14)\n",
"R^2 of KNN w/ 21 neighbors: 0.25 (+/- 0.14)\n",
"R^2 of KNN w/ 22 neighbors: 0.25 (+/- 0.14)\n",
"R^2 of KNN w/ 23 neighbors: 0.25 (+/- 0.14)\n",
"R^2 of KNN w/ 24 neighbors: 0.24 (+/- 0.14)\n",
"R^2 of KNN w/ 25 neighbors: 0.24 (+/- 0.14)\n",
"R^2 of KNN w/ 26 neighbors: 0.24 (+/- 0.13)\n",
"R^2 of KNN w/ 27 neighbors: 0.24 (+/- 0.13)\n",
"R^2 of KNN w/ 28 neighbors: 0.24 (+/- 0.13)\n",
"R^2 of KNN w/ 29 neighbors: 0.23 (+/- 0.13)\n",
"Regression on section done\n",
"\n",
"Regression on cluster: 4\n",
"R^2 of KNN w/ 5 neighbors: 0.11 (+/- 0.25)\n",
"R^2 of KNN w/ 6 neighbors: 0.13 (+/- 0.24)\n",
"R^2 of KNN w/ 7 neighbors: 0.14 (+/- 0.23)\n",
"R^2 of KNN w/ 8 neighbors: 0.15 (+/- 0.21)\n",
"R^2 of KNN w/ 9 neighbors: 0.16 (+/- 0.21)\n",
"R^2 of KNN w/ 10 neighbors: 0.16 (+/- 0.21)\n",
"R^2 of KNN w/ 11 neighbors: 0.16 (+/- 0.21)\n",
"R^2 of KNN w/ 12 neighbors: 0.18 (+/- 0.21)\n",
"R^2 of KNN w/ 13 neighbors: 0.18 (+/- 0.21)\n",
"R^2 of KNN w/ 14 neighbors: 0.18 (+/- 0.20)\n",
"R^2 of KNN w/ 15 neighbors: 0.18 (+/- 0.19)\n",
"R^2 of KNN w/ 16 neighbors: 0.18 (+/- 0.19)\n",
"R^2 of KNN w/ 17 neighbors: 0.19 (+/- 0.19)\n",
"R^2 of KNN w/ 18 neighbors: 0.19 (+/- 0.18)\n",
"R^2 of KNN w/ 19 neighbors: 0.19 (+/- 0.18)\n",
"R^2 of KNN w/ 20 neighbors: 0.19 (+/- 0.18)\n",
"R^2 of KNN w/ 21 neighbors: 0.19 (+/- 0.17)\n",
"R^2 of KNN w/ 22 neighbors: 0.19 (+/- 0.18)\n",
"R^2 of KNN w/ 23 neighbors: 0.19 (+/- 0.17)\n",
"R^2 of KNN w/ 24 neighbors: 0.19 (+/- 0.17)\n",
"R^2 of KNN w/ 25 neighbors: 0.19 (+/- 0.17)\n",
"R^2 of KNN w/ 26 neighbors: 0.19 (+/- 0.16)\n",
"R^2 of KNN w/ 27 neighbors: 0.19 (+/- 0.16)\n",
"R^2 of KNN w/ 28 neighbors: 0.19 (+/- 0.16)\n",
"R^2 of KNN w/ 29 neighbors: 0.18 (+/- 0.16)\n",
"Regression on section done\n"
]
}
],
"source": [
"counter = 0\n",
"for cluster in clusters:\n",
" s = cluster.shape\n",
" cluster = cluster.reshape((s[1], s[2]))\n",
" counter += 1\n",
" print \n",
" print'Regression on cluster: ' + str(counter)\n",
" X = cluster[:, (0,1,2)] # x,y,z\n",
" Y = cluster[:,-1] # syn/unmasked from spike\n",
" for neighbor in range(5,30):\n",
" a = KNN(n_neighbors=neighbor, algorithm='auto')\n",
" scores = cross_validation.cross_val_score(a, X, Y, scoring='r2', cv=k_fold)\n",
" print(\"R^2 of KNN w/ %i neighbors: %0.2f (+/- %0.2f)\" % (neighbor, scores.mean(), scores.std() * 2))\n",
" \n",
" \n",
" print \"Regression on section done\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5) Density distrubtion in each cluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Interestingly, cluster 3 has many values around zero. "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Working on cluster: 1\n",
"Done with cluster\n",
"\n",
"Working on cluster: 2\n",
"Done with cluster\n",
"\n",
"Working on cluster: 3\n",
"Done with cluster\n",
"\n",
"Working on cluster: 4\n",
"Done with cluster\n"
]
},
{
"data": {
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WSWoDs0hSW5hHqslm57HTukVm90ZWRkR2sd+S9hcRZGbMuh/rZRatLCJYHr0/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\nkiRV4v8B7vXnYLOGWuIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1753ac18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x166d8668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x166d8e48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x166d8dd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x18a49748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"counter = 0\n",
"fig, axs = plt.subplots(1,4, figsize=(20,5))\n",
"for cluster in clusters:\n",
" s = cluster.shape\n",
" cluster = cluster.reshape((s[1], s[2]))\n",
" counter += 1\n",
" print \n",
" print'Working on cluster: ' + str(counter)\n",
" X = cluster[:, (0,1,2)] # x,y,z\n",
" Y = cluster[:,-1] # syn/unmasked from spike\n",
" figure = plt.figure()\n",
" axs[counter-1].hist(cluster[:,-1],100)\n",
" axs[counter-1].set_title('Histogram of Density w/in Cluster#: '+ str(counter))\n",
" axs[counter-1].set_xlabel('Density')\n",
" axs[counter-1].set_ylabel('Frequency')\n",
" axs[counter-1].set_ylim([0,500])\n",
" \n",
" \n",
" print \"Done with cluster\"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
ssunkara1/bqplot | examples/Marks/Pyplot/Bars.ipynb | 1 | 70331 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import bqplot.pyplot as plt\n",
"from bqplot import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"size = 100\n",
"np.random.seed(0)\n",
"\n",
"x_data = range(size)\n",
"y_data = np.random.randn(size)\n",
"y_data_2 = np.random.randn(size)\n",
"y_data_3 = np.cumsum(np.random.randn(size) * 100.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Basic Bar Chart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.figure(title='Bar Chart')\n",
"plt.bar(np.arange(10), np.random.rand(10))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Horizontal Bar Chart"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"To generate a horizontal bar chart, pass `orientation='horizontal'` to the bar."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"plt.figure(title='Horizontal Bar Chart')\n",
"plt.bar(np.arange(10), np.random.uniform(-1, 1, 10), orientation='horizontal')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Changing the reference value from which the Bars are drawn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"# assign the output of the plt.bar to a mark object\n",
"bar = plt.bar(x_data[:20], np.abs(y_data_2[:20]), base=1.0)\n",
"# render the figure\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# changing the base\n",
"bar.base = 2.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"bar.align = 'right'"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Bar Chart Properties"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Increasing the spacing between the bars using padding property\n",
"fig = plt.figure()\n",
"bar = plt.bar(x_data[:20], y_data[:20], padding=0.3)\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# changing basic properties like stroke and opacity\n",
"bar.stroke = 'red'\n",
"bar.opacities = [0.5, 0.2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"bar.orientation = 'horizontal'\n",
"fig.axes[0].orientation = 'vertical'\n",
"fig.axes[1].orientation = 'horizontal'"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Stacked Bar Chart for 2-d data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"bar = plt.bar(x_data, [y_data[:20], y_data_2[:20]], \n",
" padding=0.2,\n",
" colors=CATEGORY10)\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Grouped Bar Chart"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"bar.type = 'grouped' # equivalent to saying\n",
"# plt.bar(x_data, y_data, padding=0.2, type='grouped')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"bar = plt.bar(x_data, [y_data[:20], y_data_2[:20]], \n",
" padding=0.2, \n",
" colors=CATEGORY10, \n",
" orientation='horizontal')\n",
"fig"
]
},
{
"cell_type": "code",
"execution_count": null,
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"bar.type = 'grouped'"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Modifying color mode"
]
},
{
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"execution_count": null,
"metadata": {
"collapsed": false,
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"## Color mode has 2 values. 'group' and 'element'. \n",
"## 'group' means for every x all bars have same color.\n",
"## 'element' means for every dimension of y, all bars have same color.\n",
"fig = plt.figure()\n",
"bar = plt.bar(x_data, [y_data[:20], y_data_2[:20]], padding=0.2, colors=CATEGORY10, color_mode='group')\n",
"fig"
]
},
{
"cell_type": "code",
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"## for 1-d array for Y.\n",
"fig = plt.figure()\n",
"bar = plt.bar(x_data, y_data[:20], padding=0.2, color_mode='element', \n",
" labels=['Values'], display_legend=True)\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
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"source": [
"## Representing additional dimension using Color"
]
},
{
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"# In this example, the color is just the magnitude of the y data\n",
"fig = plt.figure()\n",
"# add a 'reds' color scale \n",
"plt.scales(scales={'color': ColorScale(scheme='Reds')})\n",
"\n",
"bar = plt.bar(x_data[:20], y_data[:20], color=np.abs(y_data[:20]), padding=0.2)\n",
"\n",
"# give enough bottom margin to accommodate the color axis\n",
"fig.fig_margin = dict(top=50, bottom=80, left=50, right=50)\n",
"\n",
"fig"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
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"source": [
"## Adding color for 2-d data"
]
},
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"execution_count": null,
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"# By default color is applied along the axis=1\n",
"fig = plt.figure()\n",
"plt.scales(scales={'color': ColorScale(scheme='Reds')})\n",
"\n",
"y_vals = [y_data[:20], y_data_2[:20], y_data_3[:20] / 100.0]\n",
"color_data = np.mean(y_vals, axis=1)\n",
"\n",
"bar = plt.bar(x_data, y_vals, color=color_data, padding=0.2,\n",
" labels=['Dim 1', 'Dim 2', 'Dim 3'], display_legend=True)\n",
"\n",
"fig.fig_margin = dict(top=50, bottom=80, left=50, right=50)\n",
"fig"
]
},
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"# Applying color along the axis=0\n",
"fig = plt.figure()\n",
"plt.scales(scales={'color': ColorScale(mid=0.)})\n",
"\n",
"y_vals = [y_data[:20], y_data_2[:20], y_data_3[:20] / 100.0]\n",
"color_data = np.mean(y_vals, axis=0)\n",
"\n",
"bar = plt.bar(x_data, y_vals, color=color_data, padding=0.2, \n",
" color_mode='group', stroke='orange')\n",
"fig.fig_margin = dict(top=50, bottom=80, left=50, right=50)\n",
"fig"
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"model_module": "jupyter-js-widgets",
"model_module_version": "~2.1.4",
"model_name": "LayoutModel",
"state": {
"_model_module_version": "~2.1.4",
"_view_module_version": "~2.1.4",
"min_width": "125px"
}
},
"db4bd53e38f744e69e9d4e1829bdbf01": {
"model_module": "bqplot",
"model_module_version": "^0.2.3",
"model_name": "OrdinalScaleModel",
"state": {
"domain": []
}
},
"dcf7319185934757bfdea2b7c3c336ba": {
"model_module": "bqplot",
"model_module_version": "^0.2.3",
"model_name": "LinearScaleModel",
"state": {
"allow_padding": false,
"max": 1,
"min": 0,
"stabilized": false
}
},
"dd10d27948674dc3bbee4e150fa94531": {
"model_module": "bqplot",
"model_module_version": "^0.2.3",
"model_name": "AxisModel",
"state": {
"orientation": "vertical",
"scale": "IPY_MODEL_0cbe01dc362d4792bcdef7e867c53b0d",
"side": "left",
"tick_format": "0.2f",
"tick_values": {
"type": null,
"values": null
}
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},
"e636bc22baa14c168991038c548a885e": {
"model_module": "jupyter-js-widgets",
"model_module_version": "~2.1.4",
"model_name": "LayoutModel",
"state": {
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"min_width": "125px"
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},
"e8448452028f4f7a92b89e8e0daf7875": {
"model_module": "bqplot",
"model_module_version": "^0.2.3",
"model_name": "LinearScaleModel",
"state": {
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"max": 1,
"min": 0,
"stabilized": false
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},
"f42bb1d5325943baa713b686f4689454": {
"model_module": "bqplot",
"model_module_version": "^0.2.3",
"model_name": "LinearScaleModel",
"state": {
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"f593111c6f5349bdb51a3891ad028a2b": {
"model_module": "bqplot",
"model_module_version": "^0.2.3",
"model_name": "LinearScaleModel",
"state": {
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"version_major": 1,
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| apache-2.0 |
arcyfelix/Courses | 18-03-07-Deep Learning With Python by François Chollet/Chapter 5.1 - Introduction to convnets.ipynb | 2 | 10937 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Chapter 5.1 - Introduction to convnets"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"# Instatiating a small convolutional neural network\n",
"from keras.models import Sequential\n",
"from keras.layers import Conv2D, MaxPooling2D"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = Sequential()\n",
"model.add(Conv2D(filters = 32, \n",
" kernel_size = (3, 3), \n",
" activation = 'relu', \n",
" input_shape = (28, 28, 1)))\n",
"model.add(MaxPooling2D(pool_size = (2, 2)))\n",
"model.add(Conv2D(filters = 64, \n",
" kernel_size = (3, 3), \n",
" activation ='relu'))\n",
"model.add(MaxPooling2D(pool_size = (2, 2)))\n",
"model.add(Conv2D(filters = 64, \n",
" kernel_size = (3, 3), \n",
" activation = 'relu'))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_1 (Conv2D) (None, 26, 26, 32) 320 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 11, 11, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 3, 3, 64) 36928 \n",
"=================================================================\n",
"Total params: 55,744\n",
"Trainable params: 55,744\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Prompting summary of the model\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.layers import Dense, Flatten"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Adding classifier part of the model\n",
"model.add(Flatten())\n",
"model.add(Dense(units = 64, \n",
" activation = 'relu'))\n",
"model.add(Dense(units = 10, \n",
" activation = 'softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_1 (Conv2D) (None, 26, 26, 32) 320 \n",
"_________________________________________________________________\n",
"max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_2 (Conv2D) (None, 11, 11, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_2 (MaxPooling2 (None, 5, 5, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_3 (Conv2D) (None, 3, 3, 64) 36928 \n",
"_________________________________________________________________\n",
"flatten_1 (Flatten) (None, 576) 0 \n",
"_________________________________________________________________\n",
"dense_1 (Dense) (None, 64) 36928 \n",
"_________________________________________________________________\n",
"dense_2 (Dense) (None, 10) 650 \n",
"=================================================================\n",
"Total params: 93,322\n",
"Trainable params: 93,322\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"# Prompting summary of the model\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preparing the dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Input images"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.datasets import mnist"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Importing the dataset\n",
"(train_images, train_labels), (test_images, test_labels) = mnist.load_data()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Reshaping the dataset\n",
"train_images = train_images.reshape((60000, 28, 28, 1))\n",
"test_images = test_images.reshape((10000, 28, 28, 1))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Changing type to one used by Keras\n",
"train_images = train_images.astype('float32')\n",
"test_images = test_images.astype('float32')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Normalization\n",
"# The data has values from 0 to 255. \n",
"# It is preferred that to be between 0 and 1.\n",
"train_images /= 255\n",
"test_images /= 255"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Output labels"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.utils import to_categorical"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_labels[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# One-hot enconding\n",
"train_labels = to_categorical(train_labels)\n",
"test_labels = to_categorical(test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_labels[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Compiling the network\n",
"model.compile(optimizer = 'rmsprop',\n",
" loss = 'categorical_crossentropy',\n",
" metrics = ['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"60000/60000 [==============================] - 11s - loss: 0.1733 - acc: 0.9456 \n",
"Epoch 2/5\n",
"60000/60000 [==============================] - 9s - loss: 0.0444 - acc: 0.9864 \n",
"Epoch 3/5\n",
"60000/60000 [==============================] - 9s - loss: 0.0316 - acc: 0.9901 \n",
"Epoch 4/5\n",
"60000/60000 [==============================] - 9s - loss: 0.0234 - acc: 0.9927 \n",
"Epoch 5/5\n",
"60000/60000 [==============================] - 9s - loss: 0.0181 - acc: 0.9945 \n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x1fed0192f60>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Training\n",
"model.fit(train_images, \n",
" train_labels, \n",
" epochs = 5, \n",
" batch_size = 64)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evalutation"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 9920/10000 [============================>.] - ETA: 0s"
]
}
],
"source": [
"test_loss, test_acc = model.evaluate(test_images, test_labels)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.99250000000000005"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_acc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The network improves the prediction (comparing to previous architectures) despite that it was trained for only 5 epochs."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
MadDataScience/DAT4 | DS_Lec01-Intro.ipynb | 1 | 64390 | {
"metadata": {
"celltoolbar": "Slideshow",
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Data Science\n",
"====\n",
"### Introduction and Overview\n",
"\n",
"Alessandro Gagliardi \n",
"Sr. Data Scientist, [Glassdoor.com](Glassdoor.com)"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Welcome!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Welcome to General Assembly's Data Science course."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table border=\"0\" style=\"border-collapse:collapse;\" cellspacing=\"0\">\n",
"<tr><td>Instructor:<td>Alessandro Gagliardi</td></tr>\n",
"<tr><td></td><td>[[email protected]](mailto:[email protected])</td></tr>\n",
"<tr><td>TA:<td>Kevin Perko</td></tr>\n",
"<tr><td></td><td>[[email protected]](mailto:[email protected])</td></tr>\n",
"<tr><td>Classes:<td>6:00pm-9:00pm, Mondays and Wednesdays</td></tr>\n",
"<tr><td></td><td>January 20 \u2013 April 7, 2014 (no class February 17)</td></tr>\n",
"<tr><td>Office Hours:<td>9:00pm-10:00pm Wednesdays after class</td></tr>\n",
"<tr><td></td><td>or by appointment</td></tr>\n",
"<!--tr><td>Outside of classwork:<td>Roughly 3 hours a week.-->\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"The class will meet every Monday and Wednesday until April 7 except for February 17 which is Presidents' Day. \n",
"\n",
"Kevin, your TA, will hold office hours will be held immediately following class on Wednesdays and either of us will be available by appointment."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Who am I?\n",
"\n",
"- 1997 - 2001 - Studied Computer Science at UC Santa Cruz \n",
"- 2001 - 2002 - Developed web-based educational CRM at TMP.Worldwide in New York \n",
"- 2002 - 2003 - Took some time off \n",
"- 2003 - 2005 - Worked as a independent consultant for startups in New York\n",
"- 2005 - 2010 - Studient Integrative Neuroscience at Rutgers\n",
"- 2010 - 2011 - Taught Psychology and Neuroscience at USF, NDNU, CIIS \n",
"- 2011 - 2014 - Returned to industry as a Data Scientist at Socialize (R.I.P.), Path, and Glassdoor"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Who are you?\n",
"\n",
"- Your name\n",
"- Where you work and what you do there\n",
"- What you hope to get out of this course (in 1 sentence, please)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"1. Lecture \n",
" A. What is Data Science? \n",
" B. Goals of the Course \n",
"2. Lab \n",
" A. Git setup \n",
" B. IPython Notebook setup \n",
" C. Working in Python"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"But first..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"Since today's my birthday, I thought I might have us warm up our brains with..."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# The Birthday Paradox!\n",
"(credit to [Balthazar Rouberol](https://gist.github.com/brouberol/6524605) for preparing what follows)\n",
"\n",
"Given a sample of _n_ people, we would like to calculate the probability _p_ that _at least_ one person has the same birthday as _any_ other person in the group. \n",
"<br />"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"First: how many know this paradox? Keep the answer to yourselves. \n",
"\n",
"The rest of you: how big do you think this class would have to be in order for there to be >50% chance that two people have the same birthday? \n",
"\n",
"Alternatively, what are the chances that two people in this class have the same birthday (including the TA and me)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Assumptions:\n",
"\n",
"* the probability distribution is uniform\n",
"* all events are independant from each other \n",
"<br />"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"P(A) is the probability of at least two people sharing the same birthday.\n",
"P(A') is the probability that all birthdays are different.\n",
"\n",
"\\begin{equation}\n",
" P(A') = 1 - P(A)\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Calculating the probability\n",
"\n",
"From [Wikipedia](http://en.wikipedia.org/wiki/Birthday_problem)\n",
"\n",
"P(A') could be calculated as P(1) \u00d7 P(2) \u00d7 P(3) \u00d7 ... \u00d7 P(20).\n",
"\n",
"The 20 independent events correspond to the 20 people, and can be defined in order. Each event can be defined as the corresponding person not sharing his/her birthday with any of the previously analyzed people. For Event 1, there are no previously analyzed people. Therefore, the probability, P(1), that person number 1 does not share his/her birthday with previously analyzed people is 1, or 100%. Ignoring leap years for this analysis, the probability of 1 can also be written as 365/365, for reasons that will become clear below.\n",
" \n",
"For Event 2, the only previously analyzed people is Person 1. Assuming that birthdays are equally likely to happen on each of the 365 days of the year, the probability, P(2), that Person 2 has a different birthday than Person 1 is 364/365. This is because, if Person 2 was born on any of the other 364 days of the year, Persons 1 and 2 will not share the same birthday."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Similarly, if Person 3 is born on any of the 363 days of the year other than the birthdays of Persons 1 and 2, Person 3 will not share their birthday. This makes the probability P(3) = 363/365.\n",
" \n",
"This analysis continues until Person 20 is reached, whose probability of not sharing his/her birthday with people analyzed before, P(20), is 346/365.\n",
" \n",
"P(A') is equal to the product of these individual probabilities:\n",
"\n",
" (1) P(A') = 365/365 \u00d7 364/365 \u00d7 363/365 \u00d7 362/365 \u00d7 ... \u00d7 346/365\n",
"\n",
"The terms of equation (1) can be collected to arrive at:\n",
"\n",
" (2) P(A') = (1/365)^23 \u00d7 (365 \u00d7 364 \u00d7 363 \u00d7 ... \u00d7 346)\n",
" = 0.588\n",
"\n",
" (3) P(A) = 1 - P(A') = 0.411 = 41.1%"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"## Generalization"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"\\begin{eqnarray}\n",
"P(n') &=& 1 \\times (1 - \\dfrac{1}{365}) \\times (1 - \\dfrac{2}{365}) \\times ... \\times (1 - \\dfrac{n+1}{365}) \\\\\n",
" &=& \\dfrac{365 \\times 364 \\times ... \\times (365 - n + 1)}{365^{n}} \\\\\n",
" &=& \\dfrac{365!}{365^{n} . (365 - n)!}\\\\\n",
"\\end{eqnarray}"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"\\begin{equation}\n",
"P(n) = 1 - P(n') = 1 - \\dfrac{365!}{365^{n} . (365 - n)!}\n",
" \\end{equation}"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import division\n",
"\n",
"import math\n",
"\n",
"def pn_dash(n):\n",
" \"\"\"Returns probability that no birthday occur the same day in a group of n people.\"\"\"\n",
" return math.factorial(365) / (365**n * math.factorial(365 - n)) \n",
"\n",
"def pn(n):\n",
" \"\"\"Returns probability that birthday of at least 2 people occur same day in group of n people.\"\"\"\n",
" return 1 - pn_dash(n)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Let's calculate it for 20 people\n",
"print '{:0.2f}%' . format(pn(20) * 100)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"41.14%\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nb_people = range(0, 85, 5)\n",
"p_birthday = [pn(n) for n in nb_people]\n",
"\n",
"for n, p in zip(nb_people, p_birthday):\n",
" print 'n = {:2} -> p = {:.2f}%'.format(n, p * 100)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"n = 0 -> p = 0.00%\n",
"n = 5 -> p = 2.71%\n",
"n = 10 -> p = 11.69%\n",
"n = 15 -> p = 25.29%\n",
"n = 20 -> p = 41.14%\n",
"n = 25 -> p = 56.87%\n",
"n = 30 -> p = 70.63%\n",
"n = 35 -> p = 81.44%\n",
"n = 40 -> p = 89.12%\n",
"n = 45 -> p = 94.10%\n",
"n = 50 -> p = 97.04%\n",
"n = 55 -> p = 98.63%\n",
"n = 60 -> p = 99.41%\n",
"n = 65 -> p = 99.77%\n",
"n = 70 -> p = 99.92%\n",
"n = 75 -> p = 99.97%\n",
"n = 80 -> p = 99.99%\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Main plot layout\n",
"f, ax = plt.subplots()\n",
"ax.set_yticks(np.arange(0, 1.1, 0.1))\n",
"plt.ylabel('probability')\n",
"f.text(x=0.5, y=0.975, s='Probability distribution of birthday collision for a sample of n people', horizontalalignment='center', verticalalignment='top')\n",
"\n",
"plt.plot(nb_people, p_birthday, label='$p(n)$', color='r')\n",
"plt.plot(nb_people, [pn_dash(n) for n in nb_people],label='$p(\\overline{n})$',color='b')\n",
"\n",
"n_p50, p50 = [(n, pn(n)) for n in xrange(0, 100) if round(pn(n), 2) in [0.5, 0.51]][0]\n",
"plt.axhline(y=p50, xmax=n_p50/80., linestyle='--', color='grey')\n",
"plt.axvline(x=n_p50, ymax=p50, linestyle='--', color=\"grey\")\n",
"\n",
"plt.legend(loc='center right')\n",
"plt.text(n_p50 - 1, -0.055, '23')"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<matplotlib.text.Text at 0x105bd0ad0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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FF1C5slw/8NNPMGGCTt6hbMfMbqcFgQ3AXcgsaEuQ4bDXeSzzAXAUeBeoBHxqLO8tIt1O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\n",
"text": [
"<matplotlib.figure.Figure at 0x10579f1d0>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Conclusion\n",
"\n",
"In a group of 23 people, there is a probability of approximately 50% of finding at least two people with the same birthday, contrary to what your intuition could tell you! "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"A. What is Data Science?\n",
"====\n",
"(And what does the birthday paradox have to do with it?)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"List examples: \n",
"\n",
"- Recommendation Engines\n",
" - Collaborative Filtering (Amazon, Netflix)\n",
" - PYMK (LinkedIn, etc.)\n",
" - Other (Pandora, etc.)\n",
"- Data Viz (NYT, etc.)\n",
"- Fraud detection\n",
"- Business Intelligence (Obama 2012, etc.)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Data Science = Data + Science"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Data can be:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"1. Most data does not conform to any predetermined schema. Must ETL it first. Will cover next class.\n",
"2. Machine-readable but unpredictable. Good for communication. Bad for analysis. Mongo, XML, JSON, etc. Will cover Monday.\n",
"3. Must be structured prior to analysis. Fields always there, always same type. SQL, R, Pandas. Next week and beyond.\n",
" - Excel is semi-structured but has some structured capabilities. Special case."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Unstructured (e.g. Email, Photos, Books, etc.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Semi-Structured (e.g. XML, JSON, NoSQL, APIs, etc.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Structured (e.g. SQL, Data Frames, etc.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"1. Most data in the world is unstructured. That is, it does not conform to any predetermined computer-readable form. Before we can work with this sort of data, we need to extract it, transform it into something usable, and load it into our system, whatever that system may be. Extract, transform, load is ETL and its one of the less glamorous and more time consuming parts of the job, so it is important to know how to do it efficiently. We will cover this in the next class.\n",
"2. Semi-structured data is a relatively new thing and has a lot to do with the web. Semi-structured data is machine readable but does not conform to a rigid structure. This is both a blessing and a curse. The flexibility it provides makes it a lot easier for different systems to talk to one another. But that same flexibility makes working with the data in aggregate more difficult. Many \"NoSQL\" databases use a semi-structured schema. Those of you who are over 30 probably remember XML which was a popular standard for semi-structured in the late 90's and early 2000's. Fortunately, XML has largely been replaced by JSON which is easier to read by both humans (because it doesn't have so many angle brackets) and computers (because it is less flexible and therefore less ambiguous). We will be covering how to work with this sort of data next Monday. \n",
"3. Ultimately, Data Scientists need our data to be structured before we can do anything with it. Relational databases are structured. And once we get into Pandas and R, we will learn about data frames which are also structured. Structured data are consistent throughout. For example, a given field will have the same data type no matter what. This is extremely important because it makes it possible to work across all of the data in aggregate. This opens up the possibility to do everything from calculate sums so measuring relationships and predicting outcomes. We will cover the basics of how to work with this kind of data next week and for the most part, the rest of this course will deal with data in this form. \n",
" 1. As an aside, you might be wondering where Excel spreadsheets would fit in this list. I would place it somewhere between Structured and Semi-structured. We might call it \"mostly structured\". Unlike fully structured data, an Excel spreadsheet can accept different datatypes in a column. But unlike semi-structured data, it will complain when you do this (at least, as soon as you try to do an operation on that column). It probably belongs in the semi-structured category but because of the ability to do structured operations in a spreadsheet, I'm reluctant to put it there."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Science can be:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "notes"
}
},
"source": [
"1. Once we've got data in a form we can use, first we look at it. Sometimes that is enough to derive valuable insights. Time permitting, we will begin this week 3.\n",
"2. Then we might try to model the data which can be useful for making inferences and predictions"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Explorations / Explanations\n",
"- Data Visualization (e.g. ggplot2, Tableau, d3.js, etc.)\n",
"- Unsupervised Machine Learning (e.g. clustering, etc.)\n",
"- etc....\n",
"<br />"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"### Inferences / Predictions\n",
"- Regression Models (e.g. Linear Models, Logistic Regression)\n",
"- Supervised Machine Learning (e.g. Neural Nets, Genetic Algorithms)\n",
"- etc.... \n",
"<br />"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Data Science Workflow\n",
"=====================\n",
"From [a Taxonomy of Data Science](http://www.dataists.com/2010/09/a-taxonomy-of-data-science/) (by Dataists)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"A. Obtain"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"B. Scrub"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"C. Explore"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"D. Model"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"E. Interpret"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Workflow Example:\n",
"### Problem: what are the leading indicators that a user will make a new purchase?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"A. Collect data around user retention, user actions within the product, potentially find data outside of company"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"B. Extract aggregated values from raw data \n",
"\n",
"1. How many times did a user share through Facebook within a week? A month?\n",
"2. How often did they open up our emails?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"C. Examine data to find common distributions and correlations"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"D. Extract new meaning to predict if user would purchase again"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"E. Share results (and probably also go back to the drawing board)"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"B. Goals of the Course"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"At the completion of this course, you will be able to:\n",
"\n",
"- Employ the Map/Reduce paradigm to transform big unstructured data\n",
"- Access data from web-based application programming interfaces (APIs)\n",
"- Use Structured Query Language (SQL) functions like JOIN and GROUP\n",
"- Explore and present data through visualizations\n",
"- Apply generalized linear models (GLMs)\n",
"- Detect clusters in multivariate data\n",
"- Predict categories using supervised machine learning techniques"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### At the completion of this course, you will be able to:"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Employ the Map/Reduce paradigm to transform big unstructured data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Access data from web-based application programming interfaces (APIs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Use Structured Query Language (SQL) functions like JOIN and GROUP"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Explore and present data through visualizations"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Apply generalized linear models (GLMs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Detect clusters in multivariate data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"- Predict categories using supervised machine learning techniques"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Tentative Course outline:\n",
"\n",
"1. Intro and Overview\n",
"2. Big Data I: Hadoop\n",
"3. Big Data II: IPython.parallel\n",
"4. APIs and semi-structured data - First Project Proposals Due 1/27\n",
"5. SQL and Data Frames\n",
"6. Data Exploration & Visualization (feedback on proposals returned)\n",
"7. Linear Regression\n",
"8. Logistic Regression - Formal Project Proposals Due 2/3 (including data and methods chosen) \n",
"9. Dimensionality Reduction\n",
"10. Unsupervised Machine Learning: K-Means Clustering (feedback on proposals returned)\n",
"11. Network Analysis\n",
"12. Supervised Machine Learning: K-Nearest Neighbors - Project live on Github 2/19 (no class 2/17)\n",
"13. Supervised Machine Learning: Naive Bayes\n",
"14. Machine Learning in Python: Scikit-Learn \n",
"15. Supervised Machine Learning: Decision Trees & Random Forests - Peer Feedback Due 3/3\n",
"16. Ensemble Techniques\n",
"17. Recommendation Systems\n",
"18. Final Project Working Session\n",
"19. Final Project Working Session\n",
"20. Where to Go Next\n",
"21. Final Project Presentations (10 min. each)\n",
"22. Final Project Presentations (10 min. each)"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"C. Lab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Checklist\n",
"\n",
"- Install Anaconda\n",
"- Setup Git\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<small><i>The following was put together by [Jake Vanderplas](http://www.vanderplas.com)</i></small>\n",
"\n",
"# Getting Started: Four Ways to Use Python\n",
"\n",
"### 1. The Python command-line interpreter\n",
"\n",
"### 2. Editing Python (.py) files\n",
"\n",
"### 3. The IPython command-line interpreter\n",
"\n",
"### 4. The IPython notebook"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 1. The Python command-line Interpreter\n",
"\n",
"If you have never used the command-line, you're in for a treat\n",
"\n",
"- Mac OSX: in Finder/Applications, search for \"Terminal\"\n",
"\n",
"- Linux/Unix: ``Ctrl-Alt-t``\n",
"\n",
"- Windows: run \"cmd\"\n",
"\n",
"<img src=\"assets/OSX_terminal.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 1. The Python command-line Interpreter\n",
"\n",
"Type ``python`` at the command-line to start the interpreter\n",
"\n",
"<img src=\"assets/OSX_terminal_python.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 1. The Python Command-line Interpreter\n",
"\n",
"Execute a command: Type ``print \"hello world\"``\n",
"\n",
"<img src=\"assets/OSX_terminal_hello.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 1. The Python Command-line Interpreter\n",
"\n",
"Closing the terminal:\n",
"\n",
"- Either type ``exit()`` or type ``Ctrl-d``\n",
"\n",
"<img src=\"assets/OSX_terminal_exit.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 2. Editing Python (.py) files\n",
"\n",
"### This requires a text editor.\n",
"\n",
"The best option is one which includes code highlighting\n",
"\n",
"- Linux: gedit, emacs, nano, vim...\n",
"- Mac OSX: textmate, emacs, nano, vim...\n",
"- Windows: NotePad...\n",
"\n",
"### GUI-based editors with bells & whistles\n",
"\n",
"- Linux: KWrite, Scribes, eggy\n",
"- Mac OSX: TextWrangler, SublimeText\n",
"- Windows: NotePad++, SublimeText"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 2. Editing Python (.py) files\n",
"\n",
"Use your editor to open ``hello_world.py`` (here we use OSX's ``mate`` though any text editor will do)\n",
"\n",
"<img src=\"assets/OSX_mate.png\">\n",
"\n",
"Edit the file to say ``print \"hello world\"``\n",
"\n",
"<img src=\"assets/mate_hello_world.png\">\n",
"\n",
"In the terminal, run ``python hello_world.py``\n",
"\n",
"<img src=\"assets/run_hello_world.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 3. The IPython command-line Interpreter\n",
"\n",
"IPython provides an enhanced command-line interface\n",
"\n",
"It can be started by typing ``ipython``:\n",
"\n",
"<img src=\"assets/ipython_start.png\">\n",
"\n",
"Useful features include tab completion, help (``?``), etc."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 3. The IPython command-line Interpreter\n",
"\n",
"Basic use is just like the standard interpreter:\n",
"\n",
"<img src=\"assets/ipython_hello.png\">"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 4. The IPython Notebook\n",
"\n",
"The IPython notebook can be started by typing ``ipython notebook``:\n",
"\n",
"<img src=\"assets/ipynb_command.png\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## 4. The IPython Notebook\n",
"\n",
"\n",
"Your web browser should open to an interactive notebook page\n",
"\n",
"<img src=\"assets/ipynb_start_page.png\">\n",
"\n",
"Notice that this slideshow is written as an IPython notebook!\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The most basic data structure is the `None` type. This is the equivalent of `NULL` in other languages. \n",
"There are four numeric types: **`int, float, bool, complex`**."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(1)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"int"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(2.5)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"float"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(True)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"bool"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(2+3j)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"complex"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The next basic data type is the Python list. \n",
"**A list is an ordered collection of elements, and these elements can be of arbitrary type. Lists are mutable, meaning they can be changed in-place.**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k = [1, 'b', True]"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k[2]"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"True"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k[1] = 'a'"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"k"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"[1, 'a', True]"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Likewise, **tuples are immutable arrays of arbitrary elements.**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = (1, 'a', 2.5)"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"(1, 'a', 2.5)"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x[0]"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"1"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x[0] = 'b'"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-1d938b100406>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'b'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The string type in Python represents an immutable ordered array of characters (note there is no char type).\n",
"\n",
"**Strings support slicing and indexing operations like arrays, and have many other string-specific functions as well.**\n",
"\n",
"String processing is one area where Python excels."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Associative arrays (or hash tables) are implemented in Python as the dictionary type."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"this_class = {'subject': 'Data Science', 'instructor': 'Alessandro', 'time': 1800, 'is_cool': True}"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"this_class['subject']"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"'Data Science'"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"this_class['is_cool']"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"True"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"**Dictionaries are unordered collections of key-value pairs, and dictionary keys must be immutable.**"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Another basic Python data type is the set. Sets are unordered mutable collections of distinct elements."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y = set([1, 1, 2, 3, 5, 8])"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y"
],
"language": "python",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"{1, 2, 3, 5, 8}"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"These are particularly useful for checking membership of an element and for ensuring element uniqueness."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### More:\n",
"\n",
"- [Basic Training](http://nbviewer.ipython.org/url/www.astro.washington.edu/users/vanderplas/Astr599/notebooks/01_basic_training.ipynb)\n",
"- [Advanced Data Structures](http://nbviewer.ipython.org/url/www.astro.washington.edu/users/vanderplas/Astr599/notebooks/02_advanced_data_structures.ipynb)"
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Discussion"
]
}
],
"metadata": {}
}
]
} | artistic-2.0 |
nilutz/Connectfour | notebooks/connectfour-2_usestatistcs.ipynb | 1 | 19651 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"probs = np.genfromtxt('normed_count.csv',delimiter=',') \n",
"\n",
"\n",
"def move_is_correct(grid,num):\n",
" '''\n",
" @param grid: 6x7 grid containing the current game state\n",
" @param num: column\n",
"\n",
" returns True if move is allowed on that column\n",
" '''\n",
"\n",
" #if 0 is in column\n",
" if 0 in grid[:,num]:\n",
" \n",
" #move is allowed\n",
" return True\n",
"\n",
" else:\n",
"\n",
" return False\n",
"\n",
"def move_still_possible(S):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" returns True if grid contains no 0, therefore no move possible anymore\n",
" '''\n",
" return not(S[S==0].size == 0)\n",
"\n",
"\n",
"def move(S,p,col_num):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" @param p: current player\n",
" @param col_num: column number\n",
" \n",
" sets the player's number on the grid and returns the grid\n",
" '''\n",
" \n",
" #sanity check\n",
" if 0 in S[:,col_num]: \n",
" y = np.where(S[:,col_num]==0)[0][-1]\n",
" S[y,col_num] = p\n",
" return S , y, col_num\n",
" else:\n",
" return S, None, None\n",
" return \n",
" \n",
"def move_probabilistic(S, p):\n",
" \n",
" #all available columns that are not already full\n",
" _ , col = np.where(S == 0)\n",
" col_num=np.unique(col)\n",
"\n",
" #x of available all columns\n",
" x_to_col_num=[np.where(S[:,x] == 0)[0][-1] for x in np.unique(col)]\n",
" \n",
" #determine free position with max prob \n",
" m = max(probs[x_to_col_num,col_num])\n",
"\n",
" #and the index to that value\n",
" _ , xy = np.where(probs==m)\n",
" return xy\n",
"\n",
"def move_at_random(S):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" moves at random\n",
" '''\n",
" return np.random.randint(0,S.shape[1])\n",
"\n",
"\n",
"#neat and ugly but the fastest way to search a matrix for a vector is a string find\n",
"player1 = '1 1 1 1'\n",
"oponent = '2 2 2 2'\n",
"\n",
"def move_was_winning_move(S, p):\n",
" '''\n",
" @param S: 6x7 grid containing the current game state\n",
" @param p: current player\n",
" \n",
" combines all the allowed formations of the grid and string_finds with \n",
" the currents player vector. Returns true if match.\n",
" '''\n",
" if p == 1:\n",
" match = player1\n",
" else:\n",
" match = oponent \n",
"\n",
" l=[]\n",
" #for every possible diag\n",
" for i in range(-2,4):\n",
" l.append(np.diag(S,k = i))\n",
" l.append(np.diag(np.fliplr(S),k=i))\n",
" #left to right\n",
" l.append(S)\n",
" #top to bottom\n",
" l.append(np.rot90(S)) \n",
"\n",
" if ''.join(np.array_str(e) for e in l).find(match) > -1:\n",
" return True\n",
" return False\n",
"\n",
"\n",
"\n",
"# relate numbers (1, -1, 0) to symbols ('x', 'o', ' ')\n",
"symbols = {1:'b', 2:'r', 0:' '}\n",
"\n",
"# print game state matrix using symbols\n",
"def print_game_state(S):\n",
" B = np.copy(S).astype(object)\n",
" for n in [1, 2, 0]:\n",
" B[B==n] = symbols[n]\n",
" print B\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
" \n",
" outcomes = []\n",
"\n",
" for i in range(2000):\n",
"\n",
"\n",
" # initialize 6x7 connectfour board\n",
" gameState = np.zeros((6,7), dtype=int)\n",
"\n",
" # initialize player number, move counter\n",
" player = 1\n",
" mvcntr = 1\n",
"\n",
" # initialize flag that indicates win\n",
" noWinnerYet = True\n",
" while move_still_possible(gameState) and noWinnerYet:\n",
"\n",
" while True:\n",
" # get player symbol\n",
" name = symbols[player]\n",
" #print '%s moves' % name\n",
"\n",
" # let player move at random\n",
" if player == 1:\n",
" col_num = move_at_random(gameState)\n",
" #col_num, _ = move_probabilistic(gameState, player)\n",
" # player o/r uses statistic\n",
" else:\n",
" col_num = move_probabilistic(gameState, player)\n",
"\n",
" if move_is_correct(gameState, col_num):\n",
" gameState, _ , _ = move(gameState,player,col_num)\n",
"\n",
" # print current game state\n",
" #print_game_state(gameState)\n",
"\n",
" # evaluate game state\n",
" if move_was_winning_move(gameState, player):\n",
" #print 'player %s wins after %d moves' % (name, mvcntr)\n",
" noWinnerYet = False\n",
" outcomes.append(player)\n",
"\n",
" # switch player and increase move counter\n",
" if player == 1:\n",
" player = 2\n",
" elif player == 2:\n",
" player = 1\n",
"\n",
" mvcntr += 1\n",
"\n",
" break\n",
"\n",
"\n",
"\n",
"\n",
" if noWinnerYet:\n",
" #print 'game ended in a draw'\n",
" outcomes.append(0)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-3-58ad659dbc81>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-3-58ad659dbc81>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m DISCUSSION EXAMPLE 1.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"DISCUSSION EXAMPLE 1.\n",
"\n",
"[[' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" [' ' ' ' ' ' ' ' ' ' ' ' ' ']\n",
" ['b' ' ' ' ' 'r' 'r' 'b' ' ']\n",
" ['b' ' ' 'b' 'r' 'r' 'b' ' ']\n",
" ['b' ' ' 'r' 'r' 'r' 'b' 'b']]\n",
"\n",
"\n",
"this demonstartes quite clearly the problem of that approach. Player o seems to want to be \n",
"extremyl sure to win look at the corresponding probs\n",
"he build his o extractly like the corresponding heatmap\n",
"\n",
"[[ 0.14303329 0.15659679 0.1405672 0.11960543 0.14426634 0.14303329\n",
" 0.15289766]\n",
" [ 0.13980583 0.14045307 0.14174757 0.14627832 0.14822006 0.13980583\n",
" 0.14368932]\n",
" [ 0.13335518 0.14089122 0.15104849 0.14777195 0.14482307 0.14711664\n",
" 0.13499345]\n",
" [ 0.13774898 0.14470842 0.14134869 0.15166787 0.15022798 0.14350852\n",
" 0.13078954]\n",
" [ 0.12749728 0.1422085 0.14965492 0.16345805 0.15201598 0.1394842\n",
" 0.12568108]\n",
" [ 0.13320377 0.13858574 0.15278816 0.17312005 0.14785469 0.13006428\n",
" 0.12438332]]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tournament Random vs Prob"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.text.Text at 0x10e5b4b10>,\n",
" <matplotlib.text.Text at 0x10b006bd0>,\n",
" <matplotlib.text.Text at 0x10b06b3d0>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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lpYDwAVrrqv19pHq6m5ZIWpe89yVtjU9cm5kNCfWExA+Aa4BtJH0LmAZ8u6G1MjOzAaHb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\nNEkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e57b750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"his = plt.hist(outcomes,bins=3)\n",
"offset = -.3\n",
"plt.title(\"2nd Tournament, draws and wins\")\n",
"#plt.xlabel(\"left: o wins, middle: draw, right: x wins\")\n",
"plt.ylabel(\"# Games\") \n",
"axes = plt.gca()\n",
"axes.set_ylim([0,2100]) # y axis should include all 2000 games\n",
"axes.set_xlim([0,2.0])\n",
"axes.set_xticks(his[1][1:]+offset)\n",
"axes.set_xticklabels( ('draw', 'Blue wins', 'Red wins') )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tyamamot/h29iro | codes/supplement1_LSA.ipynb | 1 | 48922 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 情報組織化・検索論 補足資料1\n",
"## 潜在的意味解析\n",
" - このページは演習ではありません．講義の理解を深めるために役立ててください．\n",
" - 参考文献: Christopher D. Manning et al., [Introduction to Information Retrieval](https://nlp.stanford.edu/IR-book/), Cambridge University Press. 2008.\n",
" \n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.linalg as lg\n",
"from scipy.spatial.distance import cosine\n",
"import matplotlib.pyplot as plt \n",
"import pandas as pd\n",
"np.set_printoptions(precision=2)\n",
"pd.set_option('precision', 2)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'%.2f'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%precision 2"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sim(v1,v2): #コサイン類似度\n",
" return 1-cosine(v1,v2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 1 0 0 0]\n",
" [0 1 0 0 0 0]\n",
" [1 1 0 0 0 0]\n",
" [1 0 0 1 1 0]\n",
" [0 0 0 1 0 1]]\n",
"(5, 6)\n",
"Rank(M) = 5\n"
]
}
],
"source": [
"# IIR exercise 18.4 の内容を用意\n",
"M = np.array(\n",
" [[1,0,1,0,0,0],\n",
" [0,1,0,0,0,0],\n",
" [1,1,0,0,0,0],\n",
" [1,0,0,1,1,0],\n",
" [0,0,0,1,0,1]]\n",
")\n",
"print(M)\n",
"print(M.shape)\n",
"print(\"Rank(M) =\", np.linalg.matrix_rank(M)) #行列Mのランクは5"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>d1</th>\n",
" <th>d2</th>\n",
" <th>d3</th>\n",
" <th>d4</th>\n",
" <th>d5</th>\n",
" <th>d6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ship</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>boat</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ocean</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>voyge</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>trip</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" d1 d2 d3 d4 d5 d6\n",
"ship 1 0 1 0 0 0\n",
"boat 0 1 0 0 0 0\n",
"ocean 1 1 0 0 0 0\n",
"voyge 1 0 0 1 1 0\n",
"trip 0 0 0 1 0 1"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"doc_names = [\"d1\", \"d2\", \"d3\", \"d4\", \"d5\", \"d6\"]\n",
"term_names = [\"ship\", \"boat\", \"ocean\", \"voyge\", \"trip\"]\n",
"df = pd.DataFrame(M, \n",
" columns=doc_names, index=term_names) \n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 特異値分解"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"U, sigma, Vt = lg.svd(M) #SVD．なお，sigmaは行列ではなく特異値が降順に並んだ配列"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5, 6)\n",
"[[ 2.16 0. 0. 0. 0. 0. ]\n",
" [ 0. 1.59 0. 0. 0. 0. ]\n",
" [ 0. 0. 1.28 0. 0. 0. ]\n",
" [ 0. 0. 0. 1. 0. 0. ]\n",
" [ 0. 0. 0. 0. 0.39 0. ]]\n"
]
}
],
"source": [
"Sigma = lg.diagsvd(sigma, M.shape[0], M.shape[1]) #確認のため truncateしていないSigmaを作成する．特異値集合からMxN対角行列を作成する．\n",
"\n",
"print(Sigma.shape)\n",
"print(Sigma)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.00"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M_r = np.dot(np.dot(U, Sigma), Vt) #分解した結果が本当にMと一致するのか確認 M = U x Sigma x V^T\n",
"np.linalg.norm(M - M_r) # フロベニウスノルム"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 低ランク近似"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"k = 2 #次元数"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"U_k =\n",
"[[ 0.44 -0.3 ]\n",
" [ 0.13 -0.33]\n",
" [ 0.48 -0.51]\n",
" [ 0.7 0.35]\n",
" [ 0.26 0.65]]\n",
"Sigma_k=\n",
"[[ 2.16 0. ]\n",
" [ 0. 1.59]]\n",
"V_k^T =\n",
"[[ 0.75 0.28 0.2 0.45 0.33 0.12]\n",
" [-0.29 -0.53 -0.19 0.63 0.22 0.41]]\n"
]
}
],
"source": [
"U_k = U[:, :k] #m-k行列にカット\n",
"Vt_k = Vt[:k,:] #k-n行列にカット\n",
"Sigma_k = Sigma[:k,:k] #特異値上位k個のみを用いる\n",
"print(\"U_k =\"),\n",
"print(U_k)\n",
"print(\"Sigma_k=\")\n",
"print(Sigma_k)\n",
"print(\"V_k^T =\"),\n",
"print(Vt_k)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"M_k=\n",
"[[ 0.85 0.52 0.28 0.13 0.21 -0.08]\n",
" [ 0.36 0.36 0.16 -0.21 -0.03 -0.18]\n",
" [ 1. 0.72 0.36 -0.05 0.16 -0.21]\n",
" [ 0.98 0.13 0.21 1.03 0.62 0.41]\n",
" [ 0.13 -0.39 -0.08 0.9 0.41 0.49]]\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>d1</th>\n",
" <th>d2</th>\n",
" <th>d3</th>\n",
" <th>d4</th>\n",
" <th>d5</th>\n",
" <th>d6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ship</th>\n",
" <td>0.85</td>\n",
" <td>0.52</td>\n",
" <td>0.28</td>\n",
" <td>0.13</td>\n",
" <td>0.21</td>\n",
" <td>-0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>boat</th>\n",
" <td>0.36</td>\n",
" <td>0.36</td>\n",
" <td>0.16</td>\n",
" <td>-0.21</td>\n",
" <td>-0.03</td>\n",
" <td>-0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ocean</th>\n",
" <td>1.00</td>\n",
" <td>0.72</td>\n",
" <td>0.36</td>\n",
" <td>-0.05</td>\n",
" <td>0.16</td>\n",
" <td>-0.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>voyge</th>\n",
" <td>0.98</td>\n",
" <td>0.13</td>\n",
" <td>0.21</td>\n",
" <td>1.03</td>\n",
" <td>0.62</td>\n",
" <td>0.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>trip</th>\n",
" <td>0.13</td>\n",
" <td>-0.39</td>\n",
" <td>-0.08</td>\n",
" <td>0.90</td>\n",
" <td>0.41</td>\n",
" <td>0.49</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" d1 d2 d3 d4 d5 d6\n",
"ship 0.85 0.52 0.28 0.13 0.21 -0.08\n",
"boat 0.36 0.36 0.16 -0.21 -0.03 -0.18\n",
"ocean 1.00 0.72 0.36 -0.05 0.16 -0.21\n",
"voyge 0.98 0.13 0.21 1.03 0.62 0.41\n",
"trip 0.13 -0.39 -0.08 0.90 0.41 0.49"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"M_k = np.dot(np.dot(U_k, Sigma_k), Vt_k) #低ランク近似\n",
"print(\"M_k=\")\n",
"print(M_k)\n",
"doc_names = [\"d1\", \"d2\", \"d3\", \"d4\", \"d5\", \"d6\"]\n",
"term_names = [\"ship\", \"boat\", \"ocean\", \"voyge\", \"trip\"]\n",
"df = pd.DataFrame(M_k, \n",
" columns=doc_names, index=term_names) \n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"|| M - M_k || = 1.66779328766\n",
"Rank(M_k) = 2\n"
]
}
],
"source": [
"print(\"|| M - M_k || =\", lg.norm(M-M_k)) # フロベニウスノルム\n",
"print(\"Rank(M_k) =\", np.linalg.matrix_rank(M_k)) #ランク2の行列になっていることを確認"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.78153445035\n",
"2.78153445035\n"
]
}
],
"source": [
"print(lg.norm(M-M_k)**2) # フロベニウスノルムの二乗が，k+1以降の特異値の平方和に等しいことを確認\n",
"print(sum(map(lambda x: x ** 2, sigma[k:]))) # k+1以降の特異値の平方和"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 文書ベクトルの次元削減\n",
"\n",
"$M_k = U_k \\Sigma_k V_t^T$\n",
"\n",
"$M_k$は$m\\times n$行列であった．これを，特徴-文書，つまり$k \\times n$行列で表現することを考える．\n",
"\n",
"両辺に左から$U_k^T$を掛けると， \n",
"\n",
"$U_k^T M_k = \\Sigma_k V_k^T$ \n",
"\n",
"ここで，$U_k U_k^T=I$を利用した（$U$が正規直交行列のため）．\n",
"\n",
"$U_k^T M_k=D_k = ({\\bf d}_1^{(k)}, \\ldots, {\\bf d}_n^{(k)})$とおくと，\n",
"\n",
"$D_k = \\Sigma_k V_k^T $\n",
"\n",
"${\\bf d}_i^{(k)}$を特徴空間上での文書ベクトルとして利用することができる． なお， ${\\bf d}_{i}^{(k)} は {\\bf d}_{i}^{(k)} = U_k^T {\\bf d}_j$としても求められる．"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"D_k=\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>d1</th>\n",
" <th>d2</th>\n",
" <th>d3</th>\n",
" <th>d4</th>\n",
" <th>d5</th>\n",
" <th>d6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>z1</th>\n",
" <td>1.62</td>\n",
" <td>0.60</td>\n",
" <td>0.44</td>\n",
" <td>0.97</td>\n",
" <td>0.70</td>\n",
" <td>0.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>z2</th>\n",
" <td>-0.46</td>\n",
" <td>-0.84</td>\n",
" <td>-0.30</td>\n",
" <td>1.00</td>\n",
" <td>0.35</td>\n",
" <td>0.65</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" d1 d2 d3 d4 d5 d6\n",
"z1 1.62 0.60 0.44 0.97 0.70 0.26\n",
"z2 -0.46 -0.84 -0.30 1.00 0.35 0.65"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D_k = np.dot(Sigma_k, Vt_k)\n",
"D_k\n",
"axis_names = [\"z1\", \"z2\"]\n",
"doc_names = [\"d1\", \"d2\", \"d3\", \"d4\", \"d5\", \"d6\"]\n",
"df = pd.DataFrame(D_k.T, \n",
" columns=axis_names, index=doc_names) # np.r_ は行列同士の連結\n",
"print(\"D_k=\")\n",
"df.T"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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Uifnz51NfX8+mTZuoqamhpaWF1atXdx8/7bTTeOedd7jhhhuYPXs2X/3qV5k8eTJPPvmk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\n/wNadfQP3ZhGUAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118a29f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"df.plot.scatter(x=\"z1\", y=\"z2\", ax=ax)\n",
"ax.axvline(x=0, lw=2, color='red') #x軸とy軸に線を引く\n",
"ax.axhline(y=0, lw=2, color='red') \n",
"ax.set_xlim(-0.5, 2.0)\n",
"ax.set_ylim(-1.0, 1.5)\n",
"ax.grid(True)\n",
"for k, v in df.iterrows():\n",
" ax.annotate(k, xy=(v[0]+0.05,v[1]+0.05),size=15) #データ点にラベル名を付与"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"文書$d1$と$d2$の特徴空間上での類似度を計算してみよう．"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"特徴空間上でのコサイン類似度 = 0.781837380815\n",
"M_k上での文書ベクトルのコサイン類似度 = 0.781837380815\n",
"なお，元の文書ベクトル上でのコサイン類似度 = 0.408248290464\n"
]
}
],
"source": [
"# d1とd2の特徴空間上での類似度を計算する\n",
"print(\"特徴空間上でのコサイン類似度 =\",sim(D_k[:,0], D_k[:,1]))\n",
"print(\"M_k上での文書ベクトルのコサイン類似度 =\", sim(M_k[:,0], M_k[:,1]))\n",
"print(\"なお，元の文書ベクトル上でのコサイン類似度 =\", sim(M[:,0],M[:,1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"このように，次元削減された文書ベクトル$\\{{\\bf d}_i^{(k)}\\}$間のコサイン類似度が，低ランク近似された単語-文書行列$M_k$における文書ベクトル間のコサイン類似度と一致することが分かる．"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# クエリの特徴空間での表現\n",
"\n",
"いま，$m$次元クエリべトル${\\bf q}$が与えられたとき，特徴空間上でのベクトル表現${\\bf q}^{(k)}$は以下の式で得られる．\n",
"\n",
"${\\bf q}^{(k)} = U_k^T {\\bf q}$"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.62 -0.46]\n",
"sim(q, d) = 1.0\n"
]
}
],
"source": [
"q = np.array([1,0,1,1,0]) #文書d1と同じものをクエリとして用いてみる\n",
"q_k = np.dot(U_k.T, q) #k次元特徴空間へ射影\n",
"print(q_k) # d_j^{k} と一致していることを確認\n",
"print(\"sim(q, d) =\",sim(q_k, D_k[:,0])) #文書d1との特徴空間上での類似度"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 単語ベクトルの次元削減\n",
"\n",
"$M_k = U_k \\Sigma_k V_t^T$\n",
"\n",
"文書ベクトルと同様に，今度は両辺に右から$V_k$をかけると\n",
"\n",
"$M_k V_k = U_k \\Sigma_k$ （$V_k^T V_k=I$より)\n",
"\n",
"$U_k \\Sigma_k =T_k $とおくと，\n",
"\n",
"$T_k = U_k \\Sigma_k$ "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>z1</th>\n",
" <th>z2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>ship</th>\n",
" <td>0.95</td>\n",
" <td>-0.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>boat</th>\n",
" <td>0.28</td>\n",
" <td>-0.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ocean</th>\n",
" <td>1.03</td>\n",
" <td>-0.81</td>\n",
" </tr>\n",
" <tr>\n",
" <th>voyge</th>\n",
" <td>1.52</td>\n",
" <td>0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>trip</th>\n",
" <td>0.57</td>\n",
" <td>1.03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" z1 z2\n",
"ship 0.95 -0.47\n",
"boat 0.28 -0.53\n",
"ocean 1.03 -0.81\n",
"voyge 1.52 0.56\n",
"trip 0.57 1.03"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"T_k = np.dot(U_k, Sigma_k)\n",
"axis_names = [\"z1\", \"z2\"]\n",
"term_names = [\"ship\", \"boat\", \"ocean\", \"voyge\", \"trip\"]\n",
"df = pd.DataFrame(T_k, \n",
" columns=axis_names, index=term_names) # np.r_ は行列同士の連結\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$T$の$i$行目と$j$行目がそれぞれ単語$i，j$の特徴空間上でのベクトル表現になる"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MC+nwqjXzWB2ATfD7nwzMhWHPnj3Iz1+KuLgcdOzYF3FxOcjPX9rqpq+ay+qzsC4DcKjW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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122127710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# 特徴空間上の単語ベクトルをプロット\n",
"fig, ax = plt.subplots()\n",
"df.plot.scatter(x=\"z1\", y=\"z2\", ax=ax)\n",
"ax.axvline(x=0, lw=2, color='red') #x軸とy軸に線を引く\n",
"ax.axhline(y=0, lw=2, color='red') \n",
"ax.set_xlim(-0.5, 2.0)\n",
"ax.set_ylim(-1.0, 1.5)\n",
"ax.grid(True)\n",
"for k, v in df.iterrows():\n",
" ax.annotate(k, xy=(v[0]+0.05,v[1]+0.05),size=15) #データ点にラベル名を付与"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k次元特徴空間での類似度\n",
"sim(ship, boat) = 0.811763741002\n",
"元の空間での類似度\n",
"sim(ship, boat) = 0.0\n"
]
}
],
"source": [
"t_1 = T_k[0,:] #ship\n",
"t_2 = T_k[1,:] #boat\n",
"\n",
"print(\"k次元特徴空間での類似度\")\n",
"print(\"sim(ship, boat) =\", sim(t_1, t_2))\n",
"print(\"元の空間での類似度\")\n",
"print(\"sim(ship, boat) =\", sim(M[0,:], M[1,:]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"このように，元の単語-文書行列における単語ベクトルでは類似度が0であった単語間に対して高い類似度を与えることができていることが分かる．"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
phoebe-project/phoebe2-docs | 2.3/examples/binary_misaligned_spots.ipynb | 1 | 68378 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Binary with Spots\n",
"============================\n",
"\n",
"Setup\n",
"-----------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's first make sure we have the latest version of PHOEBE 2.3 installed (uncomment this line if running in an online notebook session such as colab)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#!pip install -I \"phoebe>=2.3,<2.4\""
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"As always, let's do imports and initialize a logger and a new bundle."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import phoebe\n",
"from phoebe import u # units\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"logger = phoebe.logger()\n",
"\n",
"b = phoebe.default_binary()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"b.set_value(qualifier='pitch', component='primary', value=30)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Model without Spots\n",
"--------------------------"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 73 parameters | contexts: dataset, figure, compute, constraint>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('lc', times=phoebe.linspace(0,1,101))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 101/101 [00:04<00:00, 20.76it/s]\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 3 parameters | qualifiers: comments, fluxes, times>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(irrad_method='none', model='no_spot')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Adding Spots\n",
"---------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's add a spot to the primary component in our binary, which we have already misaligned by 30 degrees in pitch.\n",
"\n",
"The 'colat' parameter defines the colatitude on the star measured from its North (spin) Pole. The 'long' parameter measures the longitude of the spot - with longitude = 0 being defined as pointing towards the other star at t0. See the [spots tutorial](../tutorials/spots.ipynb) for more details.\n",
"\n",
"We'll place this spot at the South Pole, which should be pointing towards the observer because we pitched the north pole away from the observer."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 5 parameters | contexts: compute, feature>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_feature('spot', component='primary', feature='spot01', relteff=0.9, radius=15, colat=180, long=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll also add a mesh dataset so that we can see the positioning of the spot with respect to the misaligned component."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ParameterSet: 83 parameters | contexts: dataset, figure, compute, constraint>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.add_dataset('mesh', compute_times=[0.75], columns=['teffs'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 101/101 [00:05<00:00, 19.17it/s]\n"
]
},
{
"data": {
"text/plain": [
"<ParameterSet: 15 parameters | kinds: mesh, lc>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.run_compute(irrad_method='none', model='with_spot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Location of Spot\n",
"------------------------------"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Thu, 17 Sep 2020 17:55 PARAMETERS WARNING could not find Parameter match for ec=face at time=00.750000, assuming named color\n",
"Thu, 17 Sep 2020 17:55 PARAMETERS WARNING could not find Parameter match for ec=face at time=00.750000, assuming named color\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"afig, mplfig = b.plot(kind='mesh', fc='teffs', fcmap='plasma', ec='face', show=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing Light Curves\n",
"------------------------------"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the pitch means the polar spot is always facing towards the observer slightly, and so is always visible (unless eclipsed)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"afig, mplfig = b.plot(kind='lc', show=True, legend=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/sandbox-3/ocnbgchem.ipynb | 1 | 79378 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocnbgchem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: CCCR-IITM \n",
"**Source ID**: SANDBOX-3 \n",
"**Topic**: Ocnbgchem \n",
"**Sub-Topics**: Tracers. \n",
"**Properties**: 65 (37 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocnbgchem?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:53:48"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-3', 'ocnbgchem')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties](#1.-Key-Properties) \n",
"[2. Key Properties --> Time Stepping Framework --> Passive Tracers Transport](#2.-Key-Properties--->-Time-Stepping-Framework--->-Passive-Tracers-Transport) \n",
"[3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks](#3.-Key-Properties--->-Time-Stepping-Framework--->-Biology-Sources-Sinks) \n",
"[4. Key Properties --> Transport Scheme](#4.-Key-Properties--->-Transport-Scheme) \n",
"[5. Key Properties --> Boundary Forcing](#5.-Key-Properties--->-Boundary-Forcing) \n",
"[6. Key Properties --> Gas Exchange](#6.-Key-Properties--->-Gas-Exchange) \n",
"[7. Key Properties --> Carbon Chemistry](#7.-Key-Properties--->-Carbon-Chemistry) \n",
"[8. Tracers](#8.-Tracers) \n",
"[9. Tracers --> Ecosystem](#9.-Tracers--->-Ecosystem) \n",
"[10. Tracers --> Ecosystem --> Phytoplankton](#10.-Tracers--->-Ecosystem--->-Phytoplankton) \n",
"[11. Tracers --> Ecosystem --> Zooplankton](#11.-Tracers--->-Ecosystem--->-Zooplankton) \n",
"[12. Tracers --> Disolved Organic Matter](#12.-Tracers--->-Disolved-Organic-Matter) \n",
"[13. Tracers --> Particules](#13.-Tracers--->-Particules) \n",
"[14. Tracers --> Dic Alkalinity](#14.-Tracers--->-Dic-Alkalinity) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Ocean Biogeochemistry key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of ocean biogeochemistry model code (PISCES 2.0,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Geochemical\" \n",
"# \"NPZD\" \n",
"# \"PFT\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Elemental Stoichiometry\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe elemental stoichiometry (fixed, variable, mix of the two)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Fixed\" \n",
"# \"Variable\" \n",
"# \"Mix of both\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Elemental Stoichiometry Details\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe which elements have fixed/variable stoichiometry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_details') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all prognostic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Diagnostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all diagnotic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.diagnostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.8. Damping\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any tracer damping used (such as artificial correction or relaxation to climatology,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.damping') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Time Stepping Framework --> Passive Tracers Transport \n",
"*Time stepping method for passive tracers transport in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for passive tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for passive tracers (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks \n",
"*Time stepping framework for biology sources and sinks in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for biology sources and sinks*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for biology sources and sinks (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Transport Scheme \n",
"*Transport scheme in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of transport scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Offline\" \n",
"# \"Online\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Transport scheme used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Use that of ocean model\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Use Different Scheme\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Decribe transport scheme if different than that of ocean model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Key Properties --> Boundary Forcing \n",
"*Properties of biogeochemistry boundary forcing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Atmospheric Deposition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how atmospheric deposition is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Atmospheric Chemistry model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. River Input\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how river input is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Land Surface model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Sediments From Boundary Conditions\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.4. Sediments From Explicit Model\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from explicit sediment model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Key Properties --> Gas Exchange \n",
"*Properties of gas exchange in ocean biogeochemistry *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe CO2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. O2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is O2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. O2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe O2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. DMS Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is DMS gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.6. DMS Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify DMS gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.7. N2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.8. N2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.9. N2O Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2O gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.10. N2O Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2O gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.11. CFC11 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC11 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.12. CFC11 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC11 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.13. CFC12 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC12 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.14. CFC12 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC12 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.15. SF6 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is SF6 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.16. SF6 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify SF6 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.17. 13CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 13CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.18. 13CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 13CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.19. 14CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 14CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.20. 14CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 14CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.21. Other Gases\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify any other gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Key Properties --> Carbon Chemistry \n",
"*Properties of carbon chemistry biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how carbon chemistry is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other protocol\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. PH Scale\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, describe pH scale.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sea water\" \n",
"# \"Free\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Constants If Not OMIP\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, list carbon chemistry constants.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Tracers \n",
"*Ocean biogeochemistry tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of tracers in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Sulfur Cycle Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is sulfur cycle modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Nutrients Present\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List nutrient species present in ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrogen (N)\" \n",
"# \"Phosphorous (P)\" \n",
"# \"Silicium (S)\" \n",
"# \"Iron (Fe)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Nitrous Species If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous species.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrates (NO3)\" \n",
"# \"Amonium (NH4)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Nitrous Processes If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dentrification\" \n",
"# \"N fixation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Tracers --> Ecosystem \n",
"*Ecosystem properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Upper Trophic Levels Definition\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Definition of upper trophic level (e.g. based on size) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Upper Trophic Levels Treatment\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Define how upper trophic level are treated*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Tracers --> Ecosystem --> Phytoplankton \n",
"*Phytoplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of phytoplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"PFT including size based (specify both below)\" \n",
"# \"Size based only (specify below)\" \n",
"# \"PFT only (specify below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Pft\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton functional types (PFT) (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diatoms\" \n",
"# \"Nfixers\" \n",
"# \"Calcifiers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microphytoplankton\" \n",
"# \"Nanophytoplankton\" \n",
"# \"Picophytoplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Tracers --> Ecosystem --> Zooplankton \n",
"*Zooplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of zooplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"Size based (specify below)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Zooplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microzooplankton\" \n",
"# \"Mesozooplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Tracers --> Disolved Organic Matter \n",
"*Disolved organic matter properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Bacteria Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there bacteria representation ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Lability\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe treatment of lability in dissolved organic matter*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Labile\" \n",
"# \"Semi-labile\" \n",
"# \"Refractory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Tracers --> Particules \n",
"*Particulate carbon properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is particulate carbon represented in ocean biogeochemistry?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diagnostic\" \n",
"# \"Diagnostic (Martin profile)\" \n",
"# \"Diagnostic (Balast)\" \n",
"# \"Prognostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Types If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, type(s) of particulate matter taken into account*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"POC\" \n",
"# \"PIC (calcite)\" \n",
"# \"PIC (aragonite\" \n",
"# \"BSi\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Size If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, describe if a particule size spectrum is used to represent distribution of particules in water volume*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"No size spectrum used\" \n",
"# \"Full size spectrum\" \n",
"# \"Discrete size classes (specify which below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Size If Discrete\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If prognostic and discrete size, describe which size classes are used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Sinking Speed If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, method for calculation of sinking speed of particules*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Function of particule size\" \n",
"# \"Function of particule type (balast)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Tracers --> Dic Alkalinity \n",
"*DIC and alkalinity properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Carbon Isotopes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Which carbon isotopes are modelled (C13, C14)?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"C13\" \n",
"# \"C14)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Abiotic Carbon\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is abiotic carbon modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Alkalinity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is alkalinity modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Prognostic\" \n",
"# \"Diagnostic)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
freeman-lab/regional | example.ipynb | 1 | 11124 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from regional import one, many\n",
"from showit import image\n",
"from numpy import zeros, random, asarray, round, where, ones\n",
"from scipy.ndimage.morphology import binary_closing, binary_opening, binary_fill_holes, binary_dilation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### plotting"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dims = [100,200]\n",
"margin = 20\n",
"n = 20"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### create random polygons"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def topoly(c):\n",
" tmp = zeros(dims)\n",
" coords = asarray([c[0] + random.randn(32) * 3, c[1] + random.randn(32) * 3]).astype('int')\n",
" tmp[coords.tolist()] = 1\n",
" tmp = binary_dilation(tmp, ones((3, 3)))\n",
" tmp = binary_closing(tmp, ones((7, 7)))\n",
" return asarray(where(tmp)).T\n",
"\n",
"xcenters = (dims[0] - margin) * random.random_sample(n) + margin/2\n",
"ycenters = (dims[1] - margin) * random.random_sample(n) + margin/2\n",
"centers = zip(xcenters, ycenters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### generate regions"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"regions = many([one(topoly(c)) for c in centers])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### show one"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAE3CAYAAAC3q3ViAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1061a1350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"image(regions[0].mask(dims=dims, background=[0.9,0.9,0.9]), size=10);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### show all"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAk0AAAE3CAYAAAC3q3ViAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1062741d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"v = random.rand(n)\n",
"v = v - v.min()\n",
"v = v / v.max()\n",
"image(regions.mask(dims=dims, value=v, cmap='rainbow', background=[0.9, 0.9, 0.9]), size=10);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
cernbox/entf | ROOT_testing/converted_notebooks/math/kdTreeBinning.C.nbconvert.ipynb | 1 | 155504 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kd Tree Binning\n",
"<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">\n",
"kdTreeBinning tutorial: bin the data in cells of equal content using a kd-tree\n",
"\n",
"Using TKDTree wrapper class as a data binning structure\n",
" Plot the 2D data using the TH2Poly class\n",
"\n",
"\n",
"\n",
"\n",
"**Author:** Bartolomeu Rabacal \n",
"<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-mirror/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer (Beta)</a> from the macro found in the ROOT repository on Thursday, January 19, 2017 at 04:32 PM.</small></i>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?1034h"
]
}
],
"source": [
"%%cpp -d\n",
"#include <math.h>\n",
"\n",
"#include \"TKDTreeBinning.h\"\n",
"#include \"TH2D.h\"\n",
"#include \"TH2Poly.h\"\n",
"#include \"TStyle.h\"\n",
"#include \"TGraph2D.h\"\n",
"#include \"TRandom3.h\"\n",
"#include \"TCanvas.h\"\n",
"#include <iostream>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----------------------------------------------------------------------------------------------\n",
" Create random sample with regular binning plotting"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"const UInt_t DATASZ = 10000;\n",
"const UInt_t DATADIM = 2;\n",
"const UInt_t NBINS = 50;\n",
"\n",
"Double_t smp[DATASZ * DATADIM];\n",
"\n",
"double mu[2] = {0,2};\n",
"double sig[2] = {2,3};\n",
"TRandom3 r;\n",
"r.SetSeed(1);\n",
"for (UInt_t i = 0; i < DATADIM; ++i)\n",
" for (UInt_t j = 0; j < DATASZ; ++j)\n",
" smp[DATASZ * i + j] = r.Gaus(mu[i], sig[i]);\n",
"\n",
"UInt_t h1bins = (UInt_t) sqrt(NBINS);\n",
"\n",
"TH2D* h1 = new TH2D(\"h1BinTest\", \"Regular binning\", h1bins, -5., 5., h1bins, -5., 5.);\n",
"for (UInt_t j = 0; j < DATASZ; ++j)\n",
" h1->Fill(smp[j], smp[DATASZ + j]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------------------------------------------------------------------------------------------\n",
" Create KDTreeBinning object with TH2Poly plotting"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bin with minimum density: 13\n",
"Bin with maximum density: 29\n"
]
}
],
"source": [
"TKDTreeBinning* kdBins = new TKDTreeBinning(DATASZ, DATADIM, smp, NBINS);\n",
"\n",
"UInt_t nbins = kdBins->GetNBins();\n",
"UInt_t dim = kdBins->GetDim();\n",
"\n",
"const Double_t* binsMinEdges = kdBins->GetBinsMinEdges();\n",
"const Double_t* binsMaxEdges = kdBins->GetBinsMaxEdges();\n",
"\n",
"TH2Poly* h2pol = new TH2Poly(\"h2PolyBinTest\", \"KDTree binning\", kdBins->GetDataMin(0), kdBins->GetDataMax(0), kdBins->GetDataMin(1), kdBins->GetDataMax(1));\n",
"\n",
"for (UInt_t i = 0; i < nbins; ++i) {\n",
" UInt_t edgeDim = i * dim;\n",
" h2pol->AddBin(binsMinEdges[edgeDim], binsMinEdges[edgeDim + 1], binsMaxEdges[edgeDim], binsMaxEdges[edgeDim + 1]);\n",
"}\n",
"\n",
"for (UInt_t i = 1; i <= kdBins->GetNBins(); ++i)\n",
" h2pol->SetBinContent(i, kdBins->GetBinDensity(i - 1));\n",
"\n",
"std::cout << \"Bin with minimum density: \" << kdBins->GetBinMinDensity() << std::endl;\n",
"std::cout << \"Bin with maximum density: \" << kdBins->GetBinMaxDensity() << std::endl;\n",
"\n",
"TCanvas* c1 = new TCanvas(\"glc1\", \"TH2Poly from a kdTree\",0,0,600,800);\n",
"c1->Divide(1,3);\n",
"c1->cd(1);\n",
"h1->Draw(\"lego\");\n",
"\n",
"c1->cd(2);\n",
"h2pol->Draw(\"COLZ L\");\n",
"c1->Update();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-------------------------------------------------\n",
" Draw an equivalent plot showing the data points "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"std::vector<Double_t> z = std::vector<Double_t>(DATASZ, 0.);\n",
"for (UInt_t i = 0; i < DATASZ; ++i)\n",
" z[i] = (Double_t) h2pol->GetBinContent(h2pol->FindBin(smp[i], smp[DATASZ + i]));\n",
"\n",
"TGraph2D *g = new TGraph2D(DATASZ, smp, &smp[DATASZ], &z[0]);\n",
"g->SetMarkerStyle(20);\n",
"\n",
"c1->cd(3);\n",
"g->Draw(\"pcol\");\n",
"c1->Update();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------------------------------------------------------\n",
" make a new TH2Poly where bins are ordered by the density"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TH2Poly* h2polrebin = new TH2Poly(\"h2PolyBinTest\", \"KDTree binning\", kdBins->GetDataMin(0), kdBins->GetDataMax(0), kdBins->GetDataMin(1), kdBins->GetDataMax(1));\n",
"h2polrebin->SetFloat();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---------------------------------\n",
" Sort the bins by their density "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bin with minimum density: 0\n",
"Bin with maximum density: 49\n"
]
}
],
"source": [
"kdBins->SortBinsByDensity();\n",
"\n",
"for (UInt_t i = 0; i < kdBins->GetNBins(); ++i) {\n",
" const Double_t* binMinEdges = kdBins->GetBinMinEdges(i);\n",
" const Double_t* binMaxEdges = kdBins->GetBinMaxEdges(i);\n",
" h2polrebin->AddBin(binMinEdges[0], binMinEdges[1], binMaxEdges[0], binMaxEdges[1]);\n",
"}\n",
"\n",
"for (UInt_t i = 1; i <= kdBins->GetNBins(); ++i){\n",
" h2polrebin->SetBinContent(i, kdBins->GetBinDensity(i - 1));}\n",
"\n",
"std::cout << \"Bin with minimum density: \" << kdBins->GetBinMinDensity() << std::endl;\n",
"std::cout << \"Bin with maximum density: \" << kdBins->GetBinMaxDensity() << std::endl;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now make a vector with bin number vs position"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Warning in <TROOT::Append>: Replacing existing TGraph2D: Graph2D (Potential memory leak).\n"
]
}
],
"source": [
"for (UInt_t i = 0; i < DATASZ; ++i)\n",
" z[i] = (Double_t) h2polrebin->FindBin(smp[i], smp[DATASZ + i]);\n",
"\n",
"TGraph2D *g2 = new TGraph2D(DATASZ, smp, &smp[DATASZ], &z[0]);\n",
"g2->SetMarkerStyle(20);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot new th2poly (ordered one) and tgraph2d\n",
" The new TH2Poly has to be same as old one and the TGraph2D should be similar to\n",
" the previous one. It is now made using as z value the bin number"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TCanvas* c4 = new TCanvas(\"glc4\", \"TH2Poly from a kdTree (Ordered)\",50,50,800,800);\n",
"\n",
"c4->Divide(2,2);\n",
"c4->cd(1);\n",
"h2polrebin->Draw(\"COLZ L\"); // draw as scatter plot\n",
"\n",
"c4->cd(2);\n",
"g2->Draw(\"pcol\");\n",
"\n",
"c4->Update();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make also the 1d binned histograms"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"TKDTreeBinning* kdX = new TKDTreeBinning(DATASZ, 1, &smp[0], 20);\n",
"TKDTreeBinning* kdY = new TKDTreeBinning(DATASZ, 1, &smp[DATASZ], 40);\n",
"\n",
"\n",
" kdX->SortOneDimBinEdges();\n",
" kdY->SortOneDimBinEdges();\n",
"\n",
" TH1* hX=new TH1F(\"hX\", \"X projection\", kdX->GetNBins(), kdX->GetOneDimBinEdges());\n",
" for(int i=0; i<kdX->GetNBins(); ++i){\n",
" hX->SetBinContent(i+1, kdX->GetBinDensity(i));\n",
" }\n",
"\n",
" TH1* hY=new TH1F(\"hY\", \"Y Projection\", kdY->GetNBins(), kdY->GetOneDimBinEdges());\n",
" for(int i=0; i<kdY->GetNBins(); ++i){\n",
" hY->SetBinContent(i+1, kdY->GetBinDensity(i));\n",
" }\n",
"\n",
" c4->cd(3);\n",
" hX->Draw();\n",
" c4->cd(4);\n",
" hY->Draw();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw all canvases "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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kj/fSBSsy8C5Wm0F2xpTfG8xvJnozHe3qdze2hwUliiWozFmKmr0C12ciw6/ztT3KVnXz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\nsyZj3StL2Wqa5KW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055Sortn50M0/3ZhM3mPmcEg+2Ux4tG5C9ljguZuq5u3VAOEHACCe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\nYps2PjVmbJ3QP5E5iOdPGIIMxJufYmQwD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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Kisq4/IhIa1aKGKwsRYmbN/3Wako7RFlFU1c1rExN2dlKav8JglB/glOzyadQ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KlbV3a+bmHipisnK2w6eqbBc6Rl/4mdRFHVWC/V398/ODhoHjQ9JFZOMTEx04D5IorcqEzi7xOf\n+MSjjz56rAM2dVvAqfDD+lTHhKPMg0Mj7mc3D/1kR9VxFEjAT6DBf69NuiwyQsN712R7I5OvUXj5\ngLNklvXH3zj08O21YMz15+e+c4t92327t6Gi8pZ2FEFM/gk54udn8kUSkyTajicHd5Sc1F8XLfsv\na1pFldm4efPm8BrOiaJWMTExMdPJ9ddfz8yf/OQnp3Bs/aKtWsFNy/6ePtUxBVIbt+YL4zphkau0\n0sbBUgOB6aUMWuPh6CwMgsdBAVOjagmPI5q5mQMgou1DavuQG9l++aK2b922ZP2Dhzc+MawhzFkm\nl25HmnmdTajZOWkh4vk0iWxq+tTWF8dXLG+b+KRHyzT8umi1hYHTnf4zPxAN8ddNTExMi3Ddddc9\n9NBDJ3DAcCQj/qI7sewern74S/sKJV0oMYiqyjOTArwEn/mbuS6Rd8TIU+0xGEwM7a2v4yaNYoy1\nATfc1L3lgewFvXYcriyalQzv0Ntl//XvzL/9hln/3337HnxmXPlTbRinzi5hAm3HaJCDC7utt144\nRT20e9i599H8xkcLhZLOpcWtK3PrVnX2dh1XAcDJ/nXRaoYmrWX+GRMTEzMjbNmy5frrrw/+PP57\nQGNBTCytpkyQ4zN8/N/2v3bI2ZX3zJa8tXLgmreFyfxpkwQMBIf21U7EjjwSZWSQgFa+oGGE3MY9\ntPL7uhBHQ1xmAG9LRFEF9HbZX/rQwt3Dzu98ZvfhEWd3nuvH0f5wYat03aDtjA1D7QW8bmEimxK3\n39B9hAvajN3Dzu3/sr+v2y6UGKBCiYslffuX9q9/z7zj1FU4kwoNZ1JUnQnXNyYm5pQg+Dr65Cc/\n+eijj37qU5+K7PCJT3wCwJ//+Z8f07CNtVbHPdMziFqOr6RzabHi7GRPZ+KrT47kx00UxzcuJxhX\nglDilcBeWVWdBzqaKJVQNEgDxuYAEH5vPiZvqR3XDKW8Z4m9vjThSfv+CGa0xn52YXq77H/9UO+G\nzfkNg/mRiq77dDDq6uU9I6u6UxERA0kLAFVcnUvL1ee3r1uVm9zzaSLufTTf121vCvksbNxSXLMy\nu2Fz/s53zJnCgBHOkELD08QYPiYmJuYEsnr16qBW4cEHHySiLVu2ACCiBx988OjHaVq9HjM5xgRh\n93D1Y185kB9X+XHNjPyY+s4z4/c+li+UQubdQSzJdyWoPSVE2NjJPAuiQKkYAIQa9gHs2Rz445Dp\nskfkPQCFzyNAtYomIjLmnPAVVbtNkygqgykY3/m3yzatW2DLYBwiIUgIMsae3gJGTyAGp197Xfad\nV3Xs//vl+/9+2f23923/m3MGbpozNUUFYOOjhU31zlUANm4pbny0MLUBG5mktP+0obXSf3FD5ZiY\nmGmj6S/mxx57zOingC1btqxcuTJYEnj33XffcMMNx3SiiNvCRKc+w4nEpZbPTbQnsXHrmPc0eYbi\nwf41B4SQ4PCfMn2Cm3g4cW0H814QQ4OZhPAewztwgvcoYrYpmLWRdcy6zgZd83uv6YwcHMljhrnp\niuzjL5U2PprX9acw00hIaCZX12JsgemU+fM4V/AVxt1CqRa0C1Ms68njbcfK6f2voLVEVSykYmJi\nZpZGt4WVK1euXLlyygM2zQDGqcAI//5U4V9+NNLXZRVKzIz8uP7xtrJ/gTgIEEWOChwQJlmg10jw\nFjTbsyZomr5HExpKmeQgAb5Ws2z+yGqvtimiF2+9pnPttbnermhI6aPXd//nc2Mpm17Y54anevMb\nM/Nz9m39nQPfOPjws+NH393l6Mm1Wbm0mKitzQlUVIbT+F9Ba4mqmJiYmFYjiEs9+OCDd9999wMP\nPHBMhze6LRhOyzvKsbJ7uApg6R+/lB9XSYuqKnRNfP3CfIQVXo3PHs1Cv1rskBmCwsVVTQc/wpsV\nSDEhAFzSa1+5JN3XlYCfxzR60SsAL6vbv3Jg/bvmRnRVb5e9Ye38dffuS0hUXO90N7+h4/Co/uSv\n53q77HvW9AA4Sc5St67MTdTW5oSfy3BaVq/HNVUxMTExR+a6665729ve9sADDxxr7q+RsNvC6V1f\nMjlGbQCmuS9VVV3+Lvx48qs00bO+K0F4i2dDEApHmeqryWRTOLI1yRyCHdauyM3JWB99sxem2ri1\n0NdlbXo8VAC+tbio27r3sSa1Slcsabv/Yws//KbO9gSIkEuL+Tn7OPvGHCXrVnXuPOysWZkNtpgM\n47pVJ8ZmvSmn37+CWFTFxMTEHAEiWrlyJTMbRRX22zMMDAw0tTtuSiR2dTrdUY4Jozbgez4FNE3z\nmSrnyPaaa1SDfoJW4LrtnqIKXXswB2Xf8Ku2Gr0wgrp2gCcQajUySXSkaP1vz+vzo1Abt+bDisrf\nWNy4NR+dMwC/dH33p5d/+6O9x1l7fkz0dtnr3zMvmxbZFAGcTdGJzTBOxGlWvR6n/2JiYmIm47rr\nrvuTP/mTwEyhsRMzEa1atQrHntE7w6vXN2wZLpa9kiiYl8+MiW6u7Nl51pk5cZB3o6iNEwnWioQM\nySjPgoGIAhnnX3ntFW55zgX1wo4B+MlBauoX5dGRpO1/uTy8pVCaegH4SYpITcI09K6ZiNMmFdha\noipe/RcTE9NqbNmyZcuWLXfffbf5M1K0PjAwsGrVqqCr6TGFrAxnZvX6C3tLxbIOV6Aza98Iqpmu\nqgWN/C11rk5NbJyEZQN424XpncOu4+qkLZ7eVfWGMPrJc1337KbAFBoKdafxjNp95dXgFwXgkl57\n9XlRIZJLT2sB+Ili+vUcTpdfF631jsZCKiam1dAFOdNTmAEmqi43hMMYg4ODQcgq6Bk/5ZOeObrq\nq0+Omsr0YItnT0DNWrIYB/PAFcpv8OcdwkyeV2dwDXWgzB54rrRmRZaATEq+sG+44mhvKBOyYk5I\nqrqcsKjqgoSs5SI9eUdmX/ZsFwIPc2Nm5YWs5mdFJiXXXhv1UABw6zWdxbLauLW+AHxFriN1miS8\nTiCnwa+LuKYqJiYmZups3rw5LKqmMEJkhf9pVrc7ERu35sOKykAk/FBQqBbK7wkTmF7C/I/BWhvp\nE+xPocehcxW/+uTI2mtzbzk/LWUtFcfM667tvG5528Zbevb/zXm3v2XW2y5sM0LKnCWgvniLA4Vn\n6oEEsSUw8PZZfQ0uCQDWXpvbMeSuWREqAF+R23a42lSBxeAULzSMRVVMTEzM1DHVVIbjDFOFH5+6\nN5Wjwa80ag5rDvQKe81h6pw8iYSXnyNE9A0zw48i1QZkLpTUZX/62neeGVeaAvsrS4Ch1//2vJsu\nzwFYe23O0UjIukI3hKrgPdsFLx8oAvmrmd56Ycd3nhlv+nJ6uxLr3zU3m5K1AvD6SvaYRk7dXxex\nqIqJiYk5Lo5HSzUSqV4/LTGVRk2fSkgIKcM3VPJ7xYQhIeEt2RO1CBYgiUF1CsxcT0lwtRd8CsZ4\n/zW5bMoKxE1vV+JTvz6rqvwMX6DSAL/Gyvdur3d8wKSr+czId7x9zra/WPatj/Rt+4tld759bqyo\njsgpuiowFlUxMTExU2dgYCAQVXfddVekSt3UrU9BdZHfR+WUu6kcJbde0xnOiBluvrKjp9MKthPR\n+94Y3aeeqOenhkha0fva+fMTGvVNAAE0U0LnLUh3tknTssb3WSAi4Tfkm+y9MKv5Jp0trl02AwXg\npzSnnK5qLVH14pabg//N9FxiYmJijoypo+rv7zfGCuGyKiIycmr16tXHuiQwiFfhSPfyU5SmlUYH\nR9WG980PZ8q6O2RmgpgWJrgyVZff8/qO4M+kRS/sdyY6sFEJGbUXKCqzcd21nZf0Jfo65STvRSuv\n5julObV+XbTWJyBe/RcTczrRGLYJPzhWndE6RJYmDQ4OhtcABhuD//b3909BV+G0dlswlUa+qzhn\nUyKoNLpiSfsdb5/z2EtjJq5DoKar577wg/z/a+/+Qq7Z6sOOrxU1ao7vOWmL/0KtXqW2BEKM/YM5\ndtYYeiExUhJLDEaIsZiL9CIGDD0JZNZ4oS0U0l6U4ntRArXElCilEZKrM2s4BEKthpQQSuhFNAkm\nSkg97wlqtE4vfj7LddbMnmfvmTUza2a+H15e9p5n9szs2XvW/s1v/fvr/zew5Qcv+baXP3jR4y/R\nz37lGw9erB995eLYV13XPfHSOBJ675NPvP+/fuGnfuBxv0dpV/5v//krPvjJv/ibj73g9/7kq/30\nCb35FrWjq2CbTJVzblpKHMCO1HXdv8wlf+Oc28ut5zX6/f5kibx9Gctq5i7m36z/zu/9xcxjSEta\nGimlBlsa+ZqyS73n3v2PnuhXIL73B574qSef8A2YPvtvvvuJ77j4M/cvnvzO9/xAPAfLpXblb3zd\nY//xXa96y+sfe/BirVT3om/71q87vfnWsYuqwA0yVdbauq6rqirLsqqq/d6tArhXFFTNHypzR6SU\nk8fT7rD7N+gTbtY//8Uvf/Q3PvfRT37u2ee+9vjLXvSuH3rNu9/+2le//KUTjmch4y2NfE5Lkk8+\np6WVGkwp/fKPvTLc7OAwUV3Xfe/ffnG4crTHX/rhl4fZsmj5f/vMl/7Xn/71f/rt/xseD23PV5D/\nAKEbZKrquu66zlrbNA3JKuDYrLXhkJgJh8rMnHOuruumabquq6pq2hBWIoqobrpZ//wXv/yL//73\nv/Tc15597utK6Wef+/qjv/r6L/y73//8F788+XjWN9h77sqhCvqJrq77xgu/Tb3xtS++NxK6FO39\nszc8QW++rWTeK3DtTJVzTtLgUrYeuEgFju0vf/n14yv4q1taFEmCqm1bn5o6dgkgZZ3EUtbaJL8B\nE+YK/OhvfO67XvHS//LJP/7Wkk/+8U+87TX/+b9/9uffe88nmKEoyrmUUorWiRJd//TvPaje/vIk\nYRC9+baS7fQDGwRVbdtKN5m2baPqv5FOf7RhB7LyN97/vy/9SeItY4wv8nxUkWqozF1o21YeDL5T\n37z91iTWTa3XP/rJzz37XNzP/6Of/OMHj71gj0HVJfdWIN4be2F38sxXLRhU9ZtK+LJDCgKZ7D1c\njcgJOLx+R7lDkr44/gayaZror5OblgZTptxzs/7sc1979rmvRYM5iUd/9fUvPffXT7zsXJVWRFRY\n2qpBlQgrBZbbO4BtSdzg76D8Ql8ySJOjjY5uDX5UhX5Z58+MtNaftv2widVgaPX4y170+Mte1M9U\nKaUePPbCs0VUOIZoAqINj2TQ2g3VjTHjKXEAxyAd/bTW0qZK4qeRoTKPqv8ekzQtff4svxfrQd71\nQ6/5ibe9Jlr47h9+zbt+6O9M2CmwiWBu62/eQnxrBqHMbND7r6qqqJwFkK3u2Rfc+s+/1jknXX27\nrvOxhQxT13XdaW+rfNNSaWo2Z1BQdd9cge9++2v/5M+/HMZV7/7h13zu819+99tfO/HogVWEgVT3\nfBk2pfI2CKqkPI3KWQBHdWlgzJOTsLJpmrquJ7w2WnJprsBXv/ylH/rZ73nw2AsfPPYCpboHj73g\nsZe+8EM/+z3f9YqMxqkCRD+QUkPf9jxzVGKzaWoGS9Ww9x+N1gEcUljllyq+jOYKDH91Xv3yl/78\ne1//8+99/e/83l/84+/9W0l2B6Qy3kYq26ETLmHuPwBY1fVNSyeMOO9TVv3fISIqZOJSIDX4vd1R\nRKW2mvsPAM7smqal0n3yps3636TBqkBgQ1HVnrprbx6us6/4aRBBFQCs7d6mpTLLza2bjSa0ybxJ\nLw7vUmPzrY9rQQRVALCNkQZVczpHR0Ouk7LCmgYDqf5qR43482pTBQAwxjRNM60N++BoC+q6OW2A\naaLw6MzftLyCKnr/ATg5aZk+uVfg4O/ZSOt1YJqRXntn/qblFVQRSAE4OT80qDzVWk/OWvktRAOE\nnvYHD3MMpqOu7K93qQYw6QFmIa+gCgBOLhxkYfAXy6eyroy0otbrlzYLDDrYOFJLo6E6AOyG1lqi\nrrIsb01fRa3XFzg6HMSlXnuDXxsiqhCZKgDI+9GxygAAIABJREFUVPRzJeGU/C/zBs7ZMlWBCEUZ\nqRXyT845mVP8SPNWkakCgH2QXoFzthD9UpKyOrmRcaSW/m5Ya8uyVEqVZTlhTvFs5ZWpovcfAIzw\n9/Ra66qqbn15v30VbWLOZrCN1JX1egm/LXVdy3aMMQRVSyGQAnLz7X/5pa0PAc8j09dUVTXnpyiK\nrqgKPLaE40gl+ZI454qikAfh/OIHkFdQBQAYIZmqhNEPA4Qe1U3jSK38uftxQ4qiaNt25h1CVrYJ\nqm7tEgwAkJ+ikd+/yS1/o7kCCa32aM44UpuQw3DOHalZ1QYN1cMuwYc5jwCwNCk5w8bF4V9925Sy\nLG+tT+lnMuYdKdYTfhlWm7R45m+3MUaq/9SMyQPytHayV2LSMD4Nq/ZpU7Uvf/jMO1/xI/8z+Wa/\n8Ik38k3IwR8+886u6175o5++9YVf+MQbM7kVPhWfk7DWSspq8hbUilWB1DlOcKlqb/BkJj/D0qpv\n5jb9UUWRwN6tnamSmNSPs+JjVQDAZL7lr1LKWtu27ZytMUDooj7zqc9OeFU0/IG6y0uF66wQmjjn\n6rqev52qqrTWxpiyLGcOFJKVDdpUVVUlo1Oo3jcgHFIhQuoCAC5J0n/KF8jMFbiEP/+zZz/+sc98\n/GOfefToKw8evORHfuz73vHj3//KVz0+8pKRxubXj26Q9uOTGMj/iE9mrZWU6sGq/xYMqvp1rnLu\n6rqW+UGttVFfSiInAMgEcwUm9Od/9uyH69981aufePToq0rpR4+++txzX/2w/c2n7FujuGrOOFJL\nk+FnE4ZBB4uo1MpBlbqr8pPzOHOaBQCAWGKwn3sb6+B6H//YZ1716ic+8Wu/Gyz53R995/f9+q9+\n+mfeXyYcR2o5vtv+xseRtw2q/3xl/5HG+wKADRljfNE62Fx18kA2Yb5K5fp7n7+Pf+wzjx59NVzy\nqT/4hU/9klJK/cufW3YcqVRjGPnBpeSp1jpt1uoY1g6qpA7VD/l1pOZpALAhafkrRWv/R1qqCMqy\nLIriphva/lyBxFW3evToK48efeVTf/CL4cJ/8Pc/pJRSqouWpzXno4+Er+VrcMlm56XfPI0hFXaH\nIRWOjSEV9qhftPYHr5nw6USjLah0KavD/zyHVXt3gdS3vOzBi5/+nZ/rvyTVbDDzP/pBh//UJtts\nmprBnCETKgPAHP2iVRoXz9nm4PhV/KyO6Dc2/w+/3Dz33Ff/1S+9NYyr3vHON3zHY9/ef3mqjOD8\nj/4SPvpLNhhRfcR3v/lj/t/WxwIAB+EjLa11VVW3vjyqAfSP6Wnk6efrjyP1jh///s//6Zf+9Qd/\n07/kHe98w5/+yV++48ffMLjBVFHLzI8et2JCZQA4iMGBbHxv67quU81cS+t1cf04Uq981eNP2bf+\n+q9++mUPXvzco6+87MFLvuOxb3/KvvVVr35i6YNM+9FjHEEVgDHf+cX/c+tLvrDEceAKl341Ja5K\nGPqcufX65HGkXvmqx3/m/eXPvL/89P/47Pf/w9cuepBe8o8e4wiqAODIpCf8NT+rMsj1TRsPx14/\n8C/3veNI3XQGromoZPbGmeMgXP/RI5W82lQBANKSnvBhi5/B1aSSaNou/FyBR2plFbWRCi39NmXG\nEaVUWZbzB0G496NHQhndW0SfN23V88eQCscmQyr83X/ya9NeuMQhYSHS915NrSca7B444bXbupSO\nuvIIlxiwQEZ2ZKDsHcmr+o+fUgBY38xZcsNBFnZXFTjS2Hxk4eTV7uWc8wPiM5nb7uQVVAEAVpZq\nllwfVfgKsmxDq0uBVA7hIHmpXTtaUPWHz7xzk3TXVvvdcNfhSK3r7/pUn/KG3y4cyeCAC/ITnnYG\ntwwHCO1X7Q0eWyZHi/06WlAFABg02LPPWjs+S+6EbmhhRLVtXHVv1d4SZk5g7CNd7BG9/wDgvJxz\nvlObUqrrujAUsNZKQ6uyLOeMtrBmw6DBXnuDh7fEUWmtJSQqy3JyUNW2rTy21vr2VdiFvDJVzP0H\nAPmo61oiEt/J/1ZLVwX6fFi4cKvcmIRT8v+cNuZVVWmti6JglKndyStT5Wf9G4mo5jTlGX/tnL9m\nu+ut9vuFT7xx8l9n7vpsp/re1y76xcaRRL/fvhuaVP/NrJNKnhnyCbBrxpGa063veqkmMLbWdl0n\n/8/fGtaUV1AFAMiEjMettZaky7RMVTTa05yqwP6kxSNVe9N2MV/CCYzT9h7AOvKq/gMArG+wY6A8\nkABFRgedMyNvNODC9XFPqnGkkrs0fTUTGJ8ZQRUAnN2ln39f5Zcqa3LlXIGbjCN169SHgyszgfHJ\nEVQBAAaE3dAGG1RNHjug37p8sKX5msMxSHppZm6JCYyhumxsfSYAxCZfy2kLB2xFWgVJc/WmacI/\nyXJZoaqqybu46cu20FfLty6fuZ1+I6okh4cdyWW4WwBAnqT3X7SkLMsuaG4186fk+qTUEukrGfJ0\n/rsACKoAADfzY69LvDVzwIUNB16XpuXGmHwm1cF+HbBN1YRJFRK6taljqp2qGbMi3GrDM7zyO+3v\nfeUPd9svMzCiqioZbF2laJe9TjTzgQ98IFrypje9STF4ARLaqNpxKb6CX82r5p+z95V3qoKWDUVR\nLL27oij87qI2Fktb+Z1G1v9wt/0yA6LqaZpGGiFJCVBV1frXYyr9SWBWLtZwMEfLdvr8rXPOWrvm\ntJTSsECt25k2as2wQvra70JO72pneP132t+7WvfD3fDLDIyLLv9NKs6S53Gp/sN8hxpRPe2kCrcq\nyzLJBAU3STUrwpX8GVZ3k9uvtuuV32lk/Q932y8zcK/x0RaWNmemZ2A5Rwuq5k+qMI385G9SMZ9w\nVoR7bfvTvuY7jfa7/oe74ZcZuJe1tigKrbUxZpP7SZnp2VrbNE2qcok0FebbcUP1p556Klry5JNP\nqnSTKlyyYVNHZkVY/536pvHr7C6y9JcZmExCmf5oC+vsmjwuMrV+M67lNE0Ttpdc7d1t29RRWo6v\ns6+tzrBY852GO93kw932VAM584lquTzpxoF8HK1dXti2d5OR3DZpQL3mHjNpqL6JlT/czb/MQA4G\nKyXe9ra3cXUgQzuu/htUVZXWuiiKtm03bNe8Golp7p3FPaHwDK8cPqp13+nmzvZlBgZ9+MMfjpaE\nPWYYYgpZOVqmSmxSzX8qnOHVcKqBPvK4yNMxgyoAwIFJnxWfx+XGA5kgqAIA7BJ5XOSGoAoAACCB\nQw3+CQAAsBWCKgAAgAQIqgAAABIgqAIAAEiAoAoAACABgioAAIAECKoAAAASIKgCAABIgKAKAAAg\nAYIqAACABAiqAAAAEiCoAgAASICgCgAAIAGCKgAAgAQIqgAAABIgqAIAAEiAoAoAACABgioAAIAE\nCKoAAAASIKgCAABIgKAKAAAgAYIqAACABAiqAAAAEiCoAgAASICgCgAAIAGCKgAAgAQIqgAAABIg\nqAIAAEiAoAoAACABgioAAIAECKoAAAASIKgCAABIgKAKAAAgAYIqAACABAiqAAAAEiCoAgAASICg\nCgAAIAGCKgAAgAQIqgAAABIgqAIAAEiAoAoAACABgioAAIAECKqOQ2uttb5+eXLGmOQ70lobY9Ju\nE8AmrLWXrmj5k7X20p/6Ble+EoUVFkJQdRxN0yiloqtansqflmaMKYpi5kacc2FhN3+DADIhYVDb\nts656E91XfsVBlVV1TRN0zRVVVVVJS/pb+dKFFZYSocDkau6qip5KrFUURRbHtON5Ji3PgoAi/A3\neOFCKbiaphl8iYRQ/b9u/hNGYYU+MlWHIvdtcs+nlCrL0i8cZK2Vv1prjTHRbaL/a/QnWbm/ftM0\n0RLn3OCa0Xb8ETrnZE2/a+dcdPyX9u7X7G8WQCaMMRIk+evXOde2bVEUc+rOKKyQi62jOiTms1Pj\nN39CDaWs/UuUUlL8qSDdNfIVkk1FTwfX9Herfh3Jrvnd+T3KGwkPeGTv/l1HmwWQlfDivfeX6JpM\n1baFlSxRz68WuLewijY7XlZjLwiqDshf7feGFNFqUWY+KqG6XpY+ql4My6moHBxZM3oaZdTDoOqa\nbUaF2r6qPoGT8BfvpYAp1F+naZroel+usBqJxqLCKnwVhdVpEVQdkI+N7r31icqL7vllQf+v/Ss/\nLGLCx/01Zcv+8KKArygK2elIUHX93i8tAZCJMFUzvmaYEwr100Lhq9YvrKJQjMLqnF44+GXFrklT\nKnnQDWWhQ1He2xgjfWqkfUM/K97vXdi27aWNhy0JokYD0XaubFIQvcpaW5alP1oAe+G7zl157Ueh\nlTRFCpdkXljJ3imsDo+g6mikaGiaxjlX17W19qbRXEYu+MGiJArCwjXbtu0XYb6F5q0lCw05gTPr\nR1EjBguZbQsr2ftN28Ee0fvvUCSQkn401tqiKG4dymWkEBlceKnwUhdadPmSMTqqew/y+r0DOLmR\nQobCCosiqDqUaAwFeeBrAwdF92f3prWim62R8iVaU4ZFVkPlnXOuLMtrMmrX7x3AyUXFxUiiqD8U\nAoUVpiGoOg5/yxUulKfjt0daaz9oyviAMdHWZP3BZqT9NSWFJk8lheYLJgn7Rto03Lp3ACcXjYYl\nDy4VVm3bhmvWdR0O0EBhhRss3BAeKxkZPH1kwCoVjGgl7u3l2w/aoh1dWnOwL4wX9T3264e9/8a3\nSYcaYF+u/A26ZtiFHAqr6IUUVueku/t6h+HAtNZFUYQDAV9Z5X+pdULbttE3aqQlwchO/UDD1+8d\nAPoorLAmgqpT80FVkq0NllMAkBsKKyyENlVIQxoNbH0UAHAPCissh6Dq7FLlpaUd+tNPP51kawCw\nEAorLIfqPwAAgAQYUR3IiwyQcwbc0QH7RUk1iKAKyM4Zoo3zlMjAUVFS9dGmCgAAIAGCKgAAgAQI\nqoBzGZy2rL/wmtnNAGAh/SLIOTdYUmU1ryK9/4C8aL3sVTm4/WgsxBWGRlz6bQJY1CYllda6qiof\nWsn81k3TLDdm/a1vk0wVAOVn1JbHDDYNIENVVdV17Z+WZVlVVVazABFUAafjnNNaa63Dwqhpmrqu\n5c4vmgsWANbXL6mstUVRyFO5CcytoQIZeCAvKyTV/SzaUeZcav0STgc5fhgUPsB+bVhSaa2bplm6\n4s/vi+o/AGMkZuoXRrIkq1w6gNO6VFJVVVWWpU9ZZYWgCjivoijCp3VdS5OFrHrTADi5qKTKs+JP\nMKI6AKXuku1STpVlSd0cANyKTBWAb/X7848zzKsDQOYyylQxFxiwCedcXddN0/gl0gjUOUdo1UdJ\nBeCSjDrgREVVPgcGrOkk3eL2+zb3e+RAQie5EG59mxllqhSBFKCUIhcCYA8oqfryCqoAqHPcXVAc\nA3tHSdVHQ3UAAIAECKoAAAASIKgCAABIgKAKyJcMaiD6wwf/1m/9Vvg0n/GFrbXhAQ8ePIAjMYFr\npmTIoUwIj1MmGZy/zbyCKh3Y+liALEgJ5ZzzZZC/8j/0oQ+FcVV/hejxOtzz+RI2hzIUwHLsnf74\ndv1B73IYA09G41NKWWuljJ1fTOUVVHWBrY8FyEIYoCiltNZh8fTMM89IEeYX+sdSRoRhzTrC3fnj\nlCNZ7RgArM9nqpRSUv74p9batm39Ql+g+WS8D278S1Y4Wj+lYF3XUkbVdT1zs3kFVQDGyfR8Uga9\n+c1vfvOb36zuSiJZoW1bFUQ2vhRb+ThXKxk3QU4diLRtG1b/Rblqa21RFMYYCV98ASW5IudcWZZK\nKZnNfYV7sCRJqUHTx6nyE4SFRWd/7mh/WsPVBhcC6Lv1MpGoyxdJUnaEtYcrCJNVEuQdLE1FKh2I\nFEURNTyIcuf+TyP1gFVVSYm3dHnlD6lfLznTxEyV1EEopcqy9AckC51z/u7Nh59lWYatVv3CgxW1\nQHJ1XYdJ9cgzzzzTXyhFUlgPuPRBhiTP729YpZRc7r4QwI74GjdPwgaZe9Ss1QRTdiQ1kmm3PGXu\nHgmV/AtlZhy5G5bj85Gm1rppGvkx8BPo+AfhS9RpJhICxp3kQtjv29zvkQMJRReC1toHTFFjSsmU\ny/o+bDJBu091dyvoN7JOowV//+kPo7/fW6/3iaVDmDHzZ8qn7Px5DI9GAiz1/Car0QoUVTnT+mG0\npOvet8mRHNtJLoT9vs39HvmZ+eKLUiuVhS6E5PVxM936NidW/4URVVVV6q6RWvTXkFS4jqfa9GXT\njhNpdd37/L+tjwUAbkCptQtZRVQTTO/9J5m6qqokOxVWlE6upOwum3ycAE5usESK7vEG+xyt0xEJ\ny9H6ofzb+kBwFtMzVc45aUrlF44XPb7t6rQ9AsAE0ga0XzqFvWfoUnNgJNexqpHk0CXSNKq/sCgK\nn1VqmqbruqIoqqryC6MHVVX5l4TLkSelPjLyFKmc5EJY5236DLqUSNHyfpHVUVIdy/WlllIf8f+W\nP64jOMmFcOvbnDJOlbsb2TmMzPyIFG3byhhfSilrrdzktW0rTa+UUlVVSQv/tm076vUALKZfWPmF\nVVWF+adoID0VNGmQpg7LHigyINks6goxx5TqP2ttP5RTd+0Puq7zRZUxpus6WeiT6v4pERWA9Y1X\n59GlBsBkiaepGWwydf1CAKGod/Gav9mD48REIx2HbZLCP4WjlfrJvDJplmSMkQYMc4xXFgBn454/\nZ4NbdwqHvmv6pkSr3XsrdaW85v7jhm9ffM8a+tcsRKrO5XF/9qfwafSq+ftVvZAuGuzYGOMnHw2H\nznN3w7j7jYhM7qPati3LUmtd13Xbtv2ihi41wAQybZ88ttbOn5l4Mt/vJOTHGg0rzcL7QJdomtS8\ngipu+HYkHLOKnjXL8aOWOOf8WMO+dPARjJ+3wC+fs1NfuPiQLvyTj5Z8q6MwcgofqLsYJZ85anwJ\n45ufq7sJE/06ElSF4Wx/bg0AoWjuP+HuJvkN79PC2zMbTK6cyqWtSdkl0V60jh9UvV/i3SqvoApA\nJJzVQZZIoeBjKYkGpCyQ4GBCUOUCfr9qaKKuKw/Y3yw2TRPmt/IkN9bGGD+asbrrUiMJubSFPnA8\nUeQkC6X9oi8NfCEjpYFcdIOJpTkG74Kimd2jbLSUmX7elzmm9P4DsCYTTJAsS3zuKpwYauYuoqdS\nMob3bVGpFLLPJwvDcm3bNlWDme8oNSU9bKJy1ucCFz9EYP9MMJ2fr/6LmhBEhdXkm7db+WmIL5HW\nluG8xtMQVAG5M8aEg1JWVRW2AZff/vlZ65BvXS5pGykrw/1G6/uS1De3ijYV5sCyNVjmElEdjLT+\npMXCEgYr+n36SinlQ5ZNLis3NMCKCBtRzERQBeTLl1BSEg3Wo4ULfUXh/NIhjIEu1d/5daKAqf9a\nIAeMRLUoyW37CnQV3AH6QilV06Wbjiqsc7xUwSeHFx78NBlNt87c75nT+uHI7d1gOcXt4AQnuRD2\n+zb3e+QnNFhqjS8cL+jgneRCuPVtkqnCRTfdzw0WUkkPBwCArOUVVEVT32x4JCdEqgm4EiUVgEF5\nBVUUT9tKHkJFgRohGo6BkgrAoLyCKhxJFEJRGwgAODYG/wQAAEiATBXWQ20gAODACKqwEmoDAWTC\nlz/c2iEtqv+ArJk74cDEXjhUnUxUNziocVqD43mGY6aHj5eYMBW4ROuH/t+ldcJp4MfXxPXc8ydL\n7ovG1fSF1RKFw6VtRuMSjzydLK+gSge2PhZge+EcL36aP/nT008//fTTT4cryyzxnixMXmBZa2WS\n1Gi5XygrRMef82zKOBgfMCVcE+Occ1IC+Nk/+7O+hKOo+7LCT7SV8Ei01v7/8E/hjvy0Wn5mLSk2\n589FkVf1Hx2VgZDM+qfuigA/vYMxpiiKtm0HJyL1N39hmSUlyPyk0WDx52cJ9DsNVyZNBRyelEU+\nUy4TkjrnvvGNb3zwgx9Uzw+qhBQUEgCFk4f6csM/vT7qcs7J3MnRdH5RKVTXtcQbsms5VHVhZsCb\n5BVUYU0kvfNnjOm6zufVrbU+iqrr+umnnx6ZQssHN23bLlonGN6bDq5wvKCKwT+BkBRQWuuiKMJI\nSBb68GV8I366ZXmJzCJ/a8EV5qLC1/oAbvBVVVXJ4V2aGfB6s4Kqfq4sqqFQd/WU0QyvgwuxPlLf\nmZNSSW655I7qmlfJxeUvMT+hado0e3Sc6i56Cy9qe+dgVzqBFBAJM+JhK4XBbPolYUERtme46Uj8\nMVxf7NR1LeGUD+wmmx5USR2q5Nn8Uzl9PjKVhVVVyf++8lImsg5fDqAvDKp8/ZoUMVVVRdV/EtOo\noAWoL1bk6fVh2ZXCxl6DLRK4ccJqSL1vyOdTonq3t7zlLT/4gz84/lrnnC/HXNAdZ9pNoJRIE7Lj\nyUqqbhJ/CpqmkSVVVRVF4f9aVZXEVX4Fvy//IHxJuBzrUOojp917zk5yIez3be73yI8tKlLk6TXl\nTLQmRdOVogtBKVUUhcQG4QrFnXB9SQv5P8lCCQl8/NA9P4S4UnifKdvx22+apr8v2X4RGH+b9/pm\nSmkarXWUqeruKkRluU9Z+YUqaEWrgpxW9Bgr0PrhhtV/2+49Zye5EPb7Nvd75McWFSny9JpyJhqz\niqLpSie5EG59m8kaqkt3JGnqVRRFP5Mm9aPjGxlpyHaGDw8AsDJCKCSUbJwqqfv06bJpTTdGUmqp\njhMAAGAJyYKquq7Droz9pFS/ZxAAAMBhJKv+k9q9aCgIGbLCB1t+1Bx5Gg66A8BjRgEA+aOk6ksW\nVPlR4SVm8pWAMnS9jKEga8ooWzIeNPV6QCSfi+IkDVEBTJBP4ZBVSTUrqIreRtd1kqPydXzheNB+\nNT9EGFWBAPaIEdUBDEo8Tc1gnHT9QgDIH4EUgEF5zf3H/R8AANipvIIqAikAALBTyYZUAAAAOLO8\nMlUAAFyPeZSRFYIqAAfX72ssg+eFEz8452S1cM3BhcgN88wgH1T/ATgymevdj0jsR9RTSvkHso5S\nqizLcLBiv/DeeUsBQJGpAnBgxhg/hYNwzoWTu8uYeWVZNk1jjLHWaq0lrgpHJx6ceguZo2YQ68sr\nqGJIBQAJ+YyUX3KpLi+q9VNK+Um0JNJa7iCxBOoEsYm8gioCKQCL8sGTc66u66ZpohXCPNYlIzEW\nhdjh+QQYcRv6aFMF4HSkvZRU+U14eXdZ6iNFjgincAlBFYBz0VrLtKSDEVXbtnT3OzmtH9IeC9MQ\nVAE4EWNMVVXhYApKqaIowiUSVPkW7tZa374Kh9d17yMRhcnyalOF5XDjBSil2rZt27aua3kqLais\ntTJuQtu2VVXJn6qq0loXRRF2A8TZhCUnwRbuRVB1IpQIOKcwJBoMj6Q2MBoj1ForIylQFZin1W4U\npeTkvhTXyCuoYkgFAFu5d6gF5IYbReQmr6CKQAoAAOwUDdUBAAASmBVU9YfIk1YI0Tr9GR4GFwIA\nkC2GWsC9pgdV0TSlKpidlGlKcQ0poaJ/Wx8UcD8d2PpYsBIZaoEBFzBuYpuq/jSlMpRLOBV8OGYx\n05QiMlgwEVRhF2j9CWDQxKCqP02p73gsVXt+OdOUAgCAM0jZ+6+u67quL42VxzSlAICbbDj2Jolz\nTJAsqGrb1odN1lpjzIR6PSInAEBok7E3rwng/CHRygpesiEViqLwNX39FleKaUoBAEdBo3UMShZU\nhakpP/8o05QCAICTSFb9J1V+vlGUVOQxTSkAADiJWUFVFBL5sanCekCmKQUAAGeQfpqafqjENKUA\nAODw8ppQORxSgZpBAHmipAIwKK+giuIJ/Y7T9K9BbiipAAzKK6jCyfXjJ8bfAwDsRfo2VQAAACdE\npgoAsAOMYI78EVQBAHLnAymaBCBnBFUHRKED4MAo4pCtvIIqOiqnQnocwCFRuCFneQVVBFIAAGCn\n6P0HAACQQF6ZKgDIHw0VAAwiqAKA2xBIARhE9R8AAEACZKoAAJiOUUnh5RVU0VIBALAjjEqKUF5B\nFYEUAIAABTuVV1AFAMk554wx4RJrrf/fryOrhWsOLsQ6qErDHs1qqO6cG1wYLnfOWWujNQcXYgKt\nH/b/bX1QQEacc2VZhqWN1lqKKd/eQNZRSpVl6SMtY4xfSGEF4CrdVE3TKKWapomWK6WKogjXqapK\n/peFRVH4heHL5xzMaSn1ka0PYVmHf4M5O8AlKaVNWNRUVeULqKIopFwKV/Dv2j8IX9Id4rTkb48X\n/h6P+RiyuiQnVv8ZY9q2HVwePi3LsmkaY4y1Vmstt4Bt23Z3bafIVwFYjhQvYQ+YsCrQGOPLn6jW\nTynlAzIpvhY/VgD7NzGo6hdVfmFVVWGcRFEFIB9t24YVfP2buqIo7r3TGym4OnrbACeWsqF6WZZd\n14VtP0MUVQA252/q1IVWodegOAIwKNmI6sYYaUE1x0g9ZZKDBIDxWKptW7r7AZgmWaaqbVvpKSO0\n1lEk5LPuNKICsBVrrc+m13Utt4JFUYTLJaLyzUattWF+CwAuSZap8ikl31NG3RVVfh25/6OoArAV\nCZiMMVrroijkqbW2rmtZKB2TlVJVVWmtjTF1XXMrCOAayw7+aa2VIV7ato2KqqIowm6AALCQqJzx\nQ3r6JcaYruuihZK76g8cCgCXxJV0SxgslfoL+zWGuJfWD4897vDh32DOuCQHcVpWsMcLf4/HfAxZ\nXZLJqv9GDN7ncfMHAACOJK+5/8IhFfIJPAEAAO6VV1BFIAUAAHYqr6AKAPJHTh3AIIIqALgNgRSA\nQWs0VAcAADg8gioAAIAECKoAAAASyKtNFc0/AQDATuUVVBFIAQCAnaL6DwAAIAGCKgAAgAQIqgAA\nABLIq00VAOSPLjUYpPVD/7jr3rfhkWArBFUAcBsCKfSFUVQYXeFUqP4DAABIIK9MFUl1AACwU3kF\nVQRSAABgp6j+AwAASGBWpso5Z4wJl1hr/f9+HVktXHNwITDoUpNPOtcAexdd3VzU2LvpmSrnXFmW\nzjn/VGstT/0DWUcpVZalj7SMMX6hfzk87NxPAAATDklEQVQwqOveN/hv6+MCkAYXNY5kYqbKGNO2\nbbjEOVcUhQ+SrLUSUTVNY4yx1mqtJa5q29a3nZLVph48AOA4GIkAezcxqPIZKb/kUl1eVOunlCqK\nQp5KpDXtAAAAR0KyCgeQrPefD56cc3VdN00TrRDmsS4ZibHoGAggEwz+AmBQ4iEVpFpQqvwmvJzi\nCUD+KKkADEo5pILW2hjTdd1gRNW2Ld39AADAUaWs/quqKhxMQSlVFIW1Nuz3p5TyLdyttb59FQAA\nx8Msy6eSLKhq27Zt27qu5am0oLLWyrgJbdtWVSV/qqpKa10URdgNEACAQ5JYir6NZzArqApDosHw\nSGoDozFCJXfVHzgUANYh6fOoQcKVYxcDwCVrTFNz71ALALAaPzpxOCixLJRBjGXJ4NjFADBC51MB\nF42nkM+B5Uzrh+espD/tG1+T1hmVD6lIqCTvyz+W3LlEWpKXklH0fEfm8FQc8rRs5agXcvi+/OOj\nvtnNZXVJ5jWhchfY+lgAHJAESRI/+b4yYWsEY4wfUa8/djFwJa0fyr+tDwSrSjxOFQBkrqoqqddT\ndxnxtm3DTsr9+El63vgYi2GKMY6M1GnllakCgEX5KR+6rquqSuKkcGyXazJS3WWLHTiAHSCoAnAi\nMvW7xFLWWj9s3ngsJWMXL35wAHaOoArAufQDKWmoLo/rupaqQBm72L+KoArAvQiqAJyINE6XObXK\nspSp3yVgMsbIuMQ+j1XXtSz0YxcDwIi8Gqoz9zuApUlSKhp/2I/z6ZcMjl0MACPyCqoIpACsox8q\nMUwxgJmo/gMAAEiAoAoAACABgioAAIAE8mpTBQD5o0sNgEEEVQBwGwIpAIPyCqq4/wMAADuVV1BF\nIAUAAHYqr6AKuJ7WD0f+yizxAICVzQqq+mMNy1RZ4YRZfpziwcGLGVjvXuOhw2mNx0ycNADA+qYP\nqeCcK8synNpday3Rkm8aJesopcqy9JGWTLklC8dnhofouveN/Nv66AAAgFKTM1XGGD/Tu5BpSiVI\nMsZYa621Ml+pPNVaS1zVtq1vOxVODg8AALBfE4MqiYTCznphVaAxxodKUa2fUqooCnkqkda0AwAA\nAMhKshHV27YNg6r+CpLHGs9L6ctSHScAAMASkvX+8/kndZeRmoAhFQDkjxH15qAfCQ4s5dx/47GU\npLLo7gdg77rA1seyS3S1wVElC6rCJud1XUub9KIowuEVJKjyLdylbXuqAwAAANhQsuo/SUFJzFQU\nhTyVDoDOubZtq6qSNauq0loXRRF2AwQAANg1nTas6Q8Hev1CrRMfzDFo/ZAM+a04aUlwSQ7itMx0\n2svztG98aVldkinbVKkL/f6uXwgAALBTec39R58aAACwU3kFVQRSAABgp/IKqgAAB8PAVDgPgioA\nwLJooC2i+JLTcjwEVQBwG1p/YoIohCKBd0gEVQBwGwIpAIMSD6kAAABwTnllqkiqAwCAncorqCKQ\nAgAAO5VXUAUAwEnQGfB4CKoAnI5zTqYfDefLstb6/0dWA5KgM+Ah0VAdwLlYa8uyVEqVZelDKK21\nhFC+Zadzrr8aAIzIaG7nrCaazgcTm0/ASUviqJekf1/OOWut/985p5SSvJS1VmvdNI3kqMJTcdTT\nshyux2twlibL6pIkUwXgRJxzRVHIA2OMBFLyWFbwC+Vx+MI1jxPAHuUVVOnA1scC4ICcc23baq0l\nFyX1em3bhkFV/1VFUYRBlb5s+XcAIF95NVTPJ4MH4MB89Z+0l5LclbgmI0VJBWBQXpkqAFiUMcaH\nUNfX7oWpLAC4JHFQZa2VJp/9heES3zI07d4BYJwxpm1beeyLoLA4qutayquiKMKCi6AKwL1SBlXS\nJ1nRURlAxqqq0lobY8qybJpG3QVMxhitdVEU8tRaW9e1LKyqatNDxilo/TD6t/UR4WbJOiJKqBS2\nVOi6jo7K89HPdgJOWhLHviTDHn+XlgwuPPZpWQLX4zSctytldUkmy1RJuSPxk2/4SUdlAHnqx0+D\nFXzU+gG4Xsrqv6qqyrLUWrdtK6ESHZUBAMBJJAuqnHN1XTdN03VdVVUSQk3oqHxJquMEAABYQsqg\nKmzg2e9fM4iOygAA4BhSVv/RURnAGdAyAcCgZEGVNE6nozKAw6NlAoBBKaep6U9NKk+jJcaYrusG\ney8DAADsVPppauioDAAATiivCZXDBgrk1QEAwI7kFVQRSAHArjG5Cs4sr6AKALB3TK6C00rfpgoA\nAOCECKoAAAASIKgCAABIgDZVAHAb+ikDGJRXUEVRBSB/lE4ABuUVVFFUAQCAnaJNFQAAQAIEVQAA\nAAnkVf0HpHLTsM6MVQgAmI+gagNM47C0m4IkPg4AQBIEVdsgNQIAwMHkFVQxpAIAANipvIIqAikA\nALBTeQVVAJA/cuoABhFUAcBtCKQADEo8TpVzzlrrnAsXWmuttfeuBgAAsF8pgyprbVmWSqmyLH0U\npbV2zjnnfMLcOddfDQAAYNd0wjy21t/cmk9Eyf+SkTLGGGOstVrrpmmMMeFLosfHpvVDhlTIBx/H\nJee5JG/CaRnHBZUKZ/JKWV2SyTJVzrmiKOSBMUYCKXksK/iF8jh8YapjAAAA2ErKoKptW6215KKk\nXq9t2zCo6r+qKIowqNKXpTpOABBR8wNafwKYKXFD9a7rnHNN09R1rZSS3JW4pmDqLkt7nABOzlor\nxZSg9SeA+ZINqTBSuzeYoxJt21JaAViZcy6MqKy1PmsuTT+l2420/gyz7/CYNHMFl04yba2ylTKo\nattWHvvoKkyn13XdNI1SqiiKcPlIyAUAS5CASRJR6urWnxRWEX7aF3Xp9BLO5ixl9V9VVVprY4wU\nWOquSDLGaK2LopCnknWXhVVVJTwAALiXMcZ3QBa0/gSQRMoR1SX/FN3PSTOFcIkxRppecdsHYGWS\nI48KnwmtP9MeFYBjSNxQXQ3d5w0GT0RUANbn+ylLVknap6v7YqkwlQUAl6QPquYgiw5gUc65sE9x\n13XSFN0HVXVdSzZLWn/6FxJUAbhXXkEVYygAWB+tPwEkkbJNFQDsSHjzRutPAPPllakCgK3Q+hPA\nTARVAAAACRBUAQAAJECbKgC4Tdg9mV41ALy8giqKKgD5o3QCMCivoIqiCgAA7BRtqgAAABIgqAIA\nAEiAoAoAACABgioAAIAECKoAAAASyKv3H0MqAACAncorqCKQAgAAO5VXUAUA+SOnDmDQIm2qrLXR\n02iJc85a65xbYu8AsKgusPWxAMhI+qDKWlvXtX+qtXbOOef8vZ1zrixLpVRZllGwBQAAsFM67Z2W\nD5hks5KOkoyUMcYYY63VWjdNY4xRSmn9rQMIHx+b1g+77n1bHwW+iY/jkvNckjfhtAgunK1w5iNZ\nXZKJM1VlWTZN45865yR4UkoZY3x9n18o66Q9BgAAgPWlDKqMMT4FJdq2DYOq/kuKogiDKn1ZwuME\nAABILlnvP2kdFUVORVH4x9dkpPLJ4AEAANwkWabKOde2rc8qSft0dV8sFaayAAAA9muR5l2+1Vg4\ndIJvn+5brCsaqiMDfByXnOeSvAmnRXDhbEXrh9esdp5PJ6tLctnBPyULZYxp27YoCnlqrS3LUjJb\nVVUtegAAABzJNdHSlYEXklskqApjRhlSIazjM8Z0XRctBIC9YER1AIPWmKZmMHgiogKwU8cOpEhy\nAJPlNfcf938AsLnzNMcB0sorqCKQAgAAO7XIhMoAAABnQ1AFAACQQF7VfwCwAj8DRNhjRhbK/8J3\nXqZjDYBrkKkCcC5+voeyLH20JAudc767jHOuLEtZLYy0AOCSjMYhzWpQ1EUxEnFW+DguOeQlKaFS\nNJGDTPwgkZaf8sFPAqFONvcDV8QBnOpDzOqSzCtTpQNbHwuAAzLGNE0TLQzHIjbG+BlLw1q/a6aE\nB3ByeQVVXWDrYwFwTGGVn8yUFc7sPth8qiiKMKjSly176ADylldQBQArkNq9qqqksVRRFP5P12Sk\nussWO2QAO0DvPwDnIrmoKAAan420bVvaqgO4F5kqACfinGvbNkpHSUN1eVzXtU9fhYEUoyoAuBdB\nFYATkeApagUlAZMxRmtdFIU8tdbWdS0LpekVAIzLqCNiVr0iF3Wqzq754+O45DyXpBisAewvPPxp\n4Yo4gFN9iFldknm1qQr7zuRzjgCcwWAFH7V+AK6XV1BFIAUAAHaKNlUAAAAJJM5UMU0pAAA4p5SZ\nKqYpBXAGDKEOYFCyNvNMU3q9U/XLyB8fxyXnuSRvcvjTwhVxAKf6ELO6JJNlqpimFAAAnFnK6j+m\nKQUAAKeVuPcf05QCAIBzStn7j2lKAQDAaSXLVDFNKQAAOLOUQZVimlIAAHBWa3REZJrSyKk6u+aP\nj+OS81ySNzn8aeGKOIBTfYhZXZJrTFPDNKUAAODw8ppQORw6IZ/AEwBClFQABuUVVFE8AcgfJRWA\nQWtU/wEAABxeXpkqrR9ufQgAAABT5BVUnae3AgAAOBiq/wAAABLIK1MFbIJ65ws+svUBAMCe5BVU\n0VEZ66PS+RKttVKcHAC4Vl5BFYEUAADYKdpUAQAAJJBXpgoA8kdDBQCDjhZUbTWx4oYTOvKW2S9W\n960m/DN6Ofx00q4ACbf20wnb0iX89ua5qbRbO8OBJX+PqTaVREaFdZITfcKfPd4y+z3YfjOX6rSc\n4ecz7dby3FTaraX7dkmsnzbUznBT39xaPv2NMio0Car2tWveMvs9J4KqrbaW56bSbu0MB7ZEpiqf\nkiqv6j9aKgAAgJ3Kq/df13USS41EVHMqUMdfO+ev2e56j/vdcNfH2++iu4a6cAL7C688z1e+8Jqt\nrX9gvMdbX5jne8z2wHZRWOUVVAFAPpxz1lrn3NYHAmAfCKoAYIAxxlqrlCrLkrgKwDXyalMFAJlo\n29a3QyBfBeAaBFUAEHPOFUUhj621u2jMAWBzeQVVvuRars3vVn/N9sDy/Gu2B7bHv85/+QmN56Wi\nMza5xe6cpr6Lbp8XHuOF2R5Y2hdmJaOgijEUAOSPkgrAJTRUB4CYMWbrQwCwPy+Q7i1H4pz7lV/5\nFaXU6173uvX3bq1dvzj2rWjXecsbnuGV32l/7yt/uNt+mc/sda973Xve8x4pHuX/n/zJn0y+l7Tf\n5+TfllRf+PlvM+FbW6IMSXKi0n58Sd6mcy56uWx2wptNuKnBrcnCP/qjP9q8qDxapspaW5alUqos\ny/XjRWttXdcr71RrLRdPWZYr/ORv2M985XcaWf/D3fbLjKqqtNbGmLqul/iqh9/n+Z9v8m9Lqi/8\n/Ms2YZmzRBmS5ESl/fiSfLWcc9EJl806525t2BRtSrYgT/2DOQcmciknu2Px76hpmqIo1tx10zTr\nn1LZqX+6wt79LqqqWvMMr/9O+3tfeacbfpnhNU2z0GbDz3f+VyvttyXVFz7JZZuqzFmiDEl1ohJ+\nfEm+Wr7rq//+hye/KIqqqpJs6tbPtL+1cHkO5eShgir/dVyoHBwnH/P6cWr4Zpfee3TBrx/ZbLXr\n9T/cbb/MWIH/YSiKYv7vaNpvS8Iv/MzLNm2Zk7wMSXKiFvr4utlfrTB2CQOpCdFtuKmmaQYDrGlb\n6+5O4Mr3+ZccKqiqqkpiWAlarw+l5yuKQj7jDZN/K7zl6Fu71Zs9w4e74ZcZ6/Af8fzvVdpvy0Jf\n+GkHtlCZk+SaSnWikl/sqb5aYewSRUVzgipP4tEJoWT0KnmbmQRVGQ2pcKunnnoqWvLkk0+quw7P\nUu26RA3rBz7wgWjJm970JrVKd6H+2zHGSIODuq6rqsqiRnlJ679T2dFWfcGW/jJjHYOFVV3XTdPI\n9WuMub5lScKiL21ptosCKtXBJC8ZUl3szrnJX60Rvt5N3TeK25WMMW3bynHO3I6vhM3CtjFdWltV\nToXfNrFmlc38uoPrbVv9t+Y7DXe6yYe77anG0tImYBJ+W5J/4WdetmkvhIRlSMITlfY9JvxqqWWq\n/7rZmUL1/BRaVlHN0Upqf0I3ad7Urf7jt/7b9LvbtqH6JrY61Tm8d6QlFTTyOHlD9VTflvnbSfvW\n0jZUTyirjy/hV0tdqPKbEEFeis/mH5hH9d8ipBd0URSSV9z6cBbne6X6Jd3Cwz2HZ3jpfYXWf6eb\nO9uX+VRkkJ6En2+e35Ykl22qMifnMiThx5f8qyWkkk7q7IqimFNn17Zt27Z+HIqiKI40W7nO51uV\n0OQhxXAlzvBqONXHlvbzPfC35cBvzcv/y3CGT2GmYwZVAAAAKzvaiOoAAACbIKgCAABIgKAKAAAg\nAYIqAACABAiqAAAAEiCoAgAASICgCgAAIAGCKgAAgAQIqgAAABIgqAIAAEiAoAoAACABgioAAIAE\nCKoAAAASIKgCAABIgKAKAAAgAYIqAACABAiqAAAAEiCoAgAASICgCgAAIAGCKgAAgAQIqgAAABIg\nqAIAAEiAoAoAACABgioAAIAECKoAAAASIKgCAABIgKAKAAAgAYIqAACABAiqAAAAEiCoAgAASICg\nCgAAIIH/Dwb+8uweWEalAAAAAElFTkSuQmCC\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gROOT->GetListOfCanvases()->Draw()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "ROOT C++",
"language": "c++",
"name": "root"
},
"language_info": {
"codemirror_mode": "text/x-c++src",
"file_extension": ".C",
"mimetype": " text/x-c++src",
"name": "c++"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
Computing4Physics/C4P | Resources/Schedule/DayByDay/Day10.ipynb | 1 | 3492 | {
"metadata": {
"name": "",
"signature": "sha256:27a9a8ab93ccb4f73395e7a13e76978b7e4c3449f35e1999b6601c995b3f0487"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"<img src=\"../../../C4PLogo.png\" width=300 style=\"display: inline;\"> Day 10 - Thursday, May 1, 2014"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Summary:** Project Overviews"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1.** Use overhead projector and go through each project description with the whole class, explaining some of the background, why the project is interesting and what programming skills it will entail."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**2.** After presenting projects, they will be assigned. Each class has a fixed number of each project. Pass out randomly to each student color-coded cards with the project names on them. Allow them to exchange cards with each other until they are happy with their assigned project. (Note: there was very little exchanging. Most people were happy with what they got.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**3.** Have each group of students working on a project get together and talk about how they can collaborate and support each other. Encourage the groups to discuss strategies and general approaches but remind them of the rules for academic honesty as written in the syllabus."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**4.** I initiated a verbal discussion of Program Design strategies with the remaining class period, but next time I will probably prepare a document with guidance to distribute on PolyLearn for their reference. One could present an entire course on program design, so I stuck to some general advice:\n",
" * **Design** - Identify the problem that you need to solve and/or questions that you need to answer, then create a list of steps for how to get to the solution/answer.\n",
" * **Modularization** - break a complex problem into easy-to-implement building blocks that can be independently tested.\n",
" * **Scaffolding** - like an outline of a software program in which skeletons of the main functions for the program are developed that take in the necessary inputs and just pass default information back until you are ready to implement the \"guts\"\n",
" * **Documentation** - comment your code as you go, include docstrings in all major functions\n",
" * **Testing** - include test cases for each function that demonstrate they work as expected.\n",
" * **Debugging** - where the bulk of the work takes place.\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../../../C4PLogo.png\" width=200 style=\"display: inline;\"> All content is under a modified MIT License, and can be freely used and adapted. See the full license text [here](../../../LICENSE)."
]
}
],
"metadata": {}
}
]
} | mit |
russellclarke82/CV | Pi/Booleans.ipynb | 1 | 3031 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"True"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"False # Both Must be capitalised"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"bool"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(False) "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 > 2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 == 1"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'b' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-ce7fdd8e0e5c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m \u001b[0;31m# Is not defined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
]
}
],
"source": [
"b # Is not defined"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"b = None # Assign an empty variable to malloc."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
RainFool/Udacity_Anwser_RainFool | Project_Anron/anron_data_visualization.ipynb | 1 | 785 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# 安然数据可视化"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. 导入数据"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
jonwright/ImageD11 | docs/sintheta_squared_geometry.ipynb | 1 | 15946 | {
"cells": [
{
"cell_type": "markdown",
"id": "d29237e2",
"metadata": {},
"source": [
"# 2D Detector Geometry\n",
"\n",
"Various issues come up when calibrating 2D detector geometry and doing strain refinements:\n",
"\n",
"- Numerical instabilities can arise for small angles in certain formulations\n",
"\n",
"- Exact answers for angles like 30,45,60,90,120 etc are hard to reproduce\n",
"\n",
"- ...leading to some philosophical misgivings about trigonometric functions\n",
"\n",
"- People want to get strains that are independent of orientation (despite JW resisting this idea for years)\n",
"\n",
"- There is a correlation between wavelength and distance (not addressed here)\n",
"\n",
"- Youtube videos from Norman J. Wildberger suggested looking a quadrance (length^2) and spread (sin^2 angle). He has a book \"DIVINE PROPORTIONS : Rational Trigonometry to Universal Geometry\" Wild Egg Books, Sydney 2005. ISBN: 0-9757492-0-X. It might be useful to get a copy and try to read it."
]
},
{
"cell_type": "markdown",
"id": "8b1b83cb",
"metadata": {},
"source": [
"We set up the geometry to be ready to work on data where the detector was moved. This happens to follow the fable convention and it is a bit more common than moving the X-ray beam (but that obviously happens too). Anyway, the detector itself doest not define the geometry here. We place the x-axis along the X-ray beam. The detector is wherever it is, and some function gives the x,y,z co-ordinates of each pixel. A photon is detected at pixel co-ordinates $(x,y,z)$ in 3D cartesian space, usually in the sensitive surface of the detector.\n",
"\n",
"The origin of the coordinate system is usually in the centre of the sample. If the sample is not at the origin, then subtract the sample coordinates from the detector coordinates to get a vector along the direction of the output ray below.\n",
"\n",
"The goal here is to get a formula for the metric tensor in terms of the pixel co-ordinates. Elsewhere we have formulae for the various finite strain tensors in terms of the metric tensor.\n",
"\n",
"Consider the cross product of vectors along the incident and scattered rays. This is:\n",
"\n",
"$ \\mathbf{s_0} \\times \\mathbf{s_1} = (1,0,0) \\times (x,y,z) = |s_0| |s_1| \\sin2\\theta \\mathbf{n} $\n",
"\n",
"$ \\mathbf{s_0} \\times \\mathbf{s_1} = (0,z,-y) = (1).(\\sqrt{x^2+y^2+z^2}) \\sin2\\theta \\mathbf{n} $\n",
"\n",
"...where $\\mathbf{n}$ is a unit vector normal to the incident and scattered rays. We dot this vector with itself to arrive at a scalar value:\n",
"\n",
"$ (\\mathbf{s_0} \\times \\mathbf{s_1}).(\\mathbf{s_0} \\times \\mathbf{s_1})\n",
"= (y^2 + z^2) = (x^2 + y^2 + z^2) \\sin^22\\theta $\n",
"\n",
"This gives us a relationship between $\\sin^22\\theta$ and the detector coordinates. From Bragg's law, $\\lambda = 2d\\sin\\theta$, we can find the metric tensor in terms of $\\sin^2\\theta$, (note the $\\theta$ versus $2\\theta$):\n",
"\n",
"$ \\mathbf{ h g h^T } = \\frac{1}{d^2} = \\frac{ 4 \\sin^2\\theta } {\\lambda^2} $\n",
"\n",
"... where $\\mathbf{g}$ is the reciprocal metric tensor. "
]
},
{
"cell_type": "markdown",
"id": "841bf876",
"metadata": {},
"source": [
"We convert the $2\\theta$ into $\\theta$ via trig identities:\n",
"\n",
"$ \\sin^22\\theta = (2\\sin\\theta\\cos\\theta)^2 = 4 \\sin^2\\theta \\cos^2\\theta = 4 \\sin^2\\theta (1 - \\sin^2\\theta)$\n",
"\n",
"This gives us a quadratic in $\\sin^2\\theta$:\n",
"\n",
"$ (\\sin^2\\theta)^2 - (\\sin^2\\theta) + \\frac { (y^2 + z^2) } {4 (x^2 + y^2 + z^2) } = 0 $\n",
"\n",
"This can be solved using a quadratic formula for $ax^2+bx+c$ that avoids roundoff for small scattering angles where $c$ approaches zero (https://web.physics.utah.edu/~detar/lessons/python/quadratic/node3.html):\n",
"\n",
"$ x = \\frac{2.c}{(-b \\mp \\sqrt( b^2 - 4ac )} $\n",
"\n",
"We set, $ R = z^2 + y^2 $ and $ Q = x^2 + y^2 + z^2 $ to get:\n",
"\n",
"$ Q (\\sin^2\\theta)^2 - Q (\\sin^2\\theta) + R/4 = 0 $\n",
"\n",
"$ \\sin^2\\theta = \\frac{R}{2 ( Q \\mp \\sqrt{ Q^2 - Q R } ) } $\n",
"\n",
"Note that $ Q = R + x^2 $:\n",
"\n",
"$ \\sin^2\\theta = \\frac{R}{2 ( Q \\mp \\sqrt{ Q (R + x^2) - Q R })} = \\frac{R}{2 ( Q \\mp x\\sqrt{ Q })} $\n",
"\n",
"$ \\mathbf{ h g h^T } = \\frac{1}{d^2} = \\frac{ 4 \\sin^2\\theta } {\\lambda^2} = \\frac{2 R}{ \\lambda^2 ( Q \\mp x\\sqrt{ Q } ) } $"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "36a38723",
"metadata": {},
"outputs": [],
"source": [
"from numpy import arctan2, sin, cos, sqrt, allclose, where, isfinite\n",
"\n",
"def sinth2_atan( x, y, z ):\n",
" \"\"\" Compute sin(theta)^2\n",
" Conventional approach in ImageD11 now\n",
" \"\"\"\n",
" twotheta = arctan2( sqrt(z*z + y*y), x )\n",
" sinth = sin( twotheta/2 )\n",
" return sinth**2\n",
"\n",
"def sinth2_sqrt( x, y, z ):\n",
" \"\"\" Compute sin(theta)^2 \n",
" x,y,z = co-ordinates of the pixel in cartesian space\n",
" R = hypotenuse normal to incident beam (along x)\n",
" Q = hypotenuse along the scattered beam\n",
" \"\"\"\n",
" R = y*y+z*z\n",
" Q = x*x + R\n",
" sinsqth = 0.5*R/( Q + x*sqrt(Q) ) # postive root only, not 0.5*R/( Q - x*sqrt(Q) )\n",
" return sinsqth"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "918ba0ce",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-1.1102230246251565e-16 0 1000 1000 0.4999999999999999 0.5\n",
"-5.551115123125783e-17 1 1.7320508075688772 0 0.24999999999999994 0.25\n",
"5.551115123125783e-17 1e-09 1000 1000 0.4999999999996465 0.49999999999964645\n",
"0.0 1000000000.0 0 1 2.5e-19 2.5e-19\n",
"0.0 1e+20 1 0 2.5e-41 2.5e-41\n",
"0.0 100 0 0 0.0 0.0\n",
"-2.7755575615628914e-17 1000 1000 1000 0.2113248654051871 0.21132486540518713\n",
"1.1102230246251565e-16 -123 456 789 0.5668799937442661 0.566879993744266\n"
]
}
],
"source": [
"tests = [(0,1000,1000), # two theta == 90 degrees, sin(45)=1/sqrt(2), ans=0.5\n",
" (1, sqrt(3), 0), # two theta == 60 degrees, sin(30)=1/2, ans=0.25\n",
" (1e-9,1000,1000), \n",
" (1e9,0,1),\n",
" (1e20,1,0),\n",
" (100,0,0),\n",
" (1000,1000,1000),\n",
" (-123,456,789)]\n",
"\n",
"for x,y,z in tests:\n",
" v1 = sinth2_atan(x,y,z) \n",
" v2 = sinth2_sqrt(x,y,z)\n",
" e = v1-v2\n",
" print(e,x,y,z,v1,v2)\n",
" assert allclose( v1, v2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "099f44e9",
"metadata": {},
"outputs": [],
"source": [
"# diversion : derivatives of the atan expression:\n",
"\"\"\"\n",
"[jon@pine docs]$ maxima \n",
";;; Loading #P\"/usr/lib/ecl-21.2.1/sb-bsd-sockets.fas\"\n",
";;; Loading #P\"/usr/lib/ecl-21.2.1/sockets.fas\"\n",
"Maxima 5.44.0 http://maxima.sourceforge.net\n",
"using Lisp ECL 21.2.1\n",
"Distributed under the GNU Public License. See the file COPYING.\n",
"Dedicated to the memory of William Schelter.\n",
"The function bug_report() provides bug reporting information.\n",
"(%i1) display2d : false;\n",
"(%o1) false\n",
"(%i2) s : sin( atan2( sqrt( y*y+z*z), x ) / 2 ); \n",
"(%o2) sin(atan2(sqrt(z^2+y^2),x)/2)\n",
"(%i3) diff( s,x,1); \n",
"(%o3) -(sqrt(z^2+y^2)*cos(atan2(sqrt(z^2+y^2),x)/2))/(2*(z^2+y^2+x^2))\n",
"(%i4) diff(s,y,1);\n",
"(%o4) (x*y*cos(atan2(sqrt(z^2+y^2),x)/2))/(2*sqrt(z^2+y^2)*(z^2+y^2+x^2))\n",
"(%i5) diff(s,z,1); \n",
"(%o5) (x*z*cos(atan2(sqrt(z^2+y^2),x)/2))/(2*sqrt(z^2+y^2)*(z^2+y^2+x^2))\n",
"\"\"\"\n",
"\n",
"# import numba # no great effect here\n",
"# @numba.njit \n",
"def sinth2_atan_deriv( x, y, z ):\n",
" \"\"\" Compute sin(theta)**2 and derivatives w.r.t x,y,z \"\"\"\n",
" R2 = z*z+y*y\n",
" r = sqrt( R2 )\n",
" D2 = R2 + x*x # x*x+y*y+z*z\n",
" theta = arctan2( r, x ) / 2\n",
" sinth = sin( theta )\n",
" costh = cos( theta )\n",
" div_D2 = where(isfinite(1/D2),1/D2,0)\n",
" p = sinth*costh*div_D2\n",
" dsinth2_dx = - r*p\n",
" # if r == 0 this blows up. For r==0 then y==0 as well. Should somehow determine y==0...\n",
" div_r = where(isfinite(1/r), 1/r, 0)\n",
" dsinth2_dy = x*y*p*div_r # at r == 0?\n",
" dsinth2_dz = x*z*p*div_r\n",
" return sinth*sinth, dsinth2_dx, dsinth2_dy, dsinth2_dz"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f8ceff20",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"# maxima:\n",
"(%i3) r:z*z+y*y\n",
"(%o3) z^2+y^2\n",
"(%i4) q:r+x*x\n",
"(%o4) z^2+y^2+x^2\n",
"(%i5) s:r/(2*(q+x*sqrt(q)))\n",
"(%o5) (z^2+y^2)/(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2))\n",
"(%i6) diff(s,x,1)\n",
"(%o6) -((z^2+y^2)*(sqrt(z^2+y^2+x^2)+x^2/sqrt(z^2+y^2+x^2)+2*x))\n",
" /(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)^2)\n",
"(%i7) diff(s,y,1)\n",
"(%o7) y/(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)\n",
" -((z^2+y^2)*((x*y)/sqrt(z^2+y^2+x^2)+2*y))\n",
" /(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)^2)\n",
"(%i8) diff(s,z,1)\n",
"(%o8) z/(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)\n",
" -((z^2+y^2)*((x*z)/sqrt(z^2+y^2+x^2)+2*z))\n",
" /(2*(x*sqrt(z^2+y^2+x^2)+z^2+y^2+x^2)^2)\n",
"\"\"\"\n",
"# @numba.njit\n",
"def sinth2_sqrt_deriv(x, y, z):\n",
" R = z*z + y*y\n",
" Q = R + x*x\n",
" SQ = sqrt(Q)\n",
" R2 = R/2\n",
" # at x==y==0 this is undefined.\n",
" rQ_xSQ = 1/(Q + x*SQ)\n",
" sinth2 = R2*rQ_xSQ\n",
" # some simplification and collecting terms from expressions above to get:\n",
" sr = sinth2*rQ_xSQ\n",
" p = (x/SQ+2)*sr # p should be in the range 3sr -> 2sr for x/x to 0/sqrt(R)\n",
" t = (rQ_xSQ - p) # may cancel? \n",
" sinth2_dx = -(SQ*sr+x*p)\n",
" sinth2_dy = y*t\n",
" sinth2_dz = z*t\n",
" return sinth2, sinth2_dx, sinth2_dy, sinth2_dz"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cf36f991",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xyz: 0 1000 1000 \n",
"sqrt\t (0.5, -0.00035355339059327376, 0.0, 0.0) \n",
"atan\t (0.4999999999999999, -0.00035355339059327376, 0.0, 0.0)\n",
"xyz: 1 1.7320508075688772 0 \n",
"sqrt\t (0.25, -0.1875, 0.10825317547305482, 0.0) \n",
"atan\t (0.24999999999999994, -0.18750000000000003, 0.10825317547305487, 0.0)\n",
"xyz: 1e-09 1000 1000 \n",
"sqrt\t (0.49999999999964645, -0.00035355339059327376, 1.768181277393352e-16, 1.768181277393352e-16) \n",
"atan\t (0.4999999999996465, -0.00035355339059327376, 1.767766952966369e-16, 1.767766952966369e-16)\n",
"xyz: 1000000000.0 0 1 \n",
"sqrt\t (2.5e-19, -5e-28, 0.0, 5e-19) \n",
"atan\t (2.5e-19, -5.000000000000001e-28, 0.0, 5.000000000000001e-19)\n",
"xyz: 1e+20 1 0 \n",
"sqrt\t (2.5e-41, -4.9999999999999985e-61, 5e-41, 0.0) \n",
"atan\t (2.5e-41, -4.999999999999999e-61, 5e-41, 0.0)\n",
"xyz: 100 0 0 \n",
"sqrt\t (0.0, -0.0, 0.0, 0.0) \n",
"atan\t (0.0, -0.0, 0.0, 0.0)\n",
"xyz: 1000 1000 1000 \n",
"sqrt\t (0.21132486540518713, -0.00019245008972987527, 9.622504486493763e-05, 9.622504486493763e-05) \n",
"atan\t (0.2113248654051871, -0.00019245008972987527, 9.622504486493763e-05, 9.622504486493763e-05)\n",
"xyz: -123 456 789 \n",
"sqrt\t (0.5668799937442661, -0.0005340113387752514, -3.606644048906357e-05, -6.240443321462973e-05) \n",
"atan\t (0.5668799937442661, -0.0005340113387752512, -3.60664404890636e-05, -6.240443321462979e-05)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_751591/3023911578.py:37: RuntimeWarning: divide by zero encountered in double_scalars\n",
" div_r = where(isfinite(1/r), 1/r, 0)\n"
]
}
],
"source": [
"for x,y,z in tests:\n",
" a1 = sinth2_sqrt_deriv(x,y,z)\n",
" a2 = sinth2_atan_deriv(x,y,z)\n",
" print('xyz:', x,y,z,\"\\nsqrt\\t\",a1,\"\\natan\\t\",a2)\n",
" assert allclose(a1,a2)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "265a2ad1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 629650 -4.2043328642028395e-07 -3.2161595933277716e-10 9.70597750455382e-10\n",
"-7.0338134765625 -7.033922666496423\n",
"2 54669 -3.3884238262201716e-07 5.078959545993244e-10 4.734340882978083e-10\n",
"-2211.7232267291006 -2211.8036421740667\n",
"3 629650 -4.2043328642028395e-07 -3.2161595933277716e-10 9.70597750455382e-10\n",
"-2745.3556259377906 -2745.4355099133227\n"
]
}
],
"source": [
"from numpy import isclose, random, arange\n",
"random.seed(11)\n",
"for s,o in [(1e6,0.5), (1,.25), (1e-6,0.5)]:\n",
" x,y,z = (random.random( (3,1_000_000) )-o)*s\n",
" a1 = sinth2_sqrt_deriv(x,y,z)\n",
" a2 = sinth2_atan_deriv(x,y,z)\n",
" for i in range(4):\n",
" if allclose(a1[i], a2[i]):\n",
" continue\n",
" absdiff = abs(a1[i]-a2[i])\n",
" e = absdiff.argmax()\n",
" print(i,e, x[e],y[e],z[e])\n",
" print(a1[i][e],a2[i][e])\n",
" # The derivatives start to lose precision for small values of x,y,z:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "57c81e75",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"110 ms ± 4.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%timeit a1 = sinth2_sqrt_deriv(x,y,z)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c2cdd334",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"274 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%timeit a2 = sinth2_atan_deriv(x,y,z)"
]
},
{
"cell_type": "markdown",
"id": "b52f4575",
"metadata": {},
"source": [
"## conclusion\n",
"\n",
"This method seems to be more accurate for angles where exact answers are known and appears to be slightly faster.\n",
"\n",
"Still need to do something useful with it.\n",
"\n",
"Could set up to refine the metric tensor elements directly from spot positions on the detector. Does not depend on rotation angles (except for grain origins).\n",
"\n",
"There should be some way to look at orientations to go along with this."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "700c16ca",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| gpl-2.0 |
eabdullin/maxent_model | max_ent.v2.ipynb | 1 | 15438 | {
"cells": [
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"import os\n",
"import io\n",
"import numpy as np\n",
"import random\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def unique(arr, dic=None):\n",
" if (dic is None):\n",
" dic = {}\n",
" for el in arr:\n",
" if isinstance(el, list):\n",
" unique(el, dic)\n",
" else:\n",
" if (el not in dic):\n",
" dic[el] = 1\n",
" else:\n",
" dic[el] += 1\n",
" return dic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Классификация будет происходить по след формуле:\n",
"$$p(c\\mid d,\\lambda)=\\frac\n",
"{\\exp\\sum_i^{n \\times k}{\\lambda_i}f_i\\left(c,d\\right )}\n",
"{\\sum_{\\tilde{c}\\in C}{\\exp\\sum_i^{n \\times k}{\\lambda_i}f_i\\left(\\tilde{c},d\\right )}}$$"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def predict(x, weights):\n",
" probas = np.dot(x, weights)\n",
"\n",
" # далее сглаживаем выходы через softmax\n",
" probas = np.exp(probas, dtype=np.float32)\n",
" return probas / probas.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Задачу будем решать с помощью максимизации функции правдоподобия\n",
"$$\\log p(C|D,\\lambda)\n",
"=\\sum_{(c,d)\\in(C,D)}\\log p(c|d,\\lambda)\n",
"=\\sum_{(c,d)\\in(C,D)}\\log\\frac\n",
"{\\exp\\sum_i^{n \\times k}{\\lambda_i}f_i\\left(c,d\\right )}\n",
"{\\sum_{\\tilde{c}\\in C}{\\exp\\sum_i^{n \\times k}{\\lambda_i}f_i\\left(\\tilde{c},d\\right )}}$$\n",
"\n",
"Соответственно градиент у нас будет в частных производных\n",
"\n",
"$$\\frac{\\partial\\log p(C|D,\\lambda)}{\\partial\\lambda_i}=\n",
"\\sum_{(c,d)\\in(C,D)}{f_i(c,d)}-\n",
"\\sum_{d\\in D}{\\sum_{c\\in C}{p(c|d,\\lambda)f_i(c,d)}}$$"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def fit(X, y, batch_size=64, nb_epoch=10, alpha=0.85, max_iter=100, C=0.1, random_state=None, verbose=1):\n",
" \"\"\"\n",
"\n",
" :param X:\n",
" :param y:\n",
" :param f_count:\n",
" :param c_count:\n",
" :param batch_size:\n",
" :param nb_epoch:\n",
" :param alpha:\n",
" :param max_iter:\n",
" :param C:\n",
" :param random_state:\n",
" :param verbose:\n",
" \"\"\"\n",
" n_samples = len(X)\n",
"\n",
" feature_dic = unique(X)\n",
" classes_dic = unique(y)\n",
" n_features = len(feature_dic)\n",
" n_classes = len(classes_dic)\n",
"\n",
" if batch_size is None:\n",
" batch_size = n_samples\n",
" if batch_size > n_samples:\n",
" batch_size = n_samples\n",
" if random_state is not None:\n",
" random.seed(random_state)\n",
"\n",
" # матрица весов индикаторов\n",
" weights = np.zeros((n_features, n_classes), dtype=np.float32)\n",
" features = np.zeros((n_features, n_classes), dtype=np.int8)\n",
" \n",
" #инициализация весов\n",
" for i in range(len(X)):\n",
" for xi in X[i]:\n",
" features[xi, y[i]] = 1\n",
" weights[xi, y[i]] += 1\n",
" idx = weights > 0\n",
" weights[idx] = np.log(weights[idx])\n",
"\n",
" all_iter = 0\n",
" u = 0.0\n",
" for epoch in range(nb_epoch):\n",
" if verbose:\n",
" print 'Start epoch #%d\\t' % epoch,\n",
" # SGD\n",
" # ограничим сверху max_iter итерациями\n",
" loss = 0.\n",
" prev_logl = 0.\n",
" for iter_num in range(max_iter):\n",
" if verbose and (iter_num % (max_iter / 20) == 0):\n",
" print '.',\n",
" logl = 0.\n",
" ncorrect = 0\n",
"\n",
" r = range(n_samples)\n",
" r = random.sample(r, batch_size)\n",
" iter_sample = 0\n",
" for i in r:\n",
"\n",
" # вектор признаков(контекста)\n",
" x_vec = np.zeros(n_features, dtype=np.int8)\n",
" for j in X[i]:\n",
" x_vec[j] += 1\n",
" iter_sample += 1\n",
"\n",
" y_vec = np.zeros(weights.shape[1], dtype=np.int8)\n",
" y_vec[y[i]] = 1\n",
"\n",
" all_iter += 1.0\n",
" eta = alpha ** (all_iter / n_samples)\n",
"\n",
" # предсказываем вероятности\n",
" probas = predict(x_vec, weights)\n",
"\n",
" # смотрим, правильно ли мы предсказали, это нужно только для verbose\n",
" if np.argmax(probas) == y[i]:\n",
" ncorrect += 1\n",
"\n",
" # считаем \"правдоподобие\"\n",
" logl += np.log(probas[y[i]])\n",
"\n",
" # обновляем веса\n",
" # grad = np.outer(x_vec, probas)*features - np.outer(x_vec, y_vec )\n",
" idx = x_vec != 0\n",
" me = np.outer(x_vec[idx], probas) * features[idx]\n",
" ee = np.outer(x_vec[idx], y_vec)\n",
" weights[idx] -= (me - ee) * eta + weights[idx] * eta * C\n",
"\n",
" if (iter_num > 0):\n",
" loss += (logl - prev_logl)\n",
" prev_logl = logl\n",
" if verbose:\n",
" print '\\tLoss: %.8f' % (loss / max_iter)\n",
" return weights"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Start epoch #0\t. . . . . . . . . . . . . . . . . . . . \tLoss: 0.01640967\n"
]
}
],
"source": [
"# небольшой тест\n",
"X = [[0, 1],\n",
" [2, 1],\n",
" [2, 3],\n",
" [2, 1],\n",
" [0, 1],\n",
" [2, 1, 4],\n",
" [2, 3, 4],\n",
" [2, 1, 5],\n",
" [0, 3, 5],\n",
" [0, 1, 5]]\n",
"y = [0, 0, 1, 1, 0, 1, 1, 0, 0, 0]\n",
"weights = fit(X, y, random_state=241, C=0.00001, nb_epoch=1)\n",
"# print weights\n",
"# print patterns\n",
"x = np.zeros(6)\n",
"x[0] = 1\n",
"x[3] = 1\n",
"pred = predict(x, weights)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"digits_regex = re.compile('\\d')\n",
"punc_regex = re.compile('[\\%\\(\\)\\-\\/\\:\\;\\<\\>\\«\\»\\,]')\n",
"delim_regex = re.compile('([\\.])\\s+')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read_and_tokenize(foldername):\n",
" '''\n",
" метод для считывания текстов из файлов папки\n",
" здесь применяется довольно простая токенизация\n",
" '''\n",
"\n",
" word_counts = {}\n",
" tokenized_text = []\n",
" for path, subdirs, files in os.walk('data'):\n",
" for name in files:\n",
" filename = os.path.join(path, name)\n",
" with io.open(filename, 'r', encoding='utf-8') as data_file:\n",
" for line in data_file:\n",
" if len(line) < 50:\n",
" continue\n",
" text = digits_regex.sub(u'0', line.lower())\n",
" text = punc_regex.sub(u'', text)\n",
" text = delim_regex.sub(r' \\1 ', text)\n",
" for word in text.split():\n",
" if not word:\n",
" continue\n",
" if word not in word_counts:\n",
" word_counts[word] = 1\n",
" else:\n",
" word_counts[word] += 1\n",
" tokenized_text.append(word)\n",
" word2index = {}\n",
" index2word = []\n",
" i = 0\n",
" filtered_text = []\n",
" for word in tokenized_text:\n",
" if word_counts[word] > 2:\n",
" if word not in word2index:\n",
" word2index[word] = i\n",
" index2word.append(word)\n",
" i += 1\n",
" filtered_text.append(word)\n",
"\n",
"\n",
" return filtered_text, word2index, index2word"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def generate_train(tokenized_text, word2index,context_len = 4):\n",
" '''\n",
" метод для генерации обучающих данных\n",
" '''\n",
" X = []\n",
" y = []\n",
" for i, y_word in enumerate(tokenized_text):\n",
" x = []\n",
" for j in range(i - context_len, i):\n",
" if (j >= 0):\n",
" x_word = tokenized_text[j]\n",
" x.append(word2index[x_word])\n",
" if (len(x) > 0):\n",
" X.append(x)\n",
" y.append(word2index[y_word])\n",
" return X, y"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tokenized_text, word2index, index2word = read_and_tokenize('data') "
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"all words: 45825\n",
"all unique words: 3872\n"
]
}
],
"source": [
"unique_words = len(index2word)\n",
"print 'all words:', len(tokenized_text)\n",
"print 'all unique words:', unique_words"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"context_len = 4\n",
"X,y = generate_train(tokenized_text, word2index,context_len=context_len)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Start epoch #0\t. . . . . . . . . . . . . . . . . . . . \tLoss: 0.80140741\n",
"Start epoch #1\t. . . . . . . . . . . . . . . . . . . . \tLoss: -0.16680696\n",
"Start epoch #2\t. . . . . . . . . . . . . . . . . . . . \tLoss: -0.21839911\n"
]
}
],
"source": [
"weights = fit(X, y, random_state=241, verbose=1, batch_size=32, nb_epoch=3, C=0.0001)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GENRATED TEXT:\n",
"комиссии по мнению в россии украины на а в также с из что к он с 0000 на в том\n"
]
}
],
"source": [
"test = [word2index[word] for word in [u'путин']]\n",
"last_index = index = test[-1]\n",
"print 'GENRATED TEXT:'\n",
"\n",
"for i in range(20):\n",
" x = np.zeros(len(index2word))\n",
" x[test] = 1\n",
" pred = predict(x, weights)\n",
" indicies = pred.argsort()[::-1][:20]\n",
" for index in indicies:\n",
" if index in test:\n",
" continue\n",
" else:\n",
" break\n",
" last_index = int(index)\n",
" print index2word[index],\n",
" test.append(index)\n",
" if len(test) > context_len:\n",
" del test[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Идеи по улучшению\n",
"* первое, что приходит на ум - это увеличить кол-во обучающей выборки\n",
"* использовать в качестве контекста, не слова а символы с определнным окном(context_len) равным 40 или больше\n",
"* использовать лематизацию или стемминг для словарных \"фич\", а затем скомбинировать с предыдущим пунктом(пока точно не представляю как)\n",
"* модель работает немного медленно, а на больших текстах очень медленно. поэтому можно попробовать искать оптимальные параметры обучения. также можно переписать решение на С/С++ или на Сython"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Использованная литература:\n",
"* Tsuruoka Y., Tsujii J., Ananiadou S. Stochastic gradient descent training for l1-regularized log-linear models with cumulative penalty //Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1. – Association for Computational Linguistics, 2009. – С. 477-485.\n",
"* Smith N. A., Eisner J. Contrastive estimation: Training log-linear models on unlabeled data //Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. – Association for Computational Linguistics, 2005. – С. 354-362.\n",
"* Smith N. A. Log-Linear Models // Revised version of thesis research proposal, 2004"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
liebannam/pipes | examples/Random_wavespeed.ipynb | 1 | 477002 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import sys\n",
"sys.path.append(\"..\")\n",
"from allthethings import PyNetwork, PyPipe_ps\n",
"from allthethings import PyBC_opt_dh\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import rc\n",
"from writeit import *\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample # 0\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 2, 3, 2, 2, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"198.871309451\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.986761\n",
"T = 20.000000, Volume = 75.702589\n",
"T = 30.000000, Volume = 113.418418\n",
"T = 40.000000, Volume = 151.134246\n",
"T = 50.000000, Volume = 181.632891\n",
"T = 60.000000, Volume = 204.615880\n",
"T = 70.000000, Volume = 227.598875\n",
"T = 80.000000, Volume = 250.581870\n",
"T = 90.000000, Volume = 273.564866\n",
"T = 100.000000, Volume = 296.547862\n",
"T = 110.000000, Volume = 319.530858\n",
"T = 120.000000, Volume = 342.513854\n",
"T = 130.000000, Volume = 365.496850\n",
"T = 140.000000, Volume = 388.479865\n",
"T = 150.000000, Volume = 411.462901\n",
"T = 160.000000, Volume = 434.445941\n",
"T = 170.000000, Volume = 457.428982\n",
"T = 180.000000, Volume = 480.412022\n",
"T = 190.000000, Volume = 503.395063\n",
"T = 200.000000, Volume = 526.378104\n",
"T = 210.000000, Volume = 549.361148\n",
"T = 220.000000, Volume = 572.344227\n",
"T = 230.000000, Volume = 595.327298\n",
"T = 240.000000, Volume = 618.310370\n",
"T = 250.000000, Volume = 641.293454\n",
"T = 260.000000, Volume = 664.276578\n",
"T = 270.000000, Volume = 687.259694\n",
"T = 280.000000, Volume = 710.242809\n",
"T = 290.000000, Volume = 733.225947\n",
"T = 300.000000, Volume = 756.209110\n",
"T = 310.000000, Volume = 779.192273\n",
"T = 320.000000, Volume = 802.175429\n",
"inflow volume is 802.175429 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 117.190060 s\n",
"[0.047862618695489155, 0.083141655794982505, 0.11326645345887713, 0.72422002364115934, 9.8863197696894254, 15.295333722668525, 6.5992303019425798, 7.4047213798125755, 7.5166575009866117, 7.7906520768509422, 7.4763898396572905, 6.8921595963676889, 6.9002787807702264, 15.07829765136683, 12.500658701772512, 7.9789429220265671, 7.6863221254123868, 7.8606142976903692, 6.9409703461276324, 6.9534992130997733, 14.138811793634817, 16.160745229426038, 19.597803631446499, 19.364289714375488, 18.939688026923747, 17.238778032432808, 19.207235099914303, 21.980072101210812, 13.783492783973379, 16.866109866695727, 10.31461535758428, 11.337608656567161]\n",
"[ 89.73672876 89.77912899 77.2215967 77.13511953 77.07611633\n",
" 77.03297078 68.28106643 68.18682858 68.07973547 62.38041839\n",
" 58.28976053 55.81017712 55.8131181 55.81039867 56.47007427\n",
" 59.71505585 82.92381982 72.1192972 64.57507657]\n",
"max arrival time is 317.327968\n",
"sample # 1\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"146.512099728\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.023172\n",
"T = 20.000000, Volume = 75.778039\n",
"T = 30.000000, Volume = 113.532906\n",
"T = 40.000000, Volume = 151.287773\n",
"T = 50.000000, Volume = 189.042640\n",
"T = 60.000000, Volume = 226.797507\n",
"T = 70.000000, Volume = 255.151573\n",
"T = 80.000000, Volume = 293.080514\n",
"T = 90.000000, Volume = 316.863696\n",
"T = 100.000000, Volume = 339.846717\n",
"T = 110.000000, Volume = 362.829737\n",
"T = 120.000000, Volume = 385.812779\n",
"T = 130.000000, Volume = 408.795866\n",
"T = 140.000000, Volume = 431.778943\n",
"T = 150.000000, Volume = 454.762020\n",
"T = 160.000000, Volume = 477.745149\n",
"T = 170.000000, Volume = 500.728310\n",
"T = 180.000000, Volume = 523.711468\n",
"T = 190.000000, Volume = 546.694621\n",
"T = 200.000000, Volume = 569.677849\n",
"T = 210.000000, Volume = 592.661096\n",
"T = 220.000000, Volume = 615.644361\n",
"T = 230.000000, Volume = 638.627628\n",
"T = 240.000000, Volume = 661.610895\n",
"T = 250.000000, Volume = 684.594164\n",
"T = 260.000000, Volume = 707.577433\n",
"T = 270.000000, Volume = 730.560702\n",
"T = 280.000000, Volume = 753.543972\n",
"T = 290.000000, Volume = 776.527261\n",
"T = 300.000000, Volume = 799.510591\n",
"inflow volume is 799.510591 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 80.028797 s\n",
"[0.047692131171447952, 0.081871716727299662, 0.76790774167472997, 3.4769454042135566, 4.4071913759219905, 4.7824463915939912, 148.50685957948315, 192.66566621224376, 11.954907585484808, 5.5243727357755867, 5.1598589221831119, 10.559614827605127, 11.507741575149879, 13.464301509904239, 13.528446878302651, 12.659183564428274, 12.61304796183617, 14.063902905017224, 13.766640504097401, 10.898424201904685, 9.2598154837891631, 6.8093662539748765, 6.9806425009041346, 7.2525110527417187, 7.633707260747693, 6.9684606622713536, 6.8638761686839347, 8.8170313572508103, 11.024885441723201, 13.532997242008078]\n",
"[ 230.94871467 51.91678601 51.71025218 51.48677756 51.22438544\n",
" 40.63420907 30.20149138 20.08755202 16.26054763 17.97397465\n",
" 19.47427903 19.65625987 17.56268189 17.16797814 25.04711601\n",
" 45.8653839 14.80849169 35.355489 19.58317044]\n",
"max arrival time is 299.224468\n",
"sample # 2\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"187.914360194\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.994483\n",
"T = 20.000000, Volume = 75.718697\n",
"T = 30.000000, Volume = 113.442911\n",
"T = 40.000000, Volume = 151.167125\n",
"T = 50.000000, Volume = 188.891339\n",
"T = 60.000000, Volume = 220.679902\n",
"T = 70.000000, Volume = 243.662891\n",
"T = 80.000000, Volume = 266.645881\n",
"T = 90.000000, Volume = 289.628874\n",
"T = 100.000000, Volume = 312.611873\n",
"T = 110.000000, Volume = 335.594882\n",
"T = 120.000000, Volume = 358.577891\n",
"T = 130.000000, Volume = 381.560906\n",
"T = 140.000000, Volume = 404.543946\n",
"T = 150.000000, Volume = 427.526991\n",
"T = 160.000000, Volume = 450.510040\n",
"T = 170.000000, Volume = 473.493090\n",
"T = 180.000000, Volume = 496.476140\n",
"T = 190.000000, Volume = 519.459190\n",
"T = 200.000000, Volume = 542.442240\n",
"T = 210.000000, Volume = 565.425324\n",
"T = 220.000000, Volume = 588.408408\n",
"T = 230.000000, Volume = 611.391495\n",
"T = 240.000000, Volume = 634.374595\n",
"T = 250.000000, Volume = 657.357695\n",
"T = 260.000000, Volume = 680.340798\n",
"T = 270.000000, Volume = 703.323940\n",
"T = 280.000000, Volume = 726.307074\n",
"T = 290.000000, Volume = 749.290214\n",
"T = 300.000000, Volume = 772.273364\n",
"T = 310.000000, Volume = 795.256514\n",
"inflow volume is 795.256514 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 107.057357 s\n",
"[0.047831567783833309, 0.082047619941783362, 0.10724654445337613, 2.030330026203214, 1.7582012790931343, 17.841907745498023, 15.000603900042826, 17.314057620121964, 14.11877406630521, 10.56913893858472, 2.8126834832938346, 2.7336753046154909, 10.036377277584027, 14.533910812948886, 10.575472968490059, 7.6636192693329193, 7.7878776290923053, 6.8323268860096862, 6.7538378423656251, 11.124248246848905, 13.892944659826815, 16.081474044062837, 14.433074019503819, 6.9378016879319722, 6.9488066093994547, 14.364031883530609, 14.64861523036064, 16.93873274227008, 9.1083614511629865, 7.2460849481363976, 12.157615859654317]\n",
"[ 55.58378217 55.6197478 55.3360303 41.51977296 41.23068544\n",
" 40.94665659 27.40831123 27.12000822 27.93785481 18.87938485\n",
" 20.61310217 15.88073981 18.57335669 15.99395685 21.04850879\n",
" 17.44858965 34.14130193 48.40315552 15.99395685]\n",
"max arrival time is 309.608177\n",
"sample # 3\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"76.7372404121\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.037106\n",
"T = 20.000000, Volume = 75.809558\n",
"T = 30.000000, Volume = 113.582010\n",
"T = 40.000000, Volume = 144.195598\n",
"T = 50.000000, Volume = 167.030146\n",
"T = 60.000000, Volume = 190.013317\n",
"T = 70.000000, Volume = 212.996494\n",
"T = 80.000000, Volume = 235.979675\n",
"T = 90.000000, Volume = 258.962857\n",
"T = 100.000000, Volume = 281.946039\n",
"T = 110.000000, Volume = 304.929292\n",
"T = 120.000000, Volume = 327.912748\n",
"T = 130.000000, Volume = 350.896231\n",
"T = 140.000000, Volume = 373.879719\n",
"T = 150.000000, Volume = 396.863211\n",
"T = 160.000000, Volume = 419.846706\n",
"T = 170.000000, Volume = 442.830201\n",
"T = 180.000000, Volume = 465.813688\n",
"T = 190.000000, Volume = 488.797350\n",
"T = 200.000000, Volume = 511.781144\n",
"T = 210.000000, Volume = 534.764946\n",
"T = 220.000000, Volume = 557.748750\n",
"T = 230.000000, Volume = 580.732553\n",
"T = 240.000000, Volume = 603.716370\n",
"T = 250.000000, Volume = 626.700348\n",
"T = 260.000000, Volume = 649.684401\n",
"T = 270.000000, Volume = 672.668460\n",
"T = 280.000000, Volume = 695.652563\n",
"T = 290.000000, Volume = 718.636686\n",
"T = 300.000000, Volume = 741.620811\n",
"T = 310.000000, Volume = 764.604935\n",
"T = 320.000000, Volume = 787.589107\n",
"T = 330.000000, Volume = 810.573404\n",
"inflow volume is 810.573404 gallons\n",
"simulation time is 330.000000 s\n",
"wall clock time is 46.226954 s\n",
"[0.049624788847725095, 0.085425796957643935, 0.44064623999069191, 17.356097793826457, 6.4501395076888892, 2.1227041278574159, 2.3617779282414322, 2.3876265612937644, 2.2823181990637997, 2.2915658635492004, 5.4785572684433026, 2.7661520406456503, 2.7597835892748228, 2.8611575158834675, 2.9357894704911822, 2.7410004085041551, 2.750666613514932, 4.2066612683980953, 4.7184409691120912, 3.0213303865787271, 3.1320130946868612, 3.0342328545873851, 3.05241388645705, 6.0478090674480596, 4.541411196625778, 5.0816701160009412, 4.3550371321861574, 3.7328491950956151, 3.4652853255372866, 3.4727790429075593, 4.8464385031503978, 6.8556687737062134, 5.909562697387952]\n",
"[ 28.1218208 22.88256078 22.73161653 22.58552439 17.09577331\n",
" 16.95120149 17.12697902 13.18113081 13.99445191 11.53237305\n",
" 11.55919817 11.5390625 10.98748602 11.73922607 14.48694971\n",
" 19.84195377 12.26750569 10.8626781 25.59749657]\n",
"max arrival time is 328.216893\n",
"sample # 4\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"127.242428507\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.034295\n",
"T = 20.000000, Volume = 75.801560\n",
"T = 30.000000, Volume = 113.568825\n",
"T = 40.000000, Volume = 151.336090\n",
"T = 50.000000, Volume = 182.113677\n",
"T = 60.000000, Volume = 205.096715\n",
"T = 70.000000, Volume = 228.079769\n",
"T = 80.000000, Volume = 251.062823\n",
"T = 90.000000, Volume = 274.045877\n",
"T = 100.000000, Volume = 297.028936\n",
"T = 110.000000, Volume = 320.012059\n",
"T = 120.000000, Volume = 342.995197\n",
"T = 130.000000, Volume = 365.978341\n",
"T = 140.000000, Volume = 388.961486\n",
"T = 150.000000, Volume = 411.944632\n",
"T = 160.000000, Volume = 434.927782\n",
"T = 170.000000, Volume = 457.910949\n",
"T = 180.000000, Volume = 480.894116\n",
"T = 190.000000, Volume = 503.877280\n",
"T = 200.000000, Volume = 526.860510\n",
"T = 210.000000, Volume = 549.843746\n",
"T = 220.000000, Volume = 572.827003\n",
"T = 230.000000, Volume = 595.810260\n",
"T = 240.000000, Volume = 618.793517\n",
"T = 250.000000, Volume = 641.776786\n",
"T = 260.000000, Volume = 664.760127\n",
"T = 270.000000, Volume = 687.743478\n",
"T = 280.000000, Volume = 710.726846\n",
"T = 290.000000, Volume = 733.710215\n",
"T = 300.000000, Volume = 756.693585\n",
"T = 310.000000, Volume = 779.676956\n",
"T = 320.000000, Volume = 802.660309\n",
"inflow volume is 802.660309 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 74.046830 s\n",
"[0.047603642522276959, 0.082809563981257717, 0.11318222855813888, 0.79394069608007101, 7.2500249067630591, 3.8579470254712072, 2.4184207689694741, 2.2922456816220316, 2.3006113683447018, 5.9222587291674182, 10.989236369587472, 6.0304400999265662, 5.6677702229507627, 6.0485434963148315, 6.2009884744786916, 5.2202208527381364, 2.9089812198134264, 2.9243703735531001, 5.7554699218283725, 12.32036473376972, 8.7320043146973898, 5.8932929395481519, 5.6954075075072588, 5.7033291886365003, 10.073632703960277, 11.668718207517776, 6.8018325395362016, 6.7811377621286182, 6.9240548781146298, 6.466183654604416, 6.4661945125930522, 8.6682051001693505]\n",
"[ 43.27565762 43.31449405 35.55119184 35.31255089 29.00177527\n",
" 28.93906186 28.97848885 29.04231279 25.11699576 25.18736012\n",
" 22.783803 22.93919329 23.04945119 22.38912283 22.38912283\n",
" 31.78608179 26.70614506 23.57610196 38.95401131]\n",
"max arrival time is 317.207547\n",
"sample # 5\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"145.274052664\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.023769\n",
"T = 20.000000, Volume = 75.779467\n",
"T = 30.000000, Volume = 113.535164\n",
"T = 40.000000, Volume = 145.458391\n",
"T = 50.000000, Volume = 168.441383\n",
"T = 60.000000, Volume = 191.424401\n",
"T = 70.000000, Volume = 214.407420\n",
"T = 80.000000, Volume = 237.390440\n",
"T = 90.000000, Volume = 260.373460\n",
"T = 100.000000, Volume = 283.356482\n",
"T = 110.000000, Volume = 306.339519\n",
"T = 120.000000, Volume = 329.322556\n",
"T = 130.000000, Volume = 352.305594\n",
"T = 140.000000, Volume = 375.288632\n",
"T = 150.000000, Volume = 398.271670\n",
"T = 160.000000, Volume = 421.254709\n",
"T = 170.000000, Volume = 444.237804\n",
"T = 180.000000, Volume = 467.220886\n",
"T = 190.000000, Volume = 490.203967\n",
"T = 200.000000, Volume = 513.187072\n",
"T = 210.000000, Volume = 536.170245\n",
"T = 220.000000, Volume = 559.153430\n",
"T = 230.000000, Volume = 582.136620\n",
"T = 240.000000, Volume = 605.119810\n",
"T = 250.000000, Volume = 628.103001\n",
"T = 260.000000, Volume = 651.086191\n",
"T = 270.000000, Volume = 674.069376\n",
"T = 280.000000, Volume = 697.052624\n",
"T = 290.000000, Volume = 720.035883\n",
"T = 300.000000, Volume = 743.019158\n",
"T = 310.000000, Volume = 766.002433\n",
"T = 320.000000, Volume = 788.985708\n",
"inflow volume is 788.985708 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 86.007028 s\n",
"[0.050112863666888635, 0.085882428715880949, 0.27579943394926337, 6.0526179968726694, 10.233488106188378, 4.6460842060869192, 5.6373331998936198, 5.8804268504862804, 6.2324491790446803, 5.9113504818185314, 3.495503823341962, 3.5327167325756577, 3.4213438580128281, 2.7513962610281628, 2.7650835834523191, 6.848284607320184, 11.96272144140568, 12.572388109533692, 13.0509147035655, 12.948274251988396, 11.792835589110654, 7.2027049450125178, 6.8404408704239898, 7.0444984726810054, 6.5470744066369733, 6.5555863359481235, 9.5053490787389343, 12.58366052911513, 7.8662656558087773, 6.7254715513065175, 6.4591386527560681, 6.4513095156127775]\n",
"[ 42.85426258 32.32772834 32.16538017 32.00811707 31.85081901\n",
" 31.69348601 31.53611802 20.69136938 9.78769153 9.56495811\n",
" 13.21404458 5.04650489 2.79717645 0.04746171 37.70212991\n",
" 0.04746171 26.1252107 8.96060486 15.21927477]\n",
"max arrival time is 292.666667\n",
"sample # 6\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"73.718510803\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.033520\n",
"T = 20.000000, Volume = 75.803173\n",
"T = 30.000000, Volume = 113.572826\n",
"T = 40.000000, Volume = 144.506213\n",
"T = 50.000000, Volume = 167.299297\n",
"T = 60.000000, Volume = 190.282477\n",
"T = 70.000000, Volume = 213.265665\n",
"T = 80.000000, Volume = 236.249116\n",
"T = 90.000000, Volume = 259.232615\n",
"T = 100.000000, Volume = 282.216114\n",
"T = 110.000000, Volume = 305.199656\n",
"T = 120.000000, Volume = 328.183406\n",
"T = 130.000000, Volume = 351.167179\n",
"T = 140.000000, Volume = 374.150972\n",
"T = 150.000000, Volume = 397.134812\n",
"T = 160.000000, Volume = 420.118656\n",
"T = 170.000000, Volume = 443.102500\n",
"T = 180.000000, Volume = 466.086336\n",
"T = 190.000000, Volume = 489.070308\n",
"T = 200.000000, Volume = 512.054467\n",
"T = 210.000000, Volume = 535.038648\n",
"T = 220.000000, Volume = 558.022831\n",
"T = 230.000000, Volume = 581.007015\n",
"T = 240.000000, Volume = 603.991192\n",
"T = 250.000000, Volume = 626.975523\n",
"T = 260.000000, Volume = 649.959964\n",
"T = 270.000000, Volume = 672.944426\n",
"T = 280.000000, Volume = 695.928937\n",
"T = 290.000000, Volume = 718.913470\n",
"T = 300.000000, Volume = 741.898009\n",
"T = 310.000000, Volume = 764.882554\n",
"T = 320.000000, Volume = 787.867104\n",
"T = 330.000000, Volume = 810.851660\n",
"inflow volume is 810.851660 gallons\n",
"simulation time is 330.000000 s\n",
"wall clock time is 44.314808 s\n",
"[0.049588025311687461, 0.086736327235641952, 0.45114912203930663, 21.039733567151828, 6.4784658152256664, 2.012499682337324, 3.4491914259629342, 3.501001738725471, 2.1382981798831082, 2.144720398577487, 4.7474077534016459, 3.7782504657413511, 4.2059934357515623, 3.2641250798771377, 2.7892487101028678, 2.5892207951984343, 2.5984859835155389, 3.9300626232181766, 4.612221709319428, 2.8451279768620106, 2.9934802041442148, 2.8857537365609551, 2.8987826946601052, 5.6086688801697449, 5.4534666916903882, 4.865261626248981, 4.0011343992460144, 3.5612879431516204, 3.6189997623097927, 3.705866870967149, 3.7818725868812817, 3.8510677441319614, 5.6831155642622218]\n",
"[ 28.30845329 23.31932995 18.52909948 14.68367583 15.49650411\n",
" 15.99699659 13.45085601 13.17558979 11.11350697 11.01097275\n",
" 10.9411042 10.50466492 9.68051491 9.28757196 11.85494969\n",
" 14.6207681 15.99803143 25.90725423 20.76661384]\n",
"max arrival time is 326.883822\n",
"sample # 7\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"208.281901154\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.980032\n",
"T = 20.000000, Volume = 75.688714\n",
"T = 30.000000, Volume = 113.397395\n",
"T = 40.000000, Volume = 145.209431\n",
"T = 50.000000, Volume = 168.192411\n",
"T = 60.000000, Volume = 191.175390\n",
"T = 70.000000, Volume = 214.158375\n",
"T = 80.000000, Volume = 237.141375\n",
"T = 90.000000, Volume = 260.124375\n",
"T = 100.000000, Volume = 283.107375\n",
"T = 110.000000, Volume = 306.090376\n",
"T = 120.000000, Volume = 329.073377\n",
"T = 130.000000, Volume = 352.056378\n",
"T = 140.000000, Volume = 375.039379\n",
"T = 150.000000, Volume = 398.022381\n",
"T = 160.000000, Volume = 421.005382\n",
"T = 170.000000, Volume = 443.988384\n",
"T = 180.000000, Volume = 466.971393\n",
"T = 190.000000, Volume = 489.954421\n",
"T = 200.000000, Volume = 512.937456\n",
"T = 210.000000, Volume = 535.920489\n",
"T = 220.000000, Volume = 558.903541\n",
"T = 230.000000, Volume = 581.886608\n",
"T = 240.000000, Volume = 604.869670\n",
"T = 250.000000, Volume = 627.852729\n",
"T = 260.000000, Volume = 650.835830\n",
"T = 270.000000, Volume = 673.818933\n",
"T = 280.000000, Volume = 696.802035\n",
"T = 290.000000, Volume = 719.785136\n",
"T = 300.000000, Volume = 742.768281\n",
"T = 310.000000, Volume = 765.751423\n",
"T = 320.000000, Volume = 788.734571\n",
"inflow volume is 788.734571 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 124.338394 s\n",
"[0.05030429470537693, 0.086083909680431517, 0.10480454464233183, 7.3738967431604072, 13.510535359048809, 14.814732689599175, 12.068751352009585, 3.7833308883816779, 3.8102844439247185, 3.6159145241151078, 4.1299624852133858, 3.7812132439225339, 4.2782795352938088, 4.3057462631381229, 3.8170558482814929, 2.9467453849565133, 2.9596379414345884, 13.358472607119753, 14.819632634011999, 7.217259791134059, 7.0295274008052235, 17.916308975027768, 18.464802369527213, 20.086035303139084, 24.654400035665262, 15.109090992279443, 21.085577110306382, 20.435667960954675, 17.326718791121625, 15.58519692486982, 19.951318703683121, 12.09190435710345]\n",
"[ 76.20631854 61.08988132 60.80640886 60.58185996 60.42434433\n",
" 60.26667862 60.10829124 59.94945464 44.54939773 30.24051413\n",
" 22.35630023 21.73287839 9.87048593 0.91791486 52.23076584\n",
" 68.78500097 37.20956293 15.0439947 22.64574442]\n",
"max arrival time is 318.021514\n",
"sample # 8\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"120.271964873\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.038053\n",
"T = 20.000000, Volume = 75.809076\n",
"T = 30.000000, Volume = 113.580100\n",
"T = 40.000000, Volume = 145.561817\n",
"T = 50.000000, Volume = 168.544823\n",
"T = 60.000000, Volume = 191.527862\n",
"T = 70.000000, Volume = 214.510903\n",
"T = 80.000000, Volume = 237.493946\n",
"T = 90.000000, Volume = 260.476999\n",
"T = 100.000000, Volume = 283.460065\n",
"T = 110.000000, Volume = 306.443132\n",
"T = 120.000000, Volume = 329.426200\n",
"T = 130.000000, Volume = 352.409269\n",
"T = 140.000000, Volume = 375.392339\n",
"T = 150.000000, Volume = 398.375409\n",
"T = 160.000000, Volume = 421.358480\n",
"T = 170.000000, Volume = 444.341636\n",
"T = 180.000000, Volume = 467.324804\n",
"T = 190.000000, Volume = 490.307971\n",
"T = 200.000000, Volume = 513.291159\n",
"T = 210.000000, Volume = 536.274436\n",
"T = 220.000000, Volume = 559.257725\n",
"T = 230.000000, Volume = 582.241014\n",
"T = 240.000000, Volume = 605.224330\n",
"T = 250.000000, Volume = 628.207717\n",
"T = 260.000000, Volume = 651.191128\n",
"T = 270.000000, Volume = 674.174541\n",
"T = 280.000000, Volume = 697.157957\n",
"T = 290.000000, Volume = 720.141372\n",
"T = 300.000000, Volume = 743.124804\n",
"T = 310.000000, Volume = 766.108243\n",
"T = 320.000000, Volume = 789.091684\n",
"inflow volume is 789.091684 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 70.841092 s\n",
"[0.049990959288713423, 0.085760361793221315, 0.3387498642604152, 5.5540637658861218, 7.5675345917607242, 4.2017501613368244, 5.0642613773992089, 5.3308819345220622, 4.2092097389459546, 3.1387082432746878, 3.2073577326291103, 3.2583667079566268, 3.2638006621857705, 2.7493993174712643, 2.7630391970912123, 5.902624433718084, 10.498456699028814, 5.197927419985354, 4.9846594565696636, 8.9499808770224334, 7.5732290953104595, 5.1270403851289874, 5.2668673611005898, 9.969521672497466, 7.9533242784157068, 6.2690948459088629, 6.4226050077325159, 6.7516237111714785, 5.9926941458921839, 4.118916032635525, 3.6603513954382771, 3.655792315000908]\n",
"[ 37.27312434 28.65163243 28.49140682 28.33621066 28.18096261\n",
" 28.02566658 27.87031859 18.93235718 10.85840414 4.15020749\n",
" 0.73281689 0.65843108 0.7896681 0.04760392 23.41276746\n",
" 33.0721045 0.04760392 14.4137933 7.39846037]\n",
"max arrival time is 293.825682\n",
"sample # 9\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 2, 3, 2, 2, 2, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"187.004103409\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.995276\n",
"T = 20.000000, Volume = 75.720176\n",
"T = 30.000000, Volume = 113.445076\n",
"T = 40.000000, Volume = 151.169976\n",
"T = 50.000000, Volume = 188.894876\n",
"T = 60.000000, Volume = 220.467259\n",
"T = 70.000000, Volume = 243.450248\n",
"T = 80.000000, Volume = 266.433249\n",
"T = 90.000000, Volume = 289.416257\n",
"T = 100.000000, Volume = 312.399266\n",
"T = 110.000000, Volume = 335.382274\n",
"T = 120.000000, Volume = 358.365317\n",
"T = 130.000000, Volume = 381.348350\n",
"T = 140.000000, Volume = 404.331403\n",
"T = 150.000000, Volume = 427.314462\n",
"T = 160.000000, Volume = 450.297521\n",
"T = 170.000000, Volume = 473.280581\n",
"T = 180.000000, Volume = 496.263641\n",
"T = 190.000000, Volume = 519.246701\n",
"T = 200.000000, Volume = 542.229761\n",
"T = 210.000000, Volume = 565.212848\n",
"T = 220.000000, Volume = 588.195933\n",
"T = 230.000000, Volume = 611.179019\n",
"T = 240.000000, Volume = 634.162104\n",
"T = 250.000000, Volume = 657.145246\n",
"T = 260.000000, Volume = 680.128382\n",
"T = 270.000000, Volume = 703.111517\n",
"T = 280.000000, Volume = 726.094657\n",
"T = 290.000000, Volume = 749.077850\n",
"T = 300.000000, Volume = 772.061034\n",
"T = 310.000000, Volume = 795.044234\n",
"inflow volume is 795.044234 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 106.743728 s\n",
"[0.047829024859229748, 0.082048263904071522, 0.10918323158108552, 2.033921189662574, 1.9372195241836339, 14.663848612265094, 14.027380246511267, 9.5344251938216722, 2.4538374632519964, 2.4632489940693567, 7.5213883391764425, 13.045954207609674, 16.482813628570675, 5.6256211476419464, 4.0935905758342885, 3.9597638748400517, 4.3773199964386391, 3.1634016648234993, 3.0941040084849187, 7.4290966202630102, 13.777686404686232, 17.840898721267902, 18.682225790440413, 17.035014358742817, 14.093724228949476, 17.965624508252063, 17.574180809169203, 17.622716069284259, 15.152868105594628, 14.355076743914323, 9.4132122710634647]\n",
"[ 68.48544058 68.52702664 68.3723544 54.61039191 54.44854655\n",
" 40.57864558 40.41270055 40.25143322 40.0953272 26.29978003\n",
" 19.40843592 8.43880373 2.81818728 17.58249908 13.07462336\n",
" 47.52444442 33.12333089 61.5019271 19.83691204]\n",
"max arrival time is 307.334189\n",
"sample # 10\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"224.3899881\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.968877\n",
"T = 20.000000, Volume = 75.665429\n",
"T = 30.000000, Volume = 113.361981\n",
"T = 40.000000, Volume = 151.058533\n",
"T = 50.000000, Volume = 181.566714\n",
"T = 60.000000, Volume = 204.549695\n",
"T = 70.000000, Volume = 227.532673\n",
"T = 80.000000, Volume = 250.515651\n",
"T = 90.000000, Volume = 273.498672\n",
"T = 100.000000, Volume = 296.481685\n",
"T = 110.000000, Volume = 319.464698\n",
"T = 120.000000, Volume = 342.447719\n",
"T = 130.000000, Volume = 365.430773\n",
"T = 140.000000, Volume = 388.413820\n",
"T = 150.000000, Volume = 411.396867\n",
"T = 160.000000, Volume = 434.379934\n",
"T = 170.000000, Volume = 457.363020\n",
"T = 180.000000, Volume = 480.346101\n",
"T = 190.000000, Volume = 503.329190\n",
"T = 200.000000, Volume = 526.312282\n",
"T = 210.000000, Volume = 549.295375\n",
"T = 220.000000, Volume = 572.278468\n",
"T = 230.000000, Volume = 595.261562\n",
"T = 240.000000, Volume = 618.244655\n",
"T = 250.000000, Volume = 641.227749\n",
"T = 260.000000, Volume = 664.210843\n",
"T = 270.000000, Volume = 687.193938\n",
"T = 280.000000, Volume = 710.177039\n",
"T = 290.000000, Volume = 733.160139\n",
"T = 300.000000, Volume = 756.143240\n",
"T = 310.000000, Volume = 779.126341\n",
"T = 320.000000, Volume = 802.109412\n",
"inflow volume is 802.109412 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 132.184146 s\n",
"[0.047919272271145119, 0.08321850745507281, 0.1135529380862129, 0.63484028569099238, 10.879745649404917, 17.902210705228843, 22.550048570757294, 22.714082182694867, 15.024163887907434, 20.269760075691952, 23.274129831551566, 20.460057725189316, 15.816416469044823, 19.749871996549761, 19.372255914631211, 16.646471692064917, 18.69348783898042, 20.983354878775458, 9.2975128631946067, 8.9257793804601597, 9.0633346966102604, 9.3499419207440653, 9.7357293839720054, 10.025785525080423, 10.199211199548524, 10.242236337885004, 7.2762710104847494, 4.9178077209798241, 4.8328808718099845, 3.7810667622981242, 3.7797993996998511, 12.343080074895786]\n",
"[ 122.08169309 122.12388785 107.95388989 95.40414764 84.490853\n",
" 75.24298082 75.22881244 75.25275034 75.28683406 75.32375755\n",
" 75.35839997 75.38765203 75.40974378 74.07251671 74.07251671\n",
" 79.23038546 89.30807348 101.0396231 114.40753942]\n",
"max arrival time is 315.485021\n",
"sample # 11\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"219.647560037\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.972265\n",
"T = 20.000000, Volume = 75.672415\n",
"T = 30.000000, Volume = 113.372564\n",
"T = 40.000000, Volume = 151.072713\n",
"T = 50.000000, Volume = 188.772862\n",
"T = 60.000000, Volume = 226.473012\n",
"T = 70.000000, Volume = 259.054322\n",
"T = 80.000000, Volume = 282.037306\n",
"T = 90.000000, Volume = 305.020286\n",
"T = 100.000000, Volume = 328.003265\n",
"T = 110.000000, Volume = 350.986277\n",
"T = 120.000000, Volume = 373.969293\n",
"T = 130.000000, Volume = 396.952312\n",
"T = 140.000000, Volume = 419.935338\n",
"T = 150.000000, Volume = 442.918365\n",
"T = 160.000000, Volume = 465.901392\n",
"T = 170.000000, Volume = 488.884419\n",
"T = 180.000000, Volume = 511.867446\n",
"T = 190.000000, Volume = 534.850473\n",
"T = 200.000000, Volume = 557.833513\n",
"T = 210.000000, Volume = 580.816571\n",
"T = 220.000000, Volume = 603.799624\n",
"T = 230.000000, Volume = 626.782676\n",
"T = 240.000000, Volume = 649.765728\n",
"T = 250.000000, Volume = 672.748792\n",
"T = 260.000000, Volume = 695.731856\n",
"T = 270.000000, Volume = 718.714921\n",
"T = 280.000000, Volume = 741.698016\n",
"T = 290.000000, Volume = 764.681105\n",
"T = 300.000000, Volume = 787.664194\n",
"T = 310.000000, Volume = 810.647247\n",
"inflow volume is 810.647247 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 122.582473 s\n",
"[0.047906950168797778, 0.082147738413580501, 0.096531806548370319, 5.155299885289752, 3.9705090806546881, 2.9773275921216484, 21.140148659676012, 15.065607260835627, 18.445529563439013, 18.414161208760287, 16.758112602723813, 16.703355502700923, 15.537302777920067, 8.4363166386824098, 8.5164429851657353, 8.811969726948174, 8.1611748123110353, 7.6369719498761963, 7.6461375170276025, 18.258368341772339, 17.048995798497963, 18.706658054751657, 15.871963806674449, 15.625293314044759, 7.8284911770990311, 7.7729338125968797, 16.794575604036559, 18.848137548227601, 23.707896997371552, 20.627484857641242, 11.664675446219336]\n",
"[ 154.38764846 154.43015032 154.31438507 154.21197301 142.24736231\n",
" 131.89270944 131.84331773 131.80414051 131.772673 125.55509419\n",
" 125.54095451 125.53158178 122.63057624 121.38783776 121.38783776\n",
" 123.46028284 128.04531142 147.61996995 136.4551485 ]\n",
"max arrival time is 302.298725\n",
"sample # 12\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"207.700233628\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.980498\n",
"T = 20.000000, Volume = 75.689634\n",
"T = 30.000000, Volume = 113.398771\n",
"T = 40.000000, Volume = 145.212429\n",
"T = 50.000000, Volume = 168.195409\n",
"T = 60.000000, Volume = 191.178400\n",
"T = 70.000000, Volume = 214.161392\n",
"T = 80.000000, Volume = 237.144384\n",
"T = 90.000000, Volume = 260.127389\n",
"T = 100.000000, Volume = 283.110418\n",
"T = 110.000000, Volume = 306.093448\n",
"T = 120.000000, Volume = 329.076481\n",
"T = 130.000000, Volume = 352.059514\n",
"T = 140.000000, Volume = 375.042547\n",
"T = 150.000000, Volume = 398.025581\n",
"T = 160.000000, Volume = 421.008615\n",
"T = 170.000000, Volume = 443.991649\n",
"T = 180.000000, Volume = 466.974684\n",
"T = 190.000000, Volume = 489.957718\n",
"T = 200.000000, Volume = 512.940764\n",
"T = 210.000000, Volume = 535.923833\n",
"T = 220.000000, Volume = 558.906895\n",
"T = 230.000000, Volume = 581.889958\n",
"T = 240.000000, Volume = 604.873020\n",
"T = 250.000000, Volume = 627.856078\n",
"T = 260.000000, Volume = 650.839184\n",
"T = 270.000000, Volume = 673.822287\n",
"T = 280.000000, Volume = 696.805394\n",
"T = 290.000000, Volume = 719.788510\n",
"T = 300.000000, Volume = 742.771626\n",
"T = 310.000000, Volume = 765.754742\n",
"T = 320.000000, Volume = 788.737859\n",
"inflow volume is 788.737859 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 122.944598 s\n",
"[0.050302767503641259, 0.08608442538294156, 0.10484496912181884, 7.3553694816383954, 13.76621144904216, 7.4202305669786979, 5.6515310181016458, 5.6588918052132522, 14.377642961321834, 15.501650331643045, 12.856354633224377, 7.4629806899762938, 7.8733314772511296, 8.2950928006463993, 8.6078624585079826, 8.9612695526062609, 8.3473507162061527, 7.6410752440458944, 7.6502380277241029, 15.932813364737456, 17.049769980580788, 17.898601626123263, 15.597803340206937, 15.625721357682302, 19.974914148414658, 15.017956323894746, 18.254390226490766, 13.281146131561757, 8.8942068162429599, 8.1370720063043542, 7.7151904663825936, 8.0463289618224678]\n",
"[ 6.25927622e+01 4.74350825e+01 4.71603020e+01 3.18233392e+01\n",
" 3.15419010e+01 3.12601279e+01 3.09738229e+01 3.06828649e+01\n",
" 1.57865154e+01 2.34020027e+01 8.52118471e+00 3.62681240e+00\n",
" 8.13234305e+00 4.71221315e-02 3.94736576e+01 4.71221315e-02\n",
" 1.51301280e+01 5.51545145e+01 2.32027605e+01]\n",
"max arrival time is 291.987673\n",
"sample # 13\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"151.281534882\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.020283\n",
"T = 20.000000, Volume = 75.771780\n",
"T = 30.000000, Volume = 113.523277\n",
"T = 40.000000, Volume = 151.274773\n",
"T = 50.000000, Volume = 189.026270\n",
"T = 60.000000, Volume = 220.836727\n",
"T = 70.000000, Volume = 243.819746\n",
"T = 80.000000, Volume = 266.802776\n",
"T = 90.000000, Volume = 289.785807\n",
"T = 100.000000, Volume = 312.768838\n",
"T = 110.000000, Volume = 335.751870\n",
"T = 120.000000, Volume = 358.734902\n",
"T = 130.000000, Volume = 381.717941\n",
"T = 140.000000, Volume = 404.701020\n",
"T = 150.000000, Volume = 427.684091\n",
"T = 160.000000, Volume = 450.667161\n",
"T = 170.000000, Volume = 473.650263\n",
"T = 180.000000, Volume = 496.633421\n",
"T = 190.000000, Volume = 519.616567\n",
"T = 200.000000, Volume = 542.599714\n",
"T = 210.000000, Volume = 565.582899\n",
"T = 220.000000, Volume = 588.566085\n",
"T = 230.000000, Volume = 611.549272\n",
"T = 240.000000, Volume = 634.532459\n",
"T = 250.000000, Volume = 657.515646\n",
"T = 260.000000, Volume = 680.498836\n",
"T = 270.000000, Volume = 703.482068\n",
"T = 280.000000, Volume = 726.465295\n",
"T = 290.000000, Volume = 749.448521\n",
"T = 300.000000, Volume = 772.431748\n",
"T = 310.000000, Volume = 795.414974\n",
"inflow volume is 795.414974 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 85.533857 s\n",
"[0.047711624951512301, 0.08189936371636776, 0.61298339370329735, 1.5793248355302181, 1.8412528031856696, 11.681329180752096, 5.0152529957558762, 3.0367193634712235, 3.2696134279313958, 3.2731740934641183, 2.6854713728409343, 2.6990066349028279, 7.8669936566039258, 11.622448989670138, 15.116672632892881, 15.081042871419765, 13.41330578665313, 13.492397357002817, 13.343181247827221, 9.3888035417049522, 4.4519062521278974, 4.1049950476549162, 3.9348757874681857, 3.469662941097658, 3.4793059078898536, 9.9752921514316331, 12.553577098865127, 11.480143641297516, 10.322920321423682, 10.323698945467607, 14.058904608788486]\n",
"[ 43.8949182 43.93123329 43.71079397 32.57687902 32.28111517\n",
" 31.98240994 21.23016762 10.57986564 10.31381076 11.54025084\n",
" 13.91657357 10.06183542 13.80049534 11.85821661 9.42420232\n",
" 15.87453734 26.57300692 38.08336229 11.85821661]\n",
"max arrival time is 309.608673\n",
"sample # 14\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 2, 3, 2, 2, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"140.387829672\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.027163\n",
"T = 20.000000, Volume = 75.786179\n",
"T = 30.000000, Volume = 113.545196\n",
"T = 40.000000, Volume = 151.304212\n",
"T = 50.000000, Volume = 181.994205\n",
"T = 60.000000, Volume = 204.977233\n",
"T = 70.000000, Volume = 227.960271\n",
"T = 80.000000, Volume = 250.943311\n",
"T = 90.000000, Volume = 273.926350\n",
"T = 100.000000, Volume = 296.909393\n",
"T = 110.000000, Volume = 319.892491\n",
"T = 120.000000, Volume = 342.875604\n",
"T = 130.000000, Volume = 365.858716\n",
"T = 140.000000, Volume = 388.841828\n",
"T = 150.000000, Volume = 411.824941\n",
"T = 160.000000, Volume = 434.808092\n",
"T = 170.000000, Volume = 457.791269\n",
"T = 180.000000, Volume = 480.774444\n",
"T = 190.000000, Volume = 503.757633\n",
"T = 200.000000, Volume = 526.740852\n",
"T = 210.000000, Volume = 549.724073\n",
"T = 220.000000, Volume = 572.707294\n",
"T = 230.000000, Volume = 595.690516\n",
"T = 240.000000, Volume = 618.673737\n",
"T = 250.000000, Volume = 641.656967\n",
"T = 260.000000, Volume = 664.640244\n",
"T = 270.000000, Volume = 687.623537\n",
"T = 280.000000, Volume = 710.606831\n",
"T = 290.000000, Volume = 733.590145\n",
"T = 300.000000, Volume = 756.573506\n",
"T = 310.000000, Volume = 779.556887\n",
"T = 320.000000, Volume = 802.540271\n",
"inflow volume is 802.540271 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 82.310036 s\n",
"[0.04766820105591079, 0.082889583356384092, 0.11321229633342092, 0.83413342013004355, 7.836349447080809, 4.0886994596222621, 2.4146429374782468, 2.2932826115913705, 2.301771632795679, 6.2483588813792439, 9.6934614741920111, 5.9635669120008918, 5.3701454216454909, 5.235770814941989, 5.2451697663256089, 11.344929967811881, 10.34580824824401, 11.325579560223643, 6.8470071078983663, 3.8291676886128512, 3.8931397303077553, 3.6383742841171998, 3.3029235290080403, 3.3151142569797551, 10.312349622968469, 8.7089334396746043, 5.8322673407668058, 7.6261504636072495, 10.578979197055524, 11.079706781005148, 7.0494529031453039, 9.2660995337300704]\n",
"[ 52.31707777 52.5024003 43.66401363 44.30416205 36.93266661\n",
" 37.16624573 31.09046069 30.87847191 30.76830822 30.95109043\n",
" 28.15265876 26.26777486 26.40611102 26.55219882 33.98848353\n",
" 47.59577812 28.79747841 26.99032743 39.95709963]\n",
"max arrival time is 318.129989\n",
"sample # 15\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"124.839012846\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.035768\n",
"T = 20.000000, Volume = 75.804392\n",
"T = 30.000000, Volume = 113.573016\n",
"T = 40.000000, Volume = 151.341639\n",
"T = 50.000000, Volume = 189.110263\n",
"T = 60.000000, Volume = 226.878887\n",
"T = 70.000000, Volume = 259.880632\n",
"T = 80.000000, Volume = 282.863656\n",
"T = 90.000000, Volume = 305.846694\n",
"T = 100.000000, Volume = 328.829732\n",
"T = 110.000000, Volume = 351.812835\n",
"T = 120.000000, Volume = 374.795963\n",
"T = 130.000000, Volume = 397.779115\n",
"T = 140.000000, Volume = 420.762267\n",
"T = 150.000000, Volume = 443.745419\n",
"T = 160.000000, Volume = 466.728571\n",
"T = 170.000000, Volume = 489.711812\n",
"T = 180.000000, Volume = 512.695072\n",
"T = 190.000000, Volume = 535.678338\n",
"T = 200.000000, Volume = 558.661605\n",
"T = 210.000000, Volume = 581.644871\n",
"T = 220.000000, Volume = 604.628161\n",
"T = 230.000000, Volume = 627.611518\n",
"T = 240.000000, Volume = 650.594896\n",
"T = 250.000000, Volume = 673.578278\n",
"T = 260.000000, Volume = 696.561662\n",
"T = 270.000000, Volume = 719.545046\n",
"T = 280.000000, Volume = 742.528439\n",
"T = 290.000000, Volume = 765.511845\n",
"T = 300.000000, Volume = 788.495252\n",
"inflow volume is 788.495252 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 67.593552 s\n",
"[0.047588201421002209, 0.081753163689861769, 0.73416708588639112, 3.188952535685881, 2.8778512554356523, 2.9342979770424922, 9.2726413182223357, 7.6382703617580621, 4.5868218695187846, 4.6464370887289501, 10.336874733762787, 9.0286196363026967, 5.6137824542349124, 5.1817798593532194, 5.1912252439577315, 8.3911700074725104, 10.470702167793206, 6.202801164562743, 5.5464351729213019, 5.4470899924848961, 5.501223305188125, 10.640094464766877, 8.473898103219728, 6.3866835236243178, 6.5303381027136131, 6.8752657642482475, 6.8892493419378988, 4.0608541958873055, 3.7188741453814425, 3.7132314294301234]\n",
"[ 38.63667068 38.6782353 38.52542383 38.37256713 29.20872447\n",
" 19.92982556 19.76618283 10.37855995 10.76175669 3.74255997\n",
" 0.92917363 0.69286455 0.83327287 0.04742141 0.04742141\n",
" 33.80186556 15.08400756 7.33106549 24.57813594]\n",
"max arrival time is 275.089744\n",
"sample # 16\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"124.65449828\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.035723\n",
"T = 20.000000, Volume = 75.804404\n",
"T = 30.000000, Volume = 113.573085\n",
"T = 40.000000, Volume = 145.545449\n",
"T = 50.000000, Volume = 168.528453\n",
"T = 60.000000, Volume = 191.511487\n",
"T = 70.000000, Volume = 214.494524\n",
"T = 80.000000, Volume = 237.477562\n",
"T = 90.000000, Volume = 260.460604\n",
"T = 100.000000, Volume = 283.443663\n",
"T = 110.000000, Volume = 306.426724\n",
"T = 120.000000, Volume = 329.409785\n",
"T = 130.000000, Volume = 352.392848\n",
"T = 140.000000, Volume = 375.375911\n",
"T = 150.000000, Volume = 398.358974\n",
"T = 160.000000, Volume = 421.342038\n",
"T = 170.000000, Volume = 444.325180\n",
"T = 180.000000, Volume = 467.308335\n",
"T = 190.000000, Volume = 490.291489\n",
"T = 200.000000, Volume = 513.274662\n",
"T = 210.000000, Volume = 536.257916\n",
"T = 220.000000, Volume = 559.241184\n",
"T = 230.000000, Volume = 582.224453\n",
"T = 240.000000, Volume = 605.207722\n",
"T = 250.000000, Volume = 628.190991\n",
"T = 260.000000, Volume = 651.174306\n",
"T = 270.000000, Volume = 674.157666\n",
"T = 280.000000, Volume = 697.141047\n",
"T = 290.000000, Volume = 720.124421\n",
"T = 300.000000, Volume = 743.107858\n",
"T = 310.000000, Volume = 766.091334\n",
"T = 320.000000, Volume = 789.074829\n",
"inflow volume is 789.074829 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 73.997468 s\n",
"[0.050015336599025531, 0.085786633320182326, 0.32963081730892396, 5.6566699649401269, 8.149394803604352, 4.262965260533063, 5.1570350104942158, 5.4490613111773927, 4.9015882836276941, 3.1799552652490517, 3.2462394943357316, 3.2365657807820019, 3.2864198902171262, 2.797016160044496, 2.8104062159728143, 6.1495789000962393, 11.008450425546554, 5.2410289667261809, 5.0573292779239001, 9.2625332335260655, 8.7444160034091638, 6.0274939402423744, 5.6373301587466811, 5.6476983597817751, 7.7155009399283765, 9.7425176119513441, 6.4916848508076006, 5.5088665515605397, 8.2730450399787188, 10.199046167851288, 6.8888737768624768, 6.7169073605426712]\n",
"[ 45.85267598 36.98558732 36.82739884 36.67396279 36.52083278\n",
" 36.36762577 36.21448717 27.04856226 17.76932137 17.60549342\n",
" 10.67232261 3.607529 1.77185512 0.65602229 12.92366423\n",
" 22.41770104 41.52669049 31.64146685 7.28102957]\n",
"max arrival time is 318.652118\n",
"sample # 17\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"137.068381155\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.029207\n",
"T = 20.000000, Volume = 75.790390\n",
"T = 30.000000, Volume = 113.551573\n",
"T = 40.000000, Volume = 151.312756\n",
"T = 50.000000, Volume = 182.021954\n",
"T = 60.000000, Volume = 205.004973\n",
"T = 70.000000, Volume = 227.987998\n",
"T = 80.000000, Volume = 250.971023\n",
"T = 90.000000, Volume = 273.954049\n",
"T = 100.000000, Volume = 296.937082\n",
"T = 110.000000, Volume = 319.920189\n",
"T = 120.000000, Volume = 342.903302\n",
"T = 130.000000, Volume = 365.886420\n",
"T = 140.000000, Volume = 388.869560\n",
"T = 150.000000, Volume = 411.852754\n",
"T = 160.000000, Volume = 434.835965\n",
"T = 170.000000, Volume = 457.819178\n",
"T = 180.000000, Volume = 480.802391\n",
"T = 190.000000, Volume = 503.785606\n",
"T = 200.000000, Volume = 526.768820\n",
"T = 210.000000, Volume = 549.752027\n",
"T = 220.000000, Volume = 572.735314\n",
"T = 230.000000, Volume = 595.718593\n",
"T = 240.000000, Volume = 618.701871\n",
"T = 250.000000, Volume = 641.685155\n",
"T = 260.000000, Volume = 664.668529\n",
"T = 270.000000, Volume = 687.651924\n",
"T = 280.000000, Volume = 710.635327\n",
"T = 290.000000, Volume = 733.618731\n",
"T = 300.000000, Volume = 756.602137\n",
"T = 310.000000, Volume = 779.585543\n",
"T = 320.000000, Volume = 802.568953\n",
"inflow volume is 802.568953 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 80.912475 s\n",
"[0.047653118584096273, 0.082869669049248376, 0.11320586736603884, 0.82367586449962504, 7.6913117424311821, 5.7500712025255485, 4.9553218792791549, 4.8613769341494866, 4.8715022087876578, 8.0814420095714059, 9.281303294629339, 4.8614145075274697, 4.8283621634256875, 9.5866966531619369, 10.39748317352182, 6.3356525571284337, 6.210781542740702, 6.4263330007524777, 5.9677748583593972, 5.9817595910504249, 8.8104316929767954, 10.820600223163581, 12.428336547430519, 12.763727258328798, 12.173775721706209, 11.202515133389795, 6.7089382258127914, 6.8102510084930152, 7.1289360773357142, 7.2575839079563877, 7.3144918369832563, 7.6213794240981887]\n",
"[ 52.75160928 53.00210649 44.53700848 45.12411357 37.95029636\n",
" 31.71804276 31.41914766 31.06591054 26.34061577 22.28891025\n",
" 22.13751527 22.75172016 23.84804971 24.27347932 48.24033386\n",
" 34.58408421 41.05675275 28.3502621 24.2522997 ]\n",
"max arrival time is 318.009340\n",
"sample # 18\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 2, 2, 2, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"150.761513727\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.020408\n",
"T = 20.000000, Volume = 75.772261\n",
"T = 30.000000, Volume = 113.524114\n",
"T = 40.000000, Volume = 151.275967\n",
"T = 50.000000, Volume = 189.027820\n",
"T = 60.000000, Volume = 226.779673\n",
"T = 70.000000, Volume = 259.506420\n",
"T = 80.000000, Volume = 282.489428\n",
"T = 90.000000, Volume = 305.472445\n",
"T = 100.000000, Volume = 328.455461\n",
"T = 110.000000, Volume = 351.438525\n",
"T = 120.000000, Volume = 374.421595\n",
"T = 130.000000, Volume = 397.404665\n",
"T = 140.000000, Volume = 420.387739\n",
"T = 150.000000, Volume = 443.370835\n",
"T = 160.000000, Volume = 466.353931\n",
"T = 170.000000, Volume = 489.337028\n",
"T = 180.000000, Volume = 512.320125\n",
"T = 190.000000, Volume = 535.303222\n",
"T = 200.000000, Volume = 558.286343\n",
"T = 210.000000, Volume = 581.269502\n",
"T = 220.000000, Volume = 604.252651\n",
"T = 230.000000, Volume = 627.235801\n",
"T = 240.000000, Volume = 650.219001\n",
"T = 250.000000, Volume = 673.202231\n",
"T = 260.000000, Volume = 696.185458\n",
"T = 270.000000, Volume = 719.168678\n",
"T = 280.000000, Volume = 742.151969\n",
"T = 290.000000, Volume = 765.135284\n",
"T = 300.000000, Volume = 788.118613\n",
"T = 310.000000, Volume = 811.101896\n",
"inflow volume is 811.101896 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 84.420686 s\n",
"[0.047709624536905561, 0.081894213592486817, 0.46025910194829567, 3.4132234789120983, 3.3915655206758419, 2.8603628632616398, 11.539797119688009, 7.2055229535162857, 5.0888974777851548, 5.178088585834427, 12.398114510101372, 11.085927852315852, 10.412628857546176, 10.097194770366531, 6.9557080829676314, 7.2841495317681684, 6.8526456920296726, 6.5755864288486032, 6.5871582141629599, 11.740972492985556, 13.064037063675684, 14.235450154327735, 15.566749861453667, 11.14230361912499, 14.805839980867447, 14.245661623715671, 14.640692643934578, 11.449510096260218, 7.3183039548177184, 7.4089065989484419, 8.312984727204773]\n",
"[ 79.17879516 79.22111211 79.10076052 78.99228215 70.80199688\n",
" 63.68985653 63.63887042 63.59925106 63.56690669 59.2896357\n",
" 56.15644442 54.16775744 54.15781952 54.15066815 66.83170711\n",
" 60.99697131 54.73217427 74.48384419 57.28927293]\n",
"max arrival time is 304.623142\n",
"sample # 19\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"223.755838334\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.969169\n",
"T = 20.000000, Volume = 75.666170\n",
"T = 30.000000, Volume = 113.363172\n",
"T = 40.000000, Volume = 145.145083\n",
"T = 50.000000, Volume = 168.128061\n",
"T = 60.000000, Volume = 191.111049\n",
"T = 70.000000, Volume = 214.094037\n",
"T = 80.000000, Volume = 237.077026\n",
"T = 90.000000, Volume = 260.060025\n",
"T = 100.000000, Volume = 283.043045\n",
"T = 110.000000, Volume = 306.026057\n",
"T = 120.000000, Volume = 329.009085\n",
"T = 130.000000, Volume = 351.992116\n",
"T = 140.000000, Volume = 374.975146\n",
"T = 150.000000, Volume = 397.958176\n",
"T = 160.000000, Volume = 420.941207\n",
"T = 170.000000, Volume = 443.924238\n",
"T = 180.000000, Volume = 466.907272\n",
"T = 190.000000, Volume = 489.890326\n",
"T = 200.000000, Volume = 512.873384\n",
"T = 210.000000, Volume = 535.856443\n",
"T = 220.000000, Volume = 558.839502\n",
"T = 230.000000, Volume = 581.822561\n",
"T = 240.000000, Volume = 604.805640\n",
"T = 250.000000, Volume = 627.788725\n",
"T = 260.000000, Volume = 650.771808\n",
"T = 270.000000, Volume = 673.754887\n",
"T = 280.000000, Volume = 696.738010\n",
"T = 290.000000, Volume = 719.721129\n",
"T = 300.000000, Volume = 742.704247\n",
"T = 310.000000, Volume = 765.687376\n",
"T = 320.000000, Volume = 788.670506\n",
"inflow volume is 788.670506 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 133.441598 s\n",
"[0.050336641764611736, 0.086122435208825135, 0.10454856110105742, 7.6811050131452392, 14.990299835954042, 6.7624465411350902, 5.8929594186323992, 5.8989247555932254, 15.803441365753923, 17.845863475305293, 20.258534377222027, 4.3099776983384084, 4.2200777628807113, 3.9078435514373298, 3.9527354274242366, 2.817031968785435, 2.8271378757455721, 13.73332843830979, 17.663040562944666, 9.535965367362838, 7.1798823309996731, 7.1078221547672626, 12.343956308432231, 16.002865741929458, 21.640986725141666, 23.319888208525011, 24.453794534426446, 16.163480645037154, 19.836652665823514, 16.961991062530764, 9.4078982474795776, 9.3294862868850092]\n",
"[ 83.4900729 67.21236817 66.94644152 50.48407077 50.20904483\n",
" 49.93150453 49.64764752 33.50612828 33.23004345 25.36180269\n",
" 12.05521654 5.89616987 2.07037574 1.55609406 58.69845933\n",
" 25.21001305 41.54849344 75.48636833 16.85567921]\n",
"max arrival time is 317.714592\n",
"sample # 20\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"88.7084931583\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.043261\n",
"T = 20.000000, Volume = 75.821519\n",
"T = 30.000000, Volume = 113.599777\n",
"T = 40.000000, Volume = 151.378035\n",
"T = 50.000000, Volume = 189.156292\n",
"T = 60.000000, Volume = 226.934550\n",
"T = 70.000000, Volume = 260.580396\n",
"T = 80.000000, Volume = 283.563469\n",
"T = 90.000000, Volume = 306.546573\n",
"T = 100.000000, Volume = 329.529681\n",
"T = 110.000000, Volume = 352.512815\n",
"T = 120.000000, Volume = 375.495965\n",
"T = 130.000000, Volume = 398.479115\n",
"T = 140.000000, Volume = 421.462263\n",
"T = 150.000000, Volume = 444.445561\n",
"T = 160.000000, Volume = 467.428935\n",
"T = 170.000000, Volume = 490.412312\n",
"T = 180.000000, Volume = 513.395691\n",
"T = 190.000000, Volume = 536.379073\n",
"T = 200.000000, Volume = 559.362455\n",
"T = 210.000000, Volume = 582.345838\n",
"T = 220.000000, Volume = 605.329260\n",
"T = 230.000000, Volume = 628.312822\n",
"T = 240.000000, Volume = 651.296430\n",
"T = 250.000000, Volume = 674.280041\n",
"T = 260.000000, Volume = 697.263653\n",
"T = 270.000000, Volume = 720.247265\n",
"T = 280.000000, Volume = 743.230936\n",
"T = 290.000000, Volume = 766.214690\n",
"T = 300.000000, Volume = 789.198483\n",
"inflow volume is 789.198483 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 47.821906 s\n",
"[0.047323799963216111, 0.081495347473803451, 0.84521049147832672, 2.3273762937563851, 2.6480339323281252, 2.801526206652583, 6.8326259547310215, 5.3291546877917879, 4.6583539374463028, 4.8687384523262258, 3.5649362486680771, 2.7407724113636585, 2.7502089792827769, 4.3417166135555973, 5.3435251988903607, 3.0697385003717992, 3.3909320881946585, 3.4591953493158227, 3.2960213908282952, 3.2050404469236207, 3.2588193246414829, 7.1560527510322149, 3.4198980388404845, 3.6584408755970608, 3.4822575540520413, 3.4926140181675738, 5.107834621625523, 8.0310295349815721, 5.1714165172365245, 5.157190064041087]\n",
"[ 26.53066258 26.57215514 26.42337567 26.27449118 19.81994444\n",
" 19.66208603 19.50921054 12.94222899 12.78009936 12.62309435\n",
" 5.9399086 5.99707563 2.58116238 0.04491068 4.25125024\n",
" 23.05781365 16.23661926 0.04491068 9.2927727 ]\n",
"max arrival time is 279.548736\n",
"sample # 21\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 3, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"97.854100093\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.043919\n",
"T = 20.000000, Volume = 75.822497\n",
"T = 30.000000, Volume = 113.601074\n",
"T = 40.000000, Volume = 151.379651\n",
"T = 50.000000, Volume = 189.158229\n",
"T = 60.000000, Volume = 226.936806\n",
"T = 70.000000, Volume = 260.374810\n",
"T = 80.000000, Volume = 283.357869\n",
"T = 90.000000, Volume = 306.340951\n",
"T = 100.000000, Volume = 329.324034\n",
"T = 110.000000, Volume = 352.307117\n",
"T = 120.000000, Volume = 375.290200\n",
"T = 130.000000, Volume = 398.273402\n",
"T = 140.000000, Volume = 421.256664\n",
"T = 150.000000, Volume = 444.239932\n",
"T = 160.000000, Volume = 467.223201\n",
"T = 170.000000, Volume = 490.206469\n",
"T = 180.000000, Volume = 513.189755\n",
"T = 190.000000, Volume = 536.173168\n",
"T = 200.000000, Volume = 559.156620\n",
"T = 210.000000, Volume = 582.140076\n",
"T = 220.000000, Volume = 605.123534\n",
"T = 230.000000, Volume = 628.107013\n",
"T = 240.000000, Volume = 651.090506\n",
"T = 250.000000, Volume = 674.073998\n",
"T = 260.000000, Volume = 697.057539\n",
"T = 270.000000, Volume = 720.041153\n",
"T = 280.000000, Volume = 743.024795\n",
"T = 290.000000, Volume = 766.008430\n",
"T = 300.000000, Volume = 788.992141\n",
"inflow volume is 788.992141 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 53.478829 s\n",
"[0.047406882884188616, 0.081572207814486569, 0.77442703985314409, 2.3326868937254659, 2.715437895400957, 2.9084527078646052, 7.3689832305416312, 5.3549647672377318, 4.8525306761812299, 4.6216028615101896, 4.6288599443698031, 5.7666090153724392, 8.4008204526500663, 4.7479176280150455, 5.1442832556534563, 4.931040571574675, 4.9385841087672473, 7.3766378500489589, 6.6370971402222709, 5.2921321349370078, 5.7092488550869724, 5.5335891232512289, 3.398710738172948, 3.380937576449595, 4.8913055012769195, 8.4717409031572615, 5.4604716393543562, 5.2056504229800993, 7.0651292549088014, 7.356842086899908]\n",
"[ 34.50340223 34.54528167 34.4088163 34.27265476 27.38000235\n",
" 27.20917307 20.30907918 20.13280212 13.23283346 13.06842346\n",
" 12.91241287 6.96764336 2.87924741 0.04757453 23.72495376\n",
" 4.78925199 9.44928382 30.81200397 16.66105236]\n",
"max arrival time is 299.648406\n",
"sample # 22\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 3, 2, 2, 2, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"212.897654838\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.976873\n",
"T = 20.000000, Volume = 75.682071\n",
"T = 30.000000, Volume = 113.387270\n",
"T = 40.000000, Volume = 151.092469\n",
"T = 50.000000, Volume = 181.609726\n",
"T = 60.000000, Volume = 204.592711\n",
"T = 70.000000, Volume = 227.575694\n",
"T = 80.000000, Volume = 250.558685\n",
"T = 90.000000, Volume = 273.541676\n",
"T = 100.000000, Volume = 296.524672\n",
"T = 110.000000, Volume = 319.507697\n",
"T = 120.000000, Volume = 342.490726\n",
"T = 130.000000, Volume = 365.473755\n",
"T = 140.000000, Volume = 388.456796\n",
"T = 150.000000, Volume = 411.439859\n",
"T = 160.000000, Volume = 434.422915\n",
"T = 170.000000, Volume = 457.405972\n",
"T = 180.000000, Volume = 480.389028\n",
"T = 190.000000, Volume = 503.372104\n",
"T = 200.000000, Volume = 526.355180\n",
"T = 210.000000, Volume = 549.338256\n",
"T = 220.000000, Volume = 572.321333\n",
"T = 230.000000, Volume = 595.304412\n",
"T = 240.000000, Volume = 618.287515\n",
"T = 250.000000, Volume = 641.270611\n",
"T = 260.000000, Volume = 664.253707\n",
"T = 270.000000, Volume = 687.236816\n",
"T = 280.000000, Volume = 710.219958\n",
"T = 290.000000, Volume = 733.203093\n",
"T = 300.000000, Volume = 756.186227\n",
"T = 310.000000, Volume = 779.169362\n",
"T = 320.000000, Volume = 802.152470\n",
"inflow volume is 802.152470 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 125.986053 s\n",
"[0.047895129360550133, 0.083182743320939445, 0.11327970220220089, 0.66205833895393762, 10.677886854814821, 15.675213275452641, 16.463614805387518, 6.2247933383091247, 6.2385644467046424, 12.744716458167384, 13.279296347226339, 9.0074699957013209, 6.6102917610351781, 15.260254302554509, 15.566499773361551, 17.557649483431298, 15.087610935544316, 13.095022632089519, 5.0978295416787498, 3.9267697130378476, 3.1346585787782568, 3.1469468742820892, 14.195906257768431, 18.271286716305752, 20.92657951714088, 22.676863756074322, 18.009302768485554, 17.0063456373266, 18.25968642075815, 15.275088514278361, 14.571116355546682, 18.222832217380823]\n",
"[ 107.3328938 107.37674627 94.01103793 93.95619949 82.99357599\n",
" 73.52358178 73.3970604 73.2817523 73.19132106 67.84290471\n",
" 64.31059844 64.37102658 64.40357199 64.41333175 65.38731191\n",
" 87.90977545 69.83766347 77.67879012 100.09522863]\n",
"max arrival time is 316.347238\n",
"sample # 23\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"164.992886386\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.010931\n",
"T = 20.000000, Volume = 75.752447\n",
"T = 30.000000, Volume = 113.493962\n",
"T = 40.000000, Volume = 151.235477\n",
"T = 50.000000, Volume = 188.976993\n",
"T = 60.000000, Volume = 220.657999\n",
"T = 70.000000, Volume = 243.641010\n",
"T = 80.000000, Volume = 266.624030\n",
"T = 90.000000, Volume = 289.607051\n",
"T = 100.000000, Volume = 312.590071\n",
"T = 110.000000, Volume = 335.573091\n",
"T = 120.000000, Volume = 358.556154\n",
"T = 130.000000, Volume = 381.539221\n",
"T = 140.000000, Volume = 404.522306\n",
"T = 150.000000, Volume = 427.505391\n",
"T = 160.000000, Volume = 450.488477\n",
"T = 170.000000, Volume = 473.471564\n",
"T = 180.000000, Volume = 496.454651\n",
"T = 190.000000, Volume = 519.437738\n",
"T = 200.000000, Volume = 542.420826\n",
"T = 210.000000, Volume = 565.403913\n",
"T = 220.000000, Volume = 588.386998\n",
"T = 230.000000, Volume = 611.370128\n",
"T = 240.000000, Volume = 634.353248\n",
"T = 250.000000, Volume = 657.336369\n",
"T = 260.000000, Volume = 680.319497\n",
"T = 270.000000, Volume = 703.302690\n",
"T = 280.000000, Volume = 726.285875\n",
"T = 290.000000, Volume = 749.269070\n",
"T = 300.000000, Volume = 772.252275\n",
"T = 310.000000, Volume = 795.235480\n",
"inflow volume is 795.235480 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 93.196866 s\n",
"[0.04776182557924713, 0.081964647182793227, 0.30826464892363326, 1.6608052063753473, 1.9242898427418718, 12.370040606554221, 5.27747488825253, 3.024747267397121, 2.4454111145947612, 2.4550040013707237, 6.4490050808752981, 12.232438619104808, 8.7927916772084771, 3.7228004708171247, 3.8015436359177239, 3.770251255367139, 4.0916087824520844, 3.9837578973013832, 4.1093863415162009, 3.2291335354992983, 3.2424932767756647, 7.9585338687985026, 13.46023570599907, 16.902200153410774, 17.256011824958598, 14.154817735240572, 13.630038975463339, 16.174491719799569, 7.5842808986016275, 6.8224272544139239, 10.804798681184046]\n",
"[ 48.69164621 48.72797161 48.4908084 36.3560297 36.19702942\n",
" 24.15313289 23.89731999 23.74618294 23.57477072 23.40648344\n",
" 16.61687061 8.55830302 16.02544779 4.39986658 42.4211777\n",
" 30.10903184 17.34073945 4.39986658 11.25875075]\n",
"max arrival time is 309.990301\n",
"sample # 24\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"142.697551021\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.025367\n",
"T = 20.000000, Volume = 75.782838\n",
"T = 30.000000, Volume = 113.540310\n",
"T = 40.000000, Volume = 151.297782\n",
"T = 50.000000, Volume = 189.055253\n",
"T = 60.000000, Volume = 226.812725\n",
"T = 70.000000, Volume = 259.603443\n",
"T = 80.000000, Volume = 282.586455\n",
"T = 90.000000, Volume = 305.569477\n",
"T = 100.000000, Volume = 328.552499\n",
"T = 110.000000, Volume = 351.535573\n",
"T = 120.000000, Volume = 374.518655\n",
"T = 130.000000, Volume = 397.501753\n",
"T = 140.000000, Volume = 420.484862\n",
"T = 150.000000, Volume = 443.467971\n",
"T = 160.000000, Volume = 466.451081\n",
"T = 170.000000, Volume = 489.434261\n",
"T = 180.000000, Volume = 512.417430\n",
"T = 190.000000, Volume = 535.400624\n",
"T = 200.000000, Volume = 558.383828\n",
"T = 210.000000, Volume = 581.367093\n",
"T = 220.000000, Volume = 604.350350\n",
"T = 230.000000, Volume = 627.333629\n",
"T = 240.000000, Volume = 650.316930\n",
"T = 250.000000, Volume = 673.300275\n",
"T = 260.000000, Volume = 696.283640\n",
"T = 270.000000, Volume = 719.267009\n",
"T = 280.000000, Volume = 742.250380\n",
"T = 290.000000, Volume = 765.233751\n",
"T = 300.000000, Volume = 788.217122\n",
"T = 310.000000, Volume = 811.200456\n",
"inflow volume is 811.200456 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 79.930272 s\n",
"[0.047675814493799963, 0.0818548021198367, 0.74351695029279041, 3.4347526770599326, 3.1879759830879899, 2.8562848572084345, 10.838257376123737, 9.2455000335920818, 4.8551378190635894, 4.9049795415300181, 11.524439468479274, 10.943873552965723, 8.1579187696333921, 5.4909672830742808, 5.5038069944997803, 9.6564674930777521, 11.174864357668159, 11.678381207154718, 5.4189362560450025, 10.78404605191022, 12.906739776580158, 9.9777289029438911, 6.9924886120568344, 10.61090740025497, 11.0806634496386, 7.0091371173237675, 7.0077854247120435, 7.2704802006274996, 7.5830275879920022, 6.72567873149353, 6.6719368139745985]\n",
"[ 74.03666365 74.07984334 73.98549424 73.91276614 66.16706936\n",
" 59.43170643 59.38573095 54.23011201 50.13132827 47.09469901\n",
" 47.11388957 47.13714527 47.15020743 47.15347898 56.40434964\n",
" 51.77416815 62.41832789 48.20759256 69.67343912]\n",
"max arrival time is 304.520471\n",
"sample # 25\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"145.981040594\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.023289\n",
"T = 20.000000, Volume = 75.778503\n",
"T = 30.000000, Volume = 113.533718\n",
"T = 40.000000, Volume = 145.457138\n",
"T = 50.000000, Volume = 168.440129\n",
"T = 60.000000, Volume = 191.423121\n",
"T = 70.000000, Volume = 214.406134\n",
"T = 80.000000, Volume = 237.389221\n",
"T = 90.000000, Volume = 260.372317\n",
"T = 100.000000, Volume = 283.355416\n",
"T = 110.000000, Volume = 306.338515\n",
"T = 120.000000, Volume = 329.321614\n",
"T = 130.000000, Volume = 352.304760\n",
"T = 140.000000, Volume = 375.287915\n",
"T = 150.000000, Volume = 398.271086\n",
"T = 160.000000, Volume = 421.254283\n",
"T = 170.000000, Volume = 444.237482\n",
"T = 180.000000, Volume = 467.220681\n",
"T = 190.000000, Volume = 490.203881\n",
"T = 200.000000, Volume = 513.187081\n",
"T = 210.000000, Volume = 536.170282\n",
"T = 220.000000, Volume = 559.153483\n",
"T = 230.000000, Volume = 582.136684\n",
"T = 240.000000, Volume = 605.119915\n",
"T = 250.000000, Volume = 628.103161\n",
"T = 260.000000, Volume = 651.086404\n",
"T = 270.000000, Volume = 674.069646\n",
"T = 280.000000, Volume = 697.052911\n",
"T = 290.000000, Volume = 720.036182\n",
"T = 300.000000, Volume = 743.019455\n",
"T = 310.000000, Volume = 766.002728\n",
"T = 320.000000, Volume = 788.986001\n",
"inflow volume is 788.986001 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 86.222094 s\n",
"[0.050117367366379971, 0.087228371627273094, 0.27801222037736922, 6.074040772143138, 9.8738012816092766, 12.413656459378943, 13.874212414298293, 11.615886422782074, 6.2782677943460969, 5.2040285080041988, 5.0944563124563702, 5.4726411605202196, 11.747540297442239, 10.958959219624992, 7.3052091944083966, 3.5043265620963031, 3.5097063619810687, 3.6652741415400478, 3.6437901292378081, 3.9095921698406673, 3.2230907955039911, 3.0876485696457676, 4.8008917342034803, 11.018785240138184, 10.463860257221665, 10.620944034608506, 12.466582663263749, 7.7912529919962088, 7.7465271853224298, 7.2479667847140705, 7.0196335060779926, 7.0394817739041056]\n",
"[ 43.15866761 32.58039311 21.68707573 21.52180161 10.70526564\n",
" 10.42729672 10.27277953 10.03152995 13.63815179 6.50362063\n",
" 1.22223472 2.16765995 1.4265378 0.04775816 0.04775816\n",
" 37.98068855 27.14243605 16.07001825 9.23552844]\n",
"max arrival time is 290.921053\n",
"sample # 26\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"222.347745467\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.970239\n",
"T = 20.000000, Volume = 75.668362\n",
"T = 30.000000, Volume = 113.366485\n",
"T = 40.000000, Volume = 145.154979\n",
"T = 50.000000, Volume = 168.137957\n",
"T = 60.000000, Volume = 191.120945\n",
"T = 70.000000, Volume = 214.103934\n",
"T = 80.000000, Volume = 237.086923\n",
"T = 90.000000, Volume = 260.069913\n",
"T = 100.000000, Volume = 283.052902\n",
"T = 110.000000, Volume = 306.035892\n",
"T = 120.000000, Volume = 329.018882\n",
"T = 130.000000, Volume = 352.001872\n",
"T = 140.000000, Volume = 374.984862\n",
"T = 150.000000, Volume = 397.967883\n",
"T = 160.000000, Volume = 420.950905\n",
"T = 170.000000, Volume = 443.933930\n",
"T = 180.000000, Volume = 466.916956\n",
"T = 190.000000, Volume = 489.899981\n",
"T = 200.000000, Volume = 512.883007\n",
"T = 210.000000, Volume = 535.866033\n",
"T = 220.000000, Volume = 558.849059\n",
"T = 230.000000, Volume = 581.832085\n",
"T = 240.000000, Volume = 604.815136\n",
"T = 250.000000, Volume = 627.798187\n",
"T = 260.000000, Volume = 650.781237\n",
"T = 270.000000, Volume = 673.764286\n",
"T = 280.000000, Volume = 696.747379\n",
"T = 290.000000, Volume = 719.730465\n",
"T = 300.000000, Volume = 742.713551\n",
"T = 310.000000, Volume = 765.696640\n",
"T = 320.000000, Volume = 788.679767\n",
"inflow volume is 788.679767 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 132.487463 s\n",
"[0.050333688373105895, 0.086115965479723375, 0.10454156433140262, 7.6529408474992771, 14.861436655578521, 6.7623910486881211, 7.5498661049379656, 7.7751130037203815, 8.0791116935466771, 8.5432818482299684, 8.9005376786105526, 7.399019182353114, 7.3716243468346274, 12.027141549928038, 15.799422353216995, 11.729095424231875, 8.4604311915243304, 8.6698299983086784, 8.5705026096426966, 9.2664423958604623, 8.0306626272351433, 7.8266602837610542, 13.487885692152528, 16.375908092155786, 21.793672650685959, 22.835988156619866, 22.835372885215214, 17.751834556111469, 23.931066821470147, 23.984213081870838, 17.932906405148795, 17.173140265958519]\n",
"[ 8.21985989e+01 6.60286797e+01 6.57599960e+01 6.54888902e+01\n",
" 6.52097159e+01 6.49227765e+01 4.88286229e+01 4.85426991e+01\n",
" 4.82573661e+01 4.79669323e+01 3.22706618e+01 2.42074627e+01\n",
" 4.49358415e+00 4.96409897e-02 2.44339184e+01 7.42486746e+01\n",
" 5.68457703e+01 1.62781562e+01 4.00853509e+01]\n",
"max arrival time is 319.352285\n",
"sample # 27\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"142.010288959\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.026057\n",
"T = 20.000000, Volume = 75.784000\n",
"T = 30.000000, Volume = 113.541943\n",
"T = 40.000000, Volume = 151.299886\n",
"T = 50.000000, Volume = 189.057829\n",
"T = 60.000000, Volume = 226.815772\n",
"T = 70.000000, Volume = 259.624398\n",
"T = 80.000000, Volume = 282.607410\n",
"T = 90.000000, Volume = 305.590432\n",
"T = 100.000000, Volume = 328.573455\n",
"T = 110.000000, Volume = 351.556529\n",
"T = 120.000000, Volume = 374.539612\n",
"T = 130.000000, Volume = 397.522695\n",
"T = 140.000000, Volume = 420.505778\n",
"T = 150.000000, Volume = 443.488861\n",
"T = 160.000000, Volume = 466.471950\n",
"T = 170.000000, Volume = 489.455131\n",
"T = 180.000000, Volume = 512.438302\n",
"T = 190.000000, Volume = 535.421471\n",
"T = 200.000000, Volume = 558.404678\n",
"T = 210.000000, Volume = 581.387894\n",
"T = 220.000000, Volume = 604.371110\n",
"T = 230.000000, Volume = 627.354327\n",
"T = 240.000000, Volume = 650.337544\n",
"T = 250.000000, Volume = 673.320758\n",
"T = 260.000000, Volume = 696.304016\n",
"T = 270.000000, Volume = 719.287279\n",
"T = 280.000000, Volume = 742.270541\n",
"T = 290.000000, Volume = 765.253803\n",
"T = 300.000000, Volume = 788.237065\n",
"T = 310.000000, Volume = 811.220295\n",
"inflow volume is 811.220295 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 78.843201 s\n",
"[0.047672835796164409, 0.081847533662040353, 0.76898058562374261, 3.1479018116859994, 2.9968693151430754, 2.834311296577801, 10.725102977309207, 9.2415463438124945, 4.8396857699798632, 4.8931897181884016, 11.458947180856121, 10.23788150434247, 11.456690740581106, 10.59108174735125, 10.549899095030277, 12.100934335080199, 10.898782924238436, 11.377643919257359, 10.155709905641581, 4.4070908274106886, 3.9706227913029468, 4.0830805086752999, 3.3763696826229035, 3.3865586979632227, 6.7096397764052149, 12.504485256145175, 11.088662448268169, 10.13113439246025, 9.8979945410882202, 9.8932809925704266, 9.3732823100435834]\n",
"[ 71.68389305 71.7263972 71.61153446 71.5095687 63.7867881\n",
" 57.08845362 57.03883954 51.92884271 51.90566437 51.8895602\n",
" 51.87760624 49.45759625 49.45047344 48.64187605 54.08650607\n",
" 48.64187605 67.25704712 60.04214143 50.26535946]\n",
"max arrival time is 302.912676\n",
"sample # 28\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"220.63323222\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.971391\n",
"T = 20.000000, Volume = 75.670790\n",
"T = 30.000000, Volume = 113.370188\n",
"T = 40.000000, Volume = 151.069587\n",
"T = 50.000000, Volume = 188.768986\n",
"T = 60.000000, Volume = 226.468384\n",
"T = 70.000000, Volume = 258.863805\n",
"T = 80.000000, Volume = 281.846794\n",
"T = 90.000000, Volume = 304.829791\n",
"T = 100.000000, Volume = 327.812788\n",
"T = 110.000000, Volume = 350.795785\n",
"T = 120.000000, Volume = 373.778783\n",
"T = 130.000000, Volume = 396.761780\n",
"T = 140.000000, Volume = 419.744777\n",
"T = 150.000000, Volume = 442.727800\n",
"T = 160.000000, Volume = 465.710823\n",
"T = 170.000000, Volume = 488.693850\n",
"T = 180.000000, Volume = 511.676876\n",
"T = 190.000000, Volume = 534.659903\n",
"T = 200.000000, Volume = 557.642930\n",
"T = 210.000000, Volume = 580.625958\n",
"T = 220.000000, Volume = 603.608985\n",
"T = 230.000000, Volume = 626.592011\n",
"T = 240.000000, Volume = 649.575067\n",
"T = 250.000000, Volume = 672.558119\n",
"T = 260.000000, Volume = 695.541171\n",
"T = 270.000000, Volume = 718.524225\n",
"T = 280.000000, Volume = 741.507319\n",
"T = 290.000000, Volume = 764.490408\n",
"T = 300.000000, Volume = 787.473496\n",
"T = 310.000000, Volume = 810.456548\n",
"inflow volume is 810.456548 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 122.078670 s\n",
"[0.047908946681099088, 0.08215061009763118, 0.096519323484277206, 5.0139727853285736, 4.3716551941224173, 3.0517276475912456, 17.163640451160859, 7.951516422334838, 3.9040225634847201, 4.1349899533584811, 4.2085826466167919, 2.8032110370669536, 2.814051922143388, 10.310032380968138, 15.863911037211501, 13.988770059420329, 8.2614502931671669, 8.7548803896136835, 8.7413044845385386, 9.4245636954517131, 7.9503908538178649, 7.9613193188048088, 14.186480296512757, 16.319502088890506, 19.972162778537669, 20.700401381673196, 21.483135284533013, 17.844309538085465, 23.808812392703015, 20.690949690075559, 10.016156858835549]\n",
"[ 151.48643391 151.52897195 151.41317952 151.31088553 139.27063707\n",
" 139.20108769 139.14395372 130.40418493 130.37133096 130.34644133\n",
" 130.32768904 125.74878479 122.8332175 121.58424874 127.41594228\n",
" 134.15513641 123.66731365 121.58424874 144.67758626]\n",
"max arrival time is 302.636924\n",
"sample # 29\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"189.912733948\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.993352\n",
"T = 20.000000, Volume = 75.716042\n",
"T = 30.000000, Volume = 113.438731\n",
"T = 40.000000, Volume = 151.161420\n",
"T = 50.000000, Volume = 181.669480\n",
"T = 60.000000, Volume = 204.652473\n",
"T = 70.000000, Volume = 227.635471\n",
"T = 80.000000, Volume = 250.618469\n",
"T = 90.000000, Volume = 273.601467\n",
"T = 100.000000, Volume = 296.584466\n",
"T = 110.000000, Volume = 319.567464\n",
"T = 120.000000, Volume = 342.550476\n",
"T = 130.000000, Volume = 365.533517\n",
"T = 140.000000, Volume = 388.516563\n",
"T = 150.000000, Volume = 411.499609\n",
"T = 160.000000, Volume = 434.482681\n",
"T = 170.000000, Volume = 457.465763\n",
"T = 180.000000, Volume = 480.448842\n",
"T = 190.000000, Volume = 503.431938\n",
"T = 200.000000, Volume = 526.415034\n",
"T = 210.000000, Volume = 549.398130\n",
"T = 220.000000, Volume = 572.381225\n",
"T = 230.000000, Volume = 595.364325\n",
"T = 240.000000, Volume = 618.347462\n",
"T = 250.000000, Volume = 641.330592\n",
"T = 260.000000, Volume = 664.313728\n",
"T = 270.000000, Volume = 687.296874\n",
"T = 280.000000, Volume = 710.280019\n",
"T = 290.000000, Volume = 733.263165\n",
"T = 300.000000, Volume = 756.246306\n",
"T = 310.000000, Volume = 779.229482\n",
"T = 320.000000, Volume = 802.212639\n",
"inflow volume is 802.212639 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 111.620469 s\n",
"[0.04783943109485795, 0.083108046571343255, 0.11326464581030425, 0.75299920643139617, 9.5430595264547637, 8.8462432180923969, 6.3143628364758202, 7.1168532515923255, 6.9439545286025215, 6.14884641513442, 6.1628759216262434, 13.101322961995809, 11.442803729101584, 6.1728599055897044, 6.1854317354572608, 15.623610298564502, 15.568594549876309, 17.139109488633714, 8.0663298855326211, 7.5111469948674596, 6.7885908610681129, 6.7994574795263203, 14.145849287503284, 15.412206100199024, 17.637212183507714, 10.072648032841528, 8.3433298704205789, 7.2552359622826552, 7.2663966314961046, 12.298404728218983, 14.340413290809904, 18.396095154681621]\n",
"[ 86.01994062 86.06190748 74.10900865 73.99178498 73.89892667\n",
" 64.85582785 57.27962973 57.2788131 57.26904933 52.58171963\n",
" 52.48956933 52.371947 50.47993816 50.38438515 79.52933031\n",
" 54.39572986 50.82589248 68.84158988 60.53486438]\n",
"max arrival time is 316.826802\n",
"sample # 30\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"200.795677155\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.985519\n",
"T = 20.000000, Volume = 75.699900\n",
"T = 30.000000, Volume = 113.414282\n",
"T = 40.000000, Volume = 151.128663\n",
"T = 50.000000, Volume = 181.632883\n",
"T = 60.000000, Volume = 204.615877\n",
"T = 70.000000, Volume = 227.598879\n",
"T = 80.000000, Volume = 250.581882\n",
"T = 90.000000, Volume = 273.564885\n",
"T = 100.000000, Volume = 296.547888\n",
"T = 110.000000, Volume = 319.530892\n",
"T = 120.000000, Volume = 342.513895\n",
"T = 130.000000, Volume = 365.496899\n",
"T = 140.000000, Volume = 388.479903\n",
"T = 150.000000, Volume = 411.462908\n",
"T = 160.000000, Volume = 434.445913\n",
"T = 170.000000, Volume = 457.428918\n",
"T = 180.000000, Volume = 480.411923\n",
"T = 190.000000, Volume = 503.394928\n",
"T = 200.000000, Volume = 526.377933\n",
"T = 210.000000, Volume = 549.360946\n",
"T = 220.000000, Volume = 572.343979\n",
"T = 230.000000, Volume = 595.327019\n",
"T = 240.000000, Volume = 618.310058\n",
"T = 250.000000, Volume = 641.293113\n",
"T = 260.000000, Volume = 664.276190\n",
"T = 270.000000, Volume = 687.259260\n",
"T = 280.000000, Volume = 710.242330\n",
"T = 290.000000, Volume = 733.225432\n",
"T = 300.000000, Volume = 756.208548\n",
"T = 310.000000, Volume = 779.191662\n",
"T = 320.000000, Volume = 802.174754\n",
"inflow volume is 802.174754 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 116.461751 s\n",
"[0.047867233695478469, 0.083147140403328906, 0.11326733624947026, 0.71726963325610715, 9.9919740996046631, 8.149075497200915, 3.1392664678904909, 3.6816224014959391, 3.7215314630060075, 3.8030064390026257, 3.6760890392098533, 4.1718953996254662, 3.941069597732711, 4.3163845829141136, 4.170756032770309, 4.5735916662191887, 4.3177937289559178, 4.2470552139414046, 3.251670904562042, 3.2643778192924606, 12.599613535539167, 14.756606462465211, 7.4476558196875526, 7.2202693680836152, 16.507736704017283, 18.131749302809638, 19.535584594392638, 19.019387825744765, 14.406370354489601, 17.728971740884706, 17.908822750119512, 16.025236454786921]\n",
"[ 86.26637072 86.31012784 73.40948885 73.37316089 73.36653246\n",
" 73.37357536 73.3793115 73.38021431 73.3789481 73.36826843\n",
" 67.31227119 62.84545972 59.99425953 58.76609214 79.35382497\n",
" 60.79511793 58.76609214 64.45310742 69.74100126]\n",
"max arrival time is 316.520526\n",
"sample # 31\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 3, 2, 2, 2, 3, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"135.986092193\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.029808\n",
"T = 20.000000, Volume = 75.791700\n",
"T = 30.000000, Volume = 113.553593\n",
"T = 40.000000, Volume = 145.500664\n",
"T = 50.000000, Volume = 168.483664\n",
"T = 60.000000, Volume = 191.466705\n",
"T = 70.000000, Volume = 214.449756\n",
"T = 80.000000, Volume = 237.432857\n",
"T = 90.000000, Volume = 260.415946\n",
"T = 100.000000, Volume = 283.399036\n",
"T = 110.000000, Volume = 306.382173\n",
"T = 120.000000, Volume = 329.365310\n",
"T = 130.000000, Volume = 352.348448\n",
"T = 140.000000, Volume = 375.331585\n",
"T = 150.000000, Volume = 398.314773\n",
"T = 160.000000, Volume = 421.297981\n",
"T = 170.000000, Volume = 444.281197\n",
"T = 180.000000, Volume = 467.264414\n",
"T = 190.000000, Volume = 490.247632\n",
"T = 200.000000, Volume = 513.230852\n",
"T = 210.000000, Volume = 536.214078\n",
"T = 220.000000, Volume = 559.197315\n",
"T = 230.000000, Volume = 582.180552\n",
"T = 240.000000, Volume = 605.163817\n",
"T = 250.000000, Volume = 628.147120\n",
"T = 260.000000, Volume = 651.130435\n",
"T = 270.000000, Volume = 674.113751\n",
"T = 280.000000, Volume = 697.097067\n",
"T = 290.000000, Volume = 720.080374\n",
"T = 300.000000, Volume = 743.063757\n",
"T = 310.000000, Volume = 766.047151\n",
"T = 320.000000, Volume = 789.030564\n",
"inflow volume is 789.030564 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 80.726567 s\n",
"[0.050073510385580978, 0.087182936937503661, 0.30552628084362232, 5.8587954345888607, 8.0614669935051637, 2.0649096075134397, 6.3703704132683514, 9.3213792639400062, 9.9613141254158073, 8.9218854448970895, 3.4083807250699731, 2.5664232707697097, 2.5185032751984964, 4.9973679660860579, 11.900947243990478, 6.3299689185121046, 6.0048867658880454, 6.322428704367649, 6.5172663169021297, 6.8847128003282405, 4.0498892276028542, 3.1433799371202649, 4.5067887871555286, 11.98874241276598, 7.2706202180731294, 6.3012570469585949, 5.939559051129697, 5.9473278340223583, 9.8472527307627082, 11.510830084434447, 7.291820923582522, 6.9514599389653489]\n",
"[ 50.00363398 40.28474706 30.25726273 30.09514383 29.93869963\n",
" 19.8077199 19.64248143 19.48238849 19.32611696 10.15171948\n",
" 11.93772069 4.47404455 2.36183065 0.72579465 45.2528478\n",
" 35.27905357 24.88364522 14.21710931 8.10581917]\n",
"max arrival time is 319.258387\n",
"sample # 32\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"141.562570245\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.025983\n",
"T = 20.000000, Volume = 75.784197\n",
"T = 30.000000, Volume = 113.542410\n",
"T = 40.000000, Volume = 145.475924\n",
"T = 50.000000, Volume = 168.458918\n",
"T = 60.000000, Volume = 191.441938\n",
"T = 70.000000, Volume = 214.424959\n",
"T = 80.000000, Volume = 237.407980\n",
"T = 90.000000, Volume = 260.391027\n",
"T = 100.000000, Volume = 283.374127\n",
"T = 110.000000, Volume = 306.357235\n",
"T = 120.000000, Volume = 329.340344\n",
"T = 130.000000, Volume = 352.323453\n",
"T = 140.000000, Volume = 375.306564\n",
"T = 150.000000, Volume = 398.289674\n",
"T = 160.000000, Volume = 421.272781\n",
"T = 170.000000, Volume = 444.255964\n",
"T = 180.000000, Volume = 467.239135\n",
"T = 190.000000, Volume = 490.222322\n",
"T = 200.000000, Volume = 513.205537\n",
"T = 210.000000, Volume = 536.188753\n",
"T = 220.000000, Volume = 559.171969\n",
"T = 230.000000, Volume = 582.155185\n",
"T = 240.000000, Volume = 605.138426\n",
"T = 250.000000, Volume = 628.121691\n",
"T = 260.000000, Volume = 651.104979\n",
"T = 270.000000, Volume = 674.088268\n",
"T = 280.000000, Volume = 697.071558\n",
"T = 290.000000, Volume = 720.054849\n",
"T = 300.000000, Volume = 743.038141\n",
"T = 310.000000, Volume = 766.021433\n",
"T = 320.000000, Volume = 789.004726\n",
"inflow volume is 789.004726 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 83.517128 s\n",
"[0.050097255715466048, 0.085868922733315256, 0.28842628426083966, 5.9602894624071103, 9.6212349967717685, 4.5772940774569175, 4.6358807688755652, 4.6471223529196983, 9.4197381513845091, 8.9630177154919242, 5.7033257011128127, 5.9551221902012195, 6.2363373095081753, 5.6460258912011492, 5.6562211797805766, 8.377462896688332, 10.800544597106299, 10.904505262514823, 6.9082045710330604, 3.790930513604291, 3.2905808913378665, 3.1483532590564529, 4.43360119324075, 11.233486138204487, 10.795080908909817, 6.9673824034273686, 6.998782285027068, 7.3647823078081789, 7.6130484000771252, 7.2021437459663469, 6.924302677451684, 6.9195578894228529]\n",
"[ 41.89364495 31.64722966 31.4850084 20.92725055 20.76231586\n",
" 20.60254814 9.929852 9.76214599 12.99434103 5.34146068\n",
" 2.97794894 0.8110741 0.84312275 0.04776205 26.21748853\n",
" 0.04776205 15.27765631 36.8814291 8.80452519]\n",
"max arrival time is 292.249859\n",
"sample # 33\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"194.752318936\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.989728\n",
"T = 20.000000, Volume = 75.708682\n",
"T = 30.000000, Volume = 113.427635\n",
"T = 40.000000, Volume = 145.265845\n",
"T = 50.000000, Volume = 168.248827\n",
"T = 60.000000, Volume = 191.231821\n",
"T = 70.000000, Volume = 214.214817\n",
"T = 80.000000, Volume = 237.197813\n",
"T = 90.000000, Volume = 260.180810\n",
"T = 100.000000, Volume = 283.163807\n",
"T = 110.000000, Volume = 306.146804\n",
"T = 120.000000, Volume = 329.129801\n",
"T = 130.000000, Volume = 352.112831\n",
"T = 140.000000, Volume = 375.095861\n",
"T = 150.000000, Volume = 398.078889\n",
"T = 160.000000, Volume = 421.061917\n",
"T = 170.000000, Volume = 444.045002\n",
"T = 180.000000, Volume = 467.028077\n",
"T = 190.000000, Volume = 490.011151\n",
"T = 200.000000, Volume = 512.994227\n",
"T = 210.000000, Volume = 535.977316\n",
"T = 220.000000, Volume = 558.960406\n",
"T = 230.000000, Volume = 581.943496\n",
"T = 240.000000, Volume = 604.926586\n",
"T = 250.000000, Volume = 627.909674\n",
"T = 260.000000, Volume = 650.892797\n",
"T = 270.000000, Volume = 673.875919\n",
"T = 280.000000, Volume = 696.859041\n",
"T = 290.000000, Volume = 719.842162\n",
"T = 300.000000, Volume = 742.825297\n",
"T = 310.000000, Volume = 765.808434\n",
"T = 320.000000, Volume = 788.791571\n",
"inflow volume is 788.791571 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 115.809201 s\n",
"[0.050272261411228071, 0.086048037387806267, 0.13744444574479872, 7.113091880714518, 12.936423133793944, 7.1920945996136583, 6.6360091002429389, 6.9857539755477616, 7.4529065942560848, 6.7741693111859016, 6.4341347552303292, 7.7696353408471044, 14.995060201038669, 14.495119413508668, 17.787118482044157, 17.263149709971518, 14.010846789044326, 16.246046712974348, 14.131418977096104, 13.310336096621432, 8.5616776450434031, 8.6126685857958183, 7.4559384732604697, 7.4666941498039456, 11.992668842820699, 14.424841102794693, 16.080999010033096, 14.792015050215705, 14.516541321905349, 8.4655170120742547, 7.5977932657693117, 7.7379608664242641]\n",
"[ 5.82175335e+01 4.40362869e+01 4.37494597e+01 4.34618422e+01\n",
" 4.31655820e+01 2.90236380e+01 1.49955872e+01 1.47151990e+01\n",
" 1.44423869e+01 2.04406238e+01 7.49625975e+00 3.78345896e+00\n",
" 4.90313396e+00 4.72018995e-02 5.12710431e+01 3.60703027e+01\n",
" 1.38203501e+01 4.72018995e-02 2.19816361e+01]\n",
"max arrival time is 293.541495\n",
"sample # 34\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 3, 2, 2, 3, 2, 2, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"233.17339334\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.962844\n",
"T = 20.000000, Volume = 75.652849\n",
"T = 30.000000, Volume = 113.342853\n",
"T = 40.000000, Volume = 145.107956\n",
"T = 50.000000, Volume = 168.090933\n",
"T = 60.000000, Volume = 191.073910\n",
"T = 70.000000, Volume = 214.056895\n",
"T = 80.000000, Volume = 237.039912\n",
"T = 90.000000, Volume = 260.022921\n",
"T = 100.000000, Volume = 283.005931\n",
"T = 110.000000, Volume = 305.988953\n",
"T = 120.000000, Volume = 328.971977\n",
"T = 130.000000, Volume = 351.955019\n",
"T = 140.000000, Volume = 374.938060\n",
"T = 150.000000, Volume = 397.921100\n",
"T = 160.000000, Volume = 420.904150\n",
"T = 170.000000, Volume = 443.887201\n",
"T = 180.000000, Volume = 466.870252\n",
"T = 190.000000, Volume = 489.853303\n",
"T = 200.000000, Volume = 512.836360\n",
"T = 210.000000, Volume = 535.819440\n",
"T = 220.000000, Volume = 558.802514\n",
"T = 230.000000, Volume = 581.785589\n",
"T = 240.000000, Volume = 604.768679\n",
"T = 250.000000, Volume = 627.751769\n",
"T = 260.000000, Volume = 650.734859\n",
"T = 270.000000, Volume = 673.717949\n",
"T = 280.000000, Volume = 696.701039\n",
"T = 290.000000, Volume = 719.684129\n",
"T = 300.000000, Volume = 742.667242\n",
"T = 310.000000, Volume = 765.650349\n",
"T = 320.000000, Volume = 788.633455\n",
"inflow volume is 788.633455 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 140.078736 s\n",
"[0.050356139856895826, 0.087477144641564003, 0.10884471370995512, 7.8739975137521805, 16.303730710447482, 20.729550520408591, 20.183155534293885, 16.704574064158479, 19.114014927827874, 19.295210204982531, 3.7322546981540521, 3.519911408279544, 18.56719638035894, 20.940068240664402, 21.639255498960718, 8.6823396761927167, 8.08177267207863, 7.1691273131689961, 7.1786076939401919, 18.318788945721195, 17.211338867630054, 21.291343935531486, 13.036502015566731, 4.9363394658905939, 4.6350176932665486, 4.531676902583758, 3.4733307500645112, 3.4830725483227547, 15.786808540402003, 17.070746163653215, 21.604210331067755, 16.165663987113895]\n",
"[ 87.42889792 70.44965172 53.21432894 52.93511092 35.84765262\n",
" 35.56954126 35.28946738 18.44670223 18.17910763 17.90901195\n",
" 25.63926153 10.86251056 3.87438799 3.0542128 44.37022693\n",
" 17.24579145 61.81447427 79.07647675 26.83904925]\n",
"max arrival time is 319.100892\n",
"sample # 35\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 2, 3, 3, 2, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"102.56899814\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.043939\n",
"T = 20.000000, Volume = 75.821730\n",
"T = 30.000000, Volume = 113.599521\n",
"T = 40.000000, Volume = 151.377312\n",
"T = 50.000000, Volume = 189.155103\n",
"T = 60.000000, Volume = 226.932894\n",
"T = 70.000000, Volume = 257.928212\n",
"T = 80.000000, Volume = 269.914468\n",
"T = 90.000000, Volume = 292.808991\n",
"T = 100.000000, Volume = 315.792064\n",
"T = 110.000000, Volume = 338.775138\n",
"T = 120.000000, Volume = 361.758211\n",
"T = 130.000000, Volume = 384.741317\n",
"T = 140.000000, Volume = 407.724536\n",
"T = 150.000000, Volume = 430.707774\n",
"T = 160.000000, Volume = 453.691012\n",
"T = 170.000000, Volume = 476.674291\n",
"T = 180.000000, Volume = 499.657682\n",
"T = 190.000000, Volume = 522.641089\n",
"T = 200.000000, Volume = 545.624499\n",
"T = 210.000000, Volume = 568.607911\n",
"T = 220.000000, Volume = 591.591340\n",
"T = 230.000000, Volume = 614.574782\n",
"T = 240.000000, Volume = 637.558217\n",
"T = 250.000000, Volume = 660.541737\n",
"T = 260.000000, Volume = 683.525305\n",
"T = 270.000000, Volume = 706.508884\n",
"T = 280.000000, Volume = 729.492461\n",
"T = 290.000000, Volume = 752.476138\n",
"T = 300.000000, Volume = 775.459875\n",
"T = 310.000000, Volume = 798.443627\n",
"inflow volume is 798.443627 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 58.958361 s\n",
"[0.047444178119626902, 0.081603989421161482, 0.87754502056430472, 2.3118714916171883, 2.6959374327947194, 2.7685404162643192, 39.127021378786608, 47.927793846257067, 7.6554083402953879, 4.6174254517482574, 4.6998719473032633, 4.7096295079420747, 7.3297062590469775, 6.8146549597468287, 4.5305178792479133, 4.5435295453456845, 8.0115565629389351, 5.9262630651101729, 5.4920785027408323, 5.6968381294454824, 5.6763174520461694, 3.246256975988671, 3.2407633788478143, 6.104494755531058, 8.2198845221806511, 5.1609663596483371, 5.1392934089669158, 8.0350514385303526, 6.61728893135201, 5.8858864319195439, 6.2171061928564235]\n",
"[ 58.69586625 37.90075111 37.75311596 37.60549511 30.21181366\n",
" 30.05651415 22.55897661 14.94863796 14.78594636 14.62982116\n",
" 7.6556717 3.22686386 0.58134868 0.51136936 26.3177988\n",
" 5.24328054 18.76221283 10.78498335 33.91831036]\n",
"max arrival time is 309.921997\n",
"sample # 36\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"148.307248896\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.021853\n",
"T = 20.000000, Volume = 75.775466\n",
"T = 30.000000, Volume = 113.529080\n",
"T = 40.000000, Volume = 145.448615\n",
"T = 50.000000, Volume = 168.431606\n",
"T = 60.000000, Volume = 191.414599\n",
"T = 70.000000, Volume = 214.397627\n",
"T = 80.000000, Volume = 237.380709\n",
"T = 90.000000, Volume = 260.363800\n",
"T = 100.000000, Volume = 283.346895\n",
"T = 110.000000, Volume = 306.329991\n",
"T = 120.000000, Volume = 329.313088\n",
"T = 130.000000, Volume = 352.296185\n",
"T = 140.000000, Volume = 375.279287\n",
"T = 150.000000, Volume = 398.262400\n",
"T = 160.000000, Volume = 421.245513\n",
"T = 170.000000, Volume = 444.228627\n",
"T = 180.000000, Volume = 467.211742\n",
"T = 190.000000, Volume = 490.194856\n",
"T = 200.000000, Volume = 513.177985\n",
"T = 210.000000, Volume = 536.161150\n",
"T = 220.000000, Volume = 559.144321\n",
"T = 230.000000, Volume = 582.127501\n",
"T = 240.000000, Volume = 605.110681\n",
"T = 250.000000, Volume = 628.093860\n",
"T = 260.000000, Volume = 651.077087\n",
"T = 270.000000, Volume = 674.060329\n",
"T = 280.000000, Volume = 697.043590\n",
"T = 290.000000, Volume = 720.026852\n",
"T = 300.000000, Volume = 743.010115\n",
"T = 310.000000, Volume = 765.993378\n",
"T = 320.000000, Volume = 788.976640\n",
"inflow volume is 788.976640 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 87.353318 s\n",
"[0.050126613171074669, 0.087237242466736406, 0.2723908214306287, 6.1263520763110755, 9.9612691328699317, 12.726176493186546, 8.8073780750684403, 11.19116802788149, 6.2897940888918908, 5.8573103676604852, 6.2054518537355952, 6.478085813360801, 6.8345761689126006, 5.3933086575945639, 3.5543536591296148, 3.6435972335945546, 3.3996860818623418, 3.0040583743145071, 3.0133148342099396, 10.203054963691898, 13.367762003308391, 10.391340290563907, 5.9868573144771569, 5.9943712314437301, 8.8719279764593004, 11.904524177556885, 10.488952580277051, 7.1972694000906206, 7.3494947445415519, 7.166674116798843, 6.9093428513388444, 6.9029270825641937]\n",
"[ 43.76438366 33.01208757 21.94438782 21.7793022 21.61938896\n",
" 21.45942833 21.29961194 21.13975721 11.20037975 13.85814594\n",
" 6.13065652 2.22143297 0.80223345 0.04763154 9.38846454\n",
" 0.04763154 27.48708854 15.55993422 38.49947603]\n",
"max arrival time is 293.637345\n",
"sample # 37\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"186.909025957\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.995437\n",
"T = 20.000000, Volume = 75.720413\n",
"T = 30.000000, Volume = 113.445388\n",
"T = 40.000000, Volume = 151.170364\n",
"T = 50.000000, Volume = 181.686404\n",
"T = 60.000000, Volume = 204.669398\n",
"T = 70.000000, Volume = 227.652397\n",
"T = 80.000000, Volume = 250.635396\n",
"T = 90.000000, Volume = 273.618395\n",
"T = 100.000000, Volume = 296.601395\n",
"T = 110.000000, Volume = 319.584395\n",
"T = 120.000000, Volume = 342.567396\n",
"T = 130.000000, Volume = 365.550397\n",
"T = 140.000000, Volume = 388.533404\n",
"T = 150.000000, Volume = 411.516414\n",
"T = 160.000000, Volume = 434.499424\n",
"T = 170.000000, Volume = 457.482435\n",
"T = 180.000000, Volume = 480.465475\n",
"T = 190.000000, Volume = 503.448520\n",
"T = 200.000000, Volume = 526.431572\n",
"T = 210.000000, Volume = 549.414623\n",
"T = 220.000000, Volume = 572.397674\n",
"T = 230.000000, Volume = 595.380734\n",
"T = 240.000000, Volume = 618.363828\n",
"T = 250.000000, Volume = 641.346914\n",
"T = 260.000000, Volume = 664.330000\n",
"T = 270.000000, Volume = 687.313087\n",
"T = 280.000000, Volume = 710.296173\n",
"T = 290.000000, Volume = 733.279294\n",
"T = 300.000000, Volume = 756.262434\n",
"T = 310.000000, Volume = 779.245570\n",
"T = 320.000000, Volume = 802.228685\n",
"inflow volume is 802.228685 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 109.027505 s\n",
"[0.047831340321236822, 0.083100484174112393, 0.11326224205662722, 0.75880046905253484, 9.435576093374074, 8.7342695907484114, 6.3345057677282828, 6.895958477741023, 7.2260694836360484, 7.5009180442920842, 7.7857161906557399, 8.1365017105391324, 8.0711148210056365, 5.5548830027809784, 3.0351733170665733, 2.9297631347735824, 7.1092570086051294, 13.714052536597617, 10.662886276864741, 7.3052736999004857, 6.6446968717482457, 6.6529040016314438, 14.276849917868013, 14.297218060460871, 15.715726863630659, 13.131846789813682, 13.219068324190868, 18.087106991948794, 13.755648578487174, 17.68993721045112, 16.52262008550716, 18.416207546446596]\n",
"[ 77.718775 77.76218738 65.88959077 65.8221427 65.76905658\n",
" 65.71998985 65.66910418 65.61043469 58.72982787 58.64093608\n",
" 53.90397953 53.8224417 51.25608108 50.13923431 61.61539104\n",
" 55.69914249 50.13923431 51.96504317 71.31290539]\n",
"max arrival time is 316.571062\n",
"sample # 38\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 3, 3, 2, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"155.64403582\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.017342\n",
"T = 20.000000, Volume = 75.765756\n",
"T = 30.000000, Volume = 113.514169\n",
"T = 40.000000, Volume = 151.262582\n",
"T = 50.000000, Volume = 189.010996\n",
"T = 60.000000, Volume = 226.759409\n",
"T = 70.000000, Volume = 255.031706\n",
"T = 80.000000, Volume = 292.953106\n",
"T = 90.000000, Volume = 316.413273\n",
"T = 100.000000, Volume = 339.396287\n",
"T = 110.000000, Volume = 362.379301\n",
"T = 120.000000, Volume = 385.362333\n",
"T = 130.000000, Volume = 408.345407\n",
"T = 140.000000, Volume = 431.328478\n",
"T = 150.000000, Volume = 454.311578\n",
"T = 160.000000, Volume = 477.294679\n",
"T = 170.000000, Volume = 500.277780\n",
"T = 180.000000, Volume = 523.260914\n",
"T = 190.000000, Volume = 546.244052\n",
"T = 200.000000, Volume = 569.227190\n",
"T = 210.000000, Volume = 592.210323\n",
"T = 220.000000, Volume = 615.193534\n",
"T = 230.000000, Volume = 638.176757\n",
"T = 240.000000, Volume = 661.159989\n",
"T = 250.000000, Volume = 684.143221\n",
"T = 260.000000, Volume = 707.126504\n",
"T = 270.000000, Volume = 730.109800\n",
"T = 280.000000, Volume = 753.093106\n",
"T = 290.000000, Volume = 776.076412\n",
"T = 300.000000, Volume = 799.059720\n",
"inflow volume is 799.059720 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 84.946711 s\n",
"[0.047728185352274272, 0.081917557573650893, 0.60554789644640405, 4.0234557754257638, 4.4942843667309909, 4.9492272392392413, 169.45929779134272, 118.17975652170803, 12.376403732963849, 5.6790955514913168, 5.2660125923022569, 10.678571450079271, 12.273570021188968, 10.468824838906782, 3.1870888148631131, 3.0127332020748425, 5.6197378399222195, 11.354446068825643, 14.275475958295504, 15.368900853683716, 15.148137612271936, 12.138515408936794, 5.8820261323091669, 5.9796152970655179, 11.32080868316614, 12.962429149435666, 7.4906483420046781, 7.2749259540695803, 7.6727139030557447, 10.046299982899058]\n",
"[ 290.78696175 57.21520541 57.06350211 56.91818369 56.78168216\n",
" 45.47421969 34.2237489 34.14750018 23.03847881 15.17952536\n",
" 7.08253707 15.99186052 16.80239569 17.14574224 10.32577751\n",
" 39.83306594 17.37811961 28.60740346 51.12858484]\n",
"max arrival time is 299.655527\n",
"sample # 39\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 3, 3, 2, 3, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"85.231846309\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.042336\n",
"T = 20.000000, Volume = 75.819646\n",
"T = 30.000000, Volume = 113.596956\n",
"T = 40.000000, Volume = 151.374267\n",
"T = 50.000000, Volume = 183.376389\n",
"T = 60.000000, Volume = 206.359477\n",
"T = 70.000000, Volume = 229.342581\n",
"T = 80.000000, Volume = 252.325686\n",
"T = 90.000000, Volume = 275.308791\n",
"T = 100.000000, Volume = 298.291917\n",
"T = 110.000000, Volume = 321.275262\n",
"T = 120.000000, Volume = 344.258649\n",
"T = 130.000000, Volume = 367.242035\n",
"T = 140.000000, Volume = 390.225453\n",
"T = 150.000000, Volume = 413.209025\n",
"T = 160.000000, Volume = 436.192611\n",
"T = 170.000000, Volume = 459.176196\n",
"T = 180.000000, Volume = 482.159845\n",
"T = 190.000000, Volume = 505.143632\n",
"T = 200.000000, Volume = 528.127466\n",
"T = 210.000000, Volume = 551.111304\n",
"T = 220.000000, Volume = 574.095143\n",
"T = 230.000000, Volume = 597.078969\n",
"T = 240.000000, Volume = 620.062911\n",
"T = 250.000000, Volume = 643.046965\n",
"T = 260.000000, Volume = 666.031052\n",
"T = 270.000000, Volume = 689.015144\n",
"T = 280.000000, Volume = 711.999272\n",
"T = 290.000000, Volume = 734.983415\n",
"T = 300.000000, Volume = 757.967562\n",
"T = 310.000000, Volume = 780.951712\n",
"T = 320.000000, Volume = 803.935866\n",
"inflow volume is 803.935866 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 49.998486 s\n",
"[0.04729081597084283, 0.082476799880273607, 0.11319445166543289, 0.71689383919215388, 5.401385204200162, 4.1823818898049039, 4.1546322192927043, 4.1114779689371721, 4.123886167329764, 5.5782011633366997, 4.05914450244678, 2.4510951386527124, 2.4629223073861537, 5.725428145299456, 4.2061605079556461, 4.0845901805972282, 4.3168830756660963, 7.2315038723834926, 5.1307478314949559, 4.8988600879798367, 4.7127514517020899, 4.7208184519618612, 5.9538109120450393, 6.6185442826713912, 4.811804260690236, 5.3386863346968871, 4.1673684281929058, 3.6747922233379335, 3.8016406786232571, 3.8199444619998157, 3.9376026880861406, 4.6124973645977656]\n",
"[ 32.62089038 32.66211288 26.60231729 26.45015107 20.28589331\n",
" 14.01008188 7.62476636 7.45929156 2.76480285 0.94239097\n",
" 0.85315245 1.64285179 8.22505738 9.66667259 10.82677217\n",
" 29.64207016 4.24419948 17.15564624 23.37822994]\n",
"max arrival time is 319.680751\n",
"sample # 40\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"112.871576606\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.040828\n",
"T = 20.000000, Volume = 75.815280\n",
"T = 30.000000, Volume = 113.589732\n",
"T = 40.000000, Volume = 145.593575\n",
"T = 50.000000, Volume = 168.576585\n",
"T = 60.000000, Volume = 191.559633\n",
"T = 70.000000, Volume = 214.542681\n",
"T = 80.000000, Volume = 237.525730\n",
"T = 90.000000, Volume = 260.508820\n",
"T = 100.000000, Volume = 283.491991\n",
"T = 110.000000, Volume = 306.475177\n",
"T = 120.000000, Volume = 329.458365\n",
"T = 130.000000, Volume = 352.441555\n",
"T = 140.000000, Volume = 375.424750\n",
"T = 150.000000, Volume = 398.407968\n",
"T = 160.000000, Volume = 421.391185\n",
"T = 170.000000, Volume = 444.374402\n",
"T = 180.000000, Volume = 467.357623\n",
"T = 190.000000, Volume = 490.340934\n",
"T = 200.000000, Volume = 513.324263\n",
"T = 210.000000, Volume = 536.307592\n",
"T = 220.000000, Volume = 559.290939\n",
"T = 230.000000, Volume = 582.274376\n",
"T = 240.000000, Volume = 605.257842\n",
"T = 250.000000, Volume = 628.241314\n",
"T = 260.000000, Volume = 651.224787\n",
"T = 270.000000, Volume = 674.208262\n",
"T = 280.000000, Volume = 697.191737\n",
"T = 290.000000, Volume = 720.175203\n",
"T = 300.000000, Volume = 743.158746\n",
"T = 310.000000, Volume = 766.142349\n",
"T = 320.000000, Volume = 789.125964\n",
"inflow volume is 789.125964 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 67.087761 s\n",
"[0.049946092530325031, 0.085723094463850638, 0.35723312024253473, 5.3799708398420671, 7.5574610559339739, 4.1057256171136949, 4.2062073827656699, 4.2068151369148206, 7.4528114690418379, 7.6351240980508193, 5.0013409786348246, 5.2774062832652167, 5.5919784218457433, 4.6131236174360204, 3.1642287451523274, 2.7748427623967826, 2.7876299724100977, 6.6131163448844967, 8.9613902500667599, 4.8955234615490255, 4.865096716860819, 8.6839296791381511, 9.0007769883812774, 5.9959436783945881, 6.0367908496232383, 6.2467126995044957, 5.8995788410686929, 5.909136116022788, 7.8235350658042639, 8.5336452334255135, 5.8066945315897476, 6.4530812589264759]\n",
"[ 41.60394394 33.62697589 33.47011794 25.19343969 25.03308829\n",
" 24.87732475 24.7225841 16.334696 7.83440191 7.67576994\n",
" 8.61391671 3.34540848 0.64216893 0.58318851 29.34237945\n",
" 12.09291634 37.72215753 20.53982674 5.89662816]\n",
"max arrival time is 319.184397\n",
"sample # 41\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"138.49324169\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.028264\n",
"T = 20.000000, Volume = 75.788534\n",
"T = 30.000000, Volume = 113.548804\n",
"T = 40.000000, Volume = 145.493365\n",
"T = 50.000000, Volume = 168.476359\n",
"T = 60.000000, Volume = 191.459397\n",
"T = 70.000000, Volume = 214.442438\n",
"T = 80.000000, Volume = 237.425479\n",
"T = 90.000000, Volume = 260.408520\n",
"T = 100.000000, Volume = 283.391561\n",
"T = 110.000000, Volume = 306.374635\n",
"T = 120.000000, Volume = 329.357745\n",
"T = 130.000000, Volume = 352.340876\n",
"T = 140.000000, Volume = 375.324008\n",
"T = 150.000000, Volume = 398.307187\n",
"T = 160.000000, Volume = 421.290378\n",
"T = 170.000000, Volume = 444.273585\n",
"T = 180.000000, Volume = 467.256793\n",
"T = 190.000000, Volume = 490.240002\n",
"T = 200.000000, Volume = 513.223212\n",
"T = 210.000000, Volume = 536.206425\n",
"T = 220.000000, Volume = 559.189653\n",
"T = 230.000000, Volume = 582.172882\n",
"T = 240.000000, Volume = 605.156112\n",
"T = 250.000000, Volume = 628.139342\n",
"T = 260.000000, Volume = 651.122572\n",
"T = 270.000000, Volume = 674.105796\n",
"T = 280.000000, Volume = 697.089080\n",
"T = 290.000000, Volume = 720.072358\n",
"T = 300.000000, Volume = 743.055635\n",
"T = 310.000000, Volume = 766.038914\n",
"T = 320.000000, Volume = 789.022282\n",
"inflow volume is 789.022282 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 82.111819 s\n",
"[0.05008337439171736, 0.08585254107972623, 0.29501141388639257, 5.8719629279569743, 9.6757271507663312, 2.270964537580622, 2.7588601915800055, 2.583782346541839, 2.35523624107589, 2.3668720988632135, 9.505604332460555, 6.9202701986847854, 2.5267633676240395, 4.6652699912044886, 9.7203369146042693, 8.5030643562259538, 6.1812664524559144, 6.3709382915398294, 6.5646412707662369, 6.9188827704862659, 5.3051570898353555, 3.9908107821736691, 3.9037071355842787, 4.0114882970834307, 3.3837548362321237, 3.3968112049310171, 7.5459488839122173, 12.660350385791743, 13.334043513821637, 13.422271991769399, 11.083436844316493, 11.775087129275182]\n",
"[ 49.51071077 39.61076681 39.34220321 39.05074 29.0812679\n",
" 19.31763778 19.0407252 18.76685627 18.4939398 18.25810707\n",
" 18.08631033 12.49211333 7.39661047 0.34191634 8.49941612\n",
" 24.16329965 13.33255232 33.99101512 44.68091762]\n",
"max arrival time is 319.988446\n",
"sample # 42\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 3, 2, 2, 2, 2, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"126.055873325\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.035452\n",
"T = 20.000000, Volume = 75.803349\n",
"T = 30.000000, Volume = 113.571247\n",
"T = 40.000000, Volume = 145.541738\n",
"T = 50.000000, Volume = 168.524739\n",
"T = 60.000000, Volume = 191.507772\n",
"T = 70.000000, Volume = 214.490806\n",
"T = 80.000000, Volume = 237.473840\n",
"T = 90.000000, Volume = 260.456907\n",
"T = 100.000000, Volume = 283.440040\n",
"T = 110.000000, Volume = 306.423182\n",
"T = 120.000000, Volume = 329.406325\n",
"T = 130.000000, Volume = 352.389525\n",
"T = 140.000000, Volume = 375.372745\n",
"T = 150.000000, Volume = 398.355964\n",
"T = 160.000000, Volume = 421.339216\n",
"T = 170.000000, Volume = 444.322492\n",
"T = 180.000000, Volume = 467.305770\n",
"T = 190.000000, Volume = 490.289048\n",
"T = 200.000000, Volume = 513.272328\n",
"T = 210.000000, Volume = 536.255609\n",
"T = 220.000000, Volume = 559.238890\n",
"T = 230.000000, Volume = 582.222173\n",
"T = 240.000000, Volume = 605.205455\n",
"T = 250.000000, Volume = 628.188737\n",
"T = 260.000000, Volume = 651.172063\n",
"T = 270.000000, Volume = 674.155431\n",
"T = 280.000000, Volume = 697.138805\n",
"T = 290.000000, Volume = 720.122181\n",
"T = 300.000000, Volume = 743.105556\n",
"T = 310.000000, Volume = 766.088928\n",
"T = 320.000000, Volume = 789.072378\n",
"inflow volume is 789.072378 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 74.728307 s\n",
"[0.050022928910525601, 0.085795651798548353, 0.32719340414306286, 5.6702158013463029, 8.6729553765550911, 4.3256485158320226, 4.3907615052268278, 4.4021558336602515, 8.3224831028555819, 8.5692960345467402, 4.4649524099087605, 4.5779597767076714, 10.500125776732471, 9.1397108274027126, 9.9361254623447373, 4.1818943863158058, 3.4315219206160981, 3.5217970430241281, 3.6001847187077218, 3.7211023721527692, 3.6744439877317059, 3.8649700967113469, 3.2389217636913137, 3.2368878444452758, 5.7378688923955954, 10.293949577648196, 5.8310669456253299, 6.2450079086198356, 5.9382458811310137, 5.9454077278015278, 10.010507979653697, 12.009286225604797]\n",
"[ 44.70883038 35.74677783 35.46618465 26.35839174 17.37603374\n",
" 17.11460745 16.85801226 16.71009838 16.50631295 16.32768223\n",
" 7.66451868 10.56939993 5.22137773 0.22319893 7.19602082\n",
" 30.87228415 40.35206819 12.2117143 21.8336023 ]\n",
"max arrival time is 319.980952\n",
"sample # 43\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"129.641427071\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.033210\n",
"T = 20.000000, Volume = 75.799067\n",
"T = 30.000000, Volume = 113.564923\n",
"T = 40.000000, Volume = 151.330779\n",
"T = 50.000000, Volume = 189.096636\n",
"T = 60.000000, Volume = 226.862492\n",
"T = 70.000000, Volume = 254.524467\n",
"T = 80.000000, Volume = 292.454547\n",
"T = 90.000000, Volume = 330.220404\n",
"T = 100.000000, Volume = 356.490671\n",
"T = 110.000000, Volume = 379.473709\n",
"T = 120.000000, Volume = 402.456745\n",
"T = 130.000000, Volume = 425.439799\n",
"T = 140.000000, Volume = 448.422917\n",
"T = 150.000000, Volume = 471.406067\n",
"T = 160.000000, Volume = 494.389228\n",
"T = 170.000000, Volume = 517.372390\n",
"T = 180.000000, Volume = 540.355553\n",
"T = 190.000000, Volume = 563.338716\n",
"T = 200.000000, Volume = 586.321879\n",
"T = 210.000000, Volume = 609.305086\n",
"T = 220.000000, Volume = 632.288320\n",
"T = 230.000000, Volume = 655.271568\n",
"T = 240.000000, Volume = 678.254810\n",
"T = 250.000000, Volume = 701.238125\n",
"T = 260.000000, Volume = 724.221463\n",
"T = 270.000000, Volume = 747.204817\n",
"T = 280.000000, Volume = 770.188172\n",
"T = 290.000000, Volume = 793.171527\n",
"inflow volume is 793.171527 gallons\n",
"simulation time is 290.000000 s\n",
"wall clock time is 67.026194 s\n",
"[0.04761382277206317, 0.081780654922933738, 0.85867160596773251, 2.9316505540017621, 3.8723915103595048, 4.5815871062415265, 105.34391376412972, 322.58594739581463, 362.2093915575831, 51.184247337354485, 5.0702956622510182, 4.9835737307853929, 8.9770612373202159, 9.8641710620500369, 4.2917145782314297, 3.7617123387001157, 3.8381196157722304, 3.2494810475337834, 3.2186423282901435, 5.6474138179423763, 10.92013382586916, 6.5546888885518735, 5.5957409567133736, 8.9507450548424305, 11.054638023412632, 6.8892458599874073, 6.3062544866070063, 6.1627065214736501, 6.1529633045267014]\n",
"[ 2.52128914e+02 3.83883300e+01 3.82346090e+01 3.80808456e+01\n",
" 3.79270396e+01 3.77731910e+01 2.82235034e+01 1.85582614e+01\n",
" 1.83937762e+01 1.82344847e+01 1.16541129e+01 4.57525580e+00\n",
" 1.89533772e+00 4.80559026e-02 1.33580872e+01 7.91307303e+00\n",
" 2.33997763e+01 4.80559026e-02 3.30096180e+01]\n",
"max arrival time is 262.320988\n",
"sample # 44\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"130.992525369\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.032481\n",
"T = 20.000000, Volume = 75.797496\n",
"T = 30.000000, Volume = 113.562511\n",
"T = 40.000000, Volume = 145.523670\n",
"T = 50.000000, Volume = 168.506666\n",
"T = 60.000000, Volume = 191.489690\n",
"T = 70.000000, Volume = 214.472733\n",
"T = 80.000000, Volume = 237.455847\n",
"T = 90.000000, Volume = 260.438977\n",
"T = 100.000000, Volume = 283.422107\n",
"T = 110.000000, Volume = 306.405239\n",
"T = 120.000000, Volume = 329.388372\n",
"T = 130.000000, Volume = 352.371507\n",
"T = 140.000000, Volume = 375.354660\n",
"T = 150.000000, Volume = 398.337814\n",
"T = 160.000000, Volume = 421.320968\n",
"T = 170.000000, Volume = 444.304122\n",
"T = 180.000000, Volume = 467.287283\n",
"T = 190.000000, Volume = 490.270510\n",
"T = 200.000000, Volume = 513.253746\n",
"T = 210.000000, Volume = 536.236983\n",
"T = 220.000000, Volume = 559.220237\n",
"T = 230.000000, Volume = 582.203564\n",
"T = 240.000000, Volume = 605.186904\n",
"T = 250.000000, Volume = 628.170246\n",
"T = 260.000000, Volume = 651.153589\n",
"T = 270.000000, Volume = 674.136933\n",
"T = 280.000000, Volume = 697.120278\n",
"T = 290.000000, Volume = 720.103641\n",
"T = 300.000000, Volume = 743.087005\n",
"T = 310.000000, Volume = 766.070367\n",
"T = 320.000000, Volume = 789.053782\n",
"inflow volume is 789.053782 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 77.618992 s\n",
"[0.050049548053871989, 0.087162448335825732, 0.31555435346579874, 5.7541754701676826, 8.9808996285146581, 4.902405823062745, 7.2643042300379435, 8.950574867926024, 5.1963991536380494, 5.4545837203390155, 5.8573953314246223, 6.1236327640288124, 5.3092290978080676, 3.4428349667406803, 3.2460613539945511, 2.7627032859063672, 2.7758029909431698, 7.8255007375184231, 8.3909839170765732, 5.1646463479308125, 5.181945373425676, 10.113862064556121, 9.0865767414133547, 6.2683253353644242, 6.4901681662727313, 6.8421145552667531, 7.0109624196717046, 5.2519850408591848, 3.601812146021286, 3.5971755037387179, 9.1838066576760564, 9.5666250847456844]\n",
"[ 47.00438086 37.72707112 28.24153165 28.07384852 27.89652837\n",
" 27.70995467 27.51714008 18.08801751 8.68979977 8.46878068\n",
" 8.25884676 10.8537986 3.86443353 0.0494094 42.43836846\n",
" 32.9522591 7.38961734 13.34834285 22.78024907]\n",
"max arrival time is 318.338424\n",
"sample # 45\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"148.949476547\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.021550\n",
"T = 20.000000, Volume = 75.774742\n",
"T = 30.000000, Volume = 113.527934\n",
"T = 40.000000, Volume = 145.445043\n",
"T = 50.000000, Volume = 168.428034\n",
"T = 60.000000, Volume = 191.411047\n",
"T = 70.000000, Volume = 214.394064\n",
"T = 80.000000, Volume = 237.377081\n",
"T = 90.000000, Volume = 260.360099\n",
"T = 100.000000, Volume = 283.343116\n",
"T = 110.000000, Volume = 306.326172\n",
"T = 120.000000, Volume = 329.309258\n",
"T = 130.000000, Volume = 352.292353\n",
"T = 140.000000, Volume = 375.275448\n",
"T = 150.000000, Volume = 398.258598\n",
"T = 160.000000, Volume = 421.241748\n",
"T = 170.000000, Volume = 444.224898\n",
"T = 180.000000, Volume = 467.208053\n",
"T = 190.000000, Volume = 490.191230\n",
"T = 200.000000, Volume = 513.174407\n",
"T = 210.000000, Volume = 536.157583\n",
"T = 220.000000, Volume = 559.140776\n",
"T = 230.000000, Volume = 582.124018\n",
"T = 240.000000, Volume = 605.107250\n",
"T = 250.000000, Volume = 628.090481\n",
"T = 260.000000, Volume = 651.073756\n",
"T = 270.000000, Volume = 674.057071\n",
"T = 280.000000, Volume = 697.040380\n",
"T = 290.000000, Volume = 720.023709\n",
"T = 300.000000, Volume = 743.007061\n",
"T = 310.000000, Volume = 765.990413\n",
"T = 320.000000, Volume = 788.973767\n",
"inflow volume is 788.973767 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 89.406405 s\n",
"[0.05012754702508989, 0.08589687772301291, 0.26698745209499392, 6.1181858894665933, 10.18661968182775, 5.4298389555266882, 5.5638354386957927, 5.5109728354059726, 5.2270342046150953, 5.2376064083684692, 10.887326595274953, 8.6585698391114736, 5.0591938942400789, 8.0018231236911284, 10.375447676568568, 11.131806185245841, 10.11424758197184, 10.267110959688155, 6.4487879098252252, 6.1555977729601112, 6.1681245506996518, 11.718843702202987, 13.380909825863009, 15.023756276346655, 15.093731312814016, 10.769078676779314, 12.734917924466377, 11.913815119584793, 4.7466485171597981, 4.2851793750544198, 4.5342779299904512, 4.4346747356884233]\n",
"[ 56.66837127 45.99625973 45.83681722 45.68216051 34.70336007\n",
" 23.61031386 23.44635275 23.28833226 14.48903903 5.81783621\n",
" 3.67778709 1.73398625 0.90096788 0.82848336 29.16498971\n",
" 17.70189693 51.44127877 9.86232686 40.20398728]\n",
"max arrival time is 316.974745\n",
"sample # 46\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 3, 2, 2, 3, 3, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"36.6565956631\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.869884\n",
"T = 20.000000, Volume = 75.479269\n",
"T = 30.000000, Volume = 113.088654\n",
"T = 40.000000, Volume = 150.698039\n",
"T = 50.000000, Volume = 172.932891\n",
"T = 60.000000, Volume = 195.789376\n",
"T = 70.000000, Volume = 218.772915\n",
"T = 80.000000, Volume = 241.756486\n",
"T = 90.000000, Volume = 264.740093\n",
"T = 100.000000, Volume = 287.723730\n",
"T = 110.000000, Volume = 310.707365\n",
"T = 120.000000, Volume = 333.691000\n",
"T = 130.000000, Volume = 356.675069\n",
"T = 140.000000, Volume = 379.659802\n",
"T = 150.000000, Volume = 402.644784\n",
"T = 160.000000, Volume = 425.629796\n",
"T = 170.000000, Volume = 448.614845\n",
"T = 180.000000, Volume = 471.599921\n",
"T = 190.000000, Volume = 494.584996\n",
"T = 200.000000, Volume = 517.570061\n",
"T = 210.000000, Volume = 540.555582\n",
"T = 220.000000, Volume = 563.541722\n",
"T = 230.000000, Volume = 586.528086\n",
"T = 240.000000, Volume = 609.514482\n",
"T = 250.000000, Volume = 632.501128\n",
"T = 260.000000, Volume = 655.488242\n",
"T = 270.000000, Volume = 678.475777\n",
"T = 280.000000, Volume = 701.463497\n",
"T = 290.000000, Volume = 724.451293\n",
"T = 300.000000, Volume = 747.439118\n",
"T = 310.000000, Volume = 770.427074\n",
"T = 320.000000, Volume = 793.415488\n",
"inflow volume is 793.415488 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 21.832962 s\n",
"[0.048717104622198172, 0.084643922808468747, 0.5243037888390768, 3.1488249814956677, 18.99510175711286, 2.6392149768987827, 2.2878550202641557, 2.317640177787168, 2.40833334005616, 2.427014315906376, 2.4294637163000483, 2.6596764722031954, 3.888331174090661, 2.6889632616871793, 2.7008348918678844, 2.7633650661651834, 2.8270399285169776, 2.8408477986262808, 2.8594836229139791, 3.6972012890282224, 3.4720961574138891, 2.9426798826620648, 2.9442679773299476, 3.8299268311264529, 3.8801471147530324, 3.0209198159603234, 3.1153050164769831, 3.1363158686637727, 3.1756169538936225, 4.0463782023150436, 3.6044261706152341, 3.1494999345587482]\n",
"[ 17.07956452 10.92372217 10.79539593 10.67221548 10.54999545\n",
" 8.15690637 8.02589763 7.89908234 5.48019073 2.99728096\n",
" 2.8551496 0.32316817 0.17387038 0.02682849 12.11509381\n",
" 9.3454823 1.59123102 4.23292266 6.68301941]\n",
"max arrival time is 318.318777\n",
"sample # 47\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"140.606001523\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.027009\n",
"T = 20.000000, Volume = 75.785891\n",
"T = 30.000000, Volume = 113.544773\n",
"T = 40.000000, Volume = 145.481433\n",
"T = 50.000000, Volume = 168.464425\n",
"T = 60.000000, Volume = 191.447447\n",
"T = 70.000000, Volume = 214.430469\n",
"T = 80.000000, Volume = 237.413492\n",
"T = 90.000000, Volume = 260.396516\n",
"T = 100.000000, Volume = 283.379539\n",
"T = 110.000000, Volume = 306.362606\n",
"T = 120.000000, Volume = 329.345706\n",
"T = 130.000000, Volume = 352.328816\n",
"T = 140.000000, Volume = 375.311926\n",
"T = 150.000000, Volume = 398.295097\n",
"T = 160.000000, Volume = 421.278274\n",
"T = 170.000000, Volume = 444.261474\n",
"T = 180.000000, Volume = 467.244675\n",
"T = 190.000000, Volume = 490.227877\n",
"T = 200.000000, Volume = 513.211079\n",
"T = 210.000000, Volume = 536.194281\n",
"T = 220.000000, Volume = 559.177501\n",
"T = 230.000000, Volume = 582.160775\n",
"T = 240.000000, Volume = 605.144039\n",
"T = 250.000000, Volume = 628.127300\n",
"T = 260.000000, Volume = 651.110615\n",
"T = 270.000000, Volume = 674.093972\n",
"T = 280.000000, Volume = 697.077328\n",
"T = 290.000000, Volume = 720.060708\n",
"T = 300.000000, Volume = 743.044091\n",
"T = 310.000000, Volume = 766.027475\n",
"T = 320.000000, Volume = 789.010858\n",
"inflow volume is 789.010858 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 84.315760 s\n",
"[0.05009282660099882, 0.085862258263773791, 0.29079523672048407, 5.9305356526156432, 9.7849501602691387, 4.5920526199123914, 5.401481675070376, 5.4060186850241854, 5.1834896863586994, 5.1940464590616902, 10.559279750382487, 8.3498180453553985, 4.8884767190293497, 7.4818268259564711, 9.8979913744740795, 11.073520332039225, 6.1105094789698517, 6.4220077380297607, 6.284351225624345, 6.0210731329255474, 6.0339493068107366, 11.104738398911106, 12.654577910904429, 14.099306157942824, 14.047120723526429, 10.209675738397456, 11.617777730154179, 9.0479341264379087, 6.9924861673027445, 7.2269710860525818, 7.5965579023981187, 6.1917307720835737]\n",
"[ 52.8610875 42.85443799 42.68319194 42.51543589 32.28842854\n",
" 21.98645034 21.81181501 21.64245745 13.31931986 5.4523154\n",
" 1.99213733 0.90983022 0.99660047 1.28108919 37.38940752\n",
" 47.94510609 9.08359915 16.45491594 27.12828325]\n",
"max arrival time is 317.979511\n",
"sample # 48\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 3, 2, 2, 3, 2, 3, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"173.557140419\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.004865\n",
"T = 20.000000, Volume = 75.739937\n",
"T = 30.000000, Volume = 113.475009\n",
"T = 40.000000, Volume = 151.210081\n",
"T = 50.000000, Volume = 181.762794\n",
"T = 60.000000, Volume = 204.745796\n",
"T = 70.000000, Volume = 227.728810\n",
"T = 80.000000, Volume = 250.711823\n",
"T = 90.000000, Volume = 273.694877\n",
"T = 100.000000, Volume = 296.677932\n",
"T = 110.000000, Volume = 319.660993\n",
"T = 120.000000, Volume = 342.644055\n",
"T = 130.000000, Volume = 365.627117\n",
"T = 140.000000, Volume = 388.610180\n",
"T = 150.000000, Volume = 411.593242\n",
"T = 160.000000, Volume = 434.576334\n",
"T = 170.000000, Volume = 457.559438\n",
"T = 180.000000, Volume = 480.542541\n",
"T = 190.000000, Volume = 503.525643\n",
"T = 200.000000, Volume = 526.508746\n",
"T = 210.000000, Volume = 549.491845\n",
"T = 220.000000, Volume = 572.475016\n",
"T = 230.000000, Volume = 595.458177\n",
"T = 240.000000, Volume = 618.441347\n",
"T = 250.000000, Volume = 641.424526\n",
"T = 260.000000, Volume = 664.407706\n",
"T = 270.000000, Volume = 687.390894\n",
"T = 280.000000, Volume = 710.374123\n",
"T = 290.000000, Volume = 733.357344\n",
"T = 300.000000, Volume = 756.340564\n",
"T = 310.000000, Volume = 779.323791\n",
"T = 320.000000, Volume = 802.307023\n",
"inflow volume is 802.307023 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 102.448826 s\n",
"[0.047792389501187166, 0.08305293690771863, 0.11352312204564423, 0.79988749093555067, 9.0021007693729533, 7.2445412637766697, 2.1504390749525482, 5.9879639216085652, 12.414355023525623, 10.056733563976509, 6.6021460587935694, 6.9453881007878495, 6.3844351841991935, 6.0739006569300891, 6.1254894271420532, 14.376305473814716, 13.638072053544176, 12.630919877377867, 11.358842229417197, 11.35416982630926, 15.193051606198386, 13.204647177317719, 16.018603330956282, 8.4683505120691809, 6.53715198820551, 6.7617950280646495, 14.471724359451899, 12.654723865463259, 13.143508051603034, 12.788401148537012, 6.3944215932808062, 8.0763643367218112]\n",
"[ 76.66258655 76.70922637 65.80071797 56.20502858 56.19750025\n",
" 56.14903646 49.22289809 49.15490514 44.17763573 44.15048309\n",
" 41.46386347 41.57564773 41.57623807 41.52574178 42.20835886\n",
" 70.80149479 46.13616679 60.49776382 52.21120882]\n",
"max arrival time is 317.302905\n",
"sample # 49\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 3, 3, 2, 3, 2, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"202.179726941\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.984600\n",
"T = 20.000000, Volume = 75.697915\n",
"T = 30.000000, Volume = 113.411231\n",
"T = 40.000000, Volume = 151.124546\n",
"T = 50.000000, Volume = 188.837861\n",
"T = 60.000000, Volume = 220.594340\n",
"T = 70.000000, Volume = 243.577325\n",
"T = 80.000000, Volume = 266.560307\n",
"T = 90.000000, Volume = 289.543288\n",
"T = 100.000000, Volume = 312.526314\n",
"T = 110.000000, Volume = 335.509346\n",
"T = 120.000000, Volume = 358.492382\n",
"T = 130.000000, Volume = 381.475420\n",
"T = 140.000000, Volume = 404.458495\n",
"T = 150.000000, Volume = 427.441561\n",
"T = 160.000000, Volume = 450.424636\n",
"T = 170.000000, Volume = 473.407715\n",
"T = 180.000000, Volume = 496.390795\n",
"T = 190.000000, Volume = 519.373891\n",
"T = 200.000000, Volume = 542.357004\n",
"T = 210.000000, Volume = 565.340113\n",
"T = 220.000000, Volume = 588.323239\n",
"T = 230.000000, Volume = 611.306370\n",
"T = 240.000000, Volume = 634.289501\n",
"T = 250.000000, Volume = 657.272633\n",
"T = 260.000000, Volume = 680.255765\n",
"T = 270.000000, Volume = 703.238897\n",
"T = 280.000000, Volume = 726.222029\n",
"T = 290.000000, Volume = 749.205162\n",
"T = 300.000000, Volume = 772.188295\n",
"T = 310.000000, Volume = 795.171427\n",
"inflow volume is 795.171427 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 114.991584 s\n",
"[0.047868076340505279, 0.082095587536132483, 0.098050300376962371, 1.7943604807027875, 1.986036064235928, 19.78852378806932, 14.532121351331226, 16.749823224370459, 20.563178619327633, 13.922115823951637, 9.6237330691736993, 6.322813813968521, 12.263801379290424, 14.003844930538213, 17.940131870870498, 9.8702588385813019, 6.5677155108488803, 7.1128693071742806, 16.797460175628434, 15.73937657039404, 18.114574914378192, 4.4048701591600734, 4.7388056819028117, 4.6951792891781619, 4.6785745790735165, 5.0092025480306406, 4.7913356288244815, 5.1183266996111252, 3.8538664589877016, 3.8538944795526748, 10.372615893060086]\n",
"[ 62.3726428 62.41709543 62.61180877 49.05100038 37.74977913\n",
" 28.81667611 29.92534436 24.43925189 24.43845493 24.09119323\n",
" 23.27236167 23.25826242 23.41439572 23.157417 55.27861863\n",
" 31.35815775 26.44502918 41.82873678 23.157417 ]\n",
"max arrival time is 308.990898\n",
"sample # 50\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"117.841553208\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.039422\n",
"T = 20.000000, Volume = 75.811678\n",
"T = 30.000000, Volume = 113.583934\n",
"T = 40.000000, Volume = 151.356190\n",
"T = 50.000000, Volume = 189.128445\n",
"T = 60.000000, Volume = 226.900701\n",
"T = 70.000000, Volume = 254.671490\n",
"T = 80.000000, Volume = 292.616483\n",
"T = 90.000000, Volume = 330.388739\n",
"T = 100.000000, Volume = 356.829638\n",
"T = 110.000000, Volume = 379.812690\n",
"T = 120.000000, Volume = 402.795741\n",
"T = 130.000000, Volume = 425.778812\n",
"T = 140.000000, Volume = 448.761973\n",
"T = 150.000000, Volume = 471.745151\n",
"T = 160.000000, Volume = 494.728330\n",
"T = 170.000000, Volume = 517.711511\n",
"T = 180.000000, Volume = 540.694693\n",
"T = 190.000000, Volume = 563.677875\n",
"T = 200.000000, Volume = 586.661055\n",
"T = 210.000000, Volume = 609.644304\n",
"T = 220.000000, Volume = 632.627602\n",
"T = 230.000000, Volume = 655.610912\n",
"T = 240.000000, Volume = 678.594221\n",
"T = 250.000000, Volume = 701.577531\n",
"T = 260.000000, Volume = 724.560843\n",
"T = 270.000000, Volume = 747.544234\n",
"T = 280.000000, Volume = 770.527652\n",
"T = 290.000000, Volume = 793.511087\n",
"inflow volume is 793.511087 gallons\n",
"simulation time is 290.000000 s\n",
"wall clock time is 60.999696 s\n",
"[0.047547916815034418, 0.081713440688933214, 0.80478121639098299, 2.8386193506759194, 3.7394515853158565, 4.6501927643079375, 85.699773795123448, 303.63667925223933, 323.63614298482906, 44.086196407906051, 4.9330453333675086, 4.736034301304902, 8.1709966905317035, 8.2484447370394314, 5.5656765485473612, 5.9514476236661578, 6.3085546652441176, 5.8622970559859047, 5.8617578144198683, 7.3906912841064623, 9.7761732379738842, 6.1553423860834613, 6.0762760925934201, 5.7820325116531697, 5.7939451733562715, 9.2483645736251496, 10.016221097228925, 5.6626335775877559, 5.6591213970218419]\n",
"[ 2.06724803e+02 3.49463621e+01 3.47937007e+01 3.46409808e+01\n",
" 3.44882024e+01 3.43353657e+01 2.56769166e+01 1.69034133e+01\n",
" 1.67393898e+01 1.65805425e+01 7.69618380e+00 9.36058366e+00\n",
" 5.85973183e+00 4.83767127e-02 3.00172033e+01 2.12988948e+01\n",
" 6.40294745e+00 1.21499162e+01 4.83767127e-02]\n",
"max arrival time is 265.933469\n",
"sample # 51\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"121.029702108\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.037632\n",
"T = 20.000000, Volume = 75.808296\n",
"T = 30.000000, Volume = 113.578959\n",
"T = 40.000000, Volume = 151.349623\n",
"T = 50.000000, Volume = 189.120286\n",
"T = 60.000000, Volume = 226.890950\n",
"T = 70.000000, Volume = 255.573628\n",
"T = 80.000000, Volume = 293.517372\n",
"T = 90.000000, Volume = 317.239809\n",
"T = 100.000000, Volume = 340.222854\n",
"T = 110.000000, Volume = 363.205901\n",
"T = 120.000000, Volume = 386.188950\n",
"T = 130.000000, Volume = 409.172000\n",
"T = 140.000000, Volume = 432.155063\n",
"T = 150.000000, Volume = 455.138136\n",
"T = 160.000000, Volume = 478.121209\n",
"T = 170.000000, Volume = 501.104282\n",
"T = 180.000000, Volume = 524.087418\n",
"T = 190.000000, Volume = 547.070561\n",
"T = 200.000000, Volume = 570.053751\n",
"T = 210.000000, Volume = 593.036945\n",
"T = 220.000000, Volume = 616.020139\n",
"T = 230.000000, Volume = 639.003340\n",
"T = 240.000000, Volume = 661.986608\n",
"T = 250.000000, Volume = 684.969898\n",
"T = 260.000000, Volume = 707.953189\n",
"T = 270.000000, Volume = 730.936495\n",
"T = 280.000000, Volume = 753.919878\n",
"T = 290.000000, Volume = 776.903286\n",
"T = 300.000000, Volume = 799.886697\n",
"inflow volume is 799.886697 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 65.179211 s\n",
"[0.047566715746871574, 0.081729652509201056, 0.79186051313075256, 3.1353478222255293, 3.7037609848965976, 4.2634688422195479, 88.863173897340218, 144.25592032235997, 9.4321237371066147, 5.5491799679293496, 5.7102903514729446, 6.0249259600923999, 6.2723509935302291, 4.6144498630161257, 3.1156923545035378, 3.1082317489514568, 5.1986111804007384, 10.591529571879073, 10.238004239440198, 4.1487049459164842, 3.3656713714206061, 3.3758899297759015, 8.3005252345526834, 8.6131567583573698, 5.6025152722448377, 5.5968977585885851, 10.311406935350504, 7.2684536851596935, 5.7197832336655257, 7.7826083172535023]\n",
"[ 170.70087891 35.86722086 35.71711018 35.57290065 35.43244118\n",
" 26.58234559 26.43940106 26.30088707 26.15979427 17.23968083\n",
" 17.06686723 10.21590259 8.65436956 4.49403757 31.01464233\n",
" 6.98947789 12.57117837 21.7171939 4.49403757]\n",
"max arrival time is 299.920635\n",
"sample # 52\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"200.050252428\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.985980\n",
"T = 20.000000, Volume = 75.700894\n",
"T = 30.000000, Volume = 113.415808\n",
"T = 40.000000, Volume = 151.130723\n",
"T = 50.000000, Volume = 181.631230\n",
"T = 60.000000, Volume = 204.614221\n",
"T = 70.000000, Volume = 227.597216\n",
"T = 80.000000, Volume = 250.580211\n",
"T = 90.000000, Volume = 273.563206\n",
"T = 100.000000, Volume = 296.546201\n",
"T = 110.000000, Volume = 319.529197\n",
"T = 120.000000, Volume = 342.512193\n",
"T = 130.000000, Volume = 365.495190\n",
"T = 140.000000, Volume = 388.478186\n",
"T = 150.000000, Volume = 411.461182\n",
"T = 160.000000, Volume = 434.444209\n",
"T = 170.000000, Volume = 457.427241\n",
"T = 180.000000, Volume = 480.410280\n",
"T = 190.000000, Volume = 503.393320\n",
"T = 200.000000, Volume = 526.376360\n",
"T = 210.000000, Volume = 549.359403\n",
"T = 220.000000, Volume = 572.342480\n",
"T = 230.000000, Volume = 595.325550\n",
"T = 240.000000, Volume = 618.308633\n",
"T = 250.000000, Volume = 641.291717\n",
"T = 260.000000, Volume = 664.274801\n",
"T = 270.000000, Volume = 687.257900\n",
"T = 280.000000, Volume = 710.241019\n",
"T = 290.000000, Volume = 733.224133\n",
"T = 300.000000, Volume = 756.207243\n",
"T = 310.000000, Volume = 779.190394\n",
"T = 320.000000, Volume = 802.173533\n",
"inflow volume is 802.173533 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 117.493325 s\n",
"[0.047865562251824319, 0.08314667122705896, 0.11326704850853089, 0.72159035658787996, 9.9887244262683517, 9.2592229399090531, 6.6760117916976842, 7.3761452261683322, 7.5485560061367218, 7.8160158705659804, 7.9494167615833753, 8.6331350496357384, 7.6895632697483149, 7.3220855314165432, 7.8811866243006063, 16.476545735854828, 12.161430590188687, 7.6343733630692334, 6.7142100474242481, 6.7233874957496154, 13.975104823987731, 14.612037219864659, 16.243810578776323, 7.5167298281628163, 7.0356542093311045, 8.661426680785862, 16.399967676173144, 17.741466173126682, 19.358490881228491, 22.192646302540457, 14.298650555145947, 16.820954321899976]\n",
"[ 89.23848608 89.28228945 76.58437952 76.50662513 76.43735865\n",
" 76.36411426 76.28916693 68.29053134 68.20453302 62.56158824\n",
" 62.43658593 59.09477449 57.25495284 57.22364951 82.38365686\n",
" 57.60554394 64.75923992 60.14430091 71.68797458]\n",
"max arrival time is 317.156000\n",
"sample # 53\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"140.988274631\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.026490\n",
"T = 20.000000, Volume = 75.785105\n",
"T = 30.000000, Volume = 113.543720\n",
"T = 40.000000, Volume = 151.302335\n",
"T = 50.000000, Volume = 189.060950\n",
"T = 60.000000, Volume = 226.819565\n",
"T = 70.000000, Volume = 256.398933\n",
"T = 80.000000, Volume = 278.964084\n",
"T = 90.000000, Volume = 301.947124\n",
"T = 100.000000, Volume = 324.930164\n",
"T = 110.000000, Volume = 347.913232\n",
"T = 120.000000, Volume = 370.896326\n",
"T = 130.000000, Volume = 393.879438\n",
"T = 140.000000, Volume = 416.862551\n",
"T = 150.000000, Volume = 439.845664\n",
"T = 160.000000, Volume = 462.828779\n",
"T = 170.000000, Volume = 485.811893\n",
"T = 180.000000, Volume = 508.795011\n",
"T = 190.000000, Volume = 531.778196\n",
"T = 200.000000, Volume = 554.761370\n",
"T = 210.000000, Volume = 577.744549\n",
"T = 220.000000, Volume = 600.727766\n",
"T = 230.000000, Volume = 623.710987\n",
"T = 240.000000, Volume = 646.694208\n",
"T = 250.000000, Volume = 669.677428\n",
"T = 260.000000, Volume = 692.660677\n",
"T = 270.000000, Volume = 715.643948\n",
"T = 280.000000, Volume = 738.627241\n",
"T = 290.000000, Volume = 761.610535\n",
"T = 300.000000, Volume = 784.593829\n",
"T = 310.000000, Volume = 807.577089\n",
"inflow volume is 807.577089 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 78.481848 s\n",
"[0.047668300706712699, 0.081844082145235739, 0.76868791815493387, 3.3004613511346554, 2.9019114326204907, 3.1357487844462408, 47.544103565618116, 10.08697573947974, 2.4448398996990419, 2.4583619197230471, 9.2994887372673016, 10.459252095861467, 6.4013811527566631, 6.291814827101863, 6.4348574208059652, 5.9455872233853135, 5.9564703858274539, 9.4874891322938115, 11.039370418926017, 11.307687034036961, 8.6881409250549879, 4.4437374222339896, 3.4841280457691859, 3.4503862053969074, 5.7939635651139172, 10.787858428280842, 11.252753147446985, 6.8863017308055614, 6.3804216544279395, 6.3763382177273158, 8.6987223919261609]\n",
"[ 61.48556926 61.52891746 61.43176053 61.35297392 53.63661065\n",
" 46.93161834 46.85407595 46.78283715 42.2107987 42.17463143\n",
" 42.15355398 39.7441669 39.76625908 38.94768314 44.08158178\n",
" 38.94768314 40.55133895 49.88483319 57.11797734]\n",
"max arrival time is 305.056754\n",
"sample # 54\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"139.058304246\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.027758\n",
"T = 20.000000, Volume = 75.787635\n",
"T = 30.000000, Volume = 113.547511\n",
"T = 40.000000, Volume = 145.486960\n",
"T = 50.000000, Volume = 168.469954\n",
"T = 60.000000, Volume = 191.452951\n",
"T = 70.000000, Volume = 214.435967\n",
"T = 80.000000, Volume = 237.419063\n",
"T = 90.000000, Volume = 260.402146\n",
"T = 100.000000, Volume = 283.385230\n",
"T = 110.000000, Volume = 306.368314\n",
"T = 120.000000, Volume = 329.351398\n",
"T = 130.000000, Volume = 352.334552\n",
"T = 140.000000, Volume = 375.317737\n",
"T = 150.000000, Volume = 398.300940\n",
"T = 160.000000, Volume = 421.284144\n",
"T = 170.000000, Volume = 444.267349\n",
"T = 180.000000, Volume = 467.250550\n",
"T = 190.000000, Volume = 490.233829\n",
"T = 200.000000, Volume = 513.217096\n",
"T = 210.000000, Volume = 536.200363\n",
"T = 220.000000, Volume = 559.183644\n",
"T = 230.000000, Volume = 582.167014\n",
"T = 240.000000, Volume = 605.150373\n",
"T = 250.000000, Volume = 628.133728\n",
"T = 260.000000, Volume = 651.117117\n",
"T = 270.000000, Volume = 674.100523\n",
"T = 280.000000, Volume = 697.083930\n",
"T = 290.000000, Volume = 720.067338\n",
"T = 300.000000, Volume = 743.050747\n",
"T = 310.000000, Volume = 766.034156\n",
"T = 320.000000, Volume = 789.017567\n",
"inflow volume is 789.017567 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 83.541578 s\n",
"[0.050087622891484974, 0.087195894726117773, 0.29866150528530622, 5.9081390804882323, 9.4863221893519345, 11.41714769347519, 13.020816838593587, 9.6279726069565523, 9.2407146837153853, 8.3941583100670325, 8.2990231704316706, 8.6241808011733614, 11.58171139582514, 8.7583354367265827, 5.6895404968860062, 5.216512116375787, 5.2284763063578437, 9.0101469626674113, 10.892179438968155, 12.572163786824722, 12.556928326758191, 12.654374029912306, 11.165487424644299, 10.100250015109529, 9.4744351901162656, 4.5154720989200809, 4.133656047196963, 4.1186659941676913, 4.2580632494792408, 4.3338795141128967, 4.4176516831120551, 4.5166222352906402]\n",
"[ 52.98183713 43.04813292 32.80652391 32.64466156 22.29872239\n",
" 22.13490869 13.95084266 5.64393916 1.02596089 1.95914104\n",
" 0.837179 0.79470551 0.7961462 0.79892318 9.27010336\n",
" 37.93560133 48.12362854 27.48256717 16.91799829]\n",
"max arrival time is 319.246260\n",
"sample # 55\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 3, 2, 2, 3, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"172.150887417\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.005830\n",
"T = 20.000000, Volume = 75.741958\n",
"T = 30.000000, Volume = 113.478086\n",
"T = 40.000000, Volume = 145.357030\n",
"T = 50.000000, Volume = 168.340016\n",
"T = 60.000000, Volume = 191.323020\n",
"T = 70.000000, Volume = 214.306024\n",
"T = 80.000000, Volume = 237.289028\n",
"T = 90.000000, Volume = 260.272033\n",
"T = 100.000000, Volume = 283.255038\n",
"T = 110.000000, Volume = 306.238073\n",
"T = 120.000000, Volume = 329.221129\n",
"T = 130.000000, Volume = 352.204192\n",
"T = 140.000000, Volume = 375.187254\n",
"T = 150.000000, Volume = 398.170361\n",
"T = 160.000000, Volume = 421.153465\n",
"T = 170.000000, Volume = 444.136569\n",
"T = 180.000000, Volume = 467.119673\n",
"T = 190.000000, Volume = 490.102788\n",
"T = 200.000000, Volume = 513.085922\n",
"T = 210.000000, Volume = 536.069057\n",
"T = 220.000000, Volume = 559.052200\n",
"T = 230.000000, Volume = 582.035374\n",
"T = 240.000000, Volume = 605.018538\n",
"T = 250.000000, Volume = 628.001703\n",
"T = 260.000000, Volume = 650.984867\n",
"T = 270.000000, Volume = 673.968024\n",
"T = 280.000000, Volume = 696.951245\n",
"T = 290.000000, Volume = 719.934470\n",
"T = 300.000000, Volume = 742.917704\n",
"T = 310.000000, Volume = 765.900958\n",
"T = 320.000000, Volume = 788.884213\n",
"inflow volume is 788.884213 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 103.012944 s\n",
"[0.050209461481930423, 0.085980766141692963, 0.20383559794296771, 6.617902775885649, 11.198445281149501, 5.0226089845386355, 6.1798012521357801, 5.8851560287853051, 5.5016341436661014, 5.5142665659701651, 12.698969362755344, 9.3609891522663791, 5.5791303995485064, 9.0293385794544072, 11.799519757935647, 14.265869840271613, 11.824190429539618, 11.640677150084281, 7.0010866389335211, 3.0350689641695316, 3.0438203384123943, 12.896512489093924, 14.134632045145189, 13.00861353078135, 11.469801012067817, 11.435215196735784, 14.886010941442265, 13.355857316287295, 15.503237389105619, 5.4918647025677085, 4.7568030880788417, 4.8304583405975743]\n",
"[ 65.46377116 53.03522547 52.87389225 52.71776578 39.98184441\n",
" 27.13165148 26.96571787 26.8054024 13.85768122 16.98604455\n",
" 7.48264688 3.45744436 1.89497944 1.06899356 11.52463194\n",
" 59.35976349 20.34049509 46.36033664 33.56582031]\n",
"max arrival time is 319.409577\n",
"sample # 56\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 2, 2, 3, 3, 2, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"262.760783851\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.942564\n",
"T = 20.000000, Volume = 75.611086\n",
"T = 30.000000, Volume = 113.279608\n",
"T = 40.000000, Volume = 150.948130\n",
"T = 50.000000, Volume = 181.424665\n",
"T = 60.000000, Volume = 204.407643\n",
"T = 70.000000, Volume = 227.390618\n",
"T = 80.000000, Volume = 250.373595\n",
"T = 90.000000, Volume = 273.356577\n",
"T = 100.000000, Volume = 296.339566\n",
"T = 110.000000, Volume = 319.322572\n",
"T = 120.000000, Volume = 342.305579\n",
"T = 130.000000, Volume = 365.288586\n",
"T = 140.000000, Volume = 388.271594\n",
"T = 150.000000, Volume = 411.254602\n",
"T = 160.000000, Volume = 434.237609\n",
"T = 170.000000, Volume = 457.220617\n",
"T = 180.000000, Volume = 480.203644\n",
"T = 190.000000, Volume = 503.186670\n",
"T = 200.000000, Volume = 526.169695\n",
"T = 210.000000, Volume = 549.152721\n",
"T = 220.000000, Volume = 572.135779\n",
"T = 230.000000, Volume = 595.118829\n",
"T = 240.000000, Volume = 618.101879\n",
"T = 250.000000, Volume = 641.084937\n",
"T = 260.000000, Volume = 664.067996\n",
"T = 270.000000, Volume = 687.051055\n",
"T = 280.000000, Volume = 710.034113\n",
"T = 290.000000, Volume = 733.017186\n",
"T = 300.000000, Volume = 756.000263\n",
"T = 310.000000, Volume = 778.983340\n",
"T = 320.000000, Volume = 801.966392\n",
"inflow volume is 801.966392 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 156.712254 s\n",
"[0.0479863020989044, 0.08331104951880082, 0.11331004504659965, 0.45275433475221299, 12.231032640000169, 16.846016532069513, 20.053951303286357, 24.17144562236064, 10.073947332292452, 12.942189962377551, 17.327943939781861, 12.580734037123042, 8.8284387110902962, 9.3829520912173532, 7.9578153530896536, 7.6562866026545464, 15.909588898083157, 17.580760775237557, 26.689423399442962, 28.122175323026582, 28.601111158574088, 18.235144689260949, 23.717555621678923, 21.889182064207773, 10.655394145078876, 8.4738803166511989, 8.2965400069354569, 15.909753940163942, 18.236532235961157, 22.748378293551422, 25.029302207841447, 16.885586059417324]\n",
"[ 151.94882024 151.9917434 135.38350793 135.29187 121.65824607\n",
" 121.59319843 121.53957677 111.92162779 104.32227198 104.3090185\n",
" 104.29761258 100.73330546 100.72972143 100.73050302 115.97406642\n",
" 142.94480167 101.75992234 107.36622726 127.73117431]\n",
"max arrival time is 316.281973\n",
"sample # 57\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 2, 3, 2, 2, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"176.935949064\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.002360\n",
"T = 20.000000, Volume = 75.734937\n",
"T = 30.000000, Volume = 113.467514\n",
"T = 40.000000, Volume = 151.200090\n",
"T = 50.000000, Volume = 181.738143\n",
"T = 60.000000, Volume = 204.721140\n",
"T = 70.000000, Volume = 227.704142\n",
"T = 80.000000, Volume = 250.687144\n",
"T = 90.000000, Volume = 273.670147\n",
"T = 100.000000, Volume = 296.653155\n",
"T = 110.000000, Volume = 319.636206\n",
"T = 120.000000, Volume = 342.619249\n",
"T = 130.000000, Volume = 365.602308\n",
"T = 140.000000, Volume = 388.585367\n",
"T = 150.000000, Volume = 411.568425\n",
"T = 160.000000, Volume = 434.551512\n",
"T = 170.000000, Volume = 457.534611\n",
"T = 180.000000, Volume = 480.517714\n",
"T = 190.000000, Volume = 503.500829\n",
"T = 200.000000, Volume = 526.483945\n",
"T = 210.000000, Volume = 549.467061\n",
"T = 220.000000, Volume = 572.450178\n",
"T = 230.000000, Volume = 595.433295\n",
"T = 240.000000, Volume = 618.416411\n",
"T = 250.000000, Volume = 641.399539\n",
"T = 260.000000, Volume = 664.382703\n",
"T = 270.000000, Volume = 687.365858\n",
"T = 280.000000, Volume = 710.349028\n",
"T = 290.000000, Volume = 733.332201\n",
"T = 300.000000, Volume = 756.315375\n",
"T = 310.000000, Volume = 779.298549\n",
"T = 320.000000, Volume = 802.281697\n",
"inflow volume is 802.281697 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 103.483006 s\n",
"[0.047802569274177288, 0.083061890716842404, 0.11326414387725847, 0.78953135618281733, 9.1019321108141558, 8.9748202674481039, 5.6604502732725184, 5.5252147412684618, 5.5348984743819418, 10.679342534742357, 13.797999994913017, 15.239244990012923, 6.3334561371283176, 5.9609235398000582, 5.9911244840365248, 14.564435431624421, 14.256427979376253, 11.775215828923287, 7.465932243129231, 7.5846270258863093, 7.9860473489470527, 7.6303162023850133, 7.1663104864635798, 7.1744083853603255, 13.853755612202177, 14.943700247155318, 15.427121441235286, 8.8544234134453141, 7.9775967755796948, 7.2090082915782867, 7.2076108051556362, 11.468472125641167]\n",
"[ 76.90860545 76.95142353 65.76851441 65.66842412 56.49010788\n",
" 56.41232293 49.26767644 49.19829048 49.13177895 49.07323631\n",
" 45.25085059 45.23860315 45.24125962 44.17932994 46.65739041\n",
" 70.86498523 60.5642288 44.17932994 52.32226423]\n",
"max arrival time is 316.236997\n",
"sample # 58\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 2, 3, 2, 3, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"84.7549399808\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.041830\n",
"T = 20.000000, Volume = 75.819003\n",
"T = 30.000000, Volume = 113.596177\n",
"T = 40.000000, Volume = 143.555096\n",
"T = 50.000000, Volume = 166.321212\n",
"T = 60.000000, Volume = 189.304350\n",
"T = 70.000000, Volume = 212.287495\n",
"T = 80.000000, Volume = 235.270642\n",
"T = 90.000000, Volume = 258.253790\n",
"T = 100.000000, Volume = 281.236938\n",
"T = 110.000000, Volume = 304.220143\n",
"T = 120.000000, Volume = 327.203487\n",
"T = 130.000000, Volume = 350.186838\n",
"T = 140.000000, Volume = 373.170193\n",
"T = 150.000000, Volume = 396.153547\n",
"T = 160.000000, Volume = 419.136900\n",
"T = 170.000000, Volume = 442.120392\n",
"T = 180.000000, Volume = 465.104028\n",
"T = 190.000000, Volume = 488.087675\n",
"T = 200.000000, Volume = 511.071323\n",
"T = 210.000000, Volume = 534.054971\n",
"T = 220.000000, Volume = 557.038619\n",
"T = 230.000000, Volume = 580.022406\n",
"T = 240.000000, Volume = 603.006257\n",
"T = 250.000000, Volume = 625.990115\n",
"T = 260.000000, Volume = 648.973983\n",
"T = 270.000000, Volume = 671.957888\n",
"T = 280.000000, Volume = 694.941799\n",
"T = 290.000000, Volume = 717.925714\n",
"T = 300.000000, Volume = 740.909632\n",
"T = 310.000000, Volume = 763.893553\n",
"T = 320.000000, Volume = 786.877474\n",
"T = 330.000000, Volume = 809.861354\n",
"inflow volume is 809.861354 gallons\n",
"simulation time is 330.000000 s\n",
"wall clock time is 50.817631 s\n",
"[0.049716657197934361, 0.085506920997668226, 0.42123376804754392, 23.406918517610119, 7.1491119185604068, 2.1256010791841669, 2.3708852942642156, 2.4316738111646545, 2.2908881761810518, 2.2999052611547341, 5.6735326173119249, 4.1965773022995387, 4.5262906597839629, 4.4090937696959793, 4.4187955639181666, 5.2518680768097425, 5.4248418210823708, 2.857221794348932, 3.0421805793701115, 2.8901588213759992, 2.8985958335416284, 5.7684632319867015, 4.9170194907465659, 5.0387020919637884, 5.3418822603872753, 3.8758097163946745, 3.7058576602782929, 3.8093157613882123, 3.8270430555625881, 3.8420713183170836, 3.6459338096990854, 3.6434942228416971, 5.7988624516250145]\n",
"[ 30.56459811 25.32179335 25.07922495 24.83537647 20.70137054\n",
" 20.59561616 17.29088894 17.26535663 14.83761656 14.94962912\n",
" 15.04611377 15.12835132 15.18797526 14.63870558 22.48022654\n",
" 27.74987551 18.79046483 14.63870558 15.79623726]\n",
"max arrival time is 325.486308\n",
"sample # 59\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 3, 3, 2, 2, 3, 2, 2, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"179.459507778\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.000721\n",
"T = 20.000000, Volume = 75.731327\n",
"T = 30.000000, Volume = 113.461933\n",
"T = 40.000000, Volume = 151.192539\n",
"T = 50.000000, Volume = 181.720828\n",
"T = 60.000000, Volume = 204.703825\n",
"T = 70.000000, Volume = 227.686825\n",
"T = 80.000000, Volume = 250.669825\n",
"T = 90.000000, Volume = 273.652872\n",
"T = 100.000000, Volume = 296.635925\n",
"T = 110.000000, Volume = 319.618980\n",
"T = 120.000000, Volume = 342.602042\n",
"T = 130.000000, Volume = 365.585144\n",
"T = 140.000000, Volume = 388.568236\n",
"T = 150.000000, Volume = 411.551345\n",
"T = 160.000000, Volume = 434.534455\n",
"T = 170.000000, Volume = 457.517566\n",
"T = 180.000000, Volume = 480.500676\n",
"T = 190.000000, Volume = 503.483782\n",
"T = 200.000000, Volume = 526.466933\n",
"T = 210.000000, Volume = 549.450081\n",
"T = 220.000000, Volume = 572.433229\n",
"T = 230.000000, Volume = 595.416383\n",
"T = 240.000000, Volume = 618.399558\n",
"T = 250.000000, Volume = 641.382735\n",
"T = 260.000000, Volume = 664.365911\n",
"T = 270.000000, Volume = 687.349088\n",
"T = 280.000000, Volume = 710.332266\n",
"T = 290.000000, Volume = 733.315443\n",
"T = 300.000000, Volume = 756.298617\n",
"T = 310.000000, Volume = 779.281822\n",
"T = 320.000000, Volume = 802.265000\n",
"inflow volume is 802.265000 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 105.125642 s\n",
"[0.04781034844610433, 0.083076048238678299, 0.11353216708178462, 0.78857701251693402, 9.2344670965194471, 9.1520118996425239, 5.3332775335684408, 9.2721668587065249, 12.427629998875952, 6.1624582326531607, 5.6035231150529921, 11.708491077397255, 12.531917353996809, 15.587083545435309, 7.3337115685901217, 7.342153477717245, 6.4331416217323367, 6.4430431285170267, 10.735159511072212, 13.236778425438699, 14.715991108532959, 12.6315457196316, 9.0889709696042136, 4.6746507657921121, 4.5655688643698715, 4.5822155951115802, 4.6294030189829707, 3.6741030932860639, 3.6840075719098881, 8.9325248411411486, 14.3979986780882, 15.242841201549856]\n",
"[ 82.98795497 83.03068913 71.73434847 61.75604799 53.08512449\n",
" 52.99744199 52.93169224 47.02421481 47.02403959 47.01387473\n",
" 46.987039 46.94771604 45.15838703 45.16672352 45.51366261\n",
" 56.91637338 66.24289348 49.44519942 76.87679379]\n",
"max arrival time is 316.526973\n",
"sample # 60\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"180.108077563\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.000097\n",
"T = 20.000000, Volume = 75.730247\n",
"T = 30.000000, Volume = 113.460397\n",
"T = 40.000000, Volume = 145.326492\n",
"T = 50.000000, Volume = 168.309477\n",
"T = 60.000000, Volume = 191.292477\n",
"T = 70.000000, Volume = 214.275478\n",
"T = 80.000000, Volume = 237.258480\n",
"T = 90.000000, Volume = 260.241482\n",
"T = 100.000000, Volume = 283.224484\n",
"T = 110.000000, Volume = 306.207487\n",
"T = 120.000000, Volume = 329.190490\n",
"T = 130.000000, Volume = 352.173493\n",
"T = 140.000000, Volume = 375.156497\n",
"T = 150.000000, Volume = 398.139544\n",
"T = 160.000000, Volume = 421.122596\n",
"T = 170.000000, Volume = 444.105652\n",
"T = 180.000000, Volume = 467.088713\n",
"T = 190.000000, Volume = 490.071816\n",
"T = 200.000000, Volume = 513.054910\n",
"T = 210.000000, Volume = 536.038011\n",
"T = 220.000000, Volume = 559.021121\n",
"T = 230.000000, Volume = 582.004233\n",
"T = 240.000000, Volume = 604.987344\n",
"T = 250.000000, Volume = 627.970453\n",
"T = 260.000000, Volume = 650.953599\n",
"T = 270.000000, Volume = 673.936748\n",
"T = 280.000000, Volume = 696.919900\n",
"T = 290.000000, Volume = 719.903066\n",
"T = 300.000000, Volume = 742.886232\n",
"T = 310.000000, Volume = 765.869398\n",
"T = 320.000000, Volume = 788.852606\n",
"inflow volume is 788.852606 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 107.263380 s\n",
"[0.05023277511911399, 0.086006595317894896, 0.18133346628129571, 6.8007956062857593, 11.533519247774139, 5.1240574862798383, 6.4335232908471918, 6.6630808767754308, 7.0989974910547256, 7.4574542083299944, 7.6343978281913065, 6.5807905896744403, 6.5324174697190553, 9.7799137050179432, 13.261167056015681, 7.6375322966986738, 6.075451924780146, 12.259580004917403, 13.842310572541939, 15.176722634344054, 11.603628013721856, 7.8217714981825921, 6.8140605097606839, 6.7904505922990701, 11.010824431678552, 13.480079996218223, 15.78076212596509, 11.800268967572848, 6.9517447864856328, 6.9552803840297894, 13.860564749782498, 13.891678998418431]\n",
"[ 6.52204953e+01 5.22053017e+01 5.19096624e+01 5.16138911e+01\n",
" 5.13164854e+01 5.10174120e+01 3.81946991e+01 2.55019052e+01\n",
" 2.52150260e+01 2.49331260e+01 1.25628500e+01 1.77010019e+01\n",
" 3.32573422e+00 4.96247476e-02 3.18160883e+01 1.87164081e+01\n",
" 4.45740964e+01 5.88409549e+01 1.19988282e+01]\n",
"max arrival time is 319.848956\n",
"sample # 61\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"255.452841495\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.947189\n",
"T = 20.000000, Volume = 75.620925\n",
"T = 30.000000, Volume = 113.294661\n",
"T = 40.000000, Volume = 145.014186\n",
"T = 50.000000, Volume = 167.997162\n",
"T = 60.000000, Volume = 190.980137\n",
"T = 70.000000, Volume = 213.963120\n",
"T = 80.000000, Volume = 236.946103\n",
"T = 90.000000, Volume = 259.929094\n",
"T = 100.000000, Volume = 282.912103\n",
"T = 110.000000, Volume = 305.895110\n",
"T = 120.000000, Volume = 328.878120\n",
"T = 130.000000, Volume = 351.861130\n",
"T = 140.000000, Volume = 374.844140\n",
"T = 150.000000, Volume = 397.827150\n",
"T = 160.000000, Volume = 420.810161\n",
"T = 170.000000, Volume = 443.793172\n",
"T = 180.000000, Volume = 466.776182\n",
"T = 190.000000, Volume = 489.759193\n",
"T = 200.000000, Volume = 512.742204\n",
"T = 210.000000, Volume = 535.725215\n",
"T = 220.000000, Volume = 558.708240\n",
"T = 230.000000, Volume = 581.691273\n",
"T = 240.000000, Volume = 604.674302\n",
"T = 250.000000, Volume = 627.657329\n",
"T = 260.000000, Volume = 650.640390\n",
"T = 270.000000, Volume = 673.623446\n",
"T = 280.000000, Volume = 696.606502\n",
"T = 290.000000, Volume = 719.589558\n",
"T = 300.000000, Volume = 742.572647\n",
"T = 310.000000, Volume = 765.555730\n",
"T = 320.000000, Volume = 788.538813\n",
"inflow volume is 788.538813 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 152.957402 s\n",
"[0.050392164226900078, 0.086186447656807968, 0.10416564776917384, 8.2591027945176982, 16.159087902465519, 20.082105549366549, 7.0357179043640272, 6.5059781859297772, 17.822295611290233, 18.25641792248355, 13.482769806278466, 8.847716615711219, 9.2086344369355917, 9.5726765683133745, 9.8408078401204193, 9.9654148328321757, 10.363974883315009, 10.702345489113151, 9.5610451504331682, 8.9639922788785675, 9.2376082138322158, 21.67358681338942, 22.391241223983677, 26.219350076382234, 26.749532255098874, 18.108315432111372, 23.464785827411973, 23.215587098352785, 27.44183672881864, 18.254610080206191, 22.766718400251232, 21.270686224767136]\n",
"[ 96.12369246 77.45571211 77.21006678 58.32785455 58.07386187\n",
" 57.81805123 57.55532777 57.28545076 57.00850132 38.5256318\n",
" 33.14595848 9.15678456 4.36114664 2.70307999 47.73822383\n",
" 67.75847042 86.92214524 29.27880745 19.69182897]\n",
"max arrival time is 318.932039\n",
"sample # 62\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"140.002314107\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.027517\n",
"T = 20.000000, Volume = 75.786797\n",
"T = 30.000000, Volume = 113.546077\n",
"T = 40.000000, Volume = 145.484685\n",
"T = 50.000000, Volume = 168.467680\n",
"T = 60.000000, Volume = 191.450702\n",
"T = 70.000000, Volume = 214.433724\n",
"T = 80.000000, Volume = 237.416746\n",
"T = 90.000000, Volume = 260.399795\n",
"T = 100.000000, Volume = 283.382897\n",
"T = 110.000000, Volume = 306.366008\n",
"T = 120.000000, Volume = 329.349120\n",
"T = 130.000000, Volume = 352.332233\n",
"T = 140.000000, Volume = 375.315347\n",
"T = 150.000000, Volume = 398.298462\n",
"T = 160.000000, Volume = 421.281590\n",
"T = 170.000000, Volume = 444.264722\n",
"T = 180.000000, Volume = 467.247854\n",
"T = 190.000000, Volume = 490.230987\n",
"T = 200.000000, Volume = 513.214134\n",
"T = 210.000000, Volume = 536.197320\n",
"T = 220.000000, Volume = 559.180532\n",
"T = 230.000000, Volume = 582.163755\n",
"T = 240.000000, Volume = 605.146978\n",
"T = 250.000000, Volume = 628.130200\n",
"T = 260.000000, Volume = 651.113460\n",
"T = 270.000000, Volume = 674.096731\n",
"T = 280.000000, Volume = 697.080000\n",
"T = 290.000000, Volume = 720.063283\n",
"T = 300.000000, Volume = 743.046581\n",
"T = 310.000000, Volume = 766.029876\n",
"T = 320.000000, Volume = 789.013236\n",
"inflow volume is 789.013236 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 83.196915 s\n",
"[0.050090146951591641, 0.085862705834670844, 0.29247318394427907, 5.9257523442180347, 9.3833473370018154, 4.5308916074832739, 4.607094941504208, 4.6167720643410934, 9.2846450781136838, 8.8572468014666867, 5.7349798930209728, 5.7956523495605623, 6.2504642747997234, 6.5939257305068093, 6.6045477877694969, 3.739596484462032, 3.3507362037379997, 2.9859891670141394, 2.9953509150536184, 9.4653925535500179, 10.963497846348899, 4.3747296389449009, 3.2253827663377979, 3.2342090835099975, 5.8238358368058574, 11.210293105011024, 12.205719379342899, 11.89699575640574, 6.4519740826812209, 6.2243804696732221, 10.952632023087885, 11.717672974455327]\n",
"[ 4.97544236e+01 3.97316384e+01 3.94603178e+01 2.92912035e+01\n",
" 2.89959079e+01 2.86998040e+01 2.84150898e+01 2.81549518e+01\n",
" 1.85516577e+01 1.83321370e+01 8.82754450e+00 1.21977162e+01\n",
" 3.03469588e+00 4.93905262e-02 3.43234207e+01 1.35567338e+01\n",
" 4.48619162e+01 8.28598210e+00 2.33295548e+01]\n",
"max arrival time is 319.914286\n",
"sample # 63\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"140.983052005\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.026490\n",
"T = 20.000000, Volume = 75.785105\n",
"T = 30.000000, Volume = 113.543720\n",
"T = 40.000000, Volume = 145.479796\n",
"T = 50.000000, Volume = 168.462793\n",
"T = 60.000000, Volume = 191.445812\n",
"T = 70.000000, Volume = 214.428846\n",
"T = 80.000000, Volume = 237.411941\n",
"T = 90.000000, Volume = 260.395046\n",
"T = 100.000000, Volume = 283.378154\n",
"T = 110.000000, Volume = 306.361264\n",
"T = 120.000000, Volume = 329.344374\n",
"T = 130.000000, Volume = 352.327485\n",
"T = 140.000000, Volume = 375.310606\n",
"T = 150.000000, Volume = 398.293734\n",
"T = 160.000000, Volume = 421.276863\n",
"T = 170.000000, Volume = 444.259993\n",
"T = 180.000000, Volume = 467.243123\n",
"T = 190.000000, Volume = 490.226253\n",
"T = 200.000000, Volume = 513.209399\n",
"T = 210.000000, Volume = 536.192584\n",
"T = 220.000000, Volume = 559.175759\n",
"T = 230.000000, Volume = 582.158934\n",
"T = 240.000000, Volume = 605.142161\n",
"T = 250.000000, Volume = 628.125429\n",
"T = 260.000000, Volume = 651.108692\n",
"T = 270.000000, Volume = 674.091963\n",
"T = 280.000000, Volume = 697.075255\n",
"T = 290.000000, Volume = 720.058549\n",
"T = 300.000000, Volume = 743.041842\n",
"T = 310.000000, Volume = 766.025135\n",
"T = 320.000000, Volume = 789.008492\n",
"inflow volume is 789.008492 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 83.934348 s\n",
"[0.050096064058858504, 0.087206546025851803, 0.29415224156554676, 5.9634301853599068, 8.6327914904529433, 4.2441039299179595, 7.8513514562748279, 11.230226038008498, 6.1886864420183718, 5.660202438661825, 6.0827560845015007, 6.442651752141904, 6.5724359181721095, 4.3580934923288943, 3.5289499020930104, 3.5549795720418147, 3.3142175436357868, 2.9230503044256033, 2.9351880694617796, 9.6613659388838879, 13.543032433985813, 13.089726099609877, 15.067780169872309, 10.734603871463442, 12.091426810941504, 11.842768899848267, 9.1002200070853121, 7.0480240784039649, 6.6410840748255202, 6.6502822412777922, 10.941138682799288, 13.365845006952862]\n",
"[ 5.03591971e+01 4.02673119e+01 2.99185241e+01 2.96199837e+01\n",
" 2.93294078e+01 2.90362717e+01 2.87498233e+01 2.85006119e+01\n",
" 1.88282583e+01 9.18417664e+00 8.95208371e+00 1.23298009e+01\n",
" 5.19078458e+00 4.93945393e-02 8.37991846e+00 4.54310297e+01\n",
" 3.50379339e+01 2.36545311e+01 1.40017152e+01]\n",
"max arrival time is 319.398411\n",
"sample # 64\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 3, 2, 2, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"204.851980987\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.982590\n",
"T = 20.000000, Volume = 75.693853\n",
"T = 30.000000, Volume = 113.405116\n",
"T = 40.000000, Volume = 151.116379\n",
"T = 50.000000, Volume = 188.827642\n",
"T = 60.000000, Volume = 226.538905\n",
"T = 70.000000, Volume = 258.965278\n",
"T = 80.000000, Volume = 281.948272\n",
"T = 90.000000, Volume = 304.931273\n",
"T = 100.000000, Volume = 327.914275\n",
"T = 110.000000, Volume = 350.897298\n",
"T = 120.000000, Volume = 373.880321\n",
"T = 130.000000, Volume = 396.863344\n",
"T = 140.000000, Volume = 419.846365\n",
"T = 150.000000, Volume = 442.829436\n",
"T = 160.000000, Volume = 465.812500\n",
"T = 170.000000, Volume = 488.795576\n",
"T = 180.000000, Volume = 511.778653\n",
"T = 190.000000, Volume = 534.761731\n",
"T = 200.000000, Volume = 557.744808\n",
"T = 210.000000, Volume = 580.727885\n",
"T = 220.000000, Volume = 603.710980\n",
"T = 230.000000, Volume = 626.694089\n",
"T = 240.000000, Volume = 649.677196\n",
"T = 250.000000, Volume = 672.660297\n",
"T = 260.000000, Volume = 695.643442\n",
"T = 270.000000, Volume = 718.626591\n",
"T = 280.000000, Volume = 741.609740\n",
"T = 290.000000, Volume = 764.592887\n",
"T = 300.000000, Volume = 787.576044\n",
"T = 310.000000, Volume = 810.559162\n",
"inflow volume is 810.559162 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 115.428845 s\n",
"[0.047874509793852389, 0.082104615574254136, 0.096722163434943401, 4.5970284050663244, 4.1481144531085237, 2.6049688320177142, 15.844567339868421, 6.9807320390858099, 2.4845576502301427, 2.5268074756247398, 16.213364962425338, 16.930191853316359, 19.16650158854355, 22.675115057958884, 14.044835923261733, 15.607386033252295, 8.377389144043482, 8.2133981774877114, 7.3852219089844926, 7.0529212934012646, 8.698071732471119, 16.700229823517411, 17.77727761064811, 20.44792445235089, 21.217835711504605, 14.768620243704987, 17.452293974831193, 16.059303922745244, 15.974724458695981, 6.0163314145801126, 8.0107646569780968]\n",
"[ 139.2737173 139.3161383 139.19900785 139.09434204 127.94420896\n",
" 118.29523219 110.16947987 110.13947962 110.11675405 105.08180535\n",
" 101.58566505 101.57985066 101.57691234 101.5758972 102.75470788\n",
" 107.02373894 122.54547273 113.65616761 132.9492125 ]\n",
"max arrival time is 303.511719\n",
"sample # 65\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 3, 2, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"143.578030648\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.024901\n",
"T = 20.000000, Volume = 75.781762\n",
"T = 30.000000, Volume = 113.538623\n",
"T = 40.000000, Volume = 145.466997\n",
"T = 50.000000, Volume = 168.449990\n",
"T = 60.000000, Volume = 191.432983\n",
"T = 70.000000, Volume = 214.416009\n",
"T = 80.000000, Volume = 237.399097\n",
"T = 90.000000, Volume = 260.382198\n",
"T = 100.000000, Volume = 283.365301\n",
"T = 110.000000, Volume = 306.348404\n",
"T = 120.000000, Volume = 329.331508\n",
"T = 130.000000, Volume = 352.314659\n",
"T = 140.000000, Volume = 375.297831\n",
"T = 150.000000, Volume = 398.281020\n",
"T = 160.000000, Volume = 421.264209\n",
"T = 170.000000, Volume = 444.247399\n",
"T = 180.000000, Volume = 467.230587\n",
"T = 190.000000, Volume = 490.213847\n",
"T = 200.000000, Volume = 513.197096\n",
"T = 210.000000, Volume = 536.180357\n",
"T = 220.000000, Volume = 559.163636\n",
"T = 230.000000, Volume = 582.146982\n",
"T = 240.000000, Volume = 605.130317\n",
"T = 250.000000, Volume = 628.113651\n",
"T = 260.000000, Volume = 651.097003\n",
"T = 270.000000, Volume = 674.080382\n",
"T = 280.000000, Volume = 697.063762\n",
"T = 290.000000, Volume = 720.047142\n",
"T = 300.000000, Volume = 743.030524\n",
"T = 310.000000, Volume = 766.013906\n",
"T = 320.000000, Volume = 788.997289\n",
"inflow volume is 788.997289 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 86.107407 s\n",
"[0.050107262072116611, 0.087217085906661093, 0.28641535405052593, 6.0239476139691543, 10.068868761858269, 12.124144575627309, 10.026009954959285, 11.340260878491035, 5.8697759259532853, 5.0862382099357664, 4.9764699410449111, 5.2428393103151611, 11.233305132210351, 8.0564628915193133, 5.8433222726937659, 5.366118076642227, 5.3779697381523341, 9.7289920143178552, 11.401335859603915, 13.703580630780436, 6.383913524860648, 10.888577089597939, 11.955228573413907, 10.3523846087355, 10.946308089941075, 4.6031827511785295, 4.1876419964098348, 4.094299803952377, 4.2777231097606974, 4.4360326705404507, 4.4682043805278351, 4.5452705458329028]\n",
"[ 54.6800841 44.40744843 33.82824659 33.66783688 22.98464268\n",
" 22.81912239 14.14443575 5.39816638 1.20527831 1.84157937\n",
" 0.87689392 0.83233653 0.83462289 0.83614999 9.64097602\n",
" 28.33850343 17.4344512 49.65250054 39.12594759]\n",
"max arrival time is 318.255295\n",
"sample # 66\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"163.12776326\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.012096\n",
"T = 20.000000, Volume = 75.755019\n",
"T = 30.000000, Volume = 113.497942\n",
"T = 40.000000, Volume = 145.389771\n",
"T = 50.000000, Volume = 168.372759\n",
"T = 60.000000, Volume = 191.355746\n",
"T = 70.000000, Volume = 214.338749\n",
"T = 80.000000, Volume = 237.321812\n",
"T = 90.000000, Volume = 260.304865\n",
"T = 100.000000, Volume = 283.287918\n",
"T = 110.000000, Volume = 306.271006\n",
"T = 120.000000, Volume = 329.254131\n",
"T = 130.000000, Volume = 352.237249\n",
"T = 140.000000, Volume = 375.220367\n",
"T = 150.000000, Volume = 398.203503\n",
"T = 160.000000, Volume = 421.186655\n",
"T = 170.000000, Volume = 444.169807\n",
"T = 180.000000, Volume = 467.152960\n",
"T = 190.000000, Volume = 490.136114\n",
"T = 200.000000, Volume = 513.119268\n",
"T = 210.000000, Volume = 536.102422\n",
"T = 220.000000, Volume = 559.085577\n",
"T = 230.000000, Volume = 582.068731\n",
"T = 240.000000, Volume = 605.051913\n",
"T = 250.000000, Volume = 628.035102\n",
"T = 260.000000, Volume = 651.018289\n",
"T = 270.000000, Volume = 674.001499\n",
"T = 280.000000, Volume = 696.984709\n",
"T = 290.000000, Volume = 719.967920\n",
"T = 300.000000, Volume = 742.951131\n",
"T = 310.000000, Volume = 765.934342\n",
"T = 320.000000, Volume = 788.917553\n",
"inflow volume is 788.917553 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 97.029755 s\n",
"[0.050181315224326319, 0.087294078641913089, 0.23271598923145495, 6.4493833122819453, 10.748553169977962, 14.0621756295302, 15.187536548674402, 11.995327764540082, 14.402726770417322, 14.008975259200335, 14.486855091613435, 12.183017876640195, 11.71827645637439, 10.831595921308239, 6.071357671070345, 3.6177594892955391, 3.6648680480269324, 3.813885940482741, 3.7858566054783678, 4.0833575530332293, 3.2402082512675721, 3.0936904644796095, 5.6974862688550774, 12.329650352999378, 13.264953003457284, 14.183671088509678, 7.5048517678539781, 7.7408767937312151, 8.2332036924322232, 7.6149126315050619, 7.3581274166198449, 7.6373202719372477]\n",
"[ 4.83226892e+01 3.65292180e+01 2.44872320e+01 1.25262026e+01\n",
" 1.22368289e+01 1.19441093e+01 1.16507645e+01 1.13719613e+01\n",
" 1.58541564e+01 8.38557902e+00 3.01238437e+00 2.32491402e+00\n",
" 6.51292773e+00 4.75058581e-02 1.07446350e+01 4.25702859e+01\n",
" 1.84753889e+01 3.04840876e+01 4.75058581e-02]\n",
"max arrival time is 290.794507\n",
"sample # 67\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"189.598155973\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.993656\n",
"T = 20.000000, Volume = 75.716574\n",
"T = 30.000000, Volume = 113.439493\n",
"T = 40.000000, Volume = 145.288342\n",
"T = 50.000000, Volume = 168.271325\n",
"T = 60.000000, Volume = 191.254310\n",
"T = 70.000000, Volume = 214.237307\n",
"T = 80.000000, Volume = 237.220304\n",
"T = 90.000000, Volume = 260.203317\n",
"T = 100.000000, Volume = 283.186359\n",
"T = 110.000000, Volume = 306.169405\n",
"T = 120.000000, Volume = 329.152450\n",
"T = 130.000000, Volume = 352.135526\n",
"T = 140.000000, Volume = 375.118605\n",
"T = 150.000000, Volume = 398.101692\n",
"T = 160.000000, Volume = 421.084786\n",
"T = 170.000000, Volume = 444.067881\n",
"T = 180.000000, Volume = 467.050977\n",
"T = 190.000000, Volume = 490.034072\n",
"T = 200.000000, Volume = 513.017174\n",
"T = 210.000000, Volume = 536.000311\n",
"T = 220.000000, Volume = 558.983440\n",
"T = 230.000000, Volume = 581.966578\n",
"T = 240.000000, Volume = 604.949723\n",
"T = 250.000000, Volume = 627.932868\n",
"T = 260.000000, Volume = 650.916013\n",
"T = 270.000000, Volume = 673.899152\n",
"T = 280.000000, Volume = 696.882335\n",
"T = 290.000000, Volume = 719.865514\n",
"T = 300.000000, Volume = 742.848700\n",
"T = 310.000000, Volume = 765.831894\n",
"T = 320.000000, Volume = 788.815089\n",
"inflow volume is 788.815089 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 113.637165 s\n",
"[0.050258871022857109, 0.086039873849568418, 0.15337225034673899, 7.0073815198421432, 12.364839154160565, 11.488163372956405, 5.35048536882194, 5.3684337810530121, 12.887474574649637, 11.807156456486121, 6.1716085113596577, 6.4520022397315859, 14.96459798267383, 14.264979229597797, 11.80151347138856, 7.4810842278206708, 7.1548956750030852, 6.4743905745502373, 6.4850557919367988, 14.216954783320922, 13.946374073097125, 17.314776337111184, 9.0917154042812118, 7.8968066880814352, 6.9444483414465266, 6.9560377299365941, 12.532146762078781, 14.682141354492209, 18.069177817889045, 9.3720218146906333, 8.4685790368562728, 8.5591499090117331]\n",
"[ 69.59824983 55.89046652 55.59467023 41.756666 28.03240688\n",
" 27.74166176 27.45495206 14.05017733 13.76570425 19.46420774\n",
" 7.25467799 6.09794233 1.22369329 1.19630895 20.72925138\n",
" 62.87998688 13.18049342 34.86961809 48.65152065]\n",
"max arrival time is 318.775855\n",
"sample # 68\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"185.061732055\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.996550\n",
"T = 20.000000, Volume = 75.722898\n",
"T = 30.000000, Volume = 113.449245\n",
"T = 40.000000, Volume = 145.305369\n",
"T = 50.000000, Volume = 168.288352\n",
"T = 60.000000, Volume = 191.271351\n",
"T = 70.000000, Volume = 214.254350\n",
"T = 80.000000, Volume = 237.237350\n",
"T = 90.000000, Volume = 260.220350\n",
"T = 100.000000, Volume = 283.203350\n",
"T = 110.000000, Volume = 306.186351\n",
"T = 120.000000, Volume = 329.169352\n",
"T = 130.000000, Volume = 352.152363\n",
"T = 140.000000, Volume = 375.135374\n",
"T = 150.000000, Volume = 398.118385\n",
"T = 160.000000, Volume = 421.101396\n",
"T = 170.000000, Volume = 444.084408\n",
"T = 180.000000, Volume = 467.067420\n",
"T = 190.000000, Volume = 490.050433\n",
"T = 200.000000, Volume = 513.033445\n",
"T = 210.000000, Volume = 536.016458\n",
"T = 220.000000, Volume = 558.999495\n",
"T = 230.000000, Volume = 581.982540\n",
"T = 240.000000, Volume = 604.965593\n",
"T = 250.000000, Volume = 627.948644\n",
"T = 260.000000, Volume = 650.931738\n",
"T = 270.000000, Volume = 673.914828\n",
"T = 280.000000, Volume = 696.897917\n",
"T = 290.000000, Volume = 719.881009\n",
"T = 300.000000, Volume = 742.864154\n",
"T = 310.000000, Volume = 765.847294\n",
"T = 320.000000, Volume = 788.830434\n",
"inflow volume is 788.830434 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 108.999912 s\n",
"[0.050246889737571673, 0.086021617462508554, 0.16579147049828097, 6.9206738340935834, 12.415375109322508, 5.2232066865100411, 6.5633118005552804, 6.7748607376249081, 7.2349228369422738, 7.6145311211036759, 7.7784206337453847, 7.5949446690915172, 4.1085360009435945, 3.90199655687465, 3.9511454596024365, 4.1208625608490266, 4.06991435913171, 4.4386772584433887, 3.5469459205669391, 3.2344918519585009, 3.7177826470113913, 15.739452357252267, 10.567167830076649, 6.8078637279162137, 11.404159465398632, 14.230445406680857, 17.56039989498916, 18.343357686335143, 16.357649677355461, 16.079371435839995, 18.466217120723559, 18.81326653497025]\n",
"[ 5.47964369e+01 4.13340683e+01 4.10579029e+01 4.07812024e+01\n",
" 4.05073378e+01 4.02400766e+01 3.99690898e+01 3.96845115e+01\n",
" 3.93865711e+01 3.90808036e+01 2.61089006e+01 1.91550876e+01\n",
" 6.70224774e+00 4.71787578e-02 4.71787578e-02 1.29261677e+01\n",
" 3.25607558e+01 1.96576136e+01 4.82056003e+01]\n",
"max arrival time is 301.003026\n",
"sample # 69\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 2, 2, 3, 3, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"148.748915556\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.021911\n",
"T = 20.000000, Volume = 75.775243\n",
"T = 30.000000, Volume = 113.528575\n",
"T = 40.000000, Volume = 151.281906\n",
"T = 50.000000, Volume = 181.952079\n",
"T = 60.000000, Volume = 204.935077\n",
"T = 70.000000, Volume = 227.918090\n",
"T = 80.000000, Volume = 250.901107\n",
"T = 90.000000, Volume = 273.884124\n",
"T = 100.000000, Volume = 296.867148\n",
"T = 110.000000, Volume = 319.850233\n",
"T = 120.000000, Volume = 342.833304\n",
"T = 130.000000, Volume = 365.816377\n",
"T = 140.000000, Volume = 388.799480\n",
"T = 150.000000, Volume = 411.782593\n",
"T = 160.000000, Volume = 434.765705\n",
"T = 170.000000, Volume = 457.748817\n",
"T = 180.000000, Volume = 480.731969\n",
"T = 190.000000, Volume = 503.715121\n",
"T = 200.000000, Volume = 526.698286\n",
"T = 210.000000, Volume = 549.681456\n",
"T = 220.000000, Volume = 572.664696\n",
"T = 230.000000, Volume = 595.647928\n",
"T = 240.000000, Volume = 618.631161\n",
"T = 250.000000, Volume = 641.614393\n",
"T = 260.000000, Volume = 664.597625\n",
"T = 270.000000, Volume = 687.580872\n",
"T = 280.000000, Volume = 710.564192\n",
"T = 290.000000, Volume = 733.547502\n",
"T = 300.000000, Volume = 756.530835\n",
"T = 310.000000, Volume = 779.514175\n",
"T = 320.000000, Volume = 802.497505\n",
"inflow volume is 802.497505 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 88.143588 s\n",
"[0.047703680532962492, 0.082933822425256556, 0.11323281943765223, 0.83846612061349801, 8.0371308478227306, 12.48453706045977, 9.3681124416173187, 5.1365952474155252, 5.1418788000089801, 8.8316791449577909, 11.740494511236506, 11.953849877894907, 11.530934840815496, 4.3906454529940335, 2.9698193804582558, 2.8545031962887983, 5.1611770568976709, 10.859737033055918, 13.363851543150437, 8.7262214091585069, 9.2513334606772943, 11.416787587825521, 11.316098916969841, 9.9561376853073753, 9.8024157956697664, 9.8674837860735884, 13.49381513614648, 10.962099903030799, 11.633909770156553, 7.5796721843209758, 7.572104618508912, 9.775435980909414]\n",
"[ 58.34952175 58.39445892 49.44203644 49.37394401 41.68845059\n",
" 41.54345365 41.43806303 35.74498596 31.45854863 31.79160359\n",
" 28.84450687 28.60994782 28.6442442 28.74808535 32.81148091\n",
" 38.03849948 30.22958105 45.33267886 53.49709827]\n",
"max arrival time is 317.740721\n",
"sample # 70\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"110.945645884\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.041789\n",
"T = 20.000000, Volume = 75.817033\n",
"T = 30.000000, Volume = 113.592277\n",
"T = 40.000000, Volume = 145.602307\n",
"T = 50.000000, Volume = 168.585323\n",
"T = 60.000000, Volume = 191.568369\n",
"T = 70.000000, Volume = 214.551438\n",
"T = 80.000000, Volume = 237.534606\n",
"T = 90.000000, Volume = 260.517823\n",
"T = 100.000000, Volume = 283.501041\n",
"T = 110.000000, Volume = 306.484261\n",
"T = 120.000000, Volume = 329.467482\n",
"T = 130.000000, Volume = 352.450705\n",
"T = 140.000000, Volume = 375.433929\n",
"T = 150.000000, Volume = 398.417154\n",
"T = 160.000000, Volume = 421.400380\n",
"T = 170.000000, Volume = 444.383607\n",
"T = 180.000000, Volume = 467.366837\n",
"T = 190.000000, Volume = 490.350164\n",
"T = 200.000000, Volume = 513.333509\n",
"T = 210.000000, Volume = 536.316854\n",
"T = 220.000000, Volume = 559.300200\n",
"T = 230.000000, Volume = 582.283547\n",
"T = 240.000000, Volume = 605.266931\n",
"T = 250.000000, Volume = 628.250405\n",
"T = 260.000000, Volume = 651.233894\n",
"T = 270.000000, Volume = 674.217382\n",
"T = 280.000000, Volume = 697.200918\n",
"T = 290.000000, Volume = 720.184528\n",
"T = 300.000000, Volume = 743.168162\n",
"T = 310.000000, Volume = 766.151801\n",
"T = 320.000000, Volume = 789.135440\n",
"inflow volume is 789.135440 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 65.781887 s\n",
"[0.049934769002658551, 0.087048950431215577, 0.36542592816638364, 5.3771115455607879, 6.8194086304375814, 4.2023838999197292, 6.3678125230745843, 7.1627132776243814, 2.9085584063888366, 2.7375525059584693, 2.9188522988421162, 3.0479270930526239, 3.1386921637447718, 3.142184941868758, 3.1241084399259709, 2.75756497224801, 2.7683332880852531, 6.5971976758571982, 8.0959589105491112, 5.3973581160729633, 5.3171865359971582, 5.2653466792849102, 5.7721950195468139, 9.6067238876545122, 6.2469915326922756, 5.1313689366848481, 6.9347509056652328, 9.1527978086297583, 5.5752904542106583, 6.3045957135048836, 6.4467634579554938, 6.4184597317116019]\n",
"[ 40.92799568 33.10970754 24.9996308 24.83613032 24.68888363\n",
" 24.54408671 24.40947513 16.2615366 16.10458884 9.02572089\n",
" 5.25870387 0.6623074 0.58724976 0.51534951 6.1834351\n",
" 11.99470082 20.34385814 37.12144462 29.06270181]\n",
"max arrival time is 319.242424\n",
"sample # 71\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"144.396210984\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.024614\n",
"T = 20.000000, Volume = 75.780929\n",
"T = 30.000000, Volume = 113.537244\n",
"T = 40.000000, Volume = 145.464071\n",
"T = 50.000000, Volume = 168.447063\n",
"T = 60.000000, Volume = 191.430082\n",
"T = 70.000000, Volume = 214.413101\n",
"T = 80.000000, Volume = 237.396122\n",
"T = 90.000000, Volume = 260.379142\n",
"T = 100.000000, Volume = 283.362162\n",
"T = 110.000000, Volume = 306.345224\n",
"T = 120.000000, Volume = 329.328318\n",
"T = 130.000000, Volume = 352.311422\n",
"T = 140.000000, Volume = 375.294527\n",
"T = 150.000000, Volume = 398.277632\n",
"T = 160.000000, Volume = 421.260739\n",
"T = 170.000000, Volume = 444.243847\n",
"T = 180.000000, Volume = 467.226954\n",
"T = 190.000000, Volume = 490.210062\n",
"T = 200.000000, Volume = 513.193190\n",
"T = 210.000000, Volume = 536.176368\n",
"T = 220.000000, Volume = 559.159552\n",
"T = 230.000000, Volume = 582.142744\n",
"T = 240.000000, Volume = 605.125936\n",
"T = 250.000000, Volume = 628.109127\n",
"T = 260.000000, Volume = 651.092364\n",
"T = 270.000000, Volume = 674.075618\n",
"T = 280.000000, Volume = 697.058895\n",
"T = 290.000000, Volume = 720.042173\n",
"T = 300.000000, Volume = 743.025451\n",
"T = 310.000000, Volume = 766.008726\n",
"T = 320.000000, Volume = 788.992061\n",
"inflow volume is 788.992061 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 85.702419 s\n",
"[0.050109209919377097, 0.085878477217618737, 0.27934580851857538, 6.0203196799609806, 10.264770542348707, 4.665463955158784, 5.4753504751963593, 5.4300975746282445, 5.1633467532687716, 5.1719516141150246, 10.568602292713567, 9.2829835095545938, 6.1128969382890341, 6.1049830125438111, 6.5061815721291731, 6.8113942675321839, 6.6616407493272352, 6.2815015196720951, 6.2908014433645691, 10.983758661708766, 14.608733577849653, 7.4734071195964615, 5.9899145911744274, 5.998398771392309, 8.6074425147951779, 11.484651842447072, 11.326110143487028, 6.6867513660619666, 6.2233043015992546, 6.2312372567147101, 10.903579951333638, 11.221532616847886]\n",
"[ 5.14028645e+01 4.10446916e+01 4.08420246e+01 4.06740812e+01\n",
" 3.02880825e+01 3.01282621e+01 2.99644473e+01 2.97948108e+01\n",
" 1.95349385e+01 1.93483085e+01 9.23536683e+00 1.27668012e+01\n",
" 4.71507853e+00 4.97933568e-02 3.54350929e+01 4.63377743e+01\n",
" 2.46570287e+01 1.41659927e+01 8.66459777e+00]\n",
"max arrival time is 319.113082\n",
"sample # 72\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 3, 3, 2, 2, 2, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"205.76539336\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.981720\n",
"T = 20.000000, Volume = 75.692299\n",
"T = 30.000000, Volume = 113.402877\n",
"T = 40.000000, Volume = 151.113456\n",
"T = 50.000000, Volume = 188.824035\n",
"T = 60.000000, Volume = 220.553652\n",
"T = 70.000000, Volume = 243.536641\n",
"T = 80.000000, Volume = 266.519634\n",
"T = 90.000000, Volume = 289.502627\n",
"T = 100.000000, Volume = 312.485652\n",
"T = 110.000000, Volume = 335.468674\n",
"T = 120.000000, Volume = 358.451695\n",
"T = 130.000000, Volume = 381.434720\n",
"T = 140.000000, Volume = 404.417792\n",
"T = 150.000000, Volume = 427.400854\n",
"T = 160.000000, Volume = 450.383921\n",
"T = 170.000000, Volume = 473.366997\n",
"T = 180.000000, Volume = 496.350073\n",
"T = 190.000000, Volume = 519.333149\n",
"T = 200.000000, Volume = 542.316226\n",
"T = 210.000000, Volume = 565.299303\n",
"T = 220.000000, Volume = 588.282380\n",
"T = 230.000000, Volume = 611.265457\n",
"T = 240.000000, Volume = 634.248535\n",
"T = 250.000000, Volume = 657.231648\n",
"T = 260.000000, Volume = 680.214754\n",
"T = 270.000000, Volume = 703.197859\n",
"T = 280.000000, Volume = 726.180965\n",
"T = 290.000000, Volume = 749.164071\n",
"T = 300.000000, Volume = 772.147193\n",
"T = 310.000000, Volume = 795.130347\n",
"inflow volume is 795.130347 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 118.149533 s\n",
"[0.047876577714216453, 0.082110594185604138, 0.097993187300381993, 1.7674691915800518, 1.6903720319782927, 20.635767576187792, 12.006529996878637, 6.1516659837432579, 10.037853433036245, 14.058325205959836, 19.280202600934743, 20.396719158294733, 19.198012918359304, 14.327055124063497, 17.585160117518342, 14.87541121034322, 8.1185490438662438, 8.1441157723933006, 8.7363148832894684, 9.2330335088880577, 8.9180710050347116, 7.9217007429765438, 7.9320087925173111, 14.870959030984295, 15.365854239579448, 18.20775529691506, 15.15254780202088, 15.213982463619702, 19.416132877968412, 15.837820159654964, 17.107797466245227]\n",
"[ 75.85044508 75.88722132 75.61466528 60.53219067 45.54657642\n",
" 30.65790768 30.37751543 30.09764156 29.81584838 29.53230432\n",
" 20.33471498 23.89369628 21.80740761 23.40962402 53.0126145\n",
" 21.58303898 38.07749333 22.29340867 68.04331103]\n",
"max arrival time is 309.735614\n",
"sample # 73\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 2, 3, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"127.564599486\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.033956\n",
"T = 20.000000, Volume = 75.800992\n",
"T = 30.000000, Volume = 113.568028\n",
"T = 40.000000, Volume = 145.533927\n",
"T = 50.000000, Volume = 168.516924\n",
"T = 60.000000, Volume = 191.499957\n",
"T = 70.000000, Volume = 214.482989\n",
"T = 80.000000, Volume = 237.466021\n",
"T = 90.000000, Volume = 260.449086\n",
"T = 100.000000, Volume = 283.432205\n",
"T = 110.000000, Volume = 306.415362\n",
"T = 120.000000, Volume = 329.398523\n",
"T = 130.000000, Volume = 352.381684\n",
"T = 140.000000, Volume = 375.364847\n",
"T = 150.000000, Volume = 398.348010\n",
"T = 160.000000, Volume = 421.331175\n",
"T = 170.000000, Volume = 444.314340\n",
"T = 180.000000, Volume = 467.297506\n",
"T = 190.000000, Volume = 490.280672\n",
"T = 200.000000, Volume = 513.263854\n",
"T = 210.000000, Volume = 536.247100\n",
"T = 220.000000, Volume = 559.230354\n",
"T = 230.000000, Volume = 582.213610\n",
"T = 240.000000, Volume = 605.196865\n",
"T = 250.000000, Volume = 628.180120\n",
"T = 260.000000, Volume = 651.163428\n",
"T = 270.000000, Volume = 674.146769\n",
"T = 280.000000, Volume = 697.130133\n",
"T = 290.000000, Volume = 720.113499\n",
"T = 300.000000, Volume = 743.096865\n",
"T = 310.000000, Volume = 766.080226\n",
"T = 320.000000, Volume = 789.063659\n",
"inflow volume is 789.063659 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 75.747843 s\n",
"[0.050030416491570501, 0.085803667696143615, 0.32325507081996685, 5.6506924800415099, 9.3195940542879825, 4.419586610037233, 4.4122959218286431, 4.4207449826580243, 8.4105561016389068, 9.8997876944135523, 3.1961513961478301, 3.0310239399928287, 3.2479215845812135, 3.3450527173689024, 3.4466442134816804, 3.5320545399654311, 3.2531160677825057, 2.9166356539463685, 2.926137485163983, 8.8830080633385613, 7.669312201908828, 6.0496943657059061, 5.6467877499647985, 5.654755818609086, 7.9511063137238231, 10.492983219727266, 8.1983259599129354, 6.3019069972369426, 5.9508137418347591, 5.9587853894430198, 9.9342078236922902, 10.441595846749392]\n",
"[ 45.53649374 36.51329121 36.27936216 27.03065047 26.76416395\n",
" 26.52805371 26.3935563 26.27692201 17.18362323 17.02374406\n",
" 8.25429933 10.51884139 3.81580307 0.04939207 12.79057995\n",
" 7.17266421 41.09988072 21.76813475 31.63909055]\n",
"max arrival time is 318.764115\n",
"sample # 74\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"63.1800393407\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.017524\n",
"T = 20.000000, Volume = 75.770982\n",
"T = 30.000000, Volume = 113.524440\n",
"T = 40.000000, Volume = 145.723007\n",
"T = 50.000000, Volume = 164.574530\n",
"T = 60.000000, Volume = 187.572697\n",
"T = 70.000000, Volume = 210.555950\n",
"T = 80.000000, Volume = 233.539210\n",
"T = 90.000000, Volume = 256.522478\n",
"T = 100.000000, Volume = 279.505753\n",
"T = 110.000000, Volume = 302.489029\n",
"T = 120.000000, Volume = 325.472304\n",
"T = 130.000000, Volume = 348.455694\n",
"T = 140.000000, Volume = 371.439314\n",
"T = 150.000000, Volume = 394.422968\n",
"T = 160.000000, Volume = 417.406701\n",
"T = 170.000000, Volume = 440.390438\n",
"T = 180.000000, Volume = 463.374180\n",
"T = 190.000000, Volume = 486.357923\n",
"T = 200.000000, Volume = 509.341658\n",
"T = 210.000000, Volume = 532.325603\n",
"T = 220.000000, Volume = 555.309709\n",
"T = 230.000000, Volume = 578.293852\n",
"T = 240.000000, Volume = 601.278052\n",
"T = 250.000000, Volume = 624.262253\n",
"T = 260.000000, Volume = 647.246454\n",
"T = 270.000000, Volume = 670.230854\n",
"T = 280.000000, Volume = 693.215396\n",
"T = 290.000000, Volume = 716.199966\n",
"T = 300.000000, Volume = 739.184541\n",
"T = 310.000000, Volume = 762.169324\n",
"T = 320.000000, Volume = 785.154271\n",
"T = 330.000000, Volume = 808.139306\n",
"inflow volume is 808.139306 gallons\n",
"simulation time is 330.000000 s\n",
"wall clock time is 38.443111 s\n",
"[0.049429385093412471, 0.085255325382357028, 0.47561916754948591, 18.137071117072903, 10.259056686866563, 2.7999568419615741, 2.2637152953575765, 2.4651454269980726, 2.5252116176969808, 2.5045999870165763, 2.4333043179111593, 2.4625872759338683, 5.0959736307130523, 3.8895732872337341, 3.497405976118273, 2.9748137811105337, 2.9915101479174075, 2.8776493456906378, 2.8866552508210348, 4.1210949755803972, 4.9876150263601824, 4.3992836284058709, 3.287542618115316, 3.1714956895060813, 3.1901263680922827, 5.5203041546380875, 4.717538537754538, 4.4160820914585317, 4.45543201953621, 6.0614786170177855, 4.8475686369909585, 4.2638630347116298, 3.7139926531434742]\n",
"[ 27.3866028 19.70963948 19.56378072 19.42256332 19.28159841\n",
" 14.82184396 14.67157681 14.52573456 9.9849079 9.8283478\n",
" 5.19760689 1.42404197 0.40112203 0.29521736 2.83280636\n",
" 12.25958294 17.05548983 7.51876679 21.90900887]\n",
"max arrival time is 329.024081\n",
"sample # 75\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"186.229341543\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.995910\n",
"T = 20.000000, Volume = 75.721419\n",
"T = 30.000000, Volume = 113.446929\n",
"T = 40.000000, Volume = 151.172438\n",
"T = 50.000000, Volume = 188.897948\n",
"T = 60.000000, Volume = 226.623457\n",
"T = 70.000000, Volume = 254.660858\n",
"T = 80.000000, Volume = 292.558152\n",
"T = 90.000000, Volume = 316.015417\n",
"T = 100.000000, Volume = 338.998410\n",
"T = 110.000000, Volume = 361.981405\n",
"T = 120.000000, Volume = 384.964424\n",
"T = 130.000000, Volume = 407.947467\n",
"T = 140.000000, Volume = 430.930502\n",
"T = 150.000000, Volume = 453.913537\n",
"T = 160.000000, Volume = 476.896607\n",
"T = 170.000000, Volume = 499.879695\n",
"T = 180.000000, Volume = 522.862780\n",
"T = 190.000000, Volume = 545.845862\n",
"T = 200.000000, Volume = 568.828995\n",
"T = 210.000000, Volume = 591.812132\n",
"T = 220.000000, Volume = 614.795274\n",
"T = 230.000000, Volume = 637.778425\n",
"T = 240.000000, Volume = 660.761577\n",
"T = 250.000000, Volume = 683.744730\n",
"T = 260.000000, Volume = 706.727922\n",
"T = 270.000000, Volume = 729.711109\n",
"T = 280.000000, Volume = 752.694306\n",
"T = 290.000000, Volume = 775.677509\n",
"T = 300.000000, Volume = 798.660712\n",
"inflow volume is 798.660712 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 102.810012 s\n",
"[0.047826884646547267, 0.08204018578660259, 0.10984311943410621, 4.3313291003534591, 4.8636304316754666, 5.729699928436025, 171.45680362053514, 212.7728196024093, 15.642492704264829, 14.822014053343876, 9.7696944406400039, 11.165842446966543, 14.292355852201149, 17.463764179612088, 16.937271701848953, 14.280788744805994, 15.682598858526182, 17.589086220133193, 19.779087374037612, 13.18772952801902, 17.203822974381321, 9.2630126997430065, 6.7472605761738809, 6.753649692409696, 14.30039762473101, 17.074299661983765, 18.348982005687784, 8.8509933310477873, 8.4457971728928829, 11.564740027833828]\n",
"[ 196.79718146 96.80608472 68.38426852 68.31435788 68.57405913\n",
" 56.6380669 46.7149294 38.39194266 32.2265575 31.33580556\n",
" 26.44221774 25.7660747 25.14259517 24.77539633 35.00004322\n",
" 61.96052042 27.94550059 50.23341489 41.23209312]\n",
"max arrival time is 298.964332\n",
"sample # 76\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 3, 3, 3, 2, 2, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"248.727134408\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.951995\n",
"T = 20.000000, Volume = 75.630638\n",
"T = 30.000000, Volume = 113.309281\n",
"T = 40.000000, Volume = 150.987925\n",
"T = 50.000000, Volume = 188.666568\n",
"T = 60.000000, Volume = 226.345211\n",
"T = 70.000000, Volume = 258.872655\n",
"T = 80.000000, Volume = 281.855635\n",
"T = 90.000000, Volume = 304.838614\n",
"T = 100.000000, Volume = 327.821592\n",
"T = 110.000000, Volume = 350.804595\n",
"T = 120.000000, Volume = 373.787600\n",
"T = 130.000000, Volume = 396.770604\n",
"T = 140.000000, Volume = 419.753607\n",
"T = 150.000000, Volume = 442.736646\n",
"T = 160.000000, Volume = 465.719678\n",
"T = 170.000000, Volume = 488.702711\n",
"T = 180.000000, Volume = 511.685747\n",
"T = 190.000000, Volume = 534.668815\n",
"T = 200.000000, Volume = 557.651875\n",
"T = 210.000000, Volume = 580.634936\n",
"T = 220.000000, Volume = 603.617997\n",
"T = 230.000000, Volume = 626.601067\n",
"T = 240.000000, Volume = 649.584136\n",
"T = 250.000000, Volume = 672.567206\n",
"T = 260.000000, Volume = 695.550276\n",
"T = 270.000000, Volume = 718.533346\n",
"T = 280.000000, Volume = 741.516416\n",
"T = 290.000000, Volume = 764.499487\n",
"T = 300.000000, Volume = 787.482583\n",
"T = 310.000000, Volume = 810.465622\n",
"inflow volume is 810.465622 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 140.380099 s\n",
"[0.047961203816392572, 0.082217094601563581, 0.096269839276570179, 5.2878996204994664, 4.6381275392495249, 2.7295464996396972, 25.409930002110539, 17.816602304152489, 18.380423316558485, 23.034968392567777, 19.893945588702607, 20.295205699306393, 24.02180156002953, 25.118101349642053, 17.063246151062359, 26.030247247965523, 26.486049638876636, 26.037564878202222, 18.524254494566954, 22.590827100385063, 19.773232055208304, 16.083905074547459, 10.032963338027427, 10.259972211084646, 10.570563004748065, 9.8834704552220316, 9.0284463435542719, 9.0366956272873509, 19.615849876069813, 18.268938920012182, 10.695364487687847]\n",
"[ 199.3980074 199.44048569 199.32505803 199.22303741 185.69497394\n",
" 173.99283383 164.13741543 156.14325169 156.12372115 156.11006752\n",
" 156.10083454 156.0951704 153.74656685 153.74672323 191.76845528\n",
" 159.43847347 154.21685549 179.14802399 168.36596081]\n",
"max arrival time is 302.553876\n",
"sample # 77\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 3, 2, 3, 3, 2, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"170.432621175\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.006688\n",
"T = 20.000000, Volume = 75.744168\n",
"T = 30.000000, Volume = 113.481647\n",
"T = 40.000000, Volume = 151.219127\n",
"T = 50.000000, Volume = 181.782245\n",
"T = 60.000000, Volume = 204.765236\n",
"T = 70.000000, Volume = 227.748223\n",
"T = 80.000000, Volume = 250.731227\n",
"T = 90.000000, Volume = 273.714244\n",
"T = 100.000000, Volume = 296.697262\n",
"T = 110.000000, Volume = 319.680279\n",
"T = 120.000000, Volume = 342.663308\n",
"T = 130.000000, Volume = 365.646366\n",
"T = 140.000000, Volume = 388.629432\n",
"T = 150.000000, Volume = 411.612499\n",
"T = 160.000000, Volume = 434.595565\n",
"T = 170.000000, Volume = 457.578632\n",
"T = 180.000000, Volume = 480.561737\n",
"T = 190.000000, Volume = 503.544846\n",
"T = 200.000000, Volume = 526.527955\n",
"T = 210.000000, Volume = 549.511059\n",
"T = 220.000000, Volume = 572.494236\n",
"T = 230.000000, Volume = 595.477405\n",
"T = 240.000000, Volume = 618.460574\n",
"T = 250.000000, Volume = 641.443744\n",
"T = 260.000000, Volume = 664.426943\n",
"T = 270.000000, Volume = 687.410143\n",
"T = 280.000000, Volume = 710.393342\n",
"T = 290.000000, Volume = 733.376556\n",
"T = 300.000000, Volume = 756.359791\n",
"T = 310.000000, Volume = 779.343023\n",
"T = 320.000000, Volume = 802.326256\n",
"inflow volume is 802.326256 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 101.013020 s\n",
"[0.047782115469661594, 0.083034489679441284, 0.11325152748972642, 0.80302285570644472, 8.8910568879358483, 13.337969040341745, 13.352283525779862, 9.5118288344764856, 3.1779144452278016, 2.5515185230220516, 2.5617336519077951, 9.878554771833338, 11.196736035232865, 7.2163469574935464, 5.9902759998676771, 6.0020552120528379, 9.2536904635970032, 12.023351302591124, 14.606027263043812, 16.432742071990901, 15.231053599816331, 13.126487232004783, 13.758527126257858, 11.729032846190737, 9.158295289826615, 3.7810513915210686, 3.5025173861903589, 6.5629096296059259, 12.476731690236171, 13.2656537075563, 12.041946669874422, 11.281041736572961]\n",
"[ 72.03572371 72.07867896 61.20687909 61.08341499 60.94076154\n",
" 52.72836197 52.52595215 46.23611833 41.49594433 41.83486963\n",
" 42.03983037 39.75269749 39.49719936 39.24560275 66.17706419\n",
" 43.22224551 40.49242907 56.28174519 48.88124754]\n",
"max arrival time is 317.450704\n",
"sample # 78\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 2, 2, 3, 2, 3, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"226.143823344\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.967481\n",
"T = 20.000000, Volume = 75.662763\n",
"T = 30.000000, Volume = 113.358044\n",
"T = 40.000000, Volume = 151.053326\n",
"T = 50.000000, Volume = 188.748607\n",
"T = 60.000000, Volume = 226.443889\n",
"T = 70.000000, Volume = 258.803121\n",
"T = 80.000000, Volume = 281.786105\n",
"T = 90.000000, Volume = 304.769094\n",
"T = 100.000000, Volume = 327.752083\n",
"T = 110.000000, Volume = 350.735072\n",
"T = 120.000000, Volume = 373.718061\n",
"T = 130.000000, Volume = 396.701051\n",
"T = 140.000000, Volume = 419.684040\n",
"T = 150.000000, Volume = 442.667061\n",
"T = 160.000000, Volume = 465.650074\n",
"T = 170.000000, Volume = 488.633090\n",
"T = 180.000000, Volume = 511.616121\n",
"T = 190.000000, Volume = 534.599151\n",
"T = 200.000000, Volume = 557.582189\n",
"T = 210.000000, Volume = 580.565242\n",
"T = 220.000000, Volume = 603.548289\n",
"T = 230.000000, Volume = 626.531335\n",
"T = 240.000000, Volume = 649.514412\n",
"T = 250.000000, Volume = 672.497495\n",
"T = 260.000000, Volume = 695.480576\n",
"T = 270.000000, Volume = 718.463655\n",
"T = 280.000000, Volume = 741.446773\n",
"T = 290.000000, Volume = 764.429890\n",
"T = 300.000000, Volume = 787.413005\n",
"T = 310.000000, Volume = 810.396078\n",
"inflow volume is 810.396078 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 127.546884 s\n",
"[0.047920081280772242, 0.082167984912229736, 0.096460690614310604, 4.6675217512462543, 3.7545841601403471, 2.5875690478555238, 18.649167633828853, 13.755825167863359, 7.9690100831076913, 8.2641521995747986, 8.4395879812906553, 7.0769130398416848, 7.087687092080686, 14.353603784455286, 15.994167819715216, 20.328738657785184, 15.958405420683, 3.1368064163685796, 3.150383669636585, 17.422028368653201, 20.843690997197495, 23.687620476460509, 24.726485949972076, 15.556918429714992, 20.611534126902374, 20.021499917937181, 21.92308727095779, 16.395582651459158, 19.965471862059982, 25.908642255872969, 10.102746080645403]\n",
"[ 160.71335336 160.75585103 160.63947149 160.53642309 148.20648193\n",
" 148.13734989 148.08085479 139.13236044 139.09953914 132.6969946\n",
" 127.99578497 125.00313108 125.00279669 125.00311545 135.26098605\n",
" 125.8589763 142.9734945 129.70684735 153.74248426]\n",
"max arrival time is 303.481953\n",
"sample # 79\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"161.208561703\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.013400\n",
"T = 20.000000, Volume = 75.757726\n",
"T = 30.000000, Volume = 113.502052\n",
"T = 40.000000, Volume = 145.398332\n",
"T = 50.000000, Volume = 168.381320\n",
"T = 60.000000, Volume = 191.364308\n",
"T = 70.000000, Volume = 214.347312\n",
"T = 80.000000, Volume = 237.330377\n",
"T = 90.000000, Volume = 260.313436\n",
"T = 100.000000, Volume = 283.296512\n",
"T = 110.000000, Volume = 306.279588\n",
"T = 120.000000, Volume = 329.262665\n",
"T = 130.000000, Volume = 352.245742\n",
"T = 140.000000, Volume = 375.228817\n",
"T = 150.000000, Volume = 398.211943\n",
"T = 160.000000, Volume = 421.195066\n",
"T = 170.000000, Volume = 444.178189\n",
"T = 180.000000, Volume = 467.161312\n",
"T = 190.000000, Volume = 490.144460\n",
"T = 200.000000, Volume = 513.127618\n",
"T = 210.000000, Volume = 536.110777\n",
"T = 220.000000, Volume = 559.093936\n",
"T = 230.000000, Volume = 582.077096\n",
"T = 240.000000, Volume = 605.060256\n",
"T = 250.000000, Volume = 628.043414\n",
"T = 260.000000, Volume = 651.026609\n",
"T = 270.000000, Volume = 674.009803\n",
"T = 280.000000, Volume = 696.992997\n",
"T = 290.000000, Volume = 719.976204\n",
"T = 300.000000, Volume = 742.959421\n",
"T = 310.000000, Volume = 765.942637\n",
"T = 320.000000, Volume = 788.925854\n",
"inflow volume is 788.925854 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 95.728568 s\n",
"[0.050174793255983757, 0.087286116785519383, 0.2372757749053522, 6.4112546689649239, 10.662317956048629, 14.342049148998475, 15.789706809648806, 12.216581151598676, 11.928590016876299, 6.1559763920355328, 6.5217443991253328, 5.8595185847767448, 5.7614604403230176, 8.7104341098958233, 11.432689827739669, 12.335367135521178, 11.050550181376597, 11.431400578246381, 4.8956678205802877, 4.0244746075916167, 4.1104013367804866, 4.2089285383962345, 3.3334797147939756, 3.3428045893538303, 7.0934748258507483, 12.454169930750352, 12.870046871740113, 13.50872088977424, 9.3619213881246139, 7.2521469048640324, 7.0028848801747667, 7.0591338444103693]\n",
"[ 4.76633936e+01 3.60080107e+01 2.41025262e+01 2.38115402e+01\n",
" 2.35217342e+01 1.18816374e+01 1.15934477e+01 1.13174492e+01\n",
" 1.10353574e+01 1.53061469e+01 9.26449279e+00 3.62714753e+00\n",
" 2.78354960e+00 4.75326664e-02 1.03806406e+01 4.75326664e-02\n",
" 3.00345836e+01 1.76662127e+01 4.19781216e+01]\n",
"max arrival time is 293.111663\n",
"sample # 80\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"61.8809935125\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.014213\n",
"T = 20.000000, Volume = 75.764775\n",
"T = 30.000000, Volume = 113.515337\n",
"T = 40.000000, Volume = 151.265899\n",
"T = 50.000000, Volume = 189.016461\n",
"T = 60.000000, Volume = 222.255124\n",
"T = 70.000000, Volume = 239.718123\n",
"T = 80.000000, Volume = 262.703523\n",
"T = 90.000000, Volume = 285.686793\n",
"T = 100.000000, Volume = 308.670188\n",
"T = 110.000000, Volume = 331.653830\n",
"T = 120.000000, Volume = 354.637509\n",
"T = 130.000000, Volume = 377.621252\n",
"T = 140.000000, Volume = 400.604994\n",
"T = 150.000000, Volume = 423.588734\n",
"T = 160.000000, Volume = 446.572658\n",
"T = 170.000000, Volume = 469.556837\n",
"T = 180.000000, Volume = 492.541053\n",
"T = 190.000000, Volume = 515.525278\n",
"T = 200.000000, Volume = 538.509511\n",
"T = 210.000000, Volume = 561.493748\n",
"T = 220.000000, Volume = 584.477985\n",
"T = 230.000000, Volume = 607.462237\n",
"T = 240.000000, Volume = 630.446702\n",
"T = 250.000000, Volume = 653.431305\n",
"T = 260.000000, Volume = 676.415983\n",
"T = 270.000000, Volume = 699.400706\n",
"T = 280.000000, Volume = 722.385442\n",
"T = 290.000000, Volume = 745.370181\n",
"T = 300.000000, Volume = 768.354921\n",
"T = 310.000000, Volume = 791.339707\n",
"inflow volume is 791.339707 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 35.191328 s\n",
"[0.046968190543762008, 0.081252772323539696, 0.82422966507983764, 1.4691106683716777, 1.5858819935421289, 15.546591822510935, 6.2288827756171203, 2.7349769319335033, 2.3336376016723737, 4.8234794417655698, 3.7162627067081049, 3.186292673903969, 2.5726596949975336, 2.5790631892949842, 3.5171026290466458, 4.2673928056652048, 2.8205487008084891, 3.0426999076355417, 3.0912502147725087, 3.0668854496949502, 3.0120284840939724, 3.0659933913808404, 5.5542501187238962, 4.5531052545575879, 3.9809231570027319, 3.5131121707250808, 3.5477345346188054, 3.4309076127020672, 3.4406216800232738, 4.4749981989844052, 6.0061631756710696]\n",
"[ 26.2356575 21.17245374 21.03831909 16.75067481 12.49877905\n",
" 12.42241597 8.33457793 8.15965922 7.99848266 3.91963176\n",
" 3.80744017 3.70346587 1.45878604 0.04229158 6.00008096\n",
" 2.66140144 14.60243314 18.88954088 10.38538519]\n",
"max arrival time is 309.780078\n",
"sample # 81\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"148.863931386\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.021342\n",
"T = 20.000000, Volume = 75.774536\n",
"T = 30.000000, Volume = 113.527730\n",
"T = 40.000000, Volume = 151.280924\n",
"T = 50.000000, Volume = 181.950285\n",
"T = 60.000000, Volume = 204.933306\n",
"T = 70.000000, Volume = 227.916337\n",
"T = 80.000000, Volume = 250.899369\n",
"T = 90.000000, Volume = 273.882401\n",
"T = 100.000000, Volume = 296.865434\n",
"T = 110.000000, Volume = 319.848468\n",
"T = 120.000000, Volume = 342.831502\n",
"T = 130.000000, Volume = 365.814537\n",
"T = 140.000000, Volume = 388.797572\n",
"T = 150.000000, Volume = 411.780606\n",
"T = 160.000000, Volume = 434.763680\n",
"T = 170.000000, Volume = 457.746754\n",
"T = 180.000000, Volume = 480.729829\n",
"T = 190.000000, Volume = 503.712899\n",
"T = 200.000000, Volume = 526.696059\n",
"T = 210.000000, Volume = 549.679213\n",
"T = 220.000000, Volume = 572.662385\n",
"T = 230.000000, Volume = 595.645578\n",
"T = 240.000000, Volume = 618.628773\n",
"T = 250.000000, Volume = 641.611967\n",
"T = 260.000000, Volume = 664.595161\n",
"T = 270.000000, Volume = 687.578367\n",
"T = 280.000000, Volume = 710.561611\n",
"T = 290.000000, Volume = 733.544846\n",
"T = 300.000000, Volume = 756.528081\n",
"T = 310.000000, Volume = 779.511316\n",
"T = 320.000000, Volume = 802.494527\n",
"inflow volume is 802.494527 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 86.921004 s\n",
"[0.0477044327606993, 0.082934209710893242, 0.11322618101041751, 0.83455813445654137, 8.1120693407511766, 4.2767389060321683, 2.742655340586845, 3.1716836045335288, 3.1590902160941048, 3.3775360128292995, 3.4943194074080051, 3.5790548367969062, 3.0807116582666918, 2.8464683860638873, 3.0656656176402279, 12.156291274835088, 12.512948245617729, 13.643248095601642, 14.216802177925134, 11.126573150260754, 12.697223051189043, 5.6409211205492094, 4.1748840669397751, 3.6810904522147174, 3.4649281096718689, 3.6023020524760336, 12.170580297035526, 12.424812657565402, 10.955903349097367, 10.263548256030822, 10.262663610691622, 12.851281561927136]\n",
"[ 54.56290933 54.60491207 45.01659596 44.88177497 44.77018477\n",
" 44.67560232 44.58474817 38.59955299 33.93718576 33.80028471\n",
" 33.66990144 31.42596919 31.38288271 30.6721803 41.04365255\n",
" 30.6721803 32.11527117 49.42857133 35.7075036 ]\n",
"max arrival time is 316.661290\n",
"sample # 82\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 3, 3, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"196.907871618\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.988334\n",
"T = 20.000000, Volume = 75.705685\n",
"T = 30.000000, Volume = 113.423037\n",
"T = 40.000000, Volume = 151.140388\n",
"T = 50.000000, Volume = 181.638113\n",
"T = 60.000000, Volume = 204.621105\n",
"T = 70.000000, Volume = 227.604100\n",
"T = 80.000000, Volume = 250.587095\n",
"T = 90.000000, Volume = 273.570091\n",
"T = 100.000000, Volume = 296.553091\n",
"T = 110.000000, Volume = 319.536127\n",
"T = 120.000000, Volume = 342.519167\n",
"T = 130.000000, Volume = 365.502207\n",
"T = 140.000000, Volume = 388.485260\n",
"T = 150.000000, Volume = 411.468339\n",
"T = 160.000000, Volume = 434.451411\n",
"T = 170.000000, Volume = 457.434483\n",
"T = 180.000000, Volume = 480.417556\n",
"T = 190.000000, Volume = 503.400651\n",
"T = 200.000000, Volume = 526.383746\n",
"T = 210.000000, Volume = 549.366838\n",
"T = 220.000000, Volume = 572.349964\n",
"T = 230.000000, Volume = 595.333082\n",
"T = 240.000000, Volume = 618.316199\n",
"T = 250.000000, Volume = 641.299323\n",
"T = 260.000000, Volume = 664.282495\n",
"T = 270.000000, Volume = 687.265659\n",
"T = 280.000000, Volume = 710.248835\n",
"T = 290.000000, Volume = 733.232013\n",
"T = 300.000000, Volume = 756.215191\n",
"T = 310.000000, Volume = 779.198369\n",
"T = 320.000000, Volume = 802.181523\n",
"inflow volume is 802.181523 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 116.448025 s\n",
"[0.047857603384526964, 0.083135531924600187, 0.11327516122180066, 0.73435336193801592, 10.030137660396798, 9.1234920172890313, 6.2310931020646576, 5.9898323382430885, 5.9998131598471849, 12.199036295525259, 12.627391745801695, 6.9412700361851272, 6.1789254833921365, 14.463918456077407, 14.823804554488587, 16.127575778075812, 13.331189435083036, 12.157165973030622, 2.9507689741587733, 2.9652590369006364, 10.727666760371594, 14.436764112419118, 18.587526657942544, 18.81319346339993, 17.958638022868129, 16.759979050205548, 14.338656680640376, 8.5249372379056236, 8.5950870281191616, 9.0421352286801593, 9.154474628650739, 12.810161403425292]\n",
"[ 95.02167529 95.0619683 82.62939722 82.40791918 72.09218664\n",
" 63.27396473 63.38363898 63.5037264 57.85393618 53.80990291\n",
" 53.73846959 53.61947735 53.48898298 53.39280859 76.66777937\n",
" 55.25098557 67.11208403 88.25934088 60.05878385]\n",
"max arrival time is 316.724909\n",
"sample # 83\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"123.085030097\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.036973\n",
"T = 20.000000, Volume = 75.806529\n",
"T = 30.000000, Volume = 113.576085\n",
"T = 40.000000, Volume = 151.345641\n",
"T = 50.000000, Volume = 182.161204\n",
"T = 60.000000, Volume = 205.144247\n",
"T = 70.000000, Volume = 228.127304\n",
"T = 80.000000, Volume = 251.110360\n",
"T = 90.000000, Volume = 274.093489\n",
"T = 100.000000, Volume = 297.076635\n",
"T = 110.000000, Volume = 320.059788\n",
"T = 120.000000, Volume = 343.042943\n",
"T = 130.000000, Volume = 366.026098\n",
"T = 140.000000, Volume = 389.009254\n",
"T = 150.000000, Volume = 411.992410\n",
"T = 160.000000, Volume = 434.975614\n",
"T = 170.000000, Volume = 457.958879\n",
"T = 180.000000, Volume = 480.942151\n",
"T = 190.000000, Volume = 503.925424\n",
"T = 200.000000, Volume = 526.908699\n",
"T = 210.000000, Volume = 549.891974\n",
"T = 220.000000, Volume = 572.875250\n",
"T = 230.000000, Volume = 595.858528\n",
"T = 240.000000, Volume = 618.841890\n",
"T = 250.000000, Volume = 641.825277\n",
"T = 260.000000, Volume = 664.808671\n",
"T = 270.000000, Volume = 687.792065\n",
"T = 280.000000, Volume = 710.775461\n",
"T = 290.000000, Volume = 733.758862\n",
"T = 300.000000, Volume = 756.742280\n",
"T = 310.000000, Volume = 779.725699\n",
"T = 320.000000, Volume = 802.709100\n",
"inflow volume is 802.709100 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 71.699552 s\n",
"[0.047581396238154175, 0.082786867724115323, 0.1134386299842247, 0.78576392885129154, 7.1068432197925251, 3.7974049330765203, 2.1923034182575929, 4.3464838546154967, 9.759497978548838, 6.0796716138511169, 5.3633802786811495, 5.7705869837023442, 5.5431995695024057, 5.3690896666891836, 5.3779425376420447, 10.126115448411847, 5.8417896268849452, 5.7919560420362428, 6.0948928400099573, 6.1489452747002522, 5.7862033507003279, 5.7964625110915025, 9.0506960912463583, 10.313516140924246, 6.1949084863050281, 6.3792382779327648, 6.7504323781259297, 6.9744957147485351, 4.735611860176828, 3.6853279336188232, 3.6807036335786432, 6.9930118138764641]\n",
"[ 42.84359169 42.88303932 35.45207754 28.98867202 28.77564601\n",
" 28.57537436 23.80753257 23.90196221 24.03669638 21.25082511\n",
" 21.35261277 21.40918741 21.41099967 20.70530712 22.24735212\n",
" 31.8235995 20.70530712 25.84207553 38.69475416]\n",
"max arrival time is 317.119636\n",
"sample # 84\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"204.393811108\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.982630\n",
"T = 20.000000, Volume = 75.694272\n",
"T = 30.000000, Volume = 113.405915\n",
"T = 40.000000, Volume = 151.117558\n",
"T = 50.000000, Volume = 188.829201\n",
"T = 60.000000, Volume = 226.540843\n",
"T = 70.000000, Volume = 254.470911\n",
"T = 80.000000, Volume = 292.358317\n",
"T = 90.000000, Volume = 316.711028\n",
"T = 100.000000, Volume = 339.694031\n",
"T = 110.000000, Volume = 362.677033\n",
"T = 120.000000, Volume = 385.660044\n",
"T = 130.000000, Volume = 408.643074\n",
"T = 140.000000, Volume = 431.626097\n",
"T = 150.000000, Volume = 454.609134\n",
"T = 160.000000, Volume = 477.592179\n",
"T = 170.000000, Volume = 500.575223\n",
"T = 180.000000, Volume = 523.558289\n",
"T = 190.000000, Volume = 546.541355\n",
"T = 200.000000, Volume = 569.524420\n",
"T = 210.000000, Volume = 592.507486\n",
"T = 220.000000, Volume = 615.490599\n",
"T = 230.000000, Volume = 638.473705\n",
"T = 240.000000, Volume = 661.456824\n",
"T = 250.000000, Volume = 684.439944\n",
"T = 260.000000, Volume = 707.423065\n",
"T = 270.000000, Volume = 730.406186\n",
"T = 280.000000, Volume = 753.389307\n",
"T = 290.000000, Volume = 776.372428\n",
"T = 300.000000, Volume = 799.355537\n",
"inflow volume is 799.355537 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 110.917495 s\n",
"[0.047873268939708336, 0.082101180312553576, 0.096720555810775238, 4.6159215381519498, 6.4558946271286892, 4.8694674172974306, 253.33809698133078, 579.03375071336086, 18.580462727370247, 2.6585951090248514, 2.6430341936791204, 12.81964938714523, 15.072474181627912, 18.209894260120553, 5.6394647881755198, 3.0704170430723661, 7.4832842482357167, 14.230409013405229, 20.978689072236406, 21.164944615142396, 20.225887648272614, 15.27970731856132, 18.583042929805991, 8.5634128733671151, 8.6742517390140783, 8.8563238238647113, 9.1166559728771457, 8.075635552507741, 8.0759565654927954, 13.323788016550319]\n",
"[ 426.32009043 76.72639611 76.76086163 76.81551298 76.84274005\n",
" 65.87203622 56.56082333 56.4520821 49.72885094 44.72072523\n",
" 44.72697903 44.73383485 44.74790814 43.60649018 46.62577906\n",
" 60.54238059 52.42561737 70.77124126 43.60649018]\n",
"max arrival time is 297.599843\n",
"sample # 85\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"99.7312451263\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.044493\n",
"T = 20.000000, Volume = 75.822832\n",
"T = 30.000000, Volume = 113.601171\n",
"T = 40.000000, Volume = 151.379511\n",
"T = 50.000000, Volume = 182.601987\n",
"T = 60.000000, Volume = 205.585043\n",
"T = 70.000000, Volume = 228.568110\n",
"T = 80.000000, Volume = 251.551176\n",
"T = 90.000000, Volume = 274.534404\n",
"T = 100.000000, Volume = 297.517680\n",
"T = 110.000000, Volume = 320.500958\n",
"T = 120.000000, Volume = 343.484238\n",
"T = 130.000000, Volume = 366.467520\n",
"T = 140.000000, Volume = 389.450804\n",
"T = 150.000000, Volume = 412.434090\n",
"T = 160.000000, Volume = 435.417375\n",
"T = 170.000000, Volume = 458.400661\n",
"T = 180.000000, Volume = 481.384019\n",
"T = 190.000000, Volume = 504.367433\n",
"T = 200.000000, Volume = 527.350867\n",
"T = 210.000000, Volume = 550.334303\n",
"T = 220.000000, Volume = 573.317738\n",
"T = 230.000000, Volume = 596.301168\n",
"T = 240.000000, Volume = 619.284726\n",
"T = 250.000000, Volume = 642.268335\n",
"T = 260.000000, Volume = 665.251951\n",
"T = 270.000000, Volume = 688.235569\n",
"T = 280.000000, Volume = 711.219186\n",
"T = 290.000000, Volume = 734.202819\n",
"T = 300.000000, Volume = 757.186558\n",
"T = 310.000000, Volume = 780.170347\n",
"T = 320.000000, Volume = 803.154147\n",
"inflow volume is 803.154147 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 58.437387 s\n",
"[0.047423948603873688, 0.082608796093711134, 0.11340744968475341, 0.74782111196468748, 6.0231725081225296, 4.5909266090982719, 3.863157013960723, 5.2156200214824562, 5.0316868389365199, 2.5564746533270717, 2.8234582954196612, 2.9426522366823082, 3.0451848844399461, 3.1754845314184301, 2.8157238155101716, 2.7721238460578732, 3.8292584575421071, 8.5049797442665209, 5.5852404386471166, 5.349590097868858, 5.1068709815633104, 5.1156668765225444, 7.4014366245793823, 7.1613537512740759, 5.4539041681984637, 5.4435458114324922, 5.4057281386965261, 5.6030186295718201, 8.5793756285442644, 6.4355354408106971, 6.0081616273517504, 7.1358114067239056]\n",
"[ 36.85678278 36.89813082 29.72482924 22.44243851 22.28288629\n",
" 22.12965778 21.99358855 14.76336163 14.75249204 7.69812824\n",
" 7.56072761 7.03163414 10.68878513 10.90198056 18.36306294\n",
" 33.32128944 4.92414006 11.30184814 26.09139092]\n",
"max arrival time is 319.574639\n",
"sample # 86\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 2, 3, 3, 3, 2, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"147.277155902\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.022669\n",
"T = 20.000000, Volume = 75.776982\n",
"T = 30.000000, Volume = 113.531295\n",
"T = 40.000000, Volume = 151.285608\n",
"T = 50.000000, Volume = 189.039920\n",
"T = 60.000000, Volume = 226.794233\n",
"T = 70.000000, Volume = 255.138463\n",
"T = 80.000000, Volume = 293.071897\n",
"T = 90.000000, Volume = 316.836834\n",
"T = 100.000000, Volume = 339.819848\n",
"T = 110.000000, Volume = 362.802867\n",
"T = 120.000000, Volume = 385.785907\n",
"T = 130.000000, Volume = 408.768993\n",
"T = 140.000000, Volume = 431.752069\n",
"T = 150.000000, Volume = 454.735162\n",
"T = 160.000000, Volume = 477.718298\n",
"T = 170.000000, Volume = 500.701457\n",
"T = 180.000000, Volume = 523.684613\n",
"T = 190.000000, Volume = 546.667778\n",
"T = 200.000000, Volume = 569.650976\n",
"T = 210.000000, Volume = 592.634174\n",
"T = 220.000000, Volume = 615.617372\n",
"T = 230.000000, Volume = 638.600571\n",
"T = 240.000000, Volume = 661.583816\n",
"T = 250.000000, Volume = 684.567056\n",
"T = 260.000000, Volume = 707.550296\n",
"T = 270.000000, Volume = 730.533540\n",
"T = 280.000000, Volume = 753.516807\n",
"T = 290.000000, Volume = 776.500074\n",
"T = 300.000000, Volume = 799.483339\n",
"inflow volume is 799.483339 gallons\n",
"simulation time is 300.000000 s\n",
"wall clock time is 80.277800 s\n",
"[0.047695412902472888, 0.081879867423180069, 0.77170887424503409, 3.5078398478210122, 4.4543661115960012, 4.8383266650587755, 148.29600063836398, 204.0483562227827, 12.305680046511309, 10.128267661962667, 6.2061837596983169, 10.245901901993868, 12.112088918880699, 15.015275368110602, 7.1868807343190131, 12.021190641069097, 10.5757333771415, 12.481266780632769, 7.3170932399779636, 3.8391403716433103, 3.3007654330331495, 3.3102679440238392, 9.6366606262212287, 10.919817651291332, 10.922206385513006, 10.783868326740306, 10.484546483572563, 6.9000454788444374, 6.8854996268276505, 10.259622699473958]\n",
"[ 259.39259334 44.56962571 44.76637929 45.19398768 45.6145009\n",
" 37.30184055 29.98374854 23.95027363 23.87964342 23.58859826\n",
" 19.85087787 20.1964096 20.48439311 19.53723184 19.53723184\n",
" 33.06450521 21.21799934 26.54827984 40.84665457]\n",
"max arrival time is 298.461957\n",
"sample # 87\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"168.053424093\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.008615\n",
"T = 20.000000, Volume = 75.747820\n",
"T = 30.000000, Volume = 113.487025\n",
"T = 40.000000, Volume = 151.226230\n",
"T = 50.000000, Volume = 188.965435\n",
"T = 60.000000, Volume = 220.676542\n",
"T = 70.000000, Volume = 243.659544\n",
"T = 80.000000, Volume = 266.642551\n",
"T = 90.000000, Volume = 289.625559\n",
"T = 100.000000, Volume = 312.608567\n",
"T = 110.000000, Volume = 335.591575\n",
"T = 120.000000, Volume = 358.574583\n",
"T = 130.000000, Volume = 381.557601\n",
"T = 140.000000, Volume = 404.540659\n",
"T = 150.000000, Volume = 427.523713\n",
"T = 160.000000, Volume = 450.506783\n",
"T = 170.000000, Volume = 473.489854\n",
"T = 180.000000, Volume = 496.472925\n",
"T = 190.000000, Volume = 519.455997\n",
"T = 200.000000, Volume = 542.439069\n",
"T = 210.000000, Volume = 565.422141\n",
"T = 220.000000, Volume = 588.405211\n",
"T = 230.000000, Volume = 611.388335\n",
"T = 240.000000, Volume = 634.371450\n",
"T = 250.000000, Volume = 657.354565\n",
"T = 260.000000, Volume = 680.337687\n",
"T = 270.000000, Volume = 703.320871\n",
"T = 280.000000, Volume = 726.304048\n",
"T = 290.000000, Volume = 749.287225\n",
"T = 300.000000, Volume = 772.270413\n",
"T = 310.000000, Volume = 795.253659\n",
"inflow volume is 795.253659 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 96.019482 s\n",
"[0.047772176481148364, 0.081980237389304364, 0.39556156855316565, 1.6966477695951925, 1.9557377403150602, 13.328940432516365, 7.2466051117045938, 6.1536690933979372, 6.7098851960939632, 6.7027422491522879, 5.9752039303171705, 5.9858698141874207, 11.032173133816777, 13.013907942939687, 13.257815557515809, 7.278249840282772, 7.2844200064623417, 7.5319448555119974, 7.8482969537221612, 6.9798678719553751, 6.9930389036671183, 10.641242719764319, 13.666286576931027, 17.019709889040538, 16.589279067849628, 15.370209054466841, 15.002844713353413, 15.837669089169887, 16.91904367079966, 15.360040009221967, 14.556319086539432]\n",
"[ 60.09975724 60.1378166 59.87813824 47.58396264 47.27766171\n",
" 46.97333893 35.07283661 34.77348218 34.48883027 34.21964199\n",
" 22.78945473 16.53608924 5.77630438 4.77960184 40.9828626\n",
" 28.4602053 17.12345099 11.20346123 53.672722 ]\n",
"max arrival time is 309.861905\n",
"sample # 88\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"86.8343116029\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.043107\n",
"T = 20.000000, Volume = 75.820914\n",
"T = 30.000000, Volume = 113.598722\n",
"T = 40.000000, Volume = 151.376529\n",
"T = 50.000000, Volume = 189.154336\n",
"T = 60.000000, Volume = 226.932143\n",
"T = 70.000000, Volume = 258.786388\n",
"T = 80.000000, Volume = 281.491576\n",
"T = 90.000000, Volume = 304.474685\n",
"T = 100.000000, Volume = 327.457798\n",
"T = 110.000000, Volume = 350.440938\n",
"T = 120.000000, Volume = 373.424096\n",
"T = 130.000000, Volume = 396.407258\n",
"T = 140.000000, Volume = 419.390423\n",
"T = 150.000000, Volume = 442.373591\n",
"T = 160.000000, Volume = 465.356762\n",
"T = 170.000000, Volume = 488.339936\n",
"T = 180.000000, Volume = 511.323111\n",
"T = 190.000000, Volume = 534.306285\n",
"T = 200.000000, Volume = 557.289507\n",
"T = 210.000000, Volume = 580.272844\n",
"T = 220.000000, Volume = 603.256210\n",
"T = 230.000000, Volume = 626.239574\n",
"T = 240.000000, Volume = 649.223000\n",
"T = 250.000000, Volume = 672.206555\n",
"T = 260.000000, Volume = 695.190150\n",
"T = 270.000000, Volume = 718.173747\n",
"T = 280.000000, Volume = 741.157404\n",
"T = 290.000000, Volume = 764.141174\n",
"T = 300.000000, Volume = 787.124998\n",
"T = 310.000000, Volume = 810.108829\n",
"inflow volume is 810.108829 gallons\n",
"simulation time is 310.000000 s\n",
"wall clock time is 47.858087 s\n",
"[0.047304773510657983, 0.08148422194677829, 0.79622275209980264, 2.3281307415196308, 2.5737915651341843, 2.7320317334361941, 22.114359282252135, 7.2437021206593561, 4.4370901046600073, 4.8046652857770757, 3.5935935117619806, 3.0860911252761452, 3.2448844636778902, 3.284561509033582, 3.4006884767938503, 3.4929558904745943, 3.3554925457008009, 3.2182563353926508, 3.2331767306499586, 7.03280621911499, 6.3514958431373589, 4.8742957737330457, 5.3752253026299242, 7.8017576431053426, 5.6177528292784773, 5.0060444192123397, 6.083690813098908, 7.4426187215638135, 5.0642841462397623, 5.1028175391804211, 6.6013418195816902]\n",
"[ 31.91464277 26.18859834 26.17763326 26.32018478 21.17200979\n",
" 21.51716078 21.88862348 22.14775803 22.41311646 22.64085881\n",
" 19.42448227 16.78792338 15.04065643 14.21508668 23.41018108\n",
" 17.74271082 15.53606863 20.72752826 14.21508668]\n",
"max arrival time is 307.520737\n",
"sample # 89\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 2, 3, 2, 3, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"39.2102566853\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.897753\n",
"T = 20.000000, Volume = 75.533263\n",
"T = 30.000000, Volume = 113.168773\n",
"T = 40.000000, Volume = 150.560598\n",
"T = 50.000000, Volume = 172.785467\n",
"T = 60.000000, Volume = 190.348454\n",
"T = 70.000000, Volume = 213.340752\n",
"T = 80.000000, Volume = 236.324947\n",
"T = 90.000000, Volume = 259.309515\n",
"T = 100.000000, Volume = 282.294114\n",
"T = 110.000000, Volume = 305.278743\n",
"T = 120.000000, Volume = 328.263402\n",
"T = 130.000000, Volume = 351.248095\n",
"T = 140.000000, Volume = 374.232817\n",
"T = 150.000000, Volume = 397.217541\n",
"T = 160.000000, Volume = 420.202265\n",
"T = 170.000000, Volume = 443.187232\n",
"T = 180.000000, Volume = 466.172584\n",
"T = 190.000000, Volume = 489.157992\n",
"T = 200.000000, Volume = 512.143848\n",
"T = 210.000000, Volume = 535.129803\n",
"T = 220.000000, Volume = 558.115752\n",
"T = 230.000000, Volume = 581.101892\n",
"T = 240.000000, Volume = 604.088531\n",
"T = 250.000000, Volume = 627.075557\n",
"T = 260.000000, Volume = 650.062714\n",
"T = 270.000000, Volume = 673.049907\n",
"T = 280.000000, Volume = 696.037109\n",
"T = 290.000000, Volume = 719.024428\n",
"T = 300.000000, Volume = 742.012155\n",
"T = 310.000000, Volume = 765.000274\n",
"T = 320.000000, Volume = 787.988592\n",
"T = 330.000000, Volume = 810.977016\n",
"inflow volume is 810.977016 gallons\n",
"simulation time is 330.000000 s\n",
"wall clock time is 23.873996 s\n",
"[0.048818754023384078, 0.086056785599048211, 0.52733405329993943, 3.3302768698401759, 10.544214344533728, 3.0716995091907999, 2.6434821364658143, 2.8880682344958526, 2.2028740025453342, 2.3141014919631737, 2.3847172760958704, 2.4684441302909188, 2.5671742081384377, 2.5716404498201317, 2.5692669814437439, 2.8238731936319295, 4.3072604275323876, 4.4289103539320607, 3.220884724085662, 2.8881630855051612, 2.8403123527395624, 3.4692720471388419, 4.0481073711283919, 2.8721816793515038, 3.0861076436981847, 3.0651614992433589, 3.0769117745644725, 3.9258429094986824, 4.0574551003355026, 3.0994788114800751, 3.2682507158458991, 3.350799636519691, 3.4350144129054945]\n",
"[ 14.94829027 12.53640698 9.86734571 9.72871786 9.59469204\n",
" 9.46075977 6.69995051 6.55422194 3.69178963 3.53895683\n",
" 0.79307194 0.40722001 0.2476132 0.15473225 11.20684405\n",
" 5.13169784 13.83111193 8.08801353 2.06321111]\n",
"max arrival time is 329.612245\n",
"sample # 90\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 3, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"146.212664181\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.023507\n",
"T = 20.000000, Volume = 75.778582\n",
"T = 30.000000, Volume = 113.533657\n",
"T = 40.000000, Volume = 151.288733\n",
"T = 50.000000, Volume = 181.965296\n",
"T = 60.000000, Volume = 204.948309\n",
"T = 70.000000, Volume = 227.931326\n",
"T = 80.000000, Volume = 250.914342\n",
"T = 90.000000, Volume = 273.897428\n",
"T = 100.000000, Volume = 296.880501\n",
"T = 110.000000, Volume = 319.863576\n",
"T = 120.000000, Volume = 342.846667\n",
"T = 130.000000, Volume = 365.829783\n",
"T = 140.000000, Volume = 388.812899\n",
"T = 150.000000, Volume = 411.796015\n",
"T = 160.000000, Volume = 434.779161\n",
"T = 170.000000, Volume = 457.762322\n",
"T = 180.000000, Volume = 480.745480\n",
"T = 190.000000, Volume = 503.728632\n",
"T = 200.000000, Volume = 526.711868\n",
"T = 210.000000, Volume = 549.695108\n",
"T = 220.000000, Volume = 572.678348\n",
"T = 230.000000, Volume = 595.661595\n",
"T = 240.000000, Volume = 618.644877\n",
"T = 250.000000, Volume = 641.628159\n",
"T = 260.000000, Volume = 664.611442\n",
"T = 270.000000, Volume = 687.594730\n",
"T = 280.000000, Volume = 710.578062\n",
"T = 290.000000, Volume = 733.561410\n",
"T = 300.000000, Volume = 756.544762\n",
"T = 310.000000, Volume = 779.528114\n",
"T = 320.000000, Volume = 802.511454\n",
"inflow volume is 802.511454 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 86.249681 s\n",
"[0.047693389617982855, 0.082923405873603084, 0.11348780102027758, 0.84349900240989018, 7.9504107991185782, 5.9445588137146617, 4.6862040728004795, 7.6649233360737217, 10.164392010142839, 10.392588832301973, 9.7214654896102086, 7.3915002041127913, 2.9312524534276037, 2.6163832839336822, 2.6325624832394179, 11.14412536615192, 11.847963959438241, 13.391983010546955, 14.227378106306553, 11.015738850614644, 11.128097360497208, 10.366495632930652, 7.5460267027360768, 3.6395323072955481, 3.3163714341015424, 3.3232558903503659, 11.266816320040066, 11.084579303793827, 7.1416996115887423, 7.1650133536765725, 7.552939516670385, 9.9420430211871871]\n",
"[ 58.7213979 58.75980379 49.72452551 41.60214508 41.29995701\n",
" 41.070877 35.17490317 30.80780263 31.12608531 31.18540405\n",
" 28.29155746 28.12478271 27.89648995 27.6777208 45.28394175\n",
" 37.55270684 29.61665394 32.18786623 53.75409697]\n",
"max arrival time is 317.536946\n",
"sample # 91\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"111.96039136\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.041122\n",
"T = 20.000000, Volume = 75.815939\n",
"T = 30.000000, Volume = 113.590756\n",
"T = 40.000000, Volume = 145.598898\n",
"T = 50.000000, Volume = 168.581903\n",
"T = 60.000000, Volume = 191.564933\n",
"T = 70.000000, Volume = 214.548000\n",
"T = 80.000000, Volume = 237.531160\n",
"T = 90.000000, Volume = 260.514348\n",
"T = 100.000000, Volume = 283.497537\n",
"T = 110.000000, Volume = 306.480727\n",
"T = 120.000000, Volume = 329.463920\n",
"T = 130.000000, Volume = 352.447138\n",
"T = 140.000000, Volume = 375.430358\n",
"T = 150.000000, Volume = 398.413578\n",
"T = 160.000000, Volume = 421.396799\n",
"T = 170.000000, Volume = 444.380021\n",
"T = 180.000000, Volume = 467.363247\n",
"T = 190.000000, Volume = 490.346565\n",
"T = 200.000000, Volume = 513.329903\n",
"T = 210.000000, Volume = 536.313242\n",
"T = 220.000000, Volume = 559.296582\n",
"T = 230.000000, Volume = 582.279925\n",
"T = 240.000000, Volume = 605.263267\n",
"T = 250.000000, Volume = 628.246607\n",
"T = 260.000000, Volume = 651.230014\n",
"T = 270.000000, Volume = 674.213483\n",
"T = 280.000000, Volume = 697.196967\n",
"T = 290.000000, Volume = 720.180452\n",
"T = 300.000000, Volume = 743.163937\n",
"T = 310.000000, Volume = 766.147414\n",
"T = 320.000000, Volume = 789.130981\n",
"inflow volume is 789.130981 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 66.377207 s\n",
"[0.0499418929901412, 0.087056488989951886, 0.36342641340912063, 5.3742794849341502, 8.3414319468852529, 5.9143996270329238, 6.4208699130316242, 7.903067561810575, 5.1363277717998255, 5.0261632928075546, 5.3722826623405711, 4.8339429687028002, 3.2505227435665249, 3.1356337936668064, 3.1473823384546815, 2.7765082290159357, 2.7871798539078663, 6.6615510601831405, 8.2664752813880451, 5.3804177907512436, 5.8579660568985181, 6.1382289664323171, 5.743676076933486, 5.7253205135034673, 7.0906735841096502, 9.2007039233127248, 5.5512706370958975, 5.9877969279798879, 5.7105387522125213, 5.7195164234277014, 8.7518278604618729, 8.4963313952189825]\n",
"[ 40.0342137 32.17237748 24.07381335 23.89201415 23.71455188\n",
" 23.53639821 23.357681 15.34504447 15.15897105 14.98256388\n",
" 6.98783916 8.52463086 3.53547867 0.04899861 5.84520686\n",
" 28.10083459 10.96421489 36.18205674 19.31745972]\n",
"max arrival time is 319.907076\n",
"sample # 92\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 2, 3, 2, 2, 3, 2, 3, 2, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"174.061809918\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.004213\n",
"T = 20.000000, Volume = 75.738908\n",
"T = 30.000000, Volume = 113.473603\n",
"T = 40.000000, Volume = 151.208298\n",
"T = 50.000000, Volume = 181.758894\n",
"T = 60.000000, Volume = 204.741893\n",
"T = 70.000000, Volume = 227.724896\n",
"T = 80.000000, Volume = 250.707900\n",
"T = 90.000000, Volume = 273.690905\n",
"T = 100.000000, Volume = 296.673909\n",
"T = 110.000000, Volume = 319.656914\n",
"T = 120.000000, Volume = 342.639934\n",
"T = 130.000000, Volume = 365.622986\n",
"T = 140.000000, Volume = 388.606048\n",
"T = 150.000000, Volume = 411.589111\n",
"T = 160.000000, Volume = 434.572173\n",
"T = 170.000000, Volume = 457.555235\n",
"T = 180.000000, Volume = 480.538335\n",
"T = 190.000000, Volume = 503.521437\n",
"T = 200.000000, Volume = 526.504547\n",
"T = 210.000000, Volume = 549.487668\n",
"T = 220.000000, Volume = 572.470788\n",
"T = 230.000000, Volume = 595.453912\n",
"T = 240.000000, Volume = 618.437081\n",
"T = 250.000000, Volume = 641.420242\n",
"T = 260.000000, Volume = 664.403403\n",
"T = 270.000000, Volume = 687.386577\n",
"T = 280.000000, Volume = 710.369806\n",
"T = 290.000000, Volume = 733.353025\n",
"T = 300.000000, Volume = 756.336241\n",
"T = 310.000000, Volume = 779.319476\n",
"T = 320.000000, Volume = 802.302697\n",
"inflow volume is 802.302697 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 102.966466 s\n",
"[0.047793907089814389, 0.083050038309303464, 0.11325409646635851, 0.79483062370239499, 8.9976135691707722, 8.5900818268665873, 5.9573834639417713, 6.7367721402798795, 6.56574623355404, 6.0192823291081226, 6.0284200508078127, 11.927502116153054, 12.463936448658089, 7.6889143818151711, 6.0592103495584615, 6.0695810802234744, 9.4682867550783243, 12.442593147642272, 14.636357352835111, 11.112188718012879, 6.3803574904268778, 6.3930213748803375, 12.656965019411253, 13.763161249013411, 17.064383062418152, 17.135089297344514, 14.977501377108869, 13.670634717264825, 13.917086918824072, 12.643257051521683, 8.3569298947216879, 10.936302368296568]\n",
"[ 74.36119946 74.40558562 63.18982748 63.15581809 63.1455044\n",
" 54.76970291 54.66821959 48.38673194 48.32047646 43.89885306 40.84742\n",
" 41.13460462 41.2864549 41.32109917 45.67124328 68.37805213\n",
" 51.0108624 41.83797655 58.38585327]\n",
"max arrival time is 317.397701\n",
"sample # 93\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 3, 2, 2, 2, 3, 2, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"149.699992448\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.021250\n",
"T = 20.000000, Volume = 75.773879\n",
"T = 30.000000, Volume = 113.526508\n",
"T = 40.000000, Volume = 145.442785\n",
"T = 50.000000, Volume = 168.425775\n",
"T = 60.000000, Volume = 191.408772\n",
"T = 70.000000, Volume = 214.391809\n",
"T = 80.000000, Volume = 237.374889\n",
"T = 90.000000, Volume = 260.357975\n",
"T = 100.000000, Volume = 283.341082\n",
"T = 110.000000, Volume = 306.324189\n",
"T = 120.000000, Volume = 329.307296\n",
"T = 130.000000, Volume = 352.290438\n",
"T = 140.000000, Volume = 375.273584\n",
"T = 150.000000, Volume = 398.256731\n",
"T = 160.000000, Volume = 421.239879\n",
"T = 170.000000, Volume = 444.223066\n",
"T = 180.000000, Volume = 467.206254\n",
"T = 190.000000, Volume = 490.189442\n",
"T = 200.000000, Volume = 513.172631\n",
"T = 210.000000, Volume = 536.155819\n",
"T = 220.000000, Volume = 559.139020\n",
"T = 230.000000, Volume = 582.122260\n",
"T = 240.000000, Volume = 605.105488\n",
"T = 250.000000, Volume = 628.088715\n",
"T = 260.000000, Volume = 651.071965\n",
"T = 270.000000, Volume = 674.055221\n",
"T = 280.000000, Volume = 697.038477\n",
"T = 290.000000, Volume = 720.021734\n",
"T = 300.000000, Volume = 743.004990\n",
"T = 310.000000, Volume = 765.988250\n",
"T = 320.000000, Volume = 788.971565\n",
"inflow volume is 788.971565 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 89.233263 s\n",
"[0.050132214367805315, 0.087240556714102288, 0.26905676215192936, 6.1579356223420438, 10.008556180990043, 11.686988989449532, 6.7186901707945408, 11.689013714636923, 7.7858354524270625, 2.5131716891855884, 2.3864091856670298, 2.6332887282978206, 11.420616729822067, 10.925881387791302, 10.197097782473204, 9.5693503919699037, 3.8143905041580206, 3.7765521217924789, 3.2771852203533487, 3.014140787803711, 3.0224674798429558, 11.713673803965138, 10.816834508292319, 10.868078506211143, 11.743873907527099, 7.7815640382911324, 7.7145939749185644, 7.3210066677184091, 6.9427100270487285, 6.9514477703372819, 11.504290323198145, 13.836445163432954]\n",
"[ 54.23611461 43.4852757 32.51113984 32.2201976 21.46087919\n",
" 21.17850459 20.89828118 20.62419489 10.24715474 10.00466573\n",
" 9.76121772 13.52041004 5.1205283 5.92099867 37.95947424\n",
" 9.17406148 15.39506919 26.80731398 48.97385505]\n",
"max arrival time is 318.882950\n",
"sample # 94\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"128.822636445\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.033849\n",
"T = 20.000000, Volume = 75.800180\n",
"T = 30.000000, Volume = 113.566512\n",
"T = 40.000000, Volume = 145.529558\n",
"T = 50.000000, Volume = 168.512555\n",
"T = 60.000000, Volume = 191.495584\n",
"T = 70.000000, Volume = 214.478615\n",
"T = 80.000000, Volume = 237.461646\n",
"T = 90.000000, Volume = 260.444710\n",
"T = 100.000000, Volume = 283.427835\n",
"T = 110.000000, Volume = 306.410972\n",
"T = 120.000000, Volume = 329.394110\n",
"T = 130.000000, Volume = 352.377249\n",
"T = 140.000000, Volume = 375.360389\n",
"T = 150.000000, Volume = 398.343537\n",
"T = 160.000000, Volume = 421.326697\n",
"T = 170.000000, Volume = 444.309859\n",
"T = 180.000000, Volume = 467.293021\n",
"T = 190.000000, Volume = 490.276183\n",
"T = 200.000000, Volume = 513.259362\n",
"T = 210.000000, Volume = 536.242598\n",
"T = 220.000000, Volume = 559.225846\n",
"T = 230.000000, Volume = 582.209097\n",
"T = 240.000000, Volume = 605.192348\n",
"T = 250.000000, Volume = 628.175601\n",
"T = 260.000000, Volume = 651.158857\n",
"T = 270.000000, Volume = 674.142131\n",
"T = 280.000000, Volume = 697.125404\n",
"T = 290.000000, Volume = 720.108673\n",
"T = 300.000000, Volume = 743.092006\n",
"T = 310.000000, Volume = 766.075362\n",
"T = 320.000000, Volume = 789.058722\n",
"inflow volume is 789.058722 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 75.999598 s\n",
"[0.050036903111151443, 0.08580793628397454, 0.31968749431473437, 5.6762948288235302, 9.1740974761648246, 4.7109259100042316, 4.4291869537874256, 4.4379311064879587, 8.5126046873766885, 9.741354414169006, 5.345863939278761, 5.6898070028722687, 6.0060222214547467, 6.2449201913990482, 4.9178451942473638, 3.5322878711175396, 3.2561667236772376, 2.916945142800234, 2.9264067494929598, 8.9607691761150097, 11.065712286859235, 6.2882317592979637, 6.4986108550130437, 6.8075837439262843, 6.9969590206685206, 5.1181171761182505, 3.5835124779979806, 3.5968980331935998, 8.4802214267651479, 9.2974363352715947, 5.9494443872365501, 5.8784575169019115]\n",
"[ 38.07780722 28.79541364 28.63380338 19.04106007 18.87632882\n",
" 18.71716433 18.55791805 18.3985906 8.68724408 8.51793437\n",
" 8.35552526 11.20852178 4.44179298 0.04781099 7.62120237\n",
" 23.8486906 13.55365416 33.5470982 0.04781099]\n",
"max arrival time is 296.596273\n",
"sample # 95\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 3, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"220.402296027\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.971420\n",
"T = 20.000000, Volume = 75.670968\n",
"T = 30.000000, Volume = 113.370517\n",
"T = 40.000000, Volume = 145.161125\n",
"T = 50.000000, Volume = 168.144104\n",
"T = 60.000000, Volume = 191.127082\n",
"T = 70.000000, Volume = 214.110070\n",
"T = 80.000000, Volume = 237.093092\n",
"T = 90.000000, Volume = 260.076106\n",
"T = 100.000000, Volume = 283.059123\n",
"T = 110.000000, Volume = 306.042148\n",
"T = 120.000000, Volume = 329.025174\n",
"T = 130.000000, Volume = 352.008199\n",
"T = 140.000000, Volume = 374.991223\n",
"T = 150.000000, Volume = 397.974277\n",
"T = 160.000000, Volume = 420.957328\n",
"T = 170.000000, Volume = 443.940379\n",
"T = 180.000000, Volume = 466.923436\n",
"T = 190.000000, Volume = 489.906497\n",
"T = 200.000000, Volume = 512.889559\n",
"T = 210.000000, Volume = 535.872621\n",
"T = 220.000000, Volume = 558.855693\n",
"T = 230.000000, Volume = 581.838787\n",
"T = 240.000000, Volume = 604.821874\n",
"T = 250.000000, Volume = 627.804957\n",
"T = 260.000000, Volume = 650.788077\n",
"T = 270.000000, Volume = 673.771201\n",
"T = 280.000000, Volume = 696.754326\n",
"T = 290.000000, Volume = 719.737460\n",
"T = 300.000000, Volume = 742.720594\n",
"T = 310.000000, Volume = 765.703729\n",
"T = 320.000000, Volume = 788.686864\n",
"inflow volume is 788.686864 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 132.796828 s\n",
"[0.050331486428627344, 0.087451603843874071, 0.10904495765718888, 7.6311932764785384, 14.089455409615008, 18.157917769205987, 21.681150917995588, 13.899798294640494, 17.214579196258178, 15.129670956464286, 8.3047518857787281, 6.8407009975579109, 6.6843173137387124, 12.641427002104104, 15.072240827550896, 22.265350048251417, 19.652351665769142, 11.71227848336704, 7.6738615665047964, 7.1141478883729246, 7.1249145595841084, 18.656417331915375, 21.106018022488524, 19.182038877642491, 24.464136586854671, 15.111638054020203, 18.954348237548672, 13.740411034328325, 8.9715880839571334, 8.9895792314106995, 9.6860422974434375, 9.6759741206769352]\n",
"[ 82.41218696 66.39277061 50.11378991 49.84253616 49.56788104\n",
" 33.50314478 33.2277747 32.95172148 24.86218445 14.0632469\n",
" 7.4998129 2.14984561 1.51891483 1.44044759 16.7249876\n",
" 58.23786624 41.51184964 74.53793709 25.00720162]\n",
"max arrival time is 316.511797\n",
"sample # 96\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 3, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"154.397401782\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.017959\n",
"T = 20.000000, Volume = 75.767239\n",
"T = 30.000000, Volume = 113.516520\n",
"T = 40.000000, Volume = 145.424535\n",
"T = 50.000000, Volume = 168.407524\n",
"T = 60.000000, Volume = 191.390536\n",
"T = 70.000000, Volume = 214.373549\n",
"T = 80.000000, Volume = 237.356563\n",
"T = 90.000000, Volume = 260.339577\n",
"T = 100.000000, Volume = 283.322591\n",
"T = 110.000000, Volume = 306.305641\n",
"T = 120.000000, Volume = 329.288717\n",
"T = 130.000000, Volume = 352.271804\n",
"T = 140.000000, Volume = 375.254890\n",
"T = 150.000000, Volume = 398.238028\n",
"T = 160.000000, Volume = 421.221165\n",
"T = 170.000000, Volume = 444.204302\n",
"T = 180.000000, Volume = 467.187440\n",
"T = 190.000000, Volume = 490.170660\n",
"T = 200.000000, Volume = 513.153873\n",
"T = 210.000000, Volume = 536.137107\n",
"T = 220.000000, Volume = 559.120342\n",
"T = 230.000000, Volume = 582.103578\n",
"T = 240.000000, Volume = 605.086814\n",
"T = 250.000000, Volume = 628.070051\n",
"T = 260.000000, Volume = 651.053295\n",
"T = 270.000000, Volume = 674.036547\n",
"T = 280.000000, Volume = 697.019799\n",
"T = 290.000000, Volume = 720.003051\n",
"T = 300.000000, Volume = 742.986303\n",
"T = 310.000000, Volume = 765.969556\n",
"T = 320.000000, Volume = 788.952853\n",
"inflow volume is 788.952853 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 92.439715 s\n",
"[0.050148678415106333, 0.085918539435369493, 0.25389476812812178, 6.2437760086307605, 10.967403413452532, 4.8947268549868213, 5.6831079928132056, 5.6653213999610799, 5.3964449166378445, 5.4069819798761865, 11.235192695246747, 8.6779949714250968, 5.1855237462236277, 7.8573279984329396, 10.721263760980012, 13.804634489308249, 13.981643267758283, 13.653012510489434, 12.144390922324584, 10.804156121136423, 6.9709599916824381, 6.9444174074922058, 7.233467886116741, 7.6414706792494247, 7.3180534556906673, 4.33660195952341, 4.325847597252114, 4.0621202583186076, 3.6399126473343175, 3.6492200397428802, 10.928317048368186, 12.784414098553494]\n",
"[ 5.59235293e+01 4.48150311e+01 4.45734661e+01 4.42973685e+01\n",
" 3.31156981e+01 2.20844101e+01 1.11300087e+01 1.08634641e+01\n",
" 1.05889710e+01 1.03227629e+01 1.00666900e+01 1.39415350e+01\n",
" 3.36280429e+00 4.97834164e-02 1.65777105e+01 2.75747578e+01\n",
" 5.04821965e+01 3.86503622e+01 9.44331541e+00]\n",
"max arrival time is 319.367548\n",
"sample # 97\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"203.705654636\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.983252\n",
"T = 20.000000, Volume = 75.695426\n",
"T = 30.000000, Volume = 113.407600\n",
"T = 40.000000, Volume = 145.232378\n",
"T = 50.000000, Volume = 168.215358\n",
"T = 60.000000, Volume = 191.198339\n",
"T = 70.000000, Volume = 214.181328\n",
"T = 80.000000, Volume = 237.164329\n",
"T = 90.000000, Volume = 260.147331\n",
"T = 100.000000, Volume = 283.130333\n",
"T = 110.000000, Volume = 306.113335\n",
"T = 120.000000, Volume = 329.096338\n",
"T = 130.000000, Volume = 352.079340\n",
"T = 140.000000, Volume = 375.062343\n",
"T = 150.000000, Volume = 398.045346\n",
"T = 160.000000, Volume = 421.028351\n",
"T = 170.000000, Volume = 444.011383\n",
"T = 180.000000, Volume = 466.994408\n",
"T = 190.000000, Volume = 489.977432\n",
"T = 200.000000, Volume = 512.960472\n",
"T = 210.000000, Volume = 535.943546\n",
"T = 220.000000, Volume = 558.926611\n",
"T = 230.000000, Volume = 581.909689\n",
"T = 240.000000, Volume = 604.892768\n",
"T = 250.000000, Volume = 627.875845\n",
"T = 260.000000, Volume = 650.858953\n",
"T = 270.000000, Volume = 673.842062\n",
"T = 280.000000, Volume = 696.825170\n",
"T = 290.000000, Volume = 719.808291\n",
"T = 300.000000, Volume = 742.791413\n",
"T = 310.000000, Volume = 765.774535\n",
"T = 320.000000, Volume = 788.757657\n",
"inflow volume is 788.757657 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 121.084027 s\n",
"[0.050293541144068615, 0.086071299968169854, 0.1114999454634775, 7.2847816105499392, 13.106324834537414, 14.098234287019379, 9.833512816884479, 3.4498996238154445, 3.7361885270448991, 3.5807491462659589, 4.1003217880277223, 4.121354354940328, 3.9999399371806108, 2.8929699953099655, 2.9068203100974439, 9.722934275813941, 15.225545056624327, 17.277832223315443, 18.410925025734908, 20.332514131871225, 15.453220073803019, 17.73453022394969, 7.6597888698796295, 6.9364731461855538, 12.324077312231138, 14.60511173250257, 17.308943371987517, 16.384021522546092, 9.1721767626499187, 8.0702832416119126, 7.639172670620419, 7.6953407032131889]\n",
"[ 6.07856011e+01 4.59286002e+01 4.56441572e+01 4.53556280e+01\n",
" 4.50625343e+01 4.47662946e+01 4.44634114e+01 2.98398456e+01\n",
" 1.53351554e+01 2.17911864e+01 1.32285765e+01 3.94277568e+00\n",
" 3.54272201e+00 4.71496569e-02 3.71188514e+01 4.71496569e-02\n",
" 5.34994168e+01 1.46757483e+01 2.25538116e+01]\n",
"max arrival time is 291.347211\n",
"sample # 98\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 3, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"90.1645726541\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 38.043459\n",
"T = 20.000000, Volume = 75.821966\n",
"T = 30.000000, Volume = 113.600473\n",
"T = 40.000000, Volume = 145.726364\n",
"T = 50.000000, Volume = 168.709380\n",
"T = 60.000000, Volume = 191.692497\n",
"T = 70.000000, Volume = 214.675621\n",
"T = 80.000000, Volume = 237.658746\n",
"T = 90.000000, Volume = 260.641913\n",
"T = 100.000000, Volume = 283.625211\n",
"T = 110.000000, Volume = 306.608515\n",
"T = 120.000000, Volume = 329.591822\n",
"T = 130.000000, Volume = 352.575147\n",
"T = 140.000000, Volume = 375.558497\n",
"T = 150.000000, Volume = 398.541850\n",
"T = 160.000000, Volume = 421.525204\n",
"T = 170.000000, Volume = 444.508558\n",
"T = 180.000000, Volume = 467.491908\n",
"T = 190.000000, Volume = 490.475403\n",
"T = 200.000000, Volume = 513.458937\n",
"T = 210.000000, Volume = 536.442475\n",
"T = 220.000000, Volume = 559.426014\n",
"T = 230.000000, Volume = 582.409594\n",
"T = 240.000000, Volume = 605.393174\n",
"T = 250.000000, Volume = 628.376755\n",
"T = 260.000000, Volume = 651.360381\n",
"T = 270.000000, Volume = 674.344112\n",
"T = 280.000000, Volume = 697.327871\n",
"T = 290.000000, Volume = 720.311629\n",
"T = 300.000000, Volume = 743.295445\n",
"T = 310.000000, Volume = 766.279415\n",
"T = 320.000000, Volume = 789.263430\n",
"inflow volume is 789.263430 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 53.580571 s\n",
"[0.049770016642706374, 0.085557890886662491, 0.40975496230453173, 4.7561844955032813, 6.9946080445443641, 2.3156939135766734, 2.1424144670190728, 2.1495689706056194, 5.3758826902426913, 4.1813512108453255, 4.5201988369509518, 4.743078255375722, 3.6829243201293673, 3.0538471673369689, 3.01825524265108, 2.755994272124259, 2.7662223145171514, 5.320932691841211, 6.8982336372795947, 4.9629484133303574, 5.3337180680796719, 4.5181851145336491, 3.3379829376721482, 3.2161542750687468, 4.1673638878696035, 7.8586484922815085, 5.0337262551428497, 4.9236537406416589, 6.2299159158293209, 5.49372833695283, 3.5909672580444707, 4.004413670746267]\n",
"[ 34.40163524 28.1535557 28.00057515 21.46244141 21.3059406\n",
" 21.15362798 21.00182334 14.35962709 14.19880909 14.04171944\n",
" 7.29242052 0.6313503 0.50944644 0.42481694 10.67704732\n",
" 31.38114731 17.68989844 4.29946331 24.74068416]\n",
"max arrival time is 319.467613\n",
"sample # 99\n",
"\n",
"generating random trees starting with spine of 15 nodes and adding 5 branches\n",
"[20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20]\n",
"0.007\n",
"[1, 3, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1]\n",
"[2.0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
"207.51787814\n",
"../indata/fakeinp.inp\n",
"system volume is 788.424774 gal \n",
"[20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20]\n",
"T = 10.000000, Volume = 37.980910\n",
"T = 20.000000, Volume = 75.690198\n",
"T = 30.000000, Volume = 113.399487\n",
"T = 40.000000, Volume = 145.216970\n",
"T = 50.000000, Volume = 168.199950\n",
"T = 60.000000, Volume = 191.182930\n",
"T = 70.000000, Volume = 214.165916\n",
"T = 80.000000, Volume = 237.148909\n",
"T = 90.000000, Volume = 260.131902\n",
"T = 100.000000, Volume = 283.114894\n",
"T = 110.000000, Volume = 306.097907\n",
"T = 120.000000, Volume = 329.080937\n",
"T = 130.000000, Volume = 352.063970\n",
"T = 140.000000, Volume = 375.047004\n",
"T = 150.000000, Volume = 398.030037\n",
"T = 160.000000, Volume = 421.013071\n",
"T = 170.000000, Volume = 443.996105\n",
"T = 180.000000, Volume = 466.979145\n",
"T = 190.000000, Volume = 489.962213\n",
"T = 200.000000, Volume = 512.945275\n",
"T = 210.000000, Volume = 535.928343\n",
"T = 220.000000, Volume = 558.911418\n",
"T = 230.000000, Volume = 581.894493\n",
"T = 240.000000, Volume = 604.877568\n",
"T = 250.000000, Volume = 627.860641\n",
"T = 260.000000, Volume = 650.843746\n",
"T = 270.000000, Volume = 673.826850\n",
"T = 280.000000, Volume = 696.809953\n",
"T = 290.000000, Volume = 719.793057\n",
"T = 300.000000, Volume = 742.776168\n",
"T = 310.000000, Volume = 765.759292\n",
"T = 320.000000, Volume = 788.742416\n",
"inflow volume is 788.742416 gallons\n",
"simulation time is 320.000000 s\n",
"wall clock time is 122.996094 s\n",
"[0.050302371162145455, 0.086080256535742763, 0.1048237020734262, 7.3483203278361389, 13.438637916078436, 14.495469256710727, 10.805305943845312, 6.529259929345665, 6.0496620416161573, 6.0572222928407538, 15.917770969010745, 11.272212600495013, 7.5866872865203296, 7.6383430821366254, 7.7394138848491103, 6.8696076299416795, 6.8805645296885327, 14.917722088454012, 16.069610563934667, 18.224961996312729, 13.140288081461055, 8.4718652491279567, 7.3496319182603296, 7.3528382208760119, 13.049454829455861, 15.05423339999723, 16.760112026967189, 16.201299824557321, 15.322252461824425, 6.2636727855488434, 3.8422658714457221, 3.96461938537283]\n",
"[ 6.36003702e+01 4.84043392e+01 4.82398877e+01 4.80805800e+01\n",
" 3.25697161e+01 3.24033044e+01 3.22419925e+01 1.66178890e+01\n",
" 1.64491298e+01 2.22547328e+01 8.63408607e+00 3.14420600e+00\n",
" 4.84723608e+00 4.71179930e-02 1.49923988e+01 4.71179930e-02\n",
" 4.03365548e+01 5.61148836e+01 2.44411048e+01]\n",
"max arrival time is 294.558427\n"
]
}
],
"source": [
"import time\n",
"np.random.seed(555)\n",
"Nsamples =100\n",
"Summary = []\n",
"\n",
"N = 20\n",
"\n",
"fn = '../indata/randomwithN%d'%N\n",
"m32gal = 264.172\n",
"\n",
"\n",
"#generator = 'wilson' #sample complete graph to get an admissible tree\n",
"generator = 'spine' #start with N-2 pipes comprising 'spine'; pick S random nodes between 1 and N1-1 to add spurs\n",
"\n",
" \n",
"for m in range(Nsamples):\n",
" print \"sample # %d\\n\"%m\n",
" if generator=='wilson':\n",
" print \"generating random trees using Wilson's algorithm on complete graph, followed by filter for degree <3\"\n",
" Np = N-1\n",
" Nn = N\n",
" Ds = [0.1]*Np\n",
" A2 = np.ones((N,N))-np.diag(np.ones(N),0)\n",
" Gl2 = listRep(A2)\n",
" nodeTypes = [4]*N\n",
" while max(nodeTypes)>3 or nodeTypes[0]!=1:\n",
" conn2 = randomTreeWithRoot(0,Gl2)\n",
" nl = conn2[:,0]\n",
" nr = conn2[:,1]\n",
" nodeTypes = [sum(len(find(nl==k))+len(find(nr==k))) for k in range(N)]\n",
" IN = nodeTypes.index(1)\n",
" \n",
" if generator =='spine':\n",
" S = 5\n",
" N1 = N-N/4\n",
" N2 = S\n",
" Np = N1+N2-1\n",
" Nn = N1+N2\n",
" print \"generating random trees starting with spine of %d nodes and adding %d branches\"%(N1,N2)\n",
" conn2 = np.ndarray((N1+N2-1,2),int)\n",
" for k in range(N1-1):\n",
" conn2[k,:] = (k,k+1)\n",
" avail = [i for i in range(1,N1-1)]\n",
" for k in range(N2):\n",
" where = np.random.randint(0,N1-2-k)\n",
" spur = avail.pop(where)\n",
" conn2[k+N1-1,0]=spur\n",
" conn2[k+N1-1,1] = N1+k\n",
" nl = conn2[:,0]\n",
" nr = conn2[:,1]\n",
" nodeTypes = [sum(len(find(nl==k))+len(find(nr==k))) for k in range(Nn)]\n",
" IN =0\n",
" Ds = [0.1]*(N1-1)+[.1]*N2\n",
"\n",
" Ls = [20]*Np\n",
" print Ls\n",
" Ns = [int(max(Ls))]*Np\n",
" #Ns = [int(Ls[j]) for j in range(Np)]\n",
" Mrs = [0.007]*Np\n",
" print Mrs[0]\n",
" h0s = [0.000]*Np\n",
" q0s = [0.00]*Np\n",
" T = 10.\n",
" Mi = 10\n",
" Nt = 2*N+3\n",
" Tmax = Nt*T\n",
"\n",
" a = np.random.normal(150,50)\n",
" dx = [Ls[i]/Ns[i] for i in range(Np)] \n",
"\n",
" tol = Ds[0]\n",
" print nodeTypes\n",
" fn = '../indata/randomwithN%d'%N\n",
" jt = nodeTypes\n",
" \n",
" xs = np.random.rand(Nn)\n",
" ys = np.random.rand(Nn)\n",
" #reflect everything\n",
" bt = [1]*Nn\n",
" r = [1]*Nn\n",
" r[IN]=0\n",
" bv = [0.]*Nn\n",
" bv[IN]=0.0087\n",
" elevs = [0]*Nn\n",
" elevs[IN] +=.1*Ls[0]\n",
" #elevs = [e+np.random.normal(0,elevs[0]/8.) for e in elevs]\n",
" print elevs\n",
" print a\n",
" M = int(T*a/(max(dx)*.8))\n",
"\n",
"\n",
" (fi, fc) = writePipes(fn,conn2, xs,ys, Ns, Ls, Mrs, Ds, jt, bt, bv, r, h0s, q0s, T, M, a, elevs)\n",
" arrive = np.zeros((Np,Ns[0]))\n",
" maxH = np.zeros(Np)\n",
" Vsys = np.pi*Ds[0]**2/4.*sum(Ls)\n",
" print \"system volume is %f gal \"%(Vsys*m32gal)\n",
" Hs =np.zeros((M/Mi*Nt,sum(Ns)))\n",
" n0=PyNetwork(fi,fc,1)\n",
" \n",
" print n0.Ns\n",
"\n",
" dt = T/float(M)\n",
" #Q0 = 0.0087*np.ones(M+1)\n",
" #n0.setbVal(0,Q0)\n",
" V0 = n0.getTotalVolume()\n",
" Vin = 0\n",
" Ttot = 0\n",
" count = 0\n",
" t0 = time.clock()\n",
" dH = []\n",
" while count<Nt and Vin<Vsys:\n",
" n0.runForwardProblem(dt)\n",
" Vin = n0.getTotalVolume()-V0\n",
" for k in range(0,Np):\n",
" for K in range(0,Ns[k]):\n",
" Ht = n0.pressureTimeSeries(k,K)#this function returns H in cell K of pipe k, at each time step\n",
" #Hs[count*M/Mi:(count+1)*(M/Mi),K+Ns[0]*k] =Ht[1::Mi] \n",
" maxH[k] = max(max(Ht),maxH[k])\n",
" where = find(Ht>tol)\n",
" if len(where)>0 and arrive[k,K]==0.:\n",
" arrive[k,K] = Ttot+dt*(where[0])\n",
" dH.append(mean([n0.getAveGradH(i) for i in range(M+1)]))\n",
" count+=1\n",
" Ttot +=T\n",
" print \"T = %f, Volume = %f\"%(Ttot, m32gal*Vin)\n",
" n0.reset()\n",
" tf = time.clock()\n",
" print \"inflow volume is %f gallons\"%(Vin*m32gal)\n",
" print \"simulation time is %f s\"%Ttot\n",
" print \"wall clock time is %f s\"%(tf-t0)\n",
" print dH\n",
" print maxH\n",
" print \"max arrival time is %f\"%(arrive.max())\n",
" data = {}\n",
" data['conn']=conn2\n",
" data['a'] = a\n",
" data['max_arrive_t'] = [max(arrive[k,:]) for k in range(Np)]\n",
" data['min_arrive_t'] = [min(arrive[k,:]) for k in range(Np)]\n",
" data['last_arrive_t'] = arrive.max()\n",
" data['nodeTypes'] = nodeTypes\n",
" data['mean_dH'] = mean(dH)\n",
" data['max_H'] = maxH\n",
" data['Vin'] = Vin\n",
"\n",
" Summary.append(data)\n",
" \n",
"\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"max(T_a) mean(max(H)) mean(dH) a\n",
"317.33 69.286131 10.645587 198.87 \n",
"299.22 40.892923 19.186011 146.51 \n",
"309.61 31.562048 9.434894 187.91 \n",
"328.22 16.583420 3.866504 76.74 \n",
"317.21 29.383838 5.594357 127.24 \n",
"292.67 20.191568 6.685266 145.27 \n",
"326.88 15.507708 3.927888 73.72 \n",
"318.02 43.421884 10.330287 208.28 \n",
"293.83 16.912818 4.971174 120.27 \n",
"307.33 38.178464 9.343846 187.00 \n",
"315.49 87.719792 11.843313 224.39 \n",
"302.30 134.974682 12.140594 219.65 \n",
"291.99 26.631282 10.284195 207.70 \n",
"309.61 22.815363 7.203351 151.28 \n",
"318.13 35.541200 5.814535 140.39 \n",
"275.09 17.439249 5.673632 124.84 \n",
"318.65 24.229696 5.528470 124.65 \n",
"318.01 33.983803 6.818655 137.07 \n",
"304.62 64.949796 8.433802 150.76 \n",
"317.71 39.441894 10.690326 223.76 \n",
"279.55 14.241251 3.784583 88.71 \n",
"299.65 19.331889 4.846522 97.85 \n",
"316.35 79.227072 11.472391 212.90 \n",
"309.99 25.703759 6.831252 164.99 \n",
"304.52 58.394106 7.237620 142.70 \n",
"290.92 14.519326 6.783982 145.98 \n",
"319.35 46.111447 11.675578 222.35 \n",
"302.91 57.888949 7.329899 142.01 \n",
"302.64 135.077435 10.062016 220.63 \n",
"316.83 63.326308 9.113764 189.91 \n",
"316.52 70.612275 7.958335 200.80 \n",
"319.26 21.906725 6.037585 135.99 \n",
"292.25 15.644977 6.284961 141.56 \n",
"293.54 24.508880 9.984762 194.75 \n",
"319.10 36.739256 11.922071 233.17 \n",
"309.92 21.376251 7.400276 102.57 \n",
"293.64 17.434812 6.738442 148.31 \n",
"316.57 61.430816 9.057648 186.91 \n",
"299.66 46.059184 16.910607 155.64 \n",
"319.68 14.581966 4.039044 85.23 \n",
"319.18 18.902681 5.374141 112.87 \n",
"319.99 23.866163 5.937633 138.49 \n",
"319.98 19.964059 5.559952 126.06 \n",
"262.32 32.728649 33.627121 129.64 \n",
"318.34 21.123809 5.626571 130.99 \n",
"316.97 25.002871 7.674405 148.95 \n",
"318.32 6.838264 3.267326 36.66 \n",
"317.98 23.189258 7.369719 140.61 \n",
"317.30 53.285605 8.659463 173.56 \n",
"308.99 35.635981 8.885078 202.18 \n",
"265.93 28.879573 30.911040 117.84 \n",
"299.92 29.117388 12.836128 121.03 \n",
"317.16 70.123671 9.802276 200.05 \n",
"305.06 48.188677 7.472153 140.99 \n",
"319.25 19.550080 7.489030 139.06 \n",
"319.41 29.519690 8.353360 172.15 \n",
"316.28 119.064590 14.616576 262.76 \n",
"316.24 55.229927 8.324327 176.94 \n",
"325.49 19.520954 4.285911 84.75 \n",
"316.53 56.781064 7.824682 179.46 \n",
"319.85 33.511242 8.655325 180.11 \n",
"318.93 49.270558 14.239521 255.45 \n",
"319.91 24.097583 6.232254 140.00 \n",
"319.40 23.516882 7.177871 140.98 \n",
"303.51 117.046199 10.909780 204.85 \n",
"318.26 20.134170 6.969755 143.58 \n",
"290.79 16.185704 8.105645 163.13 \n",
"318.78 28.917508 9.245752 189.60 \n",
"301.00 30.644670 8.371313 185.06 \n",
"317.74 39.683220 8.008150 148.75 \n",
"319.24 18.454606 4.851206 110.95 \n",
"319.11 25.739476 7.030201 144.40 \n",
"309.74 40.861454 11.690812 205.77 \n",
"318.76 22.244134 5.469387 127.56 \n",
"329.02 12.531065 3.994786 63.18 \n",
"298.96 53.825322 23.285322 186.23 \n",
"302.55 171.695575 15.059180 248.73 \n",
"317.45 51.460882 8.529576 170.43 \n",
"303.48 141.502437 12.663419 226.14 \n",
"293.11 17.477424 7.859457 161.21 \n",
"309.78 10.530604 3.616613 61.88 \n",
"316.66 39.765888 6.494701 148.86 \n",
"316.72 67.927738 9.777141 196.91 \n",
"317.12 27.616138 5.377509 123.09 \n",
"297.60 76.767728 36.995527 204.39 \n",
"319.57 18.933008 4.498950 99.73 \n",
"298.46 42.080251 18.962806 147.28 \n",
"309.86 34.833138 9.305257 168.05 \n",
"307.52 21.025280 4.820084 86.83 \n",
"329.61 6.765508 3.075217 39.21 \n",
"317.54 38.413617 7.133872 146.21 \n",
"319.91 18.724966 5.318835 111.96 \n",
"317.40 53.588183 9.053047 174.06 \n",
"318.88 23.131544 7.131204 149.70 \n",
"296.60 16.261862 5.515505 128.82 \n",
"316.51 35.029795 12.552348 220.40 \n",
"319.37 24.098098 7.267115 154.40 \n",
"291.35 28.822559 9.228495 203.71 \n",
"319.47 16.617159 3.996621 90.16 \n",
"294.56 27.024560 9.526704 207.52 \n"
]
}
],
"source": [
"\n",
"print \"max(T_a) mean(max(H)) mean(dH) a\"\n",
"for k in range(len(Summary)):\n",
" print\"%3.2f %2.6f %3.6f %.2f \"%(Summary[k]['last_arrive_t'],\\\n",
" mean(Summary[k]['max_H']),Summary[k]['mean_dH'],Summary[k]['a'])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
},
{
"data": {
"image/png": 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etTngOvyWetzUE7Nf25RUVJC7TgjxdluWX0ZqeEoIoQQwEcDjAO5RqtSK0pL3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\n4rUgAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109cab990>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"which = 0\n",
"conns = Summary[which]['conn']\n",
"import networkx as nx\n",
"from networkx import graphviz_layout\n",
"G=nx.Graph()\n",
"Dpos = {}\n",
"for k in range(len(Summary[which]['nodeTypes'])-1):\n",
" G.add_edge('%d'%conns[k,0],'%d'%conns[k,1])\n",
"pos=nx.spring_layout(G) # positions for all nodes\n",
"pos=nx.graphviz_layout(G,root=0)\n",
"# nodes\n",
"nx.draw_networkx_nodes(G,pos,node_size=300,node_color = '#A0CBE2', iter=100)\n",
"# edges\n",
"nx.draw_networkx_edges(G,pos,width=4,edge_cmap=plt.cm.coolwarm)\n",
"nx.draw_networkx_edges(G,pos,width=4.0,alpha=0.3)\n",
"# labels\n",
"nx.draw_networkx_labels(G,pos,font_size=8,font_family='sans-serif')\n",
"plt.axis('off')\n",
"print Summary[0]['nodeTypes'].count(3)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#generator = 'wilson'\n",
"name = \"output_data/random_networks/summary_N%d_samples%d_%s_random_wavespeed\"%(N,Nsamples,generator)\n",
"#name = \"output_data/random_networks/summary_N%d_samples%d_%s\"%(N,Nsamples,generator)\n",
"f1 = open(name+'.pkl','wb')\n",
"pickle.dump(Summary,f1)\n",
"f1.close()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(name+'.txt','w') as f2:\n",
" f2.write(\"max(T_a) mean(max(H)) mean(dH) %junction1s %junction2s %junction3s\\n\")\n",
" for k in range(len(Summary)):\n",
" c = [Summary[k]['nodeTypes'].count(j) for j in range(1,4)]\n",
" f2.write(\"%3.2f %2.6f %3.6f %.2f %.2f %.2f\\n\"%(Summary[k]['last_arrive_t'],\\\n",
" mean(Summary[k]['max_H']),Summary[k]['mean_dH'],c[0]/float(N), c[1]/float(N),c[2]/float(N)))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"output_data/random_networks/summary_N20_samples100_spine_variable_spurs\n"
]
}
],
"source": [
"name = \"output_data/random_networks/summary_N%d_samples%d_%s_variable_spurs\"%(N,Nsamples,generator)\n",
"print name"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 1, 3, 1, 2, 3, 1, 2, 1, 2, 3, 3, 3, 1, 3, 2, 1, 3, 1]\n"
]
},
{
"data": {
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ublixYgUEQcDJkycBAKGhocjKymrT/j179kCtVuPgwYPo06cPE86YmBg2Z37o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\nAa84c7SjiAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1108c0050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G = nx.Graph(A2)\n",
"#pos=nx.spectral_layout(G) # positions for all nodes\n",
"# nodes\n",
"nx.draw_circular(G,node_size=300,node_color = '#A0CBE2')\n",
"pos=nx.circular_layout(G) # positions for all nodes\n",
"# nodes\n",
"nx.draw_networkx_nodes(G,pos,node_size=300,node_color = '#A0CBE2', iter=300)\n",
"# edges\n",
"nx.draw_networkx_edges(G,pos,width=1,edge_cmap=plt.cm.coolwarm)\n",
"# labels\n",
"nx.draw_networkx_labels(G,pos,font_size=8,font_family='sans-serif')\n",
"plt.axis('off')\n",
"print Summary[0]['nodeTypes']"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'mean_dH': 58.896549754611179, 'Vin': 2.199450277223604, 'max_H': array([ 113.01333586, 11.38314344, 11.61568269, 11.80709035,\n",
" 12.06557722, 12.20831332, 12.3132504 , 12.31435275,\n",
" 12.39691881, 12.40120213, 12.36643296, 12.33271988,\n",
" 12.37653675, 12.42818228]), 'last_arrive_t': 190.648, 'conn': array([[ 0, 1],\n",
" [ 1, 2],\n",
" [ 2, 3],\n",
" [ 3, 4],\n",
" [ 4, 5],\n",
" [ 5, 6],\n",
" [ 6, 7],\n",
" [ 7, 8],\n",
" [ 8, 9],\n",
" [ 9, 10],\n",
" [10, 11],\n",
" [11, 12],\n",
" [12, 13],\n",
" [13, 14]]), 'max_arrive_t': [190.44, 42.335999999999999, 35.287999999999997, 46.68, 59.456000000000003, 70.656000000000006, 84.584000000000003, 97.951999999999998, 113.84, 131.40799999999999, 147.816, 163.82400000000001, 183.304, 190.648], 'nodeTypes': [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1], 'min_arrive_t': [44.280000000000001, 26.192, 25.84, 35.335999999999999, 46.712000000000003, 59.496000000000002, 70.671999999999997, 84.608000000000004, 101.456, 113.864, 131.42400000000001, 147.84, 163.83199999999999, 183.33600000000001]}\n"
]
}
],
"source": [
"print Summary[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
GoogleCloudPlatform/ai-notebooks-extended | dataproc-hub-example/build/infrastructure-builder/mig/files/gcs_working_folder/examples/Environment Checks/03 - spark-bigquery-connector.ipynb | 2 | 5580 | {"cells": [{"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": "# A Spark Session is how we interact with Spark SQL to create Dataframes\nfrom pyspark.sql import SparkSession\n\n# This will help catch some PySpark errors\nfrom py4j.protocol import Py4JJavaError\n\n# Create a SparkSession under the name \"bike\". Viewable via the Spark UI\nspark = SparkSession.builder.appName(\"bike\").getOrCreate()"}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": "TABLE_HIRE = \"bigquery-public-data:london_bicycles.cycle_hire\"\nTABLE_STATION = \"bigquery-public-data:london_bicycles.cycle_stations\""}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": "hire_data = spark.read.format(\"bigquery\").option(\n \"table\", TABLE_STATION).load()"}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/plain": "DataFrame[id: bigint, installed: boolean, latitude: double, locked: string, longitude: double, name: string, bikes_count: bigint, docks_count: bigint, nbEmptyDocks: bigint, temporary: boolean, terminal_name: string, install_date: date, removal_date: date]"}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": "hire_data"}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>installed</th>\n <th>latitude</th>\n <th>locked</th>\n <th>longitude</th>\n <th>name</th>\n <th>bikes_count</th>\n <th>docks_count</th>\n <th>nbEmptyDocks</th>\n <th>temporary</th>\n <th>terminal_name</th>\n <th>install_date</th>\n <th>removal_date</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>174</td>\n <td>True</td>\n <td>51.512529</td>\n <td>false</td>\n <td>-0.115163</td>\n <td>Strand, Strand</td>\n <td>35</td>\n <td>36</td>\n <td>0</td>\n <td>False</td>\n <td>1100</td>\n <td>2010-07-16</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>213</td>\n <td>True</td>\n <td>51.502740</td>\n <td>false</td>\n <td>-0.149569</td>\n <td>Wellington Arch, Hyde Park</td>\n <td>36</td>\n <td>36</td>\n <td>0</td>\n <td>False</td>\n <td>1109</td>\n <td>2010-07-19</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>532</td>\n <td>True</td>\n <td>51.503570</td>\n <td>false</td>\n <td>-0.020068</td>\n <td>Jubilee Plaza, Canary Wharf</td>\n <td>62</td>\n <td>63</td>\n <td>1</td>\n <td>False</td>\n <td>200163</td>\n <td>2012-02-10</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>570</td>\n <td>True</td>\n <td>51.503083</td>\n <td>false</td>\n <td>-0.017676</td>\n <td>Upper Bank Street, Canary Wharf</td>\n <td>33</td>\n <td>36</td>\n <td>1</td>\n <td>False</td>\n <td>200099</td>\n <td>2012-03-03</td>\n <td>None</td>\n </tr>\n <tr>\n <th>4</th>\n <td>608</td>\n <td>True</td>\n <td>51.491093</td>\n <td>false</td>\n <td>-0.216493</td>\n <td>Colet Gardens, Hammersmith</td>\n <td>29</td>\n <td>30</td>\n <td>1</td>\n <td>False</td>\n <td>200224</td>\n <td>None</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n</div>", "text/plain": " id installed latitude locked longitude \\\n0 174 True 51.512529 false -0.115163 \n1 213 True 51.502740 false -0.149569 \n2 532 True 51.503570 false -0.020068 \n3 570 True 51.503083 false -0.017676 \n4 608 True 51.491093 false -0.216493 \n\n name bikes_count docks_count nbEmptyDocks \\\n0 Strand, Strand 35 36 0 \n1 Wellington Arch, Hyde Park 36 36 0 \n2 Jubilee Plaza, Canary Wharf 62 63 1 \n3 Upper Bank Street, Canary Wharf 33 36 1 \n4 Colet Gardens, Hammersmith 29 30 1 \n\n temporary terminal_name install_date removal_date \n0 False 1100 2010-07-16 None \n1 False 1109 2010-07-19 None \n2 False 200163 2012-02-10 None \n3 False 200099 2012-03-03 None \n4 False 200224 None None "}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": "hire_data.limit(5).toPandas()"}, {"cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": ""}], "metadata": {"kernelspec": {"display_name": "PySpark", "language": "python", "name": "pyspark"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10"}}, "nbformat": 4, "nbformat_minor": 4} | apache-2.0 |
pdamodaran/yellowbrick | examples/ndanielsen/ScatterViz Example (Iris Dataset).ipynb | 1 | 28491 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/var/pyenv/versions/3.5.2/envs/yb-dev/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"import yellowbrick as yb \n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"plt.rcParams['figure.figsize'] = (12, 8)\n",
"\n",
"from yellowbrick.features.rankd import Rank2D\n",
"from yellowbrick.features.radviz import RadViz\n",
"from yellowbrick.features.pcoords import ParallelCoordinates\n",
"from yellowbrick.features.scatter import ScatterViz"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
"col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
"iris = pd.read_csv(url, header=None, names=col_names)\n",
"iris['species_num'] = iris.species.map({'Iris-setosa':0, 'Iris-versicolor':1, 'Iris-virginica':2})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"all_features = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species_num']\n",
"features = ['petal_length', 'sepal_width']\n",
"classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']\n",
"\n",
"# Extract the numpy arrays from the data frame\n",
"X = iris[all_features]\n",
"y = iris.species_num.as_matrix()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from yellowbrick.style import palettes\n",
"from matplotlib.colors import Colormap"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"colors = palettes.PALETTES['pastel']"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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+GmNcD/wW2C/FOCRJkqRBS2WpRgjhk8DLMcZfhhDO6rF7IrCq6PVqYFKSdltb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1113b3320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualizer = ScatterViz(classes=classes, features=features, color=colors)\n",
"visualizer.fit(X, y) # Fit the data to the visualizer\n",
"visualizer.poof() # Draw/show/poof the data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"['#cbd5e8', '#b3e2cd', '#fdcdac', '#f4cae4', '#fff2ae', '#e6f5c9']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"visualizer.colormap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
schlegelp/pymaid | docs/source/morph_analysis.ipynb | 1 | 279745 | {
"cells": [
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"Morphological Analyses\n",
"**********************\n",
"\n",
"This section should give you an impression of how to access and compute morphological properties of neurons. You will note that most of this uses `navis <https://navis.readthedocs.io/en/latest/>`_ functions. ``CatmaidNeuron/Lists`` are fully compatible with ``navis`` and you should most definitely check out tutorials on its docs. \n",
"\n",
"Basic properties\n",
"================\n",
"Many basic parameters are readily accessible through attributes of :class:`~pymaid.CatmaidNeuron` or :class:`~pymaid.CatmaidNeuronList`:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO : Global CATMAID instance set. (pymaid.fetch)\n",
"INFO : Looking for Annotation(s): glomerulus DA1 right excitatory (pymaid.fetch)\n",
"INFO : Found 8 skeletons with matching annotation(s) (pymaid.fetch)\n"
]
},
{
"data": {
"text/plain": [
"array([1591.51982146, 1182.10245803, 1035.09925403, 1113.15667872,\n",
" 1215.92059396, 1182.6479815 , 1059.72905251, 1109.88615456])"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pymaid\n",
"import matplotlib.pyplot as plt\n",
"\n",
"rm = pymaid.CatmaidInstance('server_url', 'api_token', 'http_user', 'http_password')\n",
"\n",
"nl = pymaid.get_neurons('annotation:glomerulus DA1 right excitatory')\n",
"\n",
"# Access single attribute: e.g. cable lengths [um]\n",
"nl.cable_length"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>neuron_name</th>\n",
" <th>skeleton_id</th>\n",
" <th>n_nodes</th>\n",
" <th>n_connectors</th>\n",
" <th>n_branch_nodes</th>\n",
" <th>n_end_nodes</th>\n",
" <th>open_ends</th>\n",
" <th>cable_length</th>\n",
" <th>review_status</th>\n",
" <th>soma</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>PN glomerulus DA1 27296 BH</td>\n",
" <td>27295</td>\n",
" <td>9969</td>\n",
" <td>463</td>\n",
" <td>211</td>\n",
" <td>218</td>\n",
" <td>58</td>\n",
" <td>1590.676589</td>\n",
" <td>NA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>PN glomerulus DA1 57312 LK</td>\n",
" <td>57311</td>\n",
" <td>4874</td>\n",
" <td>421</td>\n",
" <td>156</td>\n",
" <td>163</td>\n",
" <td>105</td>\n",
" <td>1180.597489</td>\n",
" <td>NA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>PN glomerulus DA1 57324 LK JSL</td>\n",
" <td>57323</td>\n",
" <td>4585</td>\n",
" <td>434</td>\n",
" <td>120</td>\n",
" <td>127</td>\n",
" <td>59</td>\n",
" <td>1035.076857</td>\n",
" <td>NA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>PN glomerulus DA1 57354 GA</td>\n",
" <td>57353</td>\n",
" <td>4895</td>\n",
" <td>324</td>\n",
" <td>90</td>\n",
" <td>95</td>\n",
" <td>52</td>\n",
" <td>1113.156676</td>\n",
" <td>NA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>PN glomerulus DA1 57382 ML</td>\n",
" <td>57381</td>\n",
" <td>7727</td>\n",
" <td>357</td>\n",
" <td>153</td>\n",
" <td>162</td>\n",
" <td>71</td>\n",
" <td>1215.920594</td>\n",
" <td>NA</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" neuron_name skeleton_id n_nodes n_connectors \\\n",
"0 PN glomerulus DA1 27296 BH 27295 9969 463 \n",
"1 PN glomerulus DA1 57312 LK 57311 4874 421 \n",
"2 PN glomerulus DA1 57324 LK JSL 57323 4585 434 \n",
"3 PN glomerulus DA1 57354 GA 57353 4895 324 \n",
"4 PN glomerulus DA1 57382 ML 57381 7727 357 \n",
"\n",
" n_branch_nodes n_end_nodes open_ends cable_length review_status soma \n",
"0 211 218 58 1590.676589 NA True \n",
"1 156 163 105 1180.597489 NA True \n",
"2 120 127 59 1035.076857 NA True \n",
"3 90 95 52 1113.156676 NA True \n",
"4 153 162 71 1215.920594 NA True "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# .. or get a full summary as pandas DataFrame\n",
"df = nl.summary()\n",
"df.head()"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"Rerooting, resampling, simplifying\n",
"==================================\n",
"``navis`` lets you perform (virtual) surgery on neurons. Many of the base functions are also accessible directly via :class:`~pymaid.CatmaidNeuron` and :class:`~pymaid.CatmaidNeuronList` methods. E.g. :func:`pymaid.CatmaidNeuronList.resample` is simply calling :func:`navis:navis.resample_neuron`.\n",
"\n",
"Examples continue from above code."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3005291"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reroot a single neuron to its soma\n",
"nl[0].soma"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# .soma returns the treenode ID of the soma (if existing) and can be used to reroot\n",
"nl[0].reroot(nl[0].soma, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# You can also perform this operation on the entire CatmaidNeuronList\n",
"nl.reroot(nl.soma, inplace=True)"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"Above code is equivalent to:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import navis\n",
"navis.reroot_neuron(nl, nl.soma, inplace=True)"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"We will be using the shortcut (e.g. ``nl.reroot``) and the actual function (e.g. ``navis.reroot_neuron``) interchangeably throughout the examples. This is not meant to be confusing but to be illustrative.\n",
"\n",
"If you work with large lists you may want to down-/resample before e.g. plotting."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Downsample by \"skipping\" N nodes (here: 10) \n",
"nl_downsampled = navis.downsample(nl, 10, inplace=False)\n",
"# More elaborate: resample to given resolution in nanometers (here: 1000nm = 1um)\n",
"nl_resampled = navis.resample_neuron(nl, 1000, inplace=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Plot an original neuron first\n",
"fig, ax = navis.plot2d(nl[0], color='red')\n",
"\n",
"# Shift the downsampled and resampled versions slightly and plot\n",
"n_ds = nl_downsampled[0].copy()\n",
"n_rs = nl_resampled[0].copy()\n",
"\n",
"n_ds.nodes.x += 10000\n",
"n_rs.nodes.x += 20000\n",
"\n",
"fig, ax = n_ds.plot2d(color='blue', ax=ax)\n",
"fig, ax = n_rs.plot2d(color='green', ax=ax)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"Looking closely, you will find that the downsampled (blue) and resampled (green) neurons are smoother. \n",
"\n",
".. important::\n",
" Note how we used ``inplace`` parameter when using the neuronlist's downsampling method? \n",
" This parameter works like in pandas: if ``True`` will perform the action on the neuronlist, if\n",
" ``False`` will operate on and return a copy and leave the original neuronlist unchanged."
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"Cutting, pruning, pasting\n",
"=========================\n",
"\n",
"Examples continue from above code."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Cut a neuron in two using either a node ID or (in this case) a node tag\n",
"distal, proximal = navis.cut_neuron(nl[0], cut_node='SCHLEGEL_LH')\n",
"\n",
"# Plot neuron fragments\n",
"fig, ax = navis.plot2d(distal, color='red', method='2d', connectors=False)\n",
"fig, ax = navis.plot2d(proximal, color='blue', method='2d', connectors=False, ax=ax)\n",
"\n",
"# Annotate cut point\n",
"cut_coords = distal.nodes.set_index('node_id').loc[ distal.root, ['x','y'] ].values[0]\n",
"ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), \n",
" xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',\n",
" arrowprops=dict(facecolor='black', shrink=0.01, width=1),\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"Instead of cutting a neuron in two, we can also just prune bits off a neuron objects:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n = nl[0].prune_distal_to('SCHLEGEL_LH', inplace=False)\n",
"\n",
"# Plot original neuron in black\n",
"fig, ax = nl[0].plot2d(color='black', method='2d', connectors=False, linestyle=(0, (5, 10)))\n",
"\n",
"# Plot pruned neuron in red\n",
"fig, ax = n.plot2d(color='red', method='2d', connectors=False, ax=ax)\n",
"\n",
"# Annotate cut point\n",
"ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), \n",
" xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',\n",
" arrowprops=dict(facecolor='black', shrink=0.01, width=1),\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# To undo, simply reload the neuron from server\n",
"nl[0].reload()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# These operations can also be performed on a collection of neurons\n",
"n = nl[:5].prune_distal_to('SCHLEGEL_LH', inplace=False)\n",
"\n",
"# Plot original neurons in black\n",
"fig, ax = nl[:5].plot2d(color='black', method='2d', connectors=False, linestyle=(0, (5, 10)))\n",
"\n",
"# Plot pruned neurons in red\n",
"fig, ax = n.plot2d(color='red', method='2d', connectors=False, ax=ax)\n",
"\n",
"# Annotate cut point\n",
"ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), \n",
" xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',\n",
" arrowprops=dict(facecolor='black', shrink=0.01, width=1),\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Again, let's undo\n",
"nl.reload()\n",
"nl.reroot(nl.soma)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Something more sophisticated: pruning by strahler index\n",
"n = nl[:5].prune_by_strahler( to_prune = [1,2,3], inplace=False )\n",
"\n",
"# Plot original neurons in black\n",
"fig, ax = nl[:5].plot2d(color='black', method='2d', connectors=False)\n",
"\n",
"# Plot pruned neurons in red\n",
"fig, ax = n.plot2d(color='red', method='2d', connectors=False, ax=ax, linewidth=1.5)\n",
"\n",
"# Annotate cut point\n",
"ax.annotate('cut point', xy=(cut_coords[0], -cut_coords[1]), \n",
" xytext=(cut_coords[0], -cut_coords[1]-20000), va='center', ha='center',\n",
" arrowprops=dict(facecolor='black', shrink=0.01, width=1),\n",
" )\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"We can also intersect neurons with CATMAID volumes:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Again, let's undo\n",
"nl.reload()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# Get a volume\n",
"lh = pymaid.get_volume('LH_R')\n",
"\n",
"# Prune by volume\n",
"nl_lh = navis.in_volume(nl, lh, inplace=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Set color of volume\n",
"lh['color'] = (250,250,250,.2)\n",
"\n",
"# Plot neurons that have some cable left and the volume\n",
"fig, ax = navis.plot2d([nl_lh[nl_lh.cable_length > 10], lh], \n",
" method='3d', \n",
" connectors=False, \n",
" linewidth=2)\n",
"\n",
"ax.dist=6\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {
"raw_mimetype": "text/restructuredtext"
},
"source": [
"And now go check out `navis <https://navis.readthedocs.io/en/latest/>`_!"
]
}
],
"metadata": {
"celltoolbar": "Raw Cell Format",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
} | gpl-3.0 |
fdion/stemgraphic | notebooks/Quickstart with text.ipynb | 1 | 126720 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# stemgraphic quickstart with text\n",
"\n",
"Import stem_graphic from stemgraphic.alpha"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from stemgraphic.alpha import stem_graphic"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load words from a text file on disk."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f13e4de04a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stem_graphic('/usr/share/dict/american-english');"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
OliverEvans96/rte_matrix | gen_matrices/Diagonal Dominance.ipynb | 1 | 161020 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from numpy import *\n",
"from matplotlib.pyplot import *\n",
"from scipy import io\n",
"import numpy.polynomial.legendre as leg\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def vsf(th):\n",
" return .5 * np.exp(-th/2) / (1 - np.exp(-np.pi/2))\n",
"\n",
"# Smallest difference between two angles\n",
"def angle_diff(th,thp):\n",
" return np.pi - abs(abs(th - thp) - pi)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Numerically check bound on scattering coefficients"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"# Check to see whether sum of scattering coefs. = 2\n",
"# nth = number of theta values\n",
"# kk = integer index num. of theta to diff other angles wrt.\n",
"def check_scat_sum(nth,kk):\n",
" th_un, ww_un = leg.leggauss(nth)\n",
" th_vals = pi * (th_un + 1)\n",
" weights = pi * ww_un\n",
" \n",
" th = th_vals[kk]\n",
" thp = th_vals[th_vals!=th]\n",
" ww = weights[th_vals!=th]\n",
" \n",
" return sum(vsf(angle_diff(th,thp)) * ww)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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PbJaN0TNsNE8z0MixtfivUA5hZ7uJHt9AR+MWurwk6UCEmp1lIvcU2fr/4oxZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f044c5b22b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure(figsize=[10,6])\n",
"for nth in range(10,160,10):\n",
" res = zeros(nth)\n",
" for kk in range(nth):\n",
" res[kk] = check_scat_sum(nth,kk)\n",
" plot(linspace(0,1,nth),res,label=r'$n_\\theta = {}$'.format(nth))\n",
"legend(bbox_to_anchor=[1.01,1],loc='upper left')\n",
"xlabel(r'$k / n_\\theta$')\n",
"ylabel(r\"Sum of scattering coefficients\")\n",
"title(r\"$\\sum_{l \\neq k} \\beta(|\\theta_k - \\theta_l|) w_l \\\n",
" < \\sum_{l=1}^{n_\\theta} \\beta(|\\theta_k - \\theta_l|) w_l \\\n",
" \\approx \\int_0^{2\\pi}\\beta(|\\theta_k-\\theta'|) \\,d\\theta' = 2$\"+'\\n',fontsize=16)\n",
"tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"\\begin{equation}\n",
" \\sum_{l \\neq k} \\beta(|\\theta_k - \\theta_l|) w_l \\\n",
" < \\sum_{l=1}^{n_\\theta} \\beta(|\\theta_k - \\theta_l|) w_l \\\n",
" \\approx \\int_0^{2\\pi}\\beta(|\\theta_k-\\theta'|) \\,d\\theta' = 2\n",
"\\end{equation}"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"While the coefficients are not exactly 2, they do seem to be bounded above by 2\n",
"Adding in the l=k term to the sum reduces the error significantly, so all sums are much closer to 2 ~5e-2.\n",
"However, 2 is no longer an upper bound then."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Test diagonal dominalizer :P"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Plot diagonal dominance quantity (D - Q) as a function of row number\n",
"# Add quantity mu to D to get D' = D + Q. If D' > Q <=> D' - Q = D + mu - Q > 0 (plot is positive),\n",
"# then diagonal dominance is achieved\n",
"def plot_dd(A,mu):\n",
" # Diagonal dominance quantity, D - Q (pre-shift)\n",
" ddq = zeros(nrows)\n",
" row_list = arange(nrows)\n",
" for k in row_list:\n",
" if k%100 == 0:\n",
" print('k={}'.format(k))\n",
" D = abs(A[k,k])\n",
" Q = abs(A[k,:k]).sum() + abs(A[k,k+1:]).sum()\n",
" ddq[k] = D - Q\n",
" # Post-shift DD quantity, D' - Q\n",
" ddq_p = ddq + mu\n",
" \n",
" figure(figsize=[10,6])\n",
" plot(row_list,ddq,label=\"$D - Q$ (pre)\")\n",
" plot(row_list,ddq_p,label=\"$D' - Q$ (post)\")\n",
" \n",
" print(\"Min (pre) : {:.3e}\".format(min(ddq)))\n",
" print(\"Min (post): {:.3e}\".format(min(ddq_p)))\n",
" \n",
" title('Diagonal dominance plot')\n",
" legend(bbox_to_anchor=(1.01,1),loc='upper left')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Grid parameters\n",
"nx = 20\n",
"ny = 20\n",
"nth = 24\n",
"nrows = nx * ny * nth\n",
"\n",
"# Load matrix to test\n",
"dct = io.loadmat('../mat/ddom_{}x{}x{}_012.mat'.format(nx,ny,nth))\n",
"A = dct['A']\n",
"\n",
"dx = 1/nx\n",
"dy = 1/ny\n",
"\n",
"# IOPS\n",
"a_w = 1\n",
"b_w = 1\n",
"a_k = 5\n",
"b_k = 1\n",
"\n",
"# Maximum number density of individuals \n",
"ind_up = 2\n",
"\n",
"# Max/min abs. coef.\n",
"a_up = a_k * ind_up\n",
"a_dn = a_w\n",
"# Max/min scat. coef.\n",
"b_up = a_k * ind_up\n",
"b_dn = b_w"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# Term to add to create diagonal dominance\n",
"# mu = a_up - b_dn * (2*pi - 1) - 1/dx - 4/dy # (technically correct)\n",
"# mu = a_up - b_dn * (2*pi - 1) - nx - 4*ny # (equivalent for normailized length scale)\n",
"mu = 1/dx + 4/dy + b_up * (2*pi-1) - a_dn"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k=0\n",
"k=100\n",
"k=200\n",
"k=300\n",
"k=400\n",
"k=500\n",
"k=600\n",
"k=700\n",
"k=800\n",
"k=900\n",
"k=1000\n",
"k=1100\n",
"k=1200\n",
"k=1300\n",
"k=1400\n",
"k=1500\n",
"k=1600\n",
"k=1700\n",
"k=1800\n",
"k=1900\n",
"k=2000\n",
"k=2100\n",
"k=2200\n",
"k=2300\n",
"k=2400\n",
"k=2500\n",
"k=2600\n",
"k=2700\n",
"k=2800\n",
"k=2900\n",
"k=3000\n",
"k=3100\n",
"k=3200\n",
"k=3300\n",
"k=3400\n",
"k=3500\n",
"k=3600\n",
"k=3700\n",
"k=3800\n",
"k=3900\n",
"k=4000\n",
"k=4100\n",
"k=4200\n",
"k=4300\n",
"k=4400\n",
"k=4500\n",
"k=4600\n",
"k=4700\n",
"k=4800\n",
"k=4900\n",
"k=5000\n",
"k=5100\n",
"k=5200\n",
"k=5300\n",
"k=5400\n",
"k=5500\n",
"k=5600\n",
"k=5700\n",
"k=5800\n",
"k=5900\n",
"k=6000\n",
"k=6100\n",
"k=6200\n",
"k=6300\n",
"k=6400\n",
"k=6500\n",
"k=6600\n",
"k=6700\n",
"k=6800\n",
"k=6900\n",
"k=7000\n",
"k=7100\n",
"k=7200\n",
"k=7300\n",
"k=7400\n",
"k=7500\n",
"k=7600\n",
"k=7700\n",
"k=7800\n",
"k=7900\n",
"k=8000\n",
"k=8100\n",
"k=8200\n",
"k=8300\n",
"k=8400\n",
"k=8500\n",
"k=8600\n",
"k=8700\n",
"k=8800\n",
"k=8900\n",
"k=9000\n",
"k=9100\n",
"k=9200\n",
"k=9300\n",
"k=9400\n",
"k=9500\n",
"Min (pre) : 1.000e+00\n",
"Min (post): 1.528e+02\n"
]
},
{
"data": {
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0k+uAR/cAoRY+1HhzYoeSRSTBwsmYru9D7kF13bePARfP8N3fLN7flWX3Ap0u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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0418549be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_dd(A,mu)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Try to solve DDOM w/ spsolve & gauss_seidel"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
spacedrabbit/PythonBootcamp | Zip, Enumerate, all() & any().ipynb | 1 | 7348 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zip"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[(1, 4, 10), (2, 5, 12), (3, 6, 15)]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = [1, 2, 3]\n",
"y = [4, 5, 6]\n",
"z = [10, 12, 15, 17]\n",
"\n",
"zip(x, y, z)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"2\n",
"10\n",
"4\n",
"5\n"
]
}
],
"source": [
"a = [1, 2, 3, 4, 5]\n",
"b = [2, 2, 10, 1, 1]\n",
"\n",
"for pair in zip(a,b):\n",
" print max(pair)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2, 2, 10, 4, 5]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# each iteration of a element here, which would be a tuple generated by\n",
"# zip(), gets evaluated with max() in order to return a single, large value. \n",
"map(lambda pair: max(pair), zip(a,b))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def switcharoo(d1, d2):\n",
" dout = {}\n",
" for k1, v2 in zip(d1, d2.itervalues()): #this zip creates the list of tuple ahead of time, and then iterates over it\n",
" dout[k1] = v2\n",
" return dout"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'a': -20, 'b': -10}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic1 = {'a':10, 'b':20}\n",
"dic2 = {'c':-20, 'd':-10}\n",
"\n",
"switcharoo(dic1, dic2)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"'''\n",
"todos:\n",
"1. wtf is sentinel = object() for? is it a common code design pattern \n",
"2. why the * usage in parameter? is there reference/value aka, pointer, passing in python?\n",
"3. wtf is the yield reserved word do\n",
"'''\n",
"def zip(*iterables):\n",
" # zip('ABCD', 'xy') --> Ax By\n",
" sentinel = object()\n",
" iterators = [iter(it) for it in iterables]\n",
" while iterators:\n",
" result = []\n",
" for it in iterators:\n",
" elem = next(it, sentinel)\n",
" if elem is sentinel:\n",
" return\n",
" result.append(elem)\n",
" yield tuple(result)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Enumerate"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"a\n",
"1\n",
"b\n",
"2\n",
"c\n"
]
}
],
"source": [
"l = ['a', 'b', 'c']\n",
"count = 0\n",
"for item in l:\n",
" print count\n",
" print item\n",
" count += 1"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 a\n",
"1 b\n",
"2 c\n"
]
}
],
"source": [
"for count,item in enumerate(l):\n",
" print count, item"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
"Functionally equivalent def for enumerate built-in\n",
"'''\n",
"def enumerate(sequence, start=0):\n",
" n = start\n",
" for elem in sequence:\n",
" yield n, elem\n",
" n += 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `any()` and `all()`"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''\n",
"Very simply: all() checks that all members of a list evalue to True whereas\n",
"any() check that at least 1 evaluates to true\n",
"'''\n",
"\n",
"l_bool = [True, False, True, True]\n",
"any(l_bool)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all(l_bool)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Snooze the alarm, it's too early\n",
"Really wished is was before noon, on a saturday on my vacation\n"
]
}
],
"source": [
"is_weekend = True\n",
"is_vacation = False\n",
"is_before_noon = True\n",
"snooze_alarm_requirements = [is_weekend, is_vacation, is_before_noon]\n",
"\n",
"if any(snooze_alarm_requirements):\n",
" print(\"Snooze the alarm, it's too early\")\n",
"\n",
"if all(snooze_alarm_requirements):\n",
" print(\"Come back later, ain't no body got time for this\")\n",
"else:\n",
" print(\"Really wished is was before noon, on a saturday on my vacation\")"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def all(iterable):\n",
" for element in iterable:\n",
" if not element:\n",
" return False\n",
" return True\n",
"\n",
"def any(iterable):\n",
" for element in iterable:\n",
" if element:\n",
" return True\n",
" return False"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
robertoalotufo/ia898 | deliver/Terceira-Aula.ipynb | 1 | 1588 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Aula 3\n",
"\n",
"\n",
"- [Chess](../master/chess.ipynb) - Programação matricial eficiente\n",
"- [Strides](../master/Array-strides.ipynb) - acesso aos elementos do array (cópia rasa)\n",
"- [Toolbox ia898.src](../src/ia898src-index.ipynb) - Sucessora da toolbox ia636\n",
"- [2.1 - Criação de Imagens Sintéticas](Atividade_2_1.ipynb)\n",
"- [2.2 - NumPy redução de eixo](Atividade_2_2.ipynb)\n",
"- [2.3 - Histograma e Estatística de imagem](Atividade_2_3.ipynb)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"toc": {
"colors": {
"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
},
"moveMenuLeft": true,
"nav_menu": {
"height": "30px",
"width": "252px"
},
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
kadircet/CENG | 783/HW2/task2_layers.ipynb | 1 | 28143 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Modular neural nets\n",
"In the previous HW, we computed the loss and gradient for a two-layer neural network in a single monolithic function. This isn't very difficult for a small two-layer network, but would be tedious and error-prone for larger networks. Ideally we want to build networks using a more modular design so that we can snap together different types of layers and loss functions in order to quickly experiment with different architectures.\n",
"\n",
"In this exercise we will implement this approach, and develop a number of different layer types in isolation that can then be easily plugged together. For each layer we will implement `forward` and `backward` functions. The `forward` function will receive data, weights, and other parameters, and will return both an output and a `cache` object that stores data needed for the backward pass. The `backward` function will recieve upstream derivatives and the cache object, and will return gradients with respect to the data and all of the weights. This will allow us to write code that looks like this:\n",
"\n",
"```python\n",
"def two_layer_net(X, W1, b1, W2, b2, reg):\n",
" # Forward pass; compute scores\n",
" s1, fc1_cache = affine_forward(X, W1, b1)\n",
" a1, relu_cache = relu_forward(s1)\n",
" scores, fc2_cache = affine_forward(a1, W2, b2)\n",
" \n",
" # Loss functions return data loss and gradients on scores\n",
" data_loss, dscores = svm_loss(scores, y)\n",
" \n",
" # Compute backward pass\n",
" da1, dW2, db2 = affine_backward(dscores, fc2_cache)\n",
" ds1 = relu_backward(da1, relu_cache)\n",
" dX, dW1, db1 = affine_backward(ds1, fc1_cache)\n",
" \n",
" # A real network would add regularization here\n",
" \n",
" # Return loss and gradients\n",
" return loss, dW1, db1, dW2, db2\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# As usual, a bit of setup\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
"from cs231n.layers import *\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# for auto-reloading external modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"def rel_error(x, y):\n",
" \"\"\" returns relative error \"\"\"\n",
" return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Affine layer: forward\n",
"Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
"\n",
"Once you are done we will test your implementation by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing affine_forward function:\n",
"difference: 9.76985004799e-10\n"
]
}
],
"source": [
"# Test the affine_forward function\n",
"\n",
"num_inputs = 2\n",
"input_shape = (4, 5, 6)\n",
"output_dim = 3\n",
"\n",
"input_size = num_inputs * np.prod(input_shape)\n",
"weight_size = output_dim * np.prod(input_shape)\n",
"\n",
"x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
"w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
"b = np.linspace(-0.3, 0.1, num=output_dim)\n",
"\n",
"out, _ = affine_forward(x, w, b)\n",
"correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297],\n",
" [ 3.25553199, 3.5141327, 3.77273342]])\n",
"\n",
"# Compare your output with ours. The error should be around 1e-9.\n",
"print 'Testing affine_forward function:'\n",
"print 'difference: ', rel_error(out, correct_out)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Affine layer: backward\n",
"\n",
"Now implement the `affine_backward` function in the same file. You can test your implementation using numeric gradient checking."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing affine_backward function:\n",
"dx error: 4.40621925325e-10\n",
"dw error: 8.74703506701e-11\n",
"db error: 7.78712966461e-12\n"
]
}
],
"source": [
"# Test the affine_backward function\n",
"\n",
"x = np.random.randn(10, 2, 3)\n",
"w = np.random.randn(6, 5)\n",
"b = np.random.randn(5)\n",
"dout = np.random.randn(10, 5)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
"\n",
"_, cache = affine_forward(x, w, b)\n",
"dx, dw, db = affine_backward(dout, cache)\n",
"\n",
"# The error should be less than 1e-10\n",
"print 'Testing affine_backward function:'\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"print 'dw error: ', rel_error(dw_num, dw)\n",
"print 'db error: ', rel_error(db_num, db)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# ReLU layer: forward\n",
"\n",
"Implement the `relu_forward` function and test your implementation by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing relu_forward function:\n",
"difference: 4.99999979802e-08\n"
]
}
],
"source": [
"# Test the relu_forward function\n",
"\n",
"x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
"\n",
"out, _ = relu_forward(x)\n",
"correct_out = np.array([[ 0., 0., 0., 0., ],\n",
" [ 0., 0., 0.04545455, 0.13636364,],\n",
" [ 0.22727273, 0.31818182, 0.40909091, 0.5, ]])\n",
"\n",
"# Compare your output with ours. The error should be around 1e-8\n",
"print 'Testing relu_forward function:'\n",
"print 'difference: ', rel_error(out, correct_out)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# ReLU layer: backward\n",
"\n",
"Implement the `relu_backward` function and test your implementation using numeric gradient checking:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing relu_backward function:\n",
"dx error: 3.275625139e-12\n"
]
}
],
"source": [
"x = np.random.randn(10, 10)\n",
"dout = np.random.randn(*x.shape)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
"\n",
"_, cache = relu_forward(x)\n",
"dx = relu_backward(dout, cache)\n",
"\n",
"# The error should be around 1e-12\n",
"print 'Testing relu_backward function:'\n",
"print 'dx error: ', rel_error(dx_num, dx)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Loss layers: Softmax and SVM\n",
"\n",
"You implemented these loss functions in the last assignment, so we'll give them to you for free here. It's still a good idea to test them to make sure they work correctly."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing svm_loss:\n",
"loss: 8.99979905879\n",
"dx error: 3.0387355051e-09\n",
"\n",
"Testing softmax_loss:\n",
"loss: 2.30256541897\n",
"dx error: 2.26197733833e-06\n"
]
}
],
"source": [
"num_classes, num_inputs = 10, 50\n",
"x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
"y = np.random.randint(num_classes, size=num_inputs)\n",
"\n",
"dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, False)\n",
"loss, dx = svm_loss(x, y)\n",
"\n",
"# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
"print 'Testing svm_loss:'\n",
"print 'loss: ', loss\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"\n",
"dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, False)\n",
"loss, dx = softmax_loss(x, y)\n",
"\n",
"# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
"print '\\nTesting softmax_loss:'\n",
"print 'loss: ', loss\n",
"print 'dx error: ', rel_error(dx_num, dx)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolution layer: forward naive\n",
"\n",
"We are now ready to implement the forward pass for a convolutional layer. Implement the function `conv_forward_naive` in the file `cs231n/layers.py`.\n",
"\n",
"You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
"\n",
"You can test your implementation by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_forward_naive\n",
"difference: 2.21214764175e-08\n"
]
}
],
"source": [
"x_shape = (2, 3, 4, 4)\n",
"w_shape = (3, 3, 4, 4)\n",
"x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
"w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
"b = np.linspace(-0.1, 0.2, num=3)\n",
"\n",
"conv_param = {'stride': 2, 'pad': 1}\n",
"out, _ = conv_forward_naive(x, w, b, conv_param)\n",
"correct_out = np.array([[[[[-0.08759809, -0.10987781],\n",
" [-0.18387192, -0.2109216 ]],\n",
" [[ 0.21027089, 0.21661097],\n",
" [ 0.22847626, 0.23004637]],\n",
" [[ 0.50813986, 0.54309974],\n",
" [ 0.64082444, 0.67101435]]],\n",
" [[[-0.98053589, -1.03143541],\n",
" [-1.19128892, -1.24695841]],\n",
" [[ 0.69108355, 0.66880383],\n",
" [ 0.59480972, 0.56776003]],\n",
" [[ 2.36270298, 2.36904306],\n",
" [ 2.38090835, 2.38247847]]]]])\n",
"\n",
"# Compare your output to ours; difference should be around 1e-8\n",
"print 'Testing conv_forward_naive'\n",
"print 'difference: ', rel_error(out, correct_out)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolution layer: backward naive\n",
"\n",
"Next you need to implement the function `conv_backward_naive` in the file `cs231n/layers.py`. As usual, we will check your implementation with numeric gradient checking."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_backward_naive function\n",
"dx error: 1.36736202634e-09\n",
"dw error: 2.3105967511e-09\n",
"db error: 5.56258483667e-11\n"
]
}
],
"source": [
"x = np.random.randn(4, 3, 5, 5)\n",
"w = np.random.randn(2, 3, 3, 3)\n",
"b = np.random.randn(2,)\n",
"dout = np.random.randn(4, 2, 5, 5)\n",
"conv_param = {'stride': 1, 'pad': 1}\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
"\n",
"out, cache = conv_forward_naive(x, w, b, conv_param)\n",
"dx, dw, db = conv_backward_naive(dout, cache)\n",
"\n",
"# Your errors should be around 1e-9'\n",
"print 'Testing conv_backward_naive function'\n",
"print 'dx error: ', rel_error(dx, dx_num)\n",
"print 'dw error: ', rel_error(dw, dw_num)\n",
"print 'db error: ', rel_error(db, db_num)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Max pooling layer: forward naive\n",
"\n",
"The last layer we need for a basic convolutional neural network is the max pooling layer. First implement the forward pass in the function `max_pool_forward_naive` in the file `cs231n/layers.py`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing max_pool_forward_naive function:\n",
"difference: 4.16666651573e-08\n"
]
}
],
"source": [
"x_shape = (2, 3, 4, 4)\n",
"x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
"pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
"\n",
"out, _ = max_pool_forward_naive(x, pool_param)\n",
"\n",
"correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
" [-0.20421053, -0.18947368]],\n",
" [[-0.14526316, -0.13052632],\n",
" [-0.08631579, -0.07157895]],\n",
" [[-0.02736842, -0.01263158],\n",
" [ 0.03157895, 0.04631579]]],\n",
" [[[ 0.09052632, 0.10526316],\n",
" [ 0.14947368, 0.16421053]],\n",
" [[ 0.20842105, 0.22315789],\n",
" [ 0.26736842, 0.28210526]],\n",
" [[ 0.32631579, 0.34105263],\n",
" [ 0.38526316, 0.4 ]]]])\n",
"\n",
"# Compare your output with ours. Difference should be around 1e-8.\n",
"print 'Testing max_pool_forward_naive function:'\n",
"print 'difference: ', rel_error(out, correct_out)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Max pooling layer: backward naive\n",
"Implement the backward pass for a max pooling layer in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. As always we check the correctness of the backward pass using numerical gradient checking."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing max_pool_backward_naive function:\n",
"dx error: 3.27563146336e-12\n"
]
}
],
"source": [
"x = np.random.randn(3, 2, 8, 8)\n",
"dout = np.random.randn(3, 2, 4, 4)\n",
"pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
"\n",
"out, cache = max_pool_forward_naive(x, pool_param)\n",
"dx = max_pool_backward_naive(dout, cache)\n",
"\n",
"# Your error should be around 1e-12\n",
"print 'Testing max_pool_backward_naive function:'\n",
"print 'dx error: ', rel_error(dx, dx_num)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Fast layers\n",
"Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
"\n",
"The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
"\n",
"```bash\n",
"python setup.py build_ext --inplace\n",
"```\n",
"\n",
"The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
"\n",
"**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
"\n",
"You can compare the performance of the naive and fast versions of these layers by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_forward_fast:\n",
"Naive: 7.269685s\n",
"Fast: 0.012243s\n",
"Speedup: 593.781406x\n",
"Difference: 9.08169155651e-11\n",
"\n",
"Testing conv_backward_fast:\n",
"Naive: 8.059951s\n",
"Fast: 0.012395s\n",
"Speedup: 650.250726x\n",
"dx difference: 7.33008192806e-12\n",
"dw difference: 4.33066706153e-12\n",
"db difference: 2.60761166095e-14\n"
]
}
],
"source": [
"from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
"from time import time\n",
"\n",
"x = np.random.randn(100, 3, 31, 31)\n",
"w = np.random.randn(25, 3, 3, 3)\n",
"b = np.random.randn(25,)\n",
"dout = np.random.randn(100, 25, 16, 16)\n",
"conv_param = {'stride': 2, 'pad': 1}\n",
"\n",
"t0 = time()\n",
"out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
"t1 = time()\n",
"out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
"t2 = time()\n",
"\n",
"print 'Testing conv_forward_fast:'\n",
"print 'Naive: %fs' % (t1 - t0)\n",
"print 'Fast: %fs' % (t2 - t1)\n",
"print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
"print 'Difference: ', rel_error(out_naive, out_fast)\n",
"\n",
"t0 = time()\n",
"dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
"t1 = time()\n",
"dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
"t2 = time()\n",
"\n",
"print '\\nTesting conv_backward_fast:'\n",
"print 'Naive: %fs' % (t1 - t0)\n",
"print 'Fast: %fs' % (t2 - t1)\n",
"print 'Speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
"print 'dx difference: ', rel_error(dx_naive, dx_fast)\n",
"print 'dw difference: ', rel_error(dw_naive, dw_fast)\n",
"print 'db difference: ', rel_error(db_naive, db_fast)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing pool_forward_fast:\n",
"Naive: 0.551333s\n",
"fast: 0.001666s\n",
"speedup: 330.918432x\n",
"difference: 0.0\n",
"\n",
"Testing pool_backward_fast:\n",
"Naive: 0.361591s\n",
"Fast: 0.010301s\n",
"speedup: 35.102951x\n",
"dx difference: 0.0\n"
]
}
],
"source": [
"from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
"\n",
"x = np.random.randn(100, 3, 32, 32)\n",
"dout = np.random.randn(100, 3, 16, 16)\n",
"pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
"\n",
"t0 = time()\n",
"out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
"t1 = time()\n",
"out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
"t2 = time()\n",
"\n",
"print 'Testing pool_forward_fast:'\n",
"print 'Naive: %fs' % (t1 - t0)\n",
"print 'fast: %fs' % (t2 - t1)\n",
"print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
"print 'difference: ', rel_error(out_naive, out_fast)\n",
"\n",
"t0 = time()\n",
"dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
"t1 = time()\n",
"dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
"t2 = time()\n",
"\n",
"print '\\nTesting pool_backward_fast:'\n",
"print 'Naive: %fs' % (t1 - t0)\n",
"print 'Fast: %fs' % (t2 - t1)\n",
"print 'speedup: %fx' % ((t1 - t0) / (t2 - t1))\n",
"print 'dx difference: ', rel_error(dx_naive, dx_fast)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Sandwich layers\n",
"There are a couple common layer \"sandwiches\" that frequently appear in ConvNets. For example convolutional layers are frequently followed by ReLU and pooling, and affine layers are frequently followed by ReLU. To make it more convenient to use these common patterns, we have defined several convenience layers in the file `cs231n/layer_utils.py`. Lets grad-check them to make sure that they work correctly:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_relu_pool_forward:\n",
"dx error: 3.92515373835e-09\n",
"dw error: 1.65905695488e-09\n",
"db error: 3.86221201116e-11\n"
]
}
],
"source": [
"from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
"\n",
"x = np.random.randn(2, 3, 16, 16) # N, C, H, W = X.shape\n",
"w = np.random.randn(3, 3, 3, 3)\n",
"b = np.random.randn(3,)\n",
"dout = np.random.randn(2, 3, 8, 8)\n",
"conv_param = {'stride': 1, 'pad': 1}\n",
"pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
"\n",
"out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
"dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
"\n",
"print 'Testing conv_relu_pool_forward:'\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"print 'dw error: ', rel_error(dw_num, dw)\n",
"print 'db error: ', rel_error(db_num, db)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_relu_forward:\n",
"dx error: 1.30694101227e-08\n",
"dw error: 1.66432925101e-09\n",
"db error: 1.79339302579e-11\n"
]
}
],
"source": [
"from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
"\n",
"x = np.random.randn(2, 3, 8, 8)\n",
"w = np.random.randn(3, 3, 3, 3)\n",
"b = np.random.randn(3,)\n",
"dout = np.random.randn(2, 3, 8, 8)\n",
"conv_param = {'stride': 1, 'pad': 1}\n",
"\n",
"out, cache = conv_relu_forward(x, w, b, conv_param)\n",
"dx, dw, db = conv_relu_backward(dout, cache)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
"\n",
"print 'Testing conv_relu_forward:'\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"print 'dw error: ', rel_error(dw_num, dw)\n",
"print 'db error: ', rel_error(db_num, db)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing affine_relu_forward:\n",
"dx error: 9.70481924282e-10\n",
"dw error: 1.58717471295e-09\n",
"db error: 2.54800755619e-11\n"
]
}
],
"source": [
"from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
"\n",
"x = np.random.randn(2, 3, 4)\n",
"w = np.random.randn(12, 10)\n",
"b = np.random.randn(10)\n",
"dout = np.random.randn(2, 10)\n",
"\n",
"out, cache = affine_relu_forward(x, w, b)\n",
"dx, dw, db = affine_relu_backward(dout, cache)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
"\n",
"print 'Testing affine_relu_forward:'\n",
"print 'dx error: ', rel_error(dx_num, dx)\n",
"print 'dw error: ', rel_error(dw_num, dw)\n",
"print 'db error: ', rel_error(db_num, db)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
malogrisard/NTDScourse | algorithms/04_ex_tensorflow.ipynb | 1 | 15269 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Network Tour of Data Science\n",
"### Xavier Bresson, Winter 2016/17\n",
"## Exercise 4 : Introduction to TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import libraries\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import time\n",
"import collections\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting datasets/mnist/train-images-idx3-ubyte.gz\n",
"Extracting datasets/mnist/train-labels-idx1-ubyte.gz\n",
"Extracting datasets/mnist/t10k-images-idx3-ubyte.gz\n",
"Extracting datasets/mnist/t10k-labels-idx1-ubyte.gz\n",
"(55000, 784)\n",
"(55000, 10)\n",
"(10000, 784)\n",
"(10000, 10)\n"
]
}
],
"source": [
"# Import MNIST data with TensorFlow\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets(os.path.join('datasets', 'mnist'), one_hot=True) # load data in local folder\n",
"\n",
"train_data = mnist.train.images.astype(np.float32)\n",
"train_labels = mnist.train.labels\n",
"\n",
"test_data = mnist.test.images.astype(np.float32)\n",
"test_labels = mnist.test.labels\n",
"\n",
"print(train_data.shape)\n",
"print(train_labels.shape)\n",
"print(test_data.shape)\n",
"print(test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1st Step: Construct Computational Graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1: Prepare the input variables (x,y_label) of the computational graph\n",
"\n",
"Hint: You may use the function *tf.placeholder()*"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x= Tensor(\"Placeholder:0\", shape=(100, 784), dtype=float32) (100, 784)\n",
"y_label= Tensor(\"Placeholder_1:0\", shape=(100, 10), dtype=float32) (100, 10)\n"
]
}
],
"source": [
"# computational graph inputs\n",
"batch_size = 100\n",
"d = train_data.shape[1]\n",
"lbls = train_labels.shape[1]\n",
"nc = 10\n",
"x = tf.placeholder(tf.float32,[batch_size,d]); print('x=',x,x.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,lbls]); print('y_label=',y_label,y_label.get_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2: Prepare the variables (W,b) of the computational graph\n",
"\n",
"Hint: You may use the function *tf.Variable(), tf.truncated_normal()*"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W= (784, 10)\n",
"b= (10,)\n"
]
}
],
"source": [
"# computational graph variables\n",
"initial = tf.truncated_normal([d,nc], stddev=0.1); W = tf.Variable(initial); print('W=',W.get_shape())\n",
"b = tf.Variable(tf.zeros(10)); print('b=',b.get_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3: Compute the classifier such that\n",
"$$\n",
"y=softmax(Wx +b)\n",
"$$\n",
"\n",
"Hint: You may use the function *tf.matmul(), tf.nn.softmax()*"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y1= Tensor(\"MatMul_6:0\", shape=(100, 10), dtype=float32) (100, 10)\n",
"y2= Tensor(\"add_6:0\", shape=(100, 10), dtype=float32) (100, 10)\n",
"y3= Tensor(\"Softmax:0\", shape=(100, 10), dtype=float32) (100, 10)\n"
]
}
],
"source": [
"# Construct CG / output value\n",
"y =tf.matmul(x,W); print('y1=',y,y.get_shape())\n",
"y += b; print('y2=',y,y.get_shape())\n",
"y = tf.nn.softmax(y, name=None); print('y3=',y,y.get_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 4: Construct the loss of the computational graph such that\n",
"$$\n",
"loss = cross\\ entropy(y_{label},y) = mean_{all\\ data} \\ \\sum_{all\\ classes} -\\ y_{label}.\\log(y)\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<tf.Tensor 'Mean_1:0' shape=() dtype=float32>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 5: Construct the L2 regularization of (W,b) to the computational graph such that\n",
"$$\n",
"R(W) = \\|W\\|_2^2\\\\\n",
"R(b) = \\|b\\|_2^2\n",
"$$\n",
"\n",
"Hint: You may use the function *tf.nn.l2_loss()*"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reg_loss = 2*(tf.nn.l2_loss(W)+tf.nn.l2_loss(b))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 6: Form the total loss\n",
"$$\n",
"total\\ loss = cross\\ entropy(y_{label},y) + reg\\_par* (R(W) + R(b))\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reg_par = 1e-3\n",
"total_loss = cross_entropy + reg_par*reg_loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 7: Perform optimization of the total loss for learning weight variables of the computational graph\n",
"\n",
"Hint: You may use the function *tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)*"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Update CG variables / backward pass\n",
"learning_rate = 0.1#tf.placeholder(tf.float32, shape=[])\n",
"train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 8: Evaluate the accuracy\n",
"\n",
"Hint: You may use the function *tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))*"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2nd Step: Run the Computational Graph with batches of training data\n",
"Check out the accuracy of test set"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create test set \n",
"idx = np.random.permutation(test_data.shape[0]) # rand permutation\n",
"idx = idx[:batch_size]\n",
"test_x, test_y = test_data[idx,:], test_labels[idx]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Iteration i= 0 , train accuracy= 0.11 , loss= 2.62957\n",
"test accuracy= 0.16\n",
"\n",
"Iteration i= 1 , train accuracy= 0.23 , loss= 2.3591\n",
"test accuracy= 0.23\n",
"\n",
"Iteration i= 2 , train accuracy= 0.24 , loss= 2.28646\n",
"test accuracy= 0.25\n",
"\n",
"Iteration i= 3 , train accuracy= 0.23 , loss= 2.20308\n",
"test accuracy= 0.28\n",
"\n",
"Iteration i= 4 , train accuracy= 0.19 , loss= 2.15221\n",
"test accuracy= 0.32\n",
"\n",
"Iteration i= 5 , train accuracy= 0.33 , loss= 2.10529\n",
"test accuracy= 0.33\n",
"\n",
"Iteration i= 6 , train accuracy= 0.39 , loss= 2.00862\n",
"test accuracy= 0.38\n",
"\n",
"Iteration i= 7 , train accuracy= 0.38 , loss= 1.93917\n",
"test accuracy= 0.39\n",
"\n",
"Iteration i= 8 , train accuracy= 0.42 , loss= 1.80285\n",
"test accuracy= 0.44\n",
"\n",
"Iteration i= 9 , train accuracy= 0.47 , loss= 1.74397\n",
"test accuracy= 0.52\n",
"\n",
"Iteration i= 10 , train accuracy= 0.53 , loss= 1.68617\n",
"test accuracy= 0.52\n",
"\n",
"Iteration i= 11 , train accuracy= 0.53 , loss= 1.63493\n",
"test accuracy= 0.54\n",
"\n",
"Iteration i= 12 , train accuracy= 0.55 , loss= 1.59938\n",
"test accuracy= 0.57\n",
"\n",
"Iteration i= 13 , train accuracy= 0.59 , loss= 1.554\n",
"test accuracy= 0.59\n",
"\n",
"Iteration i= 14 , train accuracy= 0.6 , loss= 1.51773\n",
"test accuracy= 0.61\n",
"\n",
"Iteration i= 15 , train accuracy= 0.63 , loss= 1.51477\n",
"test accuracy= 0.64\n",
"\n",
"Iteration i= 16 , train accuracy= 0.61 , loss= 1.47688\n",
"test accuracy= 0.63\n",
"\n",
"Iteration i= 17 , train accuracy= 0.68 , loss= 1.39394\n",
"test accuracy= 0.6\n",
"\n",
"Iteration i= 18 , train accuracy= 0.65 , loss= 1.43529\n",
"test accuracy= 0.64\n",
"\n",
"Iteration i= 19 , train accuracy= 0.58 , loss= 1.41979\n",
"test accuracy= 0.64\n",
"\n",
"Iteration i= 20 , train accuracy= 0.59 , loss= 1.42947\n",
"test accuracy= 0.65\n",
"\n",
"Iteration i= 21 , train accuracy= 0.61 , loss= 1.38538\n",
"test accuracy= 0.67\n",
"\n",
"Iteration i= 22 , train accuracy= 0.6 , loss= 1.3769\n",
"test accuracy= 0.69\n",
"\n",
"Iteration i= 23 , train accuracy= 0.63 , loss= 1.39994\n",
"test accuracy= 0.7\n",
"\n",
"Iteration i= 24 , train accuracy= 0.68 , loss= 1.25396\n",
"test accuracy= 0.69\n",
"\n",
"Iteration i= 25 , train accuracy= 0.65 , loss= 1.30319\n",
"test accuracy= 0.72\n",
"\n",
"Iteration i= 26 , train accuracy= 0.69 , loss= 1.33592\n",
"test accuracy= 0.72\n",
"\n",
"Iteration i= 27 , train accuracy= 0.68 , loss= 1.23335\n",
"test accuracy= 0.74\n",
"\n",
"Iteration i= 28 , train accuracy= 0.77 , loss= 1.14386\n",
"test accuracy= 0.73\n",
"\n",
"Iteration i= 29 , train accuracy= 0.72 , loss= 1.13069\n",
"test accuracy= 0.71\n",
"\n",
"Iteration i= 30 , train accuracy= 0.78 , loss= 1.15764\n",
"test accuracy= 0.72\n",
"\n",
"Iteration i= 31 , train accuracy= 0.73 , loss= 1.15789\n",
"test accuracy= 0.76\n",
"\n",
"Iteration i= 32 , train accuracy= 0.7 , loss= 1.14777\n",
"test accuracy= 0.75\n",
"\n",
"Iteration i= 33 , train accuracy= 0.74 , loss= 1.11072\n",
"test accuracy= 0.74\n",
"\n",
"Iteration i= 34 , train accuracy= 0.79 , loss= 1.10595\n",
"test accuracy= 0.77\n",
"\n",
"Iteration i= 35 , train accuracy= 0.72 , loss= 1.1696\n",
"test accuracy= 0.78\n",
"\n",
"Iteration i= 36 , train accuracy= 0.75 , loss= 1.0789\n",
"test accuracy= 0.77\n",
"\n",
"Iteration i= 37 , train accuracy= 0.79 , loss= 1.01109\n",
"test accuracy= 0.77\n",
"\n",
"Iteration i= 38 , train accuracy= 0.89 , loss= 0.819416\n",
"test accuracy= 0.78\n",
"\n",
"Iteration i= 39 , train accuracy= 0.7 , loss= 1.08737\n",
"test accuracy= 0.77\n",
"\n",
"Iteration i= 40 , train accuracy= 0.83 , loss= 0.915718\n",
"test accuracy= 0.77\n",
"\n",
"Iteration i= 41 , train accuracy= 0.83 , loss= 0.937208\n",
"test accuracy= 0.77\n",
"\n",
"Iteration i= 42 , train accuracy= 0.8 , loss= 1.03092\n",
"test accuracy= 0.76\n",
"\n",
"Iteration i= 43 , train accuracy= 0.83 , loss= 0.941506\n",
"test accuracy= 0.76\n",
"\n",
"Iteration i= 44 , train accuracy= 0.74 , loss= 1.01248\n",
"test accuracy= 0.79\n",
"\n",
"Iteration i= 45 , train accuracy= 0.85 , loss= 0.819709\n",
"test accuracy= 0.79\n",
"\n",
"Iteration i= 46 , train accuracy= 0.82 , loss= 0.841836\n",
"test accuracy= 0.79\n",
"\n",
"Iteration i= 47 , train accuracy= 0.76 , loss= 0.968287\n",
"test accuracy= 0.79\n",
"\n",
"Iteration i= 48 , train accuracy= 0.84 , loss= 0.942129\n",
"test accuracy= 0.78\n",
"\n",
"Iteration i= 49 , train accuracy= 0.82 , loss= 0.884058\n",
"test accuracy= 0.78\n"
]
}
],
"source": [
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"\n",
"# Running Computational Graph\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(50):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) # rand permutation\n",
" idx = [indices.popleft() for i in range(batch_size)] # extract n_batch data\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" \n",
" # Run CG for variable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={x: batch_x, y_label: batch_y})\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" \n",
" # Run CG for testset\n",
" acc_test = sess.run(accuracy, feed_dict={x: test_x, y_label: test_y})\n",
" print('test accuracy=',acc_test)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
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"metadata": {
"anaconda-cloud": {},
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"name": "python3"
},
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"name": "ipython",
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| mit |
gururajl/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | 1 | 66592 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h1 align=\"center\">TensorFlow Neural Network Lab</h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"image/notmnist.png\">\n",
"In this lab, you'll use all the tools you learned from *Introduction to TensorFlow* to label images of English letters! The data you are using, <a href=\"http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html\">notMNIST</a>, consists of images of a letter from A to J in different fonts.\n",
"\n",
"The above images are a few examples of the data you'll be training on. After training the network, you will compare your prediction model against test data. Your goal, by the end of this lab, is to make predictions against that test set with at least an 80% accuracy. Let's jump in!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To start this lab, you first need to import all the necessary modules. Run the code below. If it runs successfully, it will print \"`All modules imported`\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All modules imported.\n"
]
}
],
"source": [
"import hashlib\n",
"import os\n",
"import pickle\n",
"from urllib.request import urlretrieve\n",
"\n",
"import numpy as np\n",
"from PIL import Image\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelBinarizer\n",
"from sklearn.utils import resample\n",
"from tqdm import tqdm\n",
"from zipfile import ZipFile\n",
"\n",
"print('All modules imported.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this data, 15,000 images for each label (A-J)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading notMNIST_train.zip...\n",
"Download Finished\n",
"Downloading notMNIST_test.zip...\n",
"Download Finished\n",
"All files downloaded.\n"
]
}
],
"source": [
"def download(url, file):\n",
" \"\"\"\n",
" Download file from <url>\n",
" :param url: URL to file\n",
" :param file: Local file path\n",
" \"\"\"\n",
" if not os.path.isfile(file):\n",
" print('Downloading ' + file + '...')\n",
" urlretrieve(url, file)\n",
" print('Download Finished')\n",
"\n",
"# Download the training and test dataset.\n",
"download('https://s3.amazonaws.com/udacity-sdc/notMNIST_train.zip', 'notMNIST_train.zip')\n",
"download('https://s3.amazonaws.com/udacity-sdc/notMNIST_test.zip', 'notMNIST_test.zip')\n",
"\n",
"# Make sure the files aren't corrupted\n",
"assert hashlib.md5(open('notMNIST_train.zip', 'rb').read()).hexdigest() == 'c8673b3f28f489e9cdf3a3d74e2ac8fa',\\\n",
" 'notMNIST_train.zip file is corrupted. Remove the file and try again.'\n",
"assert hashlib.md5(open('notMNIST_test.zip', 'rb').read()).hexdigest() == '5d3c7e653e63471c88df796156a9dfa9',\\\n",
" 'notMNIST_test.zip file is corrupted. Remove the file and try again.'\n",
"\n",
"# Wait until you see that all files have been downloaded.\n",
"print('All files downloaded.')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 210001/210001 [00:42<00:00, 4994.58files/s]\n",
"100%|██████████| 10001/10001 [00:01<00:00, 5227.37files/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"All features and labels uncompressed.\n"
]
}
],
"source": [
"def uncompress_features_labels(file):\n",
" \"\"\"\n",
" Uncompress features and labels from a zip file\n",
" :param file: The zip file to extract the data from\n",
" \"\"\"\n",
" features = []\n",
" labels = []\n",
"\n",
" with ZipFile(file) as zipf:\n",
" # Progress Bar\n",
" filenames_pbar = tqdm(zipf.namelist(), unit='files')\n",
" \n",
" # Get features and labels from all files\n",
" for filename in filenames_pbar:\n",
" # Check if the file is a directory\n",
" if not filename.endswith('/'):\n",
" with zipf.open(filename) as image_file:\n",
" image = Image.open(image_file)\n",
" image.load()\n",
" # Load image data as 1 dimensional array\n",
" # We're using float32 to save on memory space\n",
" feature = np.array(image, dtype=np.float32).flatten()\n",
"\n",
" # Get the the letter from the filename. This is the letter of the image.\n",
" label = os.path.split(filename)[1][0]\n",
"\n",
" features.append(feature)\n",
" labels.append(label)\n",
" return np.array(features), np.array(labels)\n",
"\n",
"# Get the features and labels from the zip files\n",
"train_features, train_labels = uncompress_features_labels('notMNIST_train.zip')\n",
"test_features, test_labels = uncompress_features_labels('notMNIST_test.zip')\n",
"\n",
"# Limit the amount of data to work with a docker container\n",
"docker_size_limit = 150000\n",
"train_features, train_labels = resample(train_features, train_labels, n_samples=docker_size_limit)\n",
"\n",
"# Set flags for feature engineering. This will prevent you from skipping an important step.\n",
"is_features_normal = False\n",
"is_labels_encod = False\n",
"\n",
"# Wait until you see that all features and labels have been uncompressed.\n",
"print('All features and labels uncompressed.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"image/Mean_Variance_Image.png\" style=\"height: 75%;width: 75%; position: relative; right: 5%\">\n",
"## Problem 1\n",
"The first problem involves normalizing the features for your training and test data.\n",
"\n",
"Implement Min-Max scaling in the `normalize_grayscale()` function to a range of `a=0.1` and `b=0.9`. After scaling, the values of the pixels in the input data should range from 0.1 to 0.9.\n",
"\n",
"Since the raw notMNIST image data is in [grayscale](https://en.wikipedia.org/wiki/Grayscale), the current values range from a min of 0 to a max of 255.\n",
"\n",
"Min-Max Scaling:\n",
"$\n",
"X'=a+{\\frac {\\left(X-X_{\\min }\\right)\\left(b-a\\right)}{X_{\\max }-X_{\\min }}}\n",
"$\n",
"\n",
"*If you're having trouble solving problem 1, you can view the solution [here](https://github.com/udacity/deep-learning/blob/master/intro-to-tensorflow/intro_to_tensorflow_solution.ipynb).*"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed!\n"
]
}
],
"source": [
"# Problem 1 - Implement Min-Max scaling for grayscale image data\n",
"def normalize_grayscale(image_data):\n",
" \"\"\"\n",
" Normalize the image data with Min-Max scaling to a range of [0.1, 0.9]\n",
" :param image_data: The image data to be normalized\n",
" :return: Normalized image data\n",
" \"\"\"\n",
" # TODO: Implement Min-Max scaling for grayscale image data\n",
" a = 0.1\n",
" b = 0.9\n",
" min = np.min(image_data)\n",
" max = np.max(image_data)\n",
" return a + (((image_data - min)*(b-a))/(max-min))\n",
"\n",
"\n",
"### DON'T MODIFY ANYTHING BELOW ###\n",
"# Test Cases\n",
"np.testing.assert_array_almost_equal(\n",
" normalize_grayscale(np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 255])),\n",
" [0.1, 0.103137254902, 0.106274509804, 0.109411764706, 0.112549019608, 0.11568627451, 0.118823529412, 0.121960784314,\n",
" 0.125098039216, 0.128235294118, 0.13137254902, 0.9],\n",
" decimal=3)\n",
"np.testing.assert_array_almost_equal(\n",
" normalize_grayscale(np.array([0, 1, 10, 20, 30, 40, 233, 244, 254,255])),\n",
" [0.1, 0.103137254902, 0.13137254902, 0.162745098039, 0.194117647059, 0.225490196078, 0.830980392157, 0.865490196078,\n",
" 0.896862745098, 0.9])\n",
"\n",
"if not is_features_normal:\n",
" train_features = normalize_grayscale(train_features)\n",
" test_features = normalize_grayscale(test_features)\n",
" is_features_normal = True\n",
"\n",
"print('Tests Passed!')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Labels One-Hot Encoded\n"
]
}
],
"source": [
"if not is_labels_encod:\n",
" # Turn labels into numbers and apply One-Hot Encoding\n",
" encoder = LabelBinarizer()\n",
" encoder.fit(train_labels)\n",
" train_labels = encoder.transform(train_labels)\n",
" test_labels = encoder.transform(test_labels)\n",
"\n",
" # Change to float32, so it can be multiplied against the features in TensorFlow, which are float32\n",
" train_labels = train_labels.astype(np.float32)\n",
" test_labels = test_labels.astype(np.float32)\n",
" is_labels_encod = True\n",
"\n",
"print('Labels One-Hot Encoded')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training features and labels randomized and split.\n"
]
}
],
"source": [
"assert is_features_normal, 'You skipped the step to normalize the features'\n",
"assert is_labels_encod, 'You skipped the step to One-Hot Encode the labels'\n",
"\n",
"# Get randomized datasets for training and validation\n",
"train_features, valid_features, train_labels, valid_labels = train_test_split(\n",
" train_features,\n",
" train_labels,\n",
" test_size=0.05,\n",
" random_state=832289)\n",
"\n",
"print('Training features and labels randomized and split.')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saving data to pickle file...\n",
"Data cached in pickle file.\n"
]
}
],
"source": [
"# Save the data for easy access\n",
"pickle_file = 'notMNIST.pickle'\n",
"if not os.path.isfile(pickle_file):\n",
" print('Saving data to pickle file...')\n",
" try:\n",
" with open('notMNIST.pickle', 'wb') as pfile:\n",
" pickle.dump(\n",
" {\n",
" 'train_dataset': train_features,\n",
" 'train_labels': train_labels,\n",
" 'valid_dataset': valid_features,\n",
" 'valid_labels': valid_labels,\n",
" 'test_dataset': test_features,\n",
" 'test_labels': test_labels,\n",
" },\n",
" pfile, pickle.HIGHEST_PROTOCOL)\n",
" except Exception as e:\n",
" print('Unable to save data to', pickle_file, ':', e)\n",
" raise\n",
"\n",
"print('Data cached in pickle file.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Checkpoint\n",
"All your progress is now saved to the pickle file. If you need to leave and comeback to this lab, you no longer have to start from the beginning. Just run the code block below and it will load all the data and modules required to proceed."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data and modules loaded.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# Load the modules\n",
"import pickle\n",
"import math\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tqdm import tqdm\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Reload the data\n",
"pickle_file = 'notMNIST.pickle'\n",
"with open(pickle_file, 'rb') as f:\n",
" pickle_data = pickle.load(f)\n",
" train_features = pickle_data['train_dataset']\n",
" train_labels = pickle_data['train_labels']\n",
" valid_features = pickle_data['valid_dataset']\n",
" valid_labels = pickle_data['valid_labels']\n",
" test_features = pickle_data['test_dataset']\n",
" test_labels = pickle_data['test_labels']\n",
" del pickle_data # Free up memory\n",
"\n",
"print('Data and modules loaded.')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"142500"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(train_features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Problem 2\n",
"\n",
"Now it's time to build a simple neural network using TensorFlow. Here, your network will be just an input layer and an output layer.\n",
"\n",
"<img src=\"image/network_diagram.png\" style=\"height: 40%;width: 40%; position: relative; right: 10%\">\n",
"\n",
"For the input here the images have been flattened into a vector of $28 \\times 28 = 784$ features. Then, we're trying to predict the image digit so there are 10 output units, one for each label. Of course, feel free to add hidden layers if you want, but this notebook is built to guide you through a single layer network. \n",
"\n",
"For the neural network to train on your data, you need the following <a href=\"https://www.tensorflow.org/resources/dims_types.html#data-types\">float32</a> tensors:\n",
" - `features`\n",
" - Placeholder tensor for feature data (`train_features`/`valid_features`/`test_features`)\n",
" - `labels`\n",
" - Placeholder tensor for label data (`train_labels`/`valid_labels`/`test_labels`)\n",
" - `weights`\n",
" - Variable Tensor with random numbers from a truncated normal distribution.\n",
" - See <a href=\"https://www.tensorflow.org/api_docs/python/constant_op.html#truncated_normal\">`tf.truncated_normal()` documentation</a> for help.\n",
" - `biases`\n",
" - Variable Tensor with all zeros.\n",
" - See <a href=\"https://www.tensorflow.org/api_docs/python/constant_op.html#zeros\"> `tf.zeros()` documentation</a> for help.\n",
"\n",
"*If you're having trouble solving problem 2, review \"TensorFlow Linear Function\" section of the class. If that doesn't help, the solution for this problem is available [here](intro_to_tensorflow_solution.ipynb).*"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tests Passed!\n"
]
}
],
"source": [
"# All the pixels in the image (28 * 28 = 784)\n",
"features_count = 784\n",
"# All the labels\n",
"labels_count = 10\n",
"\n",
"# TODO: Set the features and labels tensors\n",
"features = tf.placeholder(tf.float32, [None, features_count])\n",
"labels = tf.placeholder(tf.float32, [None, labels_count])\n",
"\n",
"# TODO: Set the weights and biases tensors\n",
"weights = tf.Variable(tf.random_normal([features_count, labels_count]))\n",
"biases = tf.Variable(tf.zeros([labels_count]))\n",
"\n",
"\n",
"\n",
"### DON'T MODIFY ANYTHING BELOW ###\n",
"\n",
"#Test Cases\n",
"from tensorflow.python.ops.variables import Variable\n",
"\n",
"assert features._op.name.startswith('Placeholder'), 'features must be a placeholder'\n",
"assert labels._op.name.startswith('Placeholder'), 'labels must be a placeholder'\n",
"assert isinstance(weights, Variable), 'weights must be a TensorFlow variable'\n",
"assert isinstance(biases, Variable), 'biases must be a TensorFlow variable'\n",
"\n",
"assert features._shape == None or (\\\n",
" features._shape.dims[0].value is None and\\\n",
" features._shape.dims[1].value in [None, 784]), 'The shape of features is incorrect'\n",
"assert labels._shape == None or (\\\n",
" labels._shape.dims[0].value is None and\\\n",
" labels._shape.dims[1].value in [None, 10]), 'The shape of labels is incorrect'\n",
"assert weights._variable._shape == (784, 10), 'The shape of weights is incorrect'\n",
"assert biases._variable._shape == (10), 'The shape of biases is incorrect'\n",
"\n",
"assert features._dtype == tf.float32, 'features must be type float32'\n",
"assert labels._dtype == tf.float32, 'labels must be type float32'\n",
"\n",
"# Feed dicts for training, validation, and test session\n",
"train_feed_dict = {features: train_features, labels: train_labels}\n",
"valid_feed_dict = {features: valid_features, labels: valid_labels}\n",
"test_feed_dict = {features: test_features, labels: test_labels}\n",
"\n",
"# Linear Function WX + b\n",
"logits = tf.matmul(features, weights) + biases\n",
"\n",
"prediction = tf.nn.softmax(logits)\n",
"\n",
"# Cross entropy\n",
"cross_entropy = -tf.reduce_sum(labels * tf.log(prediction), reduction_indices=1)\n",
"\n",
"# Training loss\n",
"loss = tf.reduce_mean(cross_entropy)\n",
"\n",
"# Create an operation that initializes all variables\n",
"init = tf.global_variables_initializer()\n",
"\n",
"# Test Cases\n",
"with tf.Session() as session:\n",
" session.run(init)\n",
" session.run(loss, feed_dict=train_feed_dict)\n",
" session.run(loss, feed_dict=valid_feed_dict)\n",
" session.run(loss, feed_dict=test_feed_dict)\n",
" biases_data = session.run(biases)\n",
"\n",
"assert not np.count_nonzero(biases_data), 'biases must be zeros'\n",
"\n",
"print('Tests Passed!')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy function created.\n"
]
}
],
"source": [
"# Determine if the predictions are correct\n",
"is_correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(labels, 1))\n",
"# Calculate the accuracy of the predictions\n",
"accuracy = tf.reduce_mean(tf.cast(is_correct_prediction, tf.float32))\n",
"\n",
"print('Accuracy function created.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"image/Learn_Rate_Tune_Image.png\" style=\"height: 70%;width: 70%\">\n",
"## Problem 3\n",
"Below are 2 parameter configurations for training the neural network. In each configuration, one of the parameters has multiple options. For each configuration, choose the option that gives the best acccuracy.\n",
"\n",
"Parameter configurations:\n",
"\n",
"Configuration 1\n",
"* **Epochs:** 1\n",
"* **Learning Rate:**\n",
" * 0.8\n",
" * 0.5\n",
" * 0.1\n",
" * 0.05\n",
" * 0.01\n",
"\n",
"Configuration 2\n",
"* **Epochs:**\n",
" * 1\n",
" * 2\n",
" * 3\n",
" * 4\n",
" * 5\n",
"* **Learning Rate:** 0.2\n",
"\n",
"The code will print out a Loss and Accuracy graph, so you can see how well the neural network performed.\n",
"\n",
"*If you're having trouble solving problem 3, you can view the solution [here](intro_to_tensorflow_solution.ipynb).*"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 1/2: 100%|██████████| 1114/1114 [00:12<00:00, 89.61batches/s]\n",
"Epoch 2/2: 100%|██████████| 1114/1114 [00:12<00:00, 90.63batches/s]\n"
]
},
{
"data": {
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EpC6r9aGpZ8uetGrcits+uI0d+3ZEuzoiIiJSR9X60NQorhETRkxg/qb5nPfGeezN2Rvt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\nPS3uM89sc6mnNZoSfGinpgYfukOGBK9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nRG/wlrXkhBCilDl8+DAmk4kVK1bYfe7Nmzcx\nmUzMmjWrGGom7hUF6WkaA8zXWi/RWh8CRgDXgXBrJyilTEAMMAU4XpCKFlby6zGEH37RGPRd37ht\nWUtOCCEKzmQy3XFzcnJi27ai+7eyUqpQ5xbm/KLwww8/YDKZ8PDwkHm7SiG7xjQppZyBQGB6VprW\nWiulEoA2+ZwaCfyutf5IKdWhQDUtjN9/Z+usb4keeBSfXtPMDuVcS07GNwkhhO1iYmLM9hcvXkxC\nQgIxMTForbPTi2puqsaNG3Pjxg1cXFzsPrds2bLcuHEDZ2fnIqlLQS1dupRatWpx7tw51qxZw8CB\nAx1aH2EfeweCewFOwLlc6eeAxpZOUEo9DDwFPGB37YrKpEkMc10D7x2xeFjWkhNCCPvl/oO/a9cu\nEhISCAsLs+n81NRUypUrZ1eZBQmYiuLcoqC1Zvny5Tz11FP88MMPLF26tMQGTenp6QCUKXPPvS+W\nr6KackABOk+iUuWBj4FntNYXi6gs+3z7rdGVNG0aVJEZv4UQwhE2btyIyWTi008/ZcKECdSsWZPy\n5ctz69YtUlJSGDNmDPfddx/ly5enUqVK9OrViwMHDphdw9KYptDQUKpWrcqvv/5Kz5498fDwwNvb\nm5dfftnsXEtjmiZOnIjJZOLXX39l8ODBVKpUicqVKzN8+HBu3bpldv7169cZNWoUVapUoUKFCvTv\n358TJ07YNU5q8+bNnDlzhtDQUEJCQkhISLC6HuFnn31Ghw4d8PDwoFKlSrRu3ZpPPvnELM+OHTvo\n3r07np6elC9fnubNmzNv3rzs461bt6ZHjx55rh0aGmrW+5f1uX7wwQfMnj2b+vXr4+rqyrFjx0hN\nTWXy5MkEBgZSsWJFPDw86NSpEzt27Mhz3du3bzN79mzuv/9+XF1d8fb25tFHH+XHH38EoFWrVrRu\n3dri/fr4+NCnT587f4gOZm8ImQJkAN650quRt/cJoAFQF/hM/fUg2QSglLoFNNZaWx3jNGbMGCpW\nrGiWFhYWZvO/YsjIgGefhebN4ZlnbDtHCCFEsXnllVdwd3dnwoQJXLt2DScnJw4fPkx8fDz9+/en\nbt26nDlzhnnz5hEUFMSBAwfw8rL+npFSirS0NLp27UpQUBCzZ88mPj6eGTNm0KhRI4YNG5bvuUop\nHn/8cRo1asTMmTP57rvvWLhwITVq1CAyMjI7b1hYGOvXryc8PJzAwEASEhJ4/PHH7RojtXTpUvz9\n/fH396du3boMHz6cuLg4nn32WbN88+bNY9SoUTRv3pzJkydToUIF9uzZw6ZNm+jfvz8A69evp2/f\nvtStW5exY8fi7e3Nzz//zIYNGxgxYkT2/eV337l9+OGHZGRkMGrUKMqUKUPFihU5f/48S5YsITQ0\nlBEjRnDp0iUWLlxI165d2bNnD02aNMk+f9CgQcTFxfHYY49lB55bt27l+++/p1mzZgwdOpTnn3+e\nY8eOUb9+/ezzvv76a06ePMlbb71l82dZGLGxscTGxpqlXb582baTtdZ2bcA3wLs59hXwKzDeQl4X\nwC/X9inwJdAUKGOljABAJyYm6kJZsEBr0HrHjsJdRwghCiExMVEXyXdaCffcc89pk8lk8Vh8fLxW\nSmk/Pz+dlpZmduzmzZt58h89elS7uLjo2bNnZ6cdOnRIK6V0XFxcdlpoaKg2mUx6zpw5Zuf7+/vr\n9u3bZ++npqZqpZSeOXNmdtrEiRO1UkqPHj3a7NwePXro2rVrZ+/v3LlTK6X0yy+/bJYvLCxMm0wm\ns2tak5qaqitWrKinT5+endavXz/dpk0bs3znz5/Xbm5uOigoKM/nlCUtLU3XrFlTN2nSRF+9etVq\nma1bt9bBwcF50kNDQ3XTpk2z97M+Vy8vL3358mWzvBkZGTo9Pd0s7cKFC7pKlSr6ueeey077/PPP\ntVJKT5o0yWp9zp8/r11cXHRUVJRZekREhPb09LT4c1BU7vQ7mHUcCND5xEAFeVj5FrBYKZUIfIfx\nNp0bsAhAKbUE+E1r/ZLW+hZg1r+qlLpkxGr6YAHKtt2FCzBpEgwbBm3bFmtRQghRpK5fNxYUL05N\nmoCbW/GWYUF4eHiecTI5xxplZGRw+fJlKlWqRL169dizZ49N142IiDDbb9euHevXr7/jeUophg8f\nbpbWvn17Nm7cSFpaGs7OzsTHx6OUYuTIkWb5Ro8ezfLly22q39q1a7ly5QqhoaHZaWFhYTzxxBNm\nPS9ffPEFqampvPTSS1bHE3377becPn2a+fPn4+7ublP5tggNDaVChQpmaSbTX6N4tNZcunSJjIwM\nAgICzNpm1apVuLi45HksmlPlypXp0aMHS5cuZcqUKQCkpaWxatUqBgwY4PAxZ7awO2jSWq/InJNp\nKsZjur1Ad631H5lZagHpRVfFAnrlFUhLgxkzHF0TIYSwz6FDEBhYvGUkJoIDFg/2sfDWTdZYmPnz\n53PixAlu374NGAFNw4YN73jNSpUqUb58ebM0T09PLl60bSht7lXvPT09swOEqlWrcuLECcqWLUvN\nmjXN8tlStyxLly6lcePG3L59m6SkJAAaNWqEi4sLy5YtY/LkyQDZx/z9/a1eKykpCaVUvnkKwlLb\nACxcuJB33nmHI0eOZA8QB/Dz88v+/2PHjlGnTp07BnFDhw6lf//+7N69mxYtWvD5559z8eJFhgwZ\nUiT3UNwKNCxeaz0XmGvlWOc7nPtUQcq0yw8/wLx5MGcOVK9e7MUJIUSRatLECGqKuwwHcHV1zZM2\nZcoUpk+fzogRI+jUqROenp6YTCZGjhyZHUDlx8nJyWK61nneTyqW8+/k4sWLxMfHk56ejq+vr9kx\npRRLly7NDppsKdPWelkb05SRkWEx3VLbLFy4kIiICJ544glefvllvLy8cHJyIioqij/++CM7n611\n6tmzJ56ensTExNCiRQtiYmKoU6cO7dq1s+l8R7v73iXUGp57Dpo2NQaBCyFEaePm5pBeIEdZtWoV\nPXr0YO5c83+LX7hwgQYNGjioVn+pW7cuN2/e5NSpU2a9TUePHrXp/Li4ONLT04mOjsbDw8Ps2P79\n+4mKimLPnj0EBARk917t37+fGjVqWLxew4YN0Vqzf/9+2uYz/MRab9uJEydsqjcYbePv75/nMeSL\nL76Yp067du3i6tWreXr9cnJ2diYkJIS4uDgiIyPZsGED48aNs7k+jlZUUw6UHDExsHMnvP8+OHgS\nMyGEEH+x1vPh5OSUp6fi448/5vz5839Hte6oe/fuaK3zBHXvv/++TW/PLV26FD8/P4YNG0bfvn3N\ntvHjx1O2bFmWLl0KQHBwMOXKlWP69OmkpaVZvF6rVq2oWbMmc+bM4cqVK1bLbdCgAT/99JPZm2Hf\nffcdu3fvtuW2Actts23btjxjzfr168etW7eYNs18AmlLhgwZwrlz5xgxYgQ3b95k0KBBNtfH0e6u\nnqY//4Tx4yEkBDp1cnRthBBC5GDtEU7Pnj158803iYiI4KGHHmLfvn3ExcVZHWPzd2vbti2PPvoo\nM2bM4OzZs7Ro0YLNmzdz/LgxY05+gVNycjI7d+5k0qRJFo+7urrSpUsXli9fzuzZs6lcuTJvvvkm\no0ePplWrVoSEhFCxYkX27t2L1pr58+dTpkwZ5s6dS79+/WjevDnDhg3D29ubgwcPcuzYMdauXQvA\n008/zX/+8x+6devGk08+yalTp1i4cCH+/v5mY5Py07NnT0aNGkX//v3p3r07v/zyCwsWLMDPz8/s\n0ekjjzzCgAEDmDVrFgcOHKBr166kp6ezdetWevbsydNPP52dt3Xr1vj6+rJy5UoCAgLMpi0o6Upt\nT9PixRbWi4uKgitXSH7hbRYvdkSthBDi3pZfAGHt2Kuvvsrzzz/Phg0bGDt2LAcOHGDTpk1Ur149\nzzmWrpHffES59225niVxcXEMHz6cNWvWMGnSJMqUKZO9XEx+s5pnzQfUs2dPq3l69erF2bNn2bx5\nMwCjRo1i1apVuLq68tprrzFp0iR++uknHnnkEbNzNm/eTL169Zg9ezbjx49n27Zt9OrVKzvPAw88\nwKJFi0hJSWHs2LFs3LiRuLg4/P39bf4chg8fztSpU9m9ezf/93//x5YtW1i5ciX3339/nnNiY2N5\n4403OHLkCOPHj2fGjBncvn2bVq1a5bnukCFDUEoxdOhQq59LiZTffASO2rBhnqbjx7Xu1Mn4r9Za\n6/37tXZy0sdfnGueLoQQDnavzNN0r9m1a5dWSunVq1c7uiqlzowZM7Szs7M+d+7c31JeUc3TVGp7\nmrIW2g0Ph+TjGp5/nuTa7Qn/djjR0bKWnBBCiKJz8+bNPGnvvvsuZcqUKTVvfpUUWms++ugjunXr\nRrVq1RxdHbuU6jFN2YFTzz+I/DmdqAdWE73IJAGTEEKIIjV16lQOHTpEhw4dUEqxfv16Nm/ezAsv\nvEDVqlUdXb1S4erVq3z22Wds2rSJo0eP8sEHHzi6SnYr1UETgI/3DSJ/f5YgtvLVu9LDJIQQoui1\na9eOr776iqlTp3Lt2jXq1q3LtGnTmDBhgqOrVmqcOnWKQYMGUaVKFaKioujSpYujq2S3Uh80Jc/+\nhKg/RvJVzG9ERdWSR3NCCCGKXHBwMMHBwY6uRqmWNSN6aVZqxzQBJP+STvj0BkT3/JSOg2r9NcYp\n2dE1E0IIIcTdptQGTcnJEP5YCtGpA/F5/V9ArsHhyY6snRBCCCHuNqU2aNr6lSY6fRg+wX7wwAPZ\n6VmB09atjqubEEIIIe4+pXZM07Cqn8ORTfDfvNGRj4+MaxJCCCFE0Sq1PU288Qa0bQvt2zu6JkII\nIYS4B5TOnqbt22HHDli3DmycAl8IIYQQojBKZ0/TjBng7w+PPuromgghhBDiHlGgoEkp9axS6rhS\n6oZS6hul1EP55P2XUmqbUupC5vZlfvnv6McfYcMGmDgRTKUz5hNCCCFE6WN31KGUCgHmAJFAc2Af\nsFEp5WXllI7AMiAIaA38CmxSSv2jIBVm5kyoWxdCQgp0uhBCiJKvVq1aREREZO9v3rwZk8nEzp07\n73huu3bt6NatW5HWZ/LkyTg7OxfpNUXpU5CumjHAfK31Eq31IWAEcB0It5RZaz1Eaz1Pa/2j1voI\n8K/Mcu2fP/3YMVi+HP79b5AfXiGEcKjevXvj7u7OtWvXrOYZNGgQZcuW5eLFi3ZdW1kYr2opzdZz\nbXHt2jWioqLYvn27xWuaHPx048KFC7i4uODk5ERSUpJD63KvsusnQCnlDAQCm7PStNYaSADa2HgZ\nd8AZuGBP2QDMmQNVqhizVwohhHCowYMHk5qayqeffmrx+I0bN1i3bh09evTA09OzUGV16dKFGzdu\n0LZt20JdJz9Xr14lKiqKbdu25TkWFRXF1atXi61sW6xYsQJnZ2eqVavG0qVLHVqXe5W9YbMX4ASc\ny5V+Dqhu4zVmAqcwAi3bnTtnzFr5wgvg5mbXqUIIIYpe7969KV++PMuWLbN4fM2aNVy/fp1BgwYV\nSXkuLi5Fch1rjD4Ay0wmk8Mfz8XExNC7d29CQkJKdNCktebmzZuOrkaxKKq+RgVY/2nLyqTUROAJ\n4HGt9S27Snj3XShTBkaNKlgNhRCiBFu82PryT8nJxvGSdu1y5crRt29fEhISSElJyXN82bJllC9f\nnl69emWnzZw5k4cffpgqVarg5ubGQw89xJo1a+5YlrUxTR9++CENGjTAzc2NNm3aWBzzdPPmTV55\n5RUCAwOpVKkS5cuXJygoiK+//jo7T1JSEjVq1EApxeTJkzGZTJhMJqZPnw5YHtOUnp5OVFQUDRo0\noFy5ctSvX58pU6aQlpZmlq9WrVr07duXbdu20bJlS1xdXWnYsKHVYNOS5ORkdu7cSVhYGCEhIRw9\nepTdu3dbzLtr1y6Cg4Px9PSkfPnyPPjgg3zwwQdmeQ4ePMiAAQOoWrUqbm5uNG3alMjIyOzjgwcP\nxtfXN8+1c38OGRkZmEwmxo4dy8cff4y/vz/lypVj82bjgZQ97b1kyRJatmyJu7s7VapUISgoiP/9\n73+A8Zi3evXqFhf87dy5M/fff/8dPsGiYW/QlAJkAN650quRt/fJjFLq38CLQFet9c+2FDZmzBh6\n9+5N7+Bger/5Jr29vYmNj7ezykIIUfJ17Gh53czkZCO9Y8eSee1BgwaRnp7OihUrzNIvXrzIpk2b\n6NevH2XLls1Of++99wgMDOT111/njTfewGQy0a9fPzZt2nTHsnKPVZo/fz7PPvsstWvX5s0336RN\nmzb06tWL06dPm+W7dOkSixYtokuXLsyaNYtXX32Vs2fP0q1bN37+2fhzVL16dT744AO01gwYMICY\nmBhiYmJ4/PHHs8vOXf6TTz5JVFQUrVq14u2336Z9+/a8/vrrDB48OE+9Dx8+TGhoKI888ghvvfUW\nFStWZNiwYRw9evSO9w2wdOlSKlWqRHBwMG3atKFu3boWe5vi4+MJCgriyJEjjBs3jrfeeougoCA2\nbNiQnWfv3r20bt2abdu2MXLkSN577z0ee+wxszyW7je/9E2bNjFhwgQGDhzIO++8Q506dQDb2/uV\nV17hySefxNXVlddee41XX32VWrVqsWXLFgCGDh3KH3/8QUKC+UOq06dPs23bNoYMGWLT5wgQGxtr\nxBY5tjFjxth2stbarg34Bng3x77CeCNufD7njAcuAg/ZWEYAoBMTE7XWWusZM7R2cdH61CkthBCl\nTWJiojb7TrPi+HGtO3Uy/mtpvzCK69oZGRm6Ro0a+uGHHzZLnzdvnjaZTDohIcEsPTU11Ww/LS1N\n+/n56UceecQsvVatWvqZZ57J3k9ISNAmk0nv2LFDa631rVu3tJeXl27ZsqVOT083K1cppbt27WpW\nx7S0NLPrX7p0SVetWlWPGDEiO+3s2bNaKaWnTZuW5z4nT56snZ2ds/cTExO1UkqPGjXKLN+YMWO0\nyWTS27dvN7sXk8mkv/nmG7OyXFxc9KRJk/KUZYmfn59+6qmnsvcnTJig//GPf+jbt29np6Wnp+s6\ndepoX19ffeXKFavXatu2rfb09NSnT5+2mmfw4MHa19c3T3ruzyE9PV0rpbSzs7M+evRonvy2tPfh\nw4e1yWTSISEhVuuT9XM2ZMgQs/RZs2ZpJycn/euvv1o9V+s7/w5mHQcCdD7xSUEez70FRCilhiql\nmgDzADdgEYBSaolSanpWZqXUi8BrGG/XnVRKeWdu7jaVlpoKb78Nw4ZBjRoFqK4QQpQOWQuOh4cb\ni46Hhxv7RbGWZnFd22QyERoayq5duzhx4kR2+rJly/D29qZz585m+XP2Ol26dIlLly7Rrl079uzZ\nY1e53377LefPn2fkyJE4OTllp4eHh+Ph4ZGnjmXKGAtgaK25ePEiaWlptGjRwu5ys3z++ecopRg7\ndqxZ+rhx49Bam/XaADRr1oxWrVpl73t7e+Pr68uxY8fuWNaePXs4ePAgAwcOzE4LCwvj3LlzZj0v\nu3fv5tdff2XMmDGUL1/e4rXOnTvHrl27eOaZZ/jHPwo2848lXbp0oWHDhnnSbWnv1atXA5g9HszN\nZDIxcOBA1qxZw40bN7LTly1bRocOHahVq1ZR3MYd2R00aa1XAOOAqcAPQDOgu9b6j8wstTAfFD4S\n4225T4DTObZxNhW4eDH8/juMH29vVYUQotTx8YHISAgKMv5blIuPF9e1Bw0ahNaa2NhYAE6dOsX2\n7dsJCwvL8yhn3bp1tG7dGldXVypXrky1atX473//y+XLl+0q88SJEyil8vyhdnZ2xsfCjX300Uc0\na9aMcuXKUaVKFapVq0Z8fLzd5eYsv0yZMjRo0MAsvWbNmnh4eJgFkED246qcPD09bZqKISYmBg8P\nD2rXrk1SUhJJSUm4u7tTq1Yts0d0SUlJKKXw9/e3eq2sqQryy1MQlj5zsK29jx07hpOTE40bN863\njGHDhnH16lXWrl0LwM8//8y+ffsYOnRokd3HnRRoILjWeq7W2kdr7aq1bqO13p3jWGetdXiO/Xpa\naycL29Q7FpSeDrNmQf/+YGFAmhBC3G2SkyEqCr76yvivtQHcJenaAQEBNGnSJHtgc9Z/c/aMAGzZ\nsoU+ffrg4eHBvHnz+OKLL0hISCAkJMTiAN/86Mw33SyNr8k6lmXRokU8/fTTNGnShI8++oiNGzeS\nkJBAx44d7S7XWhl3OpazN8zW62Qdj4uL4+rVqzRt2hRfX198fX1p1KgRv/32G59++impqak2XcvW\nPGB9rquMjAyL6a6urnnSbG1vrbVNc2vdd999PPDAA8TExABGMOnq6kq/fv1suaUiUbIX7N282ZjQ\ncuVKR9dECCGKXdbA7KzHZlmP04riMVpxXhuM3qYpU6bw008/ERsbi6+vL4GBgWZ5Vq9ejbu7O/Hx\n8WZBxPz58+0uz8fHB601R44c4eGHH85OT0tL48SJE1Sv/tcDj1WrVtG4ceM8g9Vfeukls317JsX0\n8fEhPT2dpKQks96m06dPc/XqVerWrWvvLVm0efNmzpw5wxtvvJHnbbaUlBRGjhzJunXreOKJlVCZ\nrAAADSZJREFUJ2jYsCFaa/bv30+HDh0sXi+rZ27//v35luvp6cmlS5fypCfbEWnb2t4NGzYkPT2d\nQ4cO4efnl+81hw4dysSJE/n9999Zvnw5vXv3zvM4tjiV7MXbPvoIunWDgABH10QIIYpV7qAGzIOb\nwvQKFee1s2Q9opsyZQp79+7N8wYZGL0tJpPJrLfi2LFjfPbZZ3aX16pVKypXrsy8efPMrrdw4UKu\nXLmSp9zcduzYwffff2+W5u5uDLW1FCzk1qNHD7TWvPPOO2bpc+bMQSnFo0W0oHxMTAwVKlRg3Lhx\n9O3b12yLiIigXr162Y/oHnroIerUqcPbb7/Nn3/+afF63t7etG3bloULF3Lq1Cmr5TZo0IDz589z\n8ODB7LRTp07Z1Va2tnefPn0AYwLRO/WEDRw4kNu3bzN69GhOnjxp8eesOJXsnqajR6EA/wIRQojS\nZutWy70+WcHN1q0F7xEqzmv/dS0f2rZty9q1a1FK5Xk0B9CzZ0/ee+89unfvTlhYGGfOnGHu3Lk0\nbtw4+9X//OT8g+rs7Mxrr73Gc889R6dOnQgJCeGXX35hyZIl1KtXL0+569ato2/fvgQHB5OUlMSC\nBQvw8/Mzm4TR3d2dRo0aERsbS/369fH09KRZs2Y0bdo0T10CAgIYNGgQc+fO5fz587Rv355du3YR\nExPDE088Ydb7VVBZs60HBwdnD2TPrVevXnz44YdcuHCBypUrM3fuXPr06cODDz7IU089RfXq1Tl0\n6BCHDx9m/fr1ALz//vt07NiR5s2bExERgY+PD8eOHWPTpk3Zcz8NHDiQl156id69ezN69GiuXr3K\nvHnzaNKkCfv27bOp/ra2d6NGjZg4cSIzZsygY8eOPP7447i4uPD9999Tt25dpk79azSPt7c3Xbt2\nZeXKlXh5efHII48U9OMtmPxerXPURtaUA/fdp/Xt2/r4ca0XLcr3bUIhhCixbJ1yoLSbO3euNplM\nuk2bNlbzLFy4UDdq1Ei7urpqf39//fHHH+d5jV1rrWvXrq0jIiKy93NPOZCzzPr162tXV1fdpk0b\nvXPnTt2+fXvdrVs3s3zTpk3TPj4+2s3NTbdo0ULHx8frwYMH60aNGpnl27Fjh27RooUuV66cNplM\n2dMPTJ48Wbu4uJjlTU9P11FRUbp+/fq6bNmy2sfHR0+ZMiXP9Aa1a9fWffv2zfNZtGvXLk89c1qx\nYoU2mUw6JibGap7Nmzdrk8mkP/zww+y07du3665du+oKFSpoDw8P3bx5cz1//nyz8/bv36/79Omj\nK1eurN3d3bWfn5+eOnWqWZ6NGzfq++67T5ctW1b7+fnpuLg4i1MOmEwmPXbsWIv1s7W9tdY6Ojpa\nBwQEaFdXV12lShXduXNnvWXLljz5YmNjtVJKjx492urnkltRTTmgtI2Dwv5OSqkAIDFx9mwq9xtX\npM/dhRDi77Znzx4CAwNJTEwkQIYbCFEoq1evZsCAAezatYuWLVvadM6dfgezjgOBWmur81CU6DFN\np307SsAkhBBCiGwLFizA19fX5oCpKJXoMU1Rr5lYuVICJiGEEOJet3z5cvbu3cuXX37J3LlzHVKH\nEh00RURIwCSEEELc6zIyMhg4cCAeHh5EREQQERHhkHqU6KBpwQLo2lUCJyGEEOJe5uTkVOCJSItS\niR7TFBlZdHOICCGEEEIURokOmmrUKNrJ14QQQgghCqpEB01gPvmaEEIIIYSjlOgxTVl8fGRckxBC\nCCEcq1QETUIIcTfIuY6XEOLvU1S/exI0CSFEMfPy8sLNze1vX1xUCPEXNzc3vLy8CnUNCZqEEKKY\n1alTh4MHD5KSkuLoqghxz/Ly8qJOnTqFuoYETaJIxMbGEhYW5uhqiGIkbVw4derUKfQXdnGS9r37\nSRsXXoHenlNKPauUOq6UuqGU+kYp9dAd8g9QSh3MzL9PKRVcsOqKkio2NtbRVRDFTNr47ibte/eT\nNi48u4MmpVQIMAeIBJoD+4CNSimLDwqVUm2AZcB/gQeBNcAapZRfQSsthBBCCPF3K0hP0xhgvtZ6\nidb6EDACuA6EW8n/AvCF1votrfVhrXUksAd4rkA1FkIIIYRwALuCJqWUMxAIbM5K01prIAFoY+W0\nNpnHc9qYT34hhBBCiBLH3oHgXoATcC5X+jmgsZVzqlvJXz2fcsqBzGlSmly+fJk9e/Y4uhqiGEkb\n392kfe9+0sbW5Yg3yuWXr6jenlOALsL8PoDMaVLKBAYGOroKophJG9/dpH3vftLGd+QD7LR20N6g\nKQXIALxzpVcjb29SlrN25gfj8d0gIBlItbOOQgghhBD2KIcRMG3ML5MyhiTZTin1DfCt1vqFzH0F\nnATe01q/aSH/csBVa/1YjrQdwD6t9Si7ChdCCCGEcJCCPJ57C1islEoEvsN4m84NWASglFoC/Ka1\nfikz/7vAVqXUWGADEIYxmPyZwlVdCCGEEOLvY3fQpLVekTkn01SMx257ge5a6z8ys9QC0nPk36WU\nCgOmZW5Hgce01gcKW3khhBBCiL+L3Y/nhBBCCCHuRQVaRkUIIYQQ4l4jQZOwSCkVqZS6nWs7kON4\nWaXUB0qpFKXUFaXUJ0qparmuUVsptUEpdU0pdVYpNUspJT9zDqKUaq+UWqeUOpXZnr0t5JmqlDqt\nlLqulPpSKdUw13FPpdRSpdRlpdRFpdRCpZR7rjzNlFLbMteaPKGUGl/c9ybu3L5KqY8s/E5/niuP\ntG8JpZSapJT6Tin1p1LqnFLqU6VUo1x5iuR7WSkVpJRKVEqlKqWOKKWG/R33WBrIHzCRn/0Y49aq\nZ27tchx7B3gU6Ad0AGoAq7IOZv4Sfo4xbq41MAx4EmMsnHAMd4wxiM9iYZ40pdQEjOWNhgMtgWsY\n60q65Mi2DGgKdMFo/w7A/BzX8MB4Zfc4EACMB15VSv2rGO5HmMu3fTN9gfnvdO4l76V9S672wPtA\nK+CfgDOwSSnlmiNPob+XlVI+wHqMlT8ewHiZa6FSqmux3FVpo7WWTbY8G8aCzHusHKsA3AT65Ehr\nDNwGWmbuBwNpgFeOPMOBi0AZR9/fvb5ltlXvXGmngTG52vkG8ETmftPM85rnyNMd48WP6pn7IzHm\ncyuTI88bwAFH3/O9tFlp34+A1fmc00Tat/RsGCt03AbaZe4XyfcyMBP4MVdZscDnjr7nkrBJT5PI\nj29mV3+SUipGKVU7Mz0Q418qOdcgPIwxX1fWmoKtgZ+01ik5rrcRqAj4F3/VhT2UUvUweh5ytumf\nwLeYt+lFrfUPOU5NwOjVaJUjzzatdXqOPBuBxkqpisVUfWG7oMxHO4eUUnOVUpVzHGuDtG9pUgmj\nbS5k7hfV93JrZL1YqyRoEtZ8g9Ft2x0YAdQDtmWOb6gO3Mr8o5pTzjUFra05CPmvOygcozrGF3B+\n60RWB37PeVBrnYHxpS3tXvJ9AQwFOgMvAh2BzzMnKAZp31Ijs83eAbbrv6bvKarvZWt5Kiilyha2\n7qVdUa09J+4yWuucU8nvV0p9B5wAnsD60ja2rkEo81yUHra06Z3yZP1RlnZ3IK31ihy7PyulfgKS\ngCBgSz6nSvuWPHMBP8zHmVpTFN/L0saZpKdJ2ERrfRk4AjTEWE/QRSlVIVe2nGsKWlpzMGs/v3UH\nhWOcxfhizG+dyLOZ+9mUUk6AZ+axrDyWrgHS7iWK1vo4xvikrDckpX1LAaXUf4AeQJDW+nSOQ4X9\nXr5TG/+ptb5VmLrfDSRoEjZRSpUHGmAMFk7EGBzaJcfxRkAd/lodehdwf+bs8Vm6AZcBmQ2+hMn8\nA3oW8zatgDGWJWebVlJKNc9xaheMYOu7HHk6ZP6xzdINOJwZeIsSQilVC6gCnMlMkvYt4TIDpseA\nTlrrk7kOF/Z7+WCOPF0w1y0zXTh6JLpsJXMD3sR4ZbUu0Bb4EuNfK1Uyj8/FeO04CGMA4g7g6xzn\nm4B9GOMommGMjToHvOboe7tXN4xX0h8AHsR4o+b/MvdrZx5/ETgP9ALuB9ZgLHvkkuManwO7gYeA\nh4HDwMc5jlfACKwXYzw+CAGuAk87+v7v9i2/9s08NgsjCK6L8UdxN8YfSmdp35K/ZX7nXsSYesA7\nx1YuV55CfS8DPpltOhPj7btRwC3gn47+DErC5vAKyFYyN4xXTH/DeOX8JMb8LfVyHC+LMWdICnAF\nWAlUy3WN2hjzfVzN/MWcCZgcfW/36oYx8Pc2kJFri86R59XMP4rXMd6YaZjrGpWAGIx/mV4E/gu4\n5cpzP7A18xongX87+t7vhS2/9gXKAfEYvYmpwDHgQ6CqtG/p2Ky0bQYwNEeeIvlezvxZSsz8/j8K\nDHH0/ZeUTdaeE0IIIYSwgYxpEkIIIYSwgQRNQgghhBA2kKBJCCGEEMIGEjQJIYQQQthAgiYhhBBC\nCBtI0CSEEEIIYQMJmoQQQgghbCBBkxBCCCGEDSRoEkIIIYSwgQRNQgghhBA2kKBJCCGEEMIGEjQJ\nIYQQQtjg/wGZCLKk0oCDaQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11cf43a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation accuracy at 0.7578666806221008\n"
]
}
],
"source": [
"# Change if you have memory restrictions\n",
"batch_size = 128\n",
"\n",
"# TODO: Find the best parameters for each configuration\n",
"epochs = 2\n",
"learning_rate = 0.1\n",
"\n",
"\n",
"\n",
"### DON'T MODIFY ANYTHING BELOW ###\n",
"# Gradient Descent\n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss) \n",
"\n",
"# The accuracy measured against the validation set\n",
"validation_accuracy = 0.0\n",
"\n",
"# Measurements use for graphing loss and accuracy\n",
"log_batch_step = 50\n",
"batches = []\n",
"loss_batch = []\n",
"train_acc_batch = []\n",
"valid_acc_batch = []\n",
"\n",
"with tf.Session() as session:\n",
" session.run(init)\n",
" batch_count = int(math.ceil(len(train_features)/batch_size))\n",
"\n",
" for epoch_i in range(epochs):\n",
" \n",
" # Progress bar\n",
" batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')\n",
" \n",
" # The training cycle\n",
" for batch_i in batches_pbar:\n",
" # Get a batch of training features and labels\n",
" batch_start = batch_i*batch_size\n",
" batch_features = train_features[batch_start:batch_start + batch_size]\n",
" batch_labels = train_labels[batch_start:batch_start + batch_size]\n",
"\n",
" # Run optimizer and get loss\n",
" _, l = session.run(\n",
" [optimizer, loss],\n",
" feed_dict={features: batch_features, labels: batch_labels})\n",
"\n",
" # Log every 50 batches\n",
" if not batch_i % log_batch_step:\n",
" # Calculate Training and Validation accuracy\n",
" training_accuracy = session.run(accuracy, feed_dict=train_feed_dict)\n",
" validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n",
"\n",
" # Log batches\n",
" previous_batch = batches[-1] if batches else 0\n",
" batches.append(log_batch_step + previous_batch)\n",
" loss_batch.append(l)\n",
" train_acc_batch.append(training_accuracy)\n",
" valid_acc_batch.append(validation_accuracy)\n",
"\n",
" # Check accuracy against Validation data\n",
" validation_accuracy = session.run(accuracy, feed_dict=valid_feed_dict)\n",
"\n",
"loss_plot = plt.subplot(211)\n",
"loss_plot.set_title('Loss')\n",
"loss_plot.plot(batches, loss_batch, 'g')\n",
"loss_plot.set_xlim([batches[0], batches[-1]])\n",
"acc_plot = plt.subplot(212)\n",
"acc_plot.set_title('Accuracy')\n",
"acc_plot.plot(batches, train_acc_batch, 'r', label='Training Accuracy')\n",
"acc_plot.plot(batches, valid_acc_batch, 'x', label='Validation Accuracy')\n",
"acc_plot.set_ylim([0, 1.0])\n",
"acc_plot.set_xlim([batches[0], batches[-1]])\n",
"acc_plot.legend(loc=4)\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"print('Validation accuracy at {}'.format(validation_accuracy))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test\n",
"You're going to test your model against your hold out dataset/testing data. This will give you a good indicator of how well the model will do in the real world. You should have a test accuracy of at least 80%."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 1/2: 100%|██████████| 1114/1114 [00:00<00:00, 1326.07batches/s]\n",
"Epoch 2/2: 100%|██████████| 1114/1114 [00:00<00:00, 1387.72batches/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nice Job! Test Accuracy is 0.8278999924659729\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"### DON'T MODIFY ANYTHING BELOW ###\n",
"# The accuracy measured against the test set\n",
"test_accuracy = 0.0\n",
"\n",
"with tf.Session() as session:\n",
" \n",
" session.run(init)\n",
" batch_count = int(math.ceil(len(train_features)/batch_size))\n",
"\n",
" for epoch_i in range(epochs):\n",
" \n",
" # Progress bar\n",
" batches_pbar = tqdm(range(batch_count), desc='Epoch {:>2}/{}'.format(epoch_i+1, epochs), unit='batches')\n",
" \n",
" # The training cycle\n",
" for batch_i in batches_pbar:\n",
" # Get a batch of training features and labels\n",
" batch_start = batch_i*batch_size\n",
" batch_features = train_features[batch_start:batch_start + batch_size]\n",
" batch_labels = train_labels[batch_start:batch_start + batch_size]\n",
"\n",
" # Run optimizer\n",
" _ = session.run(optimizer, feed_dict={features: batch_features, labels: batch_labels})\n",
"\n",
" # Check accuracy against Test data\n",
" test_accuracy = session.run(accuracy, feed_dict=test_feed_dict)\n",
"\n",
"\n",
"assert test_accuracy >= 0.80, 'Test accuracy at {}, should be equal to or greater than 0.80'.format(test_accuracy)\n",
"print('Nice Job! Test Accuracy is {}'.format(test_accuracy))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multiple layers\n",
"Good job! You built a one layer TensorFlow network! However, you might want to build more than one layer. This is deep learning after all! In the next section, you will start to satisfy your need for more layers."
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
bloomberg/bqplot | examples/Tutorials.ipynb | 2 | 1304 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorials"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Basic Plotting\n",
"* [Using the Object Model](Tutorials/Object%20Model.ipynb)\n",
"* [Using `pyplot`](Tutorials/Pyplot.ipynb)\n",
"\n",
"## Interactive Plotting\n",
"* [Updating plots](Tutorials/Updating%20Plots.ipynb)\n",
"* ### Integrating with `ipywidgets`\n",
" * [Linking with ipywidgets](Tutorials/Linking%20Plots%20With%20Widgets.ipynb)\n",
" * [Application: Gaussian Density](Tutorials/Gaussian%20Density.ipynb)\n",
"\n",
"* ### Linking Plots Using Selectors\n",
" * [Fast Interval Selector](Tutorials/Fast%20Interval%20Selector.ipynb)\n",
" * [Brush Interval Selector](Tutorials/Brush%20Interval%20Selector.ipynb)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
tpin3694/tpin3694.github.io | python/pandas_dataframe_count_values.ipynb | 1 | 24677 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Count Values In Pandas Dataframe \n",
"Slug: pandas_dataframe_count_values \n",
"Summary: Count Values In Pandas Dataframe \n",
"Date: 2016-05-01 12:00 \n",
"Category: Python \n",
"Tags: Data Wrangling \n",
"Authors: Chris Albon "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import the pandas module"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create all the columns of the dataframe as series"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"year = pd.Series([1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, \n",
" 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894])\n",
"guardCorps = pd.Series([0,2,2,1,0,0,1,1,0,3,0,2,1,0,0,1,0,1,0,1])\n",
"corps1 = pd.Series([0,0,0,2,0,3,0,2,0,0,0,1,1,1,0,2,0,3,1,0])\n",
"corps2 = pd.Series([0,0,0,2,0,2,0,0,1,1,0,0,2,1,1,0,0,2,0,0])\n",
"corps3 = pd.Series([0,0,0,1,1,1,2,0,2,0,0,0,1,0,1,2,1,0,0,0])\n",
"corps4 = pd.Series([0,1,0,1,1,1,1,0,0,0,0,1,0,0,0,0,1,1,0,0])\n",
"corps5 = pd.Series([0,0,0,0,2,1,0,0,1,0,0,1,0,1,1,1,1,1,1,0])\n",
"corps6 = pd.Series([0,0,1,0,2,0,0,1,2,0,1,1,3,1,1,1,0,3,0,0])\n",
"corps7 = pd.Series([1,0,1,0,0,0,1,0,1,1,0,0,2,0,0,2,1,0,2,0])\n",
"corps8 = pd.Series([1,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,1,1,0,1])\n",
"corps9 = pd.Series([0,0,0,0,0,2,1,1,1,0,2,1,1,0,1,2,0,1,0,0])\n",
"corps10 = pd.Series([0,0,1,1,0,1,0,2,0,2,0,0,0,0,2,1,3,0,1,1])\n",
"corps11 = pd.Series([0,0,0,0,2,4,0,1,3,0,1,1,1,1,2,1,3,1,3,1])\n",
"corps14 = pd.Series([ 1,1,2,1,1,3,0,4,0,1,0,3,2,1,0,2,1,1,0,0])\n",
"corps15 = pd.Series([0,1,0,0,0,0,0,1,0,1,1,0,0,0,2,2,0,0,0,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a dictionary variable that assigns variable names"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"variables = dict(guardCorps = guardCorps, corps1 = corps1, \n",
" corps2 = corps2, corps3 = corps3, corps4 = corps4, \n",
" corps5 = corps5, corps6 = corps6, corps7 = corps7, \n",
" corps8 = corps8, corps9 = corps9, corps10 = corps10, \n",
" corps11 = corps11 , corps14 = corps14, corps15 = corps15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a dataframe and set the order of the columns using the columns attribute"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"horsekick = pd.DataFrame(variables, columns = ['guardCorps', \n",
" 'corps1', 'corps2', \n",
" 'corps3', 'corps4', \n",
" 'corps5', 'corps6', \n",
" 'corps7', 'corps8', \n",
" 'corps9', 'corps10', \n",
" 'corps11', 'corps14', \n",
" 'corps15'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set the dataframe's index to be year"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"horsekick.index = [1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, \n",
" 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### View the horsekick dataframe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>guardCorps</th>\n",
" <th>corps1</th>\n",
" <th>corps2</th>\n",
" <th>corps3</th>\n",
" <th>corps4</th>\n",
" <th>corps5</th>\n",
" <th>corps6</th>\n",
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" <th>corps8</th>\n",
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" <tr>\n",
" <th>1886</th>\n",
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" <tr>\n",
" <th>1887</th>\n",
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" <tr>\n",
" <th>1888</th>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1889</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1890</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1891</th>\n",
" <td>0</td>\n",
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" <td>3</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>1892</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1893</th>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>1894</th>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" guardCorps corps1 corps2 corps3 corps4 corps5 corps6 corps7 \\\n",
"1875 0 0 0 0 0 0 0 1 \n",
"1876 2 0 0 0 1 0 0 0 \n",
"1877 2 0 0 0 0 0 1 1 \n",
"1878 1 2 2 1 1 0 0 0 \n",
"1879 0 0 0 1 1 2 2 0 \n",
"1880 0 3 2 1 1 1 0 0 \n",
"1881 1 0 0 2 1 0 0 1 \n",
"1882 1 2 0 0 0 0 1 0 \n",
"1883 0 0 1 2 0 1 2 1 \n",
"1884 3 0 1 0 0 0 0 1 \n",
"1885 0 0 0 0 0 0 1 0 \n",
"1886 2 1 0 0 1 1 1 0 \n",
"1887 1 1 2 1 0 0 3 2 \n",
"1888 0 1 1 0 0 1 1 0 \n",
"1889 0 0 1 1 0 1 1 0 \n",
"1890 1 2 0 2 0 1 1 2 \n",
"1891 0 0 0 1 1 1 0 1 \n",
"1892 1 3 2 0 1 1 3 0 \n",
"1893 0 1 0 0 0 1 0 2 \n",
"1894 1 0 0 0 0 0 0 0 \n",
"\n",
" corps8 corps9 corps10 corps11 corps14 corps15 \n",
"1875 1 0 0 0 1 0 \n",
"1876 0 0 0 0 1 1 \n",
"1877 0 0 1 0 2 0 \n",
"1878 0 0 1 0 1 0 \n",
"1879 1 0 0 2 1 0 \n",
"1880 0 2 1 4 3 0 \n",
"1881 0 1 0 0 0 0 \n",
"1882 1 1 2 1 4 1 \n",
"1883 0 1 0 3 0 0 \n",
"1884 0 0 2 0 1 1 \n",
"1885 0 2 0 1 0 1 \n",
"1886 0 1 0 1 3 0 \n",
"1887 1 1 0 1 2 0 \n",
"1888 0 0 0 1 1 0 \n",
"1889 0 1 2 2 0 2 \n",
"1890 0 2 1 1 2 2 \n",
"1891 1 0 3 3 1 0 \n",
"1892 1 1 0 1 1 0 \n",
"1893 0 0 1 3 0 0 \n",
"1894 1 0 1 1 0 0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"horsekick"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Count the number of times each number of deaths occurs in each regiment"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th>guardCorps</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>9.0</td>\n",
" <td>11.0</td>\n",
" <td>12.0</td>\n",
" <td>11.0</td>\n",
" <td>12.0</td>\n",
" <td>10.0</td>\n",
" <td>9.0</td>\n",
" <td>11.0</td>\n",
" <td>13.0</td>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" <td>8.0</td>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>7.0</td>\n",
" <td>7.0</td>\n",
" <td>6.0</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" guardCorps corps1 corps2 corps3 corps4 corps5 corps6 corps7 corps8 \\\n",
"0 9.0 11.0 12.0 11.0 12.0 10.0 9.0 11.0 13.0 \n",
"1 7.0 4.0 4.0 6.0 8.0 9.0 7.0 6.0 7.0 \n",
"2 3.0 3.0 4.0 3.0 0.0 1.0 2.0 3.0 0.0 \n",
"3 1.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" corps9 corps10 corps11 corps14 corps15 \n",
"0 10.0 10.0 6 6 14.0 \n",
"1 7.0 6.0 8 8 4.0 \n",
"2 3.0 3.0 2 3 2.0 \n",
"3 0.0 1.0 3 2 0.0 \n",
"4 0.0 0.0 1 1 0.0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result = horsekick.apply(pd.value_counts).fillna(0); result"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Count the number of times each monthly death total appears in guardCorps"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 9\n",
"1 7\n",
"2 3\n",
"3 1\n",
"dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(horsekick['guardCorps'].values, sort=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### List all the unique values in guardCorps"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 2, 1, 3])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"horsekick['guardCorps'].unique()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
relopezbriega/mi-python-blog | content/notebooks/pyplots.ipynb | 1 | 711473 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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txQcW03NhT1nTRwgvNn7bePpFJo3o6/FoD3pV9a5rdUviv4NOD3dyGLY1dttY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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f68a0680358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"x = np.linspace(-1,1,50)\n",
"y1 = np.sqrt(x * x) + np.sqrt(1 - x * x)\n",
"y2 = np.sqrt(x * x) - np.sqrt(1 - x * x)\n",
"plt.plot(x, y1, c='r', lw = 3)\n",
"plt.plot(x, y2, c='r', lw = 3)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext version_information"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/json": {
"Software versions": [
{
"module": "Python",
"version": "3.4.3 64bit [GCC 4.9.2]"
},
{
"module": "IPython",
"version": "3.1.0"
},
{
"module": "OS",
"version": "Linux 3.19.0 30 generic x86_64 with Ubuntu 15.04 vivid"
},
{
"module": "numpy",
"version": "1.10.0.post2"
}
]
},
"text/html": [
"<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.4.3 64bit [GCC 4.9.2]</td></tr><tr><td>IPython</td><td>3.1.0</td></tr><tr><td>OS</td><td>Linux 3.19.0 30 generic x86_64 with Ubuntu 15.04 vivid</td></tr><tr><td>numpy</td><td>1.10.0.post2</td></tr><tr><td colspan='2'>Sun Oct 11 17:15:32 2015 ART</td></tr></table>"
],
"text/latex": [
"\\begin{tabular}{|l|l|}\\hline\n",
"{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
"Python & 3.4.3 64bit [GCC 4.9.2] \\\\ \\hline\n",
"IPython & 3.1.0 \\\\ \\hline\n",
"OS & Linux 3.19.0 30 generic x86\\_64 with Ubuntu 15.04 vivid \\\\ \\hline\n",
"numpy & 1.10.0.post2 \\\\ \\hline\n",
"\\hline \\multicolumn{2}{|l|}{Sun Oct 11 17:15:32 2015 ART} \\\\ \\hline\n",
"\\end{tabular}\n"
],
"text/plain": [
"Software versions\n",
"Python 3.4.3 64bit [GCC 4.9.2]\n",
"IPython 3.1.0\n",
"OS Linux 3.19.0 30 generic x86_64 with Ubuntu 15.04 vivid\n",
"numpy 1.10.0.post2\n",
"Sun Oct 11 17:15:32 2015 ART"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%version_information numpy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f85aef29208>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Rb7mLfYkggzF0Kx/qr7Kt4a+/o7X4RdxEXszgqFO2qOk91U1UumarFj8cM0uUnnDx2qkJ\nRgtEb5G+kmUAsEn3Deg3s1MQeQnd1+pyiqy3AJy9T+uW8zRuK8zHJojVnncuS4X6mjXSalG29NUx\nMii5Vm29trb1mDtXlXlKW94+xpB6eiEilxyE+EGQwTh+gNkPgCRmLYPcobwF2BYJIFuNJZeNafEl\nDa7mRmrV0mtuIi1IFjlnKwFpF4GuGyODdeCb6b4hsVP4SeAnmJ0vUcsM8mIGtWdnUVsnyHsf1pVX\ncyNhtm1f9CzOVjJoVYZaAsg1Msjt1C7gc9QxHwSuZZ8jyGAcOatAL/GrO4xeNRJmlwpujRno7ZKb\nSNfbmb8twr42gG07W1wEnptI149lkrS6iUrWhD52jCxmha7IixB5gXOe1ULkWxD5M1VyJ+NkcJZZ\nwZ/hPdvSs6stKeIdM9UVWDtnyU00Zo20WAbemJky6WyMDPQEP231Z/fQMbo08t3jctwiBBmMI3eY\nbDJay8Ajg/mUtnrMwBPCHhl4AeYp2pYOoHlCOte3EEjNXWBdHSW3xhSysKhZBlbo/iu6j8BsNb4V\neJT6fYbZdXNsu/QKox6Reu/Y1o+5iWquvtb6RSedeWShn0erZVBzE9UCyDpmoMlgzDLIx9g5RfsO\nQQbjuLD/n4X9JrPagyUDrXHojul1fC+ArDtuKYDWYkbXyEALFytQ8zE1F4PVEGtuIivsW2Y1l5Y/\nwKn31qDx0O0vcu+GfaehfE79vLysKJsmitn2rCZb732opubqq7kX265Ztyi9a5YUjzFlyLM2ahl6\nNctAu+By/TFm3UQHAkEG48gWQY4JtFgGnuDX/v+xzAdP2Jc0p1q2hSdIPDLwrlnTREuC23NneddZ\nVOBZ6HaIs49uR0bW8D6PyIOccy6DzyNyJfOkpAPAh6mvE9Sy2qeu9563PadXNpXwS23S/dezcKYo\nM62WwVg69pppk0cGmizyuMhziKybSN/DvoU3czEwIFsGuZNoMjiKbxm0prR5/k/dcZexDGg4ZsxN\nVLIMprgDrNZYE/bedUq+8gyd/eJZDlpjzsefVdrzVixbfD7zgsOSgUXLHAy7dLOu955tLYDc4gZq\nPd6bIFa6D2sZtPRvj2yOOPVenMFzE9kAcr6WJgPrJtr3sjIsg3FkMsgrGuqOM+Ymytq+1Ui8bCHr\nJqoNhkXIYM0cY7W+mumOKveOt/U1gtGCexnLwF7T4hDzbrtN9MDuFh5cfIKRyEWIXKhK9LPR7Rgj\nA1j+2XiCvVWzn2L9eX1gihJR67+2r1rLwFrGusxTusbcRJv9/3ytrBx4bqJ9Lyv3/Q0uBJHH9C6E\n3DmyLzh3kjsZiGGd4Ru0uePdxaz2cYzOPVGKCXiWQc2MbvXvtxzTquF5g7YknKxpbwVa60qorWQw\nCNFB89fP0wueHwV+jmHp6EXwbuD96nfJXaWf8VjsY1GXjiWbRSwDve25Z0qCu+R+tOfU7VzETeTV\n5zGlv3ts3bGH6cbpuQzj9AhwUm1vMO8m2kQTRPcRnMezT7HvTZ8F8SfA2+lWM0zAfegEfCaD2xks\ngzvpYgonGbIUcic73J/jGHAKv+OWOnvOcqhlW7QMQE/zrw3qqVpjacXLKaRlP6+ptz03kK23ZbXJ\nWkeAZT+O3n3TYvaLb62fi8yofSegZhks8g6XPWdJsHvnrAl7T3EpkUFJWTpFN842GMZh3j6s6rWi\nppW6O+nI4CSDm+jLdF6Bu+gUvqcCv4fIGintu09mhmXg4/0MsxD/js4yuI1OszgH+ApDJ7qDrqNo\nAriLoRPe1R93StWf6s+jrYV15i2H00zXnKA8A7mk+U+1NjwCKl3zsHNMTfv13BKHmM+Ysce3LMVt\nyWBVg1pbIC0fkqktJ22P8Z5NTeNutd5q77jk6qtZDvacJSu11r9tsPgs3fjQY+Y0w5jRYy8Twym6\ncajHqbYM7qAb71nRO4duobp7043303RL1+u27SssTQYicpWI3CQiHxOR5zj1x0XkyyLywf7vXy97\nzW3AC+g60oV0ZHAfOi3hEB1J3EHXWbJ2cY/+f+5YWfuwZGC1lCzsDzMr+LPlcEaVJbV9Bp8MSsLc\nsyY8DbAmuKe4iew57TGt8wx0O7zPd7a2o6TxDoJb5Fea3AAiFyPyp6ZU562PLTexZsq9di4yz2Ds\nHZbcQK3WX6t70V7TSyLQfXETnwxy/06Fei34rWVgx5lVyrRlcJJ5y+ArDG6iL9EtWX+SjiQeDPxb\nUtLfP9g3WMpNJN0qfy8BvpvO7/p+EXlDSunDZtd3pJSetMy1thmfp9MILgA+3G/fRdf5LqTrGLoT\nZTfRxQxkcA5d589r7mtrIXfcs3Qd/ijzxHCUWTKw295gyR8+b9H8PW2spAFitj0hW3ITTckmmqod\nT/lIj1dmtfh/DjwUqK1l9BC6FWxhWEpCP7vunGULxitbVX3JpdNyzpK1UZqf0mo5eP3uKF3/HyOD\nrOXnssNOvXXH5nGUx6Eec3mc3puBDPIaYyfpyOD2/n+2DDIZrNO99xvYp1jWMrgSuDmldEtK6Szw\nGuDJzn67a135Or5A12GyZWDJIGsPmQw8N1EW9meZtwxyx82fM9TmrdV89ACwg0WXweyHOqxGngNn\nugzqgqRm7mtB0qpVelp+STjVBGKr60m7s3TbFnETaQLJVoC+T9uOqct3L/PluVXGDKwwt+f03rFn\neXh94Ah1ZceWHaZ7ltlNpC2DPGbOMFjWnjVuYwbn9ec7zaxloMngLjqSeBDdt7f3JZYlgwcwu5Db\nJ/syjQQ8RkRuEJFrpPve627HF+hcQxcCtzKQwSkGMrCWgXYTnaQjgHW6juaZr9nnuc6sS2iKZaA1\nq3UGTXeDecvhLOWg89igLgnemqAZEyQ14VMS7DVXyVTLwJsA5gV/LXQQOO/fagG1tnNKvX52Xjs8\nS82zxKylNXaMri+5H70+oBUX3ad1X7ZkoMfEJsOYsdZ0DiDn+hY30fn9dc4yWAbaTXRvBjfR19DJ\nhn2JZbOJWrSqPwcuTymdFJEnAK8Hvs7bUUSuVj9PpJROLNm+RXGSIWf5Njpi+Bxdh7sI+Gu6YJLu\nUDmbSHe8LOyzRnIeg38zk0G2DE4xq+XkwXAh8zGFs6o+D7A8gPL3co8wLIC21m+3uommaJW1ei+4\n6AlEzzXVYhksov16pJbhBX8tukywbiJbJoOx+7BtsmU1y2BwPc3e55hLZ4r15z1vT0mwhKz7jXX5\n5H3zEi5W8Odjzu/3rVkGWfPPY8a6kXR9HnNWAfPI4B59O84wSwbn0I39ewMfY/hO8m3sMETkOHB8\n1eddlgw+BVyufl9OZx3cjZTSV9T2tSLy6yJycUrpi/ZkKaWrl2zPapBSQiT73u9kMBW1ZXA+g3l5\nAfOZCef29Tm/ueQm0lqOlxmhB4P1mXoDKJOBV5/T7awg8MiiJkQ9TdQTTiVNFVVu6xfRjksad82a\nsGSwgchR4A3AE0lJB5j/D+AVzOazWzKoCVl9vVIAeaydtfqa9VfS8lHl9vgWgtGKx7raNysumiy0\nMmMt37WR+ty/PWv6TmZdrx4Z2NjdbcBX9ec5QzeOP8lsamke+3f09zont7YbvZJ8Iv8WkReu4rx2\nIEzFB4CHiMgV0g2gp9ENorshIpdIH0iTbt0W8YhgFyIP8hwYzjGDi+i0hwsYzMvzmdc4xtxEWdhn\nwV3yf2oCsGa09blmYb7JMLPSG4C6zLqRrAaYsz28dYbGhH2LprrIQnWWTKZM1qrVZ2zSvePHM7u8\nMcC/oIuJ6XtfxDKofVO6dW2iKZq/975aidKeM5ntnBqt+9Ias/1Kl3n9tyVmoJUd7QbKY8YqWJ6b\nKGv4efJodhNly8BmE13U75eV2r0guxbCUpZBSmlduq84vZnuhfxWSunDIvKsvv5ldB+I+QkRWacT\nkj+0ZJu3C5oMYNYyyBkHmgxO0QmSrH1YN9GXGY8ZWMtgEc0pa/7ZTWSPsYPScyNtmmNqWn4tuDjF\nl95qOXizbEuWQe2aFsKgXR+le5caeoVRPX4WueYUAhl7NpZMvHaMuQJrbbIEYpUMr6/ZfleKeWmC\n0PVZ8z8PX4HKbqILnHrtJsr1p4B70gn4swyZRdnCzzGDO/o2nMvgEjrJQCLZQth3WHoGckrpWsxX\ngHoSyNsvBV667HV2AHkgaDI4TdeJvkLXye5i6LA6cyiTQXYTHaMLRI9lE7VmU6T+uBY30Zg1URq0\nJXMffN9xa1qhFT4tgrtkGSySPlkiGBv3OqTO700u0kFWPX609VS7j7F2lp7NIVNWOr4W7G1JH86E\np99hYravaCXCE+z5GE8x0YpLbrMXQLbWsrUMSjEFTQb3YjZm8HkGMtCZgNkyON1vn0tHHDC4oNiP\nM48zliaDfYzn01kBd/a/76TrUDC4ibKWoTMSzmPWMtCWg52BbDtu9n/qNLmasF8z9ZtOvR20R9Qx\nVsOzWl8+v17gbYqbqBRAxmyXBLvnmirlvS+yjIPFGgMJeEtHePc+5Zq1e2sltZY5AWPvo1SmNf9M\nDCUlYazf6Mw2W9/al2sxA+1GzVa7jhl8oT9mjUEBy0pbHqfn01ntOWZwRp3/9v65nARezbAg5b5E\nkEEJKf0nAETy5CJNBtl/eJqhY9k5BdZNZGMGFzCr5dxJ15m1FtQSID5P1esAcSmmkGMYnoZ32CnL\n23mCT55klQVRqyZqhZONQ0yxDBZxPZXqp1oGNTKYIthL97FM6mktoG/fxzrz7dCCOacsl/qFZ1Fm\nxWODcvyq5NI8pI6v9X8vtdTGDLLSlcdm3taWgc4m0rOLs4vwJCm9F3gv+xhr9V0OPE6q/7mjZI0h\nB560ZXAuwzdvtZto6jyDXJ9nKNeyhUoD0JruNmaQj9eDXp8zCwpPKOiJbPmcOQur1XIYc1vUlky2\nGvMik7W8byDomIHFYebvrXTNVsG9qGXgEVDp2ZTIW89PsZq/FvbaGlhX+2olwvYb21d1v7NzYsbc\npGMxA20ZaDI4Zso8MsgzkE+ra51hUFSy8pe9A/saQQZ1ZDLwLAMdQLbmp+2Edmq8nWfg+Ud1TvWU\n1FEbM7AanHUDefW6LGco5awRewymrEX7HavPgqs2CWorLINMvqh709BkoOu3yzJYxA1UOl4L5nUG\n11OLxVhyE9l+M+YGqvXlsZhAKR07u5GyhW4JQo/TPLZzGZQsgwOAIIM6slZQsgxyJ8pWwDnMppaW\n3EQt8wysf7RlAGk/rWc5eIPaaoBaQ8wEUzL38yxerWlqLT4HvbWQrFkOpWyhZYRoSTDbL6FBu5uo\n9Zq1QLh9Nradi8ZoWuq1Fp/fsX6HJTeQp0ToY0puojXayKAltdRLx7bzDPQ41Nt59QAYLPy8nS2D\nIIPADHJHuJ3BMsjpZbYTnVXHnMc4GYxZBqXBUnMDlbKJapaBN+itsB+b1VzKQPLaCb5AXFS7HRP2\nNSHbQhYeWq+p6717nzIBbFHNX9fn92FdQvkde4K71G9K7kXPMvBSSz3LIbe3NVvOKlBWwfLWCNNW\nQB7bJTLI4/zLHAAEGdShZx5my+CUqteWQd6+i2FBrUwGXse1mr/2j46RwZg21RozKFkGY24iz3IY\nCzp7ZAGzgmrMMmjVbnN9q2XgCVmfLET+PiJPUXVb8VUyex9jwfXSpLF8//Z5JrWv977smlZefYtF\n6SkWpb6oLQetZJTiCGPWsrYMxmIG2jLI49SzDPS8kkwCO74ExXYgyKCGlDaAbwfex0AC2q+YO5a2\nDHKHyh3PrqaYl7i2lsEROkGQJ91MIYOaNuZlaIxZBnqgjw3qseBiFj66DOrCbVHttyVDCfxzluo/\nDLyOWUy1DGrurhZ3WMuzyc87mTLvfVhhbt1E1hWoj8nxhZoSUZrz4tXX+rc3z0ArWPpLZ5oMcuwt\nt1mTQSlm0PWjbk7BtwH22xX7EkEGLUjpnT0pbPa/82SZrP3DkGYKs2SwbraPUJ6BbAeDXYelhQxq\nbiIdKCzNUC4RTEmQlCwDT/hYYvDIYpGgc4ksrJZeCkrnepul8zHmsQzBLOL/b713j3y9gL/3jnSS\nQC0mMGWeQSaYljRpz80pfZvG3ESHmLUCdMwAZglAC37PMjiFfm8pvYuU1jkACDKYBrtWDZRjBjC4\niWDopFDbrJL8AAAgAElEQVT+noEliNZsojWn3pvok8kimfqsMZWyhTzLQAuSUszApqNaywFmNdEx\nYT6FDI4W6jHbJV+9LrP+4qnfJvCOWcQCaiUDq6V779NLEvAmiJXecY0svKVRvHobM7Cavx4fpUln\n1rVq5xnA/DgcixlkC/3AIchgGrxJei1uory9qY6x/k1v8Tq93brchPW56kFryUK7FbwAsRYUVmvU\nwUMrcHK9djFYTbXkw9bPWQvOmkD0gsU1MqhZBqXxccipb7UMasK+hQxsdpZHBvodlAS7TRKw6cPW\nFeids9VNNKa4eGTgWQGeazVbBtl1m8dUdh3psrxdswwOJGIG8jT8rVOm3URJbec63QlR27XO7q3D\n0jKFf6MvK5numZR00K5kTdQCyJ4gqGminnDS9d58BlSbYLm5CxSOKQnuDebRKuxr9TVSK7mJtGC2\nK8961p0lX8/VZ7OJvHeYmCWDbFG29JuxmEF2I1kC8caHJZj8jLIw14LfcxOVLHhNBjfSrQZwoBCW\nwTS8FHig+p2Dvaj/MHTGs2Y7k8WG2i/Xe529RgZjMYNSOl8pg6MkCA47xxwy9fketD/ZaqL6mJrb\nYkrQeUo9ZrtGFoeY/9jNKtxEU+/DEsSYy2aRmEFJIdDvwyoEtZRjz2VprQ1rxY5ZBp6bKI+5xKwF\nnseX5ybK+6HKNFmcBr4DeBQHDEEGU5DSWVK6RZVoLaOWYWQtg/xfxxG8mEG2IlqFfW2hOu3/9yaq\nWUHQ4iYqkUkppjAm+D3tFXwhWRKirfMQVmkZLPKhmqmkpr8dUHueJUvN6xelAPJYmmip35SOGUuD\nrpFBFvzWTaTfjU3UAD+RY1DahhVIE0MK+SlS+gJ745srK0WQwXK4i1nBn6EzF0puovxfd1Ir7K2Z\nrAWvFzMYiymUBrCdfVrS4EpuIn28J/itW8M73hNo+jx6khSUYwri1I9p/qskgzHLoHTNsfkUWvu2\nWrx9XqWAvUcW9h2UNH/PjZQzymqJBZ7mP9ZX8/G1mIEuy/0uwxtTOo6gx6RFYlhi5nan/kAgyGA5\nfJZ2N9E6g7DaUP+1ZZD3s/Wl1LqxdD2tjekB2qrhjR1TEyRa8Lf6sGsCbxmNGupkUHITde+i/1qf\nc82xc+b/tS+2effhEWmrG8i+A0sWXpk36WzM+mu1DKZkE9ViBjC4iVD/wSeDLPiTU6aRyOP3gKSR\neogA8uJ4IB0ZPKL/rQPIWgsZcxPpju11dksWHhnoAVYLIB/FH9StGp52QXjWRik4aYWPF3OwZRsM\n8yG0wLMrpbaSQRbmFI4pCfZFvpHQSkBj91EiUo8gxqyq3AdyxliuP8b8O2yx/sZiTWNKhFVMPMXl\nKF1QV7uBdF+HeWUJs63rvfiAVtoyNoE/Ab7ZqTswCDJYFDl2IJLNS73MbckyyNCd1Qa7bGfXnXhs\nUs6YaZ4D3WMB5Fbfr0cWJTdRKWC51rfJaqJaU91gyF7x3BrgC1FG6jHbJcFtv1Gst6dYBqV0Vd3O\nDfz7yG5B7941UY655azmX3pHuh1HGPL3S5ZDKeis21SKX3mWQxb2ui/l967JAnUMzJLBmGWgtz3L\n4BQpbQJ/5tQdGKzVdwlUkH2MtzE8z9aYga7XloGn5WQrQA+gdWY1L0/bssK+RcOzgmIsgFxyE5U0\n1WwFaE3V82uXls32BB7UUzJ1X2+dh5DbactaCaaFtEr+/7F4So0oPc1+7B2NJQl4CoH33m2/Kq1a\nWopv2TLMdslyzpjiJrJy7zSBIIMV4PP9/y+Rzf4hS2GT9mwirfmU3ES2rKRZ2TI9aK0VoI/xgr01\nF0JLNtHYOWuabEscArWd7wOnHrOdLRQou3S8stb6FndWyf9f0vJLE8T0sys971JcqBZAtvWeRVly\nP3oWZ8nK1WnM4Pf/mhuoZBloC/uIKr8DuJ5AuImWRkp3MqyJf8TWUncTWctAu470YCiZyZ5mJfiD\n0tPWWn2/niCx7gId6LYCreRPPqz208JeX3PMb26FbMmN1BpAti4d6zKy1xyLKUy1DDx32RgZtDyb\nlowvjyxs/V3MuvWsNeApGTrzzca39DEwCO4SGXj9v6ZgldxEwzhN6QICQFgGq4bnj6xlPoxpPjZm\nkMt0vQ3QtWhwJWtiLIA85kKouZHGXE81gWcnrZWsDY8MpqR5elp8KcOo9ZyLuolsPKU2Aaz0bKZY\nE5YsvCSBsX6Vjy/NicnvHdWmVmWnVF8aU3bMWTeRdi8FegQZrBafN7/tzEjMtrYMcmffZJwsSoPF\nalY2jlDy7R7p26mFi87i8fzFJa2zRVPVwsmLGZQIZkzgea4pmBfCmG1P8E85ZlEyyLEIjwi9zJ+x\nZzfVmihlb7W4ArWlZvuFFfy2zAaIoU3Ye8qSFfZijvHcRHn7DPA5AnNYmgxE5CoRuUlEPiYizyns\n86t9/Q0i8shlr7mL8T8DD1e/TzPbMb3lKDZMWcIX9nqwtA4mm8JX+qCILrPWhucvrgn2Wl77FOGU\n68UcX/O1b6qyvJ2f7xTBjdlehgz0dwZKRFmKp3gB+xJZlN6RfYf2nGMEZC0HfUyJLOw1j6r9YJpl\nUEqwwGx7ChIMM4zvAP4X4OsJzGApMhCRQ8BLgKuAhwFPF5GHmn2eCDw4pfQQ4MeB31jmmrsaKd1K\nSh9SJZoM8tfPYJYgxmIKy5rROd1PD3TPMrC+X60hloKPYy6EknAqBZDHhFctSKo16hpZZLSSward\nRKX7sHMwajGBKQHkMVeeZ4m1vGNP2FslQiseawyKRatla7fHyMDG5mz9BgMZfIWUPkdKf0lgBsta\nBlcCN6eUbkkpnQVeAzzZ7PMk4JUAKaX3AvcUkUuWvO5ewSlmySBjimWQj5/iJsqWg2dNjGUTjWUb\njQkSz9+crzuW194aM9hw6u0xNcuhJefflq3SMvBiG6V5AiUXWo0otbUxNgFszC1Xqi+5fGy/sZlB\nud95wn6RALLnJsItG7L6zjIsN3EHARfLksEDgE+o35/sy2r7XLbkdfcCvouOBHMn1+ukj1kGWstp\nsQzsOb0BZk3zsWyisfqacPK0xiwUdByixdVRCzDbY2pkUdL8a2RQm4GcrT1vuQpLZGNptzU3UavV\npN1qnhvIywjTGrt2A6HK7PvyAsi6360iW0j3b+/4Ehno2eb07fgi8BQGUggYLJtamuq7APMvxz1O\nRK5WP0+klE4s0KbdgZTeBoDIyf73WUSykPBMWt3xPcuhNlh0WX7eYwOwZhnYQW8Fs5fDfliVaWth\ng9kVN63AO01dO7bCzXOvjLmRWjJ7bNkq3ES12EaLGyin6nrzRvIxNi030U6+pWwhrRhYy8Iek7dh\n1jLw+u+ms12yfMfW8/Jcq+vMy5ejvZXwR+wDiMhx4Piqz7ssGXwKuFz9vpxO8x/b57K+bA4ppauX\nbM9uhF4LJX+OUWtLnmVgtfi8by5rJYNaOmoWCt6gLqUQ2nkIY8HFTXyyOI9Z4XQni7lCvCUZxjKY\ntJYOPhlYYT9lnoEOWi9jwdh7P2buzQr2o6resyzGrA0vGOxZE7YPaGG/Rl3wl/q3PUZbE1rL12U1\nN5EeFzBL6nsevZJ8Iv8WkReu4rzLuok+ADxERK4QkaPA04A3mH3eAPwTABF5NPCllNKtS153L+F9\nwA/327lTjgW79HbW8MAfQFrwbzr12k1kYwYwTxaH8N1EHoGUtMqSILH1rTEDr97za7cIWW3VwKxg\nP+KU1TR/TRYtS0MsSgZTiLL2vL0MJS/AXNL8PfchZrvknoS2bLmploEwO6b0e/tO4EcJVLGUZZBS\nWheRZwNvputYv5VS+rCIPKuvf1lK6RoReaKI3EynAf7Y0q3eS0jpDPBqVbKO/wWm0mDBbNcsg5ZB\n6dVbsvDyya0gsULhGL5wKVkO2lrwFkYbiym0CFlvpjT4ZLCom6iFDM5VZS1rD9XiJTWyqFlq1rrL\nz8abuW7foedexGx7yQz5vxSOqZHBulPvKVCn0GSQ0tsJNGFZNxEppWuBa03Zy8zvZy97nX2Cv6Hr\n3Pnbq8MCWSlt0i2Zr8nA84lqwV3SnGyZrfcGbYksvFnN1sWQj/e0ysPMT2obS0c9Syc8W9xEWgja\nDKbsYigJUdTxG04ZtJGBjQ/UUlxbZwtvME++OX7QEm8puYlK79BTAk6pY7RCYd2LOPXgKy66f49Z\nqfoYve25iXTZSbp1wgITsTQZBCbhsXSdOlsGd+K/A2st6O0WM9ojg5o53zKotb9YVJnV4q12asli\niiskCydvvaMp7pOWa8KsgBdmNf9cViIDz4Vm21FLgS1ZBnk2+3lOvSXCksvHEoy9pl4OXb83bSVA\n3eLU/n+tuMCgGOSysThDq5tIk8FdwH8A/pDAJAQZbCdS+ru7tzsrQAuhu/diOTdRi2VgB7U+5xhZ\neG4eLzhpBdKYpuppv1Y7PsUQRPWE7LK+9hIZtMQUrGUwZsFsONestTNr/smpL6UHezGD06bMc/Vp\nNxGFc0LZDTTVMtD1y2QT6djbGbo5Tx8mMAlr9V0CW4hSIH0RMvC0rVY3kdXgSr5fK+yzsPb8yR4Z\n1AKaUwT31FnLy5JBS709p/bF65hCFsIl19KU+/BiMKV6fc7Sh2hyPeqYnFhghbC1DMaSHXL/KtV7\n58zt1PXaChjOPUww+wKBhRCWwc5hjZQSImdNuc6M0HWtbiLPNMeULWoZaLfEGVWmrYQsfGz9pqkf\nc+kcoTP3xyyHkjWhj7GuJVufhXApZtBKBtrXbuMDWUjmdmY3USn2UbKatGVwmlnBbQnXs9SSKvPI\nPb//3Dao9wtrGaDK7TE1y2C9UG+z5TbV9bM1kVO21xQpBCYiLIOdwtBpN2yNKjvNuJlcM6M9y8Az\n51ssg3wtz1ooWQ66fkx41TJiau6XqXGIrDHnmMCylkFLZlCpftn7aBH2NjPII8UxJSBve/1mzEpd\nL9R7fbFkxXaYXVoiL+2SY2/nmH0CCyAsg53H64GvMWWD/3OANyhLZGBNa5vOl8/lZXBoAWDdBeDH\nEaxwKbmJ8kfO8/o2miDGUks9gXiXUzZ2TGkmtT3eCvscnLVkoFcg9b5X7LlsamSwzmyA+JiqL8UM\nPPIcq/cstfw+MzzN37MoLVmg9vWOh3IAuUQGdvWC9buP7zLwnsmsBR1YEEEGO42UPky3pK5GFsKn\nyX7YzqUEbQHmqaml1jIYcwdYgtDCRbt5SsIpa7+eG8jTjscsh7H1jjacc+Y2tUwQw2xbMvBcQotY\nBqcKx2yYYzwy8GICntvOluXnoF1CWph7SkDJTeTFBDxhn8tqbiIYJ4NZSzqllxNYCYIMdhc+Bfw/\nDBr7aean0pe0KT0A10y91sb0oK5ZBiWywGxbglljXjhZMrACzQrE3OajhXrriy8FSRd12UCdDOxX\nyTacc061DErHMFJvA8BjZKHJJp8T9Tx1mbVCPfdhrV+01Q/zbErHZwjwaQIrR5DB7sKD6Dr/vfrf\n2f2gYQWzF0C2ZFGb9WnP2ZoV4hGM1So9TbdV87facUlgnqRzqdQE7xTBrAU7+GSQSWuDecvgdOM1\naxaOJgMr2M9hfJKf57YrpYl6bh5dvohl4MUMSgFijTEyOEpK70fkPOe4wBIIMthN6JauAJHb+5K7\nmH9HWVBBPWZQ09Zqsz49QVCzNqxWqYXXKj9uY+trBNMSM7Blmgz0PIOa4G69j2MM5FsKvlvL4Ajz\nwt7GBDZMvZiyktuPke2S+7DmspxKBlqZyXMsNLr3kNJdBFYK+6ADuwEpnQY+RPdNZWsZ5MEMZTKo\nuZFqMQPvmE1TVqq35xRmz19aWXOqFq+F35ivfUqWzhgZZI271I5F72OM1Er3Ubp3Swa5Xr+PvD3m\nEvKsBF1fCiB7iyaO9Rur9dvrnGKWLF7PPlmGejciLIPdipS6bykP30DI0GSgs41aLQM9gFvXgymR\nxZigsP5iTQaLCNHD6lwtfvEpy2JrMjiHMhm0EMy5lWueU6kvWTBUnk1+B+LUw+w71GSg3+HYMiZb\nZRlYZBcbWMsgpac6+wdWhLAMdj/eQWchZOjVN0+r8jGXjt5e1DJYNGaQt3XA0tNka35zSyBTNOop\nZKBTXMfIoJTZs6hlUFpaouWaugyn3nsf3mSuVutv3SmrHVPqi9ZNlIkcuv79djorObDFCDLY/fh3\nwKXqd87Rh7ZJaRneAC1ZBp7WWCOLGhnoeuvSsVbC2DcSSmSwCteTzUCaSgY1YZ/JJj+zUnBdB89b\nCMYGhfPz1LPZp7gPS4Lds/7GLINFAsg5IQA6N9FPAt9AYMsRbqLdjm5WpR5oZ5hdAtsOtjPMTzrT\n9SUXwZjW6GmANoBc8ydrF4Oe8ardGjb1VJNFjQymWgat30jQk85aAtm1a5Ym5LUGkD1rova8vfoM\n3X/WRuq9c7ZYBpZgdH8+w3zq9GH0DOP5/h/YIgQZ7C18D3A98NX9by9mcIZZ/zrMp5Z2aJ/ItunU\nl2Y114ST52LQlkHpYyxjZLDoMg61bwvULINDC1xzKqllIZ21/NqicqXnbeuT2tb/oT3xoCVmMGZR\n5m86axwCPgc8vV99NLBNCDfRXkJKbyWlz5MHeEpai9dkUBPcXm53qxuo1U1ktcYNUz8mvErfSNBl\nXnrlKtxEVtivMZv6uYpsoVLsozYhr5RNlJ93qxav+8CYFm/rl7EMPDdRvh8NIaVNUnoNgW1FWAZ7\nE58GPt5vW9P+DLODMf/3hLlGzTKwx7fMMxgji1bLwRP2XvqkRxDLkoF1CW1F6mgrqeXlKLRbLS9B\nPfV56wXdxuIItt57R7WYwZjlYS2DVxEfpdkxBBnsRXTWwYP6X943YqXfL0/xt1pfzTKwLoQWy8Bz\nIXjCvuRGGotjlNwnVqM+beqXIYOWmII9Z8snKEukVqq3ZOA9G+95jlli3jv2rEzMMTXrzzvGkkV+\ntjCruEBKP0JgxxBksPfxSuBC5f8/wvx71cKh5CYa0xDt8ba+JWZghVNNkHiuo6xploRsafG7fHwW\ngofxF4grWQYtH6IpWQ6aoLxMKnu8rtftmEoGXn3tHdoyW2+VjNI5PWsip0En4I5++wxwHfBdBHYc\nQQZ7HSn9pCk5wnxQrsVNNGbOl44vafG5bqy+RVO1lkHWJFuzcHSaaA5EL+om0mmclgyyYPesEUtQ\ntQCyt3bQGBm0uolKMQNPSSiRgYeaZWDbmRiyhXJsIOIDuwARQN5/uAOfDLRmvohl0Dros/ApxRS8\nD6N4BKTrvVnLLWRQCrzaei3M7ScqW91ErUtYT8mK0s9DJwHUNP8aWZQssbH3MSXWpF2FnYzRH54Z\ntmN9oV2EIIP9hUcAb3LK7dfTPIxp/p5lUPpIyaZTv6hwspaBFrxTyGCsXn/pTLuRppJBiWBayUC7\nmUrPZoqwn2qJtcSFLOw7zjil6u3Xx/LvR5LSx5xzBnYIC7uJRORi4LV0X+m6BfjHKaUvOfvdAtxO\n15nOppSuXPSagQpSugEAkVtNTRaeMDs3IcOfh+ALD62pesKnFnMo+bi9Y7RAswvejQnZZTJ/Nky9\n993lsUlnOqaQv4FcIwN9zbFnU8vsaSVfz03UYv1Z2DTRHFPI8av8rWx9ro/2+1zvnC+wg1jGMngu\ncF1K6evoPsjy3MJ+CTieUnpkEMG24bXAw9Tvc5m1DOzAnhITqFkOnmXgkcnUALIts4J/qmUwZR2h\ns5QJxmY1jZ3TsxZa7nPs2ZTekSWDKZk/JWvEg3XlaejlMAAuA364cJ7ADmMZMngSXSYL/f+njOzr\n+agDW4WU1vvPaWrkQXuGedPdSwGEcReCFfY14WXbYY8ZWzhtzBVife1Ws19mTsAybqISAa2aDPQH\nimpupKnv2Nbbcaz7gEcGZu/0mT4tOrALsUw20SUppeyOuBW4pLBfAt4qIhvAy1JK/3mJawam40eA\nzzBLBlYJaLEMbJkut4LIq/fKxoRPTUjmNmc3zpSlIc6p1G8VGdisKN1me5+tAf1F1ibyLLUaAdUC\nyHnNKQ0B3gL8QuHYwC7CKBmIyHXMrpiZ8QL9I6WURMRqmxmPTSl9RkTuC1wnIjellN5VuN7V6ueJ\nlNKJsfYFGpDSqwAQyRlGeqXTDCvM7axmjyBsfU2wjwUsS2RRizOUyOKwOlb7/KfMUPYmkE0lAy+m\nYFNHl7UMPEvNO2fNEhsyf4Yya/1Z1CwDoVMYX0BgZRCR48DxVZ93lAxSSt9TqhORW0Xk0pTS34nI\n/YDPFs7xmf7/50TkdcCVgEsGKaWrWxsemIiUziLyFuDv8N2DelDn+qT+W2Gut1sEu+cGqlkTNSFZ\nqvc+8DKWWuoJdu/jOC0xgRbLoBTsnUIGrQH/Kd+sOGTKbGKB129KlsGrgGud/QNLoleST+TfIvLC\nVZx3mZjBG4Af7bd/lO6TdDMQkfNE5IJ++3zge4Ebl7hmYBmk9Ph+JUhvDXkdYD7U76/JYMxKqAWQ\nWwhkLLulljEzRhbLpp5OsQzGyMILdNs2L5J2W6r3Pms59g6nkIVGnitwdua4lH6ElD7u7B/YpVgm\nZvBLwO+LyD+lTy0FEJH7A/85pfR9dC6mP5Quzeww8KqU0luWanFgFfh14Db1Owso8D9CrtNIS26J\nqTGDluyWmpBsIQvPPaPTQLP265GB/UTlqgPIpfuoCfspMZ4SgXhuu7Hj8zOyyEtLnAWej+9WDuwB\nLEwGKaUvAt/tlH8a+L5++2/oJkIFdhOMmcmQDw/aMhjgZRtZgqhplbbMaqJj2URT3UR5OzGs7Olp\n6d5Xx5axDMYIptROb87AspaB9zztO2z9eP38bOJZ5AlmZ0jpvzj1gT2CmIEc+AjwRgZz37MMWlJP\nO01TLzswno20yDyDqbOaS0K2FEeoCfZWMki0k4Hnnqm5bFrqx8i1ZE1o/3/GGbOfRTfRtPu2RmAP\nYxk3UWA/IKW/D4BINu/PMisMoBNiWQOsuSXuPjOzggizPUX79dxALRp16fja0hC2Pgt2L8OotCqp\nPedYO60Q1mV5vxIRlp6dV1azJmycYZ3Z723rCWR9q9KXiHlE+wJBBoGMzwPv7pcSsEtWbDBPBiXX\nkEZN+NSEl00tXSSA7NXXJoAtE3TO555CBi1uohIZLGsZzJcNS1RbMtBKws/TzSEI7BMEGQQ6pLQO\nPLb/ZTX9dWZ9x/n/MmRgBd6iM5Bb6rWQ1QvRbQUZ6GuPTSqbSgalZ6Prx2ICJTLw5pfoukwGp2au\nldL/RmBfIWIGAQ9/DLxb/c5CVMcESpq9Rs0yGIspeMJrKhmUZtnqiWglMmgNKntk0GLBePc0llpa\nWv7DC+hroe2lB5feh5c4oC2D99DNZg/sQwQZBOaR0s+S0mNViWdBJmatBStISnMTxgSarp/iKpmq\nUbdYBp4byc5q1umq+uP0U9tp60tEWyJF7z5rnza1E8z0ZEN9vYEMUvo1Urq/c97APkCQQaCGTwHv\nd8rXGASFJ0hgVojbsiluopqrpDaxyh5T0uynkMWG+p+tjdalIaZYOLVzevXaTYSq124knVYL5TWr\ncqzoKwT2NSJmEBhHSpcBIPK1pkavVe/NTYB5IajLpqSW1iyD2tfTSoLZLluxqOXgXVNP6qqRgTdb\neMoEsoyx7C57/PAFsi5Y7GWRHendTJEtdAAQZBBoxU3mtyWD2npHtqxF8x+b27CMm8g7ZgoZjC0t\n0XLNVRJhLZsIp96bQLbB7Ffw/hh4m3OOwD5FkEGgDd33EbSA0W6iM8xbBuvM5qtnrMoyqC1XUYtT\neHMPLBnYJSzGyKLlPkoB4kVcZK3Lf2TUYjzraHdeSk9wzhHYxwgyCCyC36dbjyoLlzzxSkNrmjUy\nmLI8wiKWgRd4bV3crkQWpXWGWtq5FZaBXR7EWgY6GKy/p6DxQWbfVeAAIcggMB0pPe3u7c7fnJgX\nIusMZKBXSp2SEdMqEFfhJtLbi8xDWKaddtG4KVZRq2WwySwZZMsn4xAp/akpCxwgRDZRYFm8Cfgk\n/hIW2jKwqYx2nsEYWZQyamrLNC/zKc0WN5E4x2O2WyyDqbOFS/X62dn3YecM6PrXAn9O4EAjLIPA\nckjp+wGY/9KdJQObvbKKtYtW5VqyBKTdQN7SEt5s46nXXNYq0pPJvGM8d093nymtz7yvlH7I2Tdw\nwBBkEFgV3sbskubCrJvI+z5uqxZfEog0HFOqH3MTCf5CdLa+5ZpjQeu87VlFbQHigVyt1WPJwD7/\nXwcuJBDoEWQQWA1Suga4RpUcYdYysH0tr9sD5dTTmkBchXtlzL+/yAqkML80xCosA1vvfcfae54Z\neTnuDik939kncIARZBDYCvwY8Gf4MQONMTLwrIDaEhdThOxWkoHebllqe5H7sNDkaVedhe75Xw/8\nW6cuEAgyCGwB8hevRM7rS+5gPllBC2Ev9fQsdcE/NT9/ynpIOYCc27koGXgEZI9ZxEVWswxsDEdI\n6Q5gJR9PD+w/RDZRYCtxCriRlLwZylZ4WYE4TIJqX3lzipuodvzYPINc33JOj4AWvQ/7fQdMXT7G\nWga/2/8FAkWEZRDYOnSfQvyG/pf95oGeEasDzGMCbxUBZJvTr7ettZI/eTmVDKYsKtd6HyVLCqd+\n1jJI6X909g8EZhBkENgu/AzwIPV7mKhWzojx4LlKahPZxuYuYMpKwr5EBrV01pblqD030VjwvBQg\n1pPK/h2xtlBgAoIMAtuDlN4BvMOUWg3XE8YzZ8ETrLNzF1oXt2txE+myVcQMSpbD1GM8MjgC3Nlv\nnyal9wHvc/YLBFxEzCCwE/hB4JX4S1hoYTuWgeQFUW29V9YaU1j0E5b2nC0T4VpiI2vOtTWO0n1z\n4FmkFN8eCEzGwmQgIj8oIn8lIhsi8qiR/a4SkZtE5GMi8pxFrxfYR0jpD0jp88wLe+smsparnajm\nYZmYQksaqP2U5ioWnRvLDMrt0G4gzzLYIKVESr/p1AUCVSzjJroReCrwstIOInIIeAndzNRPAe8X\nkTi+CJIAAA0mSURBVDekbjnkQOD9wIvU7zWGL2t5yyzntYKgE6i1NfttWUswd2pMoEQwtW9Fe2W6\nzrqEErMzujWeAXzIOU8g0IyFySCldBOAyOhHkK4Ebk4p3dLv+xrgyUCQQQBS+jLw3P7XDcA6KW30\n/v81xslgSoB5lZaBd51Fvko2nQy6NYWgu6/b7t47pVc45wgEJmGrA8gPAD6hfn8S+JYtvmZgb+L7\nmZ0odYRxN1ELGdiyVX4jofWcG+iYgL9IX8vSEvrch4GX0n2NLBBYCUbJQESuAy51qp6fUnpjw/nt\nLMhRiMjV6ueJlNKJKccH9jBS+qQpGVY6nYUWkqtwE5XqtyJbyGunVz+0bbCU0kx9SifpXLWBAwYR\nOQ4cX/V5R8kgpfQ9S57/U8Dl6vfldNZB6XpXL3m9wP7A84E/Bb7TqcuC0ltyYVH//rJuIltfC3Tb\nCWTe7GzrIsv3+gK6WEvggKJXkk/k3yKykiVGVpVaWgocfAB4iIhcISJHgacBb1jRNQP7FSn9Yq/5\nnnZq9eJ3lgymCO5VWQYtBDRmwXjLe3tk0GUSpfQLpHSXc75AYCksk1r6VBH5BPBo4E0icm1ffn8R\neRNASmkdeDbwZrpsh9dGJlFgAl4CWOu0lFED0xaia40Z6HOmyjElsrCwloG3vLfGw4Fvc84TCKwM\ny2QTvQ54nVP+aeD71O9rgWsXvU7gACOlLwJvNaU59dSLGVjB3PIpTfC1/NKnNO0xi2QL2dnEY1lT\nkFKkjQa2HLEcRWCv4CnA3zJuGVjBvKa2vXpG6mtuotZ5BLXJcd5M66N06wr9VOHYQGDlCDII7A2k\n9EcAiJzTl5xy9rIpmVPnGdTIYFHLwFowh9S5PDLY7L898BICgW1CrE0U2Gs4Q7fg3Zfxheym2s8K\n6VpMYVXZRPaLbN4aS9rC0bGIlwP/zdk/ENhShGUQ2FvoFnM7DnjT3/PHaGD2S2n6/9j3DGoB5EWz\nibyvvOWMoHVm5xU8k0BgBxBkENjLeC7Dx3NgWGYaOsvALhOxbGrpBu2uJ72QnTdnILu51oFnAQ8m\nENhBBBkE9i5Sej+zE7DWGDR/zzLwyvT2Kj9ef0aVee7YbBmcJqV3A+929gkEtg0RMwjsJ9xw91b3\nyc2NfjsThP5eQsky0PvmstY4gyaD06re++Rntgy8QHggsO0IyyCwX3CUToA/WpXZHH9vApldRygv\nKrfJEJJYxDI4rcrm00tTuhORo4qoAoEdRVgGgf2BlM72gvWLqtQK4UFwD0LYriPkCedF4gyZDE4z\nT0o33d3mQGCXICyDwP5CSh9B5OJCraellzT7u89YqK9lIJXI4AL8NZcCgR1FkEFg/yGl/OGXI6ZG\nZwNl1MiAQv1Y0HlDbZ9FW+DdZLJAYNchyCCwn/FzwH3Ub48MShPRNGpuIj2buNtv9kM2v6PqAoFd\niSCDwP5FSr9rSg7hfztgGcugZC1kCCm9B3hPtb2BwA4iAsiBg4QvsXrLYIM8joagtN7v5CINDQS2\nG0EGgYOCy0npHcyTgc4mWtQy8JabALiMlD66QFsDgW1HkEHgYGD4xvLY4nalD9F46xl5axdlHOmv\n+alFmhoI7AQiZhA4aHgHs9/htusZeQqStzaRtyAewCuAsAYCew5BBoGDhZQ+AFyuSuyy195y0/Mu\noSFbCPSHdlJ6xuoaGwhsH8JNFDjoWGOaZeCRRSCw5xGWQeAg4wbgBLOrmo6RQWkF0ltW3bBAYLsR\nZBA4uEjpEQCIXNSXeJaBXY5ingxSei/zgelAYE8h3ESBAHyl/1/S/OfnFAQC+wzRsQOB7tsH0KWL\nehq+tyhdxue2qlmBwHZiYTIQkR8Ukb8SkQ0RedTIfreIyF+IyAdF5H2LXi8Q2GJc0M9F8NYQ0paB\nnotwL+CPt7phgcB2YJmYwY3AU4GXVfZLwPGU0hcr+wUCO4dhNdExMticqU/pS1vcqkBg27AwGaSU\nbgIQaYqbRXAtsFdgPzizocr8r5YFAvsA2xEzSMBbReQDIvLMbbheILAMrIK0zvBx+7NOfSCwLzDa\nsUXkOuBSp+r5KaU3Nl7jsSmlz4jIfYHrROSmlNK7Cte7Wv08kVI60XiNQGBV+Pd0cw8ytGVwGng6\nUPqSWiCw5RCR48DxlZ932e9xi8jbgZ9LKf15w74vBO5IKb3YqUsppXAnBXYPRBLdWkO/CfwpcBEp\n3b6zjQoEZrEq2bkqN5HbEBE5T0Qu6LfPB76XLvAcCOwVaDfRmbEdA4G9jGVSS58qIp8AHg28SUSu\n7cvvLyJv6ne7FHiXiFwPvBf4v1NKb1m20YHANmKTwU1kg8uBwL7B0m6iVSHcRIFdh85N9HLgV4Ab\nif4Z2IXYbW6iQGC/4i7CIggcAESaXCBQxjcBH6H7jvHjdrgtgcCWItxEgUAgsIcRbqJAIBAIrAxB\nBoFAIBAIMggEAoFAkEEgEAgECDIIBAKBAEEGgUAgECDIIBAIBAIEGQQCgUCAIINAIBAIEGQQCAQC\nAYIMAoFAIECQQSAQCAQIMggEAoEAQQaBQCAQIMggEAgEAgQZBAKBQIAgg0AgEAgQZBAIBAIBggwC\ngUAgwBJkICL/u4h8WERuEJE/FJGLCvtdJSI3icjHROQ5izc1EAgEAluFZSyDtwAPTyl9I/BR4Hl2\nBxE5BLwEuAp4GPB0EXnoEtfccYjI8Z1uQw17oY0Q7Vw1op2rxV5p56qwMBmklK5LKW32P98LXObs\ndiVwc0rplpTSWeA1wJMXveYuwfGdbkADju90AxpxfKcb0IjjO92ARhzf6QY04vhON6ARx3e6AduJ\nVcUMngFc45Q/APiE+v3JviwQCAQCuwiHxypF5DrgUqfq+SmlN/b7vAA4k1L6PWe/tHwTA4FAILDV\nkJQWl9ci8j8BzwS+K6V0yql/NHB1Sumq/vfzgM2U0oucfYM4AoFAYAGklGTZc4xaBmMQkauAfwl8\nu0cEPT4APERErgA+DTwNeLq34ypuJhAIBAKLYZmYwa8B9wCuE5EPisivA4jI/UXkTQAppXXg2cCb\ngQ8Br00pfXjJNgcCgUBgxVjKTRQIBAKB/YEdn4G82yalicgtIvIXvbXzvr7sYhG5TkQ+KiJvEZF7\nqv2f17f9JhH53i1s12+LyK0icqMqm9wuEfkmEbmxr/uVbWjj1SLyyf55flBEnrCTbezPf7mIvF1E\n/kpE/lJE/nlfvtueZ6mdu+qZisgxEXmviFwvIh8SkV/sy3fb8yy1c1c9z/78h/q25ESdrX+WKaUd\n+wMOATcDVwBHgOuBh+5wmz4OXGzK/gPwr/rt5wC/1G8/rG/zkf4ebgbWtqhdjwMeCdy4YLuyFfg+\n4Mp++xrgqi1u4wuBn3X23ZE29ue8FHhEv30P4CPAQ3fh8yy1czc+0/P6/4eB9wDfutue50g7d+Pz\n/FngVcAb+t9b/ix32jLYrZPSbDD7ScAr++1XAk/pt58MvDqldDaldAvdi7hyKxqUUnoXcNsS7foW\nEbkfcEFK6X39fv9VHbNVbYT557ljbezb+Xcppev77TuAD9PNf9ltz7PUTth9z/Rkv3mUTsm7jV32\nPEfaCbvoeYrIZcATgZerdm35s9xpMtiNk9IS8FYR+YCIPLMvuySldGu/fStwSb99f7o2Z2x3+6e2\ny5Z/iu1p709Jt4bVbynzdle0UbpMt0fSzaLftc9TtfM9fdGueqYisiYi19M9t7enlP6KXfg8C+2E\n3fU8f5kuU3NTlW35s9xpMtiN0evHppQeCTwB+EkReZyuTJ3NNdbuHbmnhnbtFH4DeCDwCOAzwIt3\ntjkDROQewH8D/kVK6Su6bjc9z76df0DXzjvYhc80pbSZUnoE3bI03yYi32Hqd8XzdNp5nF30PEXk\n+4HPppQ+iG+tbNmz3Gky+BRwufp9ObNstu1IKX2m//854HV0bp9bReRSgN78+my/u23/ZX3ZdmFK\nuz7Zl19myre0vSmlz6YedGZvdqPtaBtF5AgdEfxOSun1ffGue56qnb+b27lbn2nfti8DbwK+iV34\nPJ12fvMue56PAZ4kIh8HXg18p4j8DtvxLFcZ9Jj6RxfE+Wu6wMdRdjiADJxH52cDOB/4E+B76YI3\nz+nLn8t88OYonWbx1/TBmy1q3xXMB5AntYvOHfItdFrHVgS+bBvvp7Z/Bvi9XdBGofOh/rIp31XP\nc6Sdu+qZAvcB7tlvnwu8E/iuXfg8S+28dDc9T9WWbwfeuF19c6WNX/CGn0CXJXEz8LwdbssD+wd7\nPfCXuT3AxcBb6ZbqfkvuUH3d8/u23wQ8fgvb9mq6Wdxn6OIsP7ZIu+g0thv7ul/d4jY+g06Y/QVw\nA/B6Ot/njrWxP/+30vljrwc+2P9dtQufp9fOJ+y2Zwp8PfDnfTv/AviXi46bHWrnrnqe6hrfzpBN\ntOXPMiadBQKBQGDHYwaBQCAQ2AUIMggEAoFAkEEgEAgEggwCgUAgQJBBIBAIBAgyCAQCgQBBBoFA\nIBAgyCAQCAQCwP8PQWMhqYBER9wAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f85aee8a2e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x=np.r_[-2:2:0.001]\n",
"y=(np.sqrt(np.cos(x))*np.cos(200*x)+np.sqrt(np.abs(x))-0.7)*np.power((4-x*x),0.01)\n",
"plt.plot(y, color='r')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def heart_3d(x,y,z):\n",
" return (x**2+(9/4)*y**2+z**2-1)**3-x**2*z**3-(9/80)*y**2*z**3"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#!/usr/bin/env python\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"from matplotlib.ticker import LinearLocator, FormatStrFormatter\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"def plot_implicit(fn, bbox=(-1.5,1.5)):\n",
" ''' create a plot of an implicit function\n",
" fn ...implicit function (plot where fn==0)\n",
" bbox ..the x,y,and z limits of plotted interval'''\n",
" xmin, xmax, ymin, ymax, zmin, zmax = bbox*3\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111, projection='3d')\n",
" A = np.linspace(xmin, xmax, 100) # resolution of the contour\n",
" B = np.linspace(xmin, xmax, 40) # number of slices\n",
" A1,A2 = np.meshgrid(A,A) # grid on which the contour is plotted\n",
"\n",
" for z in B: # plot contours in the XY plane\n",
" X,Y = A1,A2\n",
" Z = fn(X,Y,z)\n",
" cset = ax.contour(X, Y, Z+z, [z], zdir='z',colors=('r',))\n",
" # [z] defines the only level to plot for this contour for this value of z\n",
"\n",
" for y in B: # plot contours in the XZ plane\n",
" X,Z = A1,A2\n",
" Y = fn(X,y,Z)\n",
" cset = ax.contour(X, Y+y, Z, [y], zdir='y',colors=('red',))\n",
"\n",
" for x in B: # plot contours in the YZ plane\n",
" Y,Z = A1,A2\n",
" X = fn(x,Y,Z)\n",
" cset = ax.contour(X+x, Y, Z, [x], zdir='x',colors=('red',))\n",
"\n",
" # must set plot limits because the contour will likely extend\n",
" # way beyond the displayed level. Otherwise matplotlib extends the plot limits\n",
" # to encompass all values in the contour.\n",
" ax.set_zlim3d(zmin,zmax)\n",
" ax.set_xlim3d(xmin,xmax)\n",
" ax.set_ylim3d(ymin,ymax)\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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POjwf/qncPtd1K13XvVRKWR/MH3s0f/5YkJM97OaEAuXyuoc80IlIDWC7iHO+W1XVF4QQ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\nKwucjz9+vrBxYyFfUtIZKOXAssD+4ouDcGOjBwAAh0JOpKo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gwUXU6Wy1v/hid1xfbzez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ZRSqry8vC3L9ZcAXAng/zU/X2i5ARHFEJqiNxFRHGGHXWvTlTsVX3l6ob6+/vtEVJpKpQ7uJdvG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f85aedf53c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_implicit(heart_3d)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NXrcDewngD8AOdPqDIS1PaooYT5pg3n/71n2b9He7gYhHO763VDk5nM60GCwA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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f85acfe1e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Julia set plot\n",
"ax = plt.subplot(111)\n",
"\n",
"########################################\n",
"## c parameter for plot : change this ##\n",
"########################################\n",
"c = np.complex(-1,0)\n",
"plt.suptitle('The Filled in Juila Set with c=-1', fontsize=16)\n",
"########################################\n",
"\n",
"#########################################################\n",
"## Size of side grid for J_c plot: change for accuracy ##\n",
"#########################################################\n",
"grid = 500\n",
"#########################################################\n",
"\n",
"#############################################################\n",
"## number of iterations we use to test for escape : change ##\n",
"#############################################################\n",
"escape = 2000\n",
"#############################################################\n",
"\n",
"absc = np.abs(c)\n",
"rc = 0.5+np.sqrt(0.25+absc)\n",
"\n",
"#####################################\n",
"## Region of plot: change for zoom ##\n",
"#####################################\n",
"xmin = -2.0\n",
"xmax = +2.0\n",
"ymin = -2.0\n",
"ymax = +2.0\n",
"#xmin = 0.0\n",
"#xmax = 1.0\n",
"#ymin = 0.0\n",
"#ymax = 1.0\n",
"#xmin = 0.5\n",
"#xmax = 0.7\n",
"#ymin = 0\n",
"#ymax = 0.2\n",
"#xmin = 0.6\n",
"#xmax = 0.65\n",
"#ymin = 0\n",
"#ymax = 0.05\n",
"#xmin = 0.618\n",
"#xmax = 0.619\n",
"#ymin = 0\n",
"#ymax = 0.001\n",
"######################################\n",
"\n",
"x_range = np.arange(xmin, xmax, (xmax - xmin) / grid)\n",
"y_range = np.arange(ymin, ymax, (ymax - ymin) / grid)\n",
"\n",
"plt.xlim(xmin,xmax)\n",
"plt.ylim(ymin,ymax)\n",
"pointSize = (xmax- xmin)/grid\n",
"\n",
"# Generate keep set points\n",
"for y in y_range:\n",
" for x in x_range:\n",
" z = np.complex(x, y)\n",
" escapecount=0\n",
"\n",
" # tests if z is in the keep set (i.e. filled in Julia Set)\n",
" while np.abs(z) <= rc and escapecount < escape:\n",
" z = z*z + c\n",
" escapecount+=1\n",
" \n",
" # Write point to plot if we have tried to get out escape times and failed\n",
" if escapecount == escape :\n",
" keepSetPoint = plt.Circle((x,y), radius=pointSize, color='r')\n",
" ax.add_patch(keepSetPoint) \n",
" \n",
"# Display the Plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fb46ae55908>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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qcOVbaqIIcNmKUJQgb7ks9oF1jFwW+ykMnUYGIk23k/ynbv2e+cDJozr86P2a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tUg09xMNBJBKAroMVDUsN1SKPgVSbFoM3F+lFe+dZzdD+2h70Bf1VBlRWyBEEsV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\nzRWDQXf1foo2bi10rkT45kfMaBORTvDggw8y9uAu+v6rQRKdHcTjc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2wYpSTCd9d06lQo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\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BoKdVXbujUX5IVLNyr0zPMjM6wUzOxWw1EZUh7NIY9elNOuOxfc8aNm9uJWkBMg4Yo0ow\nt/3unIW9qYmWhIkZRkSRXDA7EoaoGaNotAIVxs/XzVKgNqGSEuVXcWQJQ5RQ5ChOzHD++beYOnZ2\nRc6liiT+9CxJw2D7pvU0uwaUZ+o7ijAtkk1ZOrZvZOcvPomZTTLw8tsrY5YCiGIVTo9QautguKmJ\n4VcO0pzNsfGx+2nv7SGZTAJx7RjEgXxl4irkhbcSWscN3ecRBQEnn32J/NkLWLZFasMavHVdi/9U\nKo7/6CUG3jrEyKkLtHa1suvhnXS3CixP1TLACkk4d38tqrut7cPNNIOvm6KspuQyzkRpIvXxlFzG\nFvKLH7vJboU5olq7AtXoPXdLE9Xuu4Y5SoMr0QjqGlwXmlgGIFgdU5ZIa5TSCMOIpY/zxl0gITFi\nGWWda9mXSGmk0gjLQNQix/lGKvWgDsCwjHhip8Awjfh1poGTSzGtDV5/5zzrs5vYkujGt1OEJlgq\nNnlZhGmAY7Hlk/dx31efwBw7g5oajg8u2QQBWJ6N05TBNAzCWmbsRpEpj8qWTtZ+6XE+82v/CDV0\nnGDkPJWKBGFiuC7CroJxSc3aEl8kMoziDKbWGEqS8Gymu1JUWgTVc4fR4wXSbS1UCkWSaZfdd/fR\n3ZYgnJpGVgJkuNDQZk2rR1c6loH6hWrtWly8GIZlIMyasFhrVKhj4xbLqOlgxdw1RIaoYh5Mk8DN\nMT0wyom//xFT/dfWzH0+lufipJMExfLctWhrynHXzi3gl9ClPJgOOB7CtKlWfZRjselzj+KHIYOv\nr5ADJsBMGXH4HLOpFP2WoPiTt2lvbaN5x0aauzqwbZv6FNN1HTLNTaTamimVSoR+EN+XK7MnNxXS\nj4O6k8++BMC6zz7Cpi99irC6sE2BkpLjz7w49/vu+7fwyC/uR42cR06NIxwHrQWyGsxJr7UMF5+0\nuovTVc6yYvH46qE+LFOUj5nBxvxDrTduv9kPv75/QUNyeUujqc954hYGDRpciUZQ1+C6qEs6bCM2\nZVkN6kYpiuWNX8JaXzsA8zoMYjQQ1AwFqI3jXEXqz/Iski0JLNeiIjVnm00GDh7A/+f/O2/0H+dC\ns0nvjCIRLv4g1p1N6B29HC5OI18/wJ0ZRasZoqMQPXwGIW06H9mNc/cOWltbOf3Dl+j/2RvXcFRL\n09PTwye+9AXu3rsXVIRoWcN4/yQvfv2b6LYsbQ/sYnsqTWtO409fzBLKMG4nsAghkH5IZ9bmUw/3\nEjQlcFtSZH2f7O5tfOGRL/Dmn3+T/pff4IVnjrKxN8P2niRCL27WHFVDourCMUzXwJwzRdFE1RCI\nJb+mY0Iti2JYFlbCxcmmsdMpDDeBkUhjpHO4RDhRCaFvrBKh77697Pnq07z7V9/l3Ctvx/sUVtGF\nCYSWoCVEARgmKuny3As/4fCR99mzZw9BGLAa03n/5AWiiRmikSncnjV0dHSQy+UwTROBnnsfbH38\nAVTC4e/+7m85/cZB1pcNvFu4X93VMvHuMaoTeQpnLx/Mn3rnFN/4vf/K/Z/YxJbdfYhUjmBiEvP0\nKfzpWYKZmonP/MyxEJiuiQjF3MLPlbjVp2QfV1OU2Po/Pmh1k2bkLqVuitKQ6t3a1K+jalzHBldJ\nI6i7DVHEqXoTwSrFW3MyHFOv3uSwPgYqNjgxxeLjiQuH9dzrDb1wQV1wZXlmNE87I4gX4OePpVVs\nXiDmbahYleQnK+RPTzAkNRU/xB+8wKH3zjPRZKBtseDLv3vrOpK5NEPH+ykmHIKuHIEuE4wPEUqb\nyAGhJEiJ7SRZv2cDPek2mtq7yR/rX5GgzpaaXFlilXwKhSKTZ4c48doxjrxyDNWRo91w0C0Oaz1F\nOohAq1iCGcm5LJNWiwOypAkbejKYjoVhG1ilCnaPwdYd3Uzv6EL2NxFVqlQmIWy2MGomE/ORgVzg\nODp3MWpXUxhg6DhTJ0SclRO2wtQwlS9THKtgZyrkem2692wmX/CZPneWnruyZNpa2PDQ3Qj7CBOn\nzl3TOdOOhcoladq7hd0/90miyTxp10BHAd3r29DFKeZuFMNgerzA0MhpjvzwBQ4fO4qj1xbYJAAA\nIABJREFUBWJ4YtHxrgRysoCcjJvbm5ZJKpXCc11MQyC0QtQmAp2benHbsnxQGoNMgo6xKnYYZ1mL\ns7NUZmZRkwUILt8O5FajNDxOaXj8iq+bHJpkaniKXNZGOB5GTqG1RKWb8XyJWQ3ic7PwgwXDFGgl\nrjoFt5qZuroscDW2XzcC+biYosQZyYvqhLrpzM1O7NkT72fDFOX2QH1M3nMNVo5GUHcbUs+iOQaL\nzEZuRaTWVJVe0jxlPvXjno9pgCeuPnsX1qRFCeuirHPOKMWz5t4xF4ZneX+0hLCGiIDmSoCO4mxQ\n12wcZlrzkkP7vvAo6/Zu4dv/9k/JF6aZnZ1lU6KFB1sEZnGGAI3p2ViWg5ey2dXeTpDtpJpswfO8\nq9z7yzN1+jyv/+GfU52aQWTSvPr/fp1Tz75MaWoGPZWnen6EMVOwdk2GRx9ZT8aODSkWGJVIhfSX\nnvxHfoRRDpCejT51HAqT7GitsuHzO5kdmAA/wDQEUTUi8hcGcEu5/akwdh8FMG0DkahJ4XQsixWm\nAQnNiQ/GePvdEYRhsPnR+3n6ya/w/lvP8ebXv83n/891bHzkPj75r7p57T9/g5f+w59d0zlTKRd/\nVy9iSw9Nzc3s/9XP8cBn7kUXp3BNiS7PxH0hDQNslzMvv8t3/uhb5KfypA3FQesVrKJPRspVlTxq\nrZEyNpEx0HETbhVfJ0doWrNJfu1Xvsr4E59m4HQ/fqWKVIpjx44x9N4HhK8cRQezq7iHNzdaa97+\n2VEOv34KYZqk7thE06fu4o5EQF8UEJVvXnOZugHUatXSRR8zUxR9iSHFrXLcodJzEr1bZZ8bNGiw\nsjSCutsQPe9ntYm/8K/dsORaqJubLGWesuh1ix6M5QsQr5Qv97fzt6G0JpDxmHZNzqlZ2BA4l3bY\n7FqYW3dSNlzOvvgW5Yk8APa8eKV37w52feYTbLu7j1xScc++NfQVswR9LWyyNdbMDLLiowyB1goz\nBdVywPFnX0e399Lz+KMr0ogYIPIDimOTnH3hTfxSmXOvv0dhLO5Tt373Frbdv4tgdJCUrmCLmrV6\n7ZxrpVFSoUK5KFM3/+wppYnQ6MlpolIZMBChxJUKqTRRNUKG6jLbmLe1eRlUFSmiqsS0Y9OauvW7\nDCK6epu5I5Pl5HTAgO/z0l98l8EDhxk7O8Rb3/gB+YlpOvbtQnjONZ8z23VJrumiMDjK8//Xf+GO\nTz9A39YN6CkLXcyjy0VAo00LwzDxiyWmzg8hwwjbMTFPDmN8CK4ShhBYloVpEMtAQ38uqBNKYmlF\na8ol6XaRyqSo+CFBGOK0ZEkkPA4OjxGeCsjM3Hjt5q1KabaCkUyx7ZMPEzgGF352iE3rXETWACEQ\npsDyrHix4Tr6yK3GHRCpeuuClb/FPg6mKPUa7fmHp3Qc0N0qxyx13Ujj1tnnBpdHEb+3LzXladDg\nSjSCugY3hNQgpSZhxpLF1eRS85Q6lxtVafBrQZEAhBlLMi/3t5qLtXxGTYopak/UA422Zo/u5gTu\n0w8z5TZR6B9ARBEYJn6pTOTHK/tr7tjGE//iH2MOvIce+IB993SjozZM18GfKVIZyyMEGLYFGiwM\nCqWQl775InSu4dHt26hUykvs5fUzeOAIgweOxMcqIBLQu2cTn/rNzxEce5dg4AzBdJGwHM6dkLil\nQYSW+rI+alprlK+I/AgKFcJynJVbSno23/zm0sc1LGiXoaRGyQghLAwrlmFqpZBByKZta9ja10N4\noczRQxd44b/8DTJfhEhy4G++y+DJM9z/L36D4hVaCxiWiZNMICNJWI6dFW3LpDWTZerwKX7wzNfx\n0kla13bjSoWIQnS1dm0sG+0ksGyTVFOaSqGECCNapny0VqtSE2E4NqZrYxomXjaD63lxf0MZoqMA\nZBifSRlBFIIV4LkJ+nq68aWmVKmSzWYBePPoYcqFwm0Z1MlaQ01D6SWNi0zHxvJcDAGdW9by0G9+\nieFDxzj1je8TfWozOtsGxAZJRsIg1CGqtnATy7XFVS28rIb8MtJQXQXXktvdFKV+SHHN0q1rE18P\nvFfjHmjw0TC3sNwI0htcB+bXvva1j3ofAPjd3/3dr+2j6aPejdsKSwisD0l+GbcF+HDGmltB1rEZ\nxLWMG9eHxJMhxOX/tj6xqY8x1wfMNRktBBzqn6b/5DDlsQn2PrCZu7/wSXb/0hcpTkwxeXYAAL9Q\n5PzB90klTVp7WvDHxvEn81QnpglmSshKsGAZvNDcy2CijfdGRjldynOk/xSj7xyFkdWxpJ+1NWfS\nikKxzOyhfpzqNClLo8Jorp5OhnIuQyfDOGOmQnVVP1ElDupUpK75R9SaRM+nnqmDWpsD28RKuNie\nQ3NLM6mmDOOOTRhE6Fq9mfQDpvsHGX//JJWp5QO71k3reOif/iqp1mZGjpwAQEeKcGKaYGCUaLZM\ncXyK6bNnacuCZ0ZovxIHTVKCViQyadbds4vyTIn8TJHWx+7G7WihOji24umOtn272PClJ9j19GPs\n+PR+OjdvwPHcuE+dDGs/EXEEouPUszAQbgI/kpTLFSYmJsjn85imiVuoEpy6fofQmxElYKzVYSZj\nk/AV5hLXoPue3ez5x7/A3Y/vZe++9bRZJRIzg7R7Id2dKRw0YTmYa0YeZ+ouFuEapphb+FgOrXSt\nXcjlqdfvXi2RZlUajIe3sSlKXU56MQt5ax5kPeiOGjb3txW383uvwdXzNjN87Wtf+91r/btGpq7B\niqB0HCwZ4vKZsxulHtDNGZsYsTmKcZV1c1KDnNP0CYRRz+KJRfuudeyuWXPMj8cxa0EHAmlajB49\nhT+V5+F7n2TN1k5ERw8jWzuJhnqwMjlmpmY5+A/PkktIbLkNb7qEKpRjd0kNwhRo6aBrX8zSSSFz\nHSgvwcz4KQZOn6V90qd15U7hAjRxpm64f4j3Tk/Q6+2kO9dROz+6FmDJWDJZk18u5/a3lImKDNQ1\nTVLns8CXoiZ/U/KiaY0QAsM045YLfoBVnsGeDhCmicwmCFrTWMUqlakZLrx6cMG2lWUQpBxaOtrp\n7Ohg/GQ/6eYM2x+5i0x7joH3j1O8MEp1aobKyQs0taRYu7sHwjyVM8cIx3MoqwktaxJH0wTDoKWj\nlbbtW8lPFNGt7WQfvoP8qfPkXzuMZoVaGtQwbAs3l6H77l1k21oZePd9hFS4jkXnhi4yOQ+CSpy5\n0xohDLRhISPJ1Pkhzr53lPHxccZnpvHD0orJfG8qDAOruxU8G1EcgTAAIXA6mhG2RTiej89jJsX6\nvRvoa7HxTxzEFLNs29qKrAaEZT/u0bgEotZKQ33IOikNSKVXLSBR3B4GDfONtOrUJau3MnXJZdjI\n5tw2KOIFhqhhjtLgBmgEdQ1WhLpc0TMEH6Y3S90cwDW55qxkpDX10hgTcM1l2ibMM0+xlEb6kr51\nzWzf3M7zz59merLAxKsHMM+dxkql2JZTbP/1h0jtvJN3Xz7Kd//grznwgzcYOnSch+/uoNkziKpR\n3GftkkxUS64Jp6ObtqEiU8fGaVESaxUnjOlIsG3WwNQCYYPhWJiOHdf5SUVUjQMCrRRRNbrs5FXW\nsnPzuZFAQUUXMyKGJbCMuJ4pVBo7YSFMA9O1sRIuoYJXvv82x46OUAwiyp0Zyru6yRwbwR1bbAAS\nJB0mN7Sw/bOP8/RTn+Gn//Y/EkyMkwxmWLOzl92//RWO/+X3GH4lbiS/cWcPn/nyPgBMU5CwAqKZ\nPIZjxxu0bITroZVEh1Xu+tJTbPyiRyGs8kHxZ5w2DG6sqcJiJg8eIxjP09bcwkxyiHf++G8oj+dJ\n5jJ8/mu/w+7H70GXp9F+Oe6zZrkoYVL1fU4+/wY/+f0/Jookk0bEB6mQzIzPxhXex48a0zTYtGkT\nRi5F8ewMUTFAGAbpe7Zht2SZevYNRt/9gML5YXr+zX9P37Z7MAdPIYSBrIZE1SA2BlqBmfNKyi+l\n0lTkrSsb/LCITbYu/Uz6iHZmBambojQCutsHqTS+/vgYEjVYHa47qBNCtADfANYB/cBXtNaLNGJC\niH6gAEgg1Frfd71jNrg2ZE2ecSVzkJUgljXGpiSWce0B1o2OG6q4Psy6hj51dQMWAES8jfrfzj9n\nc8cmib2ijQhZqKLHi3SagqQjCIammC6WsDwbryVNOpGkuaOZTEsWBHTu2cG6XeuwwiGC2UJcr6b1\nXENtLTUyiPBPfIA/NkWRMkHKITlVZvND++jYuoHjP3mFqfM3Lo8rJk1mkyZNsxEJX5Go9SyLlObg\neJGZIZctQuLMk1yqUKKkXjJI0zI2UdHR0gYoWmt0tPTfXooxrxG8nneBVASRkBiWgSkMtAbTc0l2\ntzE8XuL06QH6jw8zMxrLKy0TPK0wq+HCAUwDc10nTtYjVS5RPHSak+Yr9G3tpfW+jWSsAE8odva4\nNO/fzEyXizAtevuyZJJxXzyURlWrRFGIpV2EZSEcFyOVw8g0IxI5Mpl2ZDHi2HdeYfD1d1eu8fg8\nolKF0sgEw+cHMFyb8bMDVMamSDVn8fOT6MosujyLDiqxBFMrMEysoEiYnyR/bijejqnJJDTJCOLm\nIbcPhmHQ2dmJ29rEacchAtAa/8I46USS+7/6eSaPneHYc69y6DvPIadG2dJWwRRxzaZWi3VQhm1i\nahYYCRmWAG3E75El3gPCFBiOiY70dZmsXEq97malJ4BSx591t3KwGOmLdYC3kuHJ5ahb29cPpWGK\ncvugiAO6huSywUpwI5m6fwn8WGv974QQ/3Pt93+5xOs08JjWeuoGxmpwHdQ/+A0TjFUVRcbUTUk0\not4X+kNhztikbmpyHYe6yFDFqEn+5r0mqPer05rZ4Vmq+QqtQGvSQU37FEsRlmdRzVeItEfu3iKE\nEY5lsPOJh9nz+N0U/v4vqQ6PElbqsr14BFnPcB18k2nHppj08XsyJKYr7HjyYe780mcojIxRGJuI\nTViuY9JlWBZOwmUiZzCcBjfUJPx4XMsyMFyLgxNFLngGHc0OrTWZqwyWl1yiqWX0lni+totaxQGr\nukIxf13KNmeCM+/k61qGFMC0DNAa03NIdrVy7uAwz30vNn4xTDM2Dyn4OBPFS4eIg7pN3ZhJF/OF\nw0yceIXnnzvAL/+bf8K+z9yDnhpGl/JkRJXND65FPLwBbJcwn6dyYWDh+XTsuKehJ9DCIDSTYKRA\nu1hK409M8MG3fsyZVw+iotXpAScjydD5gVhKGARYroOXSWFGFXRxElWeiU1SIF5EEAauZWPJi4Yo\nSSnYWLz1W59ciunYeNk07V2deK1NnEu4aNMAqSgdOkVnMs1j//p36H/zXc6+eoAPnnmOwgfv0fHr\nD9KcNJa1EDbteOEhLIfzgrr4MV2VC1xb6whDYLkWERE3qsJdrXlfffGqegvNLJfa0+A2kFfORxNL\nSP1ValnR4KOjvjjjN4L0BivEjQR1Pw88Wvv/nwHPs3RQB6tbZtWgARB/8VWVwhYC+wZSkxpi5ykF\nziWZP6k1VQk6qJlPEE/YTNtESUVYDZGhRJ8d4dzfPwuFEg9sbSFx+C3Ghk8RnhtAzsY97Qw7zjpF\nfoQI4lFM1yJlmTzZleF0KeTYsXGMwjipcJw7v/wEdmuOo//wI4JS5ZqPq3vHJh7+zS/z/sFDvPjt\nH5IqX5xdbt7ZzZ33r2fW0JhE5KIQlulHN3ee6lm8JWrmVBRLMTW6ZiJx5f2r95+Toha82eacKcrc\ndkNFSNwIujIxy/Tx81THLwoEcht62fxzjzJ26ATnnnt98SCRInq/H0wTXa7G4wLDxYDz+SodlSpm\npYQszaKCaC4YldUqsrqwV5lZk3UJy2ZmeJID3z7MxFQApsXWn/8UTds30fu5TxAkHM7/+DVUuPKB\nnfIDCm+8H8sFyz57nvoE9/+jz9O3Phc7cyqFjkKIQnTgI2SEtty5dge3Mxv272PPL32W5m3rmZyZ\nprJzDdWgRGIgbj2iZYgu5dm0qZkv/7MnUJUKjqFwVUB10ieqBmh59RGYEALTMRCRQIZyVaIvDQQy\ndmxcyWSaqtUP32q1PKFavM+3cpbxUjQ1qaW6PWSjDS5Sv7aRblzbBivHjQR1nVrr0dr/R4HOZV6n\ngZ8IISTwx1rr/3QDYza4BYjNTMBcZdOUS4mVUvqazVOWIk4q6Xg7ta0YtaKYUGuoZewMITDNOMOk\ntYZaUiSqRASF9zFdk74mD9l/htlTtRq6+k4JgRAaoS7qPmU1wvMk27IuqfYU/sZWclYFtzTGpjs3\nUMoXOP69n4EBOuEgZsrgXyIxXAa3OUv7vp1s0wHB+x+AMNAyQlXLbN3Rya6dnfjTRYKZEkEhJFrG\nHALqLn06DtzmLzHqiw5+MrzGlERN0lZHiJoVvWHMnR+tNCqQRIA/U6Zkajwi1vblMB2b1IY20h3N\n5JNLN2wXApKmg2FZlISBJM40XjgxQLaridwam4RURMUSUdknqvjIqr9IUieMOFuoNUxpg3OTVd44\neIzxU2NYRZ+K7dJXqhIqhZVKXtt5uAZ0JKmeHZ77vXPHZvZ+4dPokZPo6ZE4SxcGaD8OYJER2nax\nkjbprWtpyuUQoWTsZD9+qQxCkF7TQaK1Cc/18H2fwmyBcGIGWSit2nGsJMJzsFqzpHasp3nfDlpb\nW4nOa4ykS6Kng662bmbOD6GVIvR9Wls97nxgI+FMgbAwS1AoEVSq8UxLAyLOqAst5u4DUXO91Fqg\n5zthWgag0MpY0jjoyjsPwhIgl3fTjJReccfL+DP75m9fMCc7rf2+VFB3u1A3eQlVwzjjdqNhitJg\ntbhsUCeE+DHQtcRT/8v8X7TWWoilOgAB8LDWelgI0Q78WAhxTGv90vXtboNbgajW98czbixjdr3c\niHnKfJQGX8UaLAG4xsUWEZHSlGvmKYZiThpY56KsUsbBidYgYglW/bl6QGS55sXHIklYiTNCHS0J\nPvv53TSv8WBqhCbHJUcFA41e145e1wFvn0aM5K/qeCYmJ3nx5VfZsSbHL/+rr4LtosolgoHTiGKB\n0vBEHMiUA8JKnE1cjropyqUTT601URChV8DcZW77btwj7OIY1ForRITViA19OdZt7iDZ0czwlM/L\nX/8Wk4NLq71Nx2btUw/hZNKc+KvvUx6dRIURZ595GXNinJ3//PMkvDRReYCoGgd0shosOE5hCAzX\nRkmBrga8NlrhwJTPVE8Two/IHB/hzI9f4cJr7yKVJCxVVqWmbimUUoSRxAgDhF9F+xWUX0EHcVAn\nZITWmmRvE72/+hT379uHOV3ie7/7Hxg72Y8wBL2fvI8Nn3yQrq4uRkdHOXz4MPkfv0nxvVMfyjHc\nKFZrltSjdzKRtXj9tdd47LFHacIid2qc7Npe9n7lc7zz/32DaKZIQdtk/ACnUCCYLhAWyyipMEwD\n03OQfoDQGtOxEELG/RepySk9CxFIwmq0ICtnmAbCE0hfIq8x+DIMA+GKy0ueP8aoWk+2uqPl7Twf\nrrdeuJ2P8eNKwxSlwWpx2aBOa/3p5Z4TQowKIbq01iNCiG5gbJltDNf+HRdCfBO4D1gyqDvARRlV\nDx49LL3a3uDqqWfNQF9sov0hjBm3AwCUvibzkpUa/3rNUy7dzvwP3VCBNvRcYFc3T9GArVlUyxf5\nESKsZfnMuL+aDCVCznsMAxkqDC0wrHmr+wI8z8ZrS+F6BoQ+VlilZ30Hn/hvvsDRM+foPz+IuEQS\neDmSlkFf1mZNk01rQqORRCrCT5r4ZY3vRzVDFImW6ppNUer95fQyhirXitYaLXV8DnUtsJufsQsV\nUSXE82wsRzByYYr+czPk+4fxywG257B9/120r+0Av8y59/sZODlIb1uSjh3rSIRPcf71wwweOEx1\naoby6ASyUkG7Ydy+IQiR1SCW4c2rnTJMExVGGFqDabIm4xJkmyhs3UI+cZapE6NUpmYu2xNvJUiu\n78br7aB4/BxBTYIqwgpGaRIqxbmAjjCIZZgyQocBIgpod+HuTc2sazMxs2ke+uJDHH0xwam3T7Jm\n80Z2PLiPXC5H+eUi5WPniKaXqE+8SVEVn+DcCC0dbXR0dJByLDItKfY/9SDJ7jbW7ujF+aUnGT8z\nzKkXDlBJ+2zNlJF+cLE/o1S1rHp8wwlDcKnbVNwn0cDSVq13XW0RRMRZZsOOFyLmm6cYpgF2nB1e\nMhNX+1vxIVoIR/rmloDJ2v5BHNTJ27wvW90UJVQN44zbldrUqEGDOYaoMkT1hrdzI/LL7wC/Afxe\n7d9vXfoCIUQSMLXWs0KIFPAksGwzvUbz8ZXnoomIIPEhmpdALWMmYhmk+SEn7FbCPOVSwlqdnWFc\nPJ6glhU0rIuNzAV1c5CLK+2WF2foln1MG7WgRWCYBlYygZnwEKaJkpowiDDCkDXbN9D78H6MP/gL\nBp99ExmGl53gGJaFYZnIMKQ57XH/5h6aHJ9o5Dyy6hMWy/hTBaL/n703D5Lsuu4zv/uW3Cuz9ura\nunrf0WgAjcZOECAJkQRJmbIkUh7KkmXJnPGMQ7Y0+xJShBwOTzjG1thjx9hU2JZHsiWKpAiKpEBC\nwkJsBBpAL+h9r+7at6zc33bvnT9eZtbeXWgUuhtd+UVUdHXWy7fdl/nu751zfqfiXmctc1zPFEX5\nN5FyeSOqRi1aaURMIJhLX1VS4zsBVsXHU5rDr1/j4rUcWoQ1jqnmFI/8/NPsf+oAKjfBS3/0V0wO\njtFjuezYmKZt1xcRdoThd94PNxX4eNkZgiYDDBH253N9pOPX+5SJWAQtDLQbRmdMw+TezhT7N/Qy\n1b2P436SV7/3Wr1R9UdJavdm2j95P2NeQLHoABpbuVj5CWQlPxehU9WCnMAPhZ1bocuy2ZCJIWav\nYMRTfOpvfYJYzOTaqUF6e3vYsnkTIDCmCsy+cDjsc2eZ4XHdqbP/KnKmQPn1E2S27+Dee/fTmoqR\ntmN85je+GDZjF4L+r36OKycH+eY//D1KSZctX9oXijmlUX6w6vEzTAMjbuAToJRaGLFbbJ6y4LWb\nSM1kLiN0rdCED9/uRGOR2h4FGpxb8Hm63dSOt2GKcvfTGNoGi1kcyHqXm3so/GFE3T8FvimE+LtU\nWxoACCF6gG9orZ8lTN38TvWpowX8sdb6xx9imw0afCDWyjylvr5qSqZtgF2L2BGap4jqXThqCKxF\n21J+OLEzbbNu2S/nvVbDjkeI9XSReuRJopkU1uwQx147xdDQZR77+j66OzcT2HG2P/tpVCrDe3/y\nF4yfWjktbssnD7Hp8YOc+Pbz4eTed9HmSiYZN3erqZmirIVV+0popcPIZ1XUmba57LeXm4yQ707z\n4M5ePrG7n0xpmOmXJsFz2NgCX/raw7TZLtf++jXeOV9m8MTl+nsnh2d4/j+9xP6Ht3Hfw1vxSxeQ\nbm5B1FH6Qf04jeq/7/7kLIX4DPd9/SBtrS3cqrh0Op1my84d7P0Hm4iWK5hekZ6BdpTvhP3yqstp\nGYSCTim0UmGUUSoMQiGrfRfhO2zd2ckv/o+/wOatzYjZEbDjdO7q44F/9LcZGxtj+soQztELyI9J\n1C5i2ySTKSwLUC468MIWD04JPTNBpjTLp5/dScQvf+htmREDISwCT87V2DFnnmLoatSumrZsRsLv\ngQ9iqOIrjbeGvelq0SB5h00x5wwkwv1aD+LGr1raQ1W4r4NjXo8oHZqw3U1mPg3uLG5a1FVbFHx6\nmddHgGerv18CDtz03jX42FNLhRR89L3ylmMtzVNgzlBAKIFhLDJPqWJQNVKYty0lNUKrMC2r9rRe\nalT1NVVN1SpUAgo5n2LJQhuaynSRI6dGGDoxRvvDQ5StZgKpMDJJ+p86xOUTp5mdyeJNzi7r1BdN\np2je2EPfPdtJ2wGWGUa7lGlzNZfFyRVp11UzkioCUe2fpxZMUFc8J0qvfYRumW3MmVRUWx9IgQ4M\nlFQIw6BrWz8VJZjptNl5zwD7d/Yye+oiufNTaKVJtDXTtrmF8vg0xUuTFI+N4Y4XEULQ2p4kmbCZ\nvTpKbmMzSm5BupJgsQnNPOFqSoUQkJ/IkbcEdj5LZybK1kP7GL9wlXK+RMumXgLXI3tluB7tWytU\nvoSYKbD10UN097RjlWegNIsuz4YmPIYZTtdrqZdKoaVEB34YVUIjDAMd+BhS0rkhw4YdAxiJKKI4\nA4kMrf1d7PvyMyQvX8Z6931GL41R+ZiIOtM0sW2bqStXMMt52juTWL6HzE2jPYe467BrTwd+oYSX\nqx6TmGeCo0JjI2GGadG1z+5y6cWGaSBEaKSiquXltWt2gYOrDtOKa+Y/tWVWE7Wr9R1dK9Si763b\nyXoyQJlPLQ0vuEOjpQ3Wjrm02rs7fbjB7eXDROoaNLghuhrZ0gZEb2GdyGLWyjylRqA1SukF5ik1\naimZsXkpmTCXkqm1xopaC14zLIHG4uz5aS69NYr54mUmE4JzsRKtQyU6piUv/as/JJKMozX0/Myj\ntD92L7GDO2nVPhM/fAO5TJuDyy+/Re7yNZ782ufYdXA7yWiA8Ep40RTfP3uc7MQU/9XeflrmNRas\n9dSSvkTKO8/6vtb6oDaxtpyAREuEJ772NE92tOCMXaDJ1mjXwSuUqUwXCSoBpfEihj2GYWpStuCx\nh3o5fnqKI8fGuP/BjWzb1o5fKBG1NVOHj+LOFgkqKzuL1gTvnt2diGSSyIWf0tOzma/809/k+d//\nYy68e4qHv/5LFMYmefVf/Iewv+AaMvLmUdyJLH1d3fRt7EZYUbRpAgJhRyHiIdxKOIHQ1bTCIAhF\nhJZoV2FGIghz4W1ASx+C0CAknU6zJd1OKpUiWZHkk2/wwZtp3D4C1+PN//xDnLFxPv/bv0xrPIb2\nHLTroDwH5bjoeT0EhWFgRGx0IJCuj2FbYbsIz8cwBSJmE7gBepmHGDXzFB2WxOI7wRKzE8MOxbZ0\nJRhVQ5WGKQpSa1ypq60B14+BhNTgrmH0tcGdiSaM0AUNQdfgI6Yh6tYJCo2nQkExgOaTAAAgAElE\nQVRzK+vb6qYpKnSQNG9DfV1tP9bCPGXh+haap9SjchoCQhMV22BBKqYOfaoXrkvpeh+3TFOEjkBy\nbXSMrOfhxRXSNZCeYHpwuP4eP26TLxbwO9P4EXNJ74hkVzsdB3bR3NFGe1cHfQfvQ3UkOHz8CJ3p\nBJ1tfQRZxdTJcY6VNDomyAmfe9IJemJ22GQ9UNVUx7nUsQX7rTU60NdNu1RyddG+GyEMsSDiUTNK\nkUi8ooeZLZMYGcV2iojJSTzXY8L1KQ5lcbLOnAgUAjtuYSdsonFBX2sMuauNDRmbJBIPjSyUKWYL\nVdOYmsGFWNIzr5bOmEia2DGBnp1iuhgwdj6P1d7Cts89weYHdhMUNxHkS1x89TAj75/90Oeivv1U\nHNWVwbcExXKFSq7E5JHzZE+cYtPD+2nv7MBwXXAqKN9HOu7CXnnVKLNpmGhbM3xhmEsX3kdYFtgx\nRDRFZKCP2K5t2LZNNNNEtjVOLmWRKd55Yn8x+XyewStXGDp/BZHNElQqEBXVGkMVRjDVnGGJUX2w\noQOJrJoZCSHANDBsEy3COkuzeh3oYKmZkJj3vWLHLaxYWNM6v6jXcyVDl7NIx6erKRpeV/o65ilr\nTC3b4Ha3L5Dz9kGuAwOU+cy/H62n416PSF37vOnFt/4GDdachqhbJygNrtZgLIwe3SqkhorUxM3b\ns324deYpEJ5vp/rk2VxOQGqWNvHTsG0gQ+9AhrIzTHlU0lwyqxO9hbf96fdOM3P+CvZTB0Cpajrd\nHE0D3ez55S+y7cA99G/sR2vNmdNn+P7hsxzct4tnNu2lo2QzdGaG18/MMNRqMdIT47/b209/OoFW\nHroajTQiJsI08OWiSJMibF9wndmh8tWaRCEMywh7hS2KfAZugEYj/VncH79SF16BJ5e0mQDmTFYC\nhZt36WiK0v9AN4HjUxicxCv7y06srejSRui1IVG+rDcmv3TyfX70l3/GPV//Kvu/8lk29LYRS6bo\nuW8/P/zd319TURff3k/zpw8iUzGmprNMTExx7Pm3OPetH/PsP9lMYvM2ksUZNFmkE/bcWyLqAAwT\nbUU5/855/vwPXlqwjZZH99Pzi59m566deLZgtD1CMWOTLgWs2MTmDmFqcpKT759gNpulOQiQpTwq\nHllqU1v9r2FbCMOY689YbUUiDIEZsVEiQEmJYYefh0D7123dYSciWHG7angkwocfGoKCy6Vj4wR5\nh9ZkhFj12taVmzNP+aCoahrn7RZ1vtLVljHrD63BU42Uy/VArTVFgwa3goaoa3BLqaUmRozbU2MH\nt8Y8pUagNOVALTBPCR0kAwzbqAuFmqtk4PjE0nEee3onVwOLUyVB8eQg3oXhJdvVjk9w9GJY/+Mt\njJzkLg/x/je+xegj52i6ZysXLlxg4swlglODnD58nvz3f8rVY3MC494DB/j8lx5nZ+4qVnkaI2pz\nbbzCqRPj7NvTSVdrnMC5cXSmFkGrTU7XyjxFK03gBAtMSIQZulyqQBFUwlS3mglNre5u6YpAebLe\ng09JjV/2UVKFzdQDFUbl7IUCTgYKXfaxIubCMVuUntnXk+bZL++na1uCLjlNNBdw9e0R3vjhu1x5\n69ianIsa5TODjJYdXnvpOLFoFMdxmD4/iFeu8M4fPUfp7AkefrCTuHIIKm7YpqJqlCIMA8O2UF7A\n+MQEx4+/z/lTI0u3cXGIkT/5Makv+ETaM3QOF4jPene8oAPInbpEUKxQvDSMrzU//Jff5MAn7+H+\nT+5FzoxBtSG7YZlYiSjKC1BaY0Yj4WvxaL3FAVCtMw2pPci43vVtRW2MTAa56wDjI7Oc/t5fE5Qd\nXMNgpD1Da3cLphVgBMuYp3xE1Aw5bsccs/5QrRrdvN2i8nZRG4NGyuXdTcMUpcHtoCHqGtxSpA5r\nJgwN1l1mnmJogb1oRVKDlHqBeQpKI5WsNxyvrUSrUFTELINtOzpIN7Xg+QmyqRSlVAbf9ymWimSz\nWcyKj+0FqKvLtoekMpnl2iuHmcxlMa9d4fjx4zhXx9haNBmRUJu+BwLKlmbfwAZ+5vEHiZ4WWGPh\n34rncpwdzNPckSIatTDmzQS10kss3LUKUzGlL9c84rCcmYShQsMZFEiqE++qmDNtEyNiLLcq1Pw5\nsxNQa+ZQ6w+mIzUHnDmEqvbME2HvMsMITTHCcTQQZoAyDVpaEmzY3Em83SLiZjHyDs7oNWbOXcDJ\nzrKWeKNTeKNTzHKy/lqms5n+3RsR5TyFy5fxt5pYliaoNrTXUoaizjQRhoGUikquzOS1SfLThSXb\ncMdncCdmGO1sJ9HbQWI0D6WPR/1XZXiSyvAkAEEsyrnTg3TtHsBoakYVZ8EwEaaJIQSGbSOFh7As\nLCtCYbbE9Pg0zW1NJDNRpOOgXDdc3rYAgciXUN5cu4vFmLEIZiKO7OhAeXHKiQ7GhwbJF/MkHt9O\nW3uc2OwsZsnFr5mnUE1zNsKHFlrpNfU/D+CWGpDUDFCgasyi1u8kt2GKsn5omKI0uF00RF2DW07N\nPEUJiN2OArsqa22ecj1WMk+pYZgCKxamdWmp8Apl2jPNfHpLG8Xdu8nrFNPTM5w8eYKzL79C6so0\nrRM3tmL3zg8jRqboLZVQrkFk0fyzaGvOpRR7jBwdxWGM9iZEehsA0bM5lFKcODXJ9FiRfX0pErVo\nY7Un3fyaouVe+yhRSqEdvSCN1TANzGgYvVucknojDCN8rw4UwaJJl2mFf6v1zLOrPQZX3De3gipm\n0V6FgT0D/OI/OcgL/+abvP1nH21Hlz2P3cOTX/sMwopguiUik5fwC3kCpypdqw8PtJZ1MdKcifL0\n5/dy5PBVXv7Lk0tXqmH89SOYkQjO9NoK01tFtLOF3i9+gtYHdwMgLBsjGgvLCqsFrUbERlhRzJZ2\nzr92hh995xif+dufYf/926kMXkEWC1gJTaS1BSMSoTw0hpcvoDx/WeFlxiKYhiIxfp5dG3rZ+jtf\n56V//xzHf/AiT3TEGehOYVkenlE1THJlvebGsEI3TelJVPDxrcRZrwYoy9EwRVkf1E1RGrWSDW4D\nDVG3zpBVG2tTCJaPY3z01MxTgqp5y91iniL1nBnN4gjkiuYpIhQikaYYqZ4MY5NFyiNl9u7YhvQM\nrr12ikp8nEo0Q75QoHJllFjOwV5lnZoqVaBUqba0XHp0loKMD2k7SSTdxvn3B5m8cBWAwfNjaA35\ngkvcEmijqZp6KOtROR3M2bur4NYYPdTRLBGQ9RYRQtXP7bJpmMugtAIfdK0R/Py/ocIInVWN5hFG\nVgzLwIyYmLaFYVuYEQszalUbyRsIK0KyJU26u5f9n/8E0ohw4dXD5EbGP/DhWrEonQd24UdNLl26\nhJktkSguTP9MJU26OyKoQOP5DuVCkaBYrqcQLkaYJlbUpjWdYMf+fgI7ich04ho2Y+MTZC8NUTg3\niDt9c41Q7xSSzRl2HLqfnu3dgATLRkSqn4p5oVsRS2C1dBDb7NP04INYO/ZS6epjeKpEdqhA+cIw\nVmqWZHOSnjabaDqJX6zUm4tpGV53wjQxYxGsqEVEuRhxi9jWAaKtzdiRKD279jGwpx9vJkv+9Hk4\ndzFMLy5Vazr96jW3RtQiB+oWCIr1bICyGDkvzbL2+3o9F+uBBaYojYFucBtoiLp1RqA1UkLcrKYC\n3kbuNvOU2k07ZkBkmRXVzFMUc6JOCIEZNYm1JshsauedS7NcGXXY/gu9FGYKPPfvfkyl6CxYT2fV\ntXAtSAWCnQWTnkgnunsnb7/6LQ5/8y8XLFPrz2XHreo4VYXEKoxSbjU18xSoplPGVqitW+G90g2b\nQS8RdVKjZIAVszBt6stYMRsrHsWMRTCiNkYsipWIIaJRRCSGSKbBjoBT5L6f/RTdhx7iT/7+//7B\nRZ0QRJqS7Pzyp8m3xHjn298menJ4gagThkAVc/hDF3GzedxsHm+2uKKggzCapJWFdD027+5jzzOP\nYW07yIyV5o233ubMd16gcG5wxX1aeAL10r/dzqiEqBnraJrSTezbt5eeniRMXalH6hQsyMcV0ThG\nIk3XwQ4ObdtDpqODaQ2nIhNcvHaWke++j5aSrt5mvvy1Q/R2N6GDMDqtlUY6Xpj+G4vM+4niGxY5\nR1IJNEY8SXz/o2SeehBdySOi38efGiGo+NU+eGFa81pG6AKtcW7RLHM9G6AspnEu1hcNU5QGt5uG\nqGtw2/Gqeef2MhGuW8Vamqf4OmxRYBvXj4YqXyGFxIyajAzneO9anmtDOQplyUv/+SU818db1Pxa\ntibxBzqwxnNYI9ml67RNnL4WNBAfymKs0nRhtlDk8vAYxUW97kzb5oGvfoEd+zbSPHQc98oIQaW4\nZgYo86mn5d5ACAggYoobPgjQOmyKvnhfTdu8fupkMM/IZrG48yW+oB4FtOI2yb334iVaOPzdv6al\nS/Dw/Qc59tJxzh99E2FH2fL4Qzz0Kz+PEUsBN9e4u+vBfWx86hD99+5henSc3uESXnZO7Hdv7+fQ\nz36CTd02bjaPX6wgveCGqbDKlxiWxIzYXDo1xMlvHUVk3sAVEcYnJpi9vNSgp0bbob2kdgwgZUDp\n0jCzh0+hpSLS3ETX4/cRlCqMv3ZkoePmLWTz4wfZ9fknKZVKZDpbaUpHMAJ3bgFhIOwIyAAd+AjL\nRlg2WvqMH73AW99+nVgsjOZNTU2RvzpaT1n1zQjjzQMk21JkuIbyPJQfYEZtDNPEjEc4mQ8Yy8Ij\nA110dnRhJSL0P7KPnO/w9nM/4uifPw+BhzN0FWdoCC0V6ajJQHMMw7q9D9xuBlmtIbqOIei6Qer1\n00C9wVwGVCO1tsHtpiHqGtx2ZLXORxh3h3lKGLjSWFosl/EYpp4qjanDVC0daEpll+GpMqQzZDrT\nzBYkTq5UT2e0IjbN/d147U1MpE10boV6OiEIEpGaKwuZzlYyXa0AOMUyM8OT2KkEiY5W7IhNUHaY\nHRpjemSCMz89Rm5qhmgiSuuGFoSU4Hvs2L+Fbfu3UJw5j6N03QhlsVHKalFVs5zlXvfljSdCdcMZ\nY+lyYtH1syTaIaqRR7FyWmatIXxt2dAcpbo+qdGexDAEKq4RpoHZO4CX6OL81b8gPRswMOVz4sg1\n3nv+KAB5P8aGhx6lZbOJFpDs7SS5oZ3yxMyKJhuLibVmyGzuo6WjBaNQpDuSJJfJIDKQScbYuHcT\nG/dtpknl8MankI6Hcr0winSdJ8daKpRpoLUml3c5fzFLYfQczmz+hvsUac2Q2TlAvKMVd2MvaWmg\npSTW2crGzz9JaXoWf3KW4vA4TvbG61srzFiESFuG7of2s/fnP4tTKRM1NImEheGXwitPGGBZYU8+\npUFJhBVBRONgmMhiBXd4goLr4ZTKFMcmCSpzgtAL4Np0QKpV0NaURDtmWFsnBK4nyWYdzo6WueiZ\nZHrLVGKzxGYuoZXEak1z5rsvka9GQDPdHWQ62pHFWSypELYB/tokx9dS3T+qqrz5n+VAzz2gW8/U\nUl0b52J9oPTcA43GeDe43YhbZWpwI4QQ+usM3O7dWBcIIG6Kj9wc5IMgCCfrthC31Tyl1iA9+iHP\njykgZhjL1goKAQaCmCWI2gZ2wsZsikImgv3E08Tuf4jmVJxLrx7m+X/273FLZdLdnTz1W7+G4/m8\n9O/+GHdiBlHxlqxbGwIZj4T7UPF47CvP8IlffhaAS++e5kf/z5/Sdv9e9vzCZ2lvbyd7bpBX/sW/\nx6lUSLS3UBqboqO7mWd+9Rmi5Vlyx08Sz7RhCRNvfAJ3poiTc5CunDNF+YBfIY7UeCukbKpV1pwY\nApaT3ZYBMdO47oOBWiPzmoX8dZerGq/MX06YAjtmEW9P0NSdgU8+y4TZygu/86/IDQ7R2tPO7NgM\nhZlQyCTbmmnd2MvDf++rbPn0o1w48j5nn/8JZ/7LDxYIhesRa83QsWOAL/3238bqbOZHP3qNmews\nltA8vnuAWLHC4R+8xZYNEe7b2xr2pXM8pOPdsM7RjNpEW9PkO7Yw1raDd77xTS6/9NYN9ynSlqFj\n33YOff2r9G7fQqoaFBa2hdWSZmpyiovHTnL2m88z+Fdvruo414Lkpm56nn2CgYP72bRvFxvammlN\nRolIB+EU0OUc2imDX/38xJIYyUyYhimASJyiYzCdU4yPT3D1+CmO/sfvkL14rb4NM2qT6Gznvoc2\n8bkv7MZQQTVSF+HcyRFe/N4xJvIuOdPA2dZK0o7SOy3xKi7lUgV3apagHEZan/i7P88jX3mG8nuv\n4p47SzAyjTNdwck5BJWg/mDCkZrKB0zJlLrWk+6jaXzsKl13ctQ06og04TgFjZqqdYEm/Aw0xrvB\nWvNvGURr/YEnoY1I3TolqEaTzA9p579W3InmKbVG7TezG1rXrMOXHofWINFoXbXjdyV2XJGyTGaU\nRyEo0mNrNm2wue9gPwqItLQQ5GaZHZ6Cqfyygi450E2su53i+at1Y4v8RJbRs5fZuKmNgQ0R7r2v\nl+ZdbWxsM8hOjDF96RqB5+HM5Khkc8zGBPEN/XQe2E1yYhjr0jnKk+OUci6+ExA4Qd2Rb7WmKJow\nMllbPFgDW3Ola2te/AeBJzTVDEmsZfoh1lpH1CNwKxiqaKVDswo/dCNcnIqpA0Xg+NjZCWJpaNm3\niUKlwtDpwQVpj6XpWUrTs6R+8CIlp4LuasZsSS2tSVuOVBy9oZmODa3s2NxJky4QUYJ9W9IUCyZC\nSQY6Iyhdpi3iEtNqnqDzCSrLN1MXpqgfj/ICvFyJWKZEX8LDf2gbTcLn4nvnKM3OpYuayRixTd3I\nsoNzZQxvOkf+7BVGXnmHtlicXZ96BFMoSjOznHv7fYYuXSU7ObWqqN9aIh2Pysgkl0+cZiQ/w6ef\nfILe9hZ0yUULgbBsSDbXjWzGLo9y6YU3QAbEW5roPngvlXLAyKmrzGSzzA6OLBHf0USCjffsomfv\nJuzWDoRXRnkeZjRKeqPNxkfjuG+dIHvuEnmnRFFrVNZf0BakrX8DWx/aT6azhbFTF8jkC8SUpOCr\nsK5urc4Hay/oatGotfgs3y00TDLWF7Ua+oaga3An0RB165A5cxBBzFw2Q/C2cUeZp+gwonkzu6EI\nn+Bp4/rHoXWY6ucVPSq2ybF3jzM2PETLxhTbkjaffXYn0fZ2ir7Bt/7fH3Hu2OCKKXvN9+6g44n7\nuPJHP6yLupMvHubK4ff5ws/tZ/febp5+ejNmzCa4coRX//htjr9ztb4+JWAkbWB2JPDbN6DzBYKq\nkKv/VIJVNSFfcC60Dp9e34I7n9SaSrWoxxAQF8aypjXzDVUQYMfsZaN21zNPkV6Aky3RNDFIJKHp\n/9QByrZN8dIQLGNOcuYHLzP43gkGfvnzeMXiqlo/6LYU6tB29j5+D587sBVdyKJmh3i4yUfaCul4\nyLHLBGWXJx/fGP6/+hM4PoGzvKgzI9acqAskqlDGHrxIojDJI48+yNZ7NjMzMrVA1FmZFM2fvB93\nbBp3cBytNYWxKd7+xjcx8jnuObQTQ3vkz53n5X/+Dc4fPrWsQ+lHjTM2zfBzLzPZnWJ2Vxc7tm1l\n9/atoXGKYYIdQ8SaIJ5GJNJceP59/vR/+VcAdO7Zxif/j24mT1/kpd/71/VIdK3tQY10dwdP/cNf\nY+vubqyxM6jcNKpSQkRjbHpkN1v+5na++4//gOFTF+meWj4a27dvO1/+nf+aV//we3znf/19Ht7Z\nSk86SuAEqDu8ME1qjSNVI91sHg1TlPVFwxSlwZ1IQ9Q1uCO528xTlqN2jBFDoKXGd3wGZgt0qQBR\nyZFriRNrjnP29CTnLucYvzZ13Rqsvv4+djx4P8dffp0rFxS9FYN7H9vHgSf308EslalZgrKLFY9g\nJhM8+NlDtOzYzrs//CmFmQKRSISf+8Jn2HFoJ8aRw8yeu0h5poJX9AlcuarCHKVrgnjuZqc1t8RK\nfTFagzdPTNrGCuOoWWCoYpgGhr36miZnahY7YrOjpRM34jLI8hmpWmvc2QJjP/4pyvWXGIi4TVEK\nXSmMiE1zJsMTjz5M/9YeVFqwsTkKnoP2HGSlTFAoElQcpOuFoswLUK6HdKsROsdHuisbpSgpCZzw\nb8IQGLaJCiRBqcLh514jKzI89Ku/yKOVWeTQWcyuAey+TcS3D3Dupbd5TbxaP0atFNr3wCmgpUMq\nafHUrz5L28ZuDn/3ZQJvzuynZfsAG59+CCyTIAhwXIfC5WGm3zqBdJZGn28WrTTJrEPs8iyRvEuA\nwIwmwTAhcJGRJqaGpnnvz/+EM3/1Rn3s8/kCR48dozw4hpJy2YFM7t9Gy5MHiXQ0oxJp3I6tWMk2\nLLcA0ufquWHe+8Zfc/b1Y8vuW6Ipxv2f2M2eh/swrh6n15jhwV2tZCJm/cGJvI5j6Wrxa+1a1vCj\nV3vgFTTqh+o0TFHWFw1TlAZ3Mg1Rt46p1UCIOyQFcz53knkKH8I8RevwWIxlPFMCFTbjtUQ11c9T\ndBTKWL4HFYuSm8AQiskJh6FRh4qzyMXRMmhuTaG1ZnamRHtbO9t2bCM90IbZncQo2Awc2sPDP/dJ\npl95mfzpMZyZMmbMItYSMPDANsymDCdeOkKBAqZhsKu7j22pZib/+nm8wVFUziNwA5SvQtv2ZW5k\noRFD+LqsCqk74YanoV7vU/u/IZYfx/mGKtrWCKPaCqHuzK/D1g3G0lo+v1DCmrHoTscoNFtc3NGH\nxEIKA9d1cXIFnKnZMCpbdsgeObvs/potTcQPbCPW3MSGDV3c9+UvsHtjFzo7jM5No/IzoBU6CPDL\nFYJSKOq00uggqEbo/DD10g2ua4mvpUbKUDgIM2x0rXxJUHEZOTFMOd7BQ1/9Iu3JAP+0j7VpH1b3\nZkQ0Tvm9ZP282LEIzT2dtHQ1QzmH8ivELck9T+7HLbu89/1XF4g6s7WJ2IFttPVsIBaJMnLuEn7F\nRZhr1zVTRCyMdJKk62OPl7HLPhIDM5JAmBY6iCKtGIWiy8XD7zN67lL9vZVikUvvvY+aKaxYK2q3\npjFa08wMj0LFI5ABUb9CQigybWmKlWEuvHuG7OjUkvcGERPRmmTnAwNs25TCu3SSZm+G7X1NVLIu\nXtELG9x/yM9PmPLMmggNzdxDGVUVMHfC5/t2Ep6T8PeGKcr6oWGK0uBOpyHq1jFKhymCtgijRXca\ndYt7AVHz9gnPmviKmnxg85RAa5TSRAyBvYr3qkARuAFWzERLReB47Pv80/S1bOLFf/4HXH3n/fqy\niWSUxz+7DykVLz4XOi2mbIsvb+1h58O7ORMksXZuQsRSKAVBxcev+HhlH6/g41XeYTrrIEuhk2bg\nerzxxz/gaDKKPzPNBkuzOWWDCkVN4C7fky6oplfC7YvKrYZAacoaYibXjbzqQBOoADNqVputL3xt\n2W9NrVGez6a9A/zivfcjM92UzASDg4NcfuVtLnzrBbTvL/PGOXp7e9nzhWfp6O+lra2Nru4NSFNg\nxtLguQjfxQAMN0CI3Nx2/aAaofMIKn6YVvoB0oK0UgReOEmx0OzZ0QaxGBx9kZyQBNlpxGgOM3EM\nO53EOXeuHv5p7u7gmX/wt9h+Tz/kp1BuGS0DDLeCyk0uCRNduzbEieee42985Rd4ZP99nP3D7zHx\n8rtrGqUz2zIkH92Hd20cdfJq+KIQYNpgWWBrVCBJdndw8OtfIdbRwnv/4dvhuchXCA6fRV+nFUjx\n2HmuXB0n+9yr2LaN1hqhJe19nTzzm79M3xNP8Nne7bz5jT/l+LefX/DeUkcSf1MzRb9MaXgcZ2KG\n8kQOvxKsaR3dWiK1xpVzk9g79fN9K/FVKOSgWg9+e3enwS1AA161hq4x3g3uVBqibh0TmoKEkaI7\nkfnmKUZ1vlMzv7iVu6wJhYtxE+YptXMcKBDGysY0utoqwNChYYfWULFj5NKdDGzZxMaeLcSbkgve\nYxqC5qRFPGaya2szydlh8j95hdbpabZHLXSTTXJmgunDRylcGcPJVvBLPtJXaFXByZUJKgHdSZO2\neAphG4xPjFP0FR2ZKBHbRkuNkio0RpG6Xp813/jE17emXu7Doqr1XX51AFa6jmpROVFd0DANNOFr\nhpqLKBkRi2gmwdh0hcJIiejVAnZbAavTRaV8HCNGfmwUZya3qhw4lSvinhykOFFEJ5NkzRMYysd0\ni/Tt7KOjr4tLR97CHx9mQ3MYRVS+REuJlmrBz4L1Sr0qwSA9hfQkiXgEywwIhgYJCEUj2TyGZRE0\nJZDKIXbPFtyhSYRtY3W1YaTjqKlRtFMO98ep0Npk8MCTuwikxnUCLp+8RrQ5Q//ePQz095KORZGT\nWZzx6Rvu2wdB+wFytkBrzwY6tm0n3beBQEqUEkydu8zQkZN4fkAh8BhVDqOl2bk3+wFq6vrGLkG2\nQJAtUGKuh185ZZOo5Mn8+A02HbyXyOY+jFR8yXs3bemh95HtZFIiFPlG6NSjpFpwidQ+dzcTsauZ\nmHzYirc506hGamGNuUhNrXVNg7sZVTU1gznzs4/Bra7BOqYh6hrc8dTMU6BmfvHBI2ZrwYcxT/G1\nRimIGcu7eiqpUFJhxSzMiAlAPpHmat8O0skU/X4ZoRbWYKlAUh6boqO3icfv34A7coqr3zgGUhKL\nmRxqdfHfHWcw5+CXfZQXhOmgniRwAwQCyxTs60oSy0QxmyK8emScQsHjwJZmEhq8nIvyFHJR5MJT\n4dP7jxsacGU4jsYNHFZr/fhETCzbrNyKR0lsaOH88XMceW84vCaEqLpahj9aK5TSaHnjGqmpM5eZ\nOT8YGnrMv8C05nP/23/Lw3sP8uqP/yPBtbN86asHiQiBdG8c4VK+xK+swtym2ibCTvjYCRsZ88Lj\nrg6zMAO0Voi2DE2f24L+8WGc6RKXRiaJJHx2uFm046ClxPQc+jam6f2NpxCRGNMTRf7sX/6Apm07\n+cpv/SNSzSmyV64iPoJn3nI6T+nlo+z8lZ/jyf/+12nKpPE8HxkEHPvRTxEqcUEAACAASURBVPjR\n7/7fAOSigrMdBumSZNuH3OZsa5wh28X5xn9hy5vH2PZ3fpaZmZklyz2ydytP/8yjuBfPEWRnSERb\ncPMekFuwXC1ifzMEWuOswcxTVSN0DUE3h9SaimxEatYLkjALpTHeDT4uNERdg/CmrUKhdBtbxK0K\nrcFToITGvsURO1h78xSFxpXhE8FI9eSbEYvMQDtlHTD2g9d5443TNNkW4xeu0rd3Cw988RMwO4Ee\nv0YmYVCZLuHmXfyKHzo1AoHj4xe8uoBTwVyUTcuqox/huGtP4eU9DCegP2njR0wsVxJ4MnTik2qJ\nAcoHbJd1x1Fz74usZJ5SRStN4AWYllk3T7GiFqneZhJ792A9cAiOfQspr624jtWilUKuYITz/g9e\nZurKMEPHzrChu4nolt3YuUm8XBHlS2CpaNQyjP5Kf5WDVb0mAi9cl9ZgzPtCqNW9tesyh8QMwa4M\nVtBEhxyneSKHp0oQVIWI1hiE0WShA9LNMZ78xceJdvWSkAWMkk8yYfHkr/0crX1dvPOtH9O+fwft\ne7Zy5cWfkrs8vHjvVk810iqDAE8GeL6PVIpKpUKpUCCoCmE7gB5tEF0Dp8lM1iFe9jELHk6lQrFQ\nwPPmBHekp53Uvdu4Mlnge//6+wTZLNIN+9RtiAnaoxZ+0Q/r6W5yCqmqBg5rIcJ8pRtmEPOo1ef6\nqpFquR6ojXegG+Pd4ONFQ9Q1QFbroAwTzDvOMmUhtS9bXRWgyxmQfJQsNE8RgEas0shFE4o4Y57V\nhtZhnj4C7OqrWgg820Z6YE+VGDx+meJUFoANW/vY9tA+0t4UDCfJXbhG4do05cly3cEPqk/6Kyu7\nH9b3qdqzrRYVaI9aYJvISoDrBPjV16UGtyru7gZU1dAFwojdSteR1hrta4QQoajTgDCIpuOodIbp\naBeOmUDYFnY6iQ4kfr5Ioq0FKxWnUq4QlB10yVk2BdO3BNIU2L7GvM7JvfL2Ua6+d4JMdwetWzcT\n7erFwMeMRtDaQwQLozpz4yrnxPwqB095qp5OWxNywqj28gOaKNGCj9kTx4xFMP0pxLSJb4QRRmGZ\nofmKaaANAcIgHktw75P3YKSaEU4WVJxYNM69zz5BNJ1m7MI1+p5+iN5H7yPIl9CuT3FiGmWZELWh\n7IK/usiVsC2MpjgqZuM4DpZlob2AicFrZEfG68tFJGworM3TiWTBI1kIf/dLFXKDw7i5Qv3vZipO\nbHMP1945w6m3TwEQa0qR6e4k2W2xIanrY3azaKoZAR/iM1pLefe1XmAytJ6ppaGGTdxv9940+Kip\npx3rxng3+PjREHUNPpbU0mCiN4i0fJTbd2oW+EITNYwbCjtdi3YJiF5n4WLR5dSrF8kcfIAv/dOv\n89P/78955798D4BrJy7wnd/7Ax793H0cOLQNr1CmMlVEegWkNxetUYHCd/xVtSGoI6ivQyuNGyic\nYK6e4G58aB8oTUUroubqriPpS/ySS3kyx8gLb3Lsj37KzOUh7OYm2p8+iJ/NM/XXh9nymUfpOLSP\nU6dPM330LMG755ctwpnJ2OSTFt2TLqnK9VM045kmHvuNX2LvIzuJuFP4lTxmLAIQ1syJamRIQ+DK\nsN6uNmgalK9uLBqEwIwYYBgEjgQR7pMVsxBGGMWbfxRaa6TrYVgWRsTCjNiYlrny+pVE+y5Ydl1E\n9x3YzZf/z/8ZM53CSMaI//pXaB3o450/+CblzhR6oBPx/hXE2OzK652H2ZYm8che7J39eJ5HqVQi\nd2mII3/4HcaOnlnVOj4MlcExRr/9En52TtR5w5PMfP91gnyp/lrfvbv51G/9OpHz7+G98dJHvl+r\noWaK0ojQzVEzRWlo3PVBLUrdGO8GH0caoq5BHalBsLKZx51EzYDEn+ctLYS4ZaYvSs/Z+BuAoTUW\nN2g0Tm1eH5qumCv04DOAqNBQKTN7bRQnP9cA2pQesUqWyUvXOGloYuM5gpKHDEKTC1VN39Oy+tT/\nBjemWuH/cnO41RqgzC8mv1nM29y2orbtFc1TlEb5CmEKpBvgzFSQ5TzR0SJGqUIs08SmniSx3iT5\nYDebn7iX2K4dXDx3HqrOjpVqamui6GFVUyJNqUlFY2x9dB/RosvwkVNIb3mXTCUlxakZijM5Orqi\nTE67DJ2don/XRlKtFmJkFOnnCPIuKlComiOp0qjq9aBu+Og5bCMS7hz18GWtptKwNEJAGDMTmFqH\nTcx1GNVTgUT4EmI6DO9ZNiIaQ8STiGgC7CjCMBg6cYmZ6TIDjz5EU38fGzu6cD0Px3Ho3LkFp1Rm\nanCI2YRBJR2ldGUKf5WijmozdS9fpJDLM3ZlhMl3TzHy9vuUJpfWua01QbFMUCwveE2WHGTJIb2l\nj5YH9tGcTrFl7xZ6WwQlw6fiy7BfoGUsiLivFlk1cbhZLdYwRVlKwxRlfVG7jwW6Md4NPr40RF0D\nYK6prBKCmHlrUxo/DL7S1JKyLAPM29D6QFVTE7UhMFdRlCg1OFoTNSCyjAhMxC3u39zMUG6Y7/5P\n/4RKyan/bcOGJj79me0cOTrKd3/wLvf3peiMWeiqQ2Xg1WqaVrvvmkqgl7UpX+19Ta6BMcNK5+JW\ncSPzlFAQhUY2KtA4OZe2dITu+7p4/dgEWTdgV5Bl055+og9/Cn/rLkaKNhy7THByEKRiti1JIROl\n90o+7EUItOV8MpkYT37tb+Lmi0yevUxlBVHn5Aq89m//M7kLh+j93d/gwoji1efP8vP3HaTnnl5M\nQ+HmHfxKdsHg1dIwVzug0gudTucbxEhXoqXGTlj1cxGiEdVoIVBvqK6TMTBMjEgUI9GEkW7BSDYj\n7CgIOPbC27z3wrt8+f/qY3vfRpASWf3RWtOydSP3/zdfZXJ6ivHBIQaTJ1mlpCOYzlN65Si5ZBPT\nW/q58K3nGX/9KGqF83or2fDwfvb+0ufZu30LTdMj5F54jtKFawSORJgGpm0u2zbkemjCFO4Pky7Z\nMEVZSsMUZX3RMEVpcDfQEHUNlqL5+Kg65uaqYUpk1XdQgL1CJOyj2odAU0/JFEJcd/u15VEaSwik\n0lQChfYkft7l2plpxkoelUKJrqYoXakEKlA0C03h/ATpssP25ggRN8DxwkbTOgif1HtyeZG2HLVI\n1fWWvlFtgVqDYvKaO+hiBLduHG9onqLDH6000pX4JR/DEGzuTNBvW8ScMsdPX+at8nkyxkWsWU32\n0hBUr4k9+/YR37mRme++SuvmNPd86dNceOUtho+d5vif/SUbNnbwM7/yKUaLHtOeoL+/H8MwmJic\nYOztE0weOU3gelw7cYEf/P6fMHpukGKuzJvPvc7kiXY2pX38klMfjFp0cTUR2yWHqsJUXMMy6gYx\nCxpyzf99EWH9ocXI1Wnef+84IwkT1d/FM5/7AnYxz6nvv8jpV48yOzLB6//uTzj1/E/C818VdUEQ\nkOjtpPPh/QjTxDDmV6HeGJWK4ve3UWiOkp3JUimWVuUSejMUooLxlMGB++9j06ZNDA8Pkb88jDw3\nXB/3+UwdPcNJ12e8tZmN3Sl2DfQTzTsURsKa2Zv+3r2JD6CcJwRV9f/rHX9e3ZxsmGSsCxqmKA3u\nJhqirsECNBpFaG3+MdJ1QNX8ojoxEYAwwbqFaX1ynvgxBRhGOMFdafNhL6mqC74WSKkRnkR6kqtj\nRWZ9RUwIelIRtrTFka5ESUXuwhSJiMmm5ihOzsVx52qxdLWg/2b7xtWMEubjKz7yJ/hSLz+pFITn\ncbnZrlhjk5z55immEMuvXzPXP6wS/n9DS4xoJorluly4NsF3zozQPy2XmHDs3LaF/kP38trLx+kY\n6OGRr30eUyhmro5w8aWfEntoOz/zP/xNrlQUV4uw/8C9KC14/9gpCsMTTB45DUB2IsuRFw8TMaGp\nOcG5N0+SPxej5bF+IlUhoXWYchn2JNT11+qzFsHCtgmLD1NrpB+KfWGGBiia0MxDK41Y4UMlTAPD\ntjCiUSbHJnjtB0c4abnInT1svu8J7MkCL3z7RbzpWUxfcfy5F5ZdT+d9uzFa0zhxk1KlQhA1CVJR\nPNfDkprIdTIUVTyCM9BGNiYwR0col8srL/xhaUlh7uqg52ceZvueveRefYNisYS8MLqsqCsNTyIL\nFWZMMB/Yxj0HnyLSMoFpmShThWPyAS7qWvb5B/10hr3swG0UDgHzDWJoGMSsIxqmKA3uNhqirsEC\nVPVGbwuI3I4ipzViLp0UorclJVPjqvAJ+PVMURabp3gqdFocSNj0V5dJKYWTc8LJee0psivRAspO\ngL/obnSzT9xr52z+6jS3t2C8llomltkHs3qNrvXYBkpTXsE8RVbNR2q9BAECT2JWAqyoRcbR7JyU\nRN2lO9xqK/riipipMQrT2Jfe5Z7Hd9PU18tr/+aPUJUS7sggzSUHM+8QM7KMjBQ49dy7TF0aqa8n\nvamHLT/7FBvTgpbiFC8/9w4yN69htg4dLGUwJ+hqr9XSJg3LWHAMK6EDTaAlZsTAqJqnmBGNFVv+\n1mElYkTSKcx4HGHZAHQ7gsi0JHt5iBKS8S0tRLVH03hx2XUA5C5d4/g3/ozSxhYKXU0YvWnyVj+X\nL1+mY9ZnY9lY8b1BEFAslsheucIVP6B9eprUDY/05jhwzz4e+e1fJt7cQnEqhz56meDkFVihL2H/\nEw+w47OP0ZO0aDMr6IlhqOSJtcTDZvKBwneWfeuyBPqDm3iE3/GqkWo5j9Aghg/dsL3Bx4uGKUqD\nu42GqGuwgJqZx60yHPkomW9KUjuclcxJ1praE0ChQuMTCCNLi01oFpinVFPaTBE2OLdqOyo1lWWa\nR9dSLW9WxEm9aBJTNVu4055Y1s7PYjQg1FzEbq0Mfmopqcs91AhNRxSmNufSG30FpklsQyfpokGL\nM4Rc5iSa+RlS5Rm2HNxDVHt4F87QvHMfA5syvJuwkOU83uQUZqlMPFtEBkWMgqQ3beLHTWrxpqb2\nFnY9fpAeNU30fIHN3SmKhocq+/gybBQful/ORYo0elEt3NJInTCWNlnXWqMDjWEZ9SiloRa9zzTI\nFVzGL2UZuGeA3o3NWK1dtOyKsutZydDRMxSms4y9eYxiREDZwbjBRebmiri5CzjZDG5vC/FYHMP1\niUiNdYPr03ADImM5JBLPcdD5ta2l27h/Bx2be8Ats+3R/Ty0fyenj57n2ivvUD53FT1dWPG9QcWh\nMpOj6JjYCYFKpLC7e0gZNqOHL1AsFmhaNA41ww65zMxTf0BTB6nDNLOgMZEF5hvEfPTZCA1uP7Xx\nro10wxSlwd1GQ9Q1uKuRGpzqt7YQEDfBuIVxu0Dr+gTfEIK4ubxD5vz9NIRGawNrufDUIm72flSL\ngC1O0/w43d9q/RUhHNuYAdYtNFupiSQzZmIl4jTv2UFKJUGcWHZ559pV1HgbD3z188iZGQovfQ/h\nOZjxJKI0iwoC3GwBXazgZgsEFZfWvm6++Ouf5sVvv8XolQkA0skEu7YOwOkpZs5eZHu7iRNJ42cd\nvGp0LnCCumNlnXmDu1jgAZi2iWV+sFuCMARGxGZ0MM8LPz7H59o7GXikDatnC/1bWnnm8Wd46Z/9\nAe/+4XcY+uFraAGtQYBYpaKIjuWJThYBQQZNSioMvXKUDsAu+7RdmqkersZYY/XywJc/xeO/9Axq\nehjLNjErs1z4ixd46w9/eMPavWuvvsvIW8cxBGx87H4e+s2vMbBDkJoY5tIrV8iOFLm/Px2K6Cry\nOmZGHxRPhXV0H6fP+UdJaHLVqCdcL4SZMXPj3Rj1BncbDVHXYFkCrUGFk+RVGDre0eh5v3gKguor\n1i0SAbXt11IyjeortiEWCLy55cLJV1Cz2f+QY6BZ+mR+8RPLjyuLx1YuEsIfxmTFq05+beM669Bw\ndaJMKT9CU/4tpqaK6KrjS7qzjQNfeppMQuBePk1PSwR3aBS//CpBoYQcyyLLLtKy2ZQUCGVRGp4B\nL0A6HoETMJ0fZvJcnosnr83t19goM3/5fYypEcqTxVDAlXyCkh+2tNCszhxl0d+VDJvQm5a5JGK3\nHLHWJuKdzdipOOaog+tJjv/0AoEV5/5fGqBQGeat77/OyNFT9X52MBe5Xg1CaVBz4nQ1NyyhNWKN\nHr/3f+Igya42rr3yDqWJaQDsZIJYuolg2gXHR5UiBMUCfrkChP3n7v0bz3D2xTe58OrbAPTu3c6B\nLz3Nhdff4+xPDgMwfuoi7/6H57gYEyRkhfa0Sc89G4hWPHxnUTN5Fn5WVTXaFtzEYX7cP/NrRc0c\n4274HmxwYwKt8Rvj3eAupyHqGixLLQpimGEPsbsBzcIieI2ompncGrPPxdsH6oYT8/dBE7pB1u88\nBh/Ktibc7t2dXlQTrvMnuYJqqzSWnuPV4Ktam4OqcdC8AaqZjgghmC64DE+UsaYqeJ6sR1Ri6SQ7\nPnGQ/p4kwbk4zuAQztAE5VNXCaqT9spkEa013REAk/Lw7AKzjPGZHO9dPIMPNLcmEfEktg6YfvGv\nsJXCrwQETvgjXTlXQ3cz51BqpJQIROg4Of+Bh9ZorauvCYQhCCwb14riawtPh8ueOzZINi9pu/cB\ncuPT/PQbf4pX/gBFYncIsVScVEuanvt3k+juYvzImbqow/dR5SLB9CReqYQ7XabiOlgtTSQTcbY9\nci9P/b2fx8nO1EVd145NfPLv/y0iMZup8xdAa7x8njPfeh6lNE1NUT737G56+7uYOjnC9RJGNaH9\nuqdCo6XVoAmjFHfvN8DqqZ2LhinK+qA+3qp6X23Q4C6mIeoarFsCrdEqNNu4HTWEvtbIagqcZYgV\nDVU+rDPX7TY7uV3UxHFN6NmC5VsVXAdVbZMRMULDHag6Q7oSww7NRrZsSLGtI0PHg3u4cnmKl7/9\nZuhSOjrJC7//n9jy2H52ffYQxrSHdIarIiyMPklPhlG1eRh22K8MIG0ZHOhLk+xO07S1B/u+JxAV\nD/ni87j5fBhdq4Splh9G0M1nvhlM7aGD9FX9NcMSWDGb4+9d5erEOYRpkM+WkdXjyI9P81f/5k/x\nXY/gI2ol8FGz7eBunvo7z3Lk5WO8/8KbFEcn6n/zrp6hctTGHRnm4tkxDh8ZZyIVp+1nH+OJB+9j\ne38r4sxrqPEr9fcoBL6w2Pv4XrpjX0T7HpdOXuUnf/EeleLqRa+mGsX/gNGGQNVE4Dr8IlhEwxRl\nfSG1xlOsSfpygwZ3Og1R1+C6SB2mSxm3KJp1K1HViIsBVAMNKzaf/qi2X59YXMdQZcFyDT4QC01W\nxJJ+DQbXH29NOCGu9T60hMAgTFWspSimYxZ23EJ7AfGowY5tbfX3yukh1EQ7tiUIfIlX8PDLYVQN\nQgG1RNRJA11VohbQHrNIJWzScQvL1rjlgKmShyz5aHQowtaw2l+r0FRFGALDNBCmqBrEgKlBmCZW\nzCbe0kwExayrKJUEiFmKEcEMDsX3z2AvbwB5WzFbm7A3tJJIJEkmEyQSSSIRG9s00YEHvov2HXYd\n2sWOe/qYPHGKQhr6HtyG8jyCYoGUn6N09jResYI3NYOemSKz9V6aHz7A9kcfoMvPUX73FYLpyfp2\ntVtGTQ7RpIrEWiIIbSA2tzL70FbyMoKdaaZl/0Zsr4Q6MR62jgBktT3J4tTL1Q53Lc060Ou7sfj8\n+tuaUUyDu5u5a78x3g3WDw1R12BFaumC/z97dx5cS3re9/33dp8NywXuvszce2ffh0POkBxSFiWS\nQ5HmohLFJIqoKlk2Y8usWEpSqUrKsWQn+sNVcpKyk8gKXYwtJ5ITaylFkimRDEmRFFdxyCFn4eyc\n9c7d78UOnK273yd/dDdwABwAB/tp4PupunUPgAOcPuftBvo579u/xzupFnQsP9tD8ne+c9UVgky2\n29JAlVq4s6Ef+0FitixpvhL0Nt5xNjsyGAaqLKkCkyjR9PlxPfnYBR09dUCP/ORNKg9UJTPVr0xp\n6KR0cvxFXRq7oomJpqJGNF+0dZtvyRvJd5q9OKXWVEN6+oJ8O1F7claW+PT7t+F8xbyl19d1CU9x\nYaCwVtHbPvwmPXDqJj011tYLf/WEXn7jsq4MmyYGnO64nmi0D2PlKjef0oH3PaQzZ87q7NkzOnPm\nrA4dHNXw4IA0c102MyY/fU1ha1bJpVf1lgeP6777H1F4+KSiqSnVX/mRkokZzbx+RWamYyNlve/d\nN2vi/rvl77xTQ8NDis5f1szrl9Se6mjZMDMm96PvavaV1zX57EsqVUsaOX5YH/nFH9f0sds0d+Rm\nnTk8qPZTT8v/5ZPz10S2vClKNn4NkDdTaxMJuXtFlM1UYv9YGooC7AcUdVjVfvh12PkcI2/zkzlB\ntlxvp0qrzkCVxYEujgJvC3Tbl/NAICmdiV5pvPMJv7SPXxZ0k3+Tk8Ig0I0DTgeUKL42p9jV055j\ns035uUR+NtLkq9fUnGymRds6TzSSKFE0F0mam59J2+rL/YMwWJS6mLcwWBqe8sLEjJ6cmNDZS22d\nuHFc4Vse0ODoAck5HWokqkVO1eUdOHriS4Fmjw7JSRq6NqegSwPvdf08J12uebWHqzpy9KjKZ48q\nGKxp8PCoho8eVvnAoMojQ6oeOKCwHEnltixoKJmoK5mYUxC1FDbbssvn5adn5ebqSuYaiuZakiQX\npK9Z7ZWXFY/PaPyJQZVmJ9W+MqG4ubD0tD0+qfEfPK32lQnNXppSUApUnY2VtEztA9Nyw6+reWpU\n7TcuydptRYmpkXglHbN0iWVLsXsc9yh7I2I/B0MktvA67NfXYD9JOmblvAhFwf5DUYc1pclrLu0L\nttsbs80SW3hnL3TprFlnWt9OhKp0C1QJllwLtlPhLnvdsvG2dMyl+XptkcjbfIiFN5/2FpRTqSbd\nNFqTC6SpcxNKWomSdj4tOK3xF68qaXd+bp3bqR1Yy1heCO6RS0NgFoWnlNJlmG/M1fWF6+O66+kx\n3XdiQm+67VYNltJGIYfrprAcavDwQfkkUX18clEB68oluYGKvPfy3qeBLImXNSNVhgakQ8OavmlU\nSbOtwYmGtMmiziSNV0xzoyUNnT6keLiqZGpOydScWmNTmmwnSqZm1TowonJjQuV4VgNhqCQoqRF5\n+bmm4olJtSdnFc01FTdaSpod1zBmS7jt0guy1rMakxRWQpUHSgpir6FaKZ0ymK1r/Icvy9cjtabT\nYq8x3tTsxfH0TYFySVNnDytuJkoaTcU+nWHrfB6R9TbbtBAEYvs6CCQPT2rt49dgP2G8AYo69MCb\n1PReZee6NmTeq9IeRl6dp/ahS4NVdvJl6AxUyVUCt+7QD6zOZ8vd8le1skbLi9hM9dir1opl3itp\nJvO7inlbFlyyVUEm28UnXtZMt9EFbklQSjI/M3h3raZfufUGHT9ySMdOHdfh8LpemLkkZ1krh5PH\n9e5f+VuavT6ur/327ylqtuYfo3TqiAbffrcm5qY1MzOr0ZERhWOzSp58Raff9ZBufORhXahPa+zZ\nlzXz/FX51gan/DKBSWfrgeLLbQ1Nn5fVrmm6UtELw0/p3MCASqWSSuWyyqWSXBLpxC036D2f/HlF\nhw7oxctNnYhmNNKK0sIt8Yqbcbrs1ZviVjJfvPtkYbyTdppKeuOBikbvOqyk7TVQDtSeaslnSaVS\nWjTnt+Vaakw0lbS9WjPNRddZJvkMcY9zDoSiLBzLXEu1P3jL+q4y3tjnKOqwpvSCYxW+X916pe/8\n5bdSXumJYt4OLdxEH7RedQtKcSZ1yzNfGrKC3uX7eT7egTktXbTZOXPrs/CFIGv4rY7ZtM3sF52h\nDhu1kcfvLERd4JYHpUResUt0sFzWyWpVQwdqGhiQytcu6XAyo7vuOaGLF6YU1io6cNtpjZw5ovve\ncbvOv3hB1y9NSpIOnjyi29/7sGailmbrdY2MHlRwfVatk6d16p0P6NCb7tLMo49rZqrRc4Py1ThJ\nI5GTokSanZYktbJ/3Vy77SYduesuxUNVvfTKq6oOzelgKVzU3sEnpqSdKG7EaWGXz76a5vsEStJA\nOdDQYFlJycvHXu2pVte0UylLVG0nSuJ0uWSczdLly8l6WT5IKEqq8zXr8/dRsAXy1RaMN0BRB6yL\nN6mZnXA5J9WCheV6Oyn2Nn/NXSdCVrZO51LLXC1cPt7tZPkyt1oYzLdA2Mjjtja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byfUp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\nf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agzs/ev9vWscfmHJb1vhbtckHSm4+MzSqtUbJG1xqjHn3Ep+/+ac+5PlS6b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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb46ae66b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot Julia sets\n",
"r_min, r_max = -2.0, 2.0\n",
"c_min, c_max = -2.0, 2.0\n",
"\n",
"# Even intervals for points to compute orbits of\n",
"# CHANGED\n",
"r_range = np.arange(r_min, r_max, (r_max - r_min) / 500.0)\n",
"c_range = np.arange(c_min, c_max, (c_max - c_min) / 500.0)\n",
"\n",
"c = complex(-0.624, 0.435)\n",
"xs = []\n",
"ys = []\n",
"# CHANGED\n",
"mat = np.zeros((len(c_range),len(r_range)))\n",
"colors = []\n",
"\n",
"# CHANGED\n",
"matComp = 0 # Index of the new mat values\n",
"matReal = 0\n",
"for comp in c_range:\n",
" for real in r_range:\n",
" z = complex(real, comp)\n",
"\n",
" escaped = False\n",
" for i in range(0, 50):\n",
" z = z*z + c\n",
"\n",
" if abs(z) > max(abs(c), 2):\n",
" escaped = True\n",
" # CHANGED\n",
" mat[matComp, matReal]=i\n",
"\n",
" # Colors correspond to escape speed\n",
" if i < 7:\n",
" colors.append((1.0 - .055* i, 0.0, 0.0))\n",
"\n",
" if i >= 7 and i < 14:\n",
" colors.append((1.0 - .025*i, .6 - .025*i, 0))\n",
"\n",
" if i >= 14 and i < 21:\n",
" colors.append((1.0 - .0035*i, 1.0 - .0045*i, 0.0))\n",
"\n",
" if i >= 21 and i < 28:\n",
" colors.append((0.0, 1.0 - .0045*i, 0.0))\n",
"\n",
" if i >= 28 and i < 35:\n",
" colors.append((0.0, 0.0, 1.0 - .0055*i))\n",
"\n",
" if i >= 35 and i < 42:\n",
" colors.append((.435 - .0055*i, 0.0, 1.0 - .0055*i)) \n",
"\n",
" if i >= 42:\n",
" colors.append((0.62 - .005*i, 0, 1.0 - .005*i))\n",
" break\n",
" # CHANGED\n",
" matReal += 1\n",
"\n",
"\n",
" xs.append(real)\n",
" ys.append(comp)\n",
"\n",
" # Points that don't escape are black\n",
" if escaped == False:\n",
" colors.append((0.0, 0.0, 0.0))\n",
" # CHANGED\n",
" matComp+=1\n",
" matReal=0\n",
"\n",
"#CHANGED\n",
"fig = plt.figure(figsize=(15,15))\n",
"plt.imshow(mat, cmap=\"RdGy\", extent=[-2,2,-2,2])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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rl+KYC729bZkTw4JpEt4jq+oDYbpr+SIm4GChWMuSePO/Sdz953F63VOA3EHiynMuYdbi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\nOA3Fk4rjOA3Fk4rjOA3Fk4rjOA3lfwbawrW5xlTKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb46ae8e8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Mandelbrot Set Example\n",
"def mandelbrot( h,w, maxit=20 ):\n",
" '''Returns an image of the Mandelbrot fractal of size (h,w).'''\n",
" y,x = np.ogrid[ -1.4:1.4:h*1j, -2:0.8:w*1j ]\n",
" c = x+y*1j\n",
" z = c\n",
" divtime = maxit + np.zeros(z.shape, dtype=int)\n",
" \n",
" for i in range(maxit):\n",
" z = z**2 + c\n",
" diverge = z*np.conj(z) > 2**2 # who is diverging\n",
" div_now = diverge & (divtime==maxit) # who is diverging now\n",
" divtime[div_now] = i # note when\n",
" z[diverge] = 2 # avoid diverging too much\n",
" \n",
" return divtime\n",
"\n",
"plt.imshow(mandelbrot(2000,2000))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Fermat spiral\n",
"golden_angle = np.deg2rad(137.508)\n",
"c = 200.0\n",
"n_florets = 1000\n",
"floret_radius = 10.0\n",
"\n",
"florets = np.arange(n_florets)\n",
"ratios = np.sqrt(florets) * c\n",
"thetas = florets * golden_angle\n",
"\n",
"x = ratios * np.cos(thetas)\n",
"y = ratios * np.sin(thetas)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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i4qTnMXkikJOyFUM9ZmCdkNP4MtF+/Z3UhPVevV6myOcdhNhZ3c/vSU/cr+j8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\n/dbbuhZEo53X/4FofG2RB2E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+2hyRU8ce810QpPt7crgc024i4gwY62msqGUOYY6RzZBT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\nLoOXFeZgwut709EnNHrBQQFn0Ky/v6TnByHQzlHt/1o3fUyniLZoIK2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FkByGCNWIn/Yj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dkM+t1II+XqDO07Zv6NEI42tFsNJHsrxgyGaDsf6PO+OfcszZpEYZ+ANSJmygZEoM/kU0RK/S+a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\ncBzHcXLjTsNxHMfJzf8B02P4DGc1wh8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f85ac7ee278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x, y, s=[floret_radius]*n_florets, color='b')\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def py_julia_fractal(z_re, z_im, j):\n",
" for m in range(len(z_re)):\n",
" for n in range(len(z_im)):\n",
" z = z_re[m] + 1j * z_im[n]\n",
" for t in range(256):\n",
" z = z ** 2 - 0.05 + 0.68j\n",
" if np.abs(z) > 2.0:\n",
" j[m, n] = t\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numba\n",
"\n",
"jit_julia_fractal = numba.jit(nopython=True)(py_julia_fractal)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f853cae7eb8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bW20VztHZ+TTvtrC5sxGEqqGYzAhNc74U1VXbQeX3917BmIz1RCSlOSeTUrQG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M70ObxpvWulb27dvL1IlDWTg1g8BNy4hM606/RR8TK1pZt3gYWcdM7DGr2ev0Y9FlY7EpRPx0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oK42DWSoJ4CN6HrnDpljUWGjRvI3dOSiaGRd7Phy683YG5tRdRqcQ8I5ZGMLxH+8gCJX73O\ny5Pv46pBETz60W4U2dnDQlGdnABZVpDttvbokcKU9Z/i9vSzjIk38f5zb1DbZGPptansCxpEoW8I\nGQVVVK1eS9iUyWSUm9HrRAZFe/PLtjJsFjuK3HlNOr0GLz8TnycVcWtBJF6+3kz3t+LvZWRlVj3F\nog8xfibC139O6s13cNvbO5wkyPZ56xEgEqI34wjpQYinhj+PjuKyf62kzu6Joqr0DRKpsKjExAax\n7KvlSHYLzqqQo4fOT0oa+w92mudregHOD4Dw3x5F+L3ExQsg4fjvO/9AAme2wuF0jnFckHA8MHCs\nbV0BUZfngqb7cVzHFduBxJjC3Qj33E9RWTOlmTkoklO8SdTp0Zk86dknms9GO5j8jY22VgczJ8Xy\n4JhYPrrxYcw33cHYGF9sa3/k2bU5fPjy/bQpIlsr25AVlcmRBirqmrj575/x23sP8GNmDWZV4Noo\nB3nvf4whIRY/cyHCiJn8+9l3WZF8BXarA5tVwmGTkB1KxwLyeMCg+zA4IwqCBkRRRG/U8sikECKi\nI2hzyAxSi7hijYwoCPh76nlxchQtuzewTojhyv6ByPvWsjlkHH/7KoO8vEpnKaqqonQ0e1KcSoHt\nTqsrYfF0bs6CqGmvxtCTMmEY9w4P54Znvu/gQiAIjBw3gG3LlhMxZBIr7hvCO9uK+G53PdZWO4qi\nYnTT8dbCAeSU1bEku5mdW4uQbBanIBcwaXIfRrrVMbB/Km2trZhyd/Hw8lzcR0ymrsXGlf2DMd60\nkNeHzuD66y4lPtyXnMIqmuvqKbcZ+TWjqj1CI+Lp64Z5w88sDNCTPfMKxsUHMDzKm4xDuZRpfPl0\nWwmDYnyZ2zeUUGslY185hKI4yavungY8/d0I9zWQGh/E7cnuzP4sl2fmpFDz/Tsw8Wpu+tt3WJvr\nkR1253cSOkWsTkKi+XD7I536BYBwEufxPwoSLqQbjmPnLUg4w5GE33uM439GPc72Y2/r1qbX9VxV\nu0cS2ntEqO3h7pLAGO4c4M43qw+2A4TOm0yUvoWxI/uyOq+Fm8ZG8sPWUqoabLT9toL4e27n842F\n/LCjlCW5rbxy2wSeuu5hLp8+iIOVLVTbRUJffYLGUReh1pQyon4Xn9z+HNZxk/k2x0pzyhBSI915\nYm0DwWVZZNXrqAxL4IWUJlKMZn4tsIOgczpoV1+Hoz04Mj3jkoE2uOl4bqTI4kINqeFebCtvReew\nsabUqRY5INqX3p4yX7YEEnlwJ17+Jj7KErH5BrJucx5tNnt7oyKRmL69CDBp0Zi8MLe0dIxld4XA\nE9wYD795dz3vjm0KtcVVLNtRjGQzd3GMUFpcw7gZE9ifnkOTxp1rR8UzJCGAVfsqSYz0YfrIaGyy\nSpSPgSmmCmx+4WTm1aA4bKiyAy9LM6UNZrbVqySGeLJSCuKWOA8cwSGkxQXQL301ARqZEQ/eyXjb\nIfyjovHwMPFzURt+7noKqswdVQ6yrHDTgikMvmQMCAJlrXYkaxuldc3Mi9Pzyb4mLHaFg4sW8XKJ\nLzar5Ix2qCqSQ8HDoOX1hDLi+/ZjV63ExN7BVDZbeX1TBYn+Bhr37KLQIjgVKbv1czh1pcU/zqmr\n57XzO1/Ay/k8RidrvzfdcAEkHMf+l0GC8zindu0nOq8Tbz+ahkJnA6nDOQzCYQ7JCRQUUBRW7a9F\ncQk3dbkhNytGesQEMyhvLds3l7LPoqWtqYmS4ASGu9Wxr1bmlvHxbMmz0LeukItvvYx9BFCxdR3p\nZg8+0iZSW5iLWlPC6KuuIuXquWw/UMqe4lb25NRSYjcRP7APawij1TsY9EZsfpHs2l+Fw9FEi+ze\nnltXjw8M1O7/u0DCvIgWyvx78lefcsollRirmX/sc666VQSSvFRyLVryM3O5KEjFLSyEd3MVVmwt\npaHJjqjRcM+8gXj+9Cn5qsrU3oEYBQeFdQ5EjRY3D3fsljbnKRw1DN4OuFx/j/dwTppTXEl2oDis\nXVIXakdevjC/AlWWyK1uY1LpGkJ69mJJViOPDTfxfa6V7YUNLEuvplHS0sNHwC/Qh8ycShRJoryh\njTlDYolPjOG5pZm0VJSjjw5jQ24LP6zNJx939JfOZcsTj1HWczgbq2Xe+PBHLhmVwtxEL77enMmN\n/U2k1zu7Y6YXN5JZVM3wxDBmRBvZcKiacjxoePhBtkYOwa6oTGjIIy+yF/VVrU7Ag4qHUaV/chgv\nb6nGbHBndqIvBRtXE99Uy9xLR/Pa+lp2FNdjt1q6j4+qtlf9HgYKTrRy/gNW1ufLCv1YdgEg/LF2\noQTyTNh5CBCAs/PDPtuSsCdD0upO9e/WOOfw93X0hzgs36447FyrbOPltn5sMXogSzYkWaY8p5Dt\n//yYmOhAeqsl/PLX0Tya68lN7x7k+lufZmcNFNRauK9mGVsqDWxzH8T+Fi1L1+3FavLm+Xn9+Dk1\nj7WZ1fzn+wOs21rG+iwzZrOVpesKWLM/myZjZCdAOOKkOeL1I96nwhdFHizdWMQ1e915bn0dj25v\nwmaTQIXUSG921srcHC/w1Ehfvhcj8DBq2fHbRlpb7IhaLV6B3shuWkpnXIMhIpl1zZ74902mV1of\n3n3oYtqamoCjlL+5xrurgzvxhDkrU2QZVZJQ2gWEOuaEdpln2YGiSLTV1XFFeiBPri7n3jQv1PoK\n/nlxEhOSAokVG1iVY6FY8OIJ/XpeuGeqU/7ZYePFJfs4UNrAgilJjO0RQJROIq+ogeevH/h/7J13\neFTV1sZ/p0zJpPdKSCGB0HtHmoCNKio27GKvWL4rtnst99rlKqAoiqjXAkpVkC4l9ECoIZCEkN77\n1HPO98ekEzCAQNC8zzPJTGbPOXt2Zs5a+11rvYsd6Rb+tzaZtnfcReeURLYkF9ChfTTqoo/JVIyk\n7tyB1eTL9D4SBqOMJItkWUTe++0oty08xp4SgZiZ/8ei8c/icCgIqkb7cf0I8napFUTSFAcjh8cx\noL0/AZGRZJVYeH7wLfTJOUTSc69RYJcpLCnBYa/3+W1wbTlLJcY/wXi25ATFFlV+2VLX6CKiNSfh\nTONamNpiDc41sfCsz1PbpbGZ45sxrzPmOjSRlNl4Do1f7+xCeOrzYr0ulA2eEwREWY8oybU7BEEU\nQRSR9S4EBvoRFenFc+0qmPRdoTOXQdKhlmewaloMHxVHEHTyAPOTTYiSiI+/iXaeKgO/m8P7MUOY\nPHkMi1YfQ1VUVFWjthFUddzbSU83rB4QxUahBVFocL82J0F0PpaqpaINRh2vT2iHj48nszce55Hh\nMWhvvIwjSI97p76EdW9PzupfmHisMzaLs4w0oo2R0BB/bijejG/bYO7b6kqbhMVMf3sGKUsW8Gy8\nU++gQbfC0zhjp6LxteQ0eSaNehmIooSLycR1V3Zld3IJBaUK18fYSXUL5cquoXiXFBOhy2PaRhm9\n6OCtqCruXZtHeZXNaag1Jztx503D+GpRPGGdYzFg57HwYjyAe9dacXFzwdPPm68eHsIz83bywoRo\nPtyUzcvXtWNbjp1BhlykH75AffI/7Msuo5dUwiabJzE/zWNO8CAO5ypIksji4VUUBvdiwjvxOKyV\nIDg/N0YJIgL1WDy9Gd7RH2tFGVN6hHGwHGK8jFz37A9Yy4tQVUe1c6E2cJYa7+pPZyTPNom06WO0\nEAPcFFqYuuNfhUWAc89JaJlWsBVnxMXrFX+RHcizZRP44y9xg5bAtboLDlS7rW5XW73TddjMiEYJ\ns9nB23sEZ7MixYFit6CILty/3Y2U7HLa9u6HoijYzBZyT5YwJCeJf4UMZMb4LgSLVZj0krP9tKah\nKs66ftWh4rA5avsv1Mcpf2sc5q/9UQdZlniuFyTs3Eush8jUqqNsOlGMx3338IEtjm+THdy+pop7\ny/vTK0BFkp3ORUSIP7d1cqPiismEV6QzMspAgncPbpzxPc9uo7pNdb01+yMHoYFmgtroppy6Y63P\nSFBnsGLi2jEwdz9T3ZPRuRhZnuVB5xB//j3nFxbmwLc/HcJhU3jAO4PvKw285pvDwxN7o6pOhkJV\nFOZ9uxbFZuHI2lXc4FHAv1OCyE4tRFQdlOflkZl0HLfSdA4dTeKG136ld6ieq178hc3rE7jx2xyW\nDX8cWXNw9IfFLF26Fl8XHVscLuw/aUNxqPh5GFgodSW9tILBXf1RHA5Uxc7wPm15alI0H1/pxxej\n/bldl4qpJIfMvdv593eJXPf8QhzWquomUjJU93IQqqs/4GxCiH9dB6FxJ9JLjb+Sg3A+aGUSzjSu\nhTIJcHEqHM72POfNJDTxfFNsxiljJOmU50Sx6Z4RQj2GocF8q1kMUadD0rugOmw1r3Ze3EUJyWBi\naPdA9qdXUlhQWi/uLiAIEl39VQyBoew6lFvvAqM55y80TsJsOt9FrK8EWP1YrFYFFERn34Y79vyE\n7ZnpPB1WwBdf/0rX6ybSNvMAlX4BtI2KZPC/VhI6YDDlZjtD2vvTr+ow7xxyI6fQjCSLxIS44+Ju\nJDmtmHt7eDDjq3gUmwVNdVQ7CXWql6e9UJ7Ljq8RU1TDAgmizPV9wwnu3YP96SX0DHdn8/EKPFz1\nrF+4mE+mjeX5tfno9BK3jetAv22LmenWl62/xaMqdR0stepKA1F2qiVePX44mcez2H8gqbZzZe05\nBbG6rFZG0usRZB06gwmjTsIu6NEZZKjW8hBlAZ1eYnBnP67qFMDyg0UsXbYdTVXxDQtiSeQh5reb\nzGAPB6tTSymqgsSjeQiykdysHOxWCwaTiWEDotm86yRuksI9vUx07NmDiU/Pra3UaMDenGNZ5OnQ\nmn9wdtDOJqx2maCVSWjFBcFF9+wv4hezwYWzdmer4rBU1tsdVys6Kg4UaxVThL0U5BbWxaOrY9Ka\n6iAxX2Dnwex6eve18kinxFmbexGSZZGZY0yEhHrwxLgOSJLEkpG3Mr5TAOvEWK56+Al6aWnk9rkO\nVm8k+ZeFDI1fg8/MN/A06TmSVUbfru1wNckEhpjoFShgNeoYGOlGwbZfie7cjisq053vUVUb/L9P\nv5M6R0q48RrUE2dafqiUKGsuB/bnMXjRh4x583GUciuRA4bz5JIkXE0yM7VlPNimnC8DhvJ8eCE6\no5H67cZr5qXYLThsZlb89BsHDqfgcNicjIPqQHHY6porVZ9bA1BVVIcdi93Z+tpudaAq1QyPpjJ5\nWCR3/f4ej737M0uXbUexWlAddvQuBpZHXcVzXWRMQYF88eEXZNsE7rumE3n5pSDqCGjThsk9Pflg\nuCu/jCxi68MRfJ3jwbQ526udpFNDY02t+bmi1UE4B7Tg9brYaGUSzjSulUloMk/g9EObkSvxB8c7\npSPkWTAJ9f/eJJNQX2uhca5C/d/VbX2FRrkO9UWCmn5PDePtTsXExhUZjTtenlqxIdS0mxad0sGv\n3NYDN7WEL/ZWIDpUxnf3x8fHkwEeZj6a9SP9hTLMoybi89t8fB55la8Tskn6YgFvf/w8q1aux69b\nb7rZMnhur8aDHUSK9IG8MXc1BaVmkGSsZSVO46mp9UIyp7lw/0kx48adPocP7U50cACLNh+jsrAA\n/6i25CYfQ5R0PHfXUDwy9jFhSBz4tWF9loO5G06QsHGbU5TodIxHzf+wif+jKEmIshEXD2/uvTaO\nyOI01pgD2Xgoz8kuyBKSLOLt60L3KB8KHRo35e3jLWsbyvKLKUg7gSBKjBjdB8PuX3Bz8+aB5x9E\ntlWgE1Sue3cPNqsDWScxrIsrCTkalgobnkaJIX3CMR87SEKFN4mbNzsTOus5nXCq4TxXQ9oiDXA1\nWqrz8lcNM7SKKZ0F/gpOArS8kMOFcRKaFk5q8LdmOglNhR3q32/gJNTOVWjyGEKTSZan2RGeQrM3\nFVKpQ00DIqE61PCC6wFShkwirbCKftE+mM1WDKJGVqXGwDYu7C2w42XUsWr2p7z88iNszrUSravi\neFYF0/xPkp1ZxWaHJ32GDCKiKJG71jnYvP0YVZUVznwM1VGrsFhrpE5XOfInG50aJ1CQZV4f2Za3\ndpVQWlrqfE4QECUdk3qEEduvI1+uzcHrUDzHPYKwVZY6Qw1KfXam4cW9voNWP8FVECUknYHrxg1i\nQv929DBVsHRZAr/gx+iYYIKNlfyUWE5yvoXXJkXiUmnhtWUJqN5tObo/CVtFKZqmIAgCcd070zlU\nZrCrQvdwHbk+0XTM2UN+zFCuf287Hdv5IrsbGBgkEBXiy/RP9/HGWD9Sj+czzecotx/vyInsAgqP\nJFEl67GbK5qdxHhGtLAEwMZosc5LY52QvxBaww2tuIBouV+aphyEBsa7/k6ySUnnM6BBLwO10W/t\nlCZIdbHxJsoHm0Xj18tJEJxNiFw9DMxx60dqTgkBQhWR3i5sSStl+eFiEjNL0eWepO8LDzBq+Ru8\n/q8nmDb9fToGuNFBruT+Dhp7QkZQ0aUvab4dGHnfW3SbV8GaDfvQJLG6nFFttjE5V6PTsBlU4x1y\ndbjA4eAfq45TUliE5nA4SybtdhS7lZUpVUxJWEBohDtuY4Y7E0pVR62DUNsVsJFOQ/1ugZqqNnAo\nbu3oTm5eJTfd+SJ9bvkPV4zsyBc3xOLjY+DL3SVMGhbLt8ULmH+ggv/8vIVbxg+iX5cAhvWPQVVs\n1cmSDny8Daw/bKZjv848e8STdjsX8yndWP+/lbw/rS/HM0t5wbaRItGN2WtTcVjNbKxwY5fsT4/1\nkeSVlnPnqB5MmTK0zjg18bk7uwVvuQ7Cpe7g+Ef4qzoI54NWJ6EVLRd/lARZ4yA0SggU6jkFdWGA\n+rv3xuGMPz5XA5zuIt5AqPBUR6HudWe+EDkpcph+dCG3xGoE+LkyoGMwd/X0I6mwih62bByqRpVN\n4YGvdjHr3jf5vctEfCzF5GYWUnr4AP5p28gt0njslscJdtURVJHD6JsnERToxtgr2jHpmp5NZJP/\nGQI+dca5KacAGjoNtXkBjXJAauaiKQrlBdm85jsWf8VGiGLnmiFxDasjzkadsHrsD8dtPDKpJx/N\nnsHwUQPRyzIrs+7wxEgAACAASURBVDR4802wlNAnZxdlz39ESU4O/eIi+HxDKvPmrmDlqq21eg+q\n4mDT2p0Upafy2lc7KMgoY8yucKL+9Q+OR/fgjUf+xa2jY3isKI5fftnGG0O9sJQXEpK2nxv7RhAe\nF4TeJ4B/zVrEZ99vRrFZznHNG61tS3UQWrI2A3/dMMP5ojXccKZxreGGszrXnxJuaCIM0PRz9el/\nsTqOLwHVugTUCxNU/x9Plx9wSohDanSeRvNuPF48zZxPWY/T5kSI1cepq2wQJZGwMA/G9wogKzOX\nJJuJoTF+KEBM1h4+yAuuHetulMlb/B3/e+8B0r5fzL8X7uThpybRISOBr6u8CXU30qtPHz7/9Bey\nRo4j7UgmGcfTqlttK9W5CDXGupoNUZpgSS6wVPDpPj+CIOLi7Yu1opzgAF/MOjeK0o/X9YBoONHG\nL667Wx3acHV3Z1SYniP+Xbhi/1raP3U/Zb8spmzgNbik7Ke9l4neXQM47BLBntXrSVaCOFylkL4/\nmfL8TKhfJioICJJEmw7t6RThTbZq4tCGbRjdjFjNzvndNXEQO7LNzOgjcNMH2+joUkmFzo+0whKi\nu3ciJfEItsrSU5yqs13PFrtDb8HMRi3+wmGGGrSGG/6G+Ot5vqdXoauL94t1iYWiMwFNkmVMPqG0\n69EZsab+XJT4YZIXos6IpHehKv8kgiQ7Xy+KtcJKp4gxSU07Q8IZRKVOd3HRGu/Mz3ARamwbNU0j\nM7MMu8WO0eTKY4PCGeFr5zpS+d4WicOuoCoqogDd23rx4NNT+SlXR9Gkqfyw5UsSTxax92A6ccf2\ncNPD97Jk73G2dxhG74VzyExJb/DZaVZi6hnmfrpOhmeLOgaiUUxeU6kqykexWcnIyKIw7Witg1Bf\nj6HO4al3qzkedd8Xs8WK1TuMT+/pxa9RQ0jbe5ji3qNZuO44XyTrOWCRefTzTTz30myyPEOZ0F4l\n72g6veJX1DoItbdqjQ2vigLSk05QnpKG3VpJWX4+tqpSNE2ll7+D3K2/ot+3FYe1ir35Do5lZmE3\nV3IkfgfW8uIG86w/12auXIt1EFoys1GLv4GDcD5oZRLONK6FMwk0zqa/kGe6GEzCmaoP6iv1VY/z\nCovhwUmdkW0Kb85exMJZT3LTc9+gOuwIooS/vx9TruqGT6A7RWY7H3/yW+3FVzboURx1Bqkp56Ap\nFqGp9ykIYl2CY/11Om1yY917EcRqFkSoYxFqwg2iICDpBHR6ma8e6s+0BQmAhiSJtUmNz42K4XBR\nFREeOnbnVDEo0hdFVenqq8e3LJ24h+Yz8+og5IjO5LiG8sr8HTiqKqopc0ftLk+tKSE8HZNwWgXA\nlmmcmkStFoaeqQ9M5sfvNjP8qh70Uwv5PFnEXGnDYK2kuLISxW5FUxQqctMwevkTaHCQVe6o7d7Y\n5OGr//f110TSGwnu3JO4uEDWLVyJYjX/YQXD2agqttT1b6nzagp/vc1W02itbjgL/HWchIsXcrgY\nTsKpoQanMRVkGaObO9bKytrnRVFClHXMeWQIm7IN+PvI5ORZ2XEsn/ZR/qz/PZFp47tjMBlRflnG\nkdhBFBaVsPtIFiFtAgiLDiF5fypeXu6kpmaesps4Na/h9Gsh1pRpNh57OueiXoijtpKh9nx1SYuC\nCKIkOMvxdCKyLNUqJ4qSiCSJSDqJSH8ToihgLylmaM9o3D96k6gof5Z3GU9+Tj5rdiTRdcgA9mw5\nTGlJGZrDXrfTVpTa5MX6cswNnITT0MV/hkTwxUbtuss6BFFi8KgBTBkQydyNGQztEULCsRym9gzl\nhU/XkJ9X4KygUJ1Nw+qXiDbzZAgIREWG0S0mmGWbDmGvKj81kbXeMc/m+C11/S8nB+HvxCK0hhta\ncUHxZ33xT0fbN1UiWOMgzHzlLgZF+DDJtRRJ0iGKNZK2Ev/8Lh6hspBSTWZXRjFfDld4dqgfD+mP\nMn99BmP9HOzoOJSBXX25fWJvvIP8+WRyGAIag0Jd+PCu7s7WyYJYywjUlDfWl81tPLe6edfrUtl4\nbIPH9crx6pVRzvA9zNDeYZjcDU7jL4qIooAkC9xbsQUvLxfub2/mH+PiiPCw4ygvQG/U8WisDQ9X\nPb5ueu4KtzAhfiFP+RRxtXs5tnufZHnboWRaRZ64fgAv3dSLLRsOU1FpqV1TUdKhd/NC0hkQZT3B\nHWKdksH1tAtqcCEdhKbDA2d7a3iMPzofUFudsHnNNp5+fyXtQj0I0qvMHOzKig17uO/GvtXzcToI\nqlpfgKne3JuaT329CU3leEo6P/22oy7voF6lTP1jnp2DUJfc2VLQ0isXGkP7GzkI54PLwkkQBMFH\nEITVgiAcFQThN0EQvE4zLk0QhERBEBIEQdhxsed5KdDi432NcTqmQWhoaAVRcurbCwLRvkb0ajH9\nH5qKqDNg9PDGLTCcBUPMFFg9WHesiqLla6isdDAjyZuPnn6Vax+7C58gV25YlEladjk924XSz1jK\nqN6RTF+ZT4CPC+5dYrh15h4kWY8gyYiyDtng6pT1leQ6h6BaFa9JjYYmwiNNPxYb/q5mEd6q6EJa\ncRWLHupGacKqaqZAoHzfBm6Yfjszb2zHqL7tGN3eh3cmd2Pho0MZGevFwCED8clN5surA+nZIRpv\nczG+Lg52VRhJt4tERQZxx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uA3vB/LF2/i2dlbmDwqmgLJhelztxPhUr9nQL3fjY2edi4O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\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yMk9sbIbnCPQ29xyYFcchwQ5ZL8xXsJnH4cCseA4J1jYOC9YJHlYw6xyHBGu7XpncaL3F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\nGAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f853d362ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N = 1024\n",
"j = np.zeros((N, N), np.int64)\n",
"z_real = np.linspace(-1.5, 1.5, N)\n",
"z_imag = np.linspace(-1.5, 1.5, N)\n",
"jit_julia_fractal(z_real, z_imag, j)\n",
"\n",
"fig, ax = plt.subplots(figsize=(8, 8))\n",
"ax.imshow(j, cmap=plt.cm.RdBu_r, extent=[-1.5, 1.5, -1.5, 1.5])\n",
"ax.set_xlabel(\"$\\mathrm{Re}(z)$\", fontsize=18)\n",
"ax.set_ylabel(\"$\\mathrm{Im}(z)$\", fontsize=18)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
googleinterns/loop-project | notebooks/IO_adapters.ipynb | 1 | 13170 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "IO_adapters.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "LVLE6fzffmJk",
"colab_type": "code",
"colab": {}
},
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"\n",
"import numpy as np\n",
"import unittest"
],
"execution_count": 19,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "EYCSozXYhIvR",
"colab_type": "text"
},
"source": [
"# Input adapter\n",
"This model transforms the input image into tenfor of desired shape (in our setup 16x16x40)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "nJlxo9f7f5BP",
"colab_type": "code",
"colab": {}
},
"source": [
"def create_input_adapter(input_shape, size=16, depth=40, activation=None):\n",
" \"\"\"Creates an input adapter module for the input image.\n",
" The input adapter transforms input image of given shape \n",
" into a tensor of target shape.\n",
"\n",
" Arguments: \n",
" input_shape: shape of input image (HxWxC). Image width and height \n",
" must be devidible by size. H*W*C must be less than or equal\n",
" to size*size*depth.\n",
" size: height and width of the output tensor after space2depth operation. \n",
" depth: number of channels in the output tensor.\n",
" activation: conv layer activation function.\"\"\"\n",
" h, w, c = input_shape\n",
" if h < size or w < size:\n",
" raise ValueError('Input height and width should be greater than `size`.')\n",
" # `block_size` of space2depth\n",
" block_size = min(h / size, w / size)\n",
" if depth % (block_size * block_size) != 0:\n",
" raise ValueError('depth value is not devisible by the computed block size') \n",
" \n",
" # creating an adapter model\n",
" inputs = keras.Input(shape=input_shape)\n",
" s2d = tf.nn.space_to_depth(inputs, block_size)\n",
" outputs = layers.Conv2D(filters=depth,\n",
" kernel_size=1, activation=activation)(s2d)\n",
" model = keras.Model(inputs, outputs, name='in_adapter')\n",
" return model"
],
"execution_count": 20,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "AomVWLNQRth_",
"colab_type": "text"
},
"source": [
"# Output adapter"
]
},
{
"cell_type": "code",
"metadata": {
"id": "299hzdXmRsiC",
"colab_type": "code",
"colab": {}
},
"source": [
"def create_output_adapter(input_shape, block_size=None, pool_stride=None,\n",
" activation='swish', depthwise=True):\n",
" \"\"\" Creates an output adapter module that processes tensors before \n",
" passing them to fully connected layers.\n",
" Arguments: \n",
" input_shape: shape of the input tensor (HxWxC).\n",
" block_size: tensor height and width after average pooling. Default \n",
" value is 4.\n",
" pool_stride: stride of average pooling.\n",
" activation: activation function.\n",
" depthwise: whether to use depthwise convolution.\"\"\"\n",
" if not block_size: \n",
" block_size = 4\n",
" \n",
" if not isinstance(block_size, int) or block_size < 1:\n",
" raise ValueError(\"block_size must be a positive integer.\")\n",
"\n",
" if pool_stride != None and (not isinstance(pool_stride, int) or\n",
" pool_stride < 1):\n",
" raise ValueError(\"pool_stride be a positive integer or None.\")\n",
" \n",
" if len(input_shape) != 3:\n",
" raise ValueError(\"input_shape must be a tuple of size 3.\")\n",
"\n",
" h, w, _ = input_shape\n",
" inputs = keras.Input(shape=input_shape)\n",
" kernel_size = (tf.round(h / block_size), tf.round(w / block_size))\n",
"\n",
" x = tf.keras.layers.AveragePooling2D(pool_size=kernel_size, \n",
" strides=pool_stride,\n",
" padding='valid')(inputs)\n",
" if depthwise:\n",
" x = tf.keras.layers.DepthwiseConv2D(kernel_size=1,\n",
" activation=activation)(x)\n",
" else:\n",
" x = tf.keras.layers.Activation(activation)(x)\n",
"\n",
" x = tf.keras.layers.Flatten(data_format='channels_last')(x)\n",
" outputs = tf.expand_dims(tf.expand_dims(x, axis=1), axis=1)\n",
" model = keras.Model(inputs, outputs, name='out_adapter')\n",
" return model"
],
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Lwb8x6gtr2ZJ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"outputId": "c016ed73-228f-4289-d399-ed0f21fd42f2"
},
"source": [
"input_shape = (32, 32, 40)\n",
"input_tensor = tf.Variable(np.random.rand(32, *input_shape))\n",
"out_adapter = create_output_adapter(input_shape, block_size=4, \n",
" depthwise=True)\n",
"out_tensor = out_adapter(input_tensor)\n",
"print(tf.shape(out_tensor))"
],
"execution_count": 22,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor([ 32 1 1 640], shape=(4,), dtype=int32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iQkdf0rczCVr",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
},
"outputId": "58422b2c-4fac-4a8a-b815-c6b755c96db3"
},
"source": [
"out_adapter.summary()"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"out_adapter\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"input_9 (InputLayer) [(None, 32, 32, 40)] 0 \n",
"_________________________________________________________________\n",
"average_pooling2d (AveragePo (None, 4, 4, 40) 0 \n",
"_________________________________________________________________\n",
"depthwise_conv2d (DepthwiseC (None, 4, 4, 40) 80 \n",
"_________________________________________________________________\n",
"flatten (Flatten) (None, 640) 0 \n",
"_________________________________________________________________\n",
"tf_op_layer_ExpandDims_10 (T [(None, 1, 640)] 0 \n",
"_________________________________________________________________\n",
"tf_op_layer_ExpandDims_11 (T [(None, 1, 1, 640)] 0 \n",
"=================================================================\n",
"Total params: 80\n",
"Trainable params: 80\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "h53EVoQyRmiU",
"colab_type": "text"
},
"source": [
"# I/O adapters tests"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kr7B3qXB0GYY",
"colab_type": "code",
"colab": {}
},
"source": [
"class InputAdapterTest(tf.test.TestCase):\n",
" def setUp(self):\n",
" super(InputAdapterTest, self).setUp()\n",
" self.default_size = 32\n",
" self.default_depth = 64\n",
"\n",
" # tests if the output of the adapter is of correct shape\n",
" def test_output_shape(self):\n",
" input_shape = (64, 64, 3)\n",
" batch_size = 16\n",
" expected_out_shape = (batch_size, self.default_size,\n",
" self.default_size, self.default_depth)\n",
" adapter = self._create_default_adapter(input_shape)\n",
" out = adapter(np.zeros((batch_size, *input_shape)))\n",
" self.assertShapeEqual(np.zeros(expected_out_shape), out)\n",
"\n",
" def test_small_in_shape(self):\n",
" input_shape = (28, 28, 3)\n",
" with self.assertRaises(Exception):\n",
" self._create_default_adapter(input_shape)\n",
"\n",
" def test_non_divisible(self):\n",
" input_shape = (50, 50, 3)\n",
" with self.assertRaises(Exception):\n",
" self. _create_default_adapter(input_shape)\n",
"\n",
" def _create_default_adapter(self, input_shape):\n",
" adapter = create_input_adapter(input_shape,\n",
" size=self.default_size,\n",
" depth=self.default_depth)\n",
" return adapter"
],
"execution_count": 24,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sLKPWXSIwjEy",
"colab_type": "code",
"colab": {}
},
"source": [
"class OutputAdapterTest(tf.test.TestCase):\n",
"\n",
" def setUp(self):\n",
" super(OutputAdapterTest, self).setUp()\n",
" \n",
" def test_out_shape(self):\n",
" input_shape = (32, 32, 40)\n",
" batch = 32\n",
" input_tensor = tf.random.normal([batch, *input_shape])\n",
" block_size = 4\n",
" out_adapter = create_output_adapter(\n",
" input_shape, block_size=block_size)\n",
" out = out_adapter(input_tensor)\n",
" expected_num_c = input_shape[2] * block_size * block_size\n",
" expected_out_shape = (batch, 1, 1, expected_num_c)\n",
" self.assertAllEqual(expected_out_shape, out.shape)\n",
"\n",
" def test_bad_block_size(self):\n",
" input_shape = (32, 32, 40)\n",
" with self.assertRaises(ValueError):\n",
" out_adapter = create_output_adapter(\n",
" input_shape, block_size= 3.5)\n",
" \n",
" def test_bad_pool_stride(self):\n",
" input_shape = (32, 32, 40)\n",
" with self.assertRaises(ValueError):\n",
" out_adapter = create_output_adapter(\n",
" input_shape, pool_stride = '3')\n",
" \n",
" def test_bad_input_shape(self):\n",
" input_shape = (32, 32)\n",
" with self.assertRaises(ValueError):\n",
" out_adapter = create_output_adapter(\n",
" input_shape, block_size= 4)"
],
"execution_count": 25,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "MGwT0RvYManE",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 111
},
"outputId": "44c12918-21f0-459a-9c96-27e77e4e4731"
},
"source": [
"if __name__ == '__main__':\n",
" unittest.main(argv=['first-arg-is-ignored'], exit=False)"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"..s.....s\n",
"----------------------------------------------------------------------\n",
"Ran 9 tests in 0.156s\n",
"\n",
"OK (skipped=2)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zOk9t5yzOJjY",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
} | apache-2.0 |
shionguha/cosc4600-ai-fall17 | sep14-naivebayes.ipynb | 1 | 73447 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"python version 3.5.2 |Anaconda 4.2.0 (x86_64)| (default, Jul 2 2016, 17:52:12) \n",
"[GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]\n",
"pandas version 0.18.1\n",
"numpy version 1.11.1\n",
"sk-learn version 0.17.1\n",
"seaborn version 0.7.1\n"
]
}
],
"source": [
"#import pandas and numpy libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import sys #sys needed only for python version\n",
"#import gaussian naive bayes from scikit-learn\n",
"import sklearn as sk\n",
"#seaborn for pretty plots\n",
"import seaborn as sns\n",
"\n",
"#display versions of python and packages\n",
"print('\\npython version ' + sys.version)\n",
"print('pandas version ' + pd.__version__)\n",
"print('numpy version ' + np.__version__)\n",
"print('sk-learn version ' + sk.__version__)\n",
"print('seaborn version ' + sns.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22\n",
"3913 p f f y f f f c b g e b k k p b p w o l h y g\n",
"2829 e x y n t n f c b p t b s s w p p w o p n y d\n",
"905 e f f w f n f w b p t e f s w w p w o e k s g\n",
"844 e x s y t a f c b g e c s s w w p w o p k n g\n",
"5903 e x f n f n f w n w e b s s w n p w o e w v l\n"
]
}
],
"source": [
"#read in data. it's comma-separated with no column names.\n",
"df = pd.read_csv('agaricus-lepiota.data', sep=',', header=None,\n",
" error_bad_lines=False, warn_bad_lines=True, low_memory=False)\n",
"# set pandas to output all of the columns in output\n",
"pd.options.display.max_columns = 25\n",
"#show the first 5 rows\n",
"print(df.sample(n=5))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Example values:\n",
"\n",
"class e\n",
"cap-shape x\n",
"cap-surface y\n",
"cap-color b\n",
"bruises t\n",
"odor n\n",
"gill-attachment f\n",
"gill-spacing c\n",
"gill-size b\n",
"gill-color e\n",
"stalk-shape e\n",
"stalk-root ?\n",
"stalk-surf-above-ring s\n",
"stalk-surf-below-ring s\n",
"stalk-color-above-ring e\n",
"stalk-color-below-ring w\n",
"veil-type p\n",
"veil-color w\n",
"ring-number t\n",
"ring-type e\n",
"spore-color w\n",
"population c\n",
"habitat w\n",
"Name: 3984, dtype: object\n"
]
}
],
"source": [
"#add column names from documentation (1st col is class: e=edible,p=poisonous; rest are attributes)\n",
"df.columns = ['class','cap-shape','cap-surface','cap-color','bruises','odor','gill-attachment',\n",
" 'gill-spacing','gill-size','gill-color','stalk-shape','stalk-root',\n",
" 'stalk-surf-above-ring','stalk-surf-below-ring','stalk-color-above-ring','stalk-color-below-ring',\n",
" 'veil-type','veil-color','ring-number','ring-type','spore-color','population','habitat']\n",
"\n",
"print(\"Example values:\\n\")\n",
"print(df.iloc[3984])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"e 4208\n",
"p 3916\n",
"Name: class, dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x115595c50>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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GGR7e1u1uqAMcn73pUBmjJcGo3dCwAXgAuKW+LXEs1bMU/pLql/3TwBci4hrgHKodERfW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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b711438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#show plots in notebook\n",
"%matplotlib inline\n",
"\n",
"#bar chart of classes using pandas plotting\n",
"print(df['class'].value_counts())\n",
"df['class'].value_counts().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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o6nr6wurM/A2wL/BY4ArgdOCUzPzHzBylaG4tBa4E3gjsl5m3leveDOwPHAT8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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115a3a080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#seaborn factorplot to show edible/poisonous breakdown by different factors\n",
"df_forplot = df.loc[:,('class','cap-shape','gill-color')]\n",
"g = sns.factorplot(\"class\", col=\"cap-shape\", data=df_forplot,\n",
" kind=\"count\", size=2.5, aspect=.8, col_wrap=6)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4C2OjecB95ePV5fOh7S0V4taufdRqtzrSlvzP0dy31/r1j7e7C5W1fv3jrFv3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\n1YKNLkmSJEmSJNWCjS5JkiRJkiTVwv8D/EA4+ppXl1YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1186ace80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.factorplot(\"class\", col=\"gill-color\", data=df_forplot,\n",
" kind=\"count\", size=2.5, aspect=.8, col_wrap=6)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cap-shape ['b' 'c' 'f' 'k' 's' 'x']\n",
"cap-surface ['f' 'g' 's' 'y']\n",
"cap-color ['b' 'c' 'e' 'g' 'n' 'p' 'r' 'u' 'w' 'y']\n",
"bruises ['f' 't']\n",
"odor ['a' 'c' 'f' 'l' 'm' 'n' 'p' 's' 'y']\n",
"gill-attachment ['a' 'f']\n",
"gill-spacing ['c' 'w']\n",
"gill-size ['b' 'n']\n",
"gill-color ['b' 'e' 'g' 'h' 'k' 'n' 'o' 'p' 'r' 'u' 'w' 'y']\n",
"stalk-shape ['e' 't']\n",
"stalk-root ['?' 'b' 'c' 'e' 'r']\n",
"stalk-surf-above-ring ['f' 'k' 's' 'y']\n",
"stalk-surf-below-ring ['f' 'k' 's' 'y']\n",
"stalk-color-above-ring ['b' 'c' 'e' 'g' 'n' 'o' 'p' 'w' 'y']\n",
"stalk-color-below-ring ['b' 'c' 'e' 'g' 'n' 'o' 'p' 'w' 'y']\n",
"veil-type ['p']\n",
"veil-color ['n' 'o' 'w' 'y']\n",
"ring-number ['n' 'o' 't']\n",
"ring-type ['e' 'f' 'l' 'n' 'p']\n",
"spore-color ['b' 'h' 'k' 'n' 'o' 'r' 'u' 'w' 'y']\n",
"population ['a' 'c' 'n' 's' 'v' 'y']\n",
"habitat ['d' 'g' 'l' 'm' 'p' 'u' 'w']\n",
"\n",
"Example Feature Values - row 1 in X:\n",
"cap-shape x\n",
"cap-surface s\n",
"cap-color y\n",
"bruises t\n",
"odor a\n",
"gill-attachment f\n",
"gill-spacing c\n",
"gill-size b\n",
"gill-color k\n",
"stalk-shape e\n",
"stalk-root c\n",
"stalk-surf-above-ring s\n",
"stalk-surf-below-ring s\n",
"stalk-color-above-ring w\n",
"stalk-color-below-ring w\n",
"veil-type p\n",
"veil-color w\n",
"ring-number o\n",
"ring-type p\n",
"spore-color n\n",
"population n\n",
"habitat g\n",
"Name: 1, dtype: object\n",
"\n",
"Example Encoded Feature Values - row 1 in x:\n",
"cap-shape 5\n",
"cap-surface 2\n",
"cap-color 9\n",
"bruises 1\n",
"odor 0\n",
"gill-attachment 1\n",
"gill-spacing 0\n",
"gill-size 0\n",
"gill-color 4\n",
"stalk-shape 0\n",
"stalk-root 2\n",
"stalk-surf-above-ring 2\n",
"stalk-surf-below-ring 2\n",
"stalk-color-above-ring 7\n",
"stalk-color-below-ring 7\n",
"veil-type 0\n",
"veil-color 2\n",
"ring-number 1\n",
"ring-type 4\n",
"spore-color 3\n",
"population 2\n",
"habitat 1\n",
"Name: 1, dtype: int64\n",
"\n",
"Class Values (Y):\n",
"['p' 'e' 'e' ..., 'e' 'p' 'e']\n",
"\n",
"Encoded Class Values (y):\n",
"[1 0 0 ..., 0 1 0]\n"
]
}
],
"source": [
"#put the features into X (everything except the 0th column)\n",
"X = pd.DataFrame(df, columns=df.columns[1:len(df.columns)], index=df.index)\n",
"#put the class values (0th column) into Y \n",
"Y = df['class']\n",
"\n",
"#encode the text category labels as numeric\n",
"from sklearn import preprocessing\n",
"le = preprocessing.LabelEncoder()\n",
"le.fit(Y)\n",
"#print(le.classes_)\n",
"#print(np.array(Y))\n",
"#Y values now boolean values; poison = 1\n",
"y = le.transform(Y)\n",
"#print(y_train)\n",
"\n",
"#have to initialize or get error below\n",
"x = pd.DataFrame(X,columns=[X.columns[0]])\n",
"#encode each feature column and add it to x_train\n",
"for colname in X.columns:\n",
" le.fit(X[colname])\n",
" print(colname, le.classes_)\n",
" x[colname] = le.transform(X[colname])\n",
"\n",
"print('\\nExample Feature Values - row 1 in X:')\n",
"print(X.iloc[1])\n",
"print('\\nExample Encoded Feature Values - row 1 in x:')\n",
"print(x.iloc[1])\n",
"print('\\nClass Values (Y):')\n",
"print(np.array(Y))\n",
"print('\\nEncoded Class Values (y):')\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training accuracy: 0.925592504134\n",
"Testing accuracy: 0.916076091011\n"
]
}
],
"source": [
"# actually doing the damn machine learning\n",
"#split the dataset into training and test sets\n",
"from sklearn.cross_validation import train_test_split\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)\n",
"\n",
"#initialize and fit the naive bayes classifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"skgnb = GaussianNB()\n",
"skgnb.fit(x_train,y_train)\n",
"train_predict = skgnb.predict(x_train)\n",
"#print(train_predict)\n",
"\n",
"#see how accurate the training data was fit\n",
"from sklearn import metrics\n",
"print(\"Training accuracy:\",metrics.accuracy_score(y_train, train_predict))\n",
"\n",
"#use the trained model to predict the test values\n",
"test_predict = skgnb.predict(x_test)\n",
"print(\"Testing accuracy:\",metrics.accuracy_score(y_test, test_predict))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" edible 0.92 0.92 0.92 1382\n",
" poisonous 0.91 0.91 0.91 1299\n",
"\n",
"avg / total 0.92 0.92 0.92 2681\n",
"\n",
"\n",
"Confusion Matrix:\n",
" predicted-edible predicted-poisonous\n",
"actual \n",
"edible 1269 113\n",
"poisonous 112 1187\n",
"\n",
"Score (same thing as test accuracy?): 0.916076091011\n"
]
}
],
"source": [
"print(\"\\nClassification Report:\")\n",
"print(metrics.classification_report(y_test, test_predict, target_names=['edible','poisonous']))\n",
"print(\"\\nConfusion Matrix:\")\n",
"skcm = metrics.confusion_matrix(y_test,test_predict)\n",
"#putting it into a dataframe so it prints the labels\n",
"skcm = pd.DataFrame(skcm, columns=['predicted-edible','predicted-poisonous'])\n",
"skcm['actual'] = ['edible','poisonous']\n",
"skcm = skcm.set_index('actual')\n",
"\n",
"\n",
"print(skcm)\n",
"\n",
"print(\"\\nScore (same thing as test accuracy?): \", skgnb.score(x_test,y_test))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
sdukshis/Kaggle-Shelter-Animal-Outcomes | Submission.ipynb | 1 | 56912 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Kaggle Shelter Animal Outcomes\n",
"\n",
"https://www.kaggle.com/c/shelter-animal-outcomes\n",
"\n",
"The data comes from Austin Animal Center from October 1st, 2013 to March, 2016. Outcomes represent the status of animals as they leave the Animal Center. All animals receive a unique Animal ID during intake.\n",
"\n",
"In this competition, you are going to predict the outcome of the animal as they leave the Animal Center. These outcomes include: Adoption, Died, Euthanasia, Return to owner, and Transfer.\n",
"\n",
"The train and test data are randomly split.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv('train.csv.gz', parse_dates=['DateTime'], index_col='AnimalID')\n",
"test = pd.read_csv('test.csv.gz', parse_dates=['DateTime'], index_col='ID')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 394,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import re\n",
"\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"class FeatureExtractor(object):\n",
" DAYS_OF_WEEK = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n",
" COLORS = ['Black', 'White', 'Brown', 'Brown']\n",
" def __init__(self):\n",
" self._label_encoders = dict(\n",
" AnimalType = LabelEncoder(),\n",
" Sex = LabelEncoder(),\n",
" Intact = LabelEncoder(),\n",
" Breed = LabelEncoder(),\n",
" Color = LabelEncoder(),\n",
" )\n",
" self._outcomesubtype_dist = dict()\n",
" self._age_median = None\n",
" self._color_dist = None\n",
" \n",
" def fit(self, X):\n",
" self._label_encoders['AnimalType'].fit(X['AnimalType'])\n",
" \n",
" self._label_encoders['Sex'].fit(X['SexuponOutcome'].map(self._extract_sex))\n",
" self._label_encoders['Intact'].fit(X['SexuponOutcome'].map(self._extract_intact))\n",
" \n",
" X['AgeInDays'] = X['AgeuponOutcome'].map(self._extract_age)\n",
" self._age_median = X.groupby('AnimalType')['AgeInDays'].median()\n",
" X = X.drop(['AgeInDays'], axis=1)\n",
" \n",
" self._label_encoders['Breed'].fit(X['Breed'])\n",
" for outcomesubtype in X['OutcomeSubtype'].unique():\n",
" self._outcomesubtype_dist[outcomesubtype] = X[X['OutcomeSubtype'] == outcomesubtype]['Breed'].value_counts(normalize=True)\n",
" \n",
" self._color_dist = X['Color'].value_counts(normalize=True)\n",
" self._label_encoders['Color'].fit(X['Color'])\n",
" \n",
" self._name_dist = X['Name'].value_counts(normalize=True)\n",
" \n",
" return self\n",
" \n",
" def transform(self, X):\n",
" result = pd.DataFrame(index=X.index)\n",
"\n",
" result['HasName'] = (~X['Name'].isnull()).astype(int)\n",
" result['NamePopularity'] = X['Name'].dropna().map(lambda name: self._name_dist[name]).fillna(0)\n",
" result['NameLength'] = X['Name'].dropna().map(lambda name: len(name)).fillna(0)\n",
" \n",
" result['AnimalType'] = self._label_encoders['AnimalType'].transform(X['AnimalType'])\n",
"\n",
" result['Sex'] = self._label_encoders['Sex'].transform(X['SexuponOutcome'].map(self._extract_sex))\n",
" result['Intact'] = self._label_encoders['Intact'].transform(X['SexuponOutcome'].map(self._extract_intact))\n",
"\n",
" result['AgeuponOutcome'] = X['AgeuponOutcome'].map(self._extract_age)\n",
" \n",
" result['UnknownAge'] = X['AgeuponOutcome'].isnull().astype(int)\n",
" for animaltype in self._label_encoders['AnimalType'].classes_:\n",
" result.loc[X['AnimalType'] == animaltype, 'AgeuponOutcome'] = result[X['AnimalType'] == animaltype]['AgeuponOutcome'].fillna(self._age_median[animaltype])\n",
"\n",
" result['Year'] = X['DateTime'].map(lambda dt: dt.year)\n",
" result['Quarter'] = X['DateTime'].map(lambda dt: dt.quarter)\n",
" result['Month'] = X['DateTime'].map(lambda dt: dt.month)\n",
" result['Day'] = X['DateTime'].map(lambda dt: dt.day)\n",
" result['DayOfWeek'] = X['DateTime'].map(lambda dt: dt.dayofweek)\n",
" result['Hour'] = X['DateTime'].map(lambda dt: dt.hour)\n",
" result['Minute'] = X['DateTime'].map(lambda dt: dt.minute)\n",
" result[self.DAYS_OF_WEEK] = pd.get_dummies(result['DayOfWeek'].map(\n",
" lambda d: self.DAYS_OF_WEEK[d]), columns=self.DAYS_OF_WEEK\n",
" )[self.DAYS_OF_WEEK]\n",
" \n",
" result['BreedMix'] = X['Breed'].map(lambda b: 'Mix' in b).astype(int)\n",
" result['Longhair'] = X['Breed'].map(lambda b: 'Longhair' in b).astype(int)\n",
" result['Shorthair'] = X['Breed'].map(lambda b: 'Shorthair' in b).astype(int)\n",
" result['Breed'] = self._label_encoders['Breed'].transform(X['Breed'])\n",
" for outcomesubtype, dist in self._outcomesubtype_dist.items():\n",
" result[outcomesubtype] = X['Breed'].map(lambda breed: dist.get(breed, 0))\n",
" \n",
" result['ColorPopularity'] = X['Color'].map(lambda c: self._color_dist[c])\n",
" result['SimpleColor'] = X['Color'].map(lambda color: not '/' in color).astype(int)\n",
" result['Color'] = self._label_encoders['Color'].transform(X['Color'])\n",
" result['Tabby'] = X['Color'].map(lambda color: 'Tabby' in color).astype(int)\n",
" return result\n",
" \n",
" @classmethod\n",
" def _extract_sex(cls, sex):\n",
" if pd.isnull(sex):\n",
" return 'Unknown'\n",
" if 'Female' in sex:\n",
" return 'Female'\n",
" if 'Male' in sex:\n",
" return 'Male'\n",
" return 'Unknown'\n",
" \n",
" @classmethod\n",
" def _extract_intact(cls, sex):\n",
" if pd.isnull(sex):\n",
" return 'Unknown'\n",
" if 'Intact' in sex:\n",
" return 'Intact'\n",
" if 'Spayed' in sex or 'Neutered' in sex:\n",
" return 'Spayed'\n",
" return 'Unknown'\n",
" \n",
" @classmethod\n",
" def _extract_age(cls, age):\n",
" if pd.isnull(age):\n",
" return np.nan\n",
" days_in = {\n",
" 'day': 1,\n",
" 'week': 7,\n",
" 'month': 30,\n",
" 'year': 365,\n",
" }\n",
"\n",
" m = re.match('(?P<num>\\d+)\\s+(?P<period>\\w+)', age)\n",
" num = int(m.group('num'))\n",
" period = m.group('period')\n",
" if period.endswith('s'):\n",
" period = period[:-1]\n",
" return num * days_in[period]"
]
},
{
"cell_type": "code",
"execution_count": 448,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"outcometype_encoder = LabelEncoder().fit(train['OutcomeType'])\n",
"feature_extractor = FeatureExtractor().fit(train.append(test))\n",
"\n",
"features = [\n",
" 'AgeuponOutcome',\n",
"# 'UnknownAge',\n",
" 'AnimalType',\n",
" 'Sex',\n",
" 'Intact',\n",
" 'HasName',\n",
" 'Year',\n",
" 'Month',\n",
" 'Quarter',\n",
" 'Hour',\n",
" 'Minute',\n",
"# 'BreedMix',\n",
"# 'BreedPopularity',\n",
"# 'Longhair',\n",
"# 'Shorthair',\n",
"# 'ColorPopularity',\n",
"# 'SimpleColor',\n",
" *feature_extractor.DAYS_OF_WEEK,\n",
"# *train['OutcomeSubtype'].unique(),\n",
" 'Partner',\n",
" 'Suffering',\n",
" 'Foster',\n",
" 'Aggressive',\n",
"# *outcomesubtype_columns,\n",
"# 'OutcomeSubtypeEncoded',\n",
" 'Breed',\n",
"# 'Aggressive',\n",
"# 'Color',\n",
"# 'Tabby',\n",
"# 'NamePopularity',\n",
"# 'NameLength',\n",
"]\n",
"\n",
"\n",
"X_train = feature_extractor.transform(train)[features]\n",
"y_train = outcometype_encoder.transform(train['OutcomeType'])\n",
"\n",
"X_test = feature_extractor.transform(test)[features]"
]
},
{
"cell_type": "code",
"execution_count": 402,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 2.9min\n",
"[Parallel(n_jobs=1)]: Done 199 tasks | elapsed: 15.6min\n",
"[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 42.3min finished\n"
]
},
{
"data": {
"text/plain": [
"-0.71348616299028833"
]
},
"execution_count": 402,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from xgboost import XGBClassifier\n",
"from sklearn.grid_search import RandomizedSearchCV, GridSearchCV\n",
"\n",
"base_hyperparams = dict(\n",
" n_estimators = 80,\n",
" max_depth = 10,\n",
" subsample = 0.8,\n",
" colsample_bytree = 0.7,\n",
" seed = 42,\n",
")\n",
"\n",
"xgb_grid_params = dict(\n",
" n_estimators = np.arange(80, 120, 5),\n",
" max_depth = np.arange(8, 13),\n",
")\n",
"xbg_grid_search = GridSearchCV(XGBClassifier(**base_hyperparams), rf_param_grid, scoring='log_loss', verbose=True, cv=5)\n",
"xbg_grid_search.fit(X_train, y_train).best_score_"
]
},
{
"cell_type": "code",
"execution_count": 418,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'max_depth': 8, 'n_estimators': 110}"
]
},
"execution_count": 418,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xbg_grid_search.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 408,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xgb_estimator = xbg_grid_search.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 449,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score mean: -0.716989, score std: 0.006720\n"
]
}
],
"source": [
"from sklearn.cross_validation import cross_val_score\n",
"\n",
"score = cross_val_score(xgb_estimator, X_train, y_train, cv=5, scoring='log_loss')\n",
"print('score mean: {:0.6f}, score std: {:0.6f}'.format(score.mean(), score.std()))"
]
},
{
"cell_type": "code",
"execution_count": 410,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 49 tasks | elapsed: 2.9min\n",
"[Parallel(n_jobs=1)]: Done 199 tasks | elapsed: 15.5min\n",
"[Parallel(n_jobs=1)]: Done 420 out of 420 | elapsed: 42.3min finished\n"
]
},
{
"data": {
"text/plain": [
"-0.71348616299028833"
]
},
"execution_count": 410,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xgb_grid_params = dict(\n",
" n_estimators = np.arange(80, 120, 5),\n",
" max_depth = np.arange(8, 13),\n",
" colsample_bytree = 0.9,\n",
")\n",
"xbg_grid_search = GridSearchCV(XGBClassifier(**base_hyperparams), rf_param_grid, scoring='log_loss', verbose=True, cv=5)\n",
"xbg_grid_search.fit(X_train, y_train).best_score_"
]
},
{
"cell_type": "code",
"execution_count": 450,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbab530d780>"
]
},
"execution_count": 450,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AZlX91Wm7DqP6A2dW/MnOc0SkOFBOVec79iU65ddgNGxR1b2OE28ExAFrVHWvU+9vYInT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\nmwAAAAFJREFURFmJ+GEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbab518f6a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb_estimator = xgb_estimator.fit(X_train, y_train)\n",
"from xgboost import plot_importance\n",
"\n",
"plot_importance(xgb_estimator)"
]
},
{
"cell_type": "code",
"execution_count": 386,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"xgb_pred = xgb_estimator.predict_proba(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 387,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 9.1s\n",
"[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 52.2s\n",
"[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed: 2.5min finished\n"
]
},
{
"data": {
"text/plain": [
"-0.75835911330153627"
]
},
"execution_count": 387,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.grid_search import RandomizedSearchCV, GridSearchCV\n",
"\n",
"rf_param_grid = dict(\n",
" n_estimators = np.arange(80, 150, 10),\n",
" max_depth = np.arange(8, 20),\n",
")\n",
"grid_search = GridSearchCV(RandomForestClassifier(), rf_param_grid, scoring='log_loss', verbose=True, cv=5, n_jobs=-1)\n",
"grid_search.fit(X_train, y_train).best_score_"
]
},
{
"cell_type": "code",
"execution_count": 406,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rf_estimator = grid_search.best_estimator_"
]
},
{
"cell_type": "code",
"execution_count": 419,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'max_depth': 15, 'n_estimators': 140}"
]
},
"execution_count": 419,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"grid_search.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 479,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score mean: -0.744748, score std: 0.003568\n"
]
}
],
"source": [
"score = cross_val_score(rf_estimator.set_params(criterion='gini'), X_train, y_train, cv=5, scoring='log_loss')\n",
"print('score mean: {:0.6f}, score std: {:0.6f}'.format(score.mean(), score.std()))"
]
},
{
"cell_type": "code",
"execution_count": 456,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"xgb_pred log_loss: 0.7225131235964357\n",
"rf_pred log_loss: 0.7516835678656014\n",
"xgb_calib_pred log_loss: 0.7170276848575293\n",
"rf_calib_pred log_loss: 0.7295822659274345\n",
"y_pred log_loss: 0.7126763904553242\n",
"y_pred_w log_loss: 0.7110365695141527\n"
]
}
],
"source": [
"from sklearn.metrics import log_loss\n",
"from sklearn.cross_validation import train_test_split\n",
"\n",
"from scipy.optimize import minimize\n",
"from sklearn.calibration import CalibratedClassifierCV\n",
"\n",
"def fn(X, y):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
" \n",
" xgb_pred = XGBClassifier(**xbg_grid_search.best_params_).fit(X_train, y_train).predict_proba(X_test)\n",
" rf_pred = RandomForestClassifier(**grid_search.best_params_, random_state=42).fit(X_train, y_train).predict_proba(X_test)\n",
" xgb_calib_pred = CalibratedClassifierCV(XGBClassifier(**xbg_grid_search.best_params_), cv=10, method='isotonic').fit(X_train, y_train).predict_proba(X_test)\n",
" rf_calib_pred = CalibratedClassifierCV(RandomForestClassifier(**grid_search.best_params_, random_state=42), cv=10, method='isotonic').fit(X_train, y_train).predict_proba(X_test)\n",
" \n",
" y_pred = (xgb_pred + rf_calib_pred)/2\n",
" print(\"xgb_pred log_loss: {}\".format(log_loss(y_test, xgb_pred)))\n",
" print(\"rf_pred log_loss: {}\".format(log_loss(y_test, rf_pred)))\n",
" print(\"xgb_calib_pred log_loss: {}\".format(log_loss(y_test, xgb_calib_pred)))\n",
" print(\"rf_calib_pred log_loss: {}\".format(log_loss(y_test, rf_calib_pred)))\n",
" print(\"y_pred log_loss: {}\".format(log_loss(y_test, y_pred)))\n",
" \n",
" def target_fn(x):\n",
" return log_loss(y_test, x[0]*xgb_calib_pred + x[1]*rf_calib_pred)\n",
"\n",
" def norm_consraint(x):\n",
" return np.sum(x) - 1\n",
"\n",
" xopt = minimize(target_fn, [1/2]*2, bounds=[[0, 1] for i in range(2)], constraints=({'type': 'eq', 'fun': norm_consraint}), tol=1e-14)\n",
" print(\"y_pred_w log_loss: {}\".format(xopt.fun))\n",
" return xopt.x\n",
"weigths = fn(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 477,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(21383, 10)\n"
]
},
{
"data": {
"text/plain": [
"1.4580230374687959"
]
},
"execution_count": 477,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"def stacking(X, y):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
" \n",
" xgb_calib_est = CalibratedClassifierCV(XGBClassifier(**xbg_grid_search.best_params_), cv=10, method='isotonic').fit(X_train, y_train)\n",
" xgb_calib_pred = xgb_calib_est.predict_proba(X_test)\n",
" rf_calib_est = CalibratedClassifierCV(RandomForestClassifier(**grid_search.best_params_, random_state=42), cv=10, method='isotonic').fit(X_train, y_train)\n",
" rf_calib_pred = rf_calib_est.predict_proba(X_test)\n",
" \n",
" X_pred_train = np.hstack((xgb_calib_est.predict_proba(X_train), rf_calib_est.predict_proba(X_train)))\n",
" print(X_pred_train.shape)\n",
" X_pred_test = np.hstack((xgb_calib_est.predict_proba(X_test), rf_calib_est.predict_proba(X_test)))\n",
" return log_loss(y_test, XGBClassifier(**xbg_grid_search.best_params_).fit(X_pred_train, y_train).predict_proba(X_pred_test))\n",
"\n",
"stacking(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 457,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.63189539, 0.36810461])"
]
},
"execution_count": 457,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weigths"
]
},
{
"cell_type": "code",
"execution_count": 438,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# from sklearn.ensemble import GradientBoostingClassifier\n",
"\n",
"# gb_pred = GradientBoostingClassifier(n_estimators=90, max_depth=14).fit(X_train, y_train).predict_proba(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 458,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(26729, 22)"
]
},
"execution_count": 458,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 459,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xgb_pred = CalibratedClassifierCV(XGBClassifier(**xbg_grid_search.best_params_), cv=10, method='isotonic').fit(X_train, y_train).predict_proba(X_test)\n",
"rf_pred = CalibratedClassifierCV(RandomForestClassifier(**grid_search.best_params_, random_state=42), cv=10, method='isotonic').fit(X_train, y_train).predict_proba(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 460,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_pred = weigths[0]*xgb_pred + weigths[1]*rf_pred\n",
"# y_pred = xgb_pred"
]
},
{
"cell_type": "code",
"execution_count": 461,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission = pd.DataFrame(index=test.index)\n",
"for i, outcome_type in enumerate(outcometype_encoder.classes_):\n",
" submission[outcome_type] = y_pred[:, i]"
]
},
{
"cell_type": "code",
"execution_count": 462,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"submission.to_csv('pred.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
chili-epfl/paper-JLA-deep-teaching-analytics | notebooks/kerasSeparates-Audio.ipynb | 1 | 147945 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting from separate data sources - AUDIO\n",
"\n",
"... transformed to PCA\n",
"\n",
"## Data preparation"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded csvs\n"
]
}
],
"source": [
"import numpy\n",
"import pandas\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.wrappers.scikit_learn import KerasClassifier\n",
"from sklearn.cross_validation import cross_val_score\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.pipeline import Pipeline\n",
"\n",
"# fix random seed for reproducibility\n",
"seed = 67\n",
"numpy.random.seed(seed)\n",
"\n",
"data = pandas.read_csv(\"../data/processed/train.csv\")\n",
"notnull_data = data[data.notnull().all(axis=1)]\n",
"#train = notnull_data.values\n",
"data2 = pandas.read_csv(\"../data/processed/test.csv\")\n",
"notnull_data2 = data2[data2.notnull().all(axis=1)]\n",
"test = notnull_data2.values\n",
"\n",
"# Divide the train dataset further into validation and train sets\n",
"# keeping in the val set a session from each teacher, \n",
"# with a variety of tags for Activity and Social\n",
"#pandas.crosstab(index=data[\"session\"], columns=data[\"Activity\"])\n",
"#pandas.crosstab(index=data[\"session\"], columns=data[\"Social\"])\n",
"# e.g., case1-day1-session2-teacher1, case2-day2-session2-teacher2\n",
"print 'Loaded csvs'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(969, 7560)\n",
"(3503, 7560)\n",
"Split into train, val, test sets and scaled\n"
]
}
],
"source": [
"val = notnull_data[notnull_data.session.isin(['case1-day1-session2-teacher1','case2-day2-session2-teacher2'])].values\n",
"print val.shape\n",
"tr = notnull_data[~notnull_data.session.isin(['case1-day1-session2-teacher1','case2-day2-session2-teacher2'])].values\n",
"print tr.shape\n",
"\n",
"X_train = tr[:,3:7558].astype(float)\n",
"Y_trainA = tr[:,7558] #Activity\n",
"Y_trainS = tr[:,7559] #Social\n",
"X_val = val[:,3:7558].astype(float)\n",
"Y_valA = val[:,7558] #Activity\n",
"Y_valS = val[:,7559] #Social\n",
"X_test = test[:,3:7558].astype(float)\n",
"Y_testA = test[:,7558]\n",
"Y_testS = test[:,7559]\n",
"\n",
"# One hot encoding of the response variable (using dummy variables)\n",
"from keras.utils.np_utils import to_categorical\n",
"\n",
"# encode class values as integers\n",
"encoderA = LabelEncoder()\n",
"encoderA.fit(Y_trainA)\n",
"\n",
"encoded_Y_trainA = encoderA.transform(Y_trainA)\n",
"# convert integers to dummy variables (i.e. one hot encoded)\n",
"dummy_y_trainA = to_categorical(encoded_Y_trainA)\n",
"\n",
"encoded_Y_valA = encoderA.transform(Y_valA)\n",
"# convert integers to dummy variables (i.e. one hot encoded)\n",
"dummy_y_valA = to_categorical(encoded_Y_valA)\n",
"\n",
"#encoderA.fit(Y_testA)\n",
"encoded_Y_testA = encoderA.transform(Y_testA)\n",
"# convert integers to dummy variables (i.e. one hot encoded)\n",
"dummy_y_testA = to_categorical(encoded_Y_testA)\n",
"\n",
"# encode class values as integers\n",
"encoderS = LabelEncoder()\n",
"encoderS.fit(Y_trainS)\n",
"encoded_Y_trainS = encoderS.transform(Y_trainS)\n",
"# convert integers to dummy variables (i.e. one hot encoded)\n",
"dummy_y_trainS = to_categorical(encoded_Y_trainS)\n",
"\n",
"encoded_Y_valS = encoderS.transform(Y_valS)\n",
"# convert integers to dummy variables (i.e. one hot encoded)\n",
"dummy_y_valS = to_categorical(encoded_Y_valS)\n",
"\n",
"#encoderS.fit(Y_testS)\n",
"encoded_Y_testS = encoderS.transform(Y_testS)\n",
"# convert integers to dummy variables (i.e. one hot encoded)\n",
"dummy_y_testS = to_categorical(encoded_Y_testS)\n",
"\n",
"# We standardize on the basis of the training data\n",
"scaler = StandardScaler().fit(X_train)\n",
"X_train_st = scaler.transform(X_train)\n",
"X_val_st = scaler.transform(X_val)\n",
"X_test_st = scaler.transform(X_test)\n",
"print 'Split into train, val, test sets and scaled'"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reducing AUDIO dataset with PCA 100\n",
"Total variance explained by %d components: 100\n",
"0.684495816407\n"
]
}
],
"source": [
"# Reduce the dataset to work with to the AUDIO variables\n",
"# Get the column names of the X matrix, to separate the different sources\n",
"colnamesX = list(data.columns.values)[3:7558]\n",
"# Eyetracking: colnamesX[0:10]\n",
"# Acc: colnamesX[10:150]\n",
"# Audio: colnamesX[150:6555]\n",
"# Video: colnamesX[6555:]\n",
"X_train_st_audio = X_train_st[:,150:6555]\n",
"X_val_st_audio = X_val_st[:,150:6555]\n",
"X_test_st_audio = X_test_st[:,150:6555]\n",
"# Number of components to extract from the dataset\n",
"n_components = 100\n",
"\n",
"from sklearn import decomposition\n",
"print 'Reducing AUDIO dataset with PCA',n_components\n",
"pca = decomposition.PCA(n_components=n_components)\n",
"X_train_pca = pca.fit_transform(X_train_st_audio)\n",
"X_val_pca = pca.transform(X_val_st_audio)\n",
"X_test_pca = pca.transform(X_test_st_audio)\n",
"\n",
"#print 'Variance explained:'\n",
"#print pca.explained_variance_ratio_\n",
"print 'Total variance explained by %d components:',n_components\n",
"print sum(pca.explained_variance_ratio_)\n",
"\n",
"trainX = numpy.reshape(X_train_pca, (X_train_pca.shape[0], 1, X_train_pca.shape[1]))\n",
"valX = numpy.reshape(X_val_pca, (X_val_pca.shape[0], 1, X_val_pca.shape[1]))\n",
"testX = numpy.reshape(X_test_pca, (X_test_pca.shape[0], 1, X_test_pca.shape[1]))\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ready for training!\n"
]
}
],
"source": [
"from keras.layers import Dropout\n",
"from keras.layers import LSTM\n",
"from keras.constraints import maxnorm\n",
"from keras.optimizers import SGD\n",
"\n",
"# This is our winning architecture so far\n",
"def create_LSTM3_PCA(n_outputs, batch_size = 1, trainShape1=100):\n",
" # create and fit the LSTM network\n",
" model = Sequential()\n",
" # stateful LSTM!\n",
" model.add(LSTM(200, batch_input_shape=(batch_size, 1, trainShape1), \n",
" return_sequences=True, stateful=True))\n",
" model.add(Dropout(0.2))\n",
" model.add(LSTM(100, \n",
" return_sequences=True, stateful=True))\n",
" model.add(Dropout(0.2))\n",
" model.add(LSTM(50, \n",
" return_sequences=False, stateful=True))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(50, activation='tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(20, activation='tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(n_outputs, activation='softmax'))\n",
" # Compile model\n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
" return model\n",
"\n",
"def create_LSTM2_PCA(n_outputs, batch_size = 1, trainShape1=100):\n",
" # create and fit the LSTM network\n",
" model = Sequential()\n",
" # stateful LSTM!\n",
" model.add(LSTM(300, batch_input_shape=(batch_size, 1, trainShape1), \n",
" return_sequences=True, stateful=True))\n",
" model.add(Dropout(0.2))\n",
" model.add(LSTM(50, \n",
" return_sequences=False, stateful=True))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(50, activation='tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(20, activation='tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(n_outputs, activation='softmax'))\n",
" # Compile model\n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
" return model\n",
"\n",
"def create_LSTM1_PCA(n_outputs, batch_size = 1, trainShape1=100):\n",
" # create and fit the LSTM network\n",
" model = Sequential()\n",
" # stateful LSTM!\n",
" model.add(LSTM(400, batch_input_shape=(batch_size, 1, trainShape1), \n",
" return_sequences=False, stateful=True))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(50, activation='tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(20, activation='tanh'))\n",
" model.add(Dropout(0.2))\n",
" model.add(Dense(n_outputs, activation='softmax'))\n",
" # Compile model\n",
" model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
" return model\n",
"\n",
"from keras.callbacks import ModelCheckpoint\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.metrics import confusion_matrix, roc_auc_score, accuracy_score, cohen_kappa_score\n",
"\n",
"def printValStats(model, testX, dummy_y_test, batch=1):\n",
" # Other performance/accuracy metrics\n",
" Y_pred = model.predict(testX, batch_size=batch)\n",
" model.reset_states()\n",
" print 'Performance of model on test set ----------------------------'\n",
" # Accuracy\n",
" print('Accuracy:')\n",
" print(accuracy_score(numpy.argmax(dummy_y_test, axis=1), numpy.argmax(Y_pred, axis=1)))\n",
" # Kappa\n",
" print('Kappa:')\n",
" kappa = cohen_kappa_score(numpy.argmax(dummy_y_test, axis=1), numpy.argmax(Y_pred, axis=1))\n",
" print(kappa)\n",
" # Confusion matrix\n",
" cm = confusion_matrix(numpy.argmax(dummy_y_test, axis=1), numpy.argmax(Y_pred, axis=1))\n",
" numpy.set_printoptions(precision=2)\n",
" print('Confusion matrix:')\n",
" print(cm)\n",
" # AUC\n",
" roc = roc_auc_score(dummy_y_test, Y_pred, average='macro')\n",
" print('AUC score:')\n",
" print(roc)\n",
" return kappa, roc\n",
"\n",
"def plot_training(accs, val_accs, losss, val_losss, kappas, aucs):\n",
" # summarize history for accuracy\n",
" plt.plot(accs)\n",
" plt.plot(val_accs)\n",
" plt.title('model accuracy')\n",
" plt.ylabel('accuracy')\n",
" plt.xlabel('epoch')\n",
" plt.legend(['train','test'], loc='upper left')\n",
" plt.show()\n",
" # summarize history for loss\n",
" plt.plot(losss)\n",
" plt.plot(val_losss)\n",
" plt.title('model loss')\n",
" plt.ylabel('loss')\n",
" plt.xlabel('epoch')\n",
" plt.legend(['train','test'], loc='upper left')\n",
" plt.show()\n",
" # summarize kappa and auc\n",
" plt.plot(kappas)\n",
" plt.plot(aucs)\n",
" plt.title('Other performance')\n",
" plt.ylabel('metric')\n",
" plt.xlabel('epoch')\n",
" plt.legend(['Kappa','AUC'], loc='upper left')\n",
" plt.show()\n",
" \n",
" \n",
"import operator\n",
"\n",
"def get_max_values(list):\n",
" index, value = max(enumerate(list), key=operator.itemgetter(1))\n",
" return index, value\n",
"\n",
"print 'Ready for training!'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predicting Activity from audio data\n",
"\n",
"### 3-layer LSTM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"____________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"====================================================================================================\n",
"lstm_1 (LSTM) (1, 1, 200) 240800 lstm_input_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_1 (Dropout) (1, 1, 200) 0 lstm_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm_2 (LSTM) (1, 1, 100) 120400 dropout_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_2 (Dropout) (1, 1, 100) 0 lstm_2[0][0] \n",
"____________________________________________________________________________________________________\n",
"lstm_3 (LSTM) (1, 50) 30200 dropout_2[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_3 (Dropout) (1, 50) 0 lstm_3[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_1 (Dense) (1, 50) 2550 dropout_3[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_4 (Dropout) (1, 50) 0 dense_1[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_2 (Dense) (1, 20) 1020 dropout_4[0][0] \n",
"____________________________________________________________________________________________________\n",
"dropout_5 (Dropout) (1, 20) 0 dense_2[0][0] \n",
"____________________________________________________________________________________________________\n",
"dense_3 (Dense) (1, 5) 105 dropout_5[0][0] \n",
"====================================================================================================\n",
"Total params: 395075\n",
"____________________________________________________________________________________________________\n",
"None\n",
"('Epoch', 1, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 1.2689 - acc: 0.5023Epoch 00000: val_acc improved from -inf to 0.25077, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 62s - loss: 1.2693 - acc: 0.5021 - val_loss: 1.9646 - val_acc: 0.2508\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.251805985552\n",
"Kappa:\n",
"0.0468951594851\n",
"Confusion matrix:\n",
"[[ 0 0 0 0 122]\n",
" [ 0 144 0 0 118]\n",
" [ 0 125 0 0 118]\n",
" [ 0 35 0 0 133]\n",
" [ 0 74 0 0 100]]\n",
"AUC score:\n",
"0.659596465468\n",
"('Epoch', 2, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 1.1570 - acc: 0.5485Epoch 00000: val_acc improved from 0.25077 to 0.30857, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 70s - loss: 1.1573 - acc: 0.5484 - val_loss: 1.8879 - val_acc: 0.3086\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.310629514964\n",
"Kappa:\n",
"0.13378172582\n",
"Confusion matrix:\n",
"[[116 1 5 0 0]\n",
" [ 75 143 44 0 0]\n",
" [ 69 132 42 0 0]\n",
" [120 36 12 0 0]\n",
" [ 45 91 38 0 0]]\n",
"AUC score:\n",
"0.61159059271\n",
"('Epoch', 3, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 1.0809 - acc: 0.5914Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 81s - loss: 1.0812 - acc: 0.5912 - val_loss: 2.0865 - val_acc: 0.2797\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.28689370485\n",
"Kappa:\n",
"0.0940494391752\n",
"Confusion matrix:\n",
"[[109 0 13 0 0]\n",
" [ 29 121 112 0 0]\n",
" [ 74 121 48 0 0]\n",
" [106 33 29 0 0]\n",
" [ 28 55 91 0 0]]\n",
"AUC score:\n",
"0.586630974546\n",
"('Epoch', 4, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 1.0058 - acc: 0.6325Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 84s - loss: 1.0058 - acc: 0.6323 - val_loss: 2.0634 - val_acc: 0.3034\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.301341589267\n",
"Kappa:\n",
"0.10439465924\n",
"Confusion matrix:\n",
"[[101 0 21 0 0]\n",
" [ 17 97 145 0 3]\n",
" [ 45 104 94 0 0]\n",
" [ 99 24 44 0 1]\n",
" [ 15 28 131 0 0]]\n",
"AUC score:\n",
"0.595045966039\n",
"('Epoch', 5, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.9295 - acc: 0.6710Epoch 00000: val_acc improved from 0.30857 to 0.32611, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 85s - loss: 0.9295 - acc: 0.6709 - val_loss: 1.8770 - val_acc: 0.3261\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.334365325077\n",
"Kappa:\n",
"0.134203002144\n",
"Confusion matrix:\n",
"[[ 93 0 23 0 6]\n",
" [ 12 124 98 0 28]\n",
" [ 16 131 95 0 1]\n",
" [ 47 31 87 0 3]\n",
" [ 12 74 76 0 12]]\n",
"AUC score:\n",
"0.671039582073\n",
"('Epoch', 6, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.8512 - acc: 0.7013Epoch 00000: val_acc improved from 0.32611 to 0.35913, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 90s - loss: 0.8511 - acc: 0.7014 - val_loss: 2.1873 - val_acc: 0.3591\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.37048503612\n",
"Kappa:\n",
"0.195028183342\n",
"Confusion matrix:\n",
"[[105 3 10 0 4]\n",
" [ 25 41 191 0 5]\n",
" [ 33 11 199 0 0]\n",
" [ 78 17 73 0 0]\n",
" [ 19 46 95 0 14]]\n",
"AUC score:\n",
"0.636614370532\n",
"('Epoch', 7, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.7602 - acc: 0.7479Epoch 00000: val_acc improved from 0.35913 to 0.37771, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 92s - loss: 0.7601 - acc: 0.7479 - val_loss: 1.9019 - val_acc: 0.3777\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.37048503612\n",
"Kappa:\n",
"0.186228848269\n",
"Confusion matrix:\n",
"[[ 84 1 31 6 0]\n",
" [ 4 64 146 23 25]\n",
" [ 13 48 172 6 4]\n",
" [ 42 29 83 6 8]\n",
" [ 6 59 66 10 33]]\n",
"AUC score:\n",
"0.702416028237\n",
"('Epoch', 8, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.7144 - acc: 0.7676Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 94s - loss: 0.7143 - acc: 0.7676 - val_loss: 1.9181 - val_acc: 0.3560\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.355005159959\n",
"Kappa:\n",
"0.161198151831\n",
"Confusion matrix:\n",
"[[ 83 2 36 0 1]\n",
" [ 4 62 178 4 14]\n",
" [ 12 57 169 0 5]\n",
" [ 47 31 82 2 6]\n",
" [ 6 73 64 3 28]]\n",
"AUC score:\n",
"0.675981713055\n",
"('Epoch', 9, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.6623 - acc: 0.7864Epoch 00000: val_acc improved from 0.37771 to 0.38803, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 92s - loss: 0.6621 - acc: 0.7865 - val_loss: 2.2118 - val_acc: 0.3880\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.391124871001\n",
"Kappa:\n",
"0.217428441195\n",
"Confusion matrix:\n",
"[[ 94 2 22 0 4]\n",
" [ 8 26 197 6 25]\n",
" [ 14 6 217 0 6]\n",
" [ 54 9 89 7 9]\n",
" [ 7 18 109 5 35]]\n",
"AUC score:\n",
"0.679616741276\n",
"('Epoch', 10, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.6179 - acc: 0.7975Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 93s - loss: 0.6185 - acc: 0.7973 - val_loss: 2.1570 - val_acc: 0.3860\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.418988648091\n",
"Kappa:\n",
"0.253274768474\n",
"Confusion matrix:\n",
"[[ 85 13 21 2 1]\n",
" [ 11 118 80 4 49]\n",
" [ 14 79 132 3 15]\n",
" [ 46 20 62 15 25]\n",
" [ 10 42 62 4 56]]\n",
"AUC score:\n",
"0.701809581521\n",
"('Epoch', 11, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.5884 - acc: 0.8067Epoch 00000: val_acc improved from 0.38803 to 0.46233, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 89s - loss: 0.5883 - acc: 0.8067 - val_loss: 1.8456 - val_acc: 0.4623\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.468524251806\n",
"Kappa:\n",
"0.323859448503\n",
"Confusion matrix:\n",
"[[ 82 11 18 4 7]\n",
" [ 13 101 71 19 58]\n",
" [ 15 41 161 6 20]\n",
" [ 50 31 35 29 23]\n",
" [ 8 37 34 14 81]]\n",
"AUC score:\n",
"0.727081838647\n",
"('Epoch', 12, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.5101 - acc: 0.8452Epoch 00000: val_acc improved from 0.46233 to 0.47472, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 88s - loss: 0.5100 - acc: 0.8453 - val_loss: 1.9867 - val_acc: 0.4747\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.479876160991\n",
"Kappa:\n",
"0.338037599794\n",
"Confusion matrix:\n",
"[[ 97 6 15 1 3]\n",
" [ 20 108 80 6 48]\n",
" [ 20 25 186 1 11]\n",
" [ 62 29 26 18 33]\n",
" [ 16 64 34 4 56]]\n",
"AUC score:\n",
"0.736110166754\n",
"('Epoch', 13, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.4865 - acc: 0.8555Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 87s - loss: 0.4864 - acc: 0.8556 - val_loss: 2.2015 - val_acc: 0.4231\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.422084623323\n",
"Kappa:\n",
"0.264623774237\n",
"Confusion matrix:\n",
"[[ 88 5 25 1 3]\n",
" [ 22 78 99 16 47]\n",
" [ 21 23 177 7 15]\n",
" [ 54 23 54 8 29]\n",
" [ 12 40 52 12 58]]\n",
"AUC score:\n",
"0.714565790409\n",
"('Epoch', 14, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.4324 - acc: 0.8675Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 90s - loss: 0.4323 - acc: 0.8675 - val_loss: 2.2272 - val_acc: 0.4303\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.43137254902\n",
"Kappa:\n",
"0.277056605306\n",
"Confusion matrix:\n",
"[[ 96 7 10 3 6]\n",
" [ 8 74 113 11 56]\n",
" [ 24 16 183 9 11]\n",
" [ 66 23 33 17 29]\n",
" [ 7 50 61 8 48]]\n",
"AUC score:\n",
"0.715783232956\n",
"('Epoch', 15, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.4402 - acc: 0.8601Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 92s - loss: 0.4401 - acc: 0.8601 - val_loss: 2.3535 - val_acc: 0.3953\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.389060887513\n",
"Kappa:\n",
"0.229362382973\n",
"Confusion matrix:\n",
"[[ 83 1 26 4 8]\n",
" [ 14 31 144 16 57]\n",
" [ 21 12 178 20 12]\n",
" [ 69 8 34 25 32]\n",
" [ 13 19 62 20 60]]\n",
"AUC score:\n",
"0.711972590191\n",
"('Epoch', 16, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.4029 - acc: 0.8789Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 97s - loss: 0.4028 - acc: 0.8790 - val_loss: 2.0671 - val_acc: 0.4685\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.463364293086\n",
"Kappa:\n",
"0.323050384301\n",
"Confusion matrix:\n",
"[[ 92 6 11 4 9]\n",
" [ 12 127 28 16 79]\n",
" [ 22 68 122 11 20]\n",
" [ 54 20 6 29 59]\n",
" [ 18 52 17 8 79]]\n",
"AUC score:\n",
"0.741876928989\n",
"('Epoch', 17, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.3576 - acc: 0.8992Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 88s - loss: 0.3575 - acc: 0.8992 - val_loss: 2.1797 - val_acc: 0.4737\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.488132094943\n",
"Kappa:\n",
"0.352124295339\n",
"Confusion matrix:\n",
"[[ 86 9 12 12 3]\n",
" [ 7 98 76 6 75]\n",
" [ 18 18 173 13 21]\n",
" [ 54 21 20 42 31]\n",
" [ 12 43 32 13 74]]\n",
"AUC score:\n",
"0.749655365107\n",
"('Epoch', 18, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.3204 - acc: 0.9118Epoch 00000: val_acc improved from 0.47472 to 0.48400, saving model to activity.weights--3lstmaudio.best.hdf5\n",
"3503/3503 [==============================] - 80s - loss: 0.3204 - acc: 0.9118 - val_loss: 2.1297 - val_acc: 0.4840\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.486068111455\n",
"Kappa:\n",
"0.351600836294\n",
"Confusion matrix:\n",
"[[ 95 5 5 8 9]\n",
" [ 10 93 80 13 66]\n",
" [ 19 26 170 16 12]\n",
" [ 55 23 6 39 45]\n",
" [ 16 46 26 12 74]]\n",
"AUC score:\n",
"0.747603640693\n",
"('Epoch', 19, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.3161 - acc: 0.9121Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.3160 - acc: 0.9121 - val_loss: 2.3955 - val_acc: 0.4696\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.470588235294\n",
"Kappa:\n",
"0.332975956932\n",
"Confusion matrix:\n",
"[[ 89 1 11 12 9]\n",
" [ 4 62 105 31 60]\n",
" [ 19 17 171 15 21]\n",
" [ 41 13 11 58 45]\n",
" [ 8 34 37 19 76]]\n",
"AUC score:\n",
"0.740531586247\n",
"('Epoch', 20, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.3217 - acc: 0.9132Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 86s - loss: 0.3217 - acc: 0.9132 - val_loss: 2.5254 - val_acc: 0.4087\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.410732714138\n",
"Kappa:\n",
"0.252878172877\n",
"Confusion matrix:\n",
"[[ 93 3 18 4 4]\n",
" [ 12 64 120 11 55]\n",
" [ 24 37 163 4 15]\n",
" [ 57 22 38 7 44]\n",
" [ 15 27 52 9 71]]\n",
"AUC score:\n",
"0.691995854452\n",
"('Epoch', 21, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2961 - acc: 0.9163Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 85s - loss: 0.2961 - acc: 0.9164 - val_loss: 2.4601 - val_acc: 0.4293\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.434468524252\n",
"Kappa:\n",
"0.285308784043\n",
"Confusion matrix:\n",
"[[ 89 7 8 15 3]\n",
" [ 5 121 49 29 58]\n",
" [ 18 94 78 43 10]\n",
" [ 46 28 13 60 21]\n",
" [ 17 36 30 18 73]]\n",
"AUC score:\n",
"0.703658205573\n",
"('Epoch', 22, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2719 - acc: 0.9243Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 90s - loss: 0.2718 - acc: 0.9244 - val_loss: 2.4873 - val_acc: 0.4613\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.458204334365\n",
"Kappa:\n",
"0.316805595285\n",
"Confusion matrix:\n",
"[[100 4 12 3 3]\n",
" [ 15 75 87 24 61]\n",
" [ 18 21 177 14 13]\n",
" [ 57 17 38 27 29]\n",
" [ 25 32 38 14 65]]\n",
"AUC score:\n",
"0.730885216947\n",
"('Epoch', 23, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.3155 - acc: 0.9058Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 84s - loss: 0.3154 - acc: 0.9058 - val_loss: 2.5323 - val_acc: 0.4417\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.433436532508\n",
"Kappa:\n",
"0.283551395576\n",
"Confusion matrix:\n",
"[[ 91 2 11 14 4]\n",
" [ 4 79 89 27 63]\n",
" [ 13 26 158 24 22]\n",
" [ 58 17 36 36 21]\n",
" [ 8 34 50 26 56]]\n",
"AUC score:\n",
"0.70685528936\n",
"('Epoch', 24, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2954 - acc: 0.9218Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.2954 - acc: 0.9218 - val_loss: 2.6807 - val_acc: 0.4159\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.412796697626\n",
"Kappa:\n",
"0.261869954617\n",
"Confusion matrix:\n",
"[[ 88 5 9 17 3]\n",
" [ 13 79 72 39 59]\n",
" [ 16 49 116 42 20]\n",
" [ 60 23 18 42 25]\n",
" [ 9 21 52 17 75]]\n",
"AUC score:\n",
"0.691513256187\n",
"('Epoch', 25, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2528 - acc: 0.9249Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 77s - loss: 0.2528 - acc: 0.9249 - val_loss: 2.5991 - val_acc: 0.4644\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.457172342621\n",
"Kappa:\n",
"0.305813421371\n",
"Confusion matrix:\n",
"[[ 73 6 17 21 5]\n",
" [ 3 96 98 20 45]\n",
" [ 8 18 176 23 18]\n",
" [ 37 30 34 53 14]\n",
" [ 4 50 50 25 45]]\n",
"AUC score:\n",
"0.717191200676\n",
"('Epoch', 26, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2623 - acc: 0.9300Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.2622 - acc: 0.9301 - val_loss: 2.4574 - val_acc: 0.4623\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.460268317853\n",
"Kappa:\n",
"0.31829224329\n",
"Confusion matrix:\n",
"[[ 81 3 15 16 7]\n",
" [ 4 78 69 58 53]\n",
" [ 12 29 171 18 13]\n",
" [ 44 17 36 43 28]\n",
" [ 12 18 44 27 73]]\n",
"AUC score:\n",
"0.734716951012\n",
"('Epoch', 27, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2352 - acc: 0.9372Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.2351 - acc: 0.9372 - val_loss: 2.5326 - val_acc: 0.4716\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.476780185759\n",
"Kappa:\n",
"0.342620989756\n",
"Confusion matrix:\n",
"[[ 96 1 10 9 6]\n",
" [ 8 80 77 43 54]\n",
" [ 17 26 164 26 10]\n",
" [ 57 16 19 50 26]\n",
" [ 16 23 37 26 72]]\n",
"AUC score:\n",
"0.736181016801\n",
"('Epoch', 28, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2137 - acc: 0.9403Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 74s - loss: 0.2137 - acc: 0.9403 - val_loss: 2.7601 - val_acc: 0.4293\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.427244582043\n",
"Kappa:\n",
"0.277780463632\n",
"Confusion matrix:\n",
"[[ 94 4 6 4 14]\n",
" [ 8 104 50 45 55]\n",
" [ 18 74 120 19 12]\n",
" [ 60 27 14 28 39]\n",
" [ 12 40 21 33 68]]\n",
"AUC score:\n",
"0.71564146057\n",
"('Epoch', 29, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1976 - acc: 0.9475Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.1975 - acc: 0.9475 - val_loss: 2.5526 - val_acc: 0.4551\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.449948400413\n",
"Kappa:\n",
"0.307021333691\n",
"Confusion matrix:\n",
"[[ 90 6 11 7 8]\n",
" [ 7 114 32 29 80]\n",
" [ 12 64 113 30 24]\n",
" [ 59 25 14 32 38]\n",
" [ 8 38 21 20 87]]\n",
"AUC score:\n",
"0.74238387623\n",
"('Epoch', 30, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.2145 - acc: 0.9412Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.2144 - acc: 0.9412 - val_loss: 3.0161 - val_acc: 0.4107\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.407636738906\n",
"Kappa:\n",
"0.248012568141\n",
"Confusion matrix:\n",
"[[ 80 3 18 4 17]\n",
" [ 5 97 65 17 78]\n",
" [ 13 64 125 22 19]\n",
" [ 51 29 21 19 48]\n",
" [ 5 38 44 13 74]]\n",
"AUC score:\n",
"0.691911408302\n",
"('Epoch', 31, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1923 - acc: 0.9477Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.1923 - acc: 0.9478 - val_loss: 3.3254 - val_acc: 0.3870\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.385964912281\n",
"Kappa:\n",
"0.219209804652\n",
"Confusion matrix:\n",
"[[ 80 4 14 16 8]\n",
" [ 3 91 101 22 45]\n",
" [ 14 75 102 29 23]\n",
" [ 44 20 23 35 46]\n",
" [ 7 31 62 8 66]]\n",
"AUC score:\n",
"0.670595952919\n",
"('Epoch', 32, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1932 - acc: 0.9492Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 80s - loss: 0.1932 - acc: 0.9492 - val_loss: 3.1076 - val_acc: 0.3953\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.393188854489\n",
"Kappa:\n",
"0.233533546326\n",
"Confusion matrix:\n",
"[[82 7 16 9 8]\n",
" [ 5 94 72 36 55]\n",
" [14 77 91 42 19]\n",
" [47 17 20 48 36]\n",
" [ 8 19 51 30 66]]\n",
"AUC score:\n",
"0.67551089819\n",
"('Epoch', 33, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1770 - acc: 0.9512Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 78s - loss: 0.1770 - acc: 0.9512 - val_loss: 3.0118 - val_acc: 0.4283\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.424148606811\n",
"Kappa:\n",
"0.268624838529\n",
"Confusion matrix:\n",
"[[ 81 2 19 9 11]\n",
" [ 4 85 91 26 56]\n",
" [ 10 42 150 30 11]\n",
" [ 44 19 29 31 45]\n",
" [ 8 33 51 18 64]]\n",
"AUC score:\n",
"0.697893063474\n",
"('Epoch', 34, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1919 - acc: 0.9472Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 80s - loss: 0.1918 - acc: 0.9472 - val_loss: 2.8548 - val_acc: 0.4458\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.443756449948\n",
"Kappa:\n",
"0.300646742187\n",
"Confusion matrix:\n",
"[[ 98 4 8 6 6]\n",
" [ 10 99 51 22 80]\n",
" [ 14 55 126 27 21]\n",
" [ 50 15 21 23 59]\n",
" [ 12 29 25 24 84]]\n",
"AUC score:\n",
"0.728607362262\n",
"('Epoch', 35, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1896 - acc: 0.9472Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.1896 - acc: 0.9472 - val_loss: 2.8639 - val_acc: 0.4469\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.446852425181\n",
"Kappa:\n",
"0.301112280296\n",
"Confusion matrix:\n",
"[[ 93 2 14 5 8]\n",
" [ 16 129 29 15 73]\n",
" [ 16 70 119 26 12]\n",
" [ 60 25 21 20 42]\n",
" [ 12 48 18 24 72]]\n",
"AUC score:\n",
"0.735153043969\n",
"('Epoch', 36, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1767 - acc: 0.9523Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.1767 - acc: 0.9523 - val_loss: 2.9662 - val_acc: 0.4190\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.414860681115\n",
"Kappa:\n",
"0.258340285258\n",
"Confusion matrix:\n",
"[[ 81 2 27 8 4]\n",
" [ 25 102 83 17 35]\n",
" [ 15 74 109 31 14]\n",
" [ 52 20 18 48 30]\n",
" [ 13 39 40 20 62]]\n",
"AUC score:\n",
"0.716509742014\n",
"('Epoch', 37, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1676 - acc: 0.9609Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 74s - loss: 0.1675 - acc: 0.9609 - val_loss: 3.0033 - val_acc: 0.4190\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.410732714138\n",
"Kappa:\n",
"0.246411493029\n",
"Confusion matrix:\n",
"[[ 77 2 24 10 9]\n",
" [ 12 115 79 8 48]\n",
" [ 14 87 107 22 13]\n",
" [ 39 29 33 28 39]\n",
" [ 8 47 43 5 71]]\n",
"AUC score:\n",
"0.699252473417\n",
"('Epoch', 38, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1655 - acc: 0.9572Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.1655 - acc: 0.9572 - val_loss: 2.8768 - val_acc: 0.4396\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.43653250774\n",
"Kappa:\n",
"0.284735497303\n",
"Confusion matrix:\n",
"[[ 80 2 17 17 6]\n",
" [ 10 85 103 10 54]\n",
" [ 10 40 146 23 24]\n",
" [ 35 18 33 37 45]\n",
" [ 13 32 40 14 75]]\n",
"AUC score:\n",
"0.717027330355\n",
"('Epoch', 39, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1443 - acc: 0.9597Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.1443 - acc: 0.9597 - val_loss: 3.3124 - val_acc: 0.4149\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.410732714138\n",
"Kappa:\n",
"0.253119208221\n",
"Confusion matrix:\n",
"[[ 93 3 11 9 6]\n",
" [ 13 81 114 12 42]\n",
" [ 17 52 128 28 18]\n",
" [ 41 23 30 30 44]\n",
" [ 13 34 41 20 66]]\n",
"AUC score:\n",
"0.697017441994\n",
"('Epoch', 40, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1620 - acc: 0.9572Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.1620 - acc: 0.9572 - val_loss: 3.2233 - val_acc: 0.4045\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.399380804954\n",
"Kappa:\n",
"0.242569171075\n",
"Confusion matrix:\n",
"[[ 84 0 13 17 8]\n",
" [ 13 72 101 20 56]\n",
" [ 11 65 104 43 20]\n",
" [ 39 22 19 48 40]\n",
" [ 14 26 35 20 79]]\n",
"AUC score:\n",
"0.702387456147\n",
"('Epoch', 41, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1660 - acc: 0.9532Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 73s - loss: 0.1660 - acc: 0.9532 - val_loss: 3.0240 - val_acc: 0.4252\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.422084623323\n",
"Kappa:\n",
"0.269892483245\n",
"Confusion matrix:\n",
"[[ 82 3 13 13 11]\n",
" [ 12 86 89 15 60]\n",
" [ 16 63 120 24 20]\n",
" [ 46 20 17 35 50]\n",
" [ 11 27 35 15 86]]\n",
"AUC score:\n",
"0.704468215194\n",
"('Epoch', 42, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1678 - acc: 0.9560Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 74s - loss: 0.1677 - acc: 0.9560 - val_loss: 3.2347 - val_acc: 0.4241\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.420020639835\n",
"Kappa:\n",
"0.267296786389\n",
"Confusion matrix:\n",
"[[ 94 5 6 11 6]\n",
" [ 18 71 109 6 58]\n",
" [ 16 53 135 17 22]\n",
" [ 44 22 21 32 49]\n",
" [ 17 31 39 12 75]]\n",
"AUC score:\n",
"0.701138227437\n",
"('Epoch', 43, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1431 - acc: 0.9657Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 77s - loss: 0.1430 - acc: 0.9657 - val_loss: 3.0899 - val_acc: 0.4469\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.437564499484\n",
"Kappa:\n",
"0.288552006208\n",
"Confusion matrix:\n",
"[[ 86 2 11 14 9]\n",
" [ 16 68 116 15 47]\n",
" [ 15 35 155 21 17]\n",
" [ 43 23 22 43 37]\n",
" [ 16 30 43 13 72]]\n",
"AUC score:\n",
"0.711970712901\n",
"('Epoch', 44, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1434 - acc: 0.9632Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 74s - loss: 0.1434 - acc: 0.9632 - val_loss: 3.1590 - val_acc: 0.4685\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.465428276574\n",
"Kappa:\n",
"0.322269659826\n",
"Confusion matrix:\n",
"[[ 87 2 14 12 7]\n",
" [ 18 92 91 11 50]\n",
" [ 12 44 163 8 16]\n",
" [ 57 21 35 34 21]\n",
" [ 15 24 44 16 75]]\n",
"AUC score:\n",
"0.710476513411\n",
"('Epoch', 45, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1470 - acc: 0.9629Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.1469 - acc: 0.9629 - val_loss: 3.3880 - val_acc: 0.4180\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.41382868937\n",
"Kappa:\n",
"0.255978684777\n",
"Confusion matrix:\n",
"[[ 92 0 16 9 5]\n",
" [ 16 71 116 10 49]\n",
" [ 14 49 147 20 13]\n",
" [ 57 27 32 34 18]\n",
" [ 12 28 61 16 57]]\n",
"AUC score:\n",
"0.672356573874\n",
"('Epoch', 46, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1335 - acc: 0.9683Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.1334 - acc: 0.9683 - val_loss: 3.5998 - val_acc: 0.3983\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.398348813209\n",
"Kappa:\n",
"0.236434485723\n",
"Confusion matrix:\n",
"[[ 87 0 18 8 9]\n",
" [ 12 81 96 19 54]\n",
" [ 16 65 118 25 19]\n",
" [ 46 30 34 33 25]\n",
" [ 8 29 51 19 67]]\n",
"AUC score:\n",
"0.682599329885\n",
"('Epoch', 47, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1346 - acc: 0.9686Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 74s - loss: 0.1345 - acc: 0.9686 - val_loss: 2.9412 - val_acc: 0.4489\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.452012383901\n",
"Kappa:\n",
"0.307003248555\n",
"Confusion matrix:\n",
"[[ 87 4 9 11 11]\n",
" [ 9 125 43 21 64]\n",
" [ 17 76 109 25 16]\n",
" [ 49 27 23 44 25]\n",
" [ 9 36 19 37 73]]\n",
"AUC score:\n",
"0.723529399899\n",
"('Epoch', 48, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1388 - acc: 0.9666Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 73s - loss: 0.1387 - acc: 0.9666 - val_loss: 3.3838 - val_acc: 0.4097\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.410732714138\n",
"Kappa:\n",
"0.255604168517\n",
"Confusion matrix:\n",
"[[ 90 1 8 16 7]\n",
" [ 8 88 81 30 55]\n",
" [ 20 66 123 18 16]\n",
" [ 52 19 39 33 25]\n",
" [ 16 24 40 30 64]]\n",
"AUC score:\n",
"0.701055124781\n",
"('Epoch', 49, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0901 - acc: 0.9769Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.0901 - acc: 0.9769 - val_loss: 3.5546 - val_acc: 0.4314\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.429308565531\n",
"Kappa:\n",
"0.277630913785\n",
"Confusion matrix:\n",
"[[ 82 4 16 11 9]\n",
" [ 2 84 64 16 96]\n",
" [ 10 67 126 26 14]\n",
" [ 47 17 36 34 34]\n",
" [ 6 25 40 13 90]]\n",
"AUC score:\n",
"0.708887821817\n",
"('Epoch', 86, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0948 - acc: 0.9734Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 78s - loss: 0.0948 - acc: 0.9735 - val_loss: 3.5034 - val_acc: 0.4499\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.444788441692\n",
"Kappa:\n",
"0.29603497936\n",
"Confusion matrix:\n",
"[[ 87 3 13 12 7]\n",
" [ 4 89 74 15 80]\n",
" [ 10 58 138 21 16]\n",
" [ 53 21 30 38 26]\n",
" [ 4 31 47 13 79]]\n",
"AUC score:\n",
"0.702348491006\n",
"('Epoch', 87, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0603 - acc: 0.9849Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.0603 - acc: 0.9849 - val_loss: 3.7005 - val_acc: 0.4572\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.448916408669\n",
"Kappa:\n",
"0.302762158469\n",
"Confusion matrix:\n",
"[[ 86 6 9 16 5]\n",
" [ 5 83 70 18 86]\n",
" [ 9 60 131 25 18]\n",
" [ 44 19 26 50 29]\n",
" [ 6 33 40 10 85]]\n",
"AUC score:\n",
"0.709917452895\n",
"('Epoch', 88, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0893 - acc: 0.9792Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 80s - loss: 0.0892 - acc: 0.9792 - val_loss: 3.4982 - val_acc: 0.4582\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.450980392157\n",
"Kappa:\n",
"0.306294255856\n",
"Confusion matrix:\n",
"[[ 80 1 16 14 11]\n",
" [ 6 83 54 20 99]\n",
" [ 11 63 129 24 16]\n",
" [ 41 18 22 53 34]\n",
" [ 5 37 34 6 92]]\n",
"AUC score:\n",
"0.729690534037\n",
"('Epoch', 89, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0950 - acc: 0.9769Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.0950 - acc: 0.9769 - val_loss: 3.6014 - val_acc: 0.4241\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.422084623323\n",
"Kappa:\n",
"0.265586195229\n",
"Confusion matrix:\n",
"[[ 84 5 16 9 8]\n",
" [ 11 95 59 18 79]\n",
" [ 11 82 118 20 12]\n",
" [ 43 25 37 34 29]\n",
" [ 6 45 30 15 78]]\n",
"AUC score:\n",
"0.700645718717\n",
"('Epoch', 90, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.1143 - acc: 0.9740Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 74s - loss: 0.1143 - acc: 0.9740 - val_loss: 3.2175 - val_acc: 0.4696\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.470588235294\n",
"Kappa:\n",
"0.329044283884\n",
"Confusion matrix:\n",
"[[ 86 6 9 13 8]\n",
" [ 5 81 83 5 88]\n",
" [ 14 33 166 14 16]\n",
" [ 55 22 24 40 27]\n",
" [ 4 41 42 4 83]]\n",
"AUC score:\n",
"0.723035850452\n",
"('Epoch', 91, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0923 - acc: 0.9777Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 78s - loss: 0.0923 - acc: 0.9777 - val_loss: 3.1113 - val_acc: 0.4799\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.477812177503\n",
"Kappa:\n",
"0.336182760469\n",
"Confusion matrix:\n",
"[[ 87 8 14 10 3]\n",
" [ 6 107 65 21 63]\n",
" [ 10 59 142 20 12]\n",
" [ 49 22 23 47 27]\n",
" [ 3 48 31 12 80]]\n",
"AUC score:\n",
"0.734920181861\n",
"('Epoch', 92, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0957 - acc: 0.9774Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.0957 - acc: 0.9774 - val_loss: 3.1677 - val_acc: 0.4871\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.482972136223\n",
"Kappa:\n",
"0.348723053456\n",
"Confusion matrix:\n",
"[[ 94 2 11 9 6]\n",
" [ 12 79 69 13 89]\n",
" [ 16 41 158 18 10]\n",
" [ 57 19 15 48 29]\n",
" [ 7 38 29 11 89]]\n",
"AUC score:\n",
"0.740744312834\n",
"('Epoch', 93, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0715 - acc: 0.9843Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 75s - loss: 0.0715 - acc: 0.9843 - val_loss: 3.4666 - val_acc: 0.4634\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.459236326109\n",
"Kappa:\n",
"0.317834831325\n",
"Confusion matrix:\n",
"[[ 88 5 8 12 9]\n",
" [ 14 95 51 14 88]\n",
" [ 16 65 130 18 14]\n",
" [ 52 24 16 49 27]\n",
" [ 8 40 24 19 83]]\n",
"AUC score:\n",
"0.720746407954\n",
"('Epoch', 94, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0793 - acc: 0.9803Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 77s - loss: 0.0793 - acc: 0.9803 - val_loss: 3.4702 - val_acc: 0.4747\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.46955624355\n",
"Kappa:\n",
"0.329625676511\n",
"Confusion matrix:\n",
"[[ 84 4 11 16 7]\n",
" [ 8 67 90 19 78]\n",
" [ 10 30 170 19 14]\n",
" [ 46 24 18 58 22]\n",
" [ 5 32 35 26 76]]\n",
"AUC score:\n",
"0.734806406736\n",
"('Epoch', 95, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0771 - acc: 0.9814Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 76s - loss: 0.0771 - acc: 0.9814 - val_loss: 3.3863 - val_acc: 0.4757\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.471620227038\n",
"Kappa:\n",
"0.332851030183\n",
"Confusion matrix:\n",
"[[ 85 4 10 10 13]\n",
" [ 10 80 80 13 79]\n",
" [ 9 43 143 25 23]\n",
" [ 42 25 16 54 31]\n",
" [ 7 26 36 10 95]]\n",
"AUC score:\n",
"0.714918326632\n",
"('Epoch', 96, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0836 - acc: 0.9812Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 80s - loss: 0.0835 - acc: 0.9812 - val_loss: 3.4266 - val_acc: 0.4561\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.450980392157\n",
"Kappa:\n",
"0.305018617932\n",
"Confusion matrix:\n",
"[[ 86 3 13 10 10]\n",
" [ 18 83 71 11 79]\n",
" [ 12 40 155 21 15]\n",
" [ 47 26 28 40 27]\n",
" [ 6 42 36 17 73]]\n",
"AUC score:\n",
"0.72127870954\n",
"('Epoch', 97, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0810 - acc: 0.9814Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.0810 - acc: 0.9814 - val_loss: 3.5782 - val_acc: 0.4665\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.463364293086\n",
"Kappa:\n",
"0.316727484267\n",
"Confusion matrix:\n",
"[[ 86 4 10 14 8]\n",
" [ 12 95 85 11 59]\n",
" [ 10 49 151 20 13]\n",
" [ 41 31 18 56 22]\n",
" [ 6 51 46 10 61]]\n",
"AUC score:\n",
"0.708913341916\n",
"('Epoch', 98, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0773 - acc: 0.9832Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 77s - loss: 0.0773 - acc: 0.9832 - val_loss: 3.6385 - val_acc: 0.4499\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.440660474716\n",
"Kappa:\n",
"0.287797588375\n",
"Confusion matrix:\n",
"[[ 77 4 15 19 7]\n",
" [ 9 87 95 21 50]\n",
" [ 9 41 147 32 14]\n",
" [ 35 25 27 57 24]\n",
" [ 4 42 53 16 59]]\n",
"AUC score:\n",
"0.690753100627\n",
"('Epoch', 99, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0764 - acc: 0.9834Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 79s - loss: 0.0764 - acc: 0.9834 - val_loss: 3.5415 - val_acc: 0.4634\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.457172342621\n",
"Kappa:\n",
"0.317764565102\n",
"Confusion matrix:\n",
"[[ 90 2 11 10 9]\n",
" [ 18 85 57 29 73]\n",
" [ 17 36 142 33 15]\n",
" [ 51 21 20 53 23]\n",
" [ 6 37 27 31 73]]\n",
"AUC score:\n",
"0.719833632243\n",
"('Epoch', 100, '/', 100)\n",
"Train on 3503 samples, validate on 969 samples\n",
"Epoch 1/1\n",
"3502/3503 [============================>.] - ETA: 0s - loss: 0.0619 - acc: 0.9869Epoch 00000: val_acc did not improve\n",
"3503/3503 [==============================] - 80s - loss: 0.0619 - acc: 0.9869 - val_loss: 3.6337 - val_acc: 0.4685\n",
"Performance of model on test set ----------------------------\n",
"Accuracy:\n",
"0.462332301342\n",
"Kappa:\n",
"0.321837845244\n",
"Confusion matrix:\n",
"[[ 86 3 16 13 4]\n",
" [ 13 93 49 26 81]\n",
" [ 13 45 138 28 19]\n",
" [ 42 20 26 54 26]\n",
" [ 6 39 25 27 77]]\n",
"AUC score:\n",
"0.720621435659\n",
"Best validation accuracy: (58, 0.50567595459236325)\n"
]
},
{
"data": {
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5c2woDmOMSQ2e9m6aLyL5I00XcA+lkerCwmDBAnjwQV/s3RhjMhZPq5v83D2a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\nGxfVf4B/xNXywv0L9b7h98W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8HOl+JN6hnzOaPkv6sOP0DgrnKszU3VMZ0WYED5Z/0Ndh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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8f7cac7790>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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tuKvNXUwOv7rQz+xts/lm5zeMajOqFHNWeOU+SAQGBiIi+nLwCgwMzP8mFsCW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\n2hGl7H57FiYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8f8b918710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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MeHWTzy/+Hmc0hwaJGjVqJAYJkjx27BirVavGt99+m127dmXRokVZvXp1zp49O0WQ6Nu3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\nI+cNgYHmt5PVVU1ZTYNEKpzN2eSofn1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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8eb658c510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create the model and parameters for training\n",
"numpy.random.seed(seed)\n",
"\n",
"batch = 1\n",
"epochs = 100\n",
"\n",
"modelA3 = create_LSTM3_PCA(dummy_y_trainA.shape[1], batch_size = batch, trainShape1=n_components)\n",
"print modelA3.summary()\n",
"\n",
"# To save the best model\n",
"# serialize model to JSON\n",
"modelA3_json = modelA3.to_json()\n",
"with open(\"activity.model--3lstmaudio.json\", \"w\") as json_file:\n",
" json_file.write(modelA3_json)\n",
"filepathA3=\"activity.weights--3lstmaudio.best.hdf5\"\n",
"# Define that the accuracy in cv is monitored, and that weights are stored in a file when max accuracy is achieved\n",
"checkpointA3 = ModelCheckpoint(filepathA3, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
"callbacks_listA3 = [checkpointA3]\n",
"\n",
"# Fit the model\n",
"accs =[]\n",
"val_accs =[]\n",
"losss =[]\n",
"val_losss =[]\n",
"kappas = []\n",
"aucs = []\n",
"\n",
"# Manually create epochs and reset between sessions\n",
"for i in range(epochs):\n",
" # Single epoch. Remember to not shuffle the data!\n",
" print('Epoch', i+1, '/', epochs)\n",
" history = modelA3.fit(trainX, dummy_y_trainA, validation_data=(valX, dummy_y_valA), \n",
" nb_epoch=1, batch_size=batch, shuffle=False, \n",
" verbose=1, callbacks=callbacks_listA3)\n",
" modelA3.reset_states()\n",
" kappa, auc = printValStats(modelA3, valX, dummy_y_valA, batch=batch)\n",
" accs.append(history.history['acc'][0])\n",
" val_accs.append(history.history['val_acc'][0])\n",
" losss.append(history.history['loss'][0])\n",
" val_losss.append(history.history['val_loss'][0])\n",
" kappas.append(kappa)\n",
" aucs.append(auc)\n",
" \n",
"print 'Best validation accuracy: ', get_max_values(val_accs)\n",
"plot_training(accs, val_accs, losss, val_losss, kappas, aucs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random forest baseline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SVM baseline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Baseline + HMM states in the mix\n",
"see https://github.com/hmmlearn/hmmlearn and https://github.com/larsmans/seqlearn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading and evaluating the best models\n",
"\n",
"## 3-layer LSTM (Activity)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"from keras.models import model_from_json\n",
"import numpy\n",
"import os\n",
"\n",
"# LOAD AND USE MODEL\n",
"json_file3 = open('activity.model--3lstmbis.json','r')\n",
"loaded_model_json3 = json_file3.read()\n",
"json_file3.close()\n",
"loaded_model3 = model_from_json(loaded_model_json3)\n",
"\n",
"# load weights into new model\n",
"loaded_model3.load_weights(\"activity.weights--3lstmbis.best.hdf5\")\n",
"print(\"Loaded model 3 from disk\")\n",
"# evaluate loaded model on test data\n",
"# IMPORTANT: compile the model again before use!\n",
"loaded_model3.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
"score3 = loaded_model3.evaluate(testX, dummy_y_testA, batch_size=1, verbose=0)\n",
"print \"3 Layer LSTM --- %s: %.2f%%\" % (loaded_model3.metrics_names[1], score3[1]*100)\n",
"printValStats(loaded_model3, testX, dummy_y_testA, batch=1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
martymcwizard/CS512_link_predictor | ClassificationModels.ipynb | 1 | 323701 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"import pandas as pd\n",
"import numpy as np\n",
"import cPickle as pickle\n",
"\n",
"# Suppress convergence warning\n",
"import warnings\n",
"warnings.simplefilter(\"ignore\")\n",
"\n",
"# Machine Learning\n",
"import sklearn\n",
"import sklearn.ensemble\n",
"import sklearn.svm\n",
"import sklearn.preprocessing\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.metrics import confusion_matrix, classification_report, roc_curve, auc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Plot\n",
"import matplotlib.pyplot as plt\n",
"from pylab import rcParams\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format='retina'\n",
"rcParams['figure.figsize'] = 8, 5.5\n",
"\n",
"# Plot heat map of a 2D grid search\n",
"def plotGridResults2D(x, y, x_label, y_label, grid_scores):\n",
" \n",
" scores = [s[1] for s in grid_scores]\n",
" scores = np.array(scores).reshape(len(x), len(y))\n",
"\n",
" plt.figure()\n",
" plt.grid('off')\n",
" plt.imshow(scores, interpolation='nearest', cmap=plt.cm.RdYlGn)\n",
" plt.xlabel(y_label)\n",
" plt.ylabel(x_label)\n",
" plt.colorbar()\n",
" plt.xticks(np.arange(len(y)), y, rotation=45)\n",
" plt.yticks(np.arange(len(x)), x)\n",
" plt.title('Validation accuracy')\n",
"\n",
"\n",
"def plotRoC(fpr, tpr):\n",
" plt.figure()\n",
" plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % auc(fpr, tpr))\n",
" plt.plot([0, 1], [0, 1], 'k--')\n",
" plt.xlim([0.0, 1.0])\n",
" plt.ylim([0.0, 1.005])\n",
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
" plt.title('Receiver operating characteristic')\n",
" plt.legend(loc=\"lower right\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"68.16040205955505"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import training dataset\n",
"start = time.time()\n",
"training = pickle.load(open('trainFinal.p', 'rb'))\n",
"training = training.sample(frac=1)\n",
"X_train = training.loc[:, training.columns[1:],]\n",
"y_train = training.loc[:, 'label']\n",
"end = time.time()\n",
"end - start"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8.223140001296997"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import testing dataset\n",
"start = time.time()\n",
"testing = pickle.load(open('testFinal.p', 'rb'))\n",
"testing = testing.sample(frac=1)\n",
"X_test = testing.loc[:, training.columns[1:],]\n",
"y_test = testing.loc[:, 'label']\n",
"end = time.time()\n",
"end - start"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1220.5029830932617"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# SVM CV training\n",
"start = time.time()\n",
"C_range = np.r_[np.logspace(-2, 9, 10)]\n",
"gamma_range = np.r_[np.logspace(-9, 2, 10)]\n",
"gridCoarse = GridSearchCV(sklearn.svm.SVC(C=1.0, kernel='rbf', class_weight='balanced', verbose=False, max_iter=50),\n",
" {'C' : C_range, 'gamma': gamma_range},\n",
" scoring='roc_auc', cv=10, n_jobs=4)\n",
"gridCoarse.fit(X_train, y_train)\n",
"\n",
"C_best = np.round(np.log10(gridCoarse.best_params_['C']))\n",
"gamma_best = np.round(np.log10(gridCoarse.best_params_['gamma']))\n",
"\n",
"# Fine grid\n",
"'''\n",
"Cfine_range = np.r_[np.logspace(C_best - 1, C_best + 1, 15)]\n",
"gammafine_range = np.r_[np.logspace(gamma_best - 2, gamma_best + 2, 15)]\n",
"\n",
"gridFine = GridSearchCV(sklearn.svm.SVC(C=1.0, kernel='rbf', class_weight='balanced', verbose=False, max_iter=250),\n",
" {'C' : Cfine_range, 'gamma': gammafine_range},\n",
" scoring='roc_auc', cv=10, n_jobs=-1)\n",
"gridFine.fit(X_train, y_train)\n",
"\n",
"svmbestClf = gridFine.best_estimator_\n",
"svmbestClf.probability = True\n",
"'''\n",
"end = time.time()\n",
"end - start"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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tSdyv+94cOAK4UTCc5L20oefV/Xk12jzmi4Dn0oYP94aBB7giyT5VdeRAOy8D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EAAAg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aea2790>"
]
},
"metadata": {
"image/png": {
"height": 433,
"width": 489
}
},
"output_type": "display_data"
}
],
"source": [
"# plot coarse grid\n",
"plotGridResults2D(C_range, gamma_range, 'C', 'gamma', gridCoarse.grid_scores_)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# plot fine grid\n",
"#plotGridResults2D(Cfine_range, gammafine_range, 'C', 'gamma', gridFine.grid_scores_)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"svmbestClf = gridCoarse.best_estimator_\n",
"svmbestClf.probability = True"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0.0 0.80 0.99 0.89 15216\n",
" 1.0 0.89 0.18 0.30 4622\n",
"\n",
"avg / total 0.82 0.80 0.75 19838\n",
"\n"
]
},
{
"data": {
"text/plain": [
"array([[15116, 100],\n",
" [ 3802, 820]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dqcd91VVh4EA45RRYYYVl202ZMoXevXszceJEAHr16sXw4cPp0KFDTpE3Libu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\nuMB12774CgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ae968d0>"
]
},
"metadata": {
"image/png": {
"height": 363,
"width": 503
}
},
"output_type": "display_data"
}
],
"source": [
"svmbestClf.fit(X_train, y_train)\n",
"y_pred = svmbestClf.predict(X_test)\n",
"\n",
"print sklearn.metrics.classification_report(y_test, y_pred)\n",
"\n",
"# Predict scores\n",
"y_score = svmbestClf.predict_proba(X_test)[:, 1]\n",
"\n",
"# Plot ROC\n",
"sfpr, stpr, _ = roc_curve(y_test, y_score)\n",
"\n",
"plotRoC(sfpr, stpr)\n",
"\n",
"confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1275.4229810237885"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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US5IkSQ1nUC9JkiQ13CKD7kDTRISTECRJ0qhkZgy6DyM16FinSe/VZGKmXpIkSWo4M/Vj\ndBqrD7oLI7YLf21Uf3e+/5BBd2HEpi27M/PuP2PQ3RiVyx+5btBdGLENn3sIl95+8KC7MWKzHrhr\n0F0YlZ3XOJYzrv/QoLsxYkstsviguzBi273oaM75+/6D7sao3P3YY4Puwojt+Yrj+L8/f3DQ3RiR\nPV9x3KC7MHYfeO3Etnf85RPb3kLGTL0kSZLUcGbqJUmS1CWmTezQdictLhgz9ZIkSVLDGdRLkiRJ\nDefwG0mSJHVx+E2zmKmXJEmSGs5M/RTwnyw/6C4stD77ye0H3YWF2p4HbDroLizUtt9nvUF3YaG1\n074bDLoLC7VtP+S/3Ykw0Zl6LZjI9MeO0Wjtstakdd+bpknr1DdRk9apb5qmrVPfNE1ap76JmrRO\nfZO01qlv0i6prVhn+r4bTWi7c4/5DdCs92oyMVMvSZKkLmbqm8Ux9ZIkSVLDGdRLkiRJDefwG0mS\nJHWJcPhNk5iplyRJkhrOTL0kSZK6OFG2WczUS5IkSQ1nUC9JkiQ1nMNvJEmS1MXhN81ipl6SJElq\nODP1kiRJ6mKmvlnM1EuSJEkNZ6ZekiRJXczUN4uZekmSJKnhDOolSZKkhnP4jSRJkro4/KZZzNRL\nkiRJDWemXpIkSV3M1DeLmXpJkiSp4QzqJUmSpIZz+I0kSZK6OPymWczUS5IkSQ1npl6SJEldIszU\nN4mZekmSJKnhzNRLkiSpi2Pqm8VMvSRJktRwUyaoj4jtI+KYiLgoIu6PiHkRceqg+yVJkiQtqKk0\n/OYg4JXAQ8CtwEsH2x1JkqTJy+E3zTJlMvXAfsDqmbks8CHAf6mSJElaKEyZTH1m/mrQfZAkSWoK\nM/XNMpUy9ZIkSdJCyaBekiRJjRQRK0fEiRFxW0Q8FhE3RcTREbHcKOvZKyIui4gHI+KhiLgyIt4f\nPXbgiogXR8SBEXFBRNwSEY9HxB0RMSMiNutT/+7VIi39jr3H+BY8ZcoMv5EkSdLITfbhNxGxGnAp\nsAIwA7geWB/4KPDmiNg4M2ePoJ7TgJ2BO4HTgUeALYDjgA2BPToe+TywI3At8GPgXmANYFtg24jY\nNzO/2qe5GcDVPa7/drh+DsegXpIkSU10HCWg/0hmHtu6GBFHAvsDh1EWR+krIt5OCej/Dqzf+hIQ\nEYsA3wd2jYgZmTmj7bHzgC9m5h866nod8AvgiIg4MzPv7GgugRmZOS5LqhvUj9Eu/LVv2X+yPNuz\nwgT2RpIkDco5X7uSHx67wInWSWcyZ+qrLP0WwE3tAX3lYGBvSkB+QGY+OkRVb6ME20e2Z/Uzc05E\nfAZ4K7APJcPeKusZlGfmxRFxIfBGYCPgB6N+YQvAoH6MTmP1QXdBkiRNAtt9+DVs9+HX9Czb8xXH\nTXBvpozNq/P5nQWZ+VBEXEIJ+jcAZg5Rz3Oq8009ym6szq+LiEUyc84I+vVkde51bwDrRMQzgCWA\n24CZmXnbCOodlkG9JEmSukzmTD1lDHtC36ETN1CC+tUZOqi/uzqv2qNsteq8SPXf/YdpABHxQuAN\nlDH5F/W5bd/2R4C5EXECsF9mPj5U/cNx9RtJkiQ1zbLV+f4+5a3rw62C82NKcP2xKoMOPDWm/tC2\n+57R+WC7iFgMOA1YDDg4Mzv7dRNlGM8awNLASsA7quvvB/5vmH4Oa8pk6iNiO8q4KZj/U8tGEXFS\n9d93Z+bHJ75nkiRJGpDvAO8G3gxcGxHnAI9RxsU/B7gFeD4wr18FETEN+DZlpZzvZOZRnfdk5kX8\ne/b+MeDsiLgc+AOwc0Qcnpl/GusLmTJBPbA2sFvb30n5qaX1c8sswKBekiSJST/8ppUJX7ZPeev6\nfUNVkpnzImIb4GOU4H43SsA9E/hP4Ozq1n/1er4K6E8DdqB8Qdh1hP1vtX9rRPwEeBewCWBQP5zM\nPAQ4ZND9kCRJmqoe/ulfefT8G+qo6nrKsJl+K5e8pDoPOQ4eIDPnAkdUx1MiYvGqnrsz8+bO56oh\nOqdTAvpvA7tnZo70BbS5qzovPYZnnzJlgnpJkiSN3Hhk6pfZag2W2WqNnmV37XfuaKpqTX59U2dB\nRCwDbEyZsHrZKLvYbmfKGPnTe7SxKHAmsA1wcma+dwHaeW11vnHIu4bhRFlJkiQ1SmbeSFnOcpWI\n2Kej+FBK1vvU1hr1EbFIRKxRrW//byLiaT2urU3J3N8DHN5Rthhl3fptgBNGEtBHxLo9rkVEfIoy\nFv8u4KfD1TMUM/WSJElqog8BlwBfjog3AH+hrEu/GXAdcFDbvStX5bOYv1Rly88j4lHgGuBB4GXA\n1sDDwDaZeUfH/V8HtqQE4rdHxME9+nZhZv6q7e8rI+IayqTY2yhj/jcG1qza2SUzHxrxK+/BoF6S\nJEldIib1RFky88aIWI+SmX8LJdC+HTgaOLTHspJZHZ3OBHYCdgGWpATdxwNfzMx/9rh/laqeFYDP\n9Ose0B7UHwGsT9k0a3nKajq3AF8Bjs7MWUO81BExqJckSVIjVbux7jmC+24GpvcpOxI4chRtbj78\nXV3PHDjaZ0bLoF6SJEldJvmSlurgRFlJkiSp4czUS5IkqYuZ+mYxUy9JkiQ1nEG9JEmS1HAOv5Ek\nSVIXh980i5l6SZIkqeHM1EuSJKnLNFO/jeLHJUmSJDWcQb0kSZLUcA6/kSRJUpfp4UTZJjFTL0mS\nJDWcmXpJkiR1me6Slo1ipl6SJElqODP1kiRJ6uKY+mYxUy9JkiQ1nEG9JEmS1HAOv5EkSVKX6aZ+\nG8WPS5IkSWo4M/WSJEnq4kTZZjFTL0mSJDWcQb0kSZLUcA6/kSRJUheH3zSLmXpJkiSp4czUS5Ik\nqcv0aWbqm8RMvSRJktRwZuolSZLUZbqJ+kYxUy9JkiQ1nEG9JEmS1HAOv5EkSVIXJ8o2i5l6SZIk\nqeHM1I/RO7+34aC7sND6+X2/H3QXFmo33f/AoLuw0Jqbg+7Bwu3vjz886C4s1Pz3q05uPtUsZuol\nSZKkhjOolyRJkhrO4TeSJEnq4kTZZjFTL0mSJDWcmXpJkiR1cUfZZjFTL0mSJDWcmXpJkiR1cUx9\ns5iplyRJkhrOoF6SJElqOIffSJIkqYs7yjaLmXpJkiSp4czUS5IkqYuZ+mYxUy9JkiQ1nEG9JEmS\n1HAOv5EkSVKX6aZ+G8WPS5IkSWo4M/WSJEnq4kTZZjFTL0mSJDWcmXpJkiR1mT7NTH2TmKmXJEmS\nGs6gXpIkSWo4h99IkiSpixNlm8VMvSRJktRwZuolSZLUxc2nmsWPS5IkSWo4g3pJkiQ1UkSsHBEn\nRsRtEfFYRNwUEUdHxHKjrGeviLgsIh6MiIci4sqIeH9E/4kFEbFRRPwkIu6JiEci4g8R8dGI6Btf\nR8TuEXF51c59ETEzIrYeTV/7cfiNJEmSukz2ibIRsRpwKbACMAO4Hlgf+Cjw5ojYODNnj6Ce04Cd\ngTuB04FHgC2A44ANgT16PLMdcBbwKPBd4F5gG+BoYCPgnT2e+V/gY8A/gG8AiwE7AT+KiH0y89iR\nv/puBvWSJElqouMoAf1H2gPiiDgS2B84DPjQUBVExNspAf3fgfVbXwIiYhHg+8CuETEjM2e0PfM0\n4JvAHGDTzPx9df0zwExgh4jYMTO/1/bMhpSA/gbgNZn5QHX9COAq4H8j4tzMvGWsb4bDbyRJktRl\n+rSY0GM0qiz9FsCsHhnug4GHKQH5ksNU9TYggSPbs/qZOQf4DBDAPh3PvIPyZeKMVkBfPfMEcFD1\nzAc7nvlg1c5hrYC+euYW4GvA4sB7hunrkAzqJUmS1DSbV+fzOwsy8yHgEmApYINh6nlOdb6pR9mN\n1fl1Vea+ve0EftbjmYsow3c2iohFe/S31zPnUb4IvH6Yvg7JoF6SJEldpkdM6DFKa1AC67/2Kb+h\nOq8+TD13V+dVe5StVp0XafvvVtv0ajsz51K+IDz1TEQsBawMPJSZdy5AX4dkUC9JkqSmWbY639+n\nvHV9uFVwfkzJkn8sIp7Rulhl5g9tu+8Zbf892rbr6uuQnCgrSZKkqeo7wLuBNwPXRsQ5wGPAGylD\nc24Bng/MG1gPR8igXpIkSV0m+Y6yrez2sn3KW9fvG6qSzJwXEdtQVqZ5N7AbJaifCfwncHZ1678W\noO1a+jocg3pJkiRNiKtOuYrff+vqOqq6njJspt849JdU535j7p9SjYM/ojqeEhGLV/XcnZk3d7S9\nbtX27zuemU4Znz+HaqJtZj4SEbcBK0XEij3G1Y+4r0OZMkF9RCxP+ca1FbAWZcLCE8CfgJOAkzIz\nB9dDSZKkyWM8Np96zR7r8po91u1Z9o03/N9oqppZnd/UWRARywAbU1ahuWyUXWy3M2WDqNM7rv8S\n2AV4C2XjqXabUlbduTAzn+x45t3VM6d0PLNVdb5gAfo6pSbKvoOye9f6lA/4aMpOYK8ATqD7Q5Ek\nSdIklJk3UpazXCUiOteRPxRYGjg1Mx+FMvE1Itao1rf/N9VmUp3X1qZk7u8BDu8oPouyas5OEbFu\n2zOLA/9DWZXnuI5njqf8svDpiFiu7ZlVgA9ThvycPOSLHsaUydRTfirZJjN/3H4xIv4buBLYPiLe\nnpk/GEjvJEmSJpHp9Sfq6/Yhynr0X46INwB/oaxLvxlwHWUjqJaVq/JZ/PvylAA/j4hHgWuAB4GX\nAVtTNrDaJjPvaL85Mx+MiL2AM4ELI+I7wL3AtpQhOWdm5pkdz1waEUdRdrr9Y0ScRfkV4J2UVW/2\nWZDdZGEKZeoz88LOgL66/i/mf3vabKL7JUmSpNGrsvXrUTLc61Mmu65KGY2xYfsOsa1HqqPTmcAy\nlCE1+1OGaR8PvCIzf92n7XMoQ21+RRnevQ9lWPf+lGE7vZ75L8qusbcDewG7UoaBvzUzOzP7ozaV\nMvVDaY15mjPQXkiSJGnEMvM2YM8R3HczML1P2ZHAkWNo+1LgraN85lTg1NG2NRJTPqivZinvTvnm\n9tMBd0eSJGlSmDYOE2U1fqbM8JshHE6ZLPvjzPz5oDsjSZIkjdaUztRHxL6U8VfXUjYbkCRJEo2Y\nKKs2UzZTXy1/9CXKTOfXZ+YC7eIlSZIkDcqUzNRHxH7AUcAfgTdm5t2jrWORHTv3DZjvMzu8ioN3\nXHvsHZQkSY0x85u/5cITrhp0NzTFTbmgPiIOBL4AXAVs0WO5oxGZ873da+2XJElqps33Wo/N91qv\nZ9nBr/3GBPemPtMcftMoU2r4TUR8hhLQX0nJ0I8poJckSZImkymTqY+I3YFDKGvRXwJ8NLqXapqV\nmf3H1UiSJE0RTpRtlikT1AOrUNainw58tM89vwIM6iVJktQoUyaoz8xDKJl6SZIkDWOag+obZUqN\nqZckSZIWRgb1kiRJUsNNmeE3kiRJGjknyjaLmXpJkiSp4czUS5IkqYvzZJvFTL0kSZLUcAb1kiRJ\nUsM5/EaSJEldnCjbLGbqJUmSpIYzUy9JkqQu08JUfZOYqZckSZIazky9JEmSujimvlnM1EuSJEkN\nZ1AvSZIkNZzDbyRJktTFHWWbxUy9JEmS1HBm6iVJktRluktaNoqZekmSJKnhDOolSZKkhnP4jSRJ\nkro4UbZZzNRLkiRJDWemXpIkSV3cUbZZzNRLkiRJDWemXpIkSV2mmfptFD8uSZIkqeEM6iVJkqSG\nc/iNJEmSurijbLOYqZckSZIazky9JEmSurj5VLOYqZckSZIazqBekiRJajiH30iSJKmLO8o2i5l6\nSZIkqeHM1EuSJKmLE2WbxUy9JEmS1HBm6iVJktTFzaeaxUy9JEmS1HBm6sfovPWeOeguLLRm3nzv\noLuwUHtkTg66Cwuteb614+reR58cdBcWamZlpWYzqJckSVIXJ8o2i8NvJEmSpIYzUy9JkqQubj7V\nLGbqJUmSpIYzqJckSZIazuE3kiRJ6jLNFZEaxUy9JEmS1HBm6iVJktTFibLNYqZekiRJajiDekmS\nJHWZFjGhx1hExMoRcWJE3BYRj0XETRFxdEQsN8p6to6I8yPiHxHxSET8PSK+FxEb9Lj3pIiYN8zx\n845ndh/m/r3H9Aa0cfiNJEmSGiciVgMuBVYAZgDXA+sDHwXeHBEbZ+bsEdRzOPBx4O6qnruBFwPb\nAttHxK7wXPpUAAAgAElEQVSZeXrbIz8AbupT3W7AqsBP+pTPAK7ucf23w/VzOAb1kiRJaqLjKAH9\nRzLz2NbFiDgS2B84DPjQUBVExIrAAcAdwFqZeU9b2abATOBQ4KmgPjN/CPywR13LAgcCTwCn9Ggu\ngRmZeeoIX9+oOPxGkiRJXSbz8JsqS78FMKs9oK8cDDwM7BoRSw5T1Qsp8fDl7QE9QGb+CngQeNYI\nu7UbsCRwdmbeO8JnamOmXpIkSU2zeXU+v7MgMx+KiEsoQf8GlGx7PzdQMuvrR8QzOzL1mwBPA74/\nwj7tRcnGf6NPeQDrRMQzgCWA24CZmXnbCOsfkkG9JEmSukzyzafWoATQf+1TfgMlqF+dIYL6zJwd\nEZ8AjgKujYgZwD2UMfXbAD8DPjBcZ6oJtWsC12XmRUPcum/7Y8DciDgB2C8zHx+unaEY1EuSJKlp\nlq3O9/cpb10fdhWczDwmIm4GTgTe11b0N+CUzLx7BP15P+VLxjf7lN8E7EP5ZeFWSv//A/hC9ezT\ngHePoJ2+HFMvSZKkKavK1J9FCepfBCwNrEsJxE+PiC8O8/zTgXfQf4IsmXlRZh6bmX/LzMcy887M\nPBt4PTAb2Dki1lqQ12FQL0mSpC7TYtqEHqPUysQv26e8df2+oSqpVrj5ImVVmo9n5qwq6L4aeDtl\n3PsBEbHKENXsCizFGCbIZuatzF/+cpPRPNvJ4TeSJEmaEN/98mV87yuX11HV9ZQx6av3KX9Jde43\n5r7lrZRhMxd2FmTmoxFxBfA2YB1gVp86WhNkvz5MW/3cVZ2XHuPzgEG9JEmSehiPibI777chO++3\nYc+yt7/oS6OpqjX59U2dBRGxDLAx8Ahw2TD1LF6d+y1b2br+RK/CiFgfeCVlguzFw7TVz2ur841j\nfB5w+I0kSZIaJjNvpEw6XSUi9ukoPpSS9T41Mx8FiIhFImKNan37dhdTMv57R8RK7QURsSXly8Fj\nwG/6dKU1QbbfMpatutbtcS0i4lPAhpRs/U+HqmM4ZuolSZLUZZIvaQllt9hLgC9HxBuAv1DWpd8M\nuA44qO3elavyWUB7YH8W8HPgjcBfIuIHlN1lXw5sXd1zYGbO7mw8Ip4GvBN4HBhul9grI+Ia4A+U\ncfrLUr4wrEnZKGuXzHxoJC+6H4N6SZIkNU5m3hgR61Ey828BtgRuB44GDs3MzuUuszra68iI2Ar4\nMLATZfz8UsC9wLnAMZl5QZ8u7ELZQfaMEUyQPQJYn7Jp1vLAPOAW4CvA0Zk5a9gXPAyDekmSJDVS\ntRvrniO472Zgep+yucAx1TGato8Hjh/hvQeOpu6xMKiXJElSlwYMv1EbJ8pKkiRJDWemXpIkSV2m\nmfttFD8tSZIkqeEM6iVJkqSGc/iNJEmSujhRtlnM1EuSJEkNZ6ZekiRJXczUN4uZekmSJKnhzNRL\nkiSpy7Qw99skflqSJElSw02poD4iZkXEvD7HPwfdP0mSJGksptrwmwTuA44GOmd/PDTx3ZEkSZqc\nnCjbLFMtqAe4LzM/P+hOSJIkSXWZikG9JEmShmGmvlmmYlC/eETsArwAeBj4I3BRZs4bbLckSZKk\nsZmKQf1zgFPb/g7gpoh4T2ZeNKA+SZIkSWM2pVa/AU4E3kAJ7JcG1gKOB1YBfhIRaw2ua5IkSZPH\ntIgJPbRgplSmvscE2WuBD0XEw8ABwOeA7Se6X5IkSdKCmFJB/RCOpwT1mwy6I5IkSZOBO8o2i0F9\ncVd1XnqkD2yz2lF9y3bedwPetd9GC9onSZLUANecdjV/Pv0Pg+6GpjiD+mLD6nzjSB/40Y0fG6eu\nSJKkJllzl7VZc5e1e5Z9d+tTJrg39ZnWtU+nJrMp87tKRLw0IpbqcX0V4KuU3Wa/NcHdkiRJkhbY\nVMrUvxM4ICIuAm4GHgReBGwNLA78GDhycN2TJEmSxmYqBfUzgdWBdYCNKOPn7wMuBk7NzNMG2DdJ\nkqRJxWUmm2XKBPXVxlJuLiVJkqSFzpQJ6iVJkjRyLmnZLH5akiRJUsMZ1EuSJEkN5/AbSZIkdXGi\nbLOYqZckSZIazky9JEmSupipbxYz9ZIkSVLDmamXJElSF5e0bBY/LUmSJKnhDOolSZKkhnP4jSRJ\nkro4UbZZzNRLkiRJDWemXpIkSV2mYaa+SczUS5IkSQ1nUC9JkiQ1nMNvJEmS1MWJss1ipl6SJElq\nODP1kiRJ6uKOss3ipyVJkiQ1nJl6SZIkdXFMfbOYqZckSZIazqBekiRJajiH30iSJKlLOFG2Ufy0\nJEmSpIYzUy9JkqQu08z9NoqfliRJktRwBvWSJElSwzn8RpIkSV2cKNssflqSJElSw5mplyRJUpdp\nZuobpbZPKyI2jYhzI+JfEfFkRMztccypqz1JkiRJRS1BfURsDfwC2Ap4BLgMuKjHcXEd7UmSJGl8\nBdMm9BhTHyNWjogTI+K2iHgsIm6KiKMjYrlR1rN1RJwfEf+IiEci4u8R8b2I2KDHvS+MiHlDHKcP\n0c7uEXF5RDwYEfdFxMwqjl5gdQ2/+RzwJLB1Zp5fU52SJElSTxGxGnApsAIwA7geWB/4KPDmiNg4\nM2ePoJ7DgY8Dd1f13A28GNgW2D4ids3MXoH61dX9na7p087/Ah8D/gF8A1gM2An4UUTsk5nHDtfX\nodQV1K8JfMeAXpIkSRPkOEpA/5H2gDgijgT2Bw4DPjRUBRGxInAAcAewVmbe01a2KTATOBToGdRn\n5qEj6WhEbEgJ6G8AXpOZD1TXjwCuAv43Is7NzFtGUl8vdY2pfwi4t6a6JEmSNGDTYtqEHqNRZem3\nAGb1yHAfDDwM7BoRSw5T1Qsp8fDl7QE9QGb+CngQeNaoOtfbB4EEDmsF9FUbtwBfAxYH3rMgDdQV\n1F8AbFhTXZIkSdJQNq/OXaNEMvMh4BJgKaBrTHyHG4AngPUj4pntBRGxCfA04Od9nl0pIvaOiE9V\n57VG0N+f9Sg7Dwjg9cP0dUh1Db85ELgiIg6ifAPJmuqVJEnSAIx18uoEWYOS+f5rn/IbKJn81SlD\naHrKzNkR8QngKODaiJgB3EMZU78NJQj/QJ/Ht6iOloiIC4HdM/MfbReXAlYGHszMO/v0laqvYzam\noD4iTuxx+c/AIcB7I+Jq4L4e92Rm7jmWNiebM653tNF4ueOhJwbdhYXao3PmDroLC60n5prPGE/T\nIwbdhYXaXPNxapZlq/P9fcpb14ddBSczj4mIm4ETgfe1Ff0NOCUz7+545BHKOPsZwI3VtVdSFo55\nPfCLiFg7Mx+tu69DGWumfo8hylapjl4SWCiCekmSJDVflak/DPgSZXz7HcBLgS8Cp1cB+idb92fm\nXZQAvt2vI+LNwK8pK/C8D/jK+Pd+vrEG9avW2gtJkiRNKuOxo+yRh/2Qo77wozqqamW3l+1T3rre\na+TIU6oVbr4InJ2ZH28rujoi3k4Z3nNARByfmbOGqisz50bECcBrgU2YH9TX0tfhjCmoz8ybF6RR\nSZIkTT0HfHpbDvj0tj3LVl5mr9FUdT1lcmm/cegvqc79xty3vJUykuTCzoLMfDQirgDeBqwDzBpB\nv+6qzku31fNIRNxGmVi7Yo9x9SPt65Dq2lH2s9UM4aHueV1EfLaO9iRJkjS+IqZN6DFKrcmvb+ru\ndywDbEwZ+37ZMPUsXp37LVvZuj7SCX+t1SBv7Lj+y+r8lh7PbFWdLxhhGz3V9bvK54DNhrlnE8q6\noZIkSdKYZeaNlOUsV4mIfTqKD6Vkyk9tTVaNiEUiYo1qfft2F1My/ntHxErtBRGxJeXLwWPAb9qu\nrxPRPXM/It4A7EfJ/H+7o/j4qp1PR8Rybc+sAny4auPkYV/4EOpa0nIkFgXmTWB7kiRJGqNpk3tJ\nSyi7xV4CfLkKqP9CWZd+M+A64KC2e1euymcB7YH9WZR16N8I/CUifkCZKPtyYOvqngMzc3bbM0cB\nL4mI3wC3VtdeSVn5JoGDMvPffiHIzEsj4ijKTrd/jIizgMWAd1JWvdlnQXaThYkN6l8NdC4JJEmS\nJI1aZt4YEetRMvNvAbYEbgeOBg7NzM4lJLM62uvIiNiKki3fiTJ+fingXuBc4JjM7BwWcyrwdmC9\nqt1FgTuB7wBfy8xL+vT3vyLij1Vbe1GS3b8DjsjM80b/Dvy7MQf1EfHLjkt7RMRmPW6dDjyfsg3v\nGWNtT5IkSWqXmbcxguXSq0VepvcpmwscUx0jafMk4KRRdLP92VMpXwpqtyCZ+s3a/jvpvz79PMrO\nXN+l/OQgSZKkSW4Mk1c1QGMO6jPzqU86IuYBn8vMQ2vplSRJkqQRq2tM/XuA39dUlyRJkgZsPDaf\n0vipJajPzFPqqEeSJEnS6NX6FSwidomICyLi3oiYU51/ERG71NmOJEmSpPlqydRHxKKUdT7fSllY\nfy5lm9wVKGt2bh4ROwI7ZOaTdbQpSZKk8RO9F4vRJFVXpv5TwDbA5cDmwBKZ+VxgCUpQfwUl4D+w\npvYkSZIkVeqaKLsb8Ddgs8x8onWxWvfzwmr9+muAPYD/qalNSZIkjRMnyjZLXZ/W84Bz2gP6dpn5\nOHAOZYteSZIkSTWqK1P/T8oWuUNZtLpPkiRJk1zUu56Kxlldn9bpwA4R8fRehRGxHLADcFpN7UmS\nJEmq1BXUHwr8FrgiIt4VEc+LiEWr8y7AZZTJsp+vqT1JkiRJlbqG3zxanQP4Vo/yAF4CPBYR7dcz\nM+vqgyRJkmriRNlmqSugvhjImuqSJEmSNAq1BPWZuVkd9UiSJGlyCDP1jeKnJUmSJDVc7ePZI2Jp\nYHVgmcy8uO76JUmSJP272jL11Uo3ZwOzKSvhzGwr+4+IuLbaWVaSJEmT3LQJ/j8tmFrewYh4LnA5\nsB1wLnApZcWblsuBZwPvrKM9SZIkSfPVNfzmYErQvkVmzoyIg4ENW4WZ+WREXAxsXFN7kiRJGkdO\nlG2Wuj6trYAfZubMIe65BVippvYkSZIkVerK1K8I3DDMPU8CS9fUniRJksaRm081S12f1r3A84e5\nZ3XgjprakyRJklSpK6i/BNg2Ip7TqzAiXgK8hbYVcSRJkiTVo66g/ghgCeBXEbElsBSUNeurv38E\nzAOOrKk9SZIkjaNg2oQeWjC1jKnPzMsj4v3AcZQlLVseqM5zgPdm5p/raE+SJEnSfLXtKJuZJ1bL\nVn4I2AB4JnA/cBnw1cy8vq62JEmSNL6cKNsstQX1AJl5A7B/nXX2ExHbA5sCawOvAp4GfDszdxvi\nmY2Ag4DXAktSVuw5EfhKZs4b905LkiRJ46DWoH6CHQS8EngIuBV46VA3R8R2wFnAo8B3KSv2bAMc\nDWyEu91KkiSpocYU1EfEC8baYGbeMtZnO+wH3JqZf4+ITRliZZ2IeBrwTcrY/k0z8/fV9c9Uz+0Q\nETtm5vdq6pskSVKjOXm1WcaaqZ8F5BieywVo898ryvzVKG5/B7ACcHIroK/qeCIiDgIuAD4IGNRL\nkiSpccYaYJ9Kd1C/KrAJZXLs1ZSNpp5DGfO+LHARcNMY21tQm1P6+7MeZRcBjwAbRcSimfnkhPZM\nkiRpEnKibLOMKajPzD3a/46INYBLKePTD8nMB9rKng4cAuwG7D3mni6YNarzXzsLMnNuRNwEvBxY\nDXCVHkmSJDVKXRNlvwj8KTMP6CyoAvz9I2Ld6r7/rKnN0Vi2Ot/fp7x1fbkJ6IskSdKkF2bqG6Wu\nT2sT4NfD3PNryhKUkiRJkmpUV6Z+ccr4+aE8t7pvEFqZ+GX7lLeu3zfSCk/f8uS+ZWvu8ipe+e51\nRlqVJElqsH+efQ23/+DaQXdDU1xdQf3vgZ0i4qvtq8u0VENv3gn8tqb2Rut6YF1gdUpfnxIR0ymT\nfOcAN460wnedt0eN3ZMkSU210vZrstL2a/Ys+927m7uwXoxlnUMNTF3Dbw6hZOEvi4gTI2KPiNiy\nOp8E/AZYtLpvEH4JBPCWHmWbAksBl7jyjSRJkpqorjXjfxEROwFfB/YAdm8rDmA2sHdmXlBHe2Nw\nFnA4839N+B1ARCwO/A9lucvjBtQ3SZKkySfnDboHGoW6ht+QmWdFxHnAdsCrKePU7weuAs7JzIfr\nagsgIrYD3lb92RrPv1H1ywDA3Zn58apvD0bEXsCZwIUR8R3gXmBbypCcMzPzzDr7J0mSJE2U2oJ6\ngCpwP706xtvalLXvn2qeMjZ+1ervWcDH2/p2TkRsCnyasqzmEsDfgP2Br0xAfyVJkqRxUWtQP5Ey\n8xBGOUY/My8F3jo+PZIkSVqIOPymUdxVQJIkSWq4xmbqJUmSNI7M1DeKmXpJkiSp4czUS5IkqZuZ\n+kYxUy9JkiQ13IQG9RHhLwOSJElSzWoJ6iPimxGxxDD3rAr8uo72JEmSNM7mzZvYQwukrkz9nsAV\nEfHSXoURsT1lZ9nX1NSeJEmSpEpdQf1hwMuB30bEe1oXI2KxiDgW+B4wF3h7Te1JkiRpPOW8iT20\nQGoJ6jPzM8CbgQeBEyLiWxGxHnAF8AHgN8DamfnDOtqTJEmSNF9tE1cz84KIWBs4FXhXdcwD/gf4\nXKZfwSRJkhrD0K1R6l6N5kHgLiCqv+8HfmVAL0mSJI2f2pa0jIhXUSbD7gycTxl2sxjws4g4LCJc\nE1+SJEkaB3UtabkPcCmwGvDfmfmWzPwGsC7wR+CTwMUR8fw62pMkSdI4c6Jso9SVPT8G+BewaWYe\n3rqYmTcAGwDHAhsCV9fUniRJkqRKXUH9OcA6mXlpZ0FmPpGZHwG2r6ktSZIkjbcGbD4VEStHxIkR\ncVtEPBYRN0XE0RGx3Cjr2Toizo+If0TEIxHx94j4XkRs0OPeF0fEgRFxQUTcEhGPR8QdETEjIjbr\nU//uETFviGPvMb0BbWqZKJuZw64/n5k/iIjf1tGeJEmSpraIWI0y/HsFYAZwPbA+8FHgzRGxcWbO\nHkE9hwMfB+6u6rkbeDGwLbB9ROyamae3PfJ5YEfgWuDHwL3AGtX920bEvpn51T7NzaD3yJUFjpHr\nXv1mSJn5j4lsT5IkSQut4ygB/Ucy89jWxYg4Etifsjnqh4aqICJWBA4A7gDWysx72so2BWYChwLt\nQf15wBcz8w8ddb0O+AVwREScmZl3djSXwIzMPHVUr3KEal+RJiKmR8SKEfGCXkfd7UmSJGkcTOKJ\nslWWfgtgVntAXzkYeBjYNSKWHKaqF1Li4cvbA3qAzPwVZbn2Z3VcP7UzoK+uXwxcSFn9caORv5p6\n1Japj4i1gC8CmwOL97kt62xTkiRJU9Lm1fn8zoLMfCgiLqEE/RtQsu393AA8AawfEc/syNRvAjwN\n+P4o+vVkdZ7ToyyAdSLiGcASwG3AzMy8bRT191VLgB0RLwN+U/35c2Ab4A/AncCrKT+NzARuqaM9\nSZIkjbPJvczkGpRk8V/7lN9ACepXZ4igPjNnR8QngKOAayNiBnAPZUz9NsDPKHsvDSsiXgi8AXgE\nuKjPbfu2PwLMjYgTgP0y8/GRtNNPXVnzg4BFgddk5p8iYh7wg8w8NCKWpix5uRWwR03tSZIkaepa\ntjrf36e8dX3YVXAy85iIuBk4EXhfW9HfgFMy8+7h6oiIxYDTKENvPp2Znf26CdiH8svCrVX//wP4\nAvB+yi8C7x6unaHUNaZ+M+DczPxT27UAyMyHKZ2dTZktLEmSpMluEo+pr1OVqT+LEtS/CFiasoHq\nTcDpEfHFYZ6fBnybsifTdzLzqM57MvOizDw2M/+WmY9l5p2ZeTbwekqMvHM1lH3M6srUr0D5maNl\nDrBU64/MnBMRM4Fhl76UJEnSwulzXziLQw4fzRD1vlqZ8GX7lLeu3zdUJdUKN18Ezs7Mj7cVXR0R\nb6cM7zkgIo7PzFk9np9GydDvAHwH2HXErwDIzFsj4ifAu4BNgD8N80hfdQX19wLLtP19N9C50s0T\n9H/jJUmStJD73Kd24HOf2qFnWSz3rtFUdT1lVMjqfcpfUp37jblveStlbP6FnQWZ+WhEXAG8DVgH\nmNVeHhGLUJa63IGSqd89M3Nk3f83d1Xnpcfw7FPqCur/DqzS9vfvgC0i4tmZ+a9qXP12lJ8xJEmS\nNMllzh10F4bSmvz6ps6CiFgG2JgyYfWyYepprdj4rD7lretPdLSxKHAmZTLtyZn53hH0uZ/XVucb\nF6CO2sbUnw9sXgXvAMcDywO/j4gzKT8lvBA4oab2JEmSNEVl5o2U+HOViNino/hQStb71Mx8FEpW\nPSLWqNa3b3cxJeO/d0Ss1F4QEVtSvhw8xvxVHluTYmdQAvoTRhLQR8S6Pa5FRHyKMhb/LuCnw9Uz\nlLoy9d+k/AyyJPBwZv44IvanLP6/PeWb0uGUVXAkSZI02c2b1EtaQtkt9hLgyxHxBuAvlHXpNwOu\no6zO2LJyVT4LaA/sz6Isx/5G4C8R8QPK7rIvB7au7jkwM2e3PfN1YEtKIH57RBzco28XVptXtVwZ\nEddQlny/jTIkfWNgTcpGWbtk5kOjefGdagnqM/N24Lsd174cEV+lTKL91xjHGEmSJEldMvPGiFiP\nkpl/CyXQvh04Gji0x7KSWR3tdWREbAV8GNiJMn5+Kcp80XOBYzLzgo56VqnqWQH4TL/uAe1B/RHA\n+pRNs5YH5lH2b/oKcHSvSbijNa67u2YZjHXneLYhSZKkqanajXXPEdx3MzC9T9lcymiSEY0oyczN\nh7+r65kDR/vMaI1rUC9JkqSGmtw7yqpDbUF9RDwP2B9YG3geZYfZTpmZL6qrTUmSJEk1BfURsRnw\nE2AJysZTd1bnrlvraE+SJEnjzEx9o9SVqf9/lHFKuwGnZ/qvQJIkSZoodQX1awFnZOa3a6pPkiRJ\ng2SOtlHq2nxqNmXpH0mSJEkTrK6g/lxg05rqkiRJkjQKdQ2/+W/gsoj4GvCJzHy4pnonrb/d++ig\nu7DQeuiJuYPuwkLt/sd7zWFXHRab7loA42muWxiOqyfnOtRCHRx+0yh17Sh7d0S8Bbgc2C0i/gp0\n7uJV3ZpvqKNNSZIkSUVdS1q+ApgJPKO6tE6fW82zSJIkNcE8M/VNUteY+qOAZwKfBV4ILJqZ03oc\nPbfnlSRJkjR2dY2p3xD4fmb+T031SZIkSRqhuoL6J4BZNdUlSZKkQXOibKPUNfzmQmD9muqSJEmS\nNAp1BfWfAF4eEZ+MCNd0kyRJarqcN7GHFkhdw28OAq4BDgP2ioir6b+k5Z41tSlJkiSJ+oL6Pdr+\ne9Xq6CUBg3pJkqTJzux5o9QV1PcL4iVJkiSNs7p2lL25jnokSZIkjV5dmXpJkiQtTNxRtlHqWv1G\nkiRJ0oCYqZckSVI3J8o2ipl6SZIkqeEM6iVJkqSGc/iNJEmSujn8plHM1EuSJEkNZ6ZekiRJ3VzS\nslHM1EuSJEkNZ6ZekiRJ3ebloHugUTBTL+n/b+/e420dy4WP/y6LnI+pRG+UrSS1tyjHbSEiVBLp\nrN6OSqHzjsJ6s6vdQcqpFNFJUSkidjlFyBbKzqlYYSU5LeflsOb1/nHfg2Ec5ppzrjnnmM9cv+/n\n83yeuZ7TuOczxxrjGte47vuWJEkNZ1AvSZIkNZzlN5IkSepmR9lGMVMvSZIkNZyZekmSJHUzU98o\nZuolSZKkhjOolyRJkhrO8htJkiR1c5z6RjFTL0mSJDWcmXpJkiR1s6Nso5iplyRJkhrOTL0kSZK6\nmalvFDP1kiRJUsM1NqiPiNdFxNci4vyIuCcihiLihD7Hrln391t+MNntlyRJksZLk8tvDgBeDNwP\n3AKsO4JzrgBO6bH9qnFslyRJUvM5pGWjNDmo3xe4JTP/GhEzgXNGcM4VmTlrgtslSZIkTarGBvWZ\ned6g2yBJkjRt2VG2URob1I/R6hHxHuCpwJ3ARZn5pwG3SZIkSVooi1pQv11dWiIizgX2zMybB9Mk\nSZIkaeE0dvSbUXoQmAVsCKxcl5nA2cBWwK8jYumBtU6SJGmqGcrJXbRQFomgPjNvz8yDMvOKzLy3\nLhcA2wOXAP8CvGuwrZQkSZLGZpEI6vvJzPnAt4AAthxwcyRJkqaOoaHJXbRQFrWa+l5ur+tlR3PS\n79/8o777Vt/1hTzrdesvTJskSVJD3HfaNdz/y2sH3Qwt4gzqYdO6vmE0J73s+3tMQFMkSVLTLL/z\nuiy/c+85MG/d6+eT3JpxZPa8URaJ8puI2CAiosf2l1MmsUrge5PeMEmSJGkcNDZTHxGvAXap/1yt\nrjeLiOPqz3dk5sfqz18B1omI3wG31G0vBrahBPQHZObFk9BsSZIkadw1NqgH/g14W9u/E3hOXQBm\nA62g/gTgtcBGwA7AEsBtwInAEZl54SS0V5IkqTEyHWaySRob1GfmwcDBIzz2OOC4BR4oSZIkNdAi\nUVMvSZKkUWrAkJYRsUZEHBsRcyJiXkTcGBGHRsRKo7zOThFxVkTcHBEPRsRfI+LHEbHJMOdsFhGn\nR8Sd9ZwrI2KfiOgbX0fEnhFxSUTcFxFzI+KciNhpNG3tx6BekiRJjRMRzwX+AOwJXEzpQ/lXYB/g\ndxGx8giv8wXgVEpp9xnAV4HLgFcDF0bEm3qc8xrgPGAL4KfA1ynl3YcCP+zzOF+iVI6sBnwT+C6w\nPnBqRLx/RL/0MBpbfiNJkqRF2lHAqsAHM/PI1saI+DKwH3AIMGywHBHPAD4C/AN4UWbe2bZvJnAO\nMAv4Qdv25YFjgMeAmZl5ed3+6Xr8bhHx+sz8cds5mwIfBq4HXpqZ99btX6R8MPlSRJyWmTeN8V6Y\nqZckSVIPU7j8pmbptwNmtwf01YHAA8BbI2LpBVxqTUo8fEl7QA+QmecB9wFP6zhnd8qHiR+2Avp6\n/CPAAUAAe3WcsxdlUJdDWgF9Pecm4AhgSeAdC2jrsAzqJUmS1DRb1/VZnTsy837gQmAZoG9NfHU9\n8Ajwsoh4avuOiNgSWB747x6PncCZPa53PvAgZZj1JXq0t9c5Z1A+CGyzgLYOy6BekiRJ3YZycpfR\neXylRBwAACAASURBVD4lsL6uz/7r6/p5w10kM+8GPg48A/hzRHwjIv4zIn5MCcDPBN7X47Hp9diZ\nOR+4kVLi/lyAiFgGWAO4PzNvG2tbF8SaekmSJDXNinV9T5/9re0LHAUnM78WEX8DjgXe1bbrL8Dx\nmXnHQj72uLV1OGbqJUmS1G0K19SPp4j4OHAyJahfG1gW2JCScf9BRHx+YI0bBTP1kiRJmhQHf+di\nZh1/yXhcqpXdXrHP/tb2ucNdpI5w83ngJ5n5sbZdV0TEayklNh+JiKMzc/YYH3tc2rogBvWSJEma\nFAe+fRMOfHvvvqsztj5sNJe6ltK5tF8d+jp13a/mvmVnSm3+uZ07MvOhiPg9sAuwATC77bE3rI99\nefs5ETEDeA5luMsb6nUejIg5wOoR8YwedfUjbeuwLL+RJElSt6ldfnNOXb+ic0dELAdsThmF5uIF\nXGfJuu4ctpKO7Y+0bTub8oFihx7Hz6SMunNhZj7acQ59ztmxrn+zgLYOy6BekiRJjZKZN1CGs1wr\nIvbu2D2LUhd/QmY+BBARi0fE8+v49u1+SwnQ3xMRq7fviIhXUj4czAN+17brZOAO4A0RsWHb8UsC\nn6Vk/o/qeJyj6+PsHxErtZ2zFvCB+hjfGcnv3o/lN5IkSeo2+mEmJ9v7KePRHxYRLweupoxLvxVw\nDWUiqJY16v7Z1KEmq5Mp49BvC1wdET+jzC67HrBTPeYTdehLADLzvoh4N3AScG5EnAjcBbyaUpJz\nUmae1N7QzLwoIr5Cmen2jxFxMvAUYA/KqDd7L8xssmBQL0mSpAbKzBsiYiNKZn4H4JXArcChwKzM\n7BxCMuvSfo2MiB0p2fI3UOrnl6EE6acBX8vMrrKYzPx57WS7P7ArsBRlCMz9gK/3ae9HI+KP9bHe\nDQwBlwFfzMwzRn8HnsygXpIkSY2UmXOAd47guL8BM/rsmw98rS6jeeyLKB1tR3POCcAJozlnpAzq\nJUmS1G2AY8dr9OwoK0mSJDWcmXpJkiR1M1PfKGbqJUmSpIYzUy9JkqRuU39IS7UxUy9JkiQ1nEG9\nJEmS1HCW30iSJKmbHWUbxUy9JEmS1HBm6iVJktTNTH2jmKmXJEmSGs6gXpIkSWo4y28kSZLUzXHq\nG8VMvSRJktRwZuolSZLUzY6yjWKmXpIkSWo4M/WSJEnqkvOtqW8SM/WSJElSwxnUS5IkSQ1n+Y0k\nSZK6OaRlo5iplyRJkhrOTL0kSZK62VG2UQzqx+gvt9wz6CZMW/MfnT/oJkxrsVgMugnT1kMz/PJz\nIvnaMLHSUgup0XwHkiRJkhrOTL0kSZK6+O1Ns5iplyRJkhrOTL0kSZK62VG2UczUS5IkSQ1npl6S\nJEnd5g8NugUaBTP1kiRJUsMZ1EuSJEkNZ/mNJEmSujikZbOYqZckSZIazky9JEmSujmkZaOYqZck\nSZIazqBekiRJajjLbyRJktTNjrKNYqZekiRJajgz9ZIkSeqSdpRtFDP1kiRJUsOZqZckSVK3oaFB\nt0CjYKZekiRJajiDekmSJKnhLL+RJElSNzvKNoqZekmSJKnhzNRLkiSpSzr5VKOYqZckSZIazqBe\nkiRJajjLbyRJktTNjrKNYqZekiRJajgz9ZIkSepmpr5RzNRLkiRJDWemXpIkSV0c0rJZzNRLkiRJ\nDWdQL0mSJDWc5TeSJEnqNn9o0C3QKJiplyRJkhrOoF6SJEldcigndRmLiFgjIo6NiDkRMS8iboyI\nQyNipRGev2dEDC1gebTjnONGcM5/j/Jx3jOmG9CmkeU3EbEKsCuwI/AiYA3gEeBPwHHAcZmZbcev\nCdw4zCVPzMw3TVyLJUmSNJ4i4rnARcCqwCnAtcDLgH2A7SNi88y8ewGXuQI4qM++LYGtgdM7tv+M\n/nHl24Dn9Din5ZT6mJ3+Z9hWjkAjg3pgd+Ao4O/AOcBNwDMogf63gB2A1/c47wrKzex01cQ0U5Ik\nSRPkKEpA/8HMPLK1MSK+DOwHHAK8f7gLZOaVwJW99kXE7+qP3+w45xfAL3ocvyLwCUqi+fheDwec\nkpknDNemsWpqUH8t8KrM/GX7xoj4FHAp8LqIeG1m/qzjvCsyc9ZkNVKSJKmxpvCMsjVLvx1wY3tA\nXx0IvAd4a0R8JDMfGsP11wc2AW6hf9a909uApYEfZOZdo33MhdXImvrMPLczoK/b/wkcDQSw1WS3\nS5IkSZNi67o+q3NHZt4PXAgsQwnMx+K9lMz6t9pLuhfg3fWcb/bZH8AGEbFPRHwiIt4SEWuMsX1d\nmpqpH06rM8NjPfatXjsiPBW4E7goM/80aS2TJElqiqk9o+zzKQH0dX32X0/J5D+PUqo9YhGxFPBm\nYD7w7RGeswmwPnBNZp4/zKEfaj8NmB8R3wL2zcyHR9POTtMqqI+IGcCelD/yr3ocsl1d2k6Jc4E9\nM/PmiW+hJEmSxsGKdX1Pn/2t7SMaBafDHvW8UzNzzgjPaWX2j+mz/0Zgb8o3C7dQ2r8F8Ll67vLA\nW8bQ1sc1svxmGF8AXgj8MjPbhxJ6EJgFbAisXJeZwNmUMp1fR8TSk9tUSZKkqSvn56QuU8h7KAH6\nN0ZycESsQBnEpV8HWTLz/Mw8MjP/kpnzMvO2zPwJsA1wN/DGiHjRwjR62mTqI+JDwIeBP1M6Kjwu\nM2+ne7iiCyJie+ACyvBH7wK+PvEtlSRJWjQdcsH1/OeFfx2PS7Uy8Sv22d/aPnc0F42I9YBNgZuB\nM0Z42lsp9fuj7iCbmbdExOnAmyhDaI65LHxaBPURsTfwVcrQlNtm5oj+gJnZqmPamHIjDeolSZIm\nyP5brMP+W6zTc9+yX+hVOd3XtZSa9Of12d96kH419/0sTAfZEWX2e7i9rpcd4/nANAjqI2Jf4CvA\nHykB/R2jvMSYbuRdn+j/xFtq27VZZrveT1hJkjS9zDv7rzxy7nBzXDbU1O4o2+r8+orOHRGxHLA5\npfz64pFeMCKWpNS1zweOHeE5LwNeTOkg+9uRPlaHjev6hjGeDzQ8qI+IT1A6GPwB2G4Es4b1smld\nj+pGrvKFHcbwUJIkabpZapu1WWqbtXvuu/czv57k1iwaMvOGiDgL2C4i9s7Mw9t2z6Ika49qjVEf\nEYsDawOPZma/mO/1lH6XvxhDB9l+w1hSH3/DzLysY1sAn6TEov+k9yAvI9bYoD4iPg0cTJlsavvh\nSm4iYgPKxFPZsf3lwL6UP8b3JrC5kiRJzTJ/aNAtWJD3U8ajP6zGdFdTxqXfCrgGOKDt2DXq/tnA\nc/tcr9VBdtgAvSUilqeMlPMwsKBZYi+NiKsos9fOodT8b04ZBvMB4M11fP0xa2RQHxF7UgL6xyh/\nzH3Kh50nmZ2ZrR7IXwHWqdP93lK3vZjS4ziBAzJzxF/PSJIkabBqtn4jSmZ+B+CVwK3AocCszOwc\n7jLr0iUi1qUE2Tcx8g6yb6bMIPvDEXSQ/SJlYJatgVWAofpYXwcOzczZI3zMvhoZ1ANrUf4oM4B9\n+hxzHk8MK3QC8FpgI8offQngNuBE4IjMvHAiGytJkqTxV8tk3jmC4/5GiRv77b+GUQ71nplHA0eP\n8NhPjObaY9HIoD4zD6Zk6kd6/HHAcRPXIkmSpOklp3ZHWXWYbpNPSZIkSYucRmbqJUmSNMGm1iyv\nWgAz9ZIkSVLDmamXJElSF2vqm8VMvSRJktRwBvWSJElSw1l+I0mSpC5pR9lGMVMvSZIkNZyZekmS\nJHWxo2yzmKmXJEmSGs6gXpIkSWo4y28kSZLUZciOso1ipl6SJElqODP1kiRJ6mJH2WYxUy9JkiQ1\nnJl6SZIkdcmhoUE3QaNgpl6SJElqOIN6SZIkqeEsv5EkSVKXdEjLRjFTL0mSJDWcmXpJkiR1cUjL\nZjFTL0mSJDWcQb0kSZLUcJbfSJIkqYsdZZvFTL0kSZLUcGbqJUmS1MWOss1ipl6SJElqODP1kiRJ\n6jJkpr5RzNRLkiRJDWdQL0mSJDWc5TeSJEnq4pCWzWKmXpIkSWo4M/WSJEnq4pCWzWKmXpIkSWo4\ng3pJkiSp4Sy/kSRJUhfLb5rFTL0kSZLUcGbqx+iBOx4YdBOmrYfve2TQTZjWllja//YT5dGHHht0\nE6a1pVZcctBNmNbm3fPwoJugKcYhLZvFTL0kSZLUcKbsJEmS1CWHhgbdBI2CmXpJkiSp4QzqJUmS\npIaz/EaSJEld7CjbLGbqJUmSpIYzUy9JkqQuTj7VLGbqJUmSpIYzUy9JkqQuQ2bqG8VMvSRJktRw\nBvWSJElSw1l+I0mSpC4OadksZuolSZKkhjNTL0mSpC4OadksZuolSZKkhjOolyRJkhrO8htJkiR1\nsaNss5iplyRJkhrOoF6SJEldcigndRmLiFgjIo6NiDkRMS8iboyIQyNipRGev2dEDC1gebTjnDUX\ncPwPFvB4l0TEfRExNyLOiYidxvTLd7D8RpIkSY0TEc8FLgJWBU4BrgVeBuwDbB8Rm2fm3Qu4zBXA\nQX32bQlsDZw+zLmn9Nh+VZ/2fgn4MHAz8E3gKcAbgFMjYu/MPHIBbR2WQb0kSZK6NGBIy6MoAf0H\n2wPiiPgysB9wCPD+4S6QmVcCV/baFxG/qz9+s8/pV2TmrJE0NCI2pQT01wMvzcx76/YvAn8AvhQR\np2XmTSO5Xi+W30iSJKlRapZ+O2B2jwz3gcADwFsjYukxXn99YBNgDv0z9aOxF5DAIa2AHqAG8UcA\nSwLvWJgHMKiXJElS02xd12d17sjM+4ELgWUogflYvJcShH8rM/t9ZbF6RLwnIv6jrl80gvae2WPf\nGUAA24yxrYDlN5IkSephig9p+XxK0H1dn/3XUzL5zwPOGc2FI2Ip4M3AfODbwxy6XV3aTo1zgT0z\n8+a2jcsAawD3ZeZtfdpKbeuYmamXJElS06xY1/f02d/aPqJRcDrsUc87IzPn9Nj/IDAL2BBYuS4z\ngbOBrYBfd5T9TGRbH2emXpIkSV2Gpn5H2YnyHsq3AN/otTMzb6d7xJwLImJ74ALKCDzvAr4+gW3s\nYlAvSZKkSXH4P+7gyNvuHI9LtbLbK/bZ39o+dzQXjYj1gE0pw06eMZpzM3N+RHwL2JgyHGYrqJ+Q\ntnYyqJckSdKk2Hu1Vdl7tVV77lvvymtHc6lrKZ1L+9Whr1PX/Wru+xlJB9nh3F7Xy7Y2ZOaDETGH\n0rH2GT3q6sfa1iexpl6SJEldhoYmdxmlVufXV3TuiIjlgM0pte8Xj/SCEbEk8BZKB9ljR92iYtO6\nvqFj+9l1vUOPc3as69+M8TEBg3pJkiQ1TGbeQBnOcq2I2Ltj9yxKpvyEzHwIICIWj4jn1/Ht+3k9\npdPr6X06yFKvtUFERI/tLwf2pWT6v9ex+2jKNwv7R8RKbeesBXwAmAd8Z5i2LZDlN5IkSeoyhuz5\nZHs/ZTz6w2pAfTVlXPqtgGuAA9qOXaPunw30C+xbHWT7zSDb8hVgnTrj7C1124sp48wncEBmPukb\ngsy8KCK+Qpnp9o8RcTLwFJ4YaWfvhZlNFgzqJUmS1ECZeUNEbETJzO8AvBK4FTgUmJWZnUNIZl26\nRMS6lJKdm1hwB9kTgNcCG9XHXQK4DTgROCIzL+zT3o9GxB8pmfl3A0PAZcAXM3NUnXJ7MaiXJElS\nlwZk6qllMu8cwXF/A2YMs/8aRliWnpnHAceNtI0d555A+VAw7qyplyRJkhrOoF6SJElquMYG9REx\nOyKG+ix/73POZhFxekTcGREPRsSVEbFPRDT2PkiSJE2EoZzcRQunyTX1SZl561DKEEHt7u88OCJe\nA5wMPAT8CLgLeFU9fzNK72NJkiSpcZoc1APMzcz/t6CDImJ54BjgMWBmZl5et3+aMnnBbhHx+sz8\n8YS2VpIkqSGa0FFWT1hUyk52B1YFftgK6AEy8xHKGKYB7DWgtkmSJEkLpemZ+iUj4s3As4EHgD8C\n52dm52fLrSnlOmf2uMb5lGmEN4uIJTLz0YlssCRJkjTemh7Ur8aTx/oM4MaIeEdmnt+2/fl1fV3n\nBTJzfkTcCKxHmWHs2olqrCRJUlNYftMsTS6/ORZ4OSWwXxZ4EXA0sBZwekS8qO3YFeu6c2YxOrav\nNP7NlCRJkiZWYzP1PTrI/hl4f0Q8AHwEOAh43WS3S5IkaTowU98sjQ3qh3E0Jajfsm1bKxO/Yvfh\nT9o+d6QP8vAXf9t334zNns3im6850ktJkqQmu/QWuGzOoFuhRdx0DOpvr+tl27ZdC2wIPA+4vP3g\niJgBPIcy3OUNI32QJT/27wvXSkmSND289Fll6eXoSya3LePITH2zNLmmvp9N67o9QD+b0ol2hx7H\nzwSWAS505BtJkiQ1USOD+ohYNyKW6bF9LeBwyvCV323bdTJwB/CGiNiw7fglgc/W44+awCZLkiRJ\nE6ap5Td7AB+JiPOBvwH3AWsDOwFLAr8Evtw6ODPvi4h3AycB50bEicBdwKspJTknZeZJk/srSJIk\nTV2W3zRLU4P6cyjB+AbAZpT6+bnAb4ETMvP7nSdk5s8jYiawP7ArsBTwF2A/4OuT1G5JkiRp3DUy\nqK8TS52/wAO7z7sI2Hn8WyRJkjS9mKlvlkbW1EuSJEl6gkG9JEmS1HCNLL+RJEnSxLL8plnM1EuS\nJEkNZ6ZekiRJXczUN4uZekmSJKnhzNRLkiSpS2YOugkaBTP1kiRJUsMZ1EuSJEkNZ/mNJEmSuthR\ntlnM1EuSJEkNZ6ZekiRJXczUN4uZekmSJKnhDOolSZKkhrP8RpIkSV0sv2kWM/WSJElSw5mplyRJ\nUhcz9c1ipl6SJElqODP1kiRJ6mKmvlnM1EuSJEkNZ1AvSZIkNZzlN5IkSepi+U2zmKmXJEmSGs5M\nvSRJkrqYqW8WM/WSJElSwxnUS5IkSQ1n+Y0kSZK6DOWgW6DRMFMvSZIkNZyZekmSJHWxo2yzmKlf\nBDx24d8G3YTp69JbBt2CaW3+RTcNugnTm8/fCfPoBbMH3YTpzeeu1MWgfhEw/3cGRhPmsjmDbsG0\nNnTxzYNuwvTm83fCzL/Q190J5XN3UgwNTe6ihWNQL0mSJDWcQb0kSZLUcHaUlSRJUhdLYprFTL0k\nSZLUcGbqJUmS1MVMfbNEptOFjUZEeMMkSdKoZGYMug0jNehYp0n3aiqx/EaSJElqODP1kiRJUsOZ\nqZckSZIazqBekiRJajiDekmSJKnhDOolSZKkhjOolyRJkhrOoF6SJElqOIN6SZIkqeEM6iVJkqSG\nM6iXJEmSGs6gXpIkSWo4g/pFRETEoNswXUXEMwfdhunO5+/E8Lk7+XwuTw7vsxZFkZmDboPUWBHx\nU+BeYJ/MvGfQ7ZlOIuLZwNOAB4FbM3Nu3R7pC9dC87mr6SQi/g+wKrBYZl7Wep2IiMUyc2jQ7ZMm\nw+KDboAmVkRsC6wJPAM4H/hrZt5qYLTwIuJ0YHvgQ8AjA27OtBIRh1Lu7TrAw8DvI+IXmflVn7cL\nz+fu5IuIrYB/AZYD/gBcnZm3132+Hi+EiPgasCPw3Prv84DTI+KYzJxrYK9FhZn6aSwivgu8hvIm\nAjAPuAbYLzPPG1jDpoGIOAOYCewPHNue6fQNZOFExCnADsDFwJ8pgdDLgQC+B+yfmTcProXN5nN3\n8kXE94DXAku3bT4HOCEzj6/HGNiPQX292JbyenERsAWwHuVbvt8Ab8nM23xua1Fgpn6aiogTgZ2A\nE4GTgecB21CC/HMi4gPA8Zn54OBa2UxtQdGngeN6lC4sCTzUdrxv1iMUER+jBPQHAt+oWbYlgC2B\nY4G3AKtExIcz87oBNrWRfO5Ovoj4IeW1+DvAD4H1ga2B3YGtI2KtzDy4lop4v0chIvYDdqZ8QP1W\nZt4ZEU8DXgR8gZIMOCcitsnMfxjYa7ozUz8NRcSrgJOArwGfy8y72/YdSHkBnAF8DDgyM+cNpKEN\nFBFnAtsB+wHHZOaDEbEU8CxKKcOL6s8/Bs7PzDPreb5Zj0BE/ATYBNg4M2+pnd0iM4ciYgPgcGBT\n4FTg7Zl5t/d2ZHzuTr6IeDXltfgI4LOZeVfdvhKwK/CteujnM/NTdZ/3e4Qi4kfAVsALM/OO9qA9\nIlYGfkZJCPwJ2DYzbzew13Tm6DfT03rAU4Af1aBnRkTMAMjMg4EPAw8A/wW8HhwpYCQiYiNKUPQA\n8GANip4CvA04HdibEhitDXwSOCoi3gHgm/SCRcQylFKbezLzltb2GtBHZl4OvA+4FHgV8J91v/d2\nAXzuDszzgCUo34rcVb91IjPnZuaxlOcxwCcj4pN1n/d7BCJiBUqfm8conelpC+gXq8ms7YELKM/t\nIyJieQN6TWcG9dPTsnW9EkBmzs/M+RGxWP334cBnKH//b0fERr6RLFhm/g+wC6W2+5MRsSflq9//\nBO6mlDb9G6XD1uHAWvW4zQfS4OaZD8wF1o2IXeCJAKetNOFPwPuB+4D3RsTuA2ttg/jcnVyt11pg\njbp+PkBmPtp2TGTmLym19gD718y+RiAz7wVuAlYDXgrQlrwaiogZmfkw8EbgKuCVlNIzk1iatgzq\np6dWJ8I96te8wOMvdK3A/qvAlyllOPtFxLK+0PXWeqOob8K/oNR1rwYcAnwT+AswMzNPzcxbMvNX\nlHrOb1IySRsPpuXNEFV9A/42Jbh/bUQ8tf24tsD+MuA9dfOmk9zcRomID7Z+9rk7edqywRfU9Sbw\nxHO9HtP6wPpzSinkMpTAUwvQ9l51GuWD6r5QElhtgf38+noxB/gSJdn1mrrPJJamJYP66elU4HLg\n1dQ3k5ZWBqP+80BKreFLgSV9oRte25vwKcCbgNUpnQp3ycx5EbFY2xv234Fz66mbt7521xMi4sSI\n2Dyruvl/gN8DbwXe2XlODexn1OPmAttGxHJtmVFVEXEycFhEvK21zefuxOqRGPlf4AbgwxGxU8dz\nvd0vKCOTvTUi1pzodjZZR5+DX1Pu22si4jDoCuxbx/2W8nqxQUSsONltliaLb4TTQI+A5g7gp8DT\ngUMj4kXtO+uL3uJZRr65jlLH/IJJaWyDRMTrImIWcFpEfCQiHs8K1+zajsA3gHvqtqEadLaCoKvq\n+rb2r90FEfFLSn+O9dufv5n5Z0r5xzzg8xHxwfb9EbFELSf7K+VN+h+Zeb91sk8WZRz6XYEh6rcZ\nbd+I+NydIJ0Be2ZeAxxZ//mjiHi8/KP9A0BmXg9cSMnWrzJJzW2MiHh2RGwYES8AHg/KM3M28H8p\ncy18MCK+ULfPr7e4FdzfCNxJmWztgcluvzRZDOobLCLeHBErtJfV1Dftx4DDKENZPh/4QUS8pPUm\nUmsNH2u71E3AjZPd/qksyrjSJwIHUDpbfZGS9dypdUwtVfhyZnYOAdgKgt5c1+e39k1G26e6KMMq\nbkPpsH1iW+e2Vqb4h8BH6+GHAQdExHPqvkfrsbtT6pWviojFvbdPqPd3a+DrwK3AOyPiJe1ZYp+7\n4ysito2Id0bEpyJii4h4ZmtfZn6FMvrNMpTAfqvW36Ljvi4H3E4JPlVFmYjuV5Rx6C8FfhoR+7b2\nZ+bFlATBw8DHIuLoKJ3Aycz59Rq7UV4v/gD4jbSmr8x0aeACnEKpPT4EWK5uW6xjvSLla90hSkb+\nTcDqbdfYkfKmfxqwwqB/p6myUD4MPQAcA7wE2A04jjLKwnGU+R2ix3kz2n7eBZhNKSVZbdC/01RZ\ngDMoWfj9gJUWcOwHKN86DQFnUepm1wH2obw5/x147qB/p6m01Pv7UL1HiwEH1fvX6j+zWJ/zfO6O\n/Z5/l5IBHqrLg/X5ObP9/lK+GRmqryNvan/NpdTS31T/fssN+neaKkt9n5tHKQc7sr4OzK/38QTg\n2W3Hbg/cX/f9pr5evLi+1lxR3+vWHvTv5OIykcvAG+Ayhj8afL6+cD1Q30w+2yOwn1HXK1AmPXmw\nHvtbSob0qBro3w6sO+jfaaoslMz8HXW9Stv2f63364EF3S9gT0ot7R3ACwb9O02Vpb5BP1iD9afV\nbYtTMpjvAv6DMrLNjm3n7EoZ5/vRtqBpiFJHu/6gf6eptAC/rAH9fq3nLmV425sofWeWr9u6PpC2\nXcPn7uju+YmUkZiOqUHlByljo7eep3u1B+mUb/xa+37NE52Sb/C1uOvefqwG9J+gJgAow4O+HPhb\nvYenAc9rO+df632d2/F6cbWvFy6LwjLwBriM8g9Wsmh3AX+mdCa8rgZKwwX2y1A6HZ7W9iL3AOWr\ndd+4n7i369X7eT7wzPZ7WH8+vN67jXucG5RSp+/XgOg6yoQoA/+9psJCyRQPUT5ULlu3rQzsAVzS\n8QY8H/hq27lPo9SFf5KSed6Ntm+cXBLg5/Xe7cuTM8DLUcahHwL+o8+5PnfHds9fVYPO/wJW7th3\nIKXOez4libJM2753UAYzeKT+Xe4EzvG1uOv+/gSYAzyr/jva3ts2oPRBGKrP/fYEzKqUwR/2p8yc\n/MbW67mLy3RfFkeNEWX2xz2ApYF3ZOYlEXE7pV7zI/WYz2fm/VEm35hf1w9Shgr8dpRJaJYBbqN0\nMuycJn5Rthal7vLDmXlrrTFudSp+jCeGCl2988TMzIhYnzLJyS+A/5elc5aKYyh13psDh0TEhymZ\nzaMpX4t/tq7XpDyXPxQRj2bmxzLzdkoW86KBtHyKi4hlKeUevwGOz8x7a6121NeCWcC/A1vUjsZP\n6vhan7svxOfuaHVN8gePzwtycETcSZkH4L8oH5ZOqPuPi4gfA8+hvN7cAszJzLmD+CWmohjBRHQR\n8T7KjLyvopSh7lWPuYNyvy+d/JZLgxWZ9hlpkojYANgiM79e/704JVg6mhJsfpky5fj9tfNs1jdt\npx5fgCjjou8C/Ly+MbS2R72Hb6G8Me+UmWdEj+nG6+gMt2TmfZPa+AaIiH+hlNL8K6VE4SWUuvjt\nM/P+tuNeQflW6UHg1Zl5/gCa2ygRsSRAZj7c/n+9BvfPpPQT2QTYNcuwlr2usQ7lg77P3RGod9yo\n5AAADPpJREFUH5YOALbLzN+0bX/8daF26PwKJWO/aZZJwDqHZVSH+nw+i/JhtOs52/aavCFwNrA8\nsEdmntTjWt5rLTIc/aZhMvNySgDf+vdjlBe191G+qvwIZSbI5bIOU1cPXRUcxWI4mXkn8L32gL5u\n73xDeKhub71xz6wBP5l5tUFRb5n5F2B34ErKLJp3ADvXD6CLw+MB0VnA8ZT+IE8fVHubJDMfzjJ5\n15Oer1n8ndKvBuANEbF0PHmY0NaEdNf73B2VMU/yZ5DZLZ48yd+4TUTnvdaixKC+gXp8fT6fEtjv\nRcl8fgT4VFug9C7gjIjY2Be44bUCo3ZtH4Tm1/XSbfu2B74KfLrzjUfdamD/euA84MhattAahrXd\nbXW92qQ2cJppe+7+DLiM0slwjVYZAzxp9lONjpP8TYC2+3M5TkQnjYpP/mmiLbB/HyWw3xc4sNYd\nHkiZXMqazbFpBUatN+mH4fGA/nOU2s/da6ZfC5Blop03UDpwPv4mXoP7VoD5AuBu4IKBNHKaaAuQ\n7qB0SH4qZXbTJQwuR6dHsOgkf+Mg+kzyl5l/ogxj6UR00ggZ1E8jNbA/j5LV+DtlaLsjKCNgbJyZ\n1w6weY3V9iaxZF3Pi4gtKUOL/guwWWb+cSCNa6jMvC0zW9n4VtlNK7jfHdiKMrrF7IE0cBppK/f4\nPKVk5GWUGmSNQDjJ34SJ/pP8vQogM79PHQQCJ6KTFsigfhqpgdEjlMD+p5RRbuZSOtZeNezJGonW\nm8WOlDrZtSn39k+Da1LzdXQsfCPwGcoEPR91RJCF16o/Bu6hfPPxEp6YMVbDiIhTKJ3jP9Hqp9T6\nAFrX9wPvpnTsfiElQH1jRKyeT8xmuiOwBXAVZXIkARFxMqVvzXeAjShlecdTnp+7tnX+PopSWnoX\nZUjbb0TEvhGxTkTsQ5nf4k7g8Mx8zG+gtChz9JtpKCLeQRkicBlg88z884Cb1GhtIy3sRfnm4z7K\nB+LNzdCPjyjDtX4B2IkyTOCOfhAdfxGxGyWI2jgz/3fAzZnSIuLzwMcpHePnA1+jbWSxVt18LbNZ\noe5/PeUD6ZWUfgzrUPoxrAz8e2ZeM4jfZaqJiAMoJaJfpfStuatu/1fKCFlrABu236+avX8bZYSy\nGW2Xuw7YzdcLyaB+2omILSiTyDwd2Mg37vETEW8CvkeZuGsT7+34iDL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"text/plain": [
"<matplotlib.figure.Figure at 0x11a9d6dd0>"
]
},
"metadata": {
"image/png": {
"height": 374,
"width": 378
}
},
"output_type": "display_data"
}
],
"source": [
"######## Random Forest ##########\n",
"start = time.time()\n",
"depth_range = np.array([1, 2, 5, 10, 15, 25, 50])\n",
"ntree_range = np.array([10, 25, 50, 75, 100, 200])\n",
"grid = GridSearchCV(sklearn.ensemble.RandomForestClassifier(n_estimators=50, max_depth=None,\n",
" max_features='auto', class_weight='balanced'),\n",
" {'max_depth' : depth_range,\n",
" 'n_estimators' : ntree_range},\n",
" cv=10, n_jobs=4, scoring='roc_auc') #sklearn.cross_validation.StratifiedKFold(y_train, 10)\n",
"grid.fit(X_train, y_train)\n",
"\n",
"plotGridResults2D(depth_range, ntree_range, 'max depth', 'n estimators', grid.grid_scores_)\n",
"\n",
"rbestClf = grid.best_estimator_\n",
"#print rbestClf\n",
"end = time.time()\n",
"end - start"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0.0 1.00 0.97 0.98 15216\n",
" 1.0 0.91 1.00 0.95 4622\n",
"\n",
"avg / total 0.98 0.98 0.98 19838\n",
"\n"
]
},
{
"data": {
"text/plain": [
"array([[14754, 462],\n",
" [ 0, 4622]])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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S1MW9+eabfO9732PhwoXstttuTJkyhQ022KDosNRCJu6SJEmS1MWts846XHDB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a+QJMW/z9l0F0nSfmlE3Jiz/1RgLnCvpD4RsUFSD2A08HREDMlzjh7biyEibpHUkzRxj4inigx/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\nzMysiv0vgV69d9Xb36wAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aef3750>"
]
},
"metadata": {
"image/png": {
"height": 363,
"width": 503
}
},
"output_type": "display_data"
}
],
"source": [
"# Learn on train for test\n",
"rbestClf.fit(X_train, y_train)\n",
"y_pred = rbestClf.predict(X_test)\n",
"\n",
"# Classification report\n",
"print sklearn.metrics.classification_report(y_test, y_pred)\n",
"\n",
"y_score = rbestClf.predict_proba(X_test)[:,1]\n",
"\n",
"# ROC\n",
"rfpr, rtpr, _ = roc_curve(y_test, y_score)\n",
"\n",
"plotRoC(rfpr, rtpr)\n",
"\n",
"confusion_matrix(y_test, y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AAADQASEOAAAAQAeEOAAAAAAdEOIAAAAAdECIAwAAANABIQ4AAABA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\nSZIkSVIX+D+MfGD/9/gahAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11aef39d0>"
]
},
"metadata": {
"image/png": {
"height": 389,
"width": 568
}
},
"output_type": "display_data"
}
],
"source": [
"indices = np.argsort(rbestClf.feature_importances_)[::-1]\n",
"\n",
"# list features and scores\n",
"#for f in range(X_train.shape[1]):\n",
"# print(\"%2d) %-*s %f\" % (f+1, 30, X_train.columns[f], bestClf.feature_importances_[indices[f]]))\n",
"\n",
"# plot bar chart\n",
"plt.title('Feature Importance')\n",
"plt.bar(range(10),\n",
" rbestClf.feature_importances_[indices[:10]],\n",
" color='lightblue',\n",
" align='center')\n",
"plt.xticks(range(10),\n",
" X_train.columns, rotation=45)\n",
"plt.xlim([-1, 10])\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
arunchaganty/obviousli | third-party/stanza/examples/text_classification.ipynb | 1 | 56272 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Text Classification\n",
"\n",
"Authors: Victor Zhong, Kelvin Guu\n",
"\n",
"We are going to tackle a relatively straightforward text classification problem with Stanza and Tensorflow."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset\n",
"\n",
"First, we'll grab the 20 newsgroup data, which is conveniently downloaded by `sklearn`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Counter({'alt.atheism': 480, 'soc.religion.christian': 599})"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.datasets import fetch_20newsgroups\n",
"classes = ['alt.atheism', 'soc.religion.christian']\n",
"newsgroups_train = fetch_20newsgroups(subset='train', categories=classes)\n",
"\n",
"from collections import Counter\n",
"Counter([classes[t] for t in newsgroups_train.target])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From: [email protected] (Nigel Allen)\n",
"Subject: library of congress to host dead sea scroll symposium april 21-22\n",
"Lines: 96\n",
"\n",
"\n",
" Library of Congress to Host Dead Sea Scroll Symposium April 21-22\n",
" To: National and Assignment desks, Daybook Editor\n",
" Contact: John Sullivan, 202-707-9216, or Lucy Suddreth, 202-707-9191\n",
" both of the Library of Congress\n",
"\n",
" WASHINGTON, April 19 -- A symposium on the Dead Sea \n",
"Scrolls will be held at the Library of Congress on Wednesday,\n",
"April 21, and Thursday, April 22. The two-day program, cosponsored\n",
"by the library and Baltimore Hebrew University, with additional\n",
"support from the Project Judaica Foundation, will be held in the\n",
"library's Mumford Room, sixth floor, Madison Building.\n",
" Seating is limited, and admission to any session of the symposium\n",
"must be requested in writing (see Note A).\n",
" The symposium will be held one week before the public opening of a\n",
"major exhibition, \"Scrolls from the Dead Sea: The Ancient Library of\n",
"Qumran and Modern Scholarship,\" that opens at the Library of Congress\n",
"on April 29. On view will be fragmentary scrolls and archaeological\n",
"artifacts excavated at Qumran, on loan from the Israel Antiquities\n",
"Authority. Approximately 50 items from Library of Congress special\n",
"collections will augment these materials. The exhibition, on view in\n",
"the Madison Gallery, through Aug. 1, is made possible by a generous\n",
"gift from the Project Judaica Foundation of Washington, D.C.\n",
" The Dead Sea Scrolls have been the focus of public and scholarly\n",
"interest since 1947, when they were discovered in the desert 13 miles\n",
"east of Jerusalem. The symposium will explore the origin and meaning\n",
"of the scrolls and current scholarship. Scholars from diverse\n",
"academic backgrounds and religious affiliations, will offer their\n",
"disparate views, ensuring a lively discussion.\n",
" The symposium schedule includes opening remarks on April 21, at\n",
"2 p.m., by Librarian of Congress James H. Billington, and by\n",
"Dr. Norma Furst, president, Baltimore Hebrew University. Co-chairing\n",
"the symposium are Joseph Baumgarten, professor of Rabbinic Literature\n",
"and Institutions, Baltimore Hebrew University and Michael Grunberger,\n",
"head, Hebraic Section, Library of Congress.\n",
" Geza Vermes, professor emeritus of Jewish studies, Oxford\n",
"University, will give the keynote address on the current state of\n",
"scroll research, focusing on where we stand today. On the second\n",
"day, the closing address will be given by Shmaryahu Talmon, who will\n",
"propose a research agenda, picking up the theme of how the Qumran\n",
"studies might proceed.\n",
" On Wednesday, April 21, other speakers will include:\n",
"\n",
" -- Eugene Ulrich, professor of Hebrew Scriptures, University of\n",
"Notre Dame and chief editor, Biblical Scrolls from Qumran, on \"The\n",
"Bible at Qumran;\"\n",
" -- Michael Stone, National Endowment for the Humanities\n",
"distinguished visiting professor of religious studies, University of\n",
"Richmond, on \"The Dead Sea Scrolls and the Pseudepigrapha.\"\n",
" -- From 5 p.m. to 6:30 p.m. a special preview of the exhibition\n",
"will be given to symposium participants and guests.\n",
"\n",
" On Thursday, April 22, beginning at 9 a.m., speakers will include:\n",
"\n",
" -- Magen Broshi, curator, shrine of the Book, Israel Museum,\n",
"Jerusalem, on \"Qumran: The Archaeological Evidence;\"\n",
" -- P. Kyle McCarter, Albright professor of Biblical and ancient\n",
"near Eastern studies, The Johns Hopkins University, on \"The Copper\n",
"Scroll;\"\n",
" -- Lawrence H. Schiffman, professor of Hebrew and Judaic studies,\n",
"New York University, on \"The Dead Sea Scrolls and the History of\n",
"Judaism;\" and\n",
" -- James VanderKam, professor of theology, University of Notre\n",
"Dame, on \"Messianism in the Scrolls and in Early Christianity.\"\n",
"\n",
" The Thursday afternoon sessions, at 1:30 p.m., include:\n",
"\n",
" -- Devorah Dimant, associate professor of Bible and Ancient Jewish\n",
"Thought, University of Haifa, on \"Qumran Manuscripts: Library of a\n",
"Jewish Community;\"\n",
" -- Norman Golb, Rosenberger professor of Jewish history and\n",
"civilization, Oriental Institute, University of Chicago, on \"The\n",
"Current Status of the Jerusalem Origin of the Scrolls;\"\n",
" -- Shmaryahu Talmon, J.L. Magnas professor emeritus of Biblical\n",
"studies, Hebrew University, Jerusalem, on \"The Essential 'Commune of\n",
"the Renewed Covenant': How Should Qumran Studies Proceed?\" will close\n",
"the symposium.\n",
"\n",
" There will be ample time for question and answer periods at the\n",
"end of each session.\n",
"\n",
" Also on Wednesday, April 21, at 11 a.m.:\n",
" The Library of Congress and The Israel Antiquities Authority\n",
"will hold a lecture by Esther Boyd-Alkalay, consulting conservator,\n",
"Israel Antiquities Authority, on \"Preserving the Dead Sea Scrolls\"\n",
"in the Mumford Room, LM-649, James Madison Memorial Building, The\n",
"Library of Congress, 101 Independence Ave., S.E., Washington, D.C.\n",
" ------\n",
" NOTE A: For more information about admission to the symposium,\n",
"please contact, in writing, Dr. Michael Grunberger, head, Hebraic\n",
"Section, African and Middle Eastern Division, Library of Congress,\n",
"Washington, D.C. 20540.\n",
" -30-\n",
"--\n",
"Canada Remote Systems - Toronto, Ontario\n",
"416-629-7000/629-7044\n",
"\n"
]
}
],
"source": [
"print newsgroups_train.data[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Annotating using CoreNLP\n",
"\n",
"If you do not have CoreNLP, download it from here:\n",
"\n",
"http://stanfordnlp.github.io/CoreNLP/index.html#download\n",
"\n",
"We are going to use the Java server feature of CoreNLP to annotate data in python. In the CoreNLP directory, run the server:\n",
"\n",
"```bash\n",
"java -mx4g -cp \"*\" edu.stanford.nlp.pipeline.StanfordCoreNLPServer\n",
"```\n",
"\n",
"Next, we'll annotate an example to see how the server works."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
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"{u'index': 0,\n",
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" u'before': u' ',\n",
" u'characterOffsetBegin': 401,\n",
" u'characterOffsetEnd': 403,\n",
" u'index': 69,\n",
" u'originalText': u'on',\n",
" u'pos': u'IN',\n",
" u'word': u'on'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 404,\n",
" u'characterOffsetEnd': 407,\n",
" u'index': 70,\n",
" u'originalText': u'the',\n",
" u'pos': u'DT',\n",
" u'word': u'the'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 408,\n",
" u'characterOffsetEnd': 412,\n",
" u'index': 71,\n",
" u'originalText': u'Dead',\n",
" u'pos': u'NNP',\n",
" u'word': u'Dead'},\n",
" {u'after': u' \\n',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 413,\n",
" u'characterOffsetEnd': 416,\n",
" u'index': 72,\n",
" u'originalText': u'Sea',\n",
" u'pos': u'NNP',\n",
" u'word': u'Sea'},\n",
" {u'after': u' ',\n",
" u'before': u' \\n',\n",
" u'characterOffsetBegin': 418,\n",
" u'characterOffsetEnd': 425,\n",
" u'index': 73,\n",
" u'originalText': u'Scrolls',\n",
" u'pos': u'NNP',\n",
" u'word': u'Scrolls'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 426,\n",
" u'characterOffsetEnd': 430,\n",
" u'index': 74,\n",
" u'originalText': u'will',\n",
" u'pos': u'MD',\n",
" u'word': u'will'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 431,\n",
" u'characterOffsetEnd': 433,\n",
" u'index': 75,\n",
" u'originalText': u'be',\n",
" u'pos': u'VB',\n",
" u'word': u'be'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 434,\n",
" u'characterOffsetEnd': 438,\n",
" u'index': 76,\n",
" u'originalText': u'held',\n",
" u'pos': u'VBN',\n",
" u'word': u'held'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 439,\n",
" u'characterOffsetEnd': 441,\n",
" u'index': 77,\n",
" u'originalText': u'at',\n",
" u'pos': u'IN',\n",
" u'word': u'at'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 442,\n",
" u'characterOffsetEnd': 445,\n",
" u'index': 78,\n",
" u'originalText': u'the',\n",
" u'pos': u'DT',\n",
" u'word': u'the'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 446,\n",
" u'characterOffsetEnd': 453,\n",
" u'index': 79,\n",
" u'originalText': u'Library',\n",
" u'pos': u'NNP',\n",
" u'word': u'Library'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 454,\n",
" u'characterOffsetEnd': 456,\n",
" u'index': 80,\n",
" u'originalText': u'of',\n",
" u'pos': u'IN',\n",
" u'word': u'of'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 457,\n",
" u'characterOffsetEnd': 465,\n",
" u'index': 81,\n",
" u'originalText': u'Congress',\n",
" u'pos': u'NNP',\n",
" u'word': u'Congress'},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 466,\n",
" u'characterOffsetEnd': 468,\n",
" u'index': 82,\n",
" u'originalText': u'on',\n",
" u'pos': u'IN',\n",
" u'word': u'on'},\n",
" {u'after': u'',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 469,\n",
" u'characterOffsetEnd': 478,\n",
" u'index': 83,\n",
" u'originalText': u'Wednesday',\n",
" u'pos': u'NNP',\n",
" u'word': u'Wednesday'},\n",
" {u'after': u'\\n',\n",
" u'before': u'',\n",
" u'characterOffsetBegin': 478,\n",
" u'characterOffsetEnd': 479,\n",
" u'index': 84,\n",
" u'originalText': u',',\n",
" u'pos': u',',\n",
" u'word': u','},\n",
" {u'after': u' ',\n",
" u'before': u'\\n',\n",
" u'characterOffsetBegin': 480,\n",
" u'characterOffsetEnd': 485,\n",
" u'index': 85,\n",
" u'originalText': u'April',\n",
" u'pos': u'NNP',\n",
" u'word': u'April'},\n",
" {u'after': u'',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 486,\n",
" u'characterOffsetEnd': 488,\n",
" u'index': 86,\n",
" u'originalText': u'21',\n",
" u'pos': u'CD',\n",
" u'word': u'21'},\n",
" {u'after': u' ',\n",
" u'before': u'',\n",
" u'characterOffsetBegin': 488,\n",
" u'characterOffsetEnd': 489,\n",
" u'index': 87,\n",
" u'originalText': u',',\n",
" u'pos': u',',\n",
" u'word': u','},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 490,\n",
" u'characterOffsetEnd': 493,\n",
" u'index': 88,\n",
" u'originalText': u'and',\n",
" u'pos': u'CC',\n",
" u'word': u'and'},\n",
" {u'after': u'',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 494,\n",
" u'characterOffsetEnd': 502,\n",
" u'index': 89,\n",
" u'originalText': u'Thursday',\n",
" u'pos': u'NNP',\n",
" u'word': u'Thursday'},\n",
" {u'after': u' ',\n",
" u'before': u'',\n",
" u'characterOffsetBegin': 502,\n",
" u'characterOffsetEnd': 503,\n",
" u'index': 90,\n",
" u'originalText': u',',\n",
" u'pos': u',',\n",
" u'word': u','},\n",
" {u'after': u' ',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 504,\n",
" u'characterOffsetEnd': 509,\n",
" u'index': 91,\n",
" u'originalText': u'April',\n",
" u'pos': u'NNP',\n",
" u'word': u'April'},\n",
" {u'after': u'',\n",
" u'before': u' ',\n",
" u'characterOffsetBegin': 510,\n",
" u'characterOffsetEnd': 512,\n",
" u'index': 92,\n",
" u'originalText': u'22',\n",
" u'pos': u'CD',\n",
" u'word': u'22'},\n",
" {u'after': u' ',\n",
" u'before': u'',\n",
" u'characterOffsetBegin': 512,\n",
" u'characterOffsetEnd': 513,\n",
" u'index': 93,\n",
" u'originalText': u'.',\n",
" u'pos': u'.',\n",
" u'word': u'.'}]}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from stanza.corenlp.client import Client\n",
"\n",
"client = Client()\n",
"annotation = client.annotate(newsgroups_train.data[0], properties={'annotators': 'tokenize,ssplit,pos'})\n",
"annotation['sentences'][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That was rather long, but the gist is that the annotation is organized into sentences, which is then organized into tokens. Each token carries a number of annotations (we've only asked for the POS tags)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"From IN\n",
": :\n",
"[email protected] NNP\n",
"-LRB- -LRB-\n",
"Nigel NNP\n",
"Allen NNP\n",
"-RRB- -RRB-\n",
"Subject NNP\n",
": :\n",
"library NN\n",
"of IN\n",
"congress NN\n",
"to TO\n",
"host NN\n",
"dead JJ\n",
"sea NN\n",
"scroll NN\n",
"symposium NN\n",
"april NNP\n",
"21-22 CD\n",
"Lines NNPS\n",
": :\n",
"96 CD\n",
"Library NNP\n",
"of IN\n",
"Congress NNP\n",
"to TO\n",
"Host NNP\n",
"Dead NNP\n",
"Sea NNP\n",
"Scroll NNP\n",
"Symposium NNP\n",
"April NNP\n",
"21-22 CD\n",
"To TO\n",
": :\n",
"National NNP\n",
"and CC\n",
"Assignment NNP\n",
"desks NNS\n",
", ,\n",
"Daybook NNP\n",
"Editor NNP\n",
"Contact NN\n",
": :\n",
"John NNP\n",
"Sullivan NNP\n",
", ,\n",
"202-707-9216 CD\n",
", ,\n",
"or CC\n",
"Lucy NNP\n",
"Suddreth NNP\n",
", ,\n",
"202-707-9191 CD\n",
"both DT\n",
"of IN\n",
"the DT\n",
"Library NNP\n",
"of IN\n",
"Congress NNP\n",
"WASHINGTON NNP\n",
", ,\n",
"April NNP\n",
"19 CD\n",
"-- :\n",
"A DT\n",
"symposium NN\n",
"on IN\n",
"the DT\n",
"Dead NNP\n",
"Sea NNP\n",
"Scrolls NNP\n",
"will MD\n",
"be VB\n",
"held VBN\n",
"at IN\n",
"the DT\n",
"Library NNP\n",
"of IN\n",
"Congress NNP\n",
"on IN\n",
"Wednesday NNP\n",
", ,\n",
"April NNP\n",
"21 CD\n",
", ,\n",
"and CC\n",
"Thursday NNP\n",
", ,\n",
"April NNP\n",
"22 CD\n",
". .\n"
]
}
],
"source": [
"for token in annotation['sentences'][0]['tokens']:\n",
" print token['word'], token['pos']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For our purpose, we're actually going to just take the document as a long sequence of words as opposed to a sequence of sequences (eg. a list of sentences of words). We'll do this by passing in the `ssplit.isOneSentence` flag."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"99 99\n"
]
}
],
"source": [
"docs = []\n",
"labels = []\n",
"for doc, label in zip(newsgroups_train.data, newsgroups_train.target)[:100]:\n",
" try:\n",
" annotation = client.annotate(doc, properties={'annotators': 'tokenize,ssplit', 'ssplit.isOneSentence': True})\n",
" docs.append([t['word'] for t in annotation['sentences'][0]['tokens']])\n",
" labels.append(label)\n",
" except Exception as e:\n",
" pass # we're going to punt and ignore unicode errors...\n",
"print len(docs), len(labels)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll create a lightweight dataset object out of this. A `Dataset` is really a glorified dictionary of fields, where each field corresponds to an attribute of the examples in the dataset."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Dataset(Y, X)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from stanza.text.dataset import Dataset\n",
"dataset = Dataset({'X': docs, 'Y': labels})\n",
"\n",
"# dataset supports, amongst other functionalities, shuffling:\n",
"dataset.shuffle()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Y', 'X']\n"
]
}
],
"source": [
"# indexing of a single element\n",
"print dataset[0].keys()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: 69, test: 30\n"
]
}
],
"source": [
"# indexing of multiple elements\n",
"n_train = int(0.7 * len(dataset))\n",
"train = Dataset(dataset[:n_train])\n",
"test = Dataset(dataset[n_train:])\n",
"\n",
"print 'train: {}, test: {}'.format(len(train), len(test))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating vocabulary and mapping to vector space\n",
"\n",
"Stanza provides means to convert words to vocabularies (eg. map to indices and back). We also provide convienient means of loading pretrained embeddings such as `Senna` and `Glove`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"OrderedDict([('***UNK***', 0)])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from stanza.text.vocab import Vocab\n",
"vocab = Vocab('***UNK***')\n",
"vocab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll try our hands at some conversions:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"I like cats and dogs [1, 2, 3, 4, 5]\n",
"recovered: ['I', 'like', 'cats', 'and', 'dogs']\n",
"\n",
"I like nothing [1, 2, 6]\n",
"recovered: ['I', 'like', 'nothing']\n",
"\n",
"I like cats and nothing else [1, 2, 3, 4, 6, 0]\n",
"recovered: ['I', 'like', 'cats', 'and', 'nothing', '***UNK***']\n",
"\n"
]
}
],
"source": [
"sents = ['I like cats and dogs', 'I like nothing', 'I like cats and nothing else']\n",
"inds = []\n",
"\n",
"# `vocab.update` adds the list of words to the Vocab object.\n",
"# It also returns the list of words as ints.\n",
"for s in sents[:2]:\n",
" inds.append(vocab.update(s.split()))\n",
"\n",
"# `vocab.words2indices` converts the list of words to ints (but does not update the vocab)\n",
"inds.append(vocab.words2indices(sents[2].split()))\n",
"\n",
"for s, ind in zip(sents, inds):\n",
" print '{:50}{}\\nrecovered: {}'.format(s, ind, vocab.indices2words(ind))\n",
" print"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A common operation to do with vocabular objects is to replace rare words with UNKNOWN tokens. We'll convert words that occured less than some number of times."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Counter({'***UNK***': 0,\n",
" 'I': 6,\n",
" 'and': 3,\n",
" 'cats': 3,\n",
" 'dogs': 3,\n",
" 'like': 6,\n",
" 'nothing': 3})"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vocab.counts"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['I', 'like', '***UNK***', '***UNK***', '***UNK***']\n",
"['I', 'like', '***UNK***']\n",
"['I', 'like', '***UNK***', '***UNK***', '***UNK***', '***UNK***']\n"
]
}
],
"source": [
"# this is actually a copy operation, because indices change when words are removed from the vocabulary\n",
"vocab = vocab.prune_rares(cutoff=6)\n",
"for s in sents:\n",
" inds = vocab.words2indices(s.split())\n",
" print vocab.indices2words(inds)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we'll convert the entire dataset. The `convert` function applies a transform to the specified field of the dataset. We'll apply a transform using the vocabulary."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: 69, test: 30\n",
"vocab size: Vocab(668 words)\n",
"sequence max len: 200\n",
"\n",
"OrderedDict([('Y', [0, 1]), ('X', [[1, 2, 339, 4, 340, 341, 342, 7, 8, 2, 9, 2, 0, 0, 0, 80, 2, 346, 347, 12, 348, 39, 349, 14, 2, 0, 350, 2, 0, 320, 0, 0, 16, 2, 182, 69, 70, 41, 481, 96, 136, 578, 153, 39, 58, 126, 138, 355, 182, 0, 43, 38, 0, 124, 39, 111, 69, 0, 67, 37, 77, 49, 69, 70, 257, 269, 257, 12, 31, 182, 0, 138, 89, 151, 73, 0, 39, 28, 58, 37, 399, 596, 90, 43, 49, 17, 0, 194, 0, 0, 0, 0, 195, 37, 73, 0, 131, 31, 0, 39, 28, 31, 0, 17, 48, 73, 0, 12, 31, 0, 39, 0, 48, 0, 17, 0, 283, 33, 0, 245, 111, 0, 49, 479, 39, 51, 481, 0, 31, 0, 26, 226, 149, 147, 0, 12, 31, 0, 26, 17, 238, 0, 39, 69, 52, 425, 67, 133, 61, 0, 66, 0, 49, 17, 0, 39, 142, 323, 0, 0, 131, 95, 455, 39, 159, 67, 37, 231, 142, 17, 481, 149, 52, 0, 33, 590, 49, 104, 37, 0, 367, 148, 26, 10, 0, 0, 26, 0, 75, 89, 0, 0, 26, 107, 0, 0, 26, 10, 542, 217], [1, 2, 0, 4, 0, 486, 7, 8, 2, 0, 2, 124, 85, 61, 73, 639, 26, 4, 105, 9, 2, 409, 0, 0, 0, 26, 80, 2, 486, 0, 14, 2, 0, 0, 4, 0, 4, 0, 7, 0, 7, 16, 2, 17, 18, 0, 0, 39, 31, 0, 90, 31, 0, 12, 31, 0, 37, 17, 194, 0, 496, 0, 195, 375, 67, 37, 0, 0, 33, 0, 4, 0, 7, 28, 31, 17, 0, 0, 49, 479, 37, 31, 0, 0, 4, 0, 0, 0, 7, 0, 17, 0, 152, 75, 0, 31, 145, 639, 26, 69, 105, 151, 267, 0, 470, 0, 28, 0, 31, 0, 131, 231, 12, 31, 0, 105, 0, 39, 0, 28, 0, 49, 18, 31, 0, 194, 0, 90, 0, 195, 31, 0, 0, 133, 105, 194, 0, 496, 0, 49, 195, 524, 87, 0, 0, 67, 151, 31, 486, 0, 28, 0, 0, 33, 0, 155, 0, 90, 0, 49, 468, 0, 33, 31, 0, 36, 61, 67, 0, 0, 13, 12, 0, 0, 28, 31, 237, 2, 69, 76, 0, 461, 69, 500, 0, 131, 67, 0, 305, 31, 0, 48, 13, 12, 0, 0, 0, 31, 0, 107]])])\n"
]
}
],
"source": [
"from stanza.text.vocab import SennaVocab\n",
"vocab = SennaVocab()\n",
"\n",
"# we'll actually just use the first 200 tokens of the document\n",
"max_len = 200\n",
"train = train.convert({'X': lambda x: x[:max_len]}, in_place=True)\n",
"test = test.convert({'X': lambda x: x[:max_len]}, in_place=True)\n",
" \n",
"# make a backup\n",
"train_orig = train\n",
"test_orig = test\n",
"\n",
"train = train_orig.convert({'X': vocab.update}, in_place=False)\n",
"vocab = vocab.prune_rares(cutoff=3)\n",
"train = train_orig.convert({'X': vocab.words2indices}, in_place=False)\n",
"test = test_orig.convert({'X': vocab.words2indices}, in_place=False)\n",
"pad_index = vocab.add('***PAD***', count=100)\n",
"\n",
"max_len = max([len(x) for x in train.fields['X'] + test.fields['X']])\n",
"\n",
"print 'train: {}, test: {}'.format(len(train), len(test))\n",
"print 'vocab size: {}'.format(vocab)\n",
"print 'sequence max len: {}'.format(max_len)\n",
"print\n",
"print test[:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training a model\n",
"\n",
"At this point, you're welcome to use whatever program/model/package you like to run your experiments. We'll go with TensorFlow. In particular, we'll define a LSTM classifier."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model definition\n",
"\n",
"We'll define a lookup table, a LSTM, and a linear classifier."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import tensorflow as tf \n",
"from tensorflow.models.rnn import rnn \n",
"from tensorflow.models.rnn.rnn_cell import LSTMCell\n",
"from stanza.ml.tensorflow_utils import labels_to_onehots\n",
"import numpy as np\n",
"\n",
"np.random.seed(42) \n",
"embedding_size = 50\n",
"hidden_size = 100\n",
"seq_len = max_len\n",
"vocab_size = len(vocab)\n",
"class_size = len(classes)\n",
"\n",
"# symbolic variable for word indices\n",
"indices = tf.placeholder(tf.int32, [None, seq_len])\n",
"# symbolic variable for labels\n",
"labels = tf.placeholder(tf.float32, [None, class_size])\n",
"\n",
"# lookup table\n",
"with tf.device('/cpu:0'), tf.name_scope(\"embedding\"):\n",
" E = tf.Variable(\n",
" tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),\n",
" name=\"emb\")\n",
" embeddings = tf.nn.embedding_lookup(E, indices)\n",
" embeddings_list = [tf.squeeze(t, [1]) for t in tf.split(1, seq_len, embeddings)]\n",
"\n",
"# rnn\n",
"cell = LSTMCell(hidden_size, embedding_size) \n",
"outputs, states = rnn.rnn(cell, embeddings_list, dtype=tf.float32)\n",
"final_output = outputs[-1]\n",
"\n",
"# classifier\n",
"def weights(shape):\n",
" return tf.Variable(tf.random_normal(shape, stddev=0.01))\n",
"scores = tf.matmul(final_output, weights((hidden_size, class_size)))\n",
"\n",
"# objective\n",
"cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(scores, labels))\n",
"\n",
"# operations\n",
"train_op = tf.train.AdamOptimizer(0.001, 0.9).minimize(cost)\n",
"predict_op = tf.argmax(scores, 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training\n",
"\n",
"We'll train the network for a fixed number of epochs and then evaluate on the test set. This is a relatively simple procedure without tuning, regularization and early stopping."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0\n",
"train cost: 0.693798243999, acc: 0.463768115942\n",
"time elapsed: 2.13884997368\n",
"epoch: 1\n",
"train cost: 0.689658164978, acc: 0.608695652174\n",
"time elapsed: 0.947463989258\n",
"epoch: 2\n",
"train cost: 0.685618042946, acc: 0.608695652174\n",
"time elapsed: 0.931604862213\n",
"epoch: 3\n",
"train cost: 0.681350648403, acc: 0.594202898551\n",
"time elapsed: 0.989146947861\n",
"epoch: 4\n",
"train cost: 0.676672458649, acc: 0.608695652174\n",
"time elapsed: 0.967782974243\n",
"epoch: 5\n",
"train cost: 0.6715965271, acc: 0.608695652174\n",
"time elapsed: 0.938482046127\n",
"epoch: 6\n",
"train cost: 0.666440963745, acc: 0.594202898551\n",
"time elapsed: 1.01319694519\n",
"epoch: 7\n",
"train cost: 0.661608576775, acc: 0.594202898551\n",
"time elapsed: 0.951257944107\n",
"epoch: 8\n",
"train cost: 0.656547665596, acc: 0.594202898551\n",
"time elapsed: 0.969254016876\n",
"epoch: 9\n",
"train cost: 0.64949887991, acc: 0.594202898551\n",
"time elapsed: 0.979185819626\n",
"--------------------\n",
"test cost: 0.700526297092, acc: 0.566666666667\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"from time import time\n",
"batch_size = 128\n",
"num_epochs = 10\n",
"\n",
"def run_epoch(split, train=False):\n",
" epoch_cost = 0\n",
" epoch_pred = []\n",
" for i in xrange(0, len(split), batch_size):\n",
" batch = split[i: i+batch_size]\n",
" n = len(batch['Y'])\n",
" X = Dataset.pad(batch['X'], pad_index, seq_len)\n",
" Y = np.zeros((n, class_size))\n",
" Y[np.arange(n), np.array(batch['Y'])] = 1\n",
" if train:\n",
" batch_cost, batch_pred, _ = session.run(\n",
" [cost, predict_op, train_op], {indices: X, labels: Y})\n",
" else:\n",
" batch_cost, batch_pred = session.run(\n",
" [cost, predict_op], {indices: X, labels: Y})\n",
" epoch_cost += batch_cost * n\n",
" epoch_pred += batch_pred.flatten().tolist()\n",
" return epoch_cost, epoch_pred\n",
"\n",
"def train_eval(session):\n",
" for epoch in xrange(num_epochs):\n",
" start = time()\n",
" print 'epoch: {}'.format(epoch)\n",
" epoch_cost, epoch_pred = run_epoch(train, True)\n",
" print 'train cost: {}, acc: {}'.format(epoch_cost/len(train),\n",
" accuracy_score(train.fields['Y'], epoch_pred))\n",
" print 'time elapsed: {}'.format(time() - start)\n",
" \n",
" test_cost, test_pred = run_epoch(test, False)\n",
" print '-' * 20\n",
" print 'test cost: {}, acc: {}'.format(test_cost/len(test),\n",
" accuracy_score(test.fields['Y'], test_pred))\n",
"\n",
"with tf.Session() as session:\n",
" tf.set_random_seed(123)\n",
" session.run(tf.initialize_all_variables())\n",
" train_eval(session)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember how we used `SennaVocab`? Let's see what happens if we preinitialize our embeddings:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch: 0\n",
"train cost: 0.691315352917, acc: 0.536231884058\n",
"time elapsed: 2.2313709259\n",
"epoch: 1\n",
"train cost: 0.685225009918, acc: 0.565217391304\n",
"time elapsed: 0.93435382843\n",
"epoch: 2\n",
"train cost: 0.679915368557, acc: 0.594202898551\n",
"time elapsed: 0.934975862503\n",
"epoch: 3\n",
"train cost: 0.675073981285, acc: 0.594202898551\n",
"time elapsed: 0.968421220779\n",
"epoch: 4\n",
"train cost: 0.670495569706, acc: 0.594202898551\n",
"time elapsed: 0.991052865982\n",
"epoch: 5\n",
"train cost: 0.666101515293, acc: 0.594202898551\n",
"time elapsed: 0.95667886734\n",
"epoch: 6\n",
"train cost: 0.661866605282, acc: 0.594202898551\n",
"time elapsed: 0.931576013565\n",
"epoch: 7\n",
"train cost: 0.657688558102, acc: 0.594202898551\n",
"time elapsed: 0.932205915451\n",
"epoch: 8\n",
"train cost: 0.653176009655, acc: 0.594202898551\n",
"time elapsed: 1.01794791222\n",
"epoch: 9\n",
"train cost: 0.647635579109, acc: 0.594202898551\n",
"time elapsed: 0.996000051498\n",
"--------------------\n",
"test cost: 0.698161303997, acc: 0.566666666667\n"
]
}
],
"source": [
"preinit_op = E.assign(vocab.get_embeddings())\n",
"with tf.Session() as session:\n",
" tf.set_random_seed(123)\n",
" session.run(tf.initialize_all_variables())\n",
" session.run(preinit_op)\n",
" train_eval(session)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
dwhswenson/openpathsampling | examples/alanine_dipeptide_tps/AD_tps_4_advanced.ipynb | 3 | 94383 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Advanced analysis techniques\n",
"\n",
"Now we'll move on to a few more advanced analysis techniques. (These are discussed in Paper II.)\n",
"\n",
"With the fixed path length ensemble, we should check for recrossings. To do this, we create an ensemble which represents the recrossing paths: a frame in $\\beta$, possible frames in neither $\\alpha$ nor $\\beta$, and then a frame in $\\alpha$.\n",
"\n",
"Then we check whether any subtrajectory of a trial trajectory matches that ensemble, by using the `Ensemble.split()` function. We can then further refine to see which steps that included trials with recrossings were actually accepted."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"%matplotlib inline\n",
"import openpathsampling as paths\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from tqdm.auto import tqdm\n",
"import os\n",
"import openpathsampling.visualize as ops_vis\n",
"from IPython.display import SVG"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 8.36 s, sys: 1.61 s, total: 9.97 s\n",
"Wall time: 34.6 s\n"
]
}
],
"source": [
"%%time\n",
"flexible = paths.Storage(\"ad_tps.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 24.6 s, sys: 5.35 s, total: 30 s\n",
"Wall time: 1min 40s\n"
]
}
],
"source": [
"%%time\n",
"fixed = paths.Storage(\"ad_fixed_tps.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"flex_network = flexible.networks['tps_network']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# create the ensemble that identifies recrossings\n",
"alpha = fixed.volumes['C_7eq']\n",
"beta = fixed.volumes['alpha_R']\n",
"recrossing_ensemble = paths.SequentialEnsemble([\n",
" paths.LengthEnsemble(1) & paths.AllInXEnsemble(beta),\n",
" paths.OptionalEnsemble(paths.AllOutXEnsemble(alpha | beta)),\n",
" paths.LengthEnsemble(1) & paths.AllInXEnsemble(alpha) \n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "246784e0794347ffb070136d42a07e74",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=10001.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"CPU times: user 27min 18s, sys: 7.71 s, total: 27min 26s\n",
"Wall time: 27min 54s\n"
]
}
],
"source": [
"%%time\n",
"# now we check each step to see if its trial has a recrossing\n",
"steps_with_recrossing = []\n",
"for step in tqdm(fixed.steps):\n",
" # trials is a list of samples: with shooting, only one in the list\n",
" recrossings = [] # default for initial empty move (no trials in step[0].change)\n",
" for trial in step.change.trials:\n",
" recrossings = recrossing_ensemble.split(trial.trajectory)\n",
" # recrossing contains a list with the recrossing trajectories\n",
" # (len(recrossing) == 0 if no recrossings)\n",
" if len(recrossings) > 0:\n",
" steps_with_recrossing += [step] # save for later analysis"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"accepted_recrossings = [step for step in steps_with_recrossing if step.change.accepted is True]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trials with recrossings: 532\n",
"Accepted trials with recrossings: 113\n"
]
}
],
"source": [
"print(\"Trials with recrossings:\", len(steps_with_recrossing))\n",
"print(\"Accepted trials with recrossings:\", len(accepted_recrossings))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the accepted trials with recrossing does not account for how long the trial remained active. It also doesn't tell us whether the trial represented a new recrossing event, or was correlated with the previous recrossing event."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at one of the accepted trajectories with a recrossing event. We'll plot the value of $\\psi$, since this is what distinguishes the two states. We'll also select the frames that are actually inside each state and color them (red for $\\alpha$, blue for $\\beta$)."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x14a421da4d10>]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psi = fixed.cvs['psi']\n",
"trajectory = accepted_recrossings[0].active[0].trajectory\n",
"in_alpha_indices = [trajectory.index(s) for s in trajectory if alpha(s)]\n",
"in_alpha_psi = [psi(trajectory)[i] for i in in_alpha_indices]\n",
"in_beta_indices = [trajectory.index(s) for s in trajectory if beta(s)]\n",
"in_beta_psi = [psi(trajectory)[i] for i in in_beta_indices]\n",
"\n",
"plt.plot(psi(trajectory), 'k-')\n",
"plt.plot(in_alpha_indices, in_alpha_psi, 'ro') # alpha in red\n",
"plt.plot(in_beta_indices, in_beta_psi, 'bo') # beta in blue"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's see how many recrossing events there are in each accepted trial. If there's one recrossing, then the trajectory must go $\\alpha\\to\\beta\\to\\alpha\\to\\beta$ to be accepted. Two recrossings would mean $\\alpha\\to\\beta\\to\\alpha\\to\\beta\\to\\alpha\\to\\beta$."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"recrossings_per = []\n",
"for step in accepted_recrossings:\n",
" for test in step.change.trials:\n",
" recrossings_per.append(len(recrossing_ensemble.split(test.trajectory)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
]
}
],
"source": [
"print(recrossings_per)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"113\n",
"113\n",
"0\n"
]
}
],
"source": [
"# these numbers come from accepted trial steps, not all steps\n",
"print(sum(recrossings_per))\n",
"print(len(recrossings_per))\n",
"print(len([x for x in recrossings_per if x==2]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing the fixed and flexible simulations"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "378f736f3e634a37aa8eb9a6bbe73b88",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=10001.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"CPU times: user 6h 11min 58s, sys: 1min 21s, total: 6h 13min 20s\n",
"Wall time: 6h 16min 54s\n"
]
}
],
"source": [
"%%time\n",
"# transition path length distribution\n",
"flex_ens = flex_network.sampling_ensembles[0]\n",
"fixed_transition_segments = sum([flex_ens.split(step.active[0].trajectory)\n",
" for step in tqdm(fixed.steps)],[])\n",
"fixed_transition_length = [len(traj) for traj in fixed_transition_segments]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"flexible_transition_length = [len(s.active[0].trajectory) for s in flexible.steps]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10232\n"
]
}
],
"source": [
"print(len(fixed_transition_length))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.linspace(0, 400, 80);\n",
"plt.hist(flexible_transition_length, bins, alpha=0.5, density=True, label=\"flexible\");\n",
"plt.hist(fixed_transition_length, bins, alpha=0.5, density=True, label=\"fixed\");\n",
"plt.legend(loc='upper right');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Identifying different mechanisms using custom ensembles\n",
"\n",
"We expected the plot above to be very similar for both cases. However, we know that the $\\alpha\\to\\beta$ transition in alanine dipeptide can occur via two mechanisms: since $\\psi$ is periodic, the transition can occur due to an overall increase in $\\psi$, or due to an overall decrease in $\\psi$. We also know that the alanine dipeptide transitions aren't actually all that rare, so they will occur spontaneously in long simulations.\n",
"\n",
"\n",
"This section shows how to create custom ensembles to identify whether the transition occurred with an increasing $\\psi$ or a decreasing $\\psi$. We also need to account for (unlikely) edge cases where the path starts in one direction but completes the transition from the other."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we'll create a few more `Volume` objects. In this case, we will completely tile the Ramachandran space; while a complete tiling isn't necessary, it is often useful."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# first, we fully subdivide the Ramachandran space\n",
"phi = fixed.cvs['phi']\n",
"deg = 180.0/np.pi\n",
"nml_plus = paths.PeriodicCVDefinedVolume(psi, -160/deg, -100/deg, -np.pi, np.pi)\n",
"nml_minus = paths.PeriodicCVDefinedVolume(psi, 0/deg, 100/deg, -np.pi, np.pi)\n",
"nml_alpha = (paths.PeriodicCVDefinedVolume(phi, 0/deg, 180/deg, -np.pi, np.pi) &\n",
" paths.PeriodicCVDefinedVolume(psi, 100/deg, 200/deg, -np.pi, np.pi))\n",
"nml_beta = (paths.PeriodicCVDefinedVolume(phi, 0/deg, 180/deg, -np.pi, np.pi) &\n",
" paths.PeriodicCVDefinedVolume(psi, -100/deg, 0/deg, -np.pi, np.pi))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"#TODO: plot to display where these volumes are"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll create ensembles for the \"increasing\" and \"decreasing\" transitions. These transitions mark a crossing of either the `nml_plus` or the `nml_minus`. These aren't necessarily $\\alpha\\to\\beta$ transitions. However, any $\\alpha\\to\\beta$ transition must contain at least one subtrajectory which satsifies one of these ensembles."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"increasing = paths.SequentialEnsemble([\n",
" paths.AllInXEnsemble(alpha | nml_alpha),\n",
" paths.AllInXEnsemble(nml_plus),\n",
" paths.AllInXEnsemble(beta | nml_beta)\n",
"])\n",
"decreasing = paths.SequentialEnsemble([\n",
" paths.AllInXEnsemble(alpha | nml_alpha),\n",
" paths.AllInXEnsemble(nml_minus),\n",
" paths.AllInXEnsemble(beta | nml_beta)\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we'll write a little function that characterizes a set of trajectories according to these ensembles. It returns a dictionary mapping the ensemble (`increasing` or `decreasing`) to a list of trajectories that have a subtrajectory that satisfies it (at least one entry in `ensemble.split(trajectory)`). That dictionary also contains keys for `'multiple'` matched ensembles and `None` if no ensemble was matched. Trajectories for either of these keys would need to be investigated further."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"def categorize_transitions(ensembles, trajectories):\n",
" results = {ens : [] for ens in ensembles + ['multiple', None]}\n",
" for traj in trajectories:\n",
" matched_ens = None\n",
" for ens in ensembles:\n",
" if len(ens.split(traj)) > 0:\n",
" if matched_ens is not None:\n",
" matched_ens = 'multiple'\n",
" else:\n",
" matched_ens = ens\n",
" results[matched_ens].append(traj)\n",
" return results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With that function defined, let's use it!"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"categorized = categorize_transitions(ensembles=[increasing, decreasing],\n",
" trajectories=fixed_transition_segments)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"increasing: 7185\n",
"decreasing: 3047\n",
" multiple: 0\n",
" None: 0\n"
]
}
],
"source": [
"print(\"increasing:\", len(categorized[increasing]))\n",
"print(\"decreasing:\", len(categorized[decreasing]))\n",
"print(\" multiple:\", len(categorized['multiple']))\n",
"print(\" None:\", len(categorized[None]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing to the flexible length simulation:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"flex_trajs = [step.active[0].trajectory for step in flexible.steps]\n",
"flex_categorized = categorize_transitions(ensembles=[increasing, decreasing],\n",
" trajectories=flex_trajs[::10])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"increasing: 0\n",
"decreasing: 1001\n",
" multiple: 0\n",
" None: 0\n"
]
}
],
"source": [
"print(\"increasing:\", len(flex_categorized[increasing]))\n",
"print(\"decreasing:\", len(flex_categorized[decreasing]))\n",
"print(\" multiple:\", len(flex_categorized['multiple']))\n",
"print(\" None:\", len(flex_categorized[None]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the fixed length sampling is somehow capturing both kinds of transitions (probably because they are not really that rare). Let's see what the path length distribution from only the decreasing transitions looks"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD4CAYAAADlwTGnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAR00lEQVR4nO3df6xkZX3H8fenK9immiCymg0/utCsTTdNg2QDa2wM1NrubpuuxtpAU6CUZiVhE6WautSk0viH1Ki0JGQ3KBuhtVITbdyYTSihGGNSlEURWSmypRQWNuz6C21IStFv/5izZZj768z9Nffe5/1KJnfOmefM/c6Te+czz3N+TKoKSVJ7fm7SBUiSJsMAkKRGGQCS1CgDQJIaZQBIUqNeMekCxnHGGWfUxo0bJ12GJK0qDzzwwPeqav3o+lUVABs3buTQoUOTLkOSVpUk/zXdeqeAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUavqTOA1496PvHz5kusnU4ekpjkCkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqVK8ASLItyaNJjiTZM83jSXJz9/hDSS7o1p+d5N4kjyQ5nOQ9Q9ucnuTuJI91P1+zeC9LkjSXOQMgyTrgFmA7sBm4LMnmkWbbgU3dbRewt1v/IvC+qvpVYCtw7dC2e4B7qmoTcE+3LElaJn1GABcCR6rq8ap6AbgT2DnSZidwRw3cB5yWZENVHauqbwBU1U+AR4Azh7a5vbt/O/D2hb0USdI4+gTAmcBTQ8tHeelNvHebJBuBNwJf61a9vqqOAXQ/XzfdL0+yK8mhJIdOnDjRo1xJUh99AiDTrKtx2iR5FfB54L1V9eP+5UFV3VpVW6pqy/r168fZVJI0iz4BcBQ4e2j5LOCZvm2SnMLgzf8zVfWFoTbPJtnQtdkAHB+vdEnSQvQJgPuBTUnOTXIqcClwYKTNAeCK7migrcBzVXUsSYDbgEeq6hPTbHNld/9K4IvzfhWSpLG9Yq4GVfVikt3AXcA6YH9VHU5yTff4PuAgsAM4AjwPXNVt/mbgcuDbSR7s1v1lVR0EbgQ+l+Rq4EngXYv2qiRJc5ozAAC6N+yDI+v2Dd0v4Npptvsq0+8foKq+D7x1nGIlSYvHM4ElqVEGgCQ1ygCQpEYZAJLUKANAkhplAEhSowwASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNesWkCxBw70devnzJ9ZOpQ1JTHAFIUqMMAElqlFNAK9HwlJDTQZKWiCMASWqUASBJjTIAJKlRBoAkNcoAkKRGGQCS1KheAZBkW5JHkxxJsmeax5Pk5u7xh5JcMPTY/iTHkzw8ss0NSZ5O8mB327HwlyNJ6mvOAEiyDrgF2A5sBi5Lsnmk2XZgU3fbBewdeuzTwLYZnv6mqjq/ux0cs3ZJ0gL0GQFcCBypqser6gXgTmDnSJudwB01cB9wWpINAFX1FeAHi1m0JGnh+gTAmcBTQ8tHu3XjtpnO7m7KaH+S10zXIMmuJIeSHDpx4kSPp5Qk9dEnADLNuppHm1F7gV8GzgeOAR+frlFV3VpVW6pqy/r16+d4SklSX30C4Chw9tDyWcAz82jzMlX1bFX9tKp+BnySwVSTJGmZ9LkY3P3ApiTnAk8DlwJ/NNLmAIPpnDuBi4DnqurYbE+aZMNQm3cAD8/WfkXx+v2S1oA5A6CqXkyyG7gLWAfsr6rDSa7pHt8HHAR2AEeA54GrTm6f5LPAxcAZSY4CH6qq24CPJjmfwVTRE8C7F+9lSZLm0uty0N0hmgdH1u0bul/AtTNse9kM6y/vX6YkabF5JrAkNcoAkKRGGQCS1CgDQJIaZQBIUqMMAElqlAEgSY0yACSpUQaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKANAkhrV60vhtbj+7fHvv2z5Tee9dkKVSGqZIwBJapQBIEmNMgAkqVEGgCQ1ygCQpEYZAJLUKA8DXQ73fmTSFUjSFI4AJKlRBoAkNcoAkKRGGQCS1CgDQJIa5VFAa83oEUeXXD+ZOiSteI4AJKlRvQIgybYkjyY5kmTPNI8nyc3d4w8luWDosf1Jjid5eGSb05PcneSx7udrFv5yJEl9zRkASdYBtwDbgc3AZUk2jzTbDmzqbruAvUOPfRrYNs1T7wHuqapNwD3dsiRpmfTZB3AhcKSqHgdIciewE/jOUJudwB1VVcB9SU5LsqGqjlXVV5JsnOZ5dwIXd/dvB74MfGBer2KVW8gXxNx093dftnyde3Uk9dRnCuhM4Kmh5aPdunHbjHp9VR0D6H6+brpGSXYlOZTk0IkTJ3qUK0nqo8/nxUyzrubRZl6q6lbgVoAtW7YsynOuZqOf+CVpvvoEwFHg7KHls4Bn5tFm1LMnp4mSbACO96hlVRqd4hmn/ZsuWexqJGmgzxTQ/cCmJOcmORW4FDgw0uYAcEV3NNBW4LmT0zuzOABc2d2/EvjiGHVLkhZozhFAVb2YZDdwF7AO2F9Vh5Nc0z2+DzgI7ACOAM8DV53cPslnGezsPSPJUeBDVXUbcCPwuSRXA08C71rMFzZJo9M0WydUhyTNptcxI1V1kMGb/PC6fUP3C7h2hm0vm2H994G39q5UkrSoPGhwhXOnr6Sl4qUgJKlRBoAkNcoAkKRGGQCS1CgDQJIaZQBIUqM8DHQReKimpNXIEYAkNcoAkKRGOQWkl/iF8lJTHAFIUqMMAElqlAEgSY1yH8A8TPmGr3MmU8d0pnzBvN8oJmkGBoBm5k5haU1zCkiSGuUIoHHDZzFf51+D1BT/5Ve5rU/eOukSJK1STgFJUqMMAElqlFNAa9zolUqve9sbJlSJpJXGEYAkNcoRwBJwx6yk1cAAaM3oyV28cyJlSJo8p4AkqVGOANa4KdNR57123s+1qDuUvcyENHEGwCqzlPsXplxIbgFhIWnlcwpIkhplAEhSowwASWqU+wB6Gt4BunWCdSzUSv4yG0nLywBY4TypTNJScQpIkhrVKwCSbEvyaJIjSfZM83iS3Nw9/lCSC+baNskNSZ5O8mB327E4L0mS1MecAZBkHXALsB3YDFyWZPNIs+3Apu62C9jbc9ubqur87nZwoS9GktRfnxHAhcCRqnq8ql4A7gR2jrTZCdxRA/cBpyXZ0HNbSdIE9NkJfCbw1NDyUeCiHm3O7LHt7iRXAIeA91XVD0d/eZJdDEYVnHOOh6ysaF7eQVpV+gRApllXPdvMtu1e4MPd8oeBjwN/OqVx1a3ArQBbtmwZ/b1LZvS6N5K01vQJgKPA2UPLZwHP9Gxz6kzbVtWzJ1cm+STwpd5VS5IWrM8+gPuBTUnOTXIqcClwYKTNAeCK7migrcBzVXVstm27fQQnvQN4eIGvRZI0hjlHAFX1YpLdwF3AOmB/VR1Ock33+D7gILADOAI8D1w127bdU380yfkMpoCeAN69iK9LkjSHXmcCd4doHhxZt2/ofgHX9t22W3/5WJVq4qaelfyxidQhaXF4JrAkNcprATXOaw1J7XIEIEmNcgSgGU25dLSkNcURgCQ1yhGAlszw2dTXve0NE6xE0nQcAUhSowwASWqUASBJjXIfgOZt9IqpW5+c/xfOjz6X+wykpecIQJIa5QhA8+ZZxNLq5ghAkhplAEhSo5wCWgROhYxvymUmRnYYj71TePj7iP0uYqkXRwCS1ChHAFoyLx8Z+eUx0krjCECSGmUASFKjnALSsphy1vC4TzC8kxfc0SstAkcAktQoRwBaFUYPG33TJfN/Lq87JA0YAFqVpryJ+5csjc1/m5482UvSWmMAzMA3/OU12t/3nbNrrO2Hp4jGnR5ySkitciewJDXKEUBnwYcpalbjjqgWMgJb6Cd6RwRqhQEgLaYFnq+wUsJnpdShpWUAqDkL3d8grRUGQMedvmuX03vS9NwJLEmNcgQgjWl4RLGa5sad19coA0BrXrPTe6M7pHnnRMrQymUASAsx8iY75ZpFzH5U0Oin8jXDq7euCgaA1pyFfuIf3f6mu2c+SmjKdxvP9fjj73/58hxHIM063TTyJnvTiy//hD/u9ZGWK4zGnYqaq67lnMpaa9Novf5EkmwD/g5YB3yqqm4ceTzd4zuA54E/qapvzLZtktOBfwI2Ak8Af1hVP1z4S5LWqClTOtLCzBkASdYBtwBvA44C9yc5UFXfGWq2HdjU3S4C9gIXzbHtHuCeqroxyZ5u+QOL99Kkfia5j2CccxLmGm1wzhztRx5f0Cf+OaZ4xrl891r7VL2a9BkBXAgcqarHAZLcCewEhgNgJ3BHVRVwX5LTkmxg8Ol+pm13Ahd3298OfBkDQD2tpDft1WqlnBA3tT8/trAnHA4n9z3MKoP37FkaJH8AbKuqP+uWLwcuqqrdQ22+BNxYVV/tlu9h8Ga+caZtk/yoqk4beo4fVtVrpvn9u4CTf5m/Ajw6z9d6BvC9eW67lKxrPNY1Husaz0qtCxZW2y9V1frRlX1GAJlm3WhqzNSmz7azqqpbgQV/5EpyqKq2LPR5Fpt1jce6xmNd41mpdcHS1NbnTOCjwNlDy2cBz/RsM9u2z3bTRHQ/j/cvW5K0UH0C4H5gU5Jzk5wKXAocGGlzALgiA1uB56rq2BzbHgCu7O5fCXxxga9FkjSGOaeAqurFJLuBuxgcyrm/qg4nuaZ7fB9wkMEhoEcYHAZ61Wzbdk99I/C5JFcDTwLvWtRXNtVK3XNnXeOxrvFY13hWal2wBLXNuRNYkrQ2eTVQSWqUASBJjWoiAJJsS/JokiPdWceTrOWJJN9O8mCSQ92605PcneSx7ueU8yGWoI79SY4neXho3Yx1JLm+679Hk/zOMtd1Q5Knuz57MMmOCdR1dpJ7kzyS5HCS93TrJ9pns9Q10T5L8vNJvp7kW11df92tn3R/zVTXSvgbW5fkmxmcV7U8fVVVa/rGYOfzfwDnAacC3wI2T7CeJ4AzRtZ9FNjT3d8D/M0y1PEW4ALg4bnqADZ3/fZK4NyuP9ctY103AO+fpu1y1rUBuKC7/2rgu93vn2ifzVLXRPuMwTlAr+runwJ8jcGXsU26v2aqayX8jf058I/Al7rlJe+rFkYA/38pi6p6ATh5OYqVZCeDy2HQ/Xz7Uv/CqvoK8IOedewE7qyq/6mq/2RwtNeFy1jXTJazrmPVXeCwqn4CPAKcyYT7bJa6ZrJcdVVV/Xe3eEp3KybfXzPVNZNlqSvJWcDvAp8a+d1L2lctBMCZwFNDy0eZ/R9kqRXwL0keyOAyFwCvr8F5E3Q/Xzeh2maqYyX04e4kD3VTRCeHwhOpK8lG4I0MPj2umD4bqQsm3GfdlMaDDE7yvLuqVkR/zVAXTLa//hb4C+BnQ+uWvK9aCIAFX45ikb25qi5gcAXVa5O8ZYK19DXpPtwL/DJwPnAM+Hi3ftnrSvIq4PPAe6vqx7M1nWbdktU2TV0T77Oq+mlVnc/gCgAXJvm1WZpPuq6J9VeS3wOOV9UDfTeZZt28amohAPpcymLZVNUz3c/jwD8zGLqtlMtizFTHRPuwqp7t/ml/BnySl4a7y1pXklMYvMl+pqq+0K2eeJ9NV9dK6bOulh8xuNrvNlZAf01X14T7683A7yd5gsEU9W8m+QeWoa9aCIA+l7JYFkl+McmrT94Hfht4mJVzWYyZ6jgAXJrklUnOZfC9D19frqJO/hN03sGgz5a1riQBbgMeqapPDD000T6bqa5J91mS9UlO6+7/AvBbwL8z+f6atq5J9ldVXV9VZ1XVRgbvT/9aVX/McvTVUuzNXmk3Bpep+C6DveUfnGAd5zHYe/8t4PDJWoDXAvcAj3U/T1+GWj7LYKj7vww+UVw9Wx3AB7v+exTYvsx1/T3wbeCh7o9/wwTq+g0Gw+yHgAe7245J99ksdU20z4BfB77Z/f6Hgb+a6299wnVN/G+s+10X89JRQEveV14KQpIa1cIUkCRpGgaAJDXKAJCkRhkAktQoA0CSGmUASFKjDABJatT/AR4r/PpVqzY8AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist([len(traj) for traj in flex_categorized[decreasing]], bins, alpha=0.5, density=True);\n",
"plt.hist([len(traj) for traj in categorized[decreasing]], bins, alpha=0.5, density=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Still a little off, although this might be due to bad sampling. Let's see how many of the decorrelated trajectories have this kind of transition."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"full_fixed_tree = ops_vis.PathTree(\n",
" fixed.steps,\n",
" ops_vis.ReplicaEvolution(replica=0)\n",
")\n",
"full_history = full_fixed_tree.generator"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"114\n"
]
}
],
"source": [
"# start with the decorrelated tragectories\n",
"fixed_decorrelated = full_history.decorrelated_trajectories\n",
"# find the A->B transitions from the decorrelated trajectories\n",
"decorrelated_transitions = sum([flex_ens.split(traj) for traj in fixed_decorrelated], [])\n",
"# find the A->B transition from these which are decreasing\n",
"decorrelated_decreasing = sum([decreasing.split(traj) for traj in decorrelated_transitions], [])\n",
"print(len(decorrelated_decreasing))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So this is based off of 11 decorrelated trajectory transitions. That's not a lot of statistics.\n",
"\n",
"However, we expect to see a *very* different distribution for the \"increasing\" paths:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist([len(traj) for traj in categorized[increasing]], bins, density=True, alpha=0.5, color='g');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also check whether we go back and forth between the increasing transition and the decreasing transition, or whether there's just a single change from one type to the other."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def find_switches(ensembles, trajectories):\n",
" switches = []\n",
" last_category = None\n",
" traj_num = 0\n",
" for traj in trajectories:\n",
" category = None\n",
" for ens in ensembles:\n",
" if len(ens.split(traj)) > 0:\n",
" if category is not None:\n",
" category = 'multiple'\n",
" else:\n",
" category = ens\n",
" if last_category != category:\n",
" switches.append((category, traj_num))\n",
" traj_num += 1\n",
" last_category = category\n",
" return switches"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"switches = find_switches([increasing, decreasing], fixed_transition_segments)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 2, 39, 78, 394, 513, 584, 636, 711, 718, 1359, 1392, 1504, 1623, 1952, 2225, 2339, 2438, 2633, 2639, 2642, 2705, 2925, 2977, 3160, 3295, 3296, 3371, 3723, 3812, 4094, 4208, 4942, 4955, 5005, 5232, 5245, 5349, 5511, 5520, 5576, 5655, 5697, 5745, 5839, 5848, 5931, 5958, 6000, 6012, 6214, 6244, 6485, 6503, 6538, 6545, 6629, 6635, 6905, 7338, 7756, 7775, 7967, 7974, 8008, 8049, 8065, 8296, 8524, 8530, 8539, 8591, 9051, 9252, 9287, 9293, 9343, 9349, 9634, 9637, 9912, 10088] 10232\n"
]
}
],
"source": [
"print([switch[1] for switch in switches], len(fixed_transition_segments))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So there are a lot of switches early in the simulation, and then it gets stuck in one state for much longer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Even though we know the alanine dipeptide transitions are not particularly rare, this does give us reason to re-check the temperature. First we'll check what the intergrator says its temperature is, then we'll calculate the temperature based on the kinetic energy of every 50th trajectory.\n",
"\n",
"Note that the code below is specific to using the OpenMM engine."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"every_50th_trajectory = [step.active[0].trajectory for step in fixed.steps[::50]]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"# make a set to remove duplicates, if trajs aren't decorrelated\n",
"every_50th_traj_snapshots = list(set(sum(every_50th_trajectory, [])))\n",
"# sadly, it looks like that trick with set doesn't do any good here"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d3576e09dc5547b68ab1a1f47e3865e4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(HTML(value=''), FloatProgress(value=0.0, max=79884.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"temperatures = [snap.instantaneous_temperature for snap in tqdm(every_50th_traj_snapshots)]"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean temperature: 300.40 K\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot([T / T.unit for T in temperatures])\n",
"mean_T = np.mean(temperatures)\n",
"plt.plot([mean_T / mean_T.unit]*len(temperatures), 'r')\n",
"print(\"Mean temperature:\", np.mean(temperatures).format(\"%.2f\"))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": true,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": true
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"types_to_exclude": [
"module",
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| mit |
iannesbitt/ml_bootcamp | Big-Data-and-Spark/.ipynb_checkpoints/lecture_notes-checkpoint.ipynb | 1 | 51005 | {
"cells": [
{
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"%%javascript\n",
"$.getScript('http://asimjalis.github.io/ipyn-ext/js/ipyn-present.js')"
]
},
{
"cell_type": "markdown",
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"source": [
"<!-- \n",
"The ipynb was auto-generated from markdown using notedown.\n",
"Instead of modifying the ipynb file modify the markdown source. \n",
"-->\n",
"\n",
"<h1 class=\"tocheading\">Spark</h1>\n",
"<div id=\"toc\"></div>\n",
"\n",
"<img src=\"images/spark-logo.png\">\n",
"\n",
"Apache Spark\n",
"============\n",
"\n",
"Spark Intro\n",
"-----------\n",
"\n",
"What is Spark?\n",
"\n",
"- Spark is a framework for distributed processing.\n",
"\n",
"- It is a streamlined alternative to Map-Reduce.\n",
"\n",
"- Spark applications can be written in Python, Scala, or Java.\n",
"\n",
"Why Spark\n",
"---------\n",
"\n",
"Why learn Spark?\n",
"\n",
"- Spark enables you to analyze petabytes of data.\n",
"\n",
"- Spark skills are in high demand--<http://indeed.com/salary>.\n",
"\n",
"- Spark is signficantly faster than MapReduce.\n",
"\n",
"- Paradoxically, Spark's API is simpler than the MapReduce API.\n",
"\n",
"Goals\n",
"-----\n",
"\n",
"By the end of this lecture, you will be able to:\n",
"\n",
"- Create RDDs to distribute data across a cluster\n",
"\n",
"- Use the Spark shell to compose and execute Spark commands\n",
"\n",
"- Use Spark to analyze stock market data\n",
"\n",
"Spark Version History\n",
"---------------------\n",
"\n",
"Date |Version |Changes\n",
"---- |------- |-------\n",
"May 30, 2014 |Spark 1.0.0 |APIs stabilized \n",
"September 11, 2014 |Spark 1.1.0 |New functions in MLlib, Spark SQL\n",
"December 18, 2014 |Spark 1.2.0 |Python Streaming API and better streaming fault tolerance\n",
"March 13, 2015 |Spark 1.3.0 |DataFrame API, Kafka integration in Streaming\n",
"April 17, 2015 |Spark 1.3.1 |Bug fixes, minor changes\n",
"\n",
"Matei Zaharia\n",
"-------------\n",
"\n",
"<img style=\"width:50%\" src=\"images/matei.jpg\">\n",
"\n",
"Essense of Spark\n",
"----------------\n",
"\n",
"What is the basic idea of Spark?\n",
"\n",
"- Spark takes the Map-Reduce paradigm and changes it in some critical\n",
" ways.\n",
"\n",
"- Instead of writing single Map-Reduce jobs a Spark job consists of a\n",
" series of map and reduce functions. \n",
" \n",
"- However, the intermediate data is kept in memory instead of being\n",
" written to disk or written to HDFS.\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: Since Spark keeps intermediate data in memory to get speed, what\n",
"does it make us give up? Where's the catch?\n",
"</summary>\n",
"1. Spark does a trade-off between memory and performance.\n",
"<br>\n",
"2. While Spark apps are faster, they also consume more memory.\n",
"<br>\n",
"3. Spark outshines Map-Reduce in iterative algorithms where the\n",
" overhead of saving the results of each step to HDFS slows down\n",
" Map-Reduce.\n",
"<br>\n",
"4. For non-iterative algorithms Spark is comparable to Map-Reduce.\n",
"</details>\n",
"\n",
"Spark Logging\n",
"-------------\n",
"\n",
"Q: How can I make Spark logging less verbose?\n",
"\n",
"- By default Spark logs messages at the `INFO` level.\n",
"\n",
"- Here are the steps to make it only print out warnings and errors.\n",
"\n",
"```sh\n",
"cd $SPARK_HOME/conf\n",
"cp log4j.properties.template log4j.properties\n",
"```\n",
"\n",
"- Edit `log4j.properties` and replace `rootCategory=INFO` with `rootCategory=ERROR`\n",
"\n",
"Spark Fundamentals\n",
"==================\n",
"\n",
"Spark Execution\n",
"---------------\n",
"\n",
"<img src=\"images/spark-cluster.png\">\n",
"\n",
"\n",
"Spark Terminology\n",
"-----------------\n",
"\n",
"Term |Meaning\n",
"---- |-------\n",
"Driver |Process that contains the Spark Context\n",
"Executor |Process that executes one or more Spark tasks\n",
"Master |Process which manages applications across the cluster\n",
" |E.g. Spark Master\n",
"Worker |Process which manages executors on a particular worker node\n",
" |E.g. Spark Worker\n",
"\n",
"Spark Job\n",
"---------\n",
"\n",
"Q: Flip a coin 100 times using Python's `random()` function. What\n",
"fraction of the time do you get heads?\n",
"\n",
"- Initialize Spark."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark import SparkContext\n",
"sc = SparkContext()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Import random."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"flips = 1000000\n",
"heads = sc.parallelize(xrange(flips)) \\\n",
" .map(lambda i: random.random()) \\\n",
" .filter(lambda r: r < 0.51) \\\n",
" .count()\n",
"\n",
"ratio = float(heads)/float(flips)\n",
"\n",
"print(heads)\n",
"print(ratio)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notes\n",
"-----\n",
"\n",
"- `sc.parallelize` creates an RDD.\n",
"\n",
"- `map` and `filter` are *transformations*.\n",
"\n",
"- They create new RDDs from existing RDDs.\n",
"\n",
"- `count` is an *action* and brings the data from the RDDs back to the\n",
" driver.\n",
"\n",
"Spark Terminology\n",
"-----------------\n",
"\n",
"Term |Meaning\n",
"---- |-------\n",
"RDD |*Resilient Distributed Dataset* or a distributed sequence of records\n",
"Spark Job |Sequence of transformations on data with a final action\n",
"Spark Application |Sequence of Spark jobs and other code\n",
"Transformation |Spark operation that produces an RDD\n",
"Action |Spark operation that produces a local object\n",
"\n",
"\n",
"- A Spark job consists of a series of transformations followed by an\n",
" action.\n",
"\n",
"- It pushes the data to the cluster, all computation happens on the\n",
" *executors*, then the result is sent back to the driver.\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"In this Spark job what is the transformation is what is the action? \n",
"`sc.parallelize(xrange(10)).filter(lambda x: x % 2 == 0).collect()`\n",
"</summary>\n",
"1. `filter` is the transformation.\n",
"<br>\n",
"2. `collect` is the action.\n",
"</details>\n",
"\n",
"Lambda vs Functions\n",
"-------------------\n",
"\n",
"- Instead of `lambda` you can pass in fully defined functions into\n",
" `map`, `filter`, and other RDD transformations.\n",
"\n",
"- Use `lambda` for short functions. \n",
"\n",
"- Use `def` for more substantial functions.\n",
"\n",
"Finding Primes\n",
"--------------\n",
"\n",
"Q: Find all the primes less than 100.\n",
"\n",
"- Define function to determine if a number is prime."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def is_prime(number):\n",
" factor_min = 2\n",
" factor_max = int(number**0.5)+1\n",
" for factor in xrange(factor_min,factor_max):\n",
" if number % factor == 0:\n",
" return False\n",
" return True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Use this to filter out non-primes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"numbers = xrange(2,100)\n",
"primes = sc.parallelize(numbers)\\\n",
" .filter(is_prime)\\\n",
" .collect()\n",
"print primes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<img src=\"images/spark-cluster.png\">\n",
"\n",
"<details><summary>\n",
"Q: Where does `is_prime` execute?\n",
"</summary>\n",
"On the executors.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: Where does the RDD code execute?\n",
"</summary>\n",
"On the driver.\n",
"</details>\n",
"\n",
"Transformations and Actions\n",
"===========================\n",
"\n",
"Common RDD Constructors\n",
"-----------------------\n",
"\n",
"Expression |Meaning\n",
"---------- |-------\n",
"`sc.parallelize(list1)` |Create RDD of elements of list\n",
"`sc.textFile(path)` |Create RDD of lines from file\n",
"\n",
"Common Transformations\n",
"----------------------\n",
"\n",
"Expression |Meaning\n",
"---------- |-------\n",
"`filter(lambda x: x % 2 == 0)` |Discard non-even elements\n",
"`map(lambda x: x * 2)` |Multiply each RDD element by `2`\n",
"`map(lambda x: x.split())` |Split each string into words\n",
"`flatMap(lambda x: x.split())` |Split each string into words and flatten sequence\n",
"`sample(withReplacement=True,0.25)` |Create sample of 25% of elements with replacement\n",
"`union(rdd)` |Append `rdd` to existing RDD\n",
"`distinct()` |Remove duplicates in RDD\n",
"`sortBy(lambda x: x, ascending=False)` |Sort elements in descending order\n",
"\n",
"\n",
"Common Actions\n",
"--------------\n",
"\n",
"Expression |Meaning\n",
"---------- |-------\n",
"`collect()` |Convert RDD to in-memory list \n",
"`take(3)` |First 3 elements of RDD \n",
"`top(3)` |Top 3 elements of RDD\n",
"`takeSample(withReplacement=True,3)` |Create sample of 3 elements with replacement\n",
"`sum()` |Find element sum (assumes numeric elements)\n",
"`mean()` |Find element mean (assumes numeric elements)\n",
"`stdev()` |Find element deviation (assumes numeric elements)\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"Q: What will this output?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.parallelize([1,3,2,2,1]).distinct().collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q: What will this output?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.parallelize([1,3,2,2,1]).sortBy(lambda x: x).collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q: What will this output?\n",
"\n",
"- Create this input file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%writefile input.txt\n",
"hello world\n",
"another line\n",
"yet another line\n",
"yet another another line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- What do you get when you run this code?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('input.txt') \\\n",
" .map(lambda x: x.split()) \\\n",
" .count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- What about this?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('input.txt') \\\n",
" .flatMap(lambda x: x.split()) \\\n",
" .count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Map vs FlatMap\n",
"--------------\n",
"\n",
"- Here's the difference between `map` and `flatMap`.\n",
"\n",
"- Map:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('input.txt') \\\n",
" .map(lambda x: x.split()) \\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- FlatMap:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('input.txt') \\\n",
" .flatMap(lambda x: x.split()) \\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Key Value Pairs\n",
"===============\n",
"\n",
"PairRDD\n",
"-------\n",
"\n",
"At this point we know how to aggregate values across an RDD. If we\n",
"have an RDD containing sales transactions we can find the total\n",
"revenue across all transactions.\n",
"\n",
"Q: Using the following sales data find the total revenue across all\n",
"transactions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%writefile sales.txt\n",
"#ID Date Store State Product Amount\n",
"101 11/13/2014 100 WA 331 300.00\n",
"104 11/18/2014 700 OR 329 450.00\n",
"102 11/15/2014 203 CA 321 200.00\n",
"106 11/19/2014 202 CA 331 330.00\n",
"103 11/17/2014 101 WA 373 750.00\n",
"105 11/19/2014 202 CA 321 200.00"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Read the file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .take(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Split the lines."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .take(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Remove `#`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: x[0].startswith('#'))\\\n",
" .take(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Try again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .take(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Pick off last field."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: x[-1])\\\n",
" .take(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Convert to float and then sum."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: float(x[-1]))\\\n",
" .sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ReduceByKey\n",
"-----------\n",
"\n",
"Q: Calculate revenue per state?\n",
"\n",
"- Instead of creating a sequence of revenue numbers we can create\n",
" tuples of states and revenue."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],float(x[-1])))\\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Now use `reduceByKey` to add them up."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],float(x[-1])))\\\n",
" .reduceByKey(lambda amount1,amount2: amount1+amount2)\\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q: Find the state with the highest total revenue.\n",
"\n",
"- You can either use the action `top` or the transformation `sortBy`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],float(x[-1])))\\\n",
" .reduceByKey(lambda amount1,amount2: amount1+amount2)\\\n",
" .sortBy(lambda state_amount:state_amount[1],ascending=False) \\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: What does `reduceByKey` do?\n",
"</summary>\n",
"1. It is like a reducer.\n",
"<br>\n",
"2. If the RDD is made up of key-value pairs, it combines the values\n",
" across all tuples with the same key by using the function we pass\n",
" to it.\n",
"<br>\n",
"3. It only works on RDDs made up of key-value pairs or 2-tuples.\n",
"</details>\n",
"\n",
"Notes\n",
"-----\n",
"\n",
"- `reduceByKey` only works on RDDs made up of 2-tuples.\n",
"\n",
"- `reduceByKey` works as both a reducer and a combiner.\n",
"\n",
"- It requires that the operation is associative.\n",
"\n",
"Word Count\n",
"----------\n",
"\n",
"Q: Implement word count in Spark.\n",
"\n",
"- Create some input."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%writefile input.txt\n",
"hello world\n",
"another line\n",
"yet another line\n",
"yet another another line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Count the words."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('input.txt')\\\n",
" .flatMap(lambda line: line.split())\\\n",
" .map(lambda word: (word,1))\\\n",
" .reduceByKey(lambda count1,count2: count1+count2)\\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Making List Indexing Readable\n",
"-----------------------------\n",
"\n",
"- While this code looks reasonable, the list indexes are cryptic and\n",
" hard to read."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],float(x[-1])))\\\n",
" .reduceByKey(lambda amount1,amount2: amount1+amount2)\\\n",
" .sortBy(lambda state_amount:state_amount[1],ascending=False) \\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- We can make this more readable using Python's argument unpacking\n",
" feature.\n",
"\n",
"Argument Unpacking\n",
"------------------\n",
"\n",
"Q: Which version of `getCity` is more readable and why?\n",
"\n",
"- Consider this code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"client = ('Dmitri','Smith','SF')\n",
"\n",
"def getCity1(client):\n",
" return client[2]\n",
"\n",
"def getCity2((first,last,city)):\n",
" return city\n",
"\n",
"print getCity1(client)\n",
"\n",
"print getCity2(client)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- What is the difference between `getCity1` and `getCity2`?\n",
"\n",
"- Which is more readable?\n",
"\n",
"- What is the essence of argument unpacking?\n",
"\n",
"Pop Quiz\n",
"--------\n",
"<details><summary>\n",
"Q: Can argument unpacking work for deeper nested structures?\n",
"</summary>\n",
"Yes. It can work for arbitrarily nested tuples and lists.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: How would you write `getCity` given \n",
"`client = ('Dmitri','Smith',('123 Eddy','SF','CA'))`\n",
"</summary>\n",
"`def getCity((first,last,(street,city,state))): return city`\n",
"</details>\n",
"\n",
"Argument Unpacking\n",
"------------------\n",
"\n",
"- Lets test this out."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"client = ('Dmitri','Smith',('123 Eddy','SF','CA'))\n",
"\n",
"def getCity((first,last,(street,city,state))):\n",
" return city\n",
"\n",
"getCity(client)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Whenever you find yourself indexing into a tuple consider using\n",
" argument unpacking to make it more readable.\n",
"\n",
"- Here is what `getCity` looks like with tuple indexing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def badGetCity(client):\n",
" return client[2][1]\n",
"\n",
"getCity(client)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Argument Unpacking In Spark\n",
"---------------------------\n",
"\n",
"Q: Rewrite the last Spark job using argument unpacking.\n",
"\n",
"- Here is the original version of the code."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],float(x[-1])))\\\n",
" .reduceByKey(lambda amount1,amount2: amount1+amount2)\\\n",
" .sortBy(lambda state_amount:state_amount[1],ascending=False) \\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Here is the code with argument unpacking."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda (id,date,store,state,product,amount): (state,float(amount)))\\\n",
" .reduceByKey(lambda amount1,amount2: amount1+amount2)\\\n",
" .sortBy(lambda (state,amount):amount,ascending=False) \\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- In this case because we have a long list or tuple argument unpacking\n",
" is a judgement call.\n",
"\n",
"GroupByKey\n",
"----------\n",
"\n",
"`reduceByKey` lets us aggregate values using sum, max, min, and other\n",
"associative operations. But what about non-associative operations like\n",
"average? How can we calculate them?\n",
"\n",
"- There are several ways to do this.\n",
"\n",
"- The first approach is to change the RDD tuples so that the operation\n",
" becomes associative. \n",
"\n",
"- Instead of `(state, amount)` use `(state, (amount, count))`.\n",
"\n",
"- The second approach is to use `groupByKey`, which is like\n",
" `reduceByKey` except it gathers together all the values in an\n",
" iterator. \n",
" \n",
"- The iterator can then be reduced in a `map` step immediately after\n",
" the `groupByKey`.\n",
"\n",
"Q: Calculate the average sales per state.\n",
"\n",
"- Approach 1: Restructure the tuples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],(float(x[-1]),1)))\\\n",
" .reduceByKey(lambda (amount1,count1),(amount2,count2): \\\n",
" (amount1+amount2, count1+count2))\\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Note the argument unpacking we are doing in `reduceByKey` to name\n",
" the elements of the tuples.\n",
"\n",
"- Approach 2: Use `groupByKey`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def mean(iter):\n",
" total = 0.0; count = 0\n",
" for x in iter:\n",
" total += x; count += 1\n",
" return total/count\n",
"\n",
"sc.textFile('sales.txt')\\\n",
" .map(lambda x: x.split())\\\n",
" .filter(lambda x: not x[0].startswith('#'))\\\n",
" .map(lambda x: (x[-3],float(x[-1])))\\\n",
" .groupByKey() \\\n",
" .map(lambda (state,iter): mean(iter))\\\n",
" .collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Note that we are using unpacking again.\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: What would be the disadvantage of not using unpacking?\n",
"</summary>\n",
"1. We will need to drill down into the elements.\n",
"<br>\n",
"2. The code will be harder to read.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: What are the pros and cons of `reduceByKey` vs `groupByKey`?\n",
"</summary>\n",
"1. `groupByKey` stores the values for particular key as an iterable.\n",
"<br>\n",
"2. This will take up space in memory or on disk.\n",
"<br>\n",
"3. `reduceByKey` therefore is more scalable.\n",
"<br>\n",
"4. However, `groupByKey` does not require associative reducer\n",
" operation.\n",
"<br>\n",
"5. For this reason `groupByKey` can be easier to program with.\n",
"</details>\n",
"\n",
"\n",
"Joins\n",
"-----\n",
"\n",
"Q: Given a table of employees and locations find the cities that the\n",
"employees live in.\n",
"\n",
"\n",
"- The easiest way to do this is with a `join`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Employees: emp_id, loc_id, name\n",
"employee_data = [\n",
" (101, 14, 'Alice'),\n",
" (102, 15, 'Bob'),\n",
" (103, 14, 'Chad'),\n",
" (104, 15, 'Jen'),\n",
" (105, 13, 'Dee') ]\n",
"\n",
"# Locations: loc_id, location\n",
"location_data = [\n",
" (14, 'SF'),\n",
" (15, 'Seattle'),\n",
" (16, 'Portland')]\n",
"\n",
"employees = sc.parallelize(employee_data)\n",
"locations = sc.parallelize(location_data)\n",
"\n",
"# Re-key employee records with loc_id\n",
"employees2 = employees.map(lambda (emp_id,loc_id,name):(loc_id,name));\n",
"\n",
"# Now join.\n",
"employees2.join(locations).collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: How can we keep employees that don't have a valid location ID in\n",
"the final result?\n",
"</summary>\n",
"1. Use `leftOuterJoin` to keep employees without location IDs.\n",
"<br>\n",
"2. Use `rightOuterJoin` to keep locations without employees. \n",
"<br>\n",
"3. Use `fullOuterJoin` to keep both.\n",
"<br>\n",
"</details>\n",
"\n",
"RDD Details\n",
"===========\n",
"\n",
"RDD Statistics\n",
"--------------\n",
"\n",
"Q: How would you calculate the mean, variance, and standard deviation of a sample\n",
"produced by Python's `random()` function?\n",
"\n",
"- Create an RDD and apply the statistical actions to it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"count = 1000\n",
"list = [random.random() for _ in xrange(count)]\n",
"rdd = sc.parallelize(list)\n",
"print rdd.mean()\n",
"print rdd.variance()\n",
"print rdd.stdev()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: What requirement does an RDD have to satisfy before you can apply\n",
"these statistical actions to it? \n",
"</summary>\n",
"The RDD must consist of numeric elements.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: What is the advantage of using Spark vs Numpy to calculate mean or standard deviation?\n",
"</summary>\n",
"The calculation is distributed across different machines and will be\n",
"more scalable.\n",
"</details>\n",
"\n",
"RDD Laziness\n",
"------------\n",
"\n",
"- Q: What is this Spark job doing?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"max = 10000000\n",
"%time sc.parallelize(xrange(max)).map(lambda x:x+1).count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Q: How is the following job different from the previous one? How\n",
" long do you expect it to take?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time sc.parallelize(xrange(max)).map(lambda x:x+1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: Why did the second job complete so much faster?\n",
"</summary>\n",
"1. Because Spark is lazy. \n",
"<br>\n",
"2. Transformations produce new RDDs and do no operations on the data.\n",
"<br>\n",
"3. Nothing happens until an action is applied to an RDD.\n",
"<br>\n",
"4. An RDD is the *recipe* for a transformation, rather than the\n",
" *result* of the transformation.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: What is the benefit of keeping the recipe instead of the result of\n",
"the action?\n",
"</summary>\n",
"1. It save memory.\n",
"<br>\n",
"2. It produces *resilience*. \n",
"<br>\n",
"3. If an RDD loses data on a machine, it always knows how to recompute it.\n",
"</details>\n",
"\n",
"Writing Data\n",
"------------\n",
"\n",
"Besides reading data Spark and also write data out to a file system.\n",
"\n",
"Q: Calculate the squares of integers from 1 to 100 and write them out\n",
"to `squares.txt`.\n",
"\n",
"- Make sure `squares.txt` does not exist."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!if [ -e squares.txt ] ; then rm -rf squares.txt ; fi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Create the RDD and then save it to `squares.txt`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rdd1 = sc.parallelize(xrange(10))\n",
"rdd2 = rdd1.map(lambda x: x*x)\n",
"rdd2.saveAsTextFile('squares.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Now look at the output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!cat squares.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Looks like the output is a directory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!ls -l squares.txt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Lets take a look at the files."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!for i in squares.txt/part-*; do echo $i; cat $i; done"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: What's going on? Why are there two files in the output directory?\n",
"</summary>\n",
"1. There were two threads that were processing the RDD.\n",
"<br>\n",
"2. The RDD was split up in two partitions (by default).\n",
"<br>\n",
"3. Each partition was processed in a different task.\n",
"</details>\n",
"\n",
"Partitions\n",
"----------\n",
"\n",
"Q: Can we control the number of partitions/tasks that Spark uses for\n",
"processing data? Solve the same problem as above but this time with 5\n",
"tasks.\n",
"\n",
"- Make sure `squares.txt` does not exist."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!if [ -e squares.txt ] ; then rm -rf squares.txt ; fi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Create the RDD and then save it to `squares.txt`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"partitions = 5\n",
"rdd1 = sc.parallelize(xrange(10), partitions)\n",
"rdd2 = rdd1.map(lambda x: x*x)\n",
"rdd2.saveAsTextFile('squares.txt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Now look at the output."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"!ls -l squares.txt\n",
"!for i in squares.txt/part-*; do echo $i; cat $i; done"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: How many partitions does Spark use by default?\n",
"</summary>\n",
"1. By default Spark uses 2 partitions.\n",
"<br>\n",
"2. If you read an HDFS file into an RDD Spark uses one partition per\n",
" block.\n",
"<br>\n",
"3. If you read a file into an RDD from S3 or some other source Spark\n",
" uses 1 partition per 32 MB of data.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: If I read a file that is 200 MB into an RDD, how many partitions will that have?\n",
"</summary>\n",
"1. If the file is on HDFS that will produce 2 partitions (each is 128\n",
" MB).\n",
"<br>\n",
"2. If the file is on S3 or some other file system it will produce 7\n",
" partitions.\n",
"<br>\n",
"3. You can also control the number of partitions by passing in an\n",
" additional argument into `textFile`.\n",
"</details>\n",
"\n",
"Spark Terminology\n",
"-----------------\n",
"\n",
"<img src=\"images/spark-cluster.png\">\n",
"\n",
"Term |Meaning\n",
"---- |-------\n",
"Task |Single thread in an executor\n",
"Partition |Data processed by a single task\n",
"Record |Records make up a partition that is processed by a single task\n",
"\n",
"Notes\n",
"-----\n",
"\n",
"- Every Spark application gets executors when you create a new `SparkContext`.\n",
"\n",
"- You can specify how many cores to assign to each executor.\n",
"\n",
"- A core is equivalent to a thread.\n",
"\n",
"- The number of cores determine how many tasks can run concurrently on\n",
" an executor.\n",
"\n",
"- Each task corresponds to one partition.\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: Suppose you have 2 executors, each with 2 cores--so a total of 4\n",
"cores. And you start a Spark job with 8 partitions. How many tasks\n",
"will run concurrently?\n",
"</summary>\n",
"4 tasks will execute concurrently.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: What happens to the other partitions?\n",
"</summary>\n",
"1. The other partitions wait in queue until a task thread becomes\n",
"available.\n",
"<br>\n",
"2. Think of cores as turnstile gates at a train station, and\n",
" partitions as people .\n",
"<br>\n",
"3. The number of turnstiles determine how many people can get through\n",
" at once.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: How many Spark jobs can you have in a Spark application?\n",
"</summary>\n",
"As many as you want.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: How many Spark applications and Spark jobs are in this IPython Notebook?\n",
"</summary>\n",
"1. There is one Spark application because there is one `SparkContext`.\n",
"<br>\n",
"2. There are as many Spark jobs as we have invoked actions on RDDs.\n",
"</details>\n",
"\n",
"Stock Quotes\n",
"------------\n",
"\n",
"Q: Find the date on which AAPL's stock price was the highest.\n",
"\n",
"Suppose you have stock market data from Yahoo! for AAPL from\n",
"<http://finance.yahoo.com/q/hp?s=AAPL+Historical+Prices>. The data is\n",
"in CSV format and has these values.\n",
"\n",
"Date |Open |High |Low |Close |Volume |Adj Close\n",
"---- |---- |---- |--- |----- |------ |---------\n",
"11-18-2014 |113.94 |115.69 |113.89 |115.47 |44,200,300 |115.47\n",
"11-17-2014 |114.27 |117.28 |113.30 |113.99 |46,746,700 |113.99\n",
"\n",
"Here is what the CSV looks like:\n",
" \n",
" csv = [\n",
" \"#Date,Open,High,Low,Close,Volume,Adj Close\\n\",\n",
" \"2014-11-18,113.94,115.69,113.89,115.47,44200300,115.47\\n\",\n",
" \"2014-11-17,114.27,117.28,113.30,113.99,46746700,113.99\\n\",\n",
" ]\n",
"\n",
"Lets find the date on which the price was the highest. \n",
"\n",
"\n",
"<details><summary>\n",
"Q: What two fields do we need to extract? \n",
"</summary>\n",
"1. *Date* and *Adj Close*.\n",
"<br>\n",
"2. We want to use *Adj Close* instead of *High* so our calculation is\n",
" not affected by stock splits.\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: What field should we sort on?\n",
"</summary>\n",
"*Adj Close*\n",
"</details>\n",
"\n",
"<details><summary>\n",
"Q: What sequence of operations would we need to perform?\n",
"</summary>\n",
"1. Use `filter` to remove the header line.\n",
"<br>\n",
"2. Use `map` to split each row into fields.\n",
"<br>\n",
"3. Use `map` to extract *Adj Close* and *Date*.\n",
"<br>\n",
"4. Use `sortBy` to sort descending on *Adj Close*.\n",
"<br>\n",
"5. Use `take(1)` to get the highest value.\n",
"</details>\n",
"\n",
"- Here is full source."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"csv = [\n",
" \"#Date,Open,High,Low,Close,Volume,Adj Close\\n\",\n",
" \"2014-11-18,113.94,115.69,113.89,115.47,44200300,115.47\\n\",\n",
" \"2014-11-17,114.27,117.28,113.30,113.99,46746700,113.99\\n\",\n",
"]\n",
"sc.parallelize(csv) \\\n",
" .filter(lambda line: not line.startswith(\"#\")) \\\n",
" .map(lambda line: line.split(\",\")) \\\n",
" .map(lambda fields: (float(fields[-1]),fields[0])) \\\n",
" .sortBy(lambda (close, date): close, ascending=False) \\\n",
" .take(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Here is the program for finding the high of any stock that stores\n",
" the data in memory."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import urllib2\n",
"import re\n",
"\n",
"def get_stock_high(symbol):\n",
" url = 'http://real-chart.finance.yahoo.com' + \\\n",
" '/table.csv?s='+symbol+'&g=d&ignore=.csv'\n",
" csv = urllib2.urlopen(url).read()\n",
" csv_lines = csv.split('\\n')\n",
" stock_rdd = sc.parallelize(csv_lines) \\\n",
" .filter(lambda line: re.match(r'\\d', line)) \\\n",
" .map(lambda line: line.split(\",\")) \\\n",
" .map(lambda fields: (float(fields[-1]),fields[0])) \\\n",
" .sortBy(lambda (close, date): close, ascending=False)\n",
" return stock_rdd.take(1)\n",
"\n",
"get_stock_high('AAPL')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notes\n",
"-----\n",
"\n",
"- Spark is high-level like Hive and Pig.\n",
"\n",
"- At the same time it does not invent a new language.\n",
"\n",
"- This allows it to leverage the ecosystem of tools that Python,\n",
" Scala, and Java provide.\n",
"\n",
"\n",
"\n",
"Caching and Persistence\n",
"=======================\n",
"\n",
"RDD Caching\n",
"-----------\n",
"\n",
"- Consider this Spark job."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"num_count = 500*1000\n",
"num_list = [random.random() for i in xrange(num_count)]\n",
"rdd1 = sc.parallelize(num_list)\n",
"rdd2 = rdd1.sortBy(lambda num: num)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Lets time running `count()` on `rdd2`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%time rdd2.count()\n",
"%time rdd2.count()\n",
"%time rdd2.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- The RDD does no work until an action is called. And then when an\n",
" action is called it figures out the answer and then throws away all\n",
" the data.\n",
"\n",
"- If you have an RDD that you are going to reuse in your computation\n",
" you can use `cache()` to make Spark cache the RDD.\n",
"\n",
"- Lets cache it and try again."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rdd2.cache()\n",
"%time rdd2.count()\n",
"%time rdd2.count()\n",
"%time rdd2.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Caching the RDD speeds up the job because the RDD does not have to\n",
" be computed from scratch again.\n",
"\n",
"Notes\n",
"-----\n",
"\n",
"- Calling `cache()` flips a flag on the RDD. \n",
"\n",
"- The data is not cached until an action is called.\n",
"\n",
"- You can uncache an RDD using `unpersist()`.\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: Will `unpersist` uncache the RDD immediately or does it wait for an\n",
"action?\n",
"</summary>\n",
"It unpersists immediately.\n",
"</details>\n",
"\n",
"Caching and Persistence\n",
"-----------------------\n",
"\n",
"Q: Persist RDD to disk instead of caching it in memory.\n",
"\n",
"- You can cache RDDs at different levels.\n",
"\n",
"- Here is an example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pyspark\n",
"rdd = sc.parallelize(xrange(100))\n",
"rdd.persist(pyspark.StorageLevel.DISK_ONLY)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Q: Will the RDD be stored on disk at this point?\n",
"</summary>\n",
"No. It will get stored after we call an action.\n",
"</details>\n",
"\n",
"Persistence Levels\n",
"------------------\n",
"\n",
"Level |Meaning\n",
"----- |-------\n",
"`MEMORY_ONLY` |Same as `cache()`\n",
"`MEMORY_AND_DISK` |Cache in memory then overflow to disk\n",
"`MEMORY_AND_DISK_SER` |Like above; in cache keep objects serialized instead of live \n",
"`DISK_ONLY` |Cache to disk not to memory\n",
"\n",
"Notes\n",
"-----\n",
"\n",
"- `MEMORY_AND_DISK_SER` is a good compromise between the levels. \n",
"\n",
"- Fast, but not too expensive.\n",
"\n",
"- Make sure you unpersist when you don't need the RDD any more.\n",
"\n",
"\n",
"Spark Performance\n",
"=================\n",
"\n",
"Narrow and Wide Transformations\n",
"-------------------------------\n",
"\n",
"- Spark transformations are *narrow* if each RDD has one unique child\n",
" past the transformation.\n",
"\n",
"- Spark transformations are *wide* if each RDD can have multiple\n",
" children past the transformation.\n",
"\n",
"- Narrow transformations are map-like, while wide transformations are\n",
" reduce-like.\n",
"\n",
"- Narrow transformations are faster because they do move data between\n",
" executors, while wide transformations are slower.\n",
" \n",
"Repartitioning\n",
"--------------\n",
"\n",
"- Over time partitions can get skewed. \n",
"\n",
"- Or you might have less data or more data than you started with.\n",
"\n",
"- You can rebalance your partitions using `repartition` or `coalesce`.\n",
"\n",
"- `coalesce` is narrow while `repartition` is wide.\n",
"\n",
"Pop Quiz\n",
"--------\n",
"\n",
"<details><summary>\n",
"Between `coalesce` and `repartition` which one is faster? Which one is\n",
"more effective?\n",
"</summary>\n",
"1. `coalesce` is narrow so it is faster. \n",
"<br>\n",
"2. However, it only combines partitions and does not shuffle them.\n",
"<br>\n",
"3. `repartition` is wide but it partitions more effectively because it\n",
" reshuffles the records.\n",
"</details>\n",
"\n",
"Misc\n",
"====\n",
"\n",
"Amazon S3\n",
"---------\n",
"\n",
"- *\"s3:\" URLs break when Secret Key contains a slash, even if encoded*\n",
" <https://issues.apache.org/jira/browse/HADOOP-3733>\n",
"\n",
"- *Spark 1.3.1 / Hadoop 2.6 prebuilt pacakge has broken S3 filesystem access*\n",
" <https://issues.apache.org/jira/browse/SPARK-7442>\n",
"\n",
"<!--\n",
"\n",
"Broadcast Variables\n",
"\n",
"Accumulators\n",
"\n",
"Checkpoint\n",
"\n",
"S3 key issue\n",
"\n",
"2.6\n",
"\n",
"-->"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tritemio/multispot_paper | out_notebooks/Multi-spot 5-Samples analysis - Background-out.ipynb | 1 | 10629583 | null | mit |
seldamat/Surfer-gems | bin/morphometrics.ipynb | 1 | 12951888 | null | apache-2.0 |
HrantDavtyan/Quant_Management | Python/Class_1.ipynb | 1 | 18578 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+5"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = 2+5"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b = 3"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c = a + b"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10\n"
]
}
],
"source": [
"print(c)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(c)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10/2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hrant'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"Hrant\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"name = \"Hrant\""
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hrant\n"
]
}
],
"source": [
"print(name)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"surname= 'Davtyan'"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"str"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(name)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'HrantDavtyan'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name + surname"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hrant Davtyan'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name + \" \" + surname"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for -: 'str' and 'str'",
"output_type": "error",
"traceback": [
"\u001b[0;31m-------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-23-d42d4f2b3b46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msurname\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'str' and 'str'"
]
}
],
"source": [
"surname - name"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "can't multiply sequence by non-int of type 'str'",
"output_type": "error",
"traceback": [
"\u001b[0;31m-------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-8e6e0e45a20d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mname\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0msurname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'str'"
]
}
],
"source": [
"name*surname"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'HrantHrantHrantHrantHrant'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"name*5"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"10/3"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5/2"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5//2"
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},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.5"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5.0/2"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"print(a)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 31,
"metadata": {},
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}
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},
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"data": {
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},
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],
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},
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"metadata": {
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},
"execution_count": 33,
"metadata": {},
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"metadata": {
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"source": [
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},
{
"cell_type": "code",
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"metadata": {
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},
"outputs": [
{
"data": {
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},
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"metadata": {},
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},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
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},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(name)"
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},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(name) + len(surname)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"print a"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"instructor = [\"Hrant\",surname,6]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Hrant', 'Davtyan', 6]\n"
]
}
],
"source": [
"print(instructor)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(instructor)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(instructor)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Davtyan'"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"instructor[1]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Hrant'"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"instructor[0]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Davtyan'"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"instructor[-2]"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"number_list = [5,6,13,56,75,8]"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(number_list)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[-1]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[0]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[5, 6, 13]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[0:3]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#print(10)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[13, 56, 75]"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[2:5]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[56, 75]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[3:-1]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[56, 75, 8]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[3:]"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[5, 6, 13, 56, 75]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"number_list[:5]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m-------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-63-0dea2c35ab9a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumber_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"number_list[10]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"range(5)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"range(10)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Hrant', 'Davtyan', 6]\n"
]
}
],
"source": [
"print(instructor)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_list = [\"Jack\",\"James\",\"Jimmy\"]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Jack\n",
"James\n",
"Jimmy\n"
]
}
],
"source": [
"for i in range(3):\n",
" print(my_list[i])"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n"
]
}
],
"source": [
"for i in range(3):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
rouckas/NEVF138 | 140 Deconvolution.ipynb | 1 | 283879 | {
"metadata": {
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"cells": [
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"input": [
"%matplotlib inline\n",
"from pylab import *\n",
"from numpy import *\n",
"from numpy.random import poisson, normal"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
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"input": [
"xmin = 0\n",
"xmax = 10\n",
"xlen = xmax-xmin\n",
"x = linspace(xmin, xmax, 200)\n",
"signal = zeros_like(x)\n",
"I = (x>(xmin+xlen*0.4)) & (x<(xmin+xlen*0.6))\n",
"signal[I] = x[I]\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4dd12e5c0>]"
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},
{
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"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7ff4dd9cb6a0>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.stats import norm\n",
"rv = norm(loc=xlen/2, scale=0.2)\n",
"psf = rv.pdf(x)\n",
"psf /= sum(psf)\n",
"plot(x, psf)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce5e4710>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7ff4dd180b00>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"csignal = convolve(psf, signal, mode=\"same\")\n",
"plot(x, csignal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce54e828>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7ff4dd9b1f98>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import fft as F\n",
"from numpy.fft import fft, ifft, ifftshift\n",
"H = fft(psf)\n",
"S = fft(csignal)\n",
"DS = ifftshift(ifft(S/H))\n",
"plot(x, DS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python3.4/dist-packages/numpy/core/numeric.py:462: ComplexWarning: Casting complex values to real discards the imaginary part\n",
" return array(a, dtype, copy=False, order=order)\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce55bc18>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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CIHn+cHxKIVslTyF5UXng5+p25ucODUUTchKS540N/U+X5FtagF27gvnmFSzd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\nUMQQkhcIBIIixv8PT7X+58O9dcwAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce5705c0>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"semilogy(x, abs(fftshift(H)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce5a2240>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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g/QDlswrAE0v/GJM5Z50FH34I69b5jiRY0loBiEgjERksIq+UHodFZKaIDBSR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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce56d240>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"semilogy(x, abs(fftshift(S/H)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce2639e8>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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u+/QBamvVZ/cTe3dkf/Bg8jZese/s1Nd2rtupU/07cR94QD/vq68Gtm/Pzv9S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\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce2de828>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"semilogy(x, abs(fftshift(fft(signal))))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce3737f0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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4w6OPyhLpH/zA3f7II8CFF8oGxT//eWX7sWOyAu+++4A/+zP3GOvXS82X173O\nL8BpUbipod7Z6Y/Uly6VL//WrZKNkGzP66fb7QMDpc0pXO2ARPUxVosve6aa1aqhMsGhSL5dRL63\nVyJIMw/h+vuQnePbYtDuMzHhXmCW3LwkmWtfrV0zMVE+sR+K9BculMApWQ3TniR2jbF/v0T3c+em\nF1IzdwNpFwGtpc9DD8lchMvSuftuYM0auVDce697jLe+VepIffnLle2AOARr1khWXbLaqxnj6aeB\nxx8H7ruvSbNrenoG8drXAm97W7oN8uEPA9/7nuSQu3jwQUn/WrpUJjeTmKX611+f7uHZVkpaFL50\nabyfnuxjJq4WLZLsg3nzKiOQauwaV59kZoxS5X2SJWmB+CjcF/HNnCkiMTGRL5Lv7Q1vwt1Onnxs\nbRtXH7MxyYkT4c/18GH5DDs63O2A+3ns+jiAW+RtO2fbNrFhZswotSfTMO2JWVMN05eh4zpfk3cD\nPl8/7SJgkhzmznVvnG7G+NCHxAFwacK+fRKg/vZvA3feWdkOSKrod74j1tLTT1e2P/ecBK9vfzvw\nwANNml2zYQPwq78qFRNdt01mA+K//3sRSlcN682bJQJ/1avSb82WLJEPa9u2dA/PJ+JmjHnz5DiS\nizhsq8Ul0CMjEpmYlZppdoz5gsfaNXYfV6Run2iuqK8aPz2tT0eHiISJLPOmULZTJB+anwjZOWmR\nvFLxGThZPfvZs8ttiuQ+AkmBNcJq//306eWrspN9XCIciuSTGTxplo+J5JMXmuRxxI6RZPNmCT7f\n/vb04HLzZuCP/gh47WvdfTZsEJfi+uvTbaUQdRd584GlRdC7d0vkO3u2P8r2ReHmOaZPFwsjWSf7\n0KFSVkookjfRcRaBtvOQkxeCsTE5GefPl9+XLJGTxrZ0qrVrAEm39FktWbzbkB2Tdes+I0o+EW+l\nidcZM9LLMxgrJbRYKk3A7TFCIu7L0Al59kbkzV2cbenMmydWi/n+JSN9oHrL59gx0YRFi9ztQLlA\np0XqRhN6e8vXgSTbY8YI6U7awrDkGFmeI4aGEvm0F2lHyL43M3QRSBvDtCftjbQxQlF2jNXiWoS0\neHHplrm7u9zS0Tq8kGnXrtJFwhAzaRoScCPQx4/Le5ScmLX75I3kzVhpDAw0RtmCvMybV7mZuU3I\nzjEW2YEDcRuP+NIss07e2u1jY5Wbkisln5cR0FiRt4OhZLuplmmeJ0suvnEHfCIeczdgxgi1p9lG\n5jiWLEkQsizrAAAU4ElEQVQfwwSHLSHy5kW6bpvsDyOLyNtjhATa1W4mjMytaEjEjUViT6S4/PSQ\n1WIfy86dlfVQFi4s90RjRDwtM8ZUf8wj0L4+MZF8zIrWL34R+NjH0tubhfnzJUUvjdDEbGeniOqO\nHXG+fSiST2v35drH3AXadwOjo5UXtpClU217so8rit61S+6izPG6BDZ0ETh6VDz3hQvDUbgZI/kc\ne/ZIwNTXF9augQG5oLvW34Sou8ibK1Vvr7zxydsm2+ZwvRH794ulMWdOKW83OUsdGsP+MExKoW2T\n7NghtoeZMAqNMWOGHI8twPYdCRBntdh9tm6tLBdre+VAeMcl14nY1SV3D8ePh+2a0KSpT8RjFkOZ\nx32RfFeXO9ukGQkVMAPCawZ8cxi2XRP6XGMieV9phKwlkRcvLtmnSSsGCEf6oXPRFUUnSzjE+P7J\n57DvvOfPl9eYtGNCcwfVBLBmFbxrAjhE3bNr1q8f8r6ZoTfC9sp7emQ2PLntWGhS1BZY1xjJL0XW\n9Ebfl9Ml8nbtD1cKm32SAfnsmNgoPBQ15lkM1Uq1afJiIvXQFoIxNel9n2toi8HY0ghZM7P6+kpB\nyPbtkrFjWz6hSH7BAgn07BTHkK/vsoTSMnSAsJ1jdsqqVsRjRd7oQm/vEL7whdWVnQLUVeQ/9anV\n0HoQc+fK72lvhM9Pd12VkwJczZsNhKPs5BgHDkiqmnkdrj5pFwFjT7k2drBPAJc4dnXJ3x8/XjrR\nQjnsWdIbY6Lw2bPlZDt2rHTHY9PTI+/Rvn3+uwGAIm+YNSscyYfSLPftk++H6zMJlUYICXTyLjAm\nQ8cVhJhI/8ABd9375Kpa+1zt6BCP3vQ5cULuvE2NHSAcycdm6NhWskt3krpijxHjICTXzpiqnsYm\nfsUrBvGmN61GtdRV5O0oHMh2tQsJ8P79pWXSWceIabdfBxC+UMyZI6mc9hc8uaNRKHXRpMmZSdEs\nq1XtPnk8+d5eiSpnzqysiGgfq89eiJl4bSd6e/NvBm5KN7s+k7yefHKM0IUixtdPHoedHQaIENoC\nDpSL9JYtlTV2kudrKE3T7HdrbKP+fnn/TJE+M4bvbiB5sQnZNd3dlQ7C6Kg81tPjfh2x1FXkY66G\nts3hutqFxrBnuNOeI3YMw6mnysl14kTpGJNWi31HYfLq7awQU2zLZ6XECHTMhGcRY8QsZIqpOzM2\n1h6lhIsgZp/YsTH/hdf3mdgTs1k8ebtPbBpmaHMT193C+HjpXEumaQIipCYLLXmuAtXbNSMj5RcK\nV9p0yNc362LMGEXoTlOKfMinTl4NTVqhvVlA6I1wRdATE6Wrsl2jPXaMZL69S+Tt1DHz5fXV/A5V\nd8wzeVZNXZkskT4Qv5AJCNs1jOSF3t7wRRPwe/Kxm4Vn8eTNc1Rzoaj2O24v6krrYwdLu3bJuWcT\nsmsWL5b3ySySTEb6MWMkLxRplpDP8gmN0ZQiH3oRIyMyseK79QoJdPI5krnwu3aJxWALTzKN0hUd\n2FkByVrcQFjAAfly+gS4WqvF56ebMbJ47rF2Tai4WG+vRGbTp7vbGcmXE4rkQxk4xs6JmSz3Rekx\nE6uhyV0g/3cccKeDJrdCTI5hV9MEKi2frq7yQmeu8z2pK6Esn+QYvb1iu9jZg9VG8mkra0PUXeSr\nEeiYPml+edoYaVkt9mTP7t2VC3BCfnm1X95aZcbETLzGrlb12TWxkbyvfebMcPTaTsybVxk82MRE\n8r7NwkNR+MyZYgcdOZKeulrNhuOhidda3Q3Y7aZP0vKZP78kwCY90sYW2LS7f1+kb/oY3Un6/jFj\n+Orn+6i7yNsvYtEiiayPH5ffQwI9MVFZLyOZXZMsvwuUR+EuAU9O9mTxEosS+djiYXk8+dAY5mQ/\ncCC7/2v6+KJ0paRuuWtFbTuyZo3UPUkjtv6Nr91n11Sz4Cr03Uq7G6jGEkrrU017WhZa8m6hv7+8\n3b4I7N0r31H7eUJWC1CuXUnfP9nuGmP27NIkbDXU3ZO3r4bJqnNpAm2i7D17SrdBhpNPLm08ALit\nlGQUHtr/MosAFyHysVF4yE832TdAtpIEHR1yUoROdrMqN42YKD25SXg7M2eOf+FXb69YX2kXxVmz\nJGjy2Tk+u8b0KWLy9tixuOyv0KblY2Puksi+88i+kIyNyXuWtAyrvZAk7wRmzRJP35xnrhIj9gSx\nK2U6mWufLGMCVF44YqiryG/duhrPPDNU9pi9n6grrbC/v/yKm3yzOzokJ9isPst75Z+YcH+x6mHX\n5InkfVZLaAzTJ7TJttb57BpSHb294ewbrbPbNaZPrK/v6hObix/j+5ua976SyDHnUVZbydduJoh9\nz2NrhutuYc6c8uq2+/aVr70ZGhrCa1+7uvLgA9RV5MfHV2PVqsGyx4q0MHxjhK78hw/LF+/IEblo\n+L5Yeas7njghUUB3d/YxfO2+W3KgfGI1a4qkebwdKkg2CqHFUjF2Tui7YT730IbjIfFMe46uLhFI\nX1mNvIu2urtLaZhTEXDFjBFaM+AaY3BwEDffvLryiQPUVeQB9+2bLeIhLzwmo6TaD6yzU74YY2PZ\nP9BZs0qFv9LGSC7196VYho4jbdLUvJadO/0nckzZgrwLmRjJF0tMJA+EUyhrGcnHXkhiLJ+YKNw1\nhp2GWU+RD80/JGtR+T6XaqiryDfCFTWvDZI2RleXCOyxY9mtFHMBNFFOnsJfMbsMhaJ9365N5u98\nX0pG8sUSU/bA/pmks1O+Yzt2ZPfkjXBlXRFr+hRZSK0WY0yF7tgOQtr8QxbaUuRjdkMq4otVTWpi\nVgEOTbyaMUJReMyGHkeO+J8D8Iv4ggWVC1VIdgYGKrdztImtZBn6XEN2Tcx5klbuuNoxfHcLQHbb\nqFrdqYWvbzsIafMPWWhZkS8q8yWrJZQcw9ceskliVqPGiLxPwHfuLN19pPWxfyaJsWuuuCJ9H19S\nPW98o2wmnYb5LEKWjklWSGuv5cRrzBixkT7QOJF81vr7MedztTScyBeZtWKqM4Z8/3rfIoZsEt+F\nIHYMU6jKRehENmOYvi66u+UC4Rujo6N1asE3Akqlrx4GwhdmoGT5uAqYmb+NzbXPEkGbYzB9ahGF\nFzFGSMDtPllTPZPH6bs4V0PDiXyRV1TTnnVCMyaCruY4qm2vZozQ3UDIk49ZyASk3w2Ygmv03BsH\nc2cWiuRDF4E8efIzZ0qt99BCupAdE3O3ABQj8q7j6OqSdOqYzXWypnrGHGcWmk7k7cmJqYjCQ6UA\n0tIfqzmOkMib3ezTFjL5bCXTJ+TJHz8eJ/KhPkV9MUl+TFZJns+st1e+G76J2QMHSsKWxFhBeVbN\nhiwfs9+t2Uw8S4Rs7kgmJmSs5LlUTYZOLQPDLDSdyE+bJmIXk96Y582uZs/StNvd0HEUUfirmonX\nPJkxMcXDKPKNR4yIx6Rh+r5bO3aIkPvmc2Jq6KQFKiGB7uyU5zcC7BojNgvItSG56zgo8pH4XqTZ\nWDrrdnWN4KebC0HeMfJMfJk+IU8eyFdBEgA+9Sng7LPT28nU87nPAaedlt6eNw0z9N0yzxH6Du/e\nXUrpdLXHfMd37ZIg0PUdzXu3EDMGRd5B6IpqJvPS+mS1WmbMkFvQF16Iix7yCHQRY+QVeXPHkNY+\nbZpEL3mtmKuuqlyqTerL7/xOeuYMEP5czbkRisJDdwOh73ARgUzeRVuh54hZeVvrLKAsNKzI5xHP\nkA2iVHE72cceZ+hC4rNj8i5kirFjYm7ri/rSkcYh5nO3f1bbbtpiRD5rHn3sc+Q532P62O2u12LK\nK8RM3raMyD/yyGoMDQ2VPTaVEXKeMUzNDVMJ0/ccaXcURSwrNxO+e/fWVuSZOdOaxGwxaP9MYiZb\nQ9+t0dHaVbq0n6ORNSPv5O3Q0BBWr17tPjgPdRX5Sy9djcHBwbLHplLk9+yRK2uWzBjTJ5S14hsj\ntq5MyPPs7ZXoIG0JtPnbPMXDTjqJZYBbkdDnGlo1ayY9Y76ftYrC7TFqtaq2mjFqpV2Dg4OZRL6u\nS1PqdUU1fWJ3sg99sXxZK770MiC8o5J5jle/2t1u+ph63Wnt9s+04/C1v+Y1wPe/n95OmpOPf1wC\nnTTMdyJPrn2M5bN9e2XtdIN9xxvazzarZlRzZ55Xd3wlRHp7pY78+HhxJUDaVuRjxLUIH3DnzvSU\nrNgxDhwIR+HHjqW3x5QcCJ2oSnFStRUJ7cIVa/Xl2YvWfMd9dwtdXeE73qmcvM26utdoQtp2imaM\n8XH/e14NDTfxGspnNX/XKFkrebIGYo/D/pnWJ08kFTMGaU9irb68kbyv3bSFqmHmEeiY52gU3amW\nhhN5s9jJt23ZVL/ZeTbS8GXGmD6hJd/2TxchqyVmjDPPBJYvT28n7Ul/P3Deeel3okB8rr1PXO2f\naX1C50kRWWih83nfPplfcO21SpF3kPWNmD279pOmsWMUVRPG91p97aYt5jl80diXvuTfNJq0J93d\nwKOP+vvk/f6FLgJ2n6znmtnE5+DBfEFbUfN4edJFq6UpRT70ZsdaPlPh4R0/Hr7VtX9W227aQpE+\nwBRIUht6e8MC3dWV7v/HfscBv0CHMnh8q2pNnzz264wZUoxt/35G8i+S543wtRvLJ09qYm9vXE2Y\nffvyWy2+PrEngE/A+/pkQ2CW+SW1YN688g2nk+Tdi9a0zZqVPYMsdK6aPqE7c59mdHRIFl2est61\nEPmGy64xj0/FhOb27cD556e3HzhQumD4jj+vQAPhW9nQ3cDYmL997dr0dkLy8Fd/5faoDaE7zdgF\nVXlsz9gLSWgucPt2YOlS/xgxurNiRXp7S2XX5NmNpgiR932g06eLuOf5YsX66aEUy9AYMRtkcyET\nqRV9ff5UzJCd09kpQh+K9mPOxbTniV205RsjpBmmT63r9FRLXUU+z240vvrUpk/IarF/pvXJI/Km\namOe5zD5tL4+K1cCZ52V3k5IPVm6FHjlK/19Ys61POdiEWPEPkdId0K19Y1NXNQcWkO6tL29MoHh\neyMmJqbmA/Ptll7UF8vXHrPxw+WXp7cRUm/OOAO47TZ/n1YS+dAYPu0yK2/TFktloWFF3v5ZbXs1\nY4S8bp/IF5XeGLot6++XW2JCWpW+Pv+K6qJEPk+aZqyv7+sTe5y+dQnV0tIir5Tf948Zo96RPAA8\n8IB/4wdCmp27706fjASaL5LPswsbRT6i3bT5Uq5io/Bai3xMlH766f52Qpqdl7zE3x6K9GOj7Dwi\nb7QgdDfQ05NutVDkJylK5Iu48seIfKiPzxJ685uBc85JbyeEyLzTu96V3h5zPsfm66edrzFZQEXp\nTkOLvFJqFoBvATgGYEhr/X+qHSP0RsRmreRZaWraQlun+TYwBiTzxZdX29UFLFuW3k4ICYtnVxfw\nilfkL4nc3e3fxzg0Rt49c01bQ4s8gPcAuEtr/S9KqTsAFC7ypq2WlfFMW579MQHgxhv97YSQ/CgF\n/OIX/j5FzI/lHSPWVipS5KPy5JVStyilRpVSTyQeX6WUelYptVYp9dnJhxcDGJ78v2c7gnR6e0uL\nkXx98rzZRayyW7ZMJozyktwCsZ3he1GC70WJIt6La6/1pxtPhcgXYTVXS+xiqFsBrLIfUEp1AvjG\n5OPnAPigUupsAFsAmD1eMi22moo3u6MjfGv1vvcBv/Zr6e1KARde6D/OGHgyl+B7UYLvRYki3ouV\nK4FTTvG3f/7z/jEaIbislii7Rmv9kFJqeeLh8wCs01pvBIBJa+YSAH8D4BtKqXcCuCfLQRUh8nPm\nyD8f3/42sGBBentaXRtCSOsxYwZw5ZX+Pn/yJ35dCOlO7ORto3jyti0DSAR/vtb6CICP5jmoBQv8\nk5WARNkvfWl6+6WXSuaKj8suq/7YCCHtS2jPhXe/G7joIn+fj3/cv3/r4sXpqd9ZUFrruI4Syd+r\ntf6lyd/fC2CV1vqqyd9/HSLy10SOF/fEhBBCytBap1T+qiRPJD+CkveOyf9vif3jag6SEEJINvLc\nFDwG4Ayl1HKlVBeAy5DRgyeEEFIbYlMobwfwMICVSqlhpdSVWusXAHwCwP0AngZwp9b6mdodKiGE\nkGqJ9uQLe0KlVgH4awCdANZorb88pQfQQCillgD4OwADADSAm7TWf1Pfo6ofk2m5jwHYorX2LGJv\nbZRScwCsAfBSyPfio1rrn9T3qOqDUupzAH4dwASAJwBcqbU+Vt+jmhqUUrcAeCeAHdZc6DwAdwJY\nBmAjgPdrrff5xpnSTUM8ufXtygkA12qtXwrgdQB+p83fj09C7grbfVL+egA/0FqfDeDlANryDnky\n2eMqAK+eFLlOAB+o5zFNMRXrkwD8IYD/p7VeCeDfJ3/3MtU7Q72YW6+1PgHA5Na3JVrr7VrrX0z+\n/xDkZPYs12hdlFKnAngHJIJt20l5pVQ/gDdqrW8BAK31C1rr/XU+rHpxABIIzVRKTQMwE5Lw0RZo\nrR8CsDfx8LsBfGfy/98B4FmuKUy1yLty6xdP8TE0JJNRy6sAPFrfI6kbfwXg9yG35e3MCgA7lVK3\nKqV+ppS6WSnlqXPaumit9wD4GoDNALYC2Ke1fqC+R1V3FmitRyf/PwrAs5xTmGqRb/fbcCdKqV4A\ndwP45GRE31YopX4V4jv+HG0cxU8yDcCrAXxLa/1qAIcRcUveiiilXgLg9wAsh9zh9iqlrqjrQTUQ\nWiZUg5o61SKfK7e+FVFKTQfwTwBu01p/t97HUycuAPBupdQGALcDuFgp9Xd1PqZ6sQUy8fzTyd/v\nhoh+O3IugIe11rsns/n+L+S70s6MKqUWAoBSahGAHaE/mGqRZ269hVJKAfg2gKe11n9d7+OpF1rr\nP9JaL9Far4BMrP1Qa/0b9T6ueqC13g5gWCm1cvKhtwB4qo6HVE+eBfA6pdSMyXPlLZCJ+XbmHgAf\nnvz/hwEEA8Mp3RlKa/2CUsrk1ncC+Hab59b/MiQ97HGl1M8nH/uc1vpf63hMjUC723rXAPiHyUBo\nPYBA2azWRGv935N3dI9B5mp+BuCm+h7V1DG5PukiACcrpYYB/BmA/wXgLqXUxzCZQhkcZ6rz5Akh\nhEwdU23XEEIImUIo8oQQ0sJQ5AkhpIWhyBNCSAtDkSeEkBaGIk8IIS0MRZ4QQloYijwhhLQw/x8N\niOlx7gGVWwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce17d828>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# naive regularization\n",
"DS = ifftshift(ifft(S/(H+1e-11)))\n",
"plot(x, DS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce16c748>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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L0WlknpOtmSvMJSqFuRRdNsxraiCdhubNS92j0smOzFVmkagU5lJ02TDPjsqtCV85NplI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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce16c940>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Wiener deconvolution\n",
"lamb = 1e-12\n",
"WDS = ifftshift(ifft(S*conj(H)/(H*conj(H) + lamb**2)))\n",
"plot(x, WDS)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce0059e8>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7ff4ce07e358>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"noise = normal(loc=0.0, scale=0.1, size=len(x))\n",
"ncsignal = csignal+noise\n",
"NS = fft(ncsignal)\n",
"plot(x, ncsignal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce187e80>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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GO0lUrZqdcNm507pznXaafdfZ2XbTg9des3p5t272/g4dStfNFy0CevSI5q9P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\nAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce1847f0>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# naive regularization\n",
"DS = ifftshift(ifft(NS/(H+.2)))\n",
"plot(x, DS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce00a710>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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btgXOXVZpZO0uFnNfwmGzBOoABUjMjQ5GUndkQHd/urycCnitWOEt5h9+SHej\nZjJnFBs20HH72Wee13bvJiEeNQr4+mvjnvknn1BmmRrlnZlJZTwAEvNf/5osSSsJCNGmV4u5lOTB\nHXNM8GX79ye7IhSrRSvmgPfJlZFBJ+nhw5QpMG0ajWabNIm8RiPMn++xOwKJuZHIvF8/EmR/dHZS\nJ1FqKnD11cCbb3pbLcXFgTuKlc3S7mpHYgJ75lrCYbMYicxDEfNAkfmsWfT7asW8vt4j5mvXht5+\ngPqqvvc9bzFX5+uoUXQ3acQzl5KCpdNP97zmK+YnnUTb1O6rp9GrxbymBkhJoRPGCJddBrzxhvHt\n+4q5FiFIfFX98UmT6HWjYt7SQj6fmtexuNj/RclIZD5tWuDIvLGRBF8I6gC95hrgvvvovdZWmgzb\niJh3uDs4MvchUJ65GZvF7abOvvR0/8tYEXPlT7e20uPp06mDVCvmAFksU6aYj8z/8x/grrsoaFDl\nIHbvpprpxx1Hz42I+Z491HGr1gFIzNXEM9XVdExbuYuIBSyLuRBilhCiWAixUwhxhx2NihSlpeRd\nG+XEE41nAAAk5gMH+n9fWS1bt3pqwxgV83XrKFpWkURREUUxehiJzGfMCCzm2hMaAG69lXzIAwfo\nOznqKLow6aUsdnSQIPXrxzaLHv7qmQPmbJb6ehI4p9P/MqGIueoABbyFs6KCjq3EREpZVSKek0Pj\nNgYPpuOirS30UcMHDwI7dwKFhXRBWLOGjq39+2mbqp8rPZ2Oq0AdoJ98Anz/+96F9FRk7nJ5LMAp\nU8zfRYTKI4/QBcZOLIm5EMIB4CkAswCMAXCZEMKAAx0blJRQ6qFRgh00Wtxu75NADyXm27aRDw3Q\n6M3Nm4PncX/+OXDmmZ55HXfsoMhCj6wsyjTxV32voYHW3b3bf0aLr5inp1Pe8fLllBE0cSKdEHoX\njbo6uqglJLCY62G3zRIoLVFh1jPXirnW2rvgAk/G1rRpwN1302MhgHPPDWxPdnZ6H3cbN9IE1DNn\nUh/TySfTcX7gAIlwnz6eKNtIB6ivxQJ4xLyujj6b02nNEgoFtxu44w66Y7ATq5H5VAC7pJR7pZQd\nAN4A8EPrzdJHytAi42CEKubqoDFSq6WpiaIqf4ORABLzAwdIiFVkPmAAnQxXXx1Y0NesIc+8qop6\n+6dO7V67WpGcTG33N51XQwMJ/vDh/jNafMUcIDH/8ENPeqe/0bLKYgG6PHMHe+Za7M5mCeaXA/Z4\n5loxX7TVy936AAAbJElEQVSIgguA7navvNKz/sUXU7aWPxYvpj+AIvjCQrIM1RSFJ59M/UobNnis\nxKFD6QKRmBhYzPX8csAj5spiASjltq0t/DMlVVbSBczu/QS4ETPEMADaHIYSANN8FyprtKfVpaXU\nUbFqlf/h96FQXAr0HwaUhVCkR/QH9lRTdBCI0nIgLSfwttNygI++BNJHAA0SaOha9pnXgF/8Aph/\nC/C7+7uvJyWwahNwz0PApFOB+x8HfvSjwPvKyAc27wGO0wmKD3UArYnAUROAleuBAToXuD3VQFKG\n9z4mnAzccCetO3s2kLGD9pHrc2+2owzom03rVjVXcWTugzPBibq2Ot3zpEkAda7gx2hbGwUOTiew\nvST4ce1KBQ62GDv2yxop6ClrpOO/tIEeby+hYyXYNkZPpWXX7gCG6aTPvvVfCgTKGun4m3gK8H+3\nAe6ufR81Aah3A/N+BVx6qWd/086kx02C3tdrx67d1OY+mT7v96PP4asB408CPvycApVwsXk37X/b\nAeA4GwuECWlh8jwhxIUAZkkpr+x6PhfANCnlDZplZNrMtCPrJB2ThOQCPyFkEDo76WqakhLYizZK\nXR3dxqWmGl+nooKi2IQg9zSdnRSRZmb6X+bwYbJIkpK63xYHWl872rOpieycjAwELF5VU0O3k3rL\nHDxI+zl8mPar12nb2krv+3aqVVXROoMHUwTpdHbvUG5rI6tAfcbrplyHu753l//G9jKW7ViGq/99\nte57Lhf9dllZ/tdXM1ilpdGx3NJCfmygc8TtpgjRSPVK7XnS2EjWieoIVY+NbCMxsfux4XJROxwO\n+ozNzfRaIHvSFynpGNb7LP6+C/W99u9Py6hjU9moylZqbQWciYDTxrlUWlvp3O7f3/v7aNvVhvbd\nHiO9aUUTpJRCZxP6SClN/wGYDuADzfNFAO7wWUbaxRdfSDlqlJRZWVJu3mx9ezNnSrl8eWjr5OVJ\n+d13wZdbtUrKGTMCL/PRR1ICUi5Y0P29ffukzM3VX++ll6T88Y/p8ccfS5mcLOXhw4H3demlUr72\nmv57qalSNjZKuWWLlPn5Urrd3Zf529+knD+/++u3306foalJygcflPK227ov89prtH8mdCoqpMzM\n9P/+nj1SDhlCx9rChfTaffdJuWhR4O12dEjpcEjpcgVvw0UXSfmPf9Dj3/+efnMppZw3j44LIyxb\nJuXpp3d//dlnpbzgAjqG29ulvOoqKZ96ytg2FR0dUiYk6B+3F18s5Ysvdn+9tlbK/v2lfP55Ka+4\nwrudM2fS44MHpRwwQMqMDCnvvlvKzs7Q2uWPP/+Zzpmbbw68XJd2GtZjq575OgDHCiHyhRBJAC4B\n8J7FbfqluZmuvrfeas8s8aFmswDG60AESktUqBGbqvNTy8CBFM3osX49ZdYAVF73rbeC2z7+0hM7\nOylS6NuX2tHRQVkEvuh55gDZK/n5tP7w4foDh7SeORMawTpAN22iLIwrr/R4sP6iVC1OJx3L/o4x\nLYE8c6OTjIwcqX9sLF9OFuGwYZRDvmOHdwqhEZxOunPwrZ3kdlOWl69fDtC5efgwaYDyzAHy5zdu\npPkAnnkG+PGPKSFh9Wrg8svtyUApLaXPaLdnbknMpZSdAK4H8CGAbQD+IaXcbkfD9GhqooPpoovo\nIDAzN6KWUDtAAeMZLVbFvF8/Otj0sku0o1YTE6k0aTBUeqKU3gekNn9cCBoAolfpTpvRoOW002hk\nLOC/wqQays+ETmoqHQf+3FCVMaUdUXzwoKdsRCBUJ6jbHbiz3Z+Yl5X5H6jmb19a2tspbfDss0nc\nvv2W/kaONLZNLXpB1tat1G69/jU1XqK42FvM09OB+++ni+Nf/gLcdBN9t8uXk1147bWht82X0lK6\nANs9I5PlPHMp5XIp5XFSygIp5e/taJQ/mpspUjn6aPqRtJXPQqWxkaLSUL13o+U2jYh5//70o+qV\nExDCf3QerDiYHioyf+wx4LzzPK/7RtyBxFwvMheCInLAv5hzZG6ehATKRvI3xaD6XbRiXlERmpg/\n9hilyvnDX555oFHHvgwcSG3VXjQ2baI6P1lZJObr1tF5E2qABeifl3pZLFoyM+lc8s38mT+fov0J\nEzxZZn360CC9996zPouXEvOYiswjjRJzgG7v33/f/LaUxSKMdy8AMD5FlRExF4JGnPnrQNIT8+Zm\n6jDKzzfU3CNkZ5PQPvII5airFE9fkT7zTLql3LuXIqfzzweef96/mGtRguJ7x8Ribo1AVou6sxo6\n1BPpGbFZAI+Yf/ll4GJYerVZWlvpGA/Uwa/F4aDzQZseu20bDTYCSMyXLaM5RIMlF+ihd15u2EBp\nvv7IzOwemQO0/3fe6V4WesAASsG8887Q26elpIRs0rIy81NS6tGjxFzZLABFkMuXm9+WGYsFsNdm\nCUZ6evfc8B07gIKCwPnregwZQpFKQQHdKv71r/S6r0gPHEgTW5x6KtlZ27dTRGJEzPv0oc/8zTee\nodJA8KJPTGAC5ZoroR00yFPrJ1SbZeNG+s38CYuezbJvH92RhXIc+lot27Z5It+RI0l8Q/XLFXo2\nS7AsIJXB5Svm6j29/oBrr6U7CiuuQGkpjWBNSPAUubODHiXm2sj8tNPoC/U3ECYYZsXczg7QYOiJ\nudEqj75kZ9PJesstwFVXAS+/TAeynkhfdx0JelISTWqxeTN1ihpJF5s8mWrAjxxJt/suF7WZJ3A2\nT6Ah/dpa4zk5NKrw8GFj9mFGBg2PP3CA7qb0Ru9K6Yn+AU8EvGcPDasPhYwM74u81i5UIm6nmAcL\nItRdhZ6Y+yM5mUY7q/K5odLURHe86ened1N20GPFPCWFpmIzWyvZTCYLENnIXM9mCVZ/3R9DhlBN\n9nPOoT6HiRNp9Ka/iHvuXKqMOHAg3QVt3GhMzD/4gET8iiuA3/4WeO45EvLp00NvM0MYsVkAEocN\nG+i3NmIfZmRQtseYMeQP65VebmmhOy5V50WJplkx9xeZDx1K2zbT+QnQd+Ab5YZDzAFrc46WllIQ\nqS6+dvrmPUrMtTYLQLdQZmYuBygyNyPm0Y7MzXR+AnQ7/NvfevzIyZPpwtDQELyd559P/0MZyHHX\nXTSE++67gccfD71vgvEQzGZRv0tOjkfMjZCRQTVPJk0i71pPzH0v9naJ+eHDdA6q4flC0DD+E04I\nbZuKMWO6B3bBxHzwYNpvqP05VsVc6Y7ePMBWiIiYB5pOLBS0kTlAP5RZMQ8lR1ZLKJG51VGqdkbm\nvhQUUB0WI174nDl0exnKxSkjg1K85s/3lPdlzBHMZvGNzI1aWhkZ1JE5aRLN3uNPzLUpqaoD1KqY\nf/stCbl2msNly8wFKgBNPPHvf3ueSxk8JTYzkzQk1P4nM2Le0UGF7HzFvLycOqCLi0Pbnh4REfNP\nP7VnO3pibrRYkC+hpFVp0f6QX33lf9BFOCLzzk4SYLO3olpCEfN+/WigUqh9DFdfTd47Yw0zNosR\nVNSqInO9lDvf4yMlhfKtd+4kuy4UtGK+fbvHYrGDKVPIj1eVCJubqc/HX/E5gMQ8WEEyPcyI+Ycf\n0ud96SVvMd+6lWYxu/nm0NvhS0TE/OOPza+7axdd0QD6Au2MzM2IuTYynz+fCn/5zocJhEfMd+2i\nE9XoZBqBKCigE9KImAN0ILJVEh1UvRU9tL/f0KF0vIRiswDkl48dS8JSXQ0sXaq/fcAzqXNxsbXI\nfNs281G4HgkJdAeponMjGVQnnhh43lp/mBHzigq64GzZQrn1AOnPSy/RXcXXX1OmmhViWsz37qUU\nuaeeoufNzd6eeSiT0moJVJgnGL7F+X/0IzqIfKmrs78D9JVX6CpuB7m59N2Vl4fmhTORx2hkroIT\no8d1Xh4NIOvfn4619HS6aM+d69mfXk1+NfFFqB2HgweHLzIHvK0WIzXd+/Y1dz6ZEfPKSpo8Zvt2\nqogKUECVl0djP668kiaU/vRT4JJLPMuEQkTEvKlJP3oNts7s2eSrqejULs/80CGKdoLVM9FDReaq\nquE991DErM3Rdbup/VZFUhuZt7VRZogdw4kBimSOPtp4lgoTPUKxWQDjkfngwcC//uV5vmgR8Prr\nFKlv3kyv6ZVxSEujqDzUO7VwRuYATWaxejWdK+Ec22BWzLOyqE0pKfTalCmelN9rrqHz+2c/A844\ng+74QyUiYn7aafQl++J2UwH6trbu7y1fTtHjbbd5BM0um8WsxQJ4OoCqqujgTEmhv/p6zzJlZRTp\nhNqx4otWzP/5T0onNJuHq0dBAXVEsZjHNv5sFt8c8FDF3JdrryUhOeEE6iMB9G04JeahosS8rY2K\natnRka+lXz/ywUtLSRfM+OFGsCLmvqgO4NxcmoZxyxYaBzJ/fujtioiY5+fTF+zLn/4E3Hij/vRJ\n779Pt4Bawfa1WayIudlBLGrQhJr/EPBMy6Z4+2265bOK1mZ55hkazGMnBQUkCCzmsY2/yPzwYRID\nJQjp6dThZ3WA1uTJ5OEC+mLer581Md+xg9YP1DlplhEjaEBPrEbmgTjrLGsZcBERc1+xAygT5OGH\nyUZRo8LWrqVcUbebBp/Mnk0HqFbM7YjMKyqsR+baYkZqCirFP/5BvpdVVGTe1kYnl92znxQU0H8W\n89hGifmKFRS1KfQ6Jx96yJzQatFG5v48c7NiXl1N0ef48dba6I9IibnRuYAVRsTcKlET8yVLgAUL\nqBNE+Wgvv0wi+MUXdAAVFHgLtq/NooQ+1GI1VmwWvchcK+b791PkoeZDtMLAgWTfbNlC34UZjz8Q\nLOY9A2WzrFnjnT6otVgUv/pV4BmnjDBuHNlvra36nvmsWWSdhkqfPnQXsWZN+MRc1dTviZG5VaIm\n5vX15PFpO0WqquhHmDvXkyGiolOXixLvVecB4Dk4mpooejVap8WKmKemeiZ91Yq5+nxLl1KGi9UT\nCqCMgZQUmvP0+OOtb8+XY4+l/yzmsY2KzLdt866foifmdtCnD/XNbNmib7PccIP541GVEOjpkXko\nYq6mqAs1+ydUoibm6iDRinllJfDgg+Svz55Nr6WkUOZFVRUJqW8PukpPXLLEeOK9NqoOFZVnu2uX\nfmS+fLln+LsdpKdTulI4xHz4cOCUU4zN4chEDyXmW7fSsavuRI2OETDD5MnAf/5jvMCaUTIy6HOE\nMzKPNTE/dIjSlFV9m3ARMTH3rcimDsTBgz2eeVUVFWTavJnSjBTp6fQD6Q2WUTZMcbHxCmRWInOA\noiE1gAegz6fEfPdue3vp09OBlSvDI+YOB0X9VrNumPCSmkp3srt2edJegfBF5gBd5J98ko5lM5aK\nPzIy6DwOtR6/UUaMiK7N4nYDb7wBvPqq57VIWCwAEOZrBaHEzu32FHrSRua7dtFrVVW0rK/QDhpE\nRXn0Ikgl5jt3endCBsKqmKeldY/M162j3PPycs/MO3agZmiZONG+bTI9i759yfIYNoyi8ooKz3iH\ncIn5vHlU+dLuUb8ZGeTJm5mAwgjKZhEifGKurFaXyzsQamujOUQBqvd+8cWUsRMpMY9IZJ6U1H3y\nWF+bxe327ysNGkRX20CR+a5dxsXcis0C0Ank2wFaWUkXnCFD7PHLFenpFMXwTD29l9RUOl/GjqXj\nS93lhtNmAcJTvmHw4PBZLADZGVLSIMVwibmyWn3TRZctI51bu5YuWMuW0etxJeZAd9/cV8xra+mL\n0FZRUwwaFNhmqaykH6+qKnhmS0sLFYe3ks+p7hB8UxP37rX/9jE9PTwWC9NzUMe9r5iHMzIPF7Nm\nUcQaLoSg6LypKbyzW+lZLS++SMPwhaA7GzXtXFyLuZTdPfOqKv/zCaanU2Tuz2bZsIFsE4cjeMeE\nGjBkJero148uOipaVjZSOMQ8OxuYOtXebTI9CyXmY8bQ8VBRQc/1csBjnXPPpcEx4WTECMrI0Wa+\n2Y2vmJeXU234Cy+k5xdeSCmYZWVx5pkD3p2gra2eWcdVZB5IzAcNogI1etbIoEE0WlSl2VVVBY5W\nrAwYUvTrRxGS8v1UZL5nj/1ifu+9XK2wt5OaSv/HjiU7UWuzqAp8jIfhw8M/56wS8717aZDgt98C\nF1zgufD27UuC/sorJOaRuLuOSmSu9fqU5x3o6hXIM1epTgUF+imQvuzYEXodZl/S0rzrXyQnUySw\naZP9Yp6UpG89Mb2H1FQKGEaNouNOG5n3NJslEowYETkxf+89yl5paKABW1qU1XLwYJzZLEOG6It5\nUhLdDu3aFTgyLy/3b7O43RSZ+w6r1+OLL4Bp08x/DoBOIN+7hMxM6vgIV8oV03txOGjsRUoKHXda\nz7yn2SyRIJKReUkJcOmlnkJ4WmbMoP+ffhpnYu4vMgfINy8uDizmbrf/DlAgNDG3OrlwWpq+mJeV\nsZgz4UHdnflms3Bk3p3CQuCXvwzvPrRi7m8GLiEovbOhIc7FXDtxQ0ZGYDFXHY2BxLygILiYNzbS\noB6rOdszZngPagLo8yUk2JtjzjC++HaAsph3Jy+P6oKHEyNiDlA7nE7zZYlDISodoL6ReUYGRcyB\nInPAv82iJlrQ1kjRY906mu/Qah74rFndX8vMpB+V/W0mnKjIXJsRxkQeVXAvmJgPHUoBpNVZx4wQ\nMzZLfX1wMdeLzHNyKL8zOTl4ZG6HxeKPzEy2WJjw07cveegNDRyZR5O0NPoNSks9EzT7Y8SIyLTJ\nkpgLIf4ohNguhNgkhHhbCOH3+qPtAK2v7x6ZA4GzWQB9MU9IAH76U8/62sh840bghRc8z8Mp5kOG\nsJgzkUFZLb7nERM50tIoLbF/f/tLU5vFamT+XwBjpZQTAXwLYJG/BQcOpNGXbW36NgvgPzLv149E\nO9is9L6R+cKFwG9+Q7ekbrc9mSz+uOIK4P77w7NthtEyZAhN8TZmDJd5iBZpadTPF8hiiTSWxFxK\nuUJK6e56+iUAvx9NCLJESkv9i7m/er8JCXTQBivVqhXzDRuoOFFCAuWhr1lD2w9XB2V6Ond+MpEh\nO5tSdZctC1/BKiYwaWk0kDGWxNzODtBfAHg90AL5+XRr0tDg7SMNHkziHmhOwEGDQovMH3oIuOUW\n6nxYvpyqmF12GY+mZHo+999PwUO4c6kZ/6SlUT2pHiXmQogVAPRqDN4ppVzWtcxdANqllH8PtK28\nPBJVvcjcn8WiuPhizzRn/ujblyyV9euBjz4Cnn2WZjV5+GG6Jfrss8DrM0xPYPToaLeAUS5BjxJz\nKeXMQO8LIa4AMAfAGf6WWbx4MQAS8qKiQjQ0FHqJ+bhxnk5Mf/zud8FaSlF3VhZwzTU0v2i/fsD3\nvw9cdBEwYULwiwHDMIwRwiHmRUVFKCoqMr2+kKHOhqxdWYhZAP4E4DQpZbWfZaTaxwsv0Kw5+/YB\nixfTSC27OfFE8uV37fLYMrNnU274jTfavz+GYXof69cDJ5xADsAZfsNYawghIKU0bAxb9cyfBJAE\nYIUgM3qNlPJafwsrmyWcKVXDh9NQXq2/vnRpcL+dYRjGKD3SZgmElPLYUJZXHaAOR/jE/I03uo/w\n5IEVDMPYiRLzYAOGIknEhvMDFDWXl1NJz3ANbw2UEcMwDGMHgwaRfRssXTqSWPLMDe1A45kDdFtS\nWkoTVLDwMgzD6BOqZx7xIQf5+WSDsJAzDMPYR8TFPC+P60kwDMPYTVQicxZzhmEYe2ExZxiGiQMi\nLuajRnWfco1hGIaxRsSzWaQEOjqsz/bDMAwTz8R8NosQLOQMwzB2w9WQGYZh4gAWc4ZhmDiAxZxh\nGCYOYDFnGIaJA1jMGYZh4gAWc4ZhmDiAxZxhGCYOYDFnGIaJA1jMGYZh4gAWc4ZhmDiAxZxhGCYO\nYDFnGIaJA1jMGYZh4gAWc4ZhmDiAxZxhGCYOYDFnGIaJA1jMGYZh4gAWc4ZhmDiAxZxhGCYOsCzm\nQohbhRBuIcQgOxrEMAzDhI4lMRdCDAcwE8A+e5oT3xQVFUW7CTEDfxce+LvwwN+FeaxG5o8AuN2O\nhvQG+ED1wN+FB/4uPPB3YR7TYi6E+CGAEinlZhvbwzAMw5jAGehNIcQKANk6b90FYBGAs7SL29gu\nhmEYJgSElDL0lYQYB+BjAC1dL+UCKAUwVUpZ6bNs6DtgGIZhIKU0HCSbEvNuGxFiD4ATpJSHLG+M\nYRiGCRm78sw5+mYYhokitkTmDMMwTHQJ6whQIcQsIUSxEGKnEOKOcO4rlhFCDBdCfCqE2CqE+EYI\n8atotymaCCEcQogNQohl0W5LtBFCDBRCvCmE2C6E2CaEmB7tNkULIcSirnNkixDi70KI5Gi3KVII\nIV4QQhwUQmzRvDZICLFCCPGtEOK/QoiBgbYRNjEXQjgAPAVgFoAxAC4TQowO1/5inA4AN0spxwKY\nDuC6XvxdAMCNALaB7TkAeBzA+1LK0QAmANge5fZEBSFEPoArAUyWUo4H4ABwaTTbFGGWgLRSy0IA\nK6SUI0EJJwsDbSCckflUALuklHullB0A3gDwwzDuL2aRUlZIKTd2PW4CnbBDo9uq6CCEyAUwB8Bz\n6OXprEKIAQBOlVK+AABSyk4pZX2UmxUtGkBBT6oQwgkgFZQh1yuQUq4CUOvz8nkAXup6/BKAHwXa\nRjjFfBiAA5rnJV2v9Wq6IpDjAXwZ3ZZEjUcB3AbAHe2GxABHAagSQiwRQqwXQvxNCJEa7UZFg65M\nuD8B2A+gDECdlPKj6LYq6gyRUh7senwQwJBAC4dTzPkW2gchRBqANwHc2BWh9yqEED8AUCml3IBe\nHpV34QQwGcCfpZSTATQjyK10vCKEOAbATQDyQXetaUKIn0S1UTGEpEyVgJoaTjEvBTBc83w4KDrv\nlQghEgG8BeBVKeW70W5PlDgJwHld4xJeB/B9IcTLUW5TNCkBlcRY2/X8TZC490ZOBPC5lLJGStkJ\n4G3Q8dKbOSiEyAYAIUQOgMpAC4dTzNcBOFYIkS+ESAJwCYD3wri/mEUIIQA8D2CblPKxaLcnWkgp\n75RSDpdSHgXq3PpESvmzaLcrWkgpKwAcEEKM7HrpTABbo9ikaFIMYLoQIqXrfDkT1Enem3kPwM+7\nHv8cQMAgMGBtFitIKTuFENcD+BDUM/28lLJX9tQDOBnAXACbhRAbul5bJKX8IIptigXYigNuAPBa\nV8CzG8C8KLcnKkgpN3Xdpa0D9aesB/BsdFsVOYQQrwM4DcBgIcQBAL8G8CCApUKIXwLYC+DHAbfB\ng4YYhmF6PjxtHMMwTBzAYs4wDBMHsJgzDMPEASzmDMMwcQCLOcMwTBzAYs4wDBMHsJgzDMPEASzm\nDMMwccD/AzaYRAVLqmg5AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce2ab860>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Wiener deconvolution\n",
"lamb = 0.005\n",
"WDS = ifftshift(ifft(NS*conj(H)/(H*conj(H) + lamb)))\n",
"plot(x, WDS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4ce18a240>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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ZYX4egE+Dfn4AwAMaY5gXF6u2amX+D+b/Y8+dW/cv/dRTqsOH1z7/scdUf/7z\num/rt3+/GcX+8EP4y9euNe/O8Y5yw5k717RHKivDX37JJarTpkXfTt++qjNn1n2dgoLY3hD9Zs9W\nPfXUmiMo//k9eyb2Uf+FF1T7969526HThuqUFVPi31gKG/jGQJ31/aywl61ebUZzHk/gPJ/PtOnO\nPtu0AOaEfx8IK/TTl9/+/eYTVDzhFM1NN5k3o3CefdaMrmNxySWmhRps9WrzuozUHiovN582br7Z\nDJDmzDHXj7UF6/OZTw+ffRb5Os88E/vvoGpan3/9a/TrLVhgXnPJDvMbALwS9PNwAM9pjGH++uvm\no144zz1nngyRVFaqHnec6tKltS/bvVs1Ozu2vvvLL6tec03d1/nFL0wbxAqfT/UnP6n9pAz2/PPR\n34T8fdRoH4l9PjMaifaG6DdoUO1RuaoJ95NOUv3vf2Pbjqrqzp2qt91mQmj58pqX3fDeDfred+/F\nvrE0cOlbl+qn6z+NeHmvXoG/4969pt1w9tlmxDpxovkk+skn0e/H6zUBF+7vrKo6ZYpqnz6139AT\nMX26ed6Ul4e/3D+Q27277u2sXWt+v4qK2peNHWs+OYfy+VRvvdWMyoMHEi++aB7LWNpJCxaodutW\n9yCmpCS230HV1J+TY3ri0Xg85hNNvGEuqomv5xOR6wEMVtUR1T8PB3Cuqo4Muo6O+HBE2NvP/gz4\nUWczax/q8GHgnXeBYUOBJk1qX75hA1BYCFx5ZfjaZs0ye1Z171737/DvfwO9+wCdO0W+zoEDwPTp\nZqXLUUfVvb1INm0Cvv4GGHJD7RUofmVlwL/+BQwfHlg7HqqoyKxwGDw4+n2uXm32CB00qO7rlZQA\nH39sVsK4w+ycuXy52a38ggui3+emTWYp4sknA717m68/CzZrwyw8PehpXNvj2ugbSxOXT74cPvWh\nc6vOYS9fvtz8Hbv3AJYuMcvXzj038BzZudM83wcPNsf5iGT9erNK66c/Df8cVAAf/Bs49TSgW9fE\nf59Dh4Bp75t9KDp2iHy9uQVA26OB00+PfJ1Fi8xz8pxzal/m8QBTpwL9+pnXul9hYfXveQ2QGbRw\nSgF8+om57mmn1f07zJ0LtG1bd22A2Q2/TRugZ8+6r7dxo6kpUl6F2rQJmP3rV6CqEdKiNqtrxLYB\nCI7BTgC2hl5p+4zAUfa79emGU846BR4P8PYS4O5rgeyW4Tde2A6o3AT0u7D2ZXMnA1ddBPQ5Nvxt\nM84wO88hiRg3AAAK8ElEQVQMvyhy8Vu3AlX/A665I3J4+h04FVhXAIwI/75Up9JS4N1/mZ0CTj6u\n7usu8AA5FZFfTKtmA+efAJwV4fcO1rOtWS9//GXmWB+RvD4LuOQ04NwIb2jdWgIPPwSc+lOgefPI\n2/n2W2Dxh8DIEeagWuGcfezZuPDEMH/QNPbIBY9g+c7lES//cRvg87lA0XLgsl5Abm7IFY4FOniB\nD6YBf3gQaBpm8FNZCUx91jx/63oOHj0Y+Mc/gGvy6/5bq5plrM2a1XxjKC0FXnwT6N8VuPLMyLcH\ngJxzgDffBG67NPyby+HDwOQFZue4o48Ov4121wIvTAB63WWec19+CXw3Cxj9G6BD+9rX73gp8Nxz\nwNAB4QeJgBm8vf0NcNdj0QdvrfsCr78G3DYIcNURu19NBy45o+7X7drFa7HuW7MWNUJpdYtnGB/6\nD+bNYAPMBGgW4pgAnTXL9H7rMnu2mUALtWKFabFE6j2rmiVRbdvWnnwJNnJk5InPUOXlpqdYVw8t\nnL17VS+6SPWRR2K7fn6+WaETjs9nWhdr18Z+//n5ZrVPJBs2mI900eYEbrzRtIEiWb/efBxevDj2\n2shet95q+rLhWgMPPKA6ZEhs2/nVr1Tvuiv8ZRUVZkFAly6qzZurZmWZSb277zYTqMcdZ5YJx9Kq\niTaJ+NJLqldfHX07M2eatuqxx5pVXtFeH0OGqD75ZOTL//xn0yaMhc9nJmPryoXNm83ihrKy2Lbp\nBweWJl4GYC3Mqpbfh7k8bKEjR5qJyrp4vSZAv/yy5vkjRqiOGxf9wbj3XvMkDufgQRNimzZF347f\nJ5+Y9eDfflvz/PJy1XffVR0zxvS8773XhOjvfmeu/6tfmXWjsVi6VPXEE8O/IFeuNGu245mM3LXL\nrPhZty785Xl5kSepgs2ZY5604e7b4zE93OC185R8+/ebhQOhq5hmzTJBt2NHbNspKVHt2LF2H37l\nSvMcuO46s07c5zOvoy+/VB0/3vSk490v4Zlnwi8l9vnMJGC0iX6/4mLTjz54MPp1CwvNwCPcnJrX\na96ovvoqtvtVNYOcut4o8/NV77wz9u35JT3Mo95BmDD3+UxIh06OhfPGG2ZSxj+b/9VXJiBjmXRY\ns8bM3Ieb8HjrLTORFK/33zfbfOAB1X/8w0zA5OSYbT36qNnZ5q9/NQE5blzt4I/G5zMTL+GeTH/+\ns3ljiNf48ebTQWgQf/+9CfrQ1USR6ura1ayTD/XCC2bHkPrYuYXiU1RkgvjFF82n088+Uz3mGLMu\nOh7z55vn+YsvmnXuDz5oBj+vvmrv39k/Eepfdhh8/1272jMZG86tt4b/VP7xx6q9e8f3O+7bZ0be\nGzfWvqyqSvX441WXLYu/xkYR5gsXxr7g3uczy9uef958xPvxj82se6wuvlj17bdrn9+/f/zrTv2+\n+ca82/7sZ2Y1TKRljYl6+OHaa4v9H+diXZ0SrKrKfBQOXsVQXm7Oe/rp2Lfz97+rXnppzb/bzp1m\nlLNyZfx1Uf1YutQ871u2NAODRJ/nq1aZN+k+fczoOZaVGIm44w7zaTbYddfF99yMl39HweBB4eHD\nZnnu1Knxb++hh8K3ZqZMid5OjqRRhPk995i+WqxWrDAPPGAW8cfzrjl9uup559U876OPzLt+XT13\nJ61aZT4WB49Kliwxn2YSHaksW2ZGbI8/bj4KX3utWaMfz2NZWWk+xvuf7JWVZknjb3+bWE1UvzZt\nqrk+vaHatcvMb/kPZzF1qnl9xttjjtfdd5s88efAH/+oeuWViX3y2Lev9s6K+/ebOYT58xOrr0GG\nefDItarKfHxbvz7+X87ni/+BrqoykyyTqvcgP3zYjFY++ij++0+mnj1rTgyNGhVbb7suW7aYdsuZ\nZ5pduGPpL4aaN898bJwyxcwPXH557PMBRJH87W+BPZA7dLDnmEDRVFSY5+9VV5n2Zdu2Nfcmj9eT\nT6pecEHg8AGjR5t2TqLiDXNL68xjISL65JOKMWPMzzNnmqOQff11vd5tDYWFZo30+PFmPW5JSeKH\nmUyW558H3n3XrGP1es3a2IULIy/5S6ZXXzWPI2COcNmihbP1UONXVQU8/TSwbJk5wuKddybnfg8f\nNnnUrh0wZAhwwgmJb6uqCrj3XrNGvU0bs/7/yy+B9mGWSMZCRKBxrDNPSph37aooLDSHjMzNNTvF\nJOuP5TdjhjkE58CBwD331L3uuiHw+YDLLjM7VK1YYb7YYcoUp6siomjeecfst3L99UCGhT15GmSY\nX3qpwu0GOnUy33H34YfRd9Ih885+zjnAbbeZ457zMSNKHw0yzCsrFb/7HfDpp6ZV0Lp1vd5lSlGN\nvPs/EaWuBhnm/vtgMBERxSbeME/qB3cGORFR/WAXlogoBTDMiYhSAMOciCgFMMyJiFIAw5yIKAUw\nzImIUgDDnIgoBTDMiYhSAMOciCgFMMyJiFJAwmEuIkNEZJWIeEWkt51FERFRfKyMzFcCuBbAPJtq\nSXkFBQVOl9Bg8LEI4GMRwMcicQmHuaquUdV1dhaT6vhEDeBjEcDHIoCPReLYMyciSgF1fqmRiMwG\nEO4L1saq6oz6KYmIiOJl+cspRGQugN+o6pIIl9fvt18QEaWoeL6cwsLXjdYQ8Q7jKYaIiBJjZWni\ntSKyBUBfAB+JyCf2lUVERPGo9+8AJSKi+ldvq1lEZLCIrBGR9SIypr7upzEQkU4iMrd6J6vvROTX\nTtfkJBFxi8hSEUnrSXQRyRGRaSJSKCKrRaSv0zU5RUR+X/36WCkiU0SkidM1JYuITBKRnSKyMui8\nNiIyW0TWicgsEcmJtp16CXMRcQN4HsBgAKcCGCYiPerjvhqJKgD3qeppMG2pu9P88RgFYDWAdP9Y\n+CyAj1W1B4CeAAodrscRInICgBEAeqvq6QDcAIY6WVOSvQaTlcEeADBbVbsBmFP9c53qa2R+DoDv\nVXWTqlYBeBfAT+vpvho8Vd2hqsuqT5fBvGiPdbYqZ4jI8QAuB/Aq6pg4T3Ui0gpAf1WdBACq6lHV\nUofLcsp+mAFPcxHJANAcwDZnS0oeVZ0PYG/I2VcDeKP69BsArom2nfoK8+MAbAn6eWv1eWmvehRy\nJoCvnK3EMU8D+C0An9OFOOxEALtF5DURWSIir4hIc6eLcoKqlgAYD2AzgB8A7FPVz5ytynEdVHVn\n9emdADpEu0F9hXm6f3wOS0RaAJgGYFT1CD2tiMiVAHap6lKk8ai8WgaA3gBeUNXeAMoRw0fpVCQi\nJwG4F8AJMJ9YW4jILY4W1YCoWaUSNVPrK8y3AegU9HMnmNF52hKRTADvA3hbVf/tdD0OOR/A1SKy\nEcA7AC4SkTcdrskpWwFsVdVvqn+eBhPu6egsAAtVtVhVPQD+BfNcSWc7RaQjAIjIMQB2RbtBfYX5\nYgBdReQEEckCcBOAD+vpvho8EREAEwGsVtVnnK7HKao6VlU7qeqJMBNcn6vqz52uywmqugPAFhHp\nVn3WxQBWOViSk9YA6CsizapfKxfDTJCnsw8B3Fp9+lYAUQeAdu0BWoOqekTkHgAzYWamJ6pqWs7U\nV+sHYDiAFSKytPq836vqpw7W1BCkeztuJIDJ1QOeDQBud7geR6jq8upPaIth5lKWAHjZ2aqSR0Te\nAXABgLbVO2I+DOBJAO+JSB6ATQBujLod7jRERNT48RC4REQpgGFORJQCGOZERCmAYU5ElAIY5kRE\nKYBhTkSUAhjmREQpgGFORJQC/h91b8vyfT3YRAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ce18a198>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def deconvolve_iterative(data, kernel, niter):\n",
" # http://dx.doi.org/10.1086/111605\n",
" from scipy.signal import convolve\n",
" P = kernel\n",
" I = data\n",
" O = convolve(I, P, mode=\"same\")\n",
" eps = 1e-10 # this is to avoid division by zero\n",
" for i in range(niter):\n",
" denom = convolve(O, P, mode=\"same\") + eps\n",
" fact = convolve(I/denom, P[::-1], mode=\"same\")\n",
" O = fact*O\n",
" O[O<0] = 0\n",
" return O"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"LRDS = deconvolve_iterative(ncsignal, psf, 8)\n",
"plot(x, LRDS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python3.4/dist-packages/numpy/core/fromnumeric.py:2507: VisibleDeprecationWarning: `rank` is deprecated; use the `ndim` attribute or function instead. To find the rank of a matrix see `numpy.linalg.matrix_rank`.\n",
" VisibleDeprecationWarning)\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4cdfcbf60>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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/+tzuf+c+qP6vb1F26ic7/LpzUFcHX5wEe/fBqpXeCfsTDg4egIONMO4Yb7uC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\nIpIHFNYiInng/wNjI+IYNnAB0QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4ccf99550>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# more realistic example:\n",
"xmin = 0\n",
"xmax = 10\n",
"xlen = xmax-xmin\n",
"x = linspace(xmin, xmax, 200)\n",
"signal = exp(-(x-xmax*0.4)**2/(0.2))*20\n",
"signal += exp(-(x-xmax*0.55)**2/(0.4))*30\n",
"plot(x, signal)\n",
"\n",
"#plot(x, psf)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4cc6070b8>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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1ZrbGzO5MuqakmVlvM1thZguSriVJZjbUzJ4zs3Vm1piZZ6pIZvajzN/IajP7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\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4cdfc3b70>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rv = norm(loc=xlen/2, scale=0.5)\n",
"psf = rv.pdf(x)\n",
"psf /= sum(psf)\n",
"csignal = convolve(psf, signal, mode=\"same\")\n",
"plot(x, csignal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4cc5def28>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7ff4ce145898>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy.random import poisson\n",
"seed(12)\n",
"ncsignal = poisson(csignal)\n",
"plot(x, ncsignal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4cc53b438>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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nLqJfFCQavJHK8LDz5QnQiScJingCmZrKLOK23ri7O1jEM2XiixZxpfuo6ehI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\n4oQQEmMo4oQQEmP+P8zCGKn27UWIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4cc69bdd8>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"H = fft(psf)\n",
"NS = fft(ncsignal)\n",
"lamb = 0.001\n",
"WDS = ifftshift(ifft(NS*conj(H)/(H*conj(H) + lamb)))\n",
"plot(x, WDS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4cc4d8240>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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gXngB7rsP2rWDX/wCli2DgoLo3HvNrjX06tCLti3aVh07csQF0kjXkvdnsFzA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\nmCbs/wOcu8j9yh5iUgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4cc4d8160>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"LRDS = deconvolve_iterative(ncsignal, psf, 10)\n",
"plot(x, LRDS)\n",
"plot(x, signal)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"[<matplotlib.lines.Line2D at 0x7ff4cc1ce7f0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Ewbqhc3nwg3NUFPCnJf44cG2fbd8D3jDGTAPecj9XUcZzUbOzE7ZvtxahCodg\nu1T21O8ZFiNTPLyFeH6+dU9QpXwZNMSNMWuBhj6bbwCecD9+ArjJ5rrUMODpD9+/H9LTISVMn8dm\njJtB2fGygI8bTiNTAMaNGkdHVwcNZ879uhUUaIirgQXbJ55hjKlzP64DMmyqRw0jnpEp4epK8Qh2\nhMq2um3MypgVhorCQ0T6tca1Ja4GE/JStMYYIyLG22srVqzoflxcXExxcXGob6eGkCpXFcsLllP6\nSnguanoEO1Z829FtzJkQpj6eMPGE+MWTLrae50JNDbS327cmjRpaSkpKKCkpCfr4YEO8TkQmGGOO\nishE4Ji3nXqGuDr/eLpT/rgF7rsvfO+Tl5rH4ebDnG4/zah4/waiHz91nJa2lu7ZpMNF3xEqI0bA\nxInWMMP8fAcLU2HTt4H7wAMPBHR8sN0pLwG3ux/fDrwQ5HnUMGbd0Scn7N0p8bHxFKUXsb1uu9/H\nbKvbxuwJs4fNyBQPvbipAuXPEMMngXVAoYjUiMgXgJXAMhHZByxxP1dR5GzHWepP19PZOImEBOum\nxeE0b8I8thzZ4vf+245uY3bG7DBWFB65qbn9xorrxU01kEG7U4wxt/p4aanNtahhpKaphswLMtm2\nNTas/eEe8ybOY/ORzX7vv7VuK1fnXB3GisIjL63/jTA0xNVAdMamCkplY2VERqZ4zJsYWEt869Gt\nw+6iJlh3+DnacpSzHWe7t+Xn64Qf5ZuGuAqK52YQ4VhD3JtZGbPYU7+nV7j50trRSvnJcorSi8Jf\nmM3iY+OZkjylV5eKtsTVQDTEVVA8szW3bIlMS3xk/Ejy0/LZdXzXoPvuPLaT/LR8EuMSw19YGEwb\nO63XGuq5uVBbq6sZKu80xFVQKhsrSYvJ5tQpyMmJzHvOmziPzYcH7xdfW7WWhVkLI1BReEwbO439\nJ841vRMSrGVpdSEs5Y2GuApKlauK1roc5sw5d1f5cPP34ua71e+yOHtxBCoKj74tcYDp062bbijV\nl4a4CkplYyX15dkR6UrxWDB5Ae/XvD/gPl2mi7VVa1mUvShCVdlv2thp7DupIa78oyGuAtbR1cHR\nlqMc2Do5Ihc1PS6eeDGHmg5xpPmIz312HdtFSmIKmUnDYw1xb7y1xAsLYe9ehwpSQ5qGuApYbVMt\n40ePZ3tpQkRDPDYmlqunXs2bB970uc87Ve8M664UsO5833S2iaazTd3btCWufNEQVwGraqwic3Q2\nR45Y4RKW4fRRAAAQKklEQVRJy3KX8caBN3y+/m7VuyzOGd4hHiMx5KflU37y3OBwT4gbr0vNqWim\nIa4CVuWqYkyHdVEzNjay7+0JceMlzdo723m78u1h3xKH/l0q48ZZ3+tjXpeaU9FMQ1wFrLKxkq6G\nbC65JPLvnZeWx8i4kew8trPfa6/tf43CsYVkpwyvlQu9mZamI1SUfzTEVcAqGytpqs5xJMQBrp92\nPU/verrf9j9u+yNfmPMFByqyX+G4wn5rqE+frhc3VX8a4ipg5SfLObQj37EQv3v+3fx2829pPtvc\nve3YqWO8ffBtPjnzk84UZbOi9KJ+9xUtLITdgd8bQ53nNMRVwPbVl9NSlU9BgTPvn5+Wz5KpS/j9\nlt93b3u89HFuKLyBpBFJzhRlsxnjZrDvxD46uzq7txUVaYir/kK+PZuKLqfaTtFwpoHLp2US42AT\n4LsLv8vHn/w41+VfR01TDQ9ueJB3Pv+OcwXZbHTCaDLGZHCg4QAFY62/lhdeCDv7XwpQUU5DXAWk\noqGCFJPLpZc4+yFu3sR5fHfhd1n8x8V0dHXw0q0vDas72/tjZvpMdh3f1R3iU6aAywWNjZCS4nBx\nasjQ7hQVkPKT5cS4nOsP7+nu+XdTfnc57//z+1w55Uqny7FdUXoRZcfLup/HxFhdKrsGX8hRRREN\ncRWQ8pPltFQPjRAHSBqRxIz0GU6XERZ9Qxy0S0X1pyGuArLjUDmdx/PIzXW6kvOfpzulJw1x1ZeG\nuArIjtoKpo/Pj9jys9FsRvoM9tbv7TVC5cILtTtF9aYhrgJS2VzO/IJ8p8uICmMSxjB+9HgONh7s\n3qYtcdWXhrjyW2tHK81dR7l63hSnS4kaM8fPZEfdju7nEyZAZ6euoaLO0RBXfjvYcBBpymb+pToy\nNVLmZMxhW9227uciMHOmtsbVORriym8b9u8jpjGfKdoQj5i5E+dSerS017bZs2HbNh8HqKijIa78\ntmbHbjITZuhFzQiaM2EOW49u7bVt7lzYssWhgtSQoyGu/LalZjezM8/PMdlDVW5qLg1nGjhx+kT3\ntrlzobR0gINUVNEQV36rPr2ba2YVOV1GVImRGGZPmN2rX3zmTDhwAE6fdrAwNWRoiCu/nDljaEnc\nw01Xaks80uZOmEvpkXNN74QEa23xHTsGOEhFjZBCXEQqRWS7iJSKyCa7ilJDz6p1h4gzo8gal+p0\nKVFnzoQ5bK3r3y+uXSoKQm+JG6DYGDPXGHOZHQWpoem1D8rIiNVWuBP6tsRBQ1ydY0d3io5ViAIb\nKnaftwtNDXVF6UUcbDxIS1tL9zYdoaI87GiJvykiH4rIl+0oSA09xkB5424WTdeLmk4YETeCWRmz\n2Hx4c/e22bOhrAza2hwsTA0JoYb4QmPMXOA64E4RucqGmtQQU1YGjNvNFQXaEnfKgswFbKjd0P18\nzBjIy4Pt2x0sSg0JIc2fNsYccf//cRF5HrgMWOt5fcWKFd37FhcXU1xcHMrbKYe89ZaBdO1OcdKC\nyQt4atdTvbctgPXrGTJru6vglJSUUFJSEvTxYowJ7kCRUUCsMaZZREYDq4EHjDGr3a+bYM+thpbr\nbjnMexfNoumHxxGdrumIqsYqFjy6gMP3He7+N3jsMXjrLfjLXxwuTtlKRDDG+P2LFkp3SgawVkS2\nAhuBVzwBrs4fXV3wXvlW5k6cqwHuoCnJ1oI11a7q7m2XXw4bNvg6QkWLoLtTjDEHgTk21qKGoG3b\nIDGnlAXZc50uJaqJCAsmW/3i2SnZABQWwsmT1rK048c7XKByjM7YVANaswaSCkuZO0FD3GkLMhew\nvnZ99/OYGJg/X1vj0U5DXA3ozTfh1AWlzJ2oIe60RdmLeKfqnV7bPBc3VfTSEFc+nToFaze5aKGO\ngrQCp8uJepdMuoQDDQeoP13fve2KK2Dt2gEOUuc9DXHl09tvQ8FVW7ko4yJiY2KdLifqxcfGc+WU\nKympLOnetnAhbN1q/cFV0UlDXPn06qswZb72hw8lS3KWsObgmu7no0fDvHnw3nsOFqUcpSGuvDIG\nXnsNmLhFQ3wIWTK1d4gDLFliXYBW0UlDXHm1a5c1+mFn0/tckXWF0+Uot9kTZnP89HEONR3q3qYh\nHt00xJVXf/87LLnxMA1nGnS6/RASIzEsmbqE1RXn5tXNnw979kBDg4OFKcdoiCuvnnkGpi5+n4VT\nFhIj+mMylNww7QZe3Pti9/MRI6zZmyEsv6GGMf3tVP3s2gUuFxxLfI8rs650uhzVx8emfYw1B9dw\nuv3cTTaXL4dXXnGwKOUYDXHVz9NPwy23wPs173FVtq4uPNSkjUzj4kkX8+aBN7u33XADvPwydHY6\nWJhyhIa46sUYK8Sv/8dm9tbv5eKJFztdkvLixsIbeXHPuS6V3FzIyICNGx0sSjlCQ1z1sn69FeRn\n09cxd+JcRsSNcLok5cWNhTfy8r6X6ejqOLftRnjxxQEOUuclDXHVy29/C1/5Cqyq+F8+mvdRp8tR\nPkxNnUpuam6vUSoa4tFJQ1x1a2iwQuD22w0v73uZ66dd73RJagBfmPMF/rj1j93PL74YTp/WW7ZF\nGw1x1e1Pf4LrroMTspe2zjZmZ8x2uiQ1gE9d+ClWV6zm5JmTgDU567bb4IknHC5MRZSGuAKgowP+\n8z/h61+HV/a9wvUF1+udfIa4lMQUriu4jid3PNm97fbbrdu1tbc7WJiKKA1xBVgjUiZNgquuQrtS\nhpEvzf0Sj3z4CF2mC4CCAsjPh1WrHC5MRYyGuKKrC376U/jhD+Foy1G2Hd3GkqlLnC5L+WHJ1CUk\nxCbw2v7Xurd9/vPw6KPO1aQiS0Nc8cwzMGYMLFsGT2x9gpuLbmZk/Einy1J+EBG+s/A7/Pz9n3dv\nu/VWWLcO9u51sDAVMRriUe7UKfjOd+CXvwQwPFr6KF+a9yWny1IBuLnoZg41HeK9amtR8dGjrWsb\n1r+pOt9piEe5n/3M6gdftAjWVq8lPjae+ZnznS5LBSAuJo77F9/Pt1Z/q7tv/F/+BZ57Do4ccbg4\nFXYa4lFs61b43e/gF7+wnj/y4SN8ce4XdVTKMPS52Z/DYPjz9j8DMG6cNdzwJz9xuDAVdmKMCc+J\nRUy4zq1C19xsTQ554AGrD3XnsZ1c8z/XsP+u/SSNSHK6PBWEjbUb+cQzn2DHHTtIG5nGiRNQVASr\nV8NsHfI/bIgIxhi/W1Ia4lGoqws+/WlITobf/97adtNTN7EoexH3XX6fs8WpkNy76l4qXZX8/Za/\nIyL85jfw17/CO++AfsAaHgINce1OiTLGwJ13Ql2dNbkHYF3NOjYf2cwdl9zhbHEqZCuXrqTGVcN/\nbrT+cb/8ZWhrgwcfdLgwFTZxThegIqe9He66CzZvhrfegpEjoelsE7c9fxsPffQhHVZ4HhgRN4Jn\nPvkMVz52JRljMvj0hZ/m6afhssus27hdqff4OO9oSzxKHDoEH/0o1NZaAZ6UBMYY7nj1Dq6Zeg03\nF93sdInKJrmpubz+2df5xqpv8OSOJ8nOhscfh09+EnbscLo6ZbegQ1xErhWRPSKyX0S+a2dRyj5t\nbfDwwzBnDixebK1SmJQEXaaLb6z6Bnvr9/LQtQ85Xaay2UUZF7H6c6v5wZof8O3V32bpR9t46CH4\nyEfgww+drk7ZKagQF5FY4L+Aa4Ei4FYR0Vui+1DiwB1s6+rgV7+y1tJ46SVYswbuvx9iY8HV6uK2\n52/jg8Mf8OZtbzIqflTE6nLiezFUhft7MStjFh98+QP2nNjD7N/MJvWS13n4YcN118FDDw2tW7np\nz0Xwgm2JXwaUG2MqjTHtwFPAjfaVdX6JxA/o8ePw+uvWuOBFi6CwEHbuhGeftbZfdBG0drTyWOlj\nXPjIhVyQcAGrP7ealMSUsNfWk/6ynhOJ78W4UeN46dMvsfKalXzj9W/wk+MXc8+ffsPTrxxj1ixr\n4bOzZ8NexqD05yJ4wV7YzARqejyvBXSan42MgdZWOHMGmprg5MneX4cPQ1UVVFfD/v3Q0gLz5llf\n3/8+LF5saI9povxkOY+XbuftyrdZVb6KSyZdwl8/8Ve9AXIUERFunH4jHy/8OKvKV/Hn7X9mz7Lv\nkRaTw71rFvCF3xVyaW4BV88q4JIZGczMTyJrcgxxOuxhWAj2n8mvAeAZ917fY2fT/0gx9B9Kbvrt\n5u3tjNcS+mw11nv0PY0R4+WUps8jP87vZ21nN1TyYMMa7/sZ6DLQ1WmN3+40hq5OK8RFrIX+Y+MM\n8XEQ5/mKh/h4w4jpMGI2ZCRCVoLB1dXGy20t/LWsGVepixiJITc1l5npM1mUvYgfX/1jclJyvPx3\nqWgQIzEsL1jO8oLltHW2seXIFjYd2kRp1X4+PPg6D9aVc/rYcTrXnoLWZGI6LiDGJBBLAjHEE0cC\nMSQQYxKQHh/iBel+1P9/pcdL7m29hkBbj1vW7+MR1+Zw/aef14Ka7CMiC4AVxphr3c+/D3QZY37e\nYx+d6aOUUkEI+4xNEYkD9gLXAIeBTcCtxpjdAZ9MKaVU0ILqTjHGdIjIvwCvA7HAoxrgSikVeWFb\nO0UppVT4hWXGpk4EsohIloi8LSK7RGSniNztdE1OE5FYESkVkZedrsVJIpIiIn8Tkd0iUua+zhSV\nROT77t+RHSLyVxEZ4XRNkSIij4lInYjs6LEtTUTeEJF9IrJaRAYcB2x7iOtEoF7agXuNMTOBBcCd\nUfy98LgHKMPPEU7nsf8AXjPGzABmAVHZHSkiOcCXgXnGmIuwumc/7WRNEfY4Vlb29D3gDWPMNOAt\n93OfwtES14lAbsaYo8aYre7HLVi/qJOcrco5IjIZWA78AYjahVFFJBm4yhjzGFjXmIwxLofLckoT\nVmNnlHvAxCjgkLMlRY4xZi3Q0GfzDcAT7sdPADcNdI5whLi3iUCZYXifYcXd4pgLbHS2Ekc9BHwb\n6HK6EIdNBY6LyOMiskVEfi8ikVv7YAgxxpwEfgVUY410azTGvOlsVY7LMMbUuR/XARkD7RyOEI/2\nj8n9iMgY4G/APe4WedQRkeuBY8aYUqK4Fe4WB8wD/tsYMw84xSAfmc9XIpIHfAPIwfqUOkZE/snR\nooYQ9511BszUcIT4ISCrx/MsrNZ4VBKReOA54M/GmBecrsdBVwA3iMhB4ElgiYj8j8M1OaUWqDXG\nfOB+/jesUI9GlwDrjDEnjDEdwN+xflaiWZ2ITAAQkYnAsYF2DkeIfwgUiEiOiCQAnwJeCsP7DHli\n3XH4UaDMGPPvTtfjJGPMD4wxWcaYqVgXrtYYY25zui4nGGOOAjUiMs29aSmwy8GSnLQHWCAiI92/\nL0uxLnxHs5eA292PbwcGbPzZvsSNTgTqZSHwWWC7iJS6t33fGLPKwZqGimjvdrsL+Iu7oVMBfMHh\nehxhjNnm/kT2Ida1ki3A75ytKnJE5ElgMTBORGqAHwErgWdE5ItAJXDLgOfQyT5KKTV86e3ZlFJq\nGNMQV0qpYUxDXCmlhjENcaWUGsY0xJVSahjTEFdKqWFMQ1wppYYxDXGllBrG/j9z3Zr79oVBuQAA\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7ff4cc295eb8>"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | cc0-1.0 |
alejandro-mc/Ecollect | dowloadhtmlfromWU.ipynb | 1 | 13613 | {
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160722/e20160722/mdaily > 20160722.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160723/e20160723/mdaily > 20160723.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160724/e20160724/mdaily > 20160724.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160725/e20160725/mdaily > 20160725.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160726/e20160726/mdaily > 20160726.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160727/e20160727/mdaily > 20160727.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160728/e20160728/mdaily > 20160728.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160729/e20160729/mdaily > 20160729.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160730/e20160730/mdaily > 20160730.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160731/e20160731/mdaily > 20160731.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160801/e20160801/mdaily > 20160801.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160802/e20160802/mdaily > 20160802.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160803/e20160803/mdaily > 20160803.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160804/e20160804/mdaily > 20160804.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160805/e20160805/mdaily > 20160805.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160806/e20160806/mdaily > 20160806.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160807/e20160807/mdaily > 20160807.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160808/e20160808/mdaily > 20160808.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160809/e20160809/mdaily > 20160809.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160810/e20160810/mdaily > 20160810.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160811/e20160811/mdaily > 20160811.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160812/e20160812/mdaily > 20160812.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160813/e20160813/mdaily > 20160813.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160814/e20160814/mdaily > 20160814.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160815/e20160815/mdaily > 20160815.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160816/e20160816/mdaily > 20160816.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160817/e20160817/mdaily > 20160817.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160818/e20160818/mdaily > 20160818.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160819/e20160819/mdaily > 20160819.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160820/e20160820/mdaily > 20160820.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160821/e20160821/mdaily > 20160821.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160822/e20160822/mdaily > 20160822.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160823/e20160823/mdaily > 20160823.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160824/e20160824/mdaily > 20160824.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160825/e20160825/mdaily > 20160825.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160826/e20160826/mdaily > 20160826.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160827/e20160827/mdaily > 20160827.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160828/e20160828/mdaily > 20160828.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160829/e20160829/mdaily > 20160829.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160830/e20160830/mdaily > 20160830.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160831/e20160831/mdaily > 20160831.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160901/e20160901/mdaily > 20160901.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160902/e20160902/mdaily > 20160902.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160903/e20160903/mdaily > 20160903.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160904/e20160904/mdaily > 20160904.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160905/e20160905/mdaily > 20160905.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160906/e20160906/mdaily > 20160906.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160907/e20160907/mdaily > 20160907.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160908/e20160908/mdaily > 20160908.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160909/e20160909/mdaily > 20160909.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160910/e20160910/mdaily > 20160910.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160911/e20160911/mdaily > 20160911.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160912/e20160912/mdaily > 20160912.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160913/e20160913/mdaily > 20160913.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160914/e20160914/mdaily > 20160914.html\n",
"running command: phantomjs.exe save_webpage.js https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s20160915/e20160915/mdaily > 20160915.html\n"
]
}
],
"source": [
"import os\n",
"import datetime as dt\n",
"import time\n",
"\n",
"startdate = dt.date(2016,7,22)\n",
"enddate = dt.date(2016,9,15)\n",
"\n",
"\n",
"curdate = startdate\n",
"#datestr = 20160707\n",
"\n",
"while curdate <= enddate:\n",
" \n",
" datestr = str(curdate).replace('-','')\n",
" \n",
" #phantom js s a headless web browser. it will render the page \n",
" #and then we pipe the full html to a local html file\n",
" cmd = 'phantomjs.exe save_webpage.js '+\\\n",
" 'https://www.wunderground.com/personal-weather-station/dashboard?ID=KNYNEWYO111#history/tdata/s' +\\\n",
" datestr + '/e' + datestr +'/mdaily > ' + datestr + '.html'\n",
" \n",
" print('running command: ',cmd)\n",
" \n",
" os.system(cmd)#run command\n",
"\n",
" #increment date by one day\n",
" curdate += dt.timedelta(days=1)\n",
" \n",
" time.sleep(15)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'20160707'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"str(dt.date(2016,7,7)).replace('-','')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dt.date(2016,7,7) < dt.date(2016,9,15)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
NZRS/content-analysis | Content Analysis.ipynb | 1 | 322644 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NetFlix New Zealand vs NetFlix USA Library Comparisons #\n",
"\n",
"During June 2015 NZRS logged into Netflix from within New Zealand and the USA and observed the content that was avaiable. From each page the titles offered by the service geographically were extracted and stored.\n",
"\n",
"Each title was compared against the OMDd API (http://www.omdbapi.com/) an alternative interace to the Internet Movide Database (IMDd) data. This allowed each title to be compared against data held within IMDb and the title data to be augmented.\n",
"\n",
"This included the following:\n",
"\n",
"* Plot\n",
"* Poster\n",
"* Rated\n",
"* Language\n",
"* Title\n",
"* Country\n",
"* Writer\n",
"* Metascore\n",
"* imdbRating\n",
"* Director\n",
"* Released\n",
"* Actors\n",
"* Year\n",
"* Genre\n",
"* Awards\n",
"* Runtime\n",
"* Type\n",
"* Response\n",
"* imdbVotes\n",
"* imdbID\n",
"\n",
"The data was compiled and the serialised outputs saved for further analysis. The data can be found here.[[[link to pickles]]]] This was done using the Python pickle module.\n",
"\n",
"A Python module was created to help with analysis and is available on Github (https://github.com/NZRS/content-analysis/blob/master/content_stats.py).\n",
"\n",
"The analysis focussed on titles, so does not at present identify if the title is a movies or a series. This may be able to be ascertianed from the OMDB 'Type', though this still does not give number of episodes, nor how many episodes are on Netflix. Qualitively Netflix NZ is missing the lasest series of Doctor Who as well as series from the 'classic Who'.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pickle\n",
"import plotly.plotly as py\n",
"from plotly.graph_objs import *\n",
"# module from NZRS\n",
"import content_stats\n",
"from IPython.core.display import Image\n",
"from urllib2 import quote\n",
"from IPython.display import display\n",
"import plotly.tools as tls\n",
"from IPython.display import HTML\n",
"from collections import Counter\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# load previously pickled dictionaries\n",
"\n",
"nz_data = pickle.load(open('nz/all_movies_dict.p', 'rb'))\n",
"us_data = pickle.load(open('us/all_movies_dict.p', 'rb'))\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For all charts we present; the data we use is available for exploration and reuse. There is a 'Play with this data' link at the bottom right hand side of each chart.\n",
"\n",
"If you are running these iPython notebooks yourself please note we are embedding the graphs rather than creating them. You can uncomment the creation code and comment out the embed code if you are using the notebooks interactively.\n",
"\n",
"## Library Size ##\n",
"The simplest test we can carry out is looking at library size. We are looking at count of titles not count of discreet episodes or total viewing time. Its not an unusfule test to begin to understand the libraries."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/97/\" title=\"Netflix Library Comparison- USA vs NZ - June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/97.png\" alt=\"Netflix Library Comparison- USA vs NZ - June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:97\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
"\n",
"<a href=\"https://plot.ly/~gotofftherails/97/\">Link to interactive chart and data</a> \n",
"\n",
"</div>\n",
"\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_titles_nz = len(nz_data)\n",
"total_titles_us = len(us_data)\n",
"\n",
"data = (\n",
" [Bar( x = ['NZ', 'USA'],\n",
" y = [total_titles_nz, total_titles_us],\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )]\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix Library Comparison- USA vs NZ - June 2015',\n",
" yaxis = YAxis(title = 'Count of Titles'),\n",
" xaxis = XAxis(title = 'Geographic Service'),\n",
" \n",
" ) \n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"# Run this to generate the plot.ly plot for yourself, once created we will embed it\n",
"# py.iplot(fig, filename = 'Netflix-Library-Comparison-June-2015')\n",
"\n",
"# Run this to embed the plot after creation, this is necessary for rendering in Github in particular.\n",
"\n",
"HTML('''<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/97/\" title=\"Netflix Library Comparison- USA vs NZ - June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/97.png\" alt=\"Netflix Library Comparison- USA vs NZ - June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:97\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
"\n",
"<a href=\"https://plot.ly/~gotofftherails/97/\">Link to interactive chart and data</a> \n",
"\n",
"</div>\n",
"\n",
"\n",
"'''\n",
")\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Uniqueness of Content ##\n",
"\n",
"Further up you would notice we imported a python module called 'content_stats'. This lets us apply some more exploratory stats.\n",
"\n",
"The uniqueness of content between libraries has been questioned by several people. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Titles in common between USA and NZ: 468\n",
"Titles Unique to the USA : 3860\n",
"Titles Unique to the NZ : 966\n"
]
}
],
"source": [
"# Number of titles are common\n",
"common = len(content_stats.Compare_regions(us_data, nz_data).common_titles())\n",
"print 'Titles in common between USA and NZ:', common\n",
"\n",
"\n",
"# Number of titles unique to us\n",
"unique_us = len(content_stats.Compare_regions(us_data, nz_data).unique_to_first())\n",
"print 'Titles Unique to the USA :', unique_us\n",
"\n",
"# Number of titles unique to nz\n",
"unique_nz = len(content_stats.Compare_regions(nz_data, us_data).unique_to_first())\n",
"print 'Titles Unique to the NZ :', unique_nz\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"We can represent this graphically again."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/127/\" target=\"_blank\" title=\"Unique to NZ, Common Between NZ and USA, Unique to USA\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/127.png\" alt=\"Unique to NZ, Common Between NZ and USA, Unique to USA\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:127\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
"\n",
" <a href=\"https://plot.ly/~gotofftherails/127/\">Link to interactive chart and data</a>\n",
"\n",
"</div>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace1 = Bar(\n",
" y=['Service'],\n",
" x=[unique_nz],\n",
" name='Unique to NZ',\n",
" orientation = 'h',\n",
" \n",
" marker = Marker(\n",
" color = 'rgba(255,127,14,1.0)' )\n",
" \n",
" )\n",
"\n",
"trace2 = Bar(\n",
" y=['Service'],\n",
" x=[common],\n",
" name='Common',\n",
" orientation = 'h',\n",
" marker = Marker(\n",
" color = 'rgba(44,160,44,1.0)' )\n",
" \n",
" )\n",
"\n",
"\n",
"trace3 = Bar(\n",
" y = ['Service'],\n",
" x = [unique_us],\n",
" name = 'Unique to USA',\n",
" orientation = 'h',\n",
" marker = Marker(\n",
" color = 'rgba(39,119,180,1.0)' )\n",
" \n",
" )\n",
"\n",
"\n",
"data = Data([trace1, trace2, trace3])\n",
"layout = Layout(\n",
" barmode='stack',\n",
" title = 'Content Unique to and Common Between US and NZ Netflix Services - June 2015',\n",
" yaxis = YAxis(title = ''),\n",
" xaxis = XAxis(title = 'Count of Titles')\n",
" )\n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"# used to create\n",
"#py.iplot(fig, filename = 'Netflix-Library-Comparison-June-2015-Uniqueness of Content')\n",
"\n",
"# used to embed\n",
"\n",
"HTML('''<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/127/\" target=\"_blank\" title=\"Unique to NZ, Common Between NZ and USA, Unique to USA\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/127.png\" alt=\"Unique to NZ, Common Between NZ and USA, Unique to USA\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:127\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
"\n",
" <a href=\"https://plot.ly/~gotofftherails/127/\">Link to interactive chart and data</a>\n",
"\n",
"</div>\n",
"\n",
"''')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quality of Content ##\n",
"\n",
"Quality by definition is qualitive. Though with enough measure of quality we can hopefully have some quantitive measure of quality, the same way we can hopefully say a five star hotel is normally going to better than a one star hotel. We know this is not always the case and we do have the [Napoleon Dynamite](http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?pagewanted=all&_r=0) effect where the hate and love for a title can be strong. \n",
"\n",
"To assess quality we looked looked towards IMDB, who make some of their data available via [alternative interfaces](http://www.imdb.com/interfaces) though not in a structured API. Luckily [OMDB]('http://www.omdbapi.com/') offer a RESTful API that allows querying by title or ID, and returns XML or JSON.\n",
"\n",
"We used this to query the title against OMDB. Not all returned a useful response, Doctor Who fans will be pleased to know 1995's made for TV movie was not recognised when we queried."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can look at what we did not get a response for, this is useful, it does not tell us where we got a false response, but we're hoping there were not too many of those."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Count of NZ titles we did not get a reponse for: 163\n",
"Percentage of NZ sites: 11.0 %\n"
]
}
],
"source": [
"# Count of titles we did not get a response for \n",
"nz_no_response = len([k for (k, v) in nz_data.iteritems() if v['Response'] == 'False'])\n",
"print 'Count of NZ titles we did not get a reponse for: ', nz_no_response\n",
"print 'Percentage of NZ sites: ', round(float(nz_no_response)/float(len(nz_data))*100), '%'\n",
"\n",
"#US count?\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're pretty happy with these percentages for getting an understanding of the content that is available from a quality perpective. We might not be able to be absolutely, absolute, but good enough.\n",
"\n",
"We can now look at the difference in quality.\n",
"\n",
"In our assessment of quality we are not looking at Netflix ratings, we are looking at the ratings held in IMDb. It could be that Netflix have content more suited to its customers and they would rate titles higher. Those rating IMDb may be skewed in a particular way as they may have more interest in esoteric aspects of movies and content. Perhaps another useful metric would be Rotten Tomatoes scores, though we don't have complete information on this and we were declied access to the Rotten Tomatoes API.\n",
"\n",
"\n",
"## Average Score ##"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NZ Average Score via OMDB: 6.68\n",
"US Average Score via OMDB: 6.37\n"
]
}
],
"source": [
"# Average IMDB score of NZ geographic content\n",
"nz_avg_score = content_stats.Title_stats(nz_data).average_score()\n",
"\n",
"# Average IMDB score of NZ geographic content\n",
"us_avg_score = content_stats.Title_stats(us_data).average_score()\n",
"\n",
"print 'NZ Average Score via OMDB: ', round(nz_avg_score,2)\n",
"print 'US Average Score via OMDB: ', round(us_avg_score,2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"New Zealand may have a smaller catalgoue but it does seem to have a marginally higher average quality than the US. We can look at what the top movies are (based on IMDb/OMDb ratings) between the two countries.\n",
"\n",
"##Top Movies##\n",
"\n",
"This give an interesting understanding of the top movies. At first look we can se the titles are very different."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top NZ Titles\n",
"\n",
"Title : The Shawshank Redemption\n",
"Rating: 9.3\n",
"=========\n",
"Title : Human Planet\n",
"Rating: 9.3\n",
"=========\n",
"Title : Frozen Planet\n",
"Rating: 9.3\n",
"=========\n",
"Title : Firefly\n",
"Rating: 9.2\n",
"=========\n",
"Title : The Godfather\n",
"Rating: 9.2\n",
"=========\n",
"\n",
"\n",
"Top US Titles\n",
"\n",
"Title : Generation Earth\n",
"Rating: 9.1\n",
"=========\n",
"Title : Fullmetal Alchemist: Brotherhood\n",
"Rating: 9.1\n",
"=========\n",
"Title : Long Way Round\n",
"Rating: 9.1\n",
"=========\n",
"Title : Tomb Raider\n",
"Rating: 9.1\n",
"=========\n",
"Title : Top Gear\n",
"Rating: 9.0\n",
"=========\n",
"\n",
"\n",
"Average score of top 25 Titles\n",
"\n",
"NZ top 25 average\n",
"9.0\n",
"\n",
"US top 25 average\n",
"8.916\n"
]
}
],
"source": [
"top_nz_titles = content_stats.Title_stats(nz_data).top_movies(25)\n",
"top_us_titles = content_stats.Title_stats(us_data).top_movies(25)\n",
"\n",
"print 'Top NZ Titles'\n",
"print\n",
"\n",
"# Truncate to 5\n",
"for tup in top_nz_titles[:5]:\n",
" print 'Title :', tup[0]\n",
" print 'Rating: ', tup[1]\n",
" print '========='\n",
"\n",
"print\n",
"print \n",
"print 'Top US Titles'\n",
"print\n",
" \n",
"# Truncate to 5\n",
"for tup in top_us_titles[:5]:\n",
" print 'Title :', tup[0]\n",
" print 'Rating: ', tup[1]\n",
" print '========='\n",
" \n",
"print\n",
"print\n",
"\n",
"print 'Average score of top 25 Titles'\n",
"print\n",
"print 'NZ top 25 average'\n",
"\n",
"score = 0\n",
"count = 0\n",
"for tup in top_nz_titles:\n",
" count += 1\n",
" score += tup[1]\n",
"print score/count\n",
"\n",
"print \n",
"\n",
"print 'US top 25 average'\n",
"score = 0\n",
"count = 0\n",
"for tup in top_us_titles:\n",
" count += 1\n",
" score += tup[1]\n",
"print score/count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could potentially display this visually.\n",
"\n",
"We need to do some trickery as our dictionaries have quoted title names as the keys, so 'The%20Pink%20Panther' instead of 'The Pink Panther', so we need to 'quote' our moviename to get an image.\n",
"\n",
"## Top NZ Title Visually ##"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Title : The Shawshank Redemption\n",
"Title : 9.3\n"
]
},
{
"data": {
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1sUhenhcJBq/9aW3x27DkB0HqcTnDtWU0fiRiKS63J0lgD5j8rk/T5YzlFGsX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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Title : Human Planet\n",
"Title : 9.3\n"
]
},
{
"data": {
"image/jpeg": 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IkYBZQMY9apOvabPA5lCH74q9XUbNcyqz4AY4OaCa5O0MLxTKHTHDCnfFyUqO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\njinkU6p5/wCdKlQSbis5pUqAM1mlSoA//9k=\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Title : Frozen Planet\n",
"Title : 9.3\n"
]
},
{
"data": {
"image/jpeg": 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wDzjl2DGvz8UaDLcIw0O2ito7SGDsYtHtQ0jJGwLM7FiCzEEsuCR13ANL07jDh1DLdX3B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\nhmqXq8U1ncNHcRPHID9k7GtEZ7jh59NLD9yI/lZXwDg1IQRTKuXWNsj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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Title : Firefly\n",
"Title : 9.2\n"
]
},
{
"data": {
"image/jpeg": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Title : The Godfather\n",
"Title : 9.2\n"
]
},
{
"data": {
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sfk1DSHt+F4ccOY4dfuTtNTkOc/JdG7gHrhU9kM7HgRW2kbAXVEhPpgDqglk0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\nW0Rsj6kOT3tPfJJ1WfJGPEK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hCEAIQhACEIQAhCEAIQhACEIQAhCEAIQhACEI\nQAhCEAIQhACEIQAhCEAIQhACEIQAhCEAIQhACEIQAhCEAIQhACEIQAhCEAIQhACEIQAhCEAIQhAC\nEIQAhCEAIQhACEIQH//Z\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#truncate to top 5\n",
"for tup in top_nz_titles[:5]:\n",
" print 'Title :', tup[0]\n",
" print 'Title :', tup[1]\n",
" try:\n",
" poster = Image(nz_data[quote(tup[0])]['Poster'])\n",
" display(poster)\n",
" except:\n",
" print '''\n",
" |\n",
" | No Poster\n",
" | \n",
" '''\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Bieber Effect ##\n",
"Across the top 25 both NZ and US are close, with the NZ library taking a slight lead. How do we compare in terms of the bottom ranked content."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bottom ranked NZ title Justin Bieber: Never Say Never : 1.6\n"
]
},
{
"data": {
"image/jpeg": 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pXOS5mt5GPwkj9KsDTtImS3XzpD569yP7VPFnp8O8OUG4HIx6U7xWocjPFAMk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\nKalaSXNu0SSLH90kEZYlfiAyPrjk81zrdRxpqTX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Y++y+3uMUdDD5ygtckMACC3Pr7ipt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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bottom ranked US title Biebermania! : 1.1\n"
]
},
{
"data": {
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dJUKAphBN1GqW6QE4R5b0NapmNlnJFN4/OBkmou/OqcPL5edYyoQ2Z1gsBXEg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\nSpUqUUVKlSpRT//Z\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Average score of bottom 25 Titles\n",
"\n",
"NZ bottom 25 average\n",
"3.104\n",
"\n",
"US bottom 25 average\n",
"1.832\n"
]
}
],
"source": [
"bottom_nz_titles = content_stats.Title_stats(nz_data).bottom_movies(25)\n",
"bottom_us_titles = content_stats.Title_stats(us_data).bottom_movies(25)\n",
"\n",
"print 'Bottom ranked NZ title', bottom_nz_titles[-1][0], ':' , bottom_nz_titles[-1][1]\n",
"\n",
"poster = Image(nz_data[quote(bottom_nz_titles[-1][0])]['Poster'])\n",
"display(poster)\n",
"\n",
"print 'Bottom ranked US title', bottom_us_titles[-1][0], ':' , bottom_us_titles[-1][1]\n",
"\n",
"poster = Image(us_data[quote(bottom_us_titles[-1][0])]['Poster'])\n",
"display(poster)\n",
"\n",
"\n",
"print \n",
"print\n",
"\n",
"print 'Average score of bottom 25 Titles'\n",
"print\n",
"print 'NZ bottom 25 average'\n",
"\n",
"score = 0\n",
"count = 0\n",
"for tup in bottom_nz_titles:\n",
" count += 1\n",
" score += tup[1]\n",
"print score/count\n",
"\n",
"print \n",
"\n",
"print 'US bottom 25 average'\n",
"score = 0\n",
"count = 0\n",
"for tup in bottom_us_titles:\n",
" count += 1\n",
" score += tup[1]\n",
"print score/count"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It seems NZ might have a smaller library but better quality (based on IMDB rankings). Though on an absolute value this makes some sense. We can look at a distribution to get a better understanding."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hist_nz_titles = content_stats.Title_stats(nz_data).ratings_distribution()\n",
"hist_us_titles = content_stats.Title_stats(us_data).ratings_distribution()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/207/\" target=\"_blank\" title=\"Netflix Library Comparison June 2015 Distribution of IMDB scores\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/207.png\" alt=\"Netflix Library Comparison June 2015 Distribution of IMDB scores\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:207\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"\n",
"<div>\n",
"\n",
" <a href=\"https://plot.ly/~gotofftherails/207/\">Link to interactive chart and data</a>\n",
"\n",
"</div>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trace1 = Histogram(\n",
" x = hist_nz_titles,\n",
" opacity=0.75,\n",
" name = 'Count of NZ Titles'\n",
")\n",
"trace2 = Histogram(\n",
" x = hist_us_titles,\n",
" opacity = 0.75,\n",
" name = 'Count of US Titles'\n",
")\n",
"data = Data([trace2, trace1])\n",
"layout = Layout(\n",
" barmode ='overlay',\n",
" yaxis = YAxis(title = 'Count of Titles'),\n",
" xaxis = XAxis(title = 'IMdB Score (rounded down)'),\n",
" title = 'Netflix Library Comparison June 2015 Distribution of IMDB scores'\n",
")\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"# py.iplot(fig, filename = 'Netflix-Library-Comparison-June-2015-Distribution of IMDB scores')\n",
"\n",
"HTML('''\n",
"\n",
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/207/\" target=\"_blank\" title=\"Netflix Library Comparison June 2015 Distribution of IMDB scores\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/207.png\" alt=\"Netflix Library Comparison June 2015 Distribution of IMDB scores\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:207\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"\n",
"<div>\n",
"\n",
" <a href=\"https://plot.ly/~gotofftherails/207/\">Link to interactive chart and data</a>\n",
"\n",
"</div>\n",
"\n",
"''')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles_score_count_nz = Counter(hist_nz_titles)\n",
"titles_score_count_us = Counter(hist_us_titles)\n",
"\n",
"\n",
"# make relative\n",
"nz_tot = sum(titles_score_count_nz.values())\n",
"titles_score_count_nz_relative = { k:round((float(v)/float(nz_tot))*100, 1) for (k,v) in titles_score_count_nz.items()}\n",
"\n",
"us_tot = sum(titles_score_count_us.values())\n",
"titles_score_count_us_relative = { k:round((float(v)/float(us_tot))*100,1) for (k,v) in titles_score_count_us.items()}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~gotofftherails/159.embed\" height=\"525\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"nz_x_list = []\n",
"nz_y_list = []\n",
"\n",
"for score, count in titles_score_count_nz_relative.iteritems():\n",
" nz_x_list.append(score)\n",
" nz_y_list.append(count)\n",
" \n",
" \n",
"us_x_list = []\n",
"us_y_list = []\n",
"\n",
"for score, count in titles_score_count_us_relative.iteritems():\n",
" us_x_list.append(score)\n",
" us_y_list.append(count)\n",
" \n",
" \n",
"\n",
"\n",
"\n",
"trace1 = (\n",
" Bar( x = us_x_list,\n",
" y = us_y_list,\n",
" name = 'USA',\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )\n",
" )\n",
"\n",
"trace2 = (\n",
" Bar( x = nz_x_list,\n",
" y = nz_y_list,\n",
" name = 'NZ',\n",
" marker = Marker(\n",
" color = 'rgba(255, 144, 33, 0.6)')\n",
" )\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix - IMDb scores - US and NZ June 2015 - Percentage of Titles',\n",
" yaxis = YAxis(title = 'Percentage of Titles'),\n",
" xaxis = XAxis(title = 'IMDb Score'),\n",
" barmode='group'\n",
" \n",
" ) \n",
"\n",
"data = Data([trace1, trace2])\n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"py.iplot(fig, filename = 'Netflix-Library-Comparison-Release-US-NZ-June-2015-relative')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It appears the NZ library, while smaller has a greater proportion of higher quality content."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## New Zealand as Country of Origin ##\n",
"\n",
"We can see what New Zealand content is represented within our samples. This includes anything that has the identifier New Zealand in 'Country' via IMDb. This can include co-productions."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/130/\" target=\"_blank\" title=\"Netflix - Count of Titles with Country of Origin as New Zealand via IMDb\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/130.png\" alt=\"Netflix - Count of Titles with Country of Origin as New Zealand via IMDb\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:130\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/130/\">Link to interactive chart and data</a>\n",
"</div>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# test\n",
"nz_origin_nz = content_stats.Title_stats(nz_data).nz_origin()\n",
"nz_origin_us = content_stats.Title_stats(nz_data).nz_origin()\n",
"\n",
"\n",
"data = (\n",
" [Bar( x = ['NZ', 'USA'],\n",
" y = [nz_origin_nz, nz_origin_us],\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )]\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix - Count of Titles with Country of Origin as New Zealand via IMDb',\n",
" yaxis = YAxis(title = 'Count of Titles'),\n",
" xaxis = XAxis(title = 'Geographic Service'),\n",
" \n",
" ) \n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"# py.iplot(fig, filename = 'Netflix-Library-Comparison-Country-June-2015')\n",
"\n",
"HTML('''<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/130/\" target=\"_blank\" title=\"Netflix - Count of Titles with Country of Origin as New Zealand via IMDb\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/130.png\" alt=\"Netflix - Count of Titles with Country of Origin as New Zealand via IMDb\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:130\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/130/\">Link to interactive chart and data</a>\n",
"</div>\n",
"\n",
"''')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Neck and neck. We can see what these titles are, we can get a feel as to how Kiwi they are."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Age of Titles ##\n",
"\n",
"We can look at age of titles within the catalogues. There are some interesting caveats to this as age is represented in three ways:\n",
"* A single year - this could be a movie that came out in a single year or a series that only ran within a single calendar year\n",
"* Across multiple years, for example a series that has run across multiple years (e.g. Friends, 1994–2004)\n",
"* Something that is still running (e.g. Orange is the New Black, 2013–)\n",
"\n",
"Initially we could look at year of first release. \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/132/\" target=\"_blank\" title=\"Netflix - Year of Titles First Release - NZ June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/132.png\" alt=\"Netflix - Year of Titles First Release - NZ June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:132\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
" <div>\n",
" \n",
" <a href=\"https://plot.ly/~gotofftherails/132/\">Link to interactive chart and data</a>\n",
" \n",
" </div>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"release_year = content_stats.Title_stats(nz_data).year_first_release_count()\n",
"\n",
"x_list = []\n",
"y_list = []\n",
"\n",
"for year, count in release_year.iteritems():\n",
" x_list.append(year)\n",
" y_list.append(count)\n",
"\n",
"data = (\n",
" [Bar( x = x_list,\n",
" y = y_list,\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )]\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix - Year of Titles First Release - NZ June 2015',\n",
" yaxis = YAxis(title = 'Count of Titles'),\n",
" xaxis = XAxis(title = 'Year'),\n",
" \n",
" ) \n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"#py.iplot(fig, filename = 'Netflix-Library-Comparison-Release-June-2015')\n",
"\n",
"HTML('''<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/132/\" target=\"_blank\" title=\"Netflix - Year of Titles First Release - NZ June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/132.png\" alt=\"Netflix - Year of Titles First Release - NZ June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:132\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
" <div>\n",
" \n",
" <a href=\"https://plot.ly/~gotofftherails/132/\">Link to interactive chart and data</a>\n",
" \n",
" </div>\n",
"\n",
"''')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can do the same for the USA catalogue."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/142/\" target=\"_blank\" title=\"Netflix - Year of Titles First Release - US June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/142.png\" alt=\"Netflix - Year of Titles First Release - US June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:142\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/142/\">Link to interactive chart and data</a>\n",
" \n",
"</div>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"release_year = content_stats.Title_stats(us_data).year_first_release_count()\n",
"\n",
"x_list = []\n",
"y_list = []\n",
"\n",
"for year, count in release_year.iteritems():\n",
" x_list.append(year)\n",
" y_list.append(count)\n",
"\n",
"data = (\n",
" [Bar( x = x_list,\n",
" y = y_list,\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )]\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix - Year of Titles First Release - US June 2015',\n",
" yaxis = YAxis(title = 'Count of Titles'),\n",
" xaxis = XAxis(title = 'Year'),\n",
" \n",
" ) \n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"# py.iplot(fig, filename = 'Netflix-Library-Comparison-Release-US-June-2015')\n",
"HTML('''<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/142/\" target=\"_blank\" title=\"Netflix - Year of Titles First Release - US June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/142.png\" alt=\"Netflix - Year of Titles First Release - US June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:142\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/142/\">Link to interactive chart and data</a>\n",
" \n",
"</div>\n",
"\n",
"''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But we can look at them side by side. Lets look at absolute first."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/157/\" target=\"_blank\" title=\"Netflix - Year of Titles First Release - US and NZ June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/157.png\" alt=\"Netflix - Year of Titles First Release - US and NZ June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:157\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
"\n",
" <a href=\"https://plot.ly/~gotofftherails/157/\">Link to interactive chart and data</a>\n",
" \n",
"</div>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"release_year_us = content_stats.Title_stats(us_data).year_first_release_count()\n",
"\n",
"us_x_list = []\n",
"us_y_list = []\n",
"\n",
"for year, count in release_year_us.iteritems():\n",
" us_x_list.append(year)\n",
" us_y_list.append(count)\n",
"\n",
"release_year_nz = content_stats.Title_stats(nz_data).year_first_release_count()\n",
"\n",
"nz_x_list = []\n",
"nz_y_list = []\n",
"\n",
"for year, count in release_year_nz.iteritems():\n",
" nz_x_list.append(year)\n",
" nz_y_list.append(count)\n",
"\n",
"trace1 = (\n",
" Bar( x = us_x_list,\n",
" y = us_y_list,\n",
" name = 'USA',\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )\n",
" )\n",
"\n",
"trace2 = (\n",
" Bar( x = nz_x_list,\n",
" y = nz_y_list,\n",
" name = 'NZ',\n",
" marker = Marker(\n",
" color = 'rgba(255, 144, 33, 0.6)')\n",
" )\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix - Year of Titles First Release - US and NZ June 2015',\n",
" yaxis = YAxis(title = 'Count of Titles'),\n",
" xaxis = XAxis(title = 'Year'),\n",
" barmode='group'\n",
" \n",
" ) \n",
"\n",
"data = Data([trace1, trace2])\n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"#py.iplot(fig, filename = 'Netflix-Library-Comparison-Release-US-NZ-June-2015')\n",
"\n",
"HTML('''<div>\n",
" <a href=\"https://plot.ly/~gotofftherails/157/\" target=\"_blank\" title=\"Netflix - Year of Titles First Release - US and NZ June 2015\" style=\"display: block; text-align: center;\"><img src=\"https://plot.ly/~gotofftherails/157.png\" alt=\"Netflix - Year of Titles First Release - US and NZ June 2015\" style=\"max-width: 100%;\" onerror=\"this.onerror=null;this.src='https://plot.ly/404.png';\" /></a>\n",
" <script data-plotly=\"gotofftherails:157\" src=\"https://plot.ly/embed.js\" async></script>\n",
"</div>\n",
"\n",
"<div>\n",
"\n",
" <a href=\"https://plot.ly/~gotofftherails/157/\">Link to interactive chart and data</a>\n",
" \n",
"</div>\n",
"\n",
"''')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The smaller New Zealand library makes this a bit tricker to understand which service has newer and older content when put against the larger US library."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~gotofftherails/272.embed\" height=\"525\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_us_titles = sum(us_y_list)\n",
"us_y_list = [(float(count)/(float(total_us_titles)) * 100) for count in us_y_list]\n",
"\n",
"total_nz_titles = sum(nz_y_list)\n",
"nz_y_list = [(float(count)/(float(total_nz_titles)) * 100) for count in nz_y_list]\n",
"\n",
"trace1 = (\n",
" Bar( x = us_x_list,\n",
" y = us_y_list,\n",
" name = 'USA',\n",
" marker = Marker(\n",
" color = 'rgba(34, 95, 250, 0.6)')\n",
" )\n",
" )\n",
"\n",
"trace2 = (\n",
" Bar( x = nz_x_list,\n",
" y = nz_y_list,\n",
" name = 'NZ',\n",
" marker = Marker(\n",
" color = 'rgba(255, 144, 33, 0.6)')\n",
" )\n",
" )\n",
"\n",
"layout = Layout(\n",
" title ='Netflix - Year of Titles First Release - US and NZ June 2015 - Percentage',\n",
" yaxis = YAxis(title = 'Percentage of Titles'),\n",
" xaxis = XAxis(title = 'Year'),\n",
" barmode='group'\n",
" \n",
" ) \n",
"\n",
"data = Data([trace1, trace2])\n",
"\n",
"fig = Figure(data=data, layout=layout)\n",
"\n",
"py.iplot(fig, filename = 'Relative- Netflix-Library-Comparison-Release-US-NZ-June-2015')\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Actors ##"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nz_actors_count = content_stats.Title_stats(nz_data).top_actors(21)\n",
"us_actors_count = content_stats.Title_stats(us_data).top_actors(21)\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<table>\n",
" <tr><th>Actor Name</th><th>Count of Titles with Actor Name</th><tr>\n",
" <tr>\n",
" <td>\n",
" Adam Sandler\n",
" </td>\n",
" <td>\n",
" 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Johnny Depp\n",
" </td>\n",
" <td>\n",
" 9\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Mel Gibson\n",
" </td>\n",
" <td>\n",
" 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Christopher Walken\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Sylvester Stallone\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Nicolas Cage\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Mike Myers\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Burt Young\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Morgan Freeman\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Tom Hanks\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Denzel Washington\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Angelina Jolie\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Josh Lucas\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Talia Shire\n",
" </td>\n",
" <td>\n",
" 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Dustin Hoffman\n",
" </td>\n",
" <td>\n",
" 5\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Jason Statham\n",
" </td>\n",
" <td>\n",
" 5\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Alec Baldwin\n",
" </td>\n",
" <td>\n",
" 5\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Al Pacino\n",
" </td>\n",
" <td>\n",
" 5\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Justin Bartha\n",
" </td>\n",
" <td>\n",
" 5\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Ashleigh Ball\n",
" </td>\n",
" <td>\n",
" 5\n",
" </td>\n",
" </tr>\n",
"</table>\n",
"\n",
"=====================\n",
"<table>\n",
" <tr><th>Actor Name</th><th>Count of Titles with Actor Name</th><tr>\n",
" <tr>\n",
" <td>\n",
" Samuel L. Jackson\n",
" </td>\n",
" <td>\n",
" 12\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Eddie Murphy\n",
" </td>\n",
" <td>\n",
" 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Jeff Bennett\n",
" </td>\n",
" <td>\n",
" 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Ewan McGregor\n",
" </td>\n",
" <td>\n",
" 9\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Nicolas Cage\n",
" </td>\n",
" <td>\n",
" 9\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Julianne Moore\n",
" </td>\n",
" <td>\n",
" 9\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Arnold Schwarzenegger\n",
" </td>\n",
" <td>\n",
" 9\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" David A.R. White\n",
" </td>\n",
" <td>\n",
" 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Nicole Kidman\n",
" </td>\n",
" <td>\n",
" 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Laura Bailey\n",
" </td>\n",
" <td>\n",
" 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Cam Clarke\n",
" </td>\n",
" <td>\n",
" 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Jean-Claude Van Damme\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Christopher Lloyd\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Lucy Liu\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Jim Cummings\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Danny Glover\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Debi Derryberry\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Tommy Lee Jones\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Cary Grant\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" Sam Neill\n",
" </td>\n",
" <td>\n",
" 7\n",
" </td>\n",
" </tr>\n",
"</table>\n"
]
}
],
"source": [
"print '<table>'\n",
"print ' <tr><th>Actor Name</th><th>Count of Titles with Actor Name</th><tr>'\n",
"\n",
"for tup in nz_actors_count[1:]:\n",
" print ' <tr>'\n",
" print ' <td>'\n",
" print ' ', tup[0].lstrip()\n",
" print ' </td>'\n",
" print ' <td>'\n",
" print ' ', tup[1]\n",
" print ' </td>'\n",
" print ' </tr>'\n",
"print '</table>' \n",
" \n",
" \n",
"print\n",
"print '====================='\n",
"\n",
"print '<table>'\n",
"print ' <tr><th>Actor Name</th><th>Count of Titles with Actor Name</th><tr>'\n",
"for tup in us_actors_count[1:]:\n",
" print ' <tr>'\n",
" print ' <td>'\n",
" print ' ', tup[0].lstrip()\n",
" print ' </td>'\n",
" print ' <td>'\n",
" print ' ', tup[1]\n",
" print ' </td>'\n",
" print ' </tr>'\n",
"\n",
"print '</table>'"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<table>\n",
"<tr><th></th><th>NZ Service</th><th></th><th>US Service</th><th></th></tr>\n",
"<tr><th>Rank</th><th>Actor Name</th><th>Count of Titles with Actor Name</th><th>Actor Name</th><th>Count of Titles with Actor Name</th></tr>\n",
"<tr>\n",
"<td>\n",
"1\n",
"</td>\n",
"<td>\n",
"Adam Sandler\n",
"</td>\n",
"<td>\n",
"10\n",
"</td>\n",
"<td>\n",
"Samuel L. Jackson\n",
"</td>\n",
"<td>\n",
"12\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"2\n",
"</td>\n",
"<td>\n",
"Johnny Depp\n",
"</td>\n",
"<td>\n",
"9\n",
"</td>\n",
"<td>\n",
"Eddie Murphy\n",
"</td>\n",
"<td>\n",
"10\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"3\n",
"</td>\n",
"<td>\n",
"Mel Gibson\n",
"</td>\n",
"<td>\n",
"8\n",
"</td>\n",
"<td>\n",
"Jeff Bennett\n",
"</td>\n",
"<td>\n",
"10\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"4\n",
"</td>\n",
"<td>\n",
"Christopher Walken\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"<td>\n",
"Ewan McGregor\n",
"</td>\n",
"<td>\n",
"9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Sylvester Stallone\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"<td>\n",
"Nicolas Cage\n",
"</td>\n",
"<td>\n",
"9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Nicolas Cage\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"<td>\n",
"Julianne Moore\n",
"</td>\n",
"<td>\n",
"9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"7\n",
"</td>\n",
"<td>\n",
"Mike Myers\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Arnold Schwarzenegger\n",
"</td>\n",
"<td>\n",
"9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"8\n",
"</td>\n",
"<td>\n",
"Burt Young\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"David A.R. White\n",
"</td>\n",
"<td>\n",
"8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"9\n",
"</td>\n",
"<td>\n",
"Morgan Freeman\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Nicole Kidman\n",
"</td>\n",
"<td>\n",
"8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"10\n",
"</td>\n",
"<td>\n",
"Tom Hanks\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Laura Bailey\n",
"</td>\n",
"<td>\n",
"8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"11\n",
"</td>\n",
"<td>\n",
"Denzel Washington\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Cam Clarke\n",
"</td>\n",
"<td>\n",
"8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"12\n",
"</td>\n",
"<td>\n",
"Angelina Jolie\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Jean-Claude Van Damme\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"13\n",
"</td>\n",
"<td>\n",
"Josh Lucas\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Christopher Lloyd\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"14\n",
"</td>\n",
"<td>\n",
"Talia Shire\n",
"</td>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Lucy Liu\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"15\n",
"</td>\n",
"<td>\n",
"Dustin Hoffman\n",
"</td>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Jim Cummings\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"16\n",
"</td>\n",
"<td>\n",
"Jason Statham\n",
"</td>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Danny Glover\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"17\n",
"</td>\n",
"<td>\n",
"Alec Baldwin\n",
"</td>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Debi Derryberry\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"18\n",
"</td>\n",
"<td>\n",
"Al Pacino\n",
"</td>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Tommy Lee Jones\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"19\n",
"</td>\n",
"<td>\n",
"Justin Bartha\n",
"</td>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Cary Grant\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"20\n",
"</td>\n",
"<td>\n",
"Ashleigh Ball\n",
"</td>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"Sam Neill\n",
"</td>\n",
"<td>\n",
"7\n",
"</td>\n",
"</tr>\n",
"</table>\n"
]
}
],
"source": [
"print '<table>'\n",
"print '<tr><th></th><th>NZ Service</th><th></th><th>US Service</th><th></th></tr>'\n",
"print '<tr><th>Rank</th><th>Actor Name</th><th>Count of Titles with Actor Name</th><th>Actor Name</th><th>Count of Titles with Actor Name</th></tr>'\n",
"\n",
"for x in range(1,len(nz_actors_count)):\n",
" print '<tr>'\n",
" print '<td>'\n",
" print str(x)\n",
" print '</td>'\n",
" print '<td>'\n",
" print nz_actors_count[x][0].lstrip()\n",
" print '</td>'\n",
" print '<td>'\n",
" print nz_actors_count[x][1]\n",
" print '</td>'\n",
" print '<td>'\n",
" print us_actors_count[x][0].lstrip()\n",
" print '</td>'\n",
" print '<td>'\n",
" print us_actors_count[x][1]\n",
" print '</td>'\n",
" print '</tr>'\n",
" \n",
"print '</table>'\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<table>\n",
"<tr><th>New Zealand Service </th></tr>\n",
"<tr><th>Movie Name</th><th>IMDb Score</th></tr>\n",
"<tr>\n",
"<td> The Shawshank Redemption </td> <td> 9.3 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Human Planet </td> <td> 9.3 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Frozen Planet </td> <td> 9.3 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Firefly </td> <td> 9.2 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Godfather </td> <td> 9.2 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Greg Fleet: Thai Die </td> <td> 9.2 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Fullmetal Alchemist: Brotherhood </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Godfather: Part II </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Arrested Development </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Freaks and Geeks </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Chef's Table </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Top Gear </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> North & South </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Doctor Who </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Fight Club </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Horrible Histories </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Fawlty Towers </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Good, the Bad and the Ugly </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Lord of the Rings: The Return of the King </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Forensic Files </td> <td> 8.8 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Lord of the Rings: The Two Towers </td> <td> 8.8 </td>\n",
"</tr>\n",
"</table>\n",
"\n",
"\n",
"===============\n",
"<table>\n",
"<tr><th>Movie Name</th><th>IMDb Score</th></tr>\n",
"<tr>\n",
"<td> Generation Earth </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Fullmetal Alchemist: Brotherhood </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Long Way Round </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Tomb Raider </td> <td> 9.1 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Top Gear </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> North & South </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Death Note </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Life of Birds </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Friends </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> 24/7 Flyers/Rangers: Road to the NHL Winter Classic </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Chef's Table </td> <td> 9.0 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Dexter </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Pulp Fiction </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Attack on Titan </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Charlie Don't Surf </td> <td> 8.9 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Slugterra: Slug Fu Showdown </td> <td> 8.8 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Andaz Apna Apna </td> <td> 8.8 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Aerial America </td> <td> 8.8 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> The Phantom of the Opera at the Royal Albert Hall </td> <td> 8.8 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> Never Sleep Again: The Elm Street Legacy </td> <td> 8.8 </td>\n",
"</tr>\n",
"<tr>\n",
"<td> It's Always Sunny in Philadelphia </td> <td> 8.8 </td>\n",
"</tr>\n",
"</table>\n"
]
}
],
"source": [
"\n",
"print '<table>'\n",
"print '<tr><th>New Zealand Service </th></tr>'\n",
"print '<tr><th>Movie Name</th><th>IMDb Score</th></tr>'\n",
"nzx = []\n",
"for tup in top_nz_titles[:21]:\n",
" print '<tr>'\n",
" print '<td>',tup[0],'</td>','<td>', tup[1],'</td>' \n",
" print '</tr>'\n",
" nzx.append(tup[0])\n",
" \n",
"print '</table>'\n",
"\n",
"print\n",
"print \n",
"print '==============='\n",
"\n",
"\n",
"print '<table>'\n",
"print '<tr><th>Movie Name</th><th>IMDb Score</th></tr>'\n",
"usx = []\n",
"for tup in top_us_titles[:21]:\n",
" print '<tr>'\n",
" print '<td>',tup[0],'</td>','<td>', tup[1],'</td>' \n",
" print '</tr>'\n",
" usx.append(tup[0])\n",
"print '</table>'\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<table>\n",
"<tr><th></th><th>NZ Service</th><th></th><th>US Service</th><th></th></tr>\n",
"<tr><th>Rank</th><th>Movie Name</th><th>IMDb Score</th><th>Movie Name</th><th>IMDb Score</th></tr>\n",
"<tr>\n",
"<td>\n",
"1\n",
"</td>\n",
"<td>\n",
"The Shawshank Redemption\n",
"</td>\n",
"<td>\n",
"9.3\n",
"</td>\n",
"<td>\n",
"Generation Earth\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"2\n",
"</td>\n",
"<td>\n",
"Human Planet\n",
"</td>\n",
"<td>\n",
"9.3\n",
"</td>\n",
"<td>\n",
"Fullmetal Alchemist: Brotherhood\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"3\n",
"</td>\n",
"<td>\n",
"Frozen Planet\n",
"</td>\n",
"<td>\n",
"9.3\n",
"</td>\n",
"<td>\n",
"Long Way Round\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"4\n",
"</td>\n",
"<td>\n",
"Firefly\n",
"</td>\n",
"<td>\n",
"9.2\n",
"</td>\n",
"<td>\n",
"Tomb Raider\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"5\n",
"</td>\n",
"<td>\n",
"The Godfather\n",
"</td>\n",
"<td>\n",
"9.2\n",
"</td>\n",
"<td>\n",
"Top Gear\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"6\n",
"</td>\n",
"<td>\n",
"Greg Fleet: Thai Die\n",
"</td>\n",
"<td>\n",
"9.2\n",
"</td>\n",
"<td>\n",
"North & South\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"7\n",
"</td>\n",
"<td>\n",
"Fullmetal Alchemist: Brotherhood\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"<td>\n",
"Death Note\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"8\n",
"</td>\n",
"<td>\n",
"The Godfather: Part II\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"<td>\n",
"The Life of Birds\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"9\n",
"</td>\n",
"<td>\n",
"Arrested Development\n",
"</td>\n",
"<td>\n",
"9.1\n",
"</td>\n",
"<td>\n",
"Friends\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"10\n",
"</td>\n",
"<td>\n",
"Freaks and Geeks\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"<td>\n",
"24/7 Flyers/Rangers: Road to the NHL Winter Classic\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"11\n",
"</td>\n",
"<td>\n",
"Chef's Table\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"<td>\n",
"Chef's Table\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"12\n",
"</td>\n",
"<td>\n",
"Top Gear\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"<td>\n",
"Dexter\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"13\n",
"</td>\n",
"<td>\n",
"North & South\n",
"</td>\n",
"<td>\n",
"9.0\n",
"</td>\n",
"<td>\n",
"Pulp Fiction\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"14\n",
"</td>\n",
"<td>\n",
"Doctor Who\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"<td>\n",
"Attack on Titan\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"15\n",
"</td>\n",
"<td>\n",
"Fight Club\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"<td>\n",
"Charlie Don't Surf\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"16\n",
"</td>\n",
"<td>\n",
"Horrible Histories\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"<td>\n",
"Slugterra: Slug Fu Showdown\n",
"</td>\n",
"<td>\n",
"8.8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"17\n",
"</td>\n",
"<td>\n",
"Fawlty Towers\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"<td>\n",
"Andaz Apna Apna\n",
"</td>\n",
"<td>\n",
"8.8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"18\n",
"</td>\n",
"<td>\n",
"The Good, the Bad and the Ugly\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"<td>\n",
"Aerial America\n",
"</td>\n",
"<td>\n",
"8.8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"19\n",
"</td>\n",
"<td>\n",
"The Lord of the Rings: The Return of the King\n",
"</td>\n",
"<td>\n",
"8.9\n",
"</td>\n",
"<td>\n",
"The Phantom of the Opera at the Royal Albert Hall\n",
"</td>\n",
"<td>\n",
"8.8\n",
"</td>\n",
"</tr>\n",
"<tr>\n",
"<td>\n",
"20\n",
"</td>\n",
"<td>\n",
"Forensic Files\n",
"</td>\n",
"<td>\n",
"8.8\n",
"</td>\n",
"<td>\n",
"Never Sleep Again: The Elm Street Legacy\n",
"</td>\n",
"<td>\n",
"8.8\n",
"</td>\n",
"</tr>\n",
"</table>\n"
]
}
],
"source": [
"print '<table>'\n",
"print '<tr><th></th><th>NZ Service</th><th></th><th>US Service</th><th></th></tr>'\n",
"print '<tr><th>Rank</th><th>Movie Name</th><th>IMDb Score</th><th>Movie Name</th><th>IMDb Score</th></tr>'\n",
"\n",
"for x in range(0,len(top_nz_titles[:20])):\n",
" print '<tr>'\n",
" print '<td>'\n",
" print str(x+1)\n",
" print '</td>'\n",
" print '<td>'\n",
" print top_nz_titles[x][0].lstrip()\n",
" print '</td>'\n",
" print '<td>'\n",
" print top_nz_titles[x][1]\n",
" print '</td>'\n",
" print '<td>'\n",
" print top_us_titles[x][0].lstrip()\n",
" print '</td>'\n",
" print '<td>'\n",
" print top_us_titles[x][1]\n",
" print '</td>'\n",
" print '</tr>'\n",
" \n",
"print '</table>'"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"* Fullmetal Alchemist: Brotherhood\n",
"* Chef's Table\n",
"* Top Gear\n",
"* North & South\n"
]
}
],
"source": [
"my_set = (set(usx)).intersection(set(nzx))\n",
"\n",
"for title in my_set:\n",
" print '* ', title"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| agpl-3.0 |
rohinkumar/galsurveystudy | old/Parallel Computing with Python public.ipynb | 1 | 20767 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parallel Computing with Python\n",
"================================\n",
"\n",
"**[Rodrigo Nemmen](http://rodrigonemmen.com), IAG USP**\n",
"\n",
"This IPython notebook illustrates a few simple ways of doing parallel computing.\n",
"\n",
"Practical examples included:\n",
"\n",
"1. Parallel function mapping to a list of arguments (multiprocessing module)\n",
"2. Parallel execution of array function (scatter/gather) + parallel execution of scripts\n",
"4. Easy parallel Monte Carlo (parallel magics)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Mapping a model to a grid of parameters \n",
"\n",
"<!---\n",
"Inspired on \"useful parallel\".\n",
"-->\n",
"\n",
"Uses the *multiprocessing* module that comes by default with python, i.e. method independent of IPython.\n",
"\n",
"Idea: you have a function $f(\\mathbf{x},\\mathbf{y})$ of two parameters (e.g., $f$ may represent your model) stored in the arrays $(\\mathbf{x},\\mathbf{y})$. Given the arrays $\\mathbf{x}$ and $\\mathbf{y}$, you want to compute the values of $f(\\mathbf{x},\\mathbf{y})$. Let's assume for simplicity that there is no dependence on the neighbours. This is an embarassingly parallel problem.\n",
"\n",
"<!---\n",
"### TODO\n",
"\n",
"* Random sampling of parameter space if desired\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import multiprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Time wasting function that depends on two parameters. Here, I generate 1E5 random numbers based on the normal distribution and then sum them. The two parameters are $\\mu$ and $\\sigma$."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import scipy\n",
"\n",
"def f(z):\n",
" x=z[1]*scipy.random.standard_normal(100000)+z[0]\n",
" return x.sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Arrays of input parameters. You could easily modify this to take as input a matrix, not two arrays."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n=3000\n",
"X=numpy.linspace(-1,1,n) # mean\n",
"Y=numpy.linspace(0.1,1,n) # std. dev."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# creates list of arguments [Xi, Yi]\n",
"pargs=[]\t# this is a list of lists!\n",
"for i in range(X.size):\n",
"\tpargs.append([X[i],Y[i]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parallel execution. Check out all the cores being used with a tool like [htop](http://hisham.hm/htop/)."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ncores=multiprocessing.cpu_count() # number of cores\n",
"pool = multiprocessing.Pool(processes=ncores) # initializes parallel engine"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 53.1 ms, sys: 4.03 ms, total: 57.1 ms\n",
"Wall time: 4.37 s\n"
]
}
],
"source": [
"%%time \n",
"t=pool.map(f, pargs)\t# parallel function map"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pool.close()\t# close the parallel engine"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Serial execution"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 10.8 s, sys: 38.9 ms, total: 10.8 s\n",
"Wall time: 10.9 s\n"
]
}
],
"source": [
"%time t=map(f, pargs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"If you want to convert the list to an array use `y=array(t)`. Also note that there is a similar map method for `ipyparallel`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Parallel execution of array function\n",
"\n",
"Uses [`ipyparallel`](https://ipyparallel.readthedocs.io/en/latest/index.html). Consider a function $f(x)$ which takes an array $x$ containing the grid of input parameters. We want to split the function calls (\"split the array\") to the different cores in our machine:\n",
"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure you start the parallel engines\n",
"\n",
"\n",
"\n",
"\n",
"Alternatively, you can start the engines from the command-line:\n",
"\n",
" $ ipcluster start -n 4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Our time-waster function $f(x)$ that can be applied to an array of integers"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# test if n is prime\n",
"def isprime(n):\n",
" for i in range(3, n):\n",
" if n % i == 0:\n",
" return False\n",
" return True\n",
"\n",
"# tests each element of an array if it is prime\n",
"def f(x):\n",
" return map(isprime,x) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generates big array (10k elements) of random integers between 0 and 100000"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = scipy.random.randint(0,100000, (10000,)) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Serial execution"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 24.5 s, sys: 217 ms, total: 24.7 s\n",
"Wall time: 25 s\n"
]
}
],
"source": [
"%time y=f(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now explain how IPython parallel works **(here I show a slide)**. See documentation at the end of the notebook for details."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import ipyparallel\n",
"client = ipyparallel.Client()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to use the direct view, which means that commands always run on all nodes. This as opposed to a balanced view, which asynchronously executes code on nodes which are idle. In addition, we are going to turn blocking on. This means that jobs will block further execution until all nodes have finished."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"direct = client[:]\n",
"direct.block = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Splits the input array $x$ between the cores"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"direct.scatter('x',x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Verify that the array was indeed divided equally"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2500, 2500, 2500, 2500]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"direct['x.size']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[array([94305, 72839, 17104, ..., 29346, 73755, 29269]),\n",
" array([31625, 37515, 37053, ..., 76381, 32938, 13199]),\n",
" array([44846, 30440, 38205, ..., 83728, 5019, 84130]),\n",
" array([29578, 88280, 80813, ..., 32620, 52857, 27595])]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"direct['x']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try to apply the function in each different core"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "CompositeError",
"evalue": "one or more exceptions from call to method: execute\n[0:execute]: NameError: name 'f' is not defined\n[1:execute]: NameError: name 'f' is not defined\n[2:execute]: NameError: name 'f' is not defined\n[3:execute]: NameError: name 'f' is not defined",
"output_type": "error",
"traceback": [
"[0:execute]: ",
"\u001b[0;31m\u001b[0m\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)\u001b[0;32m<ipython-input-1-c679c6b49964>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m",
"\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
"\u001b[0m\u001b[0;31mNameError\u001b[0m: name 'f' is not defined",
"",
"[1:execute]: ",
"\u001b[0;31m\u001b[0m\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)\u001b[0;32m<ipython-input-1-c679c6b49964>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m",
"\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
"\u001b[0m\u001b[0;31mNameError\u001b[0m: name 'f' is not defined",
"",
"[2:execute]: ",
"\u001b[0;31m\u001b[0m\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)\u001b[0;32m<ipython-input-1-c679c6b49964>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m",
"\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
"\u001b[0m\u001b[0;31mNameError\u001b[0m: name 'f' is not defined",
"",
"[3:execute]: ",
"\u001b[0;31m\u001b[0m\u001b[0;31mNameError\u001b[0mTraceback (most recent call last)\u001b[0;32m<ipython-input-1-c679c6b49964>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m",
"\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m",
"\u001b[0m\u001b[0;31mNameError\u001b[0m: name 'f' is not defined",
""
]
}
],
"source": [
"%%px\n",
"y=f(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why the errors above? Because each core does not see the local engine. They work as separate machines and you have to load all variables and modules in each engine. That's easy."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting myscript.py\n"
]
}
],
"source": [
"%%file myscript.py\n",
"# test if n is prime\n",
"def isprime(n):\n",
" for i in range(3, n):\n",
" if n % i == 0:\n",
" return False\n",
" return True\n",
"\n",
"# tests each element of an array if it is prime\n",
"def f(x):\n",
" return map(isprime,x) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Execute code which defines the methods on the different engines"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<AsyncResult: finished>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"direct.run(\"myscript.py\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now compute the \"model grid\" correctly"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 42.7 ms, sys: 8.18 ms, total: 50.8 ms\n",
"Wall time: 15.8 s\n"
]
}
],
"source": [
"%%time\n",
"%px y=f(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively to the command above, you could use\n",
"\n",
" direct.apply(f,x)\n",
"or\n",
"\n",
" direct.execute('y=f(x)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have the separate arrays $y$ containing the results on each engine. How to get it back to the local engine?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[0;31mOut[0:4]: \u001b[0m2500"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\u001b[0;31mOut[1:4]: \u001b[0m2500"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\u001b[0;31mOut[2:4]: \u001b[0m2500"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"\u001b[0;31mOut[3:4]: \u001b[0m2500"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%px \n",
"import numpy\n",
"numpy.size(y)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"y=direct.gather('y')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have the array magically reassembled back in the local engine. :)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Easy parallel Monte Carlo\n",
"\n",
"Suppose you need to do 100k Monte Carlo simulations. Wouldn't it be great if you could easily split them among your (hopefully many) cores?\n",
"\n",
"In this example, I will perform 100k realizations of a 300x300 array of random floats."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# number of desired random sets\n",
"nboot=100000\n",
"\n",
"# number of sets that will be computed by each engine\n",
"n=nboot/size(client.ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Passes variables to the engines"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"importing scipy on engine(s)\n"
]
}
],
"source": [
"direct.push(dict(n=n))\n",
"\n",
"with direct.sync_imports():\n",
" import scipy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now everything below is executed in parallel! (IPython magic)\n",
"\n",
"<!---\n",
"Have a look also at the %autopx command.\n",
"-->"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 110 ms, sys: 19.9 ms, total: 130 ms\n",
"Wall time: 1min 3s\n"
]
}
],
"source": [
"%%time\n",
"%%px\n",
"\n",
"for i in range(n):\n",
" x = scipy.random.random((300,300)) # 300x300 array of floats (values in the range [0,1) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For comparison, how long does it take to do the same simulation in serial mode?"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 42s, sys: 551 ms, total: 1min 42s\n",
"Wall time: 1min 43s\n"
]
}
],
"source": [
"%%time\n",
"for i in range(nboot):\n",
" x = scipy.random.random((300,300)) # 100x100 array of floats (values in the range [0,1) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Useful reference\n",
"\n",
"## IPython video tutorials\n",
"\n",
"* [IPython in depth: high productivity interactive and parallel python - PyCon 2014](http://youtu.be/XFw1JVXKJss)\n",
"* [The IPython Notebook Revolution](http://youtu.be/t_TzRaK9kpU)\n",
"\n",
"## Parallel computing\n",
"\n",
"### General\n",
"\n",
"* [Introduction to parallel programming](https://computing.llnl.gov/tutorials/parallel_comp)\n",
"\n",
"### Python\n",
"\n",
"* [AstroPython blog](http://astropython.blogspot.com.br), maintained (not so often anymore) by the [speaker](http://rodrigonemmen.com)\n",
"* [Using IPython for parallel computing](https://ipyparallel.readthedocs.io/en/latest/index.html). Documentation of IPython.parallel\n",
"* [Parallel computing with IPython](http://www.astro.washington.edu/users/vanderplas/Astr599/notebooks/21_IPythonParallel). Nice tutorial\n",
"* [Simple python parallelism](http://scottsievert.github.io/blog/2014/07/30/simple-python-parallelism/) and [easily distributing a parallel IPy Notebook on a cluster](http://twiecki.github.io/blog/2014/02/24/ipython-nb-cluster/). Some ideas on making a function parallel easily\n",
"\n",
"### Executing general (non-python) jobs in parallel\n",
"\n",
"* [Submit non-python commands to a running IPython cluster](https://gist.github.com/zonca/8994544)\n",
"* [GNU Parallel](http://www.gnu.org/software/parallel/)\n",
"* [xjobs](http://www.maier-komor.de/xjobs.html)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-2/ocnbgchem.ipynb | 1 | 79376 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocnbgchem \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NIMS-KMA \n",
"**Source ID**: SANDBOX-2 \n",
"**Topic**: Ocnbgchem \n",
"**Sub-Topics**: Tracers. \n",
"**Properties**: 65 (37 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocnbgchem?client=jupyter-notebook) \n",
"**Initialized From**: -- \n",
"\n",
"**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n",
"**Notebook Initialised**: 2018-02-15 16:54:29"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### Document Setup \n",
"**IMPORTANT: to be executed each time you run the notebook** "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# DO NOT EDIT ! \n",
"from pyesdoc.ipython.model_topic import NotebookOutput \n",
"\n",
"# DO NOT EDIT ! \n",
"DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-2', 'ocnbgchem')"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Authors \n",
"*Set document authors*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_author(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Contributors \n",
"*Specify document contributors* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set as follows: DOC.set_contributor(\"name\", \"email\") \n",
"# TODO - please enter value(s)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Publication \n",
"*Specify document publication status* "
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Set publication status: \n",
"# 0=do not publish, 1=publish. \n",
"DOC.set_publication_status(0)"
],
"outputs": [],
"metadata": {}
},
{
"source": [
"### Document Table of Contents \n",
"[1. Key Properties](#1.-Key-Properties) \n",
"[2. Key Properties --> Time Stepping Framework --> Passive Tracers Transport](#2.-Key-Properties--->-Time-Stepping-Framework--->-Passive-Tracers-Transport) \n",
"[3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks](#3.-Key-Properties--->-Time-Stepping-Framework--->-Biology-Sources-Sinks) \n",
"[4. Key Properties --> Transport Scheme](#4.-Key-Properties--->-Transport-Scheme) \n",
"[5. Key Properties --> Boundary Forcing](#5.-Key-Properties--->-Boundary-Forcing) \n",
"[6. Key Properties --> Gas Exchange](#6.-Key-Properties--->-Gas-Exchange) \n",
"[7. Key Properties --> Carbon Chemistry](#7.-Key-Properties--->-Carbon-Chemistry) \n",
"[8. Tracers](#8.-Tracers) \n",
"[9. Tracers --> Ecosystem](#9.-Tracers--->-Ecosystem) \n",
"[10. Tracers --> Ecosystem --> Phytoplankton](#10.-Tracers--->-Ecosystem--->-Phytoplankton) \n",
"[11. Tracers --> Ecosystem --> Zooplankton](#11.-Tracers--->-Ecosystem--->-Zooplankton) \n",
"[12. Tracers --> Disolved Organic Matter](#12.-Tracers--->-Disolved-Organic-Matter) \n",
"[13. Tracers --> Particules](#13.-Tracers--->-Particules) \n",
"[14. Tracers --> Dic Alkalinity](#14.-Tracers--->-Dic-Alkalinity) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Ocean Biogeochemistry key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.2. Model Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of ocean biogeochemistry model code (PISCES 2.0,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_name') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.3. Model Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.model_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Geochemical\" \n",
"# \"NPZD\" \n",
"# \"PFT\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Elemental Stoichiometry\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe elemental stoichiometry (fixed, variable, mix of the two)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Fixed\" \n",
"# \"Variable\" \n",
"# \"Mix of both\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Elemental Stoichiometry Details\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe which elements have fixed/variable stoichiometry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.elemental_stoichiometry_details') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all prognostic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Diagnostic Variables\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.N\n",
"### *List of all diagnotic tracer variables in the ocean biogeochemistry component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.diagnostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.8. Damping\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any tracer damping used (such as artificial correction or relaxation to climatology,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.damping') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Time Stepping Framework --> Passive Tracers Transport \n",
"*Time stepping method for passive tracers transport in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for passive tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for passive tracers (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.passive_tracers_transport.timestep_if_not_from_ocean') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Time Stepping Framework --> Biology Sources Sinks \n",
"*Time stepping framework for biology sources and sinks in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time stepping framework for biology sources and sinks*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"use ocean model transport time step\" \n",
"# \"use specific time step\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Timestep If Not From Ocean\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Time step for biology sources and sinks (if different from ocean)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.time_stepping_framework.biology_sources_sinks.timestep_if_not_from_ocean') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Transport Scheme \n",
"*Transport scheme in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of transport scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Offline\" \n",
"# \"Online\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Transport scheme used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Use that of ocean model\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Use Different Scheme\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Decribe transport scheme if different than that of ocean model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.transport_scheme.use_different_scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 5. Key Properties --> Boundary Forcing \n",
"*Properties of biogeochemistry boundary forcing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Atmospheric Deposition\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how atmospheric deposition is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.atmospheric_deposition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Atmospheric Chemistry model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. River Input\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how river input is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.river_input') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"from file (climatology)\" \n",
"# \"from file (interannual variations)\" \n",
"# \"from Land Surface model\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Sediments From Boundary Conditions\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from boundary condition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_boundary_conditions') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.4. Sediments From Explicit Model\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List which sediments are speficied from explicit sediment model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.boundary_forcing.sediments_from_explicit_model') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 6. Key Properties --> Gas Exchange \n",
"*Properties of gas exchange in ocean biogeochemistry *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe CO2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. O2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is O2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. O2 Exchange Type\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Describe O2 gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.O2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. DMS Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is DMS gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.6. DMS Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify DMS gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.DMS_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.7. N2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.8. N2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.9. N2O Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is N2O gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.10. N2O Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify N2O gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.N2O_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.11. CFC11 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC11 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.12. CFC11 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC11 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC11_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.13. CFC12 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is CFC12 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.14. CFC12 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify CFC12 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.CFC12_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.15. SF6 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is SF6 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.16. SF6 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify SF6 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.SF6_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.17. 13CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 13CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.18. 13CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 13CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.13CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.19. 14CO2 Exchange Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is 14CO2 gas exchange modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.20. 14CO2 Exchange Type\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify 14CO2 gas exchange scheme type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.14CO2_exchange_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.21. Other Gases\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Specify any other gas exchange*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.gas_exchange.other_gases') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Key Properties --> Carbon Chemistry \n",
"*Properties of carbon chemistry biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe how carbon chemistry is modeled*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OMIP protocol\" \n",
"# \"Other protocol\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. PH Scale\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, describe pH scale.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.pH_scale') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Sea water\" \n",
"# \"Free\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Constants If Not OMIP\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If NOT OMIP protocol, list carbon chemistry constants.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.key_properties.carbon_chemistry.constants_if_not_OMIP') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Tracers \n",
"*Ocean biogeochemistry tracers*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of tracers in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.overview') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.2. Sulfur Cycle Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is sulfur cycle modeled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.sulfur_cycle_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Nutrients Present\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List nutrient species present in ocean biogeochemistry model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nutrients_present') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrogen (N)\" \n",
"# \"Phosphorous (P)\" \n",
"# \"Silicium (S)\" \n",
"# \"Iron (Fe)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Nitrous Species If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous species.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_species_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Nitrates (NO3)\" \n",
"# \"Amonium (NH4)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Nitrous Processes If N\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If nitrogen present, list nitrous processes.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.nitrous_processes_if_N') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dentrification\" \n",
"# \"N fixation\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 9. Tracers --> Ecosystem \n",
"*Ecosystem properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Upper Trophic Levels Definition\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Definition of upper trophic level (e.g. based on size) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_definition') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.2. Upper Trophic Levels Treatment\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Define how upper trophic level are treated*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.upper_trophic_levels_treatment') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 10. Tracers --> Ecosystem --> Phytoplankton \n",
"*Phytoplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of phytoplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"PFT including size based (specify both below)\" \n",
"# \"Size based only (specify below)\" \n",
"# \"PFT only (specify below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Pft\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton functional types (PFT) (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.pft') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diatoms\" \n",
"# \"Nfixers\" \n",
"# \"Calcifiers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.3. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Phytoplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.phytoplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microphytoplankton\" \n",
"# \"Nanophytoplankton\" \n",
"# \"Picophytoplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 11. Tracers --> Ecosystem --> Zooplankton \n",
"*Zooplankton properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of zooplankton*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Generic\" \n",
"# \"Size based (specify below)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Size Classes\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Zooplankton size classes (if applicable)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.ecosystem.zooplankton.size_classes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Microzooplankton\" \n",
"# \"Mesozooplankton\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Tracers --> Disolved Organic Matter \n",
"*Disolved organic matter properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Bacteria Present\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there bacteria representation ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.bacteria_present') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 12.2. Lability\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Describe treatment of lability in dissolved organic matter*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.disolved_organic_matter.lability') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Labile\" \n",
"# \"Semi-labile\" \n",
"# \"Refractory\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Tracers --> Particules \n",
"*Particulate carbon properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is particulate carbon represented in ocean biogeochemistry?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diagnostic\" \n",
"# \"Diagnostic (Martin profile)\" \n",
"# \"Diagnostic (Balast)\" \n",
"# \"Prognostic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Types If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *If prognostic, type(s) of particulate matter taken into account*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.types_if_prognostic') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"POC\" \n",
"# \"PIC (calcite)\" \n",
"# \"PIC (aragonite\" \n",
"# \"BSi\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.3. Size If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, describe if a particule size spectrum is used to represent distribution of particules in water volume*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"No size spectrum used\" \n",
"# \"Full size spectrum\" \n",
"# \"Discrete size classes (specify which below)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.4. Size If Discrete\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If prognostic and discrete size, describe which size classes are used*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.size_if_discrete') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.5. Sinking Speed If Prognostic\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *If prognostic, method for calculation of sinking speed of particules*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.particules.sinking_speed_if_prognostic') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Function of particule size\" \n",
"# \"Function of particule type (balast)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Tracers --> Dic Alkalinity \n",
"*DIC and alkalinity properties in ocean biogeochemistry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Carbon Isotopes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Which carbon isotopes are modelled (C13, C14)?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.carbon_isotopes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"C13\" \n",
"# \"C14)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Abiotic Carbon\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is abiotic carbon modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.abiotic_carbon') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# Valid Choices: \n",
"# True \n",
"# False \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Alkalinity\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *How is alkalinity modelled ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocnbgchem.tracers.dic_alkalinity.alkalinity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Prognostic\" \n",
"# \"Diagnostic)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### \u00a92017 [ES-DOC](https://es-doc.org) \n"
],
"cell_type": "markdown",
"metadata": {}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.10",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | gpl-3.0 |
wdwvt1/qiime | examples/ipynb/illumina_overview_tutorial_workshop_template.ipynb | 15 | 3385 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook preparation\n",
"\n",
"This notebook was designed to run the [QIIME Illumina Overview Tutorial](http://qiime.org/tutorials/illumina_overview_tutorial.html) on the QIIME 1.9.1 Amazon Web Services EC2 instance. You can find the AMI ID on the [QIIME resources page](http://qiime.org/home_static/dataFiles.html). These steps covered here are important for having multiple users working on a single IPython Notebook server, but are a little bit of overkill if you're running this on your own AWS instance. If you're running this on your own instance, you should instead work with the IPython Notebook linked from [here](http://qiime.org/tutorials/illumina_overview_tutorial.html#ipython-notebook)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting started\n",
"\n",
"We'll begin by initializing some variables to configure our IPython computing environment. Don't edit anything in this first cell. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from random import choice\n",
"from os import chdir, mkdir, makedirs\n",
"from os.path import join\n",
"from IPython.display import FileLinks, FileLink\n",
"from functools import partial\n",
"\n",
"# to support running in a multi-user environment, each user will work in\n",
"# a temporary working directory with a randomly generated name\n",
"basedir = \"temp\"\n",
"choices = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n",
"choices += choices.lower()\n",
"working_dir = join(basedir,''.join([choice(choices) for i in range(12)]))\n",
"\n",
"print \"Your working directory is %s\" % working_dir\n",
"makedirs(working_dir)\n",
"chdir(working_dir)\n",
"\n",
"!wget ftp://ftp.microbio.me/qiime/tutorial_files/moving_pictures_tutorial-1.9.0.tgz\n",
"!tar -xzf moving_pictures_tutorial-1.9.0.tgz\n",
"\n",
"chdir('moving_pictures_tutorial-1.9.0/illumina')\n",
"FileLink = partial(FileLink, url_prefix=join(working_dir, 'moving_pictures_tutorial-1.9.0/illumina/'))\n",
"FileLinks = partial(FileLinks, url_prefix=join(working_dir, 'moving_pictures_tutorial-1.9.0/illumina/'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running the Illumina Overview Tutorial\n",
"\n",
"You can now get started. Follow the steps found in the [Illumina Overview Tutorial](http://qiime.org/tutorials/illumina_overview_tutorial.html). You should begin with the steps under **Check our mapping file for errors** (the **Getting Started** steps have already been completed when you executed the steps above)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
siddhartha-gadgil/ProvingGround | notes/2020-05-25-BotMonoid.ipynb | 1 | 40958 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Monoid in an automatic mode\n",
"\n",
"We return to the old problem of proving $e_l = e_r$, but in a general autonomous/interactive framework."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[39m\u001b[36m$cp.$ \n",
"\u001b[39m\n",
"\u001b[32mimport \u001b[39m\u001b[36mprovingground._ , interface._, HoTT._, learning._ \n",
"\u001b[39m"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import $cp.bin.`provingground-core-jvm-8e93db2c2a.fat.jar`\n",
"import provingground._ , interface._, HoTT._, learning._ \n",
"repl.pprinter() = {\n",
" val p = repl.pprinter()\n",
" p.copy(\n",
" additionalHandlers = p.additionalHandlers.orElse {\n",
" translation.FansiShow.fansiHandler\n",
" }\n",
" )\n",
"}\n",
"\n",
"\n",
"Utils.logger = {\n",
" import scribe._, writer._, Utils._\n",
" logger.withHandler(writer = FileWriter().path(file.LogPath.simple(\"monoid.log\"))).replace()\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[39m\u001b[36mlibrary._, MonoidSimple._\n",
"\u001b[39m\n",
"\u001b[36mtg\u001b[39m: \u001b[32mTermGenParams\u001b[39m = \u001b[33mTermGenParams\u001b[39m(\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.1\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.3\u001b[39m,\n",
" \u001b[32m0.7\u001b[39m,\n",
" \u001b[32m0.5\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[32m0.0\u001b[39m,\n",
" \u001b[33mOrElse\u001b[39m(\n",
" \u001b[33mOrElse\u001b[39m(\u001b[33mOrElse\u001b[39m(\u001b[33mOrElse\u001b[39m(<function1>, <function1>), <function1>), <function1>),\n",
" <function1>\n",
" )\n",
")\n",
"\u001b[36mts\u001b[39m: \u001b[32mTermState\u001b[39m = \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32me_l\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32me_r\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mmul\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32meqM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(a)(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{(eqM(a)(b) \\to eqM(b)(a))}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32maxiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(mul(e_l)(a))(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(mul(a)(e_r))(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32meqM\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mmul\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → M))\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → 𝒰 ))\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(a)(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(a : M){ ∏(b : M){ (eqM(a)(b) → eqM(b)(a)) } }\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(a : M){ ∏(b : M){ ∏(c : M){ (eqM(a)(b) → (eqM(b)(c) → eqM(a)(c))) } } }\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(mul(e_l)(a))(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(mul(a)(e_r))(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → 𝒰 ))\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → M))\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m)\n",
"...\n",
"\u001b[36mlp\u001b[39m: \u001b[32mLocalProver\u001b[39m = \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32me_l\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32me_r\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mmul\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32meqM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(a)(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{(eqM(a)(b) \\to eqM(b)(a))}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32maxiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(mul(e_l)(a))(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(mul(a)(e_r))(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32meqM\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mmul\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → M))\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → 𝒰 ))\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(a)(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(a : M){ ∏(b : M){ (eqM(a)(b) → eqM(b)(a)) } }\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(a : M){ ∏(b : M){ ∏(c : M){ (eqM(a)(b) → (eqM(b)(c) → eqM(a)(c))) } } }\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(mul(e_l)(a))(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(mul(a)(e_r))(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → 𝒰 ))\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m),\n",
"..."
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import library._, MonoidSimple._\n",
"val tg = TermGenParams.zero.copy(appW = 0.1, unAppW = 0.1)\n",
"val ts = TermState(dist1, dist1.map(_.typ), goals = FiniteDistribution.unif(eqM(l)(r)))\n",
"val lp = LocalProver(ts, tg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have set up the ingredients for the statement and proof. We next create a _session_ with the set of _bots_ we actually need. The crucial choice here is to limit the generation to applications and unified applications. The other restrictions should not matter much."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[39m\u001b[36mHoTTBot._\n",
"\n",
"\u001b[39m\n",
"\u001b[36mbs\u001b[39m: \u001b[32mVector\u001b[39m[\u001b[32mTypedPostResponse\u001b[39m[\u001b[32m_1\u001b[39m, \u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)] forSome { type \u001b[32m_1\u001b[39m >: \u001b[32mHoTTMessages\u001b[39m.\u001b[32mFinalState\u001b[39m with (\u001b[32mTermState\u001b[39m, \u001b[32mSet\u001b[39m[\u001b[32mEquationNode\u001b[39m]) with \u001b[32mLocalTangentProver\u001b[39m with \u001b[32mHoTTMessages\u001b[39m.\u001b[32mUseLemma\u001b[39m with \u001b[32mHoTTMessages\u001b[39m.\u001b[32mLemmas\u001b[39m with \u001b[32mLocalProver\u001b[39m <: \u001b[32mProduct\u001b[39m with \u001b[32mSerializable\u001b[39m with \u001b[32mObject\u001b[39m }] = \u001b[33mVector\u001b[39m(\n",
" \u001b[33mMicroBot\u001b[39m(\n",
" provingground.learning.HoTTBot$$$Lambda$2691/975941609@6b0effc0,\n",
" provingground.learning.TypedPostResponse$MicroBot$$$Lambda$2689/1407547031@72eda1a6\n",
" ),\n",
" \u001b[33mMiniBot\u001b[39m(\n",
" provingground.learning.HoTTBot$$$Lambda$2692/1366091899@43316df9,\n",
" provingground.learning.MiniBot$$$Lambda$2613/558251617@37569396\n",
" ),\n",
" \u001b[33mMicroBot\u001b[39m(\n",
" provingground.learning.HoTTBot$$$Lambda$2693/319436178@2733e6a6,\n",
" provingground.learning.TypedPostResponse$MicroBot$$$Lambda$2689/1407547031@72eda1a6\n",
" ),\n",
" \u001b[33mMicroBot\u001b[39m(\n",
" provingground.learning.HoTTBot$$$Lambda$2702/1189645419@713eacd0,\n",
" provingground.learning.TypedPostResponse$MicroBot$$$Lambda$2689/1407547031@72eda1a6\n",
" ),\n",
" \u001b[33mMicroBot\u001b[39m(\n",
" provingground.learning.TypedPostResponse$MicroBot$$$Lambda$2688/157269246@39a849ab,\n",
" provingground.learning.TypedPostResponse$MicroBot$$$Lambda$2689/1407547031@72eda1a6\n",
" ),\n",
" \u001b[33mCallback\u001b[39m(\n",
" provingground.learning.TypedPostResponse$Callback$$$Lambda$2705/1496786662@3ae1a72b,\n",
" provingground.learning.TypedPostResponse$Callback$$$Lambda$2706/347237838@25f6007\n",
" )\n",
")\n",
"\u001b[36msess\u001b[39m: \u001b[32mHoTTWebSession\u001b[39m = provingground.learning.HoTTWebSession@4b33bd05"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import HoTTBot._\n",
"\n",
"val bs = Vector(lpLemmas, scaleSplitLemmas(1), lemmaTangents(), lptToTermResult, termResultToFinalState, reportSuccesses)\n",
"val sess = new HoTTWebSession(bots = bs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we post to our session to get stuff started."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div class=\"jp-RenderedText\">\n",
"<pre><code><span style=\"color: rgb(0, 187, 187)\"><span class=\"ansi-cyan-fg\">res3</span></span>: <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">concurrent</span></span>.<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Future</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">PostData</span></span>[<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">_</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">HoTTPostWeb</span></span>, (<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">Int</span></span>)]] = <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\"><style>@keyframes fadein { from { opacity: 0; } to { opacity: 1; } }</style><span style=\"animation: fadein 2s;\"><span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Success</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">PostData</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">LocalProver</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">TermState</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">e_l</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">e_r</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">mul</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">eqM</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">axiom_{eqM(a)(a)}</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">axiom_{(eqM(a)(b) \\to eqM(b)(a))}</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">axiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">axiom_{eqM(mul(e_l)(a))(a)}</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">axiom_{eqM(mul(a)(e_r))(a)}</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">eqM</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.2857142857142857</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">mul</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.2857142857142857</span></span>)\n",
" )\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">FiniteDistribution</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Vector</span></span>(\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">M</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">M</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">(M → (M → M))</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">(M → (M → 𝒰 ))</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(a : M){ eqM(a)(a) }</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(a : M){ ∏(b : M){ (eqM(a)(b) → eqM(b)(a)) } }</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(a : M){ ∏(b : M){ ∏(c : M){ (eqM(a)(b) → (eqM(b)(c) → eqM(a)(c))) } } }</span></span>,\n",
" <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>\n",
" ),\n",
" <span style=\"color: yellow\"><span class=\"ansi-yellow-fg\">Weighted</span></span>(<span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">∏(a : M){ eqM(mul(e_l)(a))(a) }</span></span>, <span style=\"color: rgb(0, 187, 0)\"><span class=\"ansi-green-fg\">0.047619047619047616</span></span>),\n",
"...</span></span></span></code></pre>\n",
"</div>"
],
"text/plain": [
"\u001b[36mres3\u001b[39m: \u001b[32mconcurrent\u001b[39m.\u001b[32mFuture\u001b[39m[\u001b[32mPostData\u001b[39m[\u001b[32m_\u001b[39m, \u001b[32mHoTTPostWeb\u001b[39m, (\u001b[32mInt\u001b[39m, \u001b[32mInt\u001b[39m)]] = \u001b[32m\u001b[33mSuccess\u001b[39m(\n",
" \u001b[33mPostData\u001b[39m(\n",
" \u001b[33mLocalProver\u001b[39m(\n",
" \u001b[33mTermState\u001b[39m(\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32me_l\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32me_r\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mmul\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32meqM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(a)(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{(eqM(a)(b) \\to eqM(b)(a))}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32maxiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(mul(e_l)(a))(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32maxiom_{eqM(mul(a)(e_r))(a)}\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32meqM\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mmul\u001b[39m, \u001b[32m0.2857142857142857\u001b[39m)\n",
" )\n",
" ),\n",
" \u001b[33mFiniteDistribution\u001b[39m(\n",
" \u001b[33mVector\u001b[39m(\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32mM\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → M))\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m(M → (M → 𝒰 ))\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(a)(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(a : M){ ∏(b : M){ (eqM(a)(b) → eqM(b)(a)) } }\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\n",
" \u001b[32m∏(a : M){ ∏(b : M){ ∏(c : M){ (eqM(a)(b) → (eqM(b)(c) → eqM(a)(c))) } } }\u001b[39m,\n",
" \u001b[32m0.047619047619047616\u001b[39m\n",
" ),\n",
" \u001b[33mWeighted\u001b[39m(\u001b[32m∏(a : M){ eqM(mul(e_l)(a))(a) }\u001b[39m, \u001b[32m0.047619047619047616\u001b[39m),\n",
"...\u001b[39m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sess.post(lp, Set())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mres4\u001b[39m: \u001b[32mString\u001b[39m = \u001b[32mSuccess: Vector((((eqM) (e_l)) (e_r),1.0,[(((((axiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}) (e_l)) (((mul) (e_l)) (e_r))) (e_r)) (lemma:((eqM) (e_l)) (((mul) (e_l)) (e_r)))) ((axiom_{eqM(mul(e_l)(a))(a)}) (e_r)) : 0.001024170092306373]))\u001b[39m"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Utils.reportText"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__Success:__ Running the above after just a few seconds gives the report of success."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logs\n",
"\n",
"We include by copy-paste the log files (included from the file). Note the success statement.\n",
"\n",
"```\n",
"2020.05.25 10:23:24 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.LocalProver], id: (1,-1349507052), content:\n",
"LocalProver(TermState([eqM : 0.2857142857142857, mul : 0.2857142857142857, e_l : 0.047619047619047616, e_r : 0.047619047619047616, mul : 0.047619047619047616, eqM : 0.047619047619047616, axiom_{eqM(a)(a)} : 0.047619047619047616, axiom_{(eqM(a)(b) \\to eqM(b)(a))} : 0.047619047619047616, axiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))} : 0.047619047619047616, axiom_{eqM(mul(e_l)(a))(a)} : 0.047619047619047616, axiom_{eqM(mul(a)(e_r))(a)} : 0.047619047619047616],[(M) → ((M) → (𝒰 _0)) : 0.2857142857142857, (M) → ((M) → (M)) : 0.2857142857142857, M : 0.047619047619047616, M : 0.047619047619047616, (M) → ((M) → (M)) : 0.047619047619047616, (M) → ((M) → (𝒰 _0)) : 0.047619047619047616, (`a : M ) ~> (((eqM) (`a)) (`a)) : 0.047619047619047616, (`a : M ) ~> ((`b : M ) ~> ((((eqM) (`a)) (`b)) → (((eqM) (`b)) (`a)))) : 0.047619047619047616, (`a : M ) ~> ((`b : M ) ~> ((`c : M ) ~> ((((eqM) (`a)) (`b)) → ((((eqM) (`b)) (`c)) → (((eqM) (`a)) (`c)))))) : 0.047619047619047616, (`a : M ) ~> (((eqM) (((mul) (e_l)) (`a))) (`a)) : 0.047619047619047616, (`a : M ) ~> (((eqM) (((mul) (`a)) (e_r))) (`a)) : 0.047619047619047616],Vector(),[],[((eqM) (e_l)) (e_r) : 1.0],Empty),TermGenParams(0.1,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3,0.7,0.5,0.0,0.0,0.0,<function1>),1.0E-4,None,12 minutes,1.01,1.0,10000,10,1.0,1.0,None,false,false,0.5,1.0)\n",
"2020.05.25 10:23:24 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalProver], id: (1,-1349507052)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Lemmas], id: (2,1419581202), content:\n",
"Lemmas(Vector((((eqM) (e_r)) (e_r),None,0.0027509184472828325), (((eqM) (e_l)) (e_l),None,0.0027509184472828325), (((eqM) (e_l)) (((mul) (e_l)) (e_r)),None,0.0012526844961492322), (((eqM) (e_l)) (((mul) (e_l)) (e_l)),None,0.0012526844961492322), (((eqM) (e_r)) (((mul) (e_l)) (e_r)),None,0.0012526844961492322), (((eqM) (e_r)) (((mul) (e_r)) (e_r)),None,0.0012526844961492322)))\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Lemmas], id: (2,1419581202)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Weight], id: (3,-397054148), content:\n",
"Weight(0.26167884451748746)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Weight], id: (8,-1403795547), content:\n",
"Weight(0.1191605777412563)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (9,-897240716), content:\n",
"UseLemma(((eqM) (e_r)) (e_r),None)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Weight], id: (7,-1403795547), content:\n",
"Weight(0.1191605777412563)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Weight], id: (6,-1403795547), content:\n",
"Weight(0.1191605777412563)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Weight], id: (4,-397054148), content:\n",
"Weight(0.26167884451748746)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.Weight], id: (5,-1403795547), content:\n",
"Weight(0.1191605777412563)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (13,-1470620296), content:\n",
"UseLemma(((eqM) (e_l)) (e_l),None)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (14,774457819), content:\n",
"UseLemma(((eqM) (e_l)) (((mul) (e_l)) (e_r)),None)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (12,1349890185), content:\n",
"UseLemma(((eqM) (e_l)) (((mul) (e_l)) (e_l)),None)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (11,-1419946672), content:\n",
"UseLemma(((eqM) (e_r)) (((mul) (e_l)) (e_r)),None)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.PostBuffer.$anon.post:244:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (10,1179944372), content:\n",
"UseLemma(((eqM) (e_r)) (((mul) (e_r)) (e_r)),None)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (9,-897240716)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (13,-1470620296)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (14,774457819)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (12,1349890185)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (11,-1419946672)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.UseLemma], id: (10,1179944372)\n",
"2020.05.25 10:23:29 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (15,-2097001952)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (16,1481277112)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (15,-2097001952)\n",
"2020.05.25 10:23:29 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (16,1481277112)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (17,1299211175)\n",
"2020.05.25 10:23:29 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (17,1299211175)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (18,1615518517)\n",
"2020.05.25 10:23:28 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (18,1615518517)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (19,1838221781)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (19,1838221781)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (20,61122141)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.LocalTangentProver], id: (20,61122141)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (21,-293827785)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (21,-293827785)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (22,-1739022371)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (22,-1739022371)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (23,388473380)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (23,388473380)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (24,1105488446)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (25,1560009545)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (24,1105488446)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.$anonfun:76:22 - Success: Vector((((eqM) (e_l)) (e_r),1.0,[(((((axiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}) (e_l)) (((mul) (e_l)) (e_r))) (e_r)) (lemma:((eqM) (e_l)) (((mul) (e_l)) (e_r)))) ((axiom_{eqM(mul(e_l)(a))(a)}) (e_r)) : 0.001024170092306373]))\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (25,1560009545)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (26,-1452315895)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (26,-1452315895)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (27,-1331912611)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (27,-1331912611)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (28,-625764359)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (28,-625764359)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (29,-174381781)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (29,-174381781)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (30,586935838)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (30,586935838)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (31,-997289120)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (31,-997289120)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (32,931037492)\n",
"2020.05.25 10:23:32 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (32,931037492)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To higlight, here is the success statement again: \n",
"```\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.$anonfun:76:22 - Success: Vector((((eqM) (e_l)) (e_r),1.0,[(((((axiom_{(eqM(a)(b) \\to (eqM(b)(c) \\to eqM(a)(c)))}) (e_l)) (((mul) (e_l)) (e_r))) (e_r)) (lemma:((eqM) (e_l)) (((mul) (e_l)) (e_r)))) ((axiom_{eqM(mul(e_l)(a))(a)}) (e_r)) : 0.001024170092306373]))\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.HoTTBot.scribeLog:691:16 - Post; tag: TypeTag[provingground.learning.TermData.TermResult], id: (25,1560009545)\n",
"2020.05.25 10:23:30 [INFO] provingground.learning.ErasablePostBuffer.$anon.post:85:22 - Post; tag: TypeTag[provingground.learning.HoTTMessages.FinalState], id: (26,-1452315895)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Conclusions\n",
"\n",
"* The simple result is proved in an autonomous way.\n",
"* The main tuning is sticking to applications and unified applications and keeping parameters at reasonable values, including the scaling for tangents. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Scala",
"language": "scala",
"name": "scala"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala",
"nbconvert_exporter": "script",
"version": "2.12.9"
}
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"nbformat_minor": 4
}
| mit |
chetan51/nupic.research | projects/dynamic_sparse/notebooks/ExperimentAnalysis-ImprovedMagvsSETcomparison.ipynb | 1 | 61888 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Experiment: \n",
"\n",
"Evaluate pruning by magnitude weighted by coactivations (more thorough evaluation), compare it to baseline (SET).\n",
"\n",
"#### Motivation.\n",
"\n",
"Check if results are consistently above baseline.\n",
"\n",
"#### Conclusion\n",
"\n",
"- No significant difference between both models\n",
"- No support for early stopping"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import os\n",
"import glob\n",
"import tabulate\n",
"import pprint\n",
"import click\n",
"import numpy as np\n",
"import pandas as pd\n",
"from ray.tune.commands import *\n",
"from nupic.research.frameworks.dynamic_sparse.common.browser import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and check data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"exps = ['improved_magpruning_eval1', ]\n",
"paths = [os.path.expanduser(\"~/nta/results/{}\".format(e)) for e in exps]\n",
"df = load_many(paths)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Experiment Name</th>\n",
" <th>train_acc_max</th>\n",
" <th>train_acc_max_epoch</th>\n",
" <th>train_acc_min</th>\n",
" <th>train_acc_min_epoch</th>\n",
" <th>train_acc_median</th>\n",
" <th>train_acc_last</th>\n",
" <th>val_acc_max</th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th>val_acc_min</th>\n",
" <th>...</th>\n",
" <th>momentum</th>\n",
" <th>network</th>\n",
" <th>num_classes</th>\n",
" <th>on_perc</th>\n",
" <th>optim_alg</th>\n",
" <th>pruning_early_stop</th>\n",
" <th>test_noise</th>\n",
" <th>use_kwinners</th>\n",
" <th>weight_decay</th>\n",
" <th>weight_prune_perc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0_model=DSNNWeightedMag,on_perc=0.2,pruning_ea...</td>\n",
" <td>0.999483</td>\n",
" <td>93</td>\n",
" <td>0.922517</td>\n",
" <td>0</td>\n",
" <td>0.998808</td>\n",
" <td>0.999083</td>\n",
" <td>0.9802</td>\n",
" <td>35</td>\n",
" <td>0.9597</td>\n",
" <td>...</td>\n",
" <td>0.9</td>\n",
" <td>MLPHeb</td>\n",
" <td>10</td>\n",
" <td>0.2</td>\n",
" <td>SGD</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1_model=DSNNMixedHeb,on_perc=0.2,pruning_early...</td>\n",
" <td>0.999433</td>\n",
" <td>87</td>\n",
" <td>0.926283</td>\n",
" <td>0</td>\n",
" <td>0.998683</td>\n",
" <td>0.999333</td>\n",
" <td>0.9798</td>\n",
" <td>35</td>\n",
" <td>0.9603</td>\n",
" <td>...</td>\n",
" <td>0.9</td>\n",
" <td>MLPHeb</td>\n",
" <td>10</td>\n",
" <td>0.2</td>\n",
" <td>SGD</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2_model=DSNNWeightedMag,on_perc=0.1,pruning_ea...</td>\n",
" <td>0.993517</td>\n",
" <td>92</td>\n",
" <td>0.908733</td>\n",
" <td>0</td>\n",
" <td>0.990750</td>\n",
" <td>0.993150</td>\n",
" <td>0.9733</td>\n",
" <td>92</td>\n",
" <td>0.9506</td>\n",
" <td>...</td>\n",
" <td>0.9</td>\n",
" <td>MLPHeb</td>\n",
" <td>10</td>\n",
" <td>0.1</td>\n",
" <td>SGD</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3_model=DSNNMixedHeb,on_perc=0.1,pruning_early...</td>\n",
" <td>0.993217</td>\n",
" <td>94</td>\n",
" <td>0.905483</td>\n",
" <td>0</td>\n",
" <td>0.990275</td>\n",
" <td>0.993017</td>\n",
" <td>0.9725</td>\n",
" <td>38</td>\n",
" <td>0.9502</td>\n",
" <td>...</td>\n",
" <td>0.9</td>\n",
" <td>MLPHeb</td>\n",
" <td>10</td>\n",
" <td>0.1</td>\n",
" <td>SGD</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4_model=DSNNWeightedMag,on_perc=0.2,pruning_ea...</td>\n",
" <td>0.999400</td>\n",
" <td>75</td>\n",
" <td>0.927883</td>\n",
" <td>0</td>\n",
" <td>0.998633</td>\n",
" <td>0.999050</td>\n",
" <td>0.9818</td>\n",
" <td>44</td>\n",
" <td>0.9640</td>\n",
" <td>...</td>\n",
" <td>0.9</td>\n",
" <td>MLPHeb</td>\n",
" <td>10</td>\n",
" <td>0.2</td>\n",
" <td>SGD</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>0.0001</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 42 columns</p>\n",
"</div>"
],
"text/plain": [
" Experiment Name train_acc_max \\\n",
"0 0_model=DSNNWeightedMag,on_perc=0.2,pruning_ea... 0.999483 \n",
"1 1_model=DSNNMixedHeb,on_perc=0.2,pruning_early... 0.999433 \n",
"2 2_model=DSNNWeightedMag,on_perc=0.1,pruning_ea... 0.993517 \n",
"3 3_model=DSNNMixedHeb,on_perc=0.1,pruning_early... 0.993217 \n",
"4 4_model=DSNNWeightedMag,on_perc=0.2,pruning_ea... 0.999400 \n",
"\n",
" train_acc_max_epoch train_acc_min train_acc_min_epoch train_acc_median \\\n",
"0 93 0.922517 0 0.998808 \n",
"1 87 0.926283 0 0.998683 \n",
"2 92 0.908733 0 0.990750 \n",
"3 94 0.905483 0 0.990275 \n",
"4 75 0.927883 0 0.998633 \n",
"\n",
" train_acc_last val_acc_max val_acc_max_epoch val_acc_min ... momentum \\\n",
"0 0.999083 0.9802 35 0.9597 ... 0.9 \n",
"1 0.999333 0.9798 35 0.9603 ... 0.9 \n",
"2 0.993150 0.9733 92 0.9506 ... 0.9 \n",
"3 0.993017 0.9725 38 0.9502 ... 0.9 \n",
"4 0.999050 0.9818 44 0.9640 ... 0.9 \n",
"\n",
" network num_classes on_perc optim_alg pruning_early_stop test_noise \\\n",
"0 MLPHeb 10 0.2 SGD 0 False \n",
"1 MLPHeb 10 0.2 SGD 0 False \n",
"2 MLPHeb 10 0.1 SGD 0 False \n",
"3 MLPHeb 10 0.1 SGD 0 False \n",
"4 MLPHeb 10 0.2 SGD 1 False \n",
"\n",
" use_kwinners weight_decay weight_prune_perc \n",
"0 False 0.0001 NaN \n",
"1 False 0.0001 NaN \n",
"2 False 0.0001 NaN \n",
"3 False 0.0001 NaN \n",
"4 False 0.0001 NaN \n",
"\n",
"[5 rows x 42 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# replace hebbian prine\n",
"df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)\n",
"df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Experiment Name', 'train_acc_max', 'train_acc_max_epoch',\n",
" 'train_acc_min', 'train_acc_min_epoch', 'train_acc_median',\n",
" 'train_acc_last', 'val_acc_max', 'val_acc_max_epoch', 'val_acc_min',\n",
" 'val_acc_min_epoch', 'val_acc_median', 'val_acc_last', 'epochs',\n",
" 'experiment_file_name', 'trial_time', 'mean_epoch_time', 'batch_norm',\n",
" 'data_dir', 'dataset_name', 'debug_sparse', 'debug_weights', 'device',\n",
" 'hebbian_grow', 'hebbian_prune_perc', 'hidden_sizes', 'input_size',\n",
" 'learning_rate', 'lr_gamma', 'lr_milestones', 'lr_scheduler', 'model',\n",
" 'momentum', 'network', 'num_classes', 'on_perc', 'optim_alg',\n",
" 'pruning_early_stop', 'test_noise', 'use_kwinners', 'weight_decay',\n",
" 'weight_prune_perc'],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(288, 42)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Experiment Name 1_model=DSNNMixedHeb,on_perc=0.2,pruning_early...\n",
"train_acc_max 0.999433\n",
"train_acc_max_epoch 87\n",
"train_acc_min 0.926283\n",
"train_acc_min_epoch 0\n",
"train_acc_median 0.998683\n",
"train_acc_last 0.999333\n",
"val_acc_max 0.9798\n",
"val_acc_max_epoch 35\n",
"val_acc_min 0.9603\n",
"val_acc_min_epoch 0\n",
"val_acc_median 0.9784\n",
"val_acc_last 0.9789\n",
"epochs 100\n",
"experiment_file_name /Users/lsouza/nta/results/improved_magpruning_...\n",
"trial_time 57.1836\n",
"mean_epoch_time 0.571836\n",
"batch_norm True\n",
"data_dir /home/ubuntu/nta/datasets\n",
"dataset_name MNIST\n",
"debug_sparse True\n",
"debug_weights True\n",
"device cuda\n",
"hebbian_grow False\n",
"hebbian_prune_perc 0\n",
"hidden_sizes 100\n",
"input_size 784\n",
"learning_rate 0.1\n",
"lr_gamma 0.1\n",
"lr_milestones 60\n",
"lr_scheduler MultiStepLR\n",
"model DSNNMixedHeb\n",
"momentum 0.9\n",
"network MLPHeb\n",
"num_classes 10\n",
"on_perc 0.2\n",
"optim_alg SGD\n",
"pruning_early_stop 0\n",
"test_noise False\n",
"use_kwinners False\n",
"weight_decay 0.0001\n",
"weight_prune_perc 0\n",
"Name: 1, dtype: object"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.iloc[1]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"model\n",
"DSNNMixedHeb 144\n",
"DSNNWeightedMag 144\n",
"Name: model, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('model')['model'].count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ## Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Experiment Details"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"base_exp_config = dict(\n",
" device=\"cuda\",\n",
" # dataset related\n",
" dataset_name=\"CIFAR10\",\n",
" input_size=3072,\n",
" num_classes=10,\n",
" stats_mean=(0.4914, 0.4822, 0.4465),\n",
" stats_std=(0.2023, 0.1994, 0.2010),\n",
" data_dir=\"~/nta/datasets\",\n",
" # model related\n",
" model=\"DSNNMixedHeb\",\n",
" network=\"MLPHeb\",\n",
" init_weights=True,\n",
" batch_norm=True,\n",
" dropout=False,\n",
" kwinners=True,\n",
" percent_on=0.3,\n",
" boost_strength=1.4,\n",
" boost_strength_factor=0.7,\n",
" # optimizer related\n",
" optim_alg=\"SGD\",\n",
" momentum=0.9,\n",
" learning_rate=0.01,\n",
" weight_decay=1e-4,\n",
" # sparse related\n",
" epsilon=100,\n",
" start_sparse=1,\n",
" end_sparse=None,\n",
" weight_prune_perc=0.45,\n",
" hebbian_prune_perc=0.45,\n",
" pruning_es=True,\n",
" pruning_es_patience=0,\n",
" pruning_es_window_size=5,\n",
" pruning_es_threshold=0.02,\n",
" pruning_interval=1,\n",
" # additional validation\n",
" test_noise=True,\n",
" noise_level=0.1,\n",
" # debugging\n",
" debug_weights=True,\n",
" debug_sparse=True,\n",
")\n",
"\n",
"# ray configurations\n",
"tune_config = dict(\n",
" name=\"hebbian-gs-test\",\n",
" num_samples=1,\n",
" local_dir=os.path.expanduser(\"~/nta/results\"),\n",
" checkpoint_freq=0,\n",
" checkpoint_at_end=False,\n",
" stop={\"training_iteration\": 1000}, # 300 in cifar\n",
" resources_per_trial={\"cpu\": 1, \"gpu\": 0.33},\n",
" loggers=DEFAULT_LOGGERS,\n",
" verbose=1,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Did any trials failed?\n",
"df[df[\"epochs\"]<30][\"epochs\"].count()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(288, 42)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Removing failed or incomplete trials\n",
"df_origin = df.copy()\n",
"df = df_origin[df_origin[\"epochs\"]>=30]\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Series([], Name: epochs, dtype: int64)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# which ones failed?\n",
"# failed, or still ongoing?\n",
"df_origin['failed'] = df_origin[\"epochs\"]<30\n",
"df_origin[df_origin['failed']]['epochs']"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"# helper functions\n",
"def mean_and_std(s):\n",
" return \"{:.3f} ± {:.3f}\".format(s.mean(), s.std())\n",
"\n",
"def round_mean(s):\n",
" return \"{:.0f}\".format(round(s.mean()))\n",
"\n",
"stats = ['min', 'max', 'mean', 'std']\n",
"\n",
"def agg(columns, filter=None, round=3):\n",
" if filter is None:\n",
" return (df.groupby(columns)\n",
" .agg({'val_acc_max_epoch': round_mean,\n",
" 'val_acc_max': stats, \n",
" 'model': ['count']})).round(round)\n",
" else:\n",
" return (df[filter].groupby(columns)\n",
" .agg({'val_acc_max_epoch': round_mean,\n",
" 'val_acc_max': stats, \n",
" 'model': ['count']})).round(round)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Does improved weight pruning outperforms regular SET"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
" <th>model</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>round_mean</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>model</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>49</td>\n",
" <td>0.972</td>\n",
" <td>0.986</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>48</td>\n",
" <td>0.973</td>\n",
" <td>0.986</td>\n",
" <td>0.981</td>\n",
" <td>0.003</td>\n",
" <td>144</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max model\n",
" round_mean min max mean std count\n",
"model \n",
"DSNNMixedHeb 49 0.972 0.986 0.982 0.003 144\n",
"DSNNWeightedMag 48 0.973 0.986 0.981 0.003 144"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['model'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th></th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
" <th>model</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>round_mean</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>on_perc</th>\n",
" <th>model</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.1</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>46</td>\n",
" <td>0.972</td>\n",
" <td>0.984</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>47</td>\n",
" <td>0.973</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.2</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>53</td>\n",
" <td>0.980</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>49</td>\n",
" <td>0.979</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max \\\n",
" round_mean min max mean std \n",
"on_perc model \n",
"0.1 DSNNMixedHeb 46 0.972 0.984 0.980 0.003 \n",
" DSNNWeightedMag 47 0.973 0.983 0.980 0.003 \n",
"0.2 DSNNMixedHeb 53 0.980 0.986 0.983 0.002 \n",
" DSNNWeightedMag 49 0.979 0.986 0.983 0.001 \n",
"\n",
" model \n",
" count \n",
"on_perc model \n",
"0.1 DSNNMixedHeb 72 \n",
" DSNNWeightedMag 72 \n",
"0.2 DSNNMixedHeb 72 \n",
" DSNNWeightedMag 72 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['on_perc', 'model'])"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
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" <th>std</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>weight_prune_perc</th>\n",
" <th>model</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.0</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>38</td>\n",
" <td>0.972</td>\n",
" <td>0.982</td>\n",
" <td>0.978</td>\n",
" <td>0.003</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>42</td>\n",
" <td>0.973</td>\n",
" <td>0.983</td>\n",
" <td>0.978</td>\n",
" <td>0.003</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.1</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>48</td>\n",
" <td>0.980</td>\n",
" <td>0.986</td>\n",
" <td>0.982</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>49</td>\n",
" <td>0.979</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.2</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>48</td>\n",
" <td>0.979</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>51</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.3</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>55</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>52</td>\n",
" <td>0.980</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.4</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>53</td>\n",
" <td>0.981</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>52</td>\n",
" <td>0.979</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">0.5</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>53</td>\n",
" <td>0.979</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>43</td>\n",
" <td>0.978</td>\n",
" <td>0.984</td>\n",
" <td>0.981</td>\n",
" <td>0.002</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max \\\n",
" round_mean min max mean \n",
"weight_prune_perc model \n",
"0.0 DSNNMixedHeb 38 0.972 0.982 0.978 \n",
" DSNNWeightedMag 42 0.973 0.983 0.978 \n",
"0.1 DSNNMixedHeb 48 0.980 0.986 0.982 \n",
" DSNNWeightedMag 49 0.979 0.985 0.983 \n",
"0.2 DSNNMixedHeb 48 0.979 0.985 0.982 \n",
" DSNNWeightedMag 51 0.980 0.985 0.982 \n",
"0.3 DSNNMixedHeb 55 0.980 0.985 0.983 \n",
" DSNNWeightedMag 52 0.980 0.986 0.983 \n",
"0.4 DSNNMixedHeb 53 0.981 0.985 0.983 \n",
" DSNNWeightedMag 52 0.979 0.985 0.982 \n",
"0.5 DSNNMixedHeb 53 0.979 0.985 0.982 \n",
" DSNNWeightedMag 43 0.978 0.984 0.981 \n",
"\n",
" model \n",
" std count \n",
"weight_prune_perc model \n",
"0.0 DSNNMixedHeb 0.003 24 \n",
" DSNNWeightedMag 0.003 24 \n",
"0.1 DSNNMixedHeb 0.002 24 \n",
" DSNNWeightedMag 0.002 24 \n",
"0.2 DSNNMixedHeb 0.002 24 \n",
" DSNNWeightedMag 0.002 24 \n",
"0.3 DSNNMixedHeb 0.001 24 \n",
" DSNNWeightedMag 0.002 24 \n",
"0.4 DSNNMixedHeb 0.002 24 \n",
" DSNNWeightedMag 0.002 24 \n",
"0.5 DSNNMixedHeb 0.002 24 \n",
" DSNNWeightedMag 0.002 24 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['weight_prune_perc', 'model'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
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" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>on_perc</th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">0.1</th>\n",
" <th rowspan=\"2\" valign=\"top\">0</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>47</td>\n",
" <td>0.972</td>\n",
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" <td>0.003</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
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" <td>0.003</td>\n",
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" <td>0.973</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>54</td>\n",
" <td>0.974</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>50</td>\n",
" <td>0.974</td>\n",
" <td>0.984</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>38</td>\n",
" <td>0.973</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">3</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>43</td>\n",
" <td>0.974</td>\n",
" <td>0.982</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>46</td>\n",
" <td>0.975</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.002</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">0.2</th>\n",
" <th rowspan=\"2\" valign=\"top\">0</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>46</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>48</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">1</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>54</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.984</td>\n",
" <td>0.002</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>49</td>\n",
" <td>0.979</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>53</td>\n",
" <td>0.980</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>49</td>\n",
" <td>0.979</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">3</th>\n",
" <th>DSNNMixedHeb</th>\n",
" <td>57</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.984</td>\n",
" <td>0.001</td>\n",
" <td>18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DSNNWeightedMag</th>\n",
" <td>52</td>\n",
" <td>0.981</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>18</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max \\\n",
" round_mean min \n",
"on_perc pruning_early_stop model \n",
"0.1 0 DSNNMixedHeb 47 0.972 \n",
" DSNNWeightedMag 52 0.973 \n",
" 1 DSNNMixedHeb 43 0.973 \n",
" DSNNWeightedMag 54 0.974 \n",
" 2 DSNNMixedHeb 50 0.974 \n",
" DSNNWeightedMag 38 0.973 \n",
" 3 DSNNMixedHeb 43 0.974 \n",
" DSNNWeightedMag 46 0.975 \n",
"0.2 0 DSNNMixedHeb 46 0.980 \n",
" DSNNWeightedMag 48 0.980 \n",
" 1 DSNNMixedHeb 54 0.980 \n",
" DSNNWeightedMag 49 0.979 \n",
" 2 DSNNMixedHeb 53 0.980 \n",
" DSNNWeightedMag 49 0.979 \n",
" 3 DSNNMixedHeb 57 0.980 \n",
" DSNNWeightedMag 52 0.981 \n",
"\n",
" model \n",
" max mean std count \n",
"on_perc pruning_early_stop model \n",
"0.1 0 DSNNMixedHeb 0.983 0.980 0.003 18 \n",
" DSNNWeightedMag 0.982 0.979 0.003 18 \n",
" 1 DSNNMixedHeb 0.983 0.980 0.003 18 \n",
" DSNNWeightedMag 0.983 0.980 0.003 18 \n",
" 2 DSNNMixedHeb 0.984 0.980 0.003 18 \n",
" DSNNWeightedMag 0.983 0.980 0.003 18 \n",
" 3 DSNNMixedHeb 0.982 0.980 0.003 18 \n",
" DSNNWeightedMag 0.983 0.980 0.002 18 \n",
"0.2 0 DSNNMixedHeb 0.985 0.983 0.002 18 \n",
" DSNNWeightedMag 0.985 0.983 0.001 18 \n",
" 1 DSNNMixedHeb 0.985 0.984 0.002 18 \n",
" DSNNWeightedMag 0.986 0.983 0.001 18 \n",
" 2 DSNNMixedHeb 0.986 0.983 0.002 18 \n",
" DSNNWeightedMag 0.985 0.983 0.002 18 \n",
" 3 DSNNMixedHeb 0.985 0.984 0.001 18 \n",
" DSNNWeightedMag 0.985 0.983 0.001 18 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['on_perc', 'pruning_early_stop', 'model'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* No significant difference between the two approaches"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### What is the impact of early stopping?"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>1</th>\n",
" <td>50</td>\n",
" <td>0.973</td>\n",
" <td>0.986</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>48</td>\n",
" <td>0.973</td>\n",
" <td>0.986</td>\n",
" <td>0.981</td>\n",
" <td>0.003</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>49</td>\n",
" <td>0.974</td>\n",
" <td>0.985</td>\n",
" <td>0.981</td>\n",
" <td>0.003</td>\n",
" <td>72</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max model\n",
" round_mean min max mean std count\n",
"pruning_early_stop \n",
"0 48 0.972 0.985 0.982 0.003 72\n",
"1 50 0.973 0.986 0.982 0.003 72\n",
"2 48 0.973 0.986 0.981 0.003 72\n",
"3 49 0.974 0.985 0.981 0.003 72"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['pruning_early_stop'])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
" <th>model</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>round_mean</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>model</th>\n",
" <th>pruning_early_stop</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">DSNNMixedHeb</th>\n",
" <th>0</th>\n",
" <td>46</td>\n",
" <td>0.972</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>49</td>\n",
" <td>0.973</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>52</td>\n",
" <td>0.974</td>\n",
" <td>0.986</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>50</td>\n",
" <td>0.974</td>\n",
" <td>0.985</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">DSNNWeightedMag</th>\n",
" <th>0</th>\n",
" <td>50</td>\n",
" <td>0.973</td>\n",
" <td>0.985</td>\n",
" <td>0.981</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51</td>\n",
" <td>0.974</td>\n",
" <td>0.986</td>\n",
" <td>0.982</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44</td>\n",
" <td>0.973</td>\n",
" <td>0.985</td>\n",
" <td>0.981</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>49</td>\n",
" <td>0.975</td>\n",
" <td>0.985</td>\n",
" <td>0.981</td>\n",
" <td>0.002</td>\n",
" <td>36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max \\\n",
" round_mean min max \n",
"model pruning_early_stop \n",
"DSNNMixedHeb 0 46 0.972 0.985 \n",
" 1 49 0.973 0.985 \n",
" 2 52 0.974 0.986 \n",
" 3 50 0.974 0.985 \n",
"DSNNWeightedMag 0 50 0.973 0.985 \n",
" 1 51 0.974 0.986 \n",
" 2 44 0.973 0.985 \n",
" 3 49 0.975 0.985 \n",
"\n",
" model \n",
" mean std count \n",
"model pruning_early_stop \n",
"DSNNMixedHeb 0 0.982 0.003 36 \n",
" 1 0.982 0.003 36 \n",
" 2 0.982 0.003 36 \n",
" 3 0.982 0.003 36 \n",
"DSNNWeightedMag 0 0.981 0.003 36 \n",
" 1 0.982 0.003 36 \n",
" 2 0.981 0.003 36 \n",
" 3 0.981 0.002 36 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['model', 'pruning_early_stop'])"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe thead tr:last-of-type th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>val_acc_max_epoch</th>\n",
" <th colspan=\"4\" halign=\"left\">val_acc_max</th>\n",
" <th>model</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>round_mean</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>on_perc</th>\n",
" <th>pruning_early_stop</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">0.1</th>\n",
" <th>0</th>\n",
" <td>49</td>\n",
" <td>0.972</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>48</td>\n",
" <td>0.973</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>44</td>\n",
" <td>0.973</td>\n",
" <td>0.984</td>\n",
" <td>0.980</td>\n",
" <td>0.003</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>44</td>\n",
" <td>0.974</td>\n",
" <td>0.983</td>\n",
" <td>0.980</td>\n",
" <td>0.002</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"4\" valign=\"top\">0.2</th>\n",
" <th>0</th>\n",
" <td>47</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51</td>\n",
" <td>0.979</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>51</td>\n",
" <td>0.979</td>\n",
" <td>0.986</td>\n",
" <td>0.983</td>\n",
" <td>0.002</td>\n",
" <td>36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>54</td>\n",
" <td>0.980</td>\n",
" <td>0.985</td>\n",
" <td>0.983</td>\n",
" <td>0.001</td>\n",
" <td>36</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" val_acc_max_epoch val_acc_max \\\n",
" round_mean min max mean std \n",
"on_perc pruning_early_stop \n",
"0.1 0 49 0.972 0.983 0.980 0.003 \n",
" 1 48 0.973 0.983 0.980 0.003 \n",
" 2 44 0.973 0.984 0.980 0.003 \n",
" 3 44 0.974 0.983 0.980 0.002 \n",
"0.2 0 47 0.980 0.985 0.983 0.001 \n",
" 1 51 0.979 0.986 0.983 0.001 \n",
" 2 51 0.979 0.986 0.983 0.002 \n",
" 3 54 0.980 0.985 0.983 0.001 \n",
"\n",
" model \n",
" count \n",
"on_perc pruning_early_stop \n",
"0.1 0 36 \n",
" 1 36 \n",
" 2 36 \n",
" 3 36 \n",
"0.2 0 36 \n",
" 1 36 \n",
" 2 36 \n",
" 3 36 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"agg(['on_perc', 'pruning_early_stop'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Results are not strong enough. But it is has a slight increase in acc (0.1%), not greater than the standard deviation, when pruning stops at the first learning rate decay for WeightedMag model and at the second learning rate decay for DSSNMixedHeb models.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
obestwalter/pet | ipynb/containers.ipynb | 1 | 7520 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Containers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create containers with literals"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iterating over <class 'str'>\n",
"1 2 3 4 5 6 \n",
"iterating over <class 'list'>\n",
"1 2.0 1j hello [] {} (1, 2) \n",
"iterating over <class 'set'>\n",
"(1, 2) 1 2.0 1j hello \n",
"iterating over <class 'tuple'>\n",
"1 2.0 1j hello [] {} (1, 2) \n",
"iterating over <class 'dict'>\n",
"(1, 2) (value: (1, 2)) 1 (value: 1) 2.0 (value: 2.0) 1j (value: 1j) hello (value: hello) dict (value: {}) list (value: []) \n"
]
}
],
"source": [
"aString = \"123456\"\n",
"aList = [1, 2.0, 1j, 'hello', [], {}, (1, 2)]\n",
"aSet = {1, 2.0, 1j, 'hello', (1, 2)}\n",
"aTuple = (1, 2.0, 1j, 'hello', [], {}, (1, 2))\n",
"aDict = {\n",
" 1: 1,\n",
" 2.0: 2.0,\n",
" 1j: 1j,\n",
" (1, 2): (1, 2),\n",
" 'hello': 'hello',\n",
" 'list': [],\n",
" 'dict': {},\n",
"}\n",
"\n",
"iterables = [\n",
" aString,\n",
" aList,\n",
" aSet,\n",
" aTuple,\n",
" aDict,\n",
"]\n",
"\n",
"for iterable in iterables:\n",
" print('iterating over %s' % (type(iterable)))\n",
" for element in iterable:\n",
" print(element, end=' ')\n",
" if isinstance(iterable, dict):\n",
" print('(value: %s)' % (str(iterable[element])), end=' ')\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create containers with constructor functions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 2.0, 'hello', []] <class 'list'>\n",
"{1, 2.0, 'hello'} <class 'set'>\n",
"['a', 'b', 'c'] <class 'str'>\n",
"{'key2': 1j, 'key1': 'value', 'key3': [1, 2, {}]} <class 'dict'>\n"
]
}
],
"source": [
"aList = list((1, 2.0, 'hello', []))\n",
"aSet = set(([1, 2.0, 'hello']))\n",
"aTuple = tuple([1, 2.0, 'hello', []])\n",
"aString = str(['a', 'b', 'c'])\n",
"aDict = dict(key1='value', key2=1j, key3=[1, 2, {}])\n",
"iterables = [\n",
" aList,\n",
" aSet,\n",
" aString,\n",
" aDict,\n",
"]\n",
"\n",
"for iterable in iterables:\n",
" print(iterable, (type(iterable)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore more containers"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 <class 'int'>; container: False; sequence: False; hashable: True\n",
"1.0 <class 'float'>; container: False; sequence: False; hashable: True\n",
"1j <class 'complex'>; container: False; sequence: False; hashable: True\n",
"s <class 'str'>; container: True; sequence: True; hashable: True\n",
"None <class 'NoneType'>; container: False; sequence: False; hashable: True\n",
"True <class 'bool'>; container: False; sequence: False; hashable: True\n",
"[] <class 'list'>; container: True; sequence: True; hashable: False\n",
"(1, 2) <class 'tuple'>; container: True; sequence: True; hashable: True\n",
"{} <class 'dict'>; container: True; sequence: True; hashable: False\n",
"{1} <class 'set'>; container: True; sequence: True; hashable: False\n",
"<class 'object'> <class 'type'>; container: False; sequence: False; hashable: True\n",
"<module 'collections' from '/home/obestwalter/.pyenv/versions/3.4.4/lib/python3.4/collections/__init__.py'> <class 'module'>; container: False; sequence: False; hashable: True\n",
"<function func at 0x7f86dc4c47b8> <class 'function'>; container: False; sequence: False; hashable: True\n",
"<class '__main__.Cnt'> <class 'type'>; container: False; sequence: False; hashable: True\n",
"<__main__.Cnt object at 0x7f86dc4c5860> <class '__main__.Cnt'>; container: True; sequence: False; hashable: True\n"
]
}
],
"source": [
"import collections\n",
"\n",
"\n",
"def func():\n",
" pass\n",
"\n",
"\n",
"class Cnt(object):\n",
" \"\"\"Container but not iterable\"\"\"\n",
" def __contains__(self, _):\n",
" return True\n",
"\n",
"\n",
"for obj in [1, 1.0, 1j, 's', None, True, [], (1, 2), {}, {1}, object, collections, func, Cnt, Cnt()]:\n",
" isContainer = isinstance(obj, collections.Container)\n",
" isIterable = isinstance(obj, collections.Iterable)\n",
" isHashable = isinstance(obj, collections.Hashable)\n",
" print (\"%s %s; container: %s; sequence: %s; hashable: %s\" %\n",
" (obj, type(obj), isContainer, isIterable, isHashable))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Iterables"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 s [1, 3] 1j "
]
}
],
"source": [
"for element in [1, 's', [1, 3], 1j]:\n",
" print(element, end=' ')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"h e l l o w o r l d "
]
}
],
"source": [
"for letter in \"hello world\":\n",
" print(letter, end=' ')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"key2: 2\n",
"key1: 1\n"
]
}
],
"source": [
"mydict = {'key1': 1, 'key2': 2}\n",
"for key in mydict:\n",
" print(\"%s: %s\" % (key, mydict[key]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE:** `dict.items()` returns a tuple with key and value of the current dictionary element whith each iteration which is then unpacked directly into the two names `key` and `value`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k1: 1\n",
"k2: 2\n"
]
}
],
"source": [
"for key, value in {'k1': 1, 'k2': 2}.items():\n",
" print(\"%s: %s\" % (key, value))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
janekg89/flutype_webapp | notebook/Untitled.ipynb | 1 | 4953 | {
"cells": [
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import csv\n"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def read_tsv_diconary(fpath):\n",
" d={}\n",
" with open(fpath, 'r') as f:\n",
" for line in f:\n",
" line =line.split(\"\\t\")\n",
" if len(line) == 1:\n",
" line.append(\"\")\n",
" key = line[0].strip()\n",
" value = line[1].strip()\n",
" if value == \"TRUE\":\n",
" value = True\n",
" if value == \"\":\n",
" value = None\n",
" if value ==\"FALSE\":\n",
" value = False\n",
" d[key] = value\n",
"\n",
" return d"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"directory = \"/home/janekg89/Develop/Pycharm_Projects/flutype_webapp/master/studies\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'comment': None, 'status': 'FINISHED', 'measurement_type': 'microwell', 'functionalization': '3D-NHS', 'hidden': True, 'batch_sid': '161118', 'manufacturer': 'PolyAn'}\n",
"{'comment': None, 'status': 'FINISHED', 'measurement_type': 'microarray', 'functionalization': '3D-NHS', 'hidden': True, 'batch_sid': 'ABC123_321', 'manufacturer': 'PolyAn'}\n",
"{'comment': None, 'status': 'FINISHED', 'measurement_type': 'elisa', 'functionalization': 'No', 'hidden': True, 'batch_sid': '12345689', 'manufacturer': 'Thermo F96 Maxisorp'}\n"
]
}
],
"source": [
"for root, dirs, files in os.walk(directory):\n",
" for dir in dirs:\n",
" if dir ==\"measurements\":\n",
" new_dir = os.path.join(root,dir)\n",
" \n",
" for dirs in os.listdir(new_dir):\n",
" meta_path = os.path.join(new_dir,dirs,\"meta.tsv\")\n",
" d = read_tsv_diconary(meta_path)\n",
" if \"status\" in d:\n",
" print(d)\n",
" \n",
" \n",
" \n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pytz import timezone\n",
"import datetime"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"tim = \"01-01-1989 9:30\"\n",
"dt = datetime.datetime.strptime(tim, '%d-%m-%Y %H:%M')\n",
"berlin = timezone('Europe/Berlin')\n",
" #tzinfo="
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'loc_dt' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-78-eba2968ede7f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mberlin_dt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloc_dt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastimezone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBerlin\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'loc_dt' is not defined"
],
"output_type": "error"
}
],
"source": [
"berlin_dt = loc_dt.astimezone(Berlin)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "flutype_webapp",
"language": "python",
"name": "flutype_webapp"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| lgpl-3.0 |
rsignell-usgs/notebook | UGRID/UGRID_Subset_Test02.ipynb | 1 | 10076 | {
"metadata": {
"name": "UGRID_Subset_Test02"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"UGRID Remote Subsetting Demo with Time"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much of the power of OPeNDAP is the ability to select and subset just the data you need from a remote dataset. This works great for structured grid data because one can subset on index ranges, but for unstructured grid (e.g. triangular grid) this is impossible. It's therefore necessary to add an additional constraint syntax to the URL and do the subsetting on the server. This is one of the goals of the OPeNDAP-Unidata Linked Servers [OPULS](http://www.unidata.ucar.edu/projects/#opuls) project, a collaboration between [Unidata](http://www.unidata.ucar.edu), [OPeNDAP, Inc.](http://www.opendap.org/) and the [University of Washington eScience Institute](http://escience.washington.edu).\n",
"\n",
"In this demo, we show how the subset expression works, extracting the Galveston Bay region from a model simulation of the entire Gulf of Mexico. The model dataset is [UGRID-compliant](http://bit.ly/ugrid_cf) FVCOM model output from the [IOOS Modeling Testbed](http://testbed.ioos.us) project. A sample dataset was moved from the testbed THREDDS Data Server to a developmental Hyrax server in the Cloud which has the subsetting enabled. The subsetting is accomplished on the server backend using [GridFields](http://code.google.com/p/gridfields/)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pylab import *\n",
"import time\n",
"# The netCDF4-Python library accesses OPeNDAP using the Unidata NetCDF-C library:\n",
"import netCDF4"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# the full dataset OPeNDAP URL:\n",
"#url='http://ec2-54-245-151-123.us-west-2.compute.amazonaws.com:8080/opendap/ugrids/fvcom_1step.nc'\n",
"#url='http://ec2-54-242-224-73.compute-1.amazonaws.com:8080/opendap/ebs/fvcom_1step.nc'\n",
"url='http://ec2-54-242-224-73.compute-1.amazonaws.com:8080/opendap/ebs/Ike/2D_varied_manning_windstress/test_dir-norename.ncml'\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# the region we want to subset, in Matlab style bbox:\n",
"bbox=[-95.0, -94.4, 29.3, 29.8] # [lonmin lonmax latmin latmax] Galveston Bay"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# construct the subset expression\n",
"# here extract every 3rd time step between 3 and 10\n",
"expr='_expr_{}{ugr5(0,ua[3:3:10][*],va[3:3:10][*],zeta[3:3:10][*],\\\"%.6f<lat&lat<%.6f&%.6f<lon&lon<%.6f\\\")}{}' % (bbox[2],bbox[3],bbox[0],bbox[1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# construct the subset expression\n",
"# here extract every 3rd time step between 3 and 10\n",
"expr='_expr_{}{ugr5(0,zeta[3:3:10][*],\\\"%.6f<lat&lat<%.6f&%.6f<lon&lon<%.6f\\\")}{}' % (bbox[2],bbox[3],bbox[0],bbox[1])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"# construct the subset url \n",
"url_subset = url + expr"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print url_subset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"http://ec2-54-242-224-73.compute-1.amazonaws.com:8080/opendap/ebs/Ike/2D_varied_manning_windstress/test_dir-norename.ncml_expr_{}{ugr5(0,zeta[3:3:10][*],\"29.300000<lat&lat<29.800000&-95.000000<lon&lon<-94.400000\")}{}\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"time0 = time.time()\n",
"ncs = netCDF4.Dataset(url_subset)\n",
"lons=ncs.variables['ugr_result.lon'][:]\n",
"lats=ncs.variables['ugr_result.lat'][:]\n",
"lonc=ncs.variables['ugr_result.lonc'][:]\n",
"latc=ncs.variables['ugr_result.latc'][:]\n",
"# read connectivity array, and convert to pythonic 0-based indexing\n",
"nvs = ncs.variables['ugr_result.nv'][:] - ncs.variables['ugr_result.nv'].start_index\n",
"zeta =ncs.variables['ugr_result.zeta'][:]\n",
"ua =ncs.variables['ugr_result.ua'][:]\n",
"va =ncs.variables['ugr_result.va'][:]\n",
"etime = time.time()- time0\n",
"print 'Elapsed time to read subset grid: %.2f seconds' % etime"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "NetCDF: I/O failure",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-19-d8ffa62d15c9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mtime0\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mncs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnetCDF4\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl_subset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mlons\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mncs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ugr_result.lon'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mlats\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mncs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ugr_result.lat'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mlonc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mncs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariables\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ugr_result.lonc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/opt/anaconda/envs/np17py27-1.5/lib/python2.7/site-packages/netCDF4.so\u001b[0m in \u001b[0;36mnetCDF4.Dataset.__init__ (netCDF4.c:19148)\u001b[1;34m()\u001b[0m\n",
"\u001b[1;31mRuntimeError\u001b[0m: NetCDF: I/O failure"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print shape(ua)\n",
"print shape(zeta)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'ua' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-9-dc86fcf08ccd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mua\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mzeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'ua' is not defined"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# subsample velocity vectors \n",
"ind=range(len(lonc))\n",
"subsample=3\n",
"np.random.shuffle(ind)\n",
"Nvec = int(len(ind) / subsample)\n",
"idv = ind[:Nvec]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# plot subset water level and vectors\n",
"\n",
"figure(figsize=(10,8),frameon=True)\n",
"# plot one of the time steps \n",
"time_step=-1\n",
"tricontourf(lons,lats,zeta[time_step,:].flatten(),triangles=nvs,levels=arange(-1,1,.1))\n",
"gca().set_aspect(1./cos(mean(lats)*pi/180))\n",
"colorbar()\n",
"Q = quiver(lonc[idv],latc[idv],ua[time_step,idv].flatten(),va[time_step,idv].flatten(),scale=5)\n",
"qk = quiverkey(Q,0.82,0.92,0.20,'0.2 m/s',labelpos='W')\n",
"axis(bbox)\n",
"title('Galveston Bay Water Level (m) and Depth-Averaged Velocity');"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/plots_boxplots.ipynb | 2 | 1020194 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Box Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following illustrates some options for the boxplot in statsmodels. These include `violin_plot` and `bean_plot`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:35.666390Z",
"iopub.status.busy": "2021-11-12T23:30:35.665882Z",
"iopub.status.idle": "2021-11-12T23:30:36.859533Z",
"shell.execute_reply": "2021-11-12T23:30:36.858453Z"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bean Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following example is taken from the docstring of `beanplot`.\n",
"\n",
"We use the American National Election Survey 1996 dataset, which has Party\n",
"Identification of respondents as independent variable and (among other\n",
"data) age as dependent variable."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:36.863778Z",
"iopub.status.busy": "2021-11-12T23:30:36.862699Z",
"iopub.status.idle": "2021-11-12T23:30:36.886221Z",
"shell.execute_reply": "2021-11-12T23:30:36.887072Z"
}
},
"outputs": [],
"source": [
"data = sm.datasets.anes96.load_pandas()\n",
"party_ID = np.arange(7)\n",
"labels = [\n",
" \"Strong Democrat\",\n",
" \"Weak Democrat\",\n",
" \"Independent-Democrat\",\n",
" \"Independent-Independent\",\n",
" \"Independent-Republican\",\n",
" \"Weak Republican\",\n",
" \"Strong Republican\",\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Group age by party ID, and create a violin plot with it:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:36.890878Z",
"iopub.status.busy": "2021-11-12T23:30:36.889745Z",
"iopub.status.idle": "2021-11-12T23:30:37.307896Z",
"shell.execute_reply": "2021-11-12T23:30:37.307461Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Age')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams[\"figure.subplot.bottom\"] = 0.23 # keep labels visible\n",
"plt.rcParams[\"figure.figsize\"] = (10.0, 8.0) # make plot larger in notebook\n",
"age = [data.exog[\"age\"][data.endog == id] for id in party_ID]\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plot_opts = {\n",
" \"cutoff_val\": 5,\n",
" \"cutoff_type\": \"abs\",\n",
" \"label_fontsize\": \"small\",\n",
" \"label_rotation\": 30,\n",
"}\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels, plot_opts=plot_opts)\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:37.313769Z",
"iopub.status.busy": "2021-11-12T23:30:37.313262Z",
"iopub.status.idle": "2021-11-12T23:30:37.316321Z",
"shell.execute_reply": "2021-11-12T23:30:37.316677Z"
}
},
"outputs": [],
"source": [
"def beanplot(data, plot_opts={}, jitter=False):\n",
" \"\"\"helper function to try out different plot options\"\"\"\n",
" fig = plt.figure()\n",
" ax = fig.add_subplot(111)\n",
" plot_opts_ = {\n",
" \"cutoff_val\": 5,\n",
" \"cutoff_type\": \"abs\",\n",
" \"label_fontsize\": \"small\",\n",
" \"label_rotation\": 30,\n",
" }\n",
" plot_opts_.update(plot_opts)\n",
" sm.graphics.beanplot(\n",
" data, ax=ax, labels=labels, jitter=jitter, plot_opts=plot_opts_\n",
" )\n",
" ax.set_xlabel(\"Party identification of respondent.\")\n",
" ax.set_ylabel(\"Age\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:37.336484Z",
"iopub.status.busy": "2021-11-12T23:30:37.333536Z",
"iopub.status.idle": "2021-11-12T23:30:37.613972Z",
"shell.execute_reply": "2021-11-12T23:30:37.614391Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, jitter=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:37.619264Z",
"iopub.status.busy": "2021-11-12T23:30:37.618782Z",
"iopub.status.idle": "2021-11-12T23:30:37.898770Z",
"shell.execute_reply": "2021-11-12T23:30:37.899535Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, plot_opts={\"violin_width\": 0.5, \"violin_fc\": \"#66c2a5\"})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:37.903109Z",
"iopub.status.busy": "2021-11-12T23:30:37.902091Z",
"iopub.status.idle": "2021-11-12T23:30:38.176186Z",
"shell.execute_reply": "2021-11-12T23:30:38.177016Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, plot_opts={\"violin_fc\": \"#66c2a5\"})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:38.180693Z",
"iopub.status.busy": "2021-11-12T23:30:38.179677Z",
"iopub.status.idle": "2021-11-12T23:30:38.479838Z",
"shell.execute_reply": "2021-11-12T23:30:38.480619Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(\n",
" age, plot_opts={\"bean_size\": 0.2, \"violin_width\": 0.75, \"violin_fc\": \"#66c2a5\"}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:38.484230Z",
"iopub.status.busy": "2021-11-12T23:30:38.483233Z",
"iopub.status.idle": "2021-11-12T23:30:38.714434Z",
"shell.execute_reply": "2021-11-12T23:30:38.715283Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(age, jitter=True, plot_opts={\"violin_fc\": \"#66c2a5\"})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:38.718928Z",
"iopub.status.busy": "2021-11-12T23:30:38.717905Z",
"iopub.status.idle": "2021-11-12T23:30:39.071462Z",
"shell.execute_reply": "2021-11-12T23:30:39.072246Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = beanplot(\n",
" age, jitter=True, plot_opts={\"violin_width\": 0.5, \"violin_fc\": \"#66c2a5\"}\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced Box Plots"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based of example script `example_enhanced_boxplots.py` (by Ralf Gommers)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:39.076049Z",
"iopub.status.busy": "2021-11-12T23:30:39.075022Z",
"iopub.status.idle": "2021-11-12T23:30:39.095190Z",
"shell.execute_reply": "2021-11-12T23:30:39.096070Z"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm\n",
"\n",
"\n",
"# Necessary to make horizontal axis labels fit\n",
"plt.rcParams[\"figure.subplot.bottom\"] = 0.23\n",
"\n",
"data = sm.datasets.anes96.load_pandas()\n",
"party_ID = np.arange(7)\n",
"labels = [\n",
" \"Strong Democrat\",\n",
" \"Weak Democrat\",\n",
" \"Independent-Democrat\",\n",
" \"Independent-Independent\",\n",
" \"Independent-Republican\",\n",
" \"Weak Republican\",\n",
" \"Strong Republican\",\n",
"]\n",
"\n",
"# Group age by party ID.\n",
"age = [data.exog[\"age\"][data.endog == id] for id in party_ID]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:39.099854Z",
"iopub.status.busy": "2021-11-12T23:30:39.098787Z",
"iopub.status.idle": "2021-11-12T23:30:39.416888Z",
"shell.execute_reply": "2021-11-12T23:30:39.417702Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a violin plot.\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"sm.graphics.violinplot(\n",
" age,\n",
" ax=ax,\n",
" labels=labels,\n",
" plot_opts={\n",
" \"cutoff_val\": 5,\n",
" \"cutoff_type\": \"abs\",\n",
" \"label_fontsize\": \"small\",\n",
" \"label_rotation\": 30,\n",
" },\n",
")\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:39.421360Z",
"iopub.status.busy": "2021-11-12T23:30:39.420306Z",
"iopub.status.idle": "2021-11-12T23:30:39.738407Z",
"shell.execute_reply": "2021-11-12T23:30:39.739205Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a bean plot.\n",
"fig2 = plt.figure()\n",
"ax = fig2.add_subplot(111)\n",
"\n",
"sm.graphics.beanplot(\n",
" age,\n",
" ax=ax,\n",
" labels=labels,\n",
" plot_opts={\n",
" \"cutoff_val\": 5,\n",
" \"cutoff_type\": \"abs\",\n",
" \"label_fontsize\": \"small\",\n",
" \"label_rotation\": 30,\n",
" },\n",
")\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:39.742914Z",
"iopub.status.busy": "2021-11-12T23:30:39.741855Z",
"iopub.status.idle": "2021-11-12T23:30:39.991382Z",
"shell.execute_reply": "2021-11-12T23:30:39.992193Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create a jitter plot.\n",
"fig3 = plt.figure()\n",
"ax = fig3.add_subplot(111)\n",
"\n",
"plot_opts = {\n",
" \"cutoff_val\": 5,\n",
" \"cutoff_type\": \"abs\",\n",
" \"label_fontsize\": \"small\",\n",
" \"label_rotation\": 30,\n",
" \"violin_fc\": (0.8, 0.8, 0.8),\n",
" \"jitter_marker\": \".\",\n",
" \"jitter_marker_size\": 3,\n",
" \"bean_color\": \"#FF6F00\",\n",
" \"bean_mean_color\": \"#009D91\",\n",
"}\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels, jitter=True, plot_opts=plot_opts)\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"execution": {
"iopub.execute_input": "2021-11-12T23:30:39.995935Z",
"iopub.status.busy": "2021-11-12T23:30:39.994874Z",
"iopub.status.idle": "2021-11-12T23:30:40.322697Z",
"shell.execute_reply": "2021-11-12T23:30:40.323473Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, \"US national election '96 - Age & Party Identification\")"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Create an asymmetrical jitter plot.\n",
"ix = data.exog[\"income\"] < 16 # incomes < $30k\n",
"age = data.exog[\"age\"][ix]\n",
"endog = data.endog[ix]\n",
"age_lower_income = [age[endog == id] for id in party_ID]\n",
"\n",
"ix = data.exog[\"income\"] >= 20 # incomes > $50k\n",
"age = data.exog[\"age\"][ix]\n",
"endog = data.endog[ix]\n",
"age_higher_income = [age[endog == id] for id in party_ID]\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"\n",
"plot_opts[\"violin_fc\"] = (0.5, 0.5, 0.5)\n",
"plot_opts[\"bean_show_mean\"] = False\n",
"plot_opts[\"bean_show_median\"] = False\n",
"plot_opts[\"bean_legend_text\"] = \"Income < \\$30k\"\n",
"plot_opts[\"cutoff_val\"] = 10\n",
"sm.graphics.beanplot(\n",
" age_lower_income,\n",
" ax=ax,\n",
" labels=labels,\n",
" side=\"left\",\n",
" jitter=True,\n",
" plot_opts=plot_opts,\n",
")\n",
"plot_opts[\"violin_fc\"] = (0.7, 0.7, 0.7)\n",
"plot_opts[\"bean_color\"] = \"#009D91\"\n",
"plot_opts[\"bean_legend_text\"] = \"Income > \\$50k\"\n",
"sm.graphics.beanplot(\n",
" age_higher_income,\n",
" ax=ax,\n",
" labels=labels,\n",
" side=\"right\",\n",
" jitter=True,\n",
" plot_opts=plot_opts,\n",
")\n",
"\n",
"ax.set_xlabel(\"Party identification of respondent.\")\n",
"ax.set_ylabel(\"Age\")\n",
"ax.set_title(\"US national election '96 - Age & Party Identification\")\n",
"\n",
"\n",
"# Show all plots.\n",
"# plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
TomTranter/OpenPNM | examples/extractions/Managing Geometrical Properties of Imported Networks.ipynb | 1 | 380037 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Managing Geometry Properties of Imported Networks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``Imported`` geometry class is used to store the geometrical properties of imported networks. When importing an extracted network into OpenPNM using any of the ``io`` classes, all the geometrical and topological properties are lumped together on the *network* object. OpenPNM is generally designed such that geometrical properties are stored on a *geometry* object, so this class address this issue. The main function of the ``Imported`` class is to automatically strip the geometrical properties off of the network and transfer them onto itself."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **What problem does the Imported class solve?** Although OpenPNM can function with the geometrical properties on the network, a problem arises if the user wishes to add *more* pores to the network, such as boundary pores. In this case, they will probably wish to add pore-scale models to calculate size information, say 'pore.volume'. If they add this to the network, this model will overwrite the pre-existing 'pore.volume' values. The solution to this problem is an intrinsic part of OpenPNM: create a separate geometry object to manage it's own 'pore.volume' model and values. However, this **won't work**! OpenPNM will not allow an array called 'pore.volume' to exist on the network *and* a geometry object. The reason is that networks store values for *every* pore, so when adding new pores the network the 'pore.volume' array will increase to accommodate them. If you attempt to put 'pore.volume' values on the geometry object, you're are essentially putting *two* values in those locations. Therefore, the ``Imported`` class solves this problem by first transferring the 'pore.volume' array (and all other geometrical properties) from the network to itself. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import openpnm as op\n",
"ws = op.Workspace()\n",
"ws.settings['loglevel'] = 50 # Supress warnings, but see error messages"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's start by generating a random network using the Delaunay class. This will repreent an imported network:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"pn = op.network.Delaunay(shape=[1, 1, 0], num_points=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This network generator adds nicely defined boundary pores around the edges/faces of the network. Let's remove these for the sake of this example:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"op.topotools.trim(network=pn, pores=pn.pores('boundary'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = op.topotools.plot_coordinates(network=pn, c='r')\n",
"fig = op.topotools.plot_connections(network=pn, fig=fig)\n",
"fig.set_size_inches((5, 5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This network does not have any geometrical properties on it when generated. To mimic the situation of an imported network, let's manually enter some values for ``'pore.diameter'``. We'll just assign random numbers to illustrate the point:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"pn['pore.diameter'] = np.random.rand(pn.Np)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now when we ``print`` the network we'll see all the topological data ('pore.coords' and 'throat.conns'), all the labels that were added by the generator (e.g. 'pore.left'), as well as the new geometry info we just added ('pore.diameter'):"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.network.Delaunay : net_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.coords 100 / 100 \n",
"2 pore.diameter 100 / 100 \n",
"3 throat.conns 270 / 270 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.all 100 \n",
"2 pore.back 0 \n",
"3 pore.bottom 100 \n",
"4 pore.boundary 0 \n",
"5 pore.front 0 \n",
"6 pore.internal 100 \n",
"7 pore.left 0 \n",
"8 pore.right 0 \n",
"9 pore.surface 26 \n",
"10 pore.top 100 \n",
"11 throat.all 270 \n",
"12 throat.boundary 0 \n",
"13 throat.internal 270 \n",
"14 throat.surface 0 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(pn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenPNM was designed to work by assigning geomtrical information to **Geometry** objects. The presence of 'pore.diameter' on the network can be a problem in some cases. For instance, let's add some boundary pores to the left edge:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"Ps = pn['pore.surface']*(pn['pore.coords'][:, 0] < 0.1)\n",
"Ps = pn.toindices(Ps)\n",
"op.topotools.add_boundary_pores(network=pn, pores=Ps, \n",
" move_to=[0, None, None], \n",
" apply_label='left')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualizing this networks shows the newl added pores where we intended:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = op.topotools.plot_coordinates(network=pn, pores=pn.pores('left', mode='not'), c='r')\n",
"fig = op.topotools.plot_coordinates(network=pn, pores=pn.pores('left'), c='g', fig=fig)\n",
"fig = op.topotools.plot_connections(network=pn, fig=fig)\n",
"fig.set_size_inches((7, 7))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we have internal pores (red) and boundary pores (green). We would like to assign geometrical information to the boundary pores that we just created. This is typically done by creating a **Geometry** object, then either assigning numerical values or attaching a pore-scale model that calculates the values. The problem is that OpenPNM prevents you from having 'pore.diameter' on the network AND a geometry object at the same time. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"Ps = pn.pores('left')\n",
"Ts = pn.find_neighbor_throats(pores=Ps)\n",
"geo_bndry = op.geometry.GenericGeometry(network=pn, pores=Ps, throats=Ts)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we we try to assign ``'pore.diameter'``, we'll get the following exception (The \"try-except\" structure is used for the purpose of this notebook example, but is not needed in an actual script):"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cannot create pore.diameter when pore.diameter is already defined\n"
]
}
],
"source": [
"try:\n",
" geo_bndry['pore.diameter'] = 0\n",
"except Exception as e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The solution is to remove the geometrical information from the network *before* adding the boundary pores, and place them on their own geometry. In this example it is easy to transfer the ``'pore.diameter'`` array, but in the case of a real extracted network there could be quite a few arrays to move. OpenPNM has a facility for doing this: the ``Imported`` geometry class."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using the Imported Geometry Class\n",
"Let's create a network and add a geometric properties again, this time *before* adding boundary pores."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"pn = op.network.Delaunay(shape=[1, 1, 0], num_points=100)\n",
"pn['pore.diameter'] = np.random.rand(pn.Np)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we pass the network to the ``Imported`` geometry class. This class literally removes all numerical data from the network to itself. Everything is moved except topological info ('pore.coords' and 'throat.conns') and labels ('pore.left')."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"geo = op.geometry.Imported(network=pn)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing ``geo`` reveals that the 'pore.diameter' array has been transferred from the network automatically:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.geometry.Imported : geo_01\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.area 135 / 135 \n",
"2 pore.diameter 135 / 135 \n",
"3 pore.volume 135 / 135 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.all 135 \n",
"2 throat.all 301 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(geo)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that the geometrical information is properly assigned to a geometry object, we can now use OpenPNM as intended. Let's extend this network by adding a single new pore."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"op.topotools.extend(network=pn, pore_coords = [[1.2, 1.2, 0]], labels='new')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The new pore can clearly be seen outside the top-right corner of the domain."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 504x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = op.topotools.plot_coordinates(network=pn, pores=pn.pores('left', mode='not'), c='r')\n",
"fig = op.topotools.plot_coordinates(network=pn, pores=pn.pores('left'), c='g', fig=fig)\n",
"fig = op.topotools.plot_connections(network=pn, fig=fig)\n",
"fig.set_size_inches((7, 7))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can now create a geometry just for this single pore and we will be free to add any properties we wish:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"geo2 = op.geometry.GenericGeometry(network=pn, pores=pn.pores('new'))\n",
"geo2['pore.diameter'] = 2.0"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"openpnm.geometry.GenericGeometry : geo_02\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Properties Valid Values\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.diameter 1 / 1 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"# Labels Assigned Locations\n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
"1 pore.all 1 \n",
"2 throat.all 0 \n",
"――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
]
}
],
"source": [
"print(geo2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the network has the ability to fetch the 'pore.diameter' array from the geometry sub-domain object and create a single full array containing the values from all the locations. In the printout below we can see the value of 2.0 in the very last element, which is where new pores are added to the list."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.81083862 0.34819194 0.2114548 0.05938319 0.87602685 0.91854645\n",
" 0.12012018 0.33447374 0.17537207 0.11589847 0.89986674 0.05687726\n",
" 0.98048566 0.09645086 0.86347065 0.56650611 0.36791749 0.34234238\n",
" 0.75736414 0.3145733 0.65731892 0.51732608 0.48496565 0.90116217\n",
" 0.55464506 0.8268616 0.72557353 0.03855725 0.77311005 0.21687025\n",
" 0.90314965 0.04292419 0.33307203 0.09973295 0.47558912 0.82002244\n",
" 0.29818736 0.1509349 0.33026704 0.81388014 0.14038396 0.22736245\n",
" 0.06885196 0.70571004 0.39523324 0.31083998 0.71862639 0.33597754\n",
" 0.72777127 0.8151994 0.21766284 0.9738187 0.16235795 0.29084091\n",
" 0.17979529 0.34550566 0.48006089 0.52217587 0.85360604 0.88944791\n",
" 0.22010386 0.62289403 0.11149606 0.45896986 0.32233354 0.31650075\n",
" 0.48258424 0.72982764 0.06918266 0.87917334 0.73481377 0.17649939\n",
" 0.93916091 0.50631222 0.99980858 0.19725947 0.5349082 0.29024804\n",
" 0.30417356 0.59106538 0.92171907 0.80526386 0.7239414 0.55917378\n",
" 0.9222985 0.49236141 0.87383218 0.83398164 0.21383535 0.77122546\n",
" 0.01217116 0.32282954 0.22956744 0.50686296 0.73685316 0.09767637\n",
" 0.5149222 0.93841202 0.22864655 0.67714114 0.59288027 0.0100637\n",
" 0.4758262 0.70877039 0.04397543 0.87952148 0.52008142 0.03066105\n",
" 0.22441361 0.9536757 0.58231973 0.10747257 0.2875445 0.45670363\n",
" 0.02095007 0.41161551 0.48945864 0.24367788 0.588639 0.75324012\n",
" 0.23583422 0.6204999 0.63962224 0.9485403 0.77827617 0.84834527\n",
" 0.49041991 0.18534859 0.99581529 0.12935576 0.47145732 0.0680931\n",
" 0.94385086 0.96492494 0.71938906 2. ]\n"
]
}
],
"source": [
"print(pn['pore.diameter'])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
},
"nbreg": {
"diff_ignore": [
"/cells/*/outputs/"
]
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
lenovor/notes-on-dirichlet-processes | 2015-07-30-sampling-from-a-hierarchical-dirichlet-process.ipynb | 4 | 114154 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This may be more readable [on NBViewer](http://nbviewer.ipython.org/github/tdhopper/stigler-diet/blob/master/content/articles/2015-07-30-sampling-from-a-hierarchical-dirichlet-process.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[As we saw earlier](http://stiglerdiet.com/blog/2015/Jul/28/dirichlet-distribution-and-dirichlet-process/) the Dirichlet process describes the _distribution_ of a random probability distribution. The Dirichlet process takes two parameters: a base distribution $H_0$ and a dispersion parameter $\\alpha$. A sample from the Dirichlet process is itself a probability distribution that _looks like_ $H_0$. On average, the larger $\\alpha$ is, the closer a sample from $\\text{DP}(\\alpha H_0)$ will be to $H_0$.\n",
"\n",
"Suppose we're feeling masochistic and want to input a distribution sampled from a Dirichlet process as base distribution to a new Dirichlet process. (It will turn out that there are good reasons for this!) Conceptually this makes sense. But can we construct such a thing in practice? Said another way, can we build a sampler that will draw samples from a probability distribution drawn from these nested Dirichlet processes? We might initially try construct a sample (a probability distribution) from the first Dirichlet process before feeding it into the second.\n",
"\n",
"But recall that fully constructing a sample (a probability distribution!) from a Dirichlet process would require drawing a countably infinite number of samples from $H_0$ and from the beta distribution to generate the weights. This would take forever, even with Hadoop!\n",
"\n",
"[Dan Roy, et al](http://danroy.org/papers/RoyManGooTen-ICMLNPB-2008.pdf) helpfully described a technique of using _stochastic memoization_ to construct a distribution sampled from a Dirichlet process in a just-in-time manner. This process provides us with the equivalent of the [Scipy `rvs`](http://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.rvs.html) method for the sampled distribution. Stochastic memoization is equivalent to the [Chinese restaurant process](http://www.cs.princeton.edu/courses/archive/fall07/cos597C/scribe/20070921.pdf): sometimes you get seated an an occupied table (i.e. sometimes you're given a sample you've seen before) and sometimes you're put at a new table (given a unique sample). \n",
"\n",
"Here is our memoization class again:"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numpy.random import choice \n",
"from scipy.stats import beta\n",
"\n",
"class DirichletProcessSample():\n",
" def __init__(self, base_measure, alpha):\n",
" self.base_measure = base_measure\n",
" self.alpha = alpha\n",
" \n",
" self.cache = []\n",
" self.weights = []\n",
" self.total_stick_used = 0.\n",
"\n",
" def __call__(self):\n",
" remaining = 1.0 - self.total_stick_used\n",
" i = DirichletProcessSample.roll_die(self.weights + [remaining])\n",
" if i is not None and i < len(self.weights) :\n",
" return self.cache[i]\n",
" else:\n",
" stick_piece = beta(1, self.alpha).rvs() * remaining\n",
" self.total_stick_used += stick_piece\n",
" self.weights.append(stick_piece)\n",
" new_value = self.base_measure()\n",
" self.cache.append(new_value)\n",
" return new_value\n",
" \n",
" @staticmethod \n",
" def roll_die(weights):\n",
" if weights:\n",
" return choice(range(len(weights)), p=weights)\n",
" else:\n",
" return None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's illustrate again with a standard normal base measure. We can construct a function `base_measure` that generates samples from it."
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from scipy.stats import norm\n",
"\n",
"base_measure = lambda: norm().rvs() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the normal distribution has continuous support, we can generate samples from it forever and we will never see the same sample twice (in theory). We can illustrate this by drawing from the distribution ten thousand times and seeing that we get ten thousand unique values."
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of unique samples after 10000 draws: 10000\n"
]
}
],
"source": [
"from pandas import Series\n",
"\n",
"ndraws = 10000\n",
"print \"Number of unique samples after {} draws:\".format(ndraws), \n",
"draws = Series([base_measure() for _ in range(ndraws)])\n",
"print draws.unique().size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, when we feed the base measure through the stochastic memoization procedure and then sample, we get many duplicate samples. The number of unique samples goes down as $\\alpha$ increases."
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of unique samples after 10000 draws: 446\n"
]
}
],
"source": [
"norm_dp = DirichletProcessSample(base_measure, alpha=100)\n",
"\n",
"print \"Number of unique samples after {} draws:\".format(ndraws), \n",
"dp_draws = Series([norm_dp() for _ in range(ndraws)])\n",
"print dp_draws.unique().size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, we have a function `dp_draws` that returns samples from a probability distribution (specifically, a probability distribution sampled from $\\text{DP}(\\alpha H_0)$). We can use `dp_draws` as a base distribution for another Dirichlet process!"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"norm_hdp = DirichletProcessSample(norm_dp, alpha=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How do we interpret this? `norm_dp` is a sampler from a probability distribution that looks like the standard normal distribution. `norm_hdp` is a sampler from a probability distribution that \"looks like\" the distribution `norm_dp` samples from. \n",
"\n",
"Here is a histogram of samples drawn from `norm_dp`, our first sampler."
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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qd4CWqtwBWqpyB2ihyh2gpSp3gEXnYm9m1gPu2U+fA/c1l/q4Ocf2c+7H2O7Z\nm5nZiI202Es6StKNkm6W9NejHHs0qtwBWqpyB2ipyh2gpSp3gBaq3AFaqnIHWHQjK/aSdgDeBxwF\nHAKcIOngUY0/GutzB2jJ+fMqOX/J2aH8/HMb5ZH9YcAtEbExIu4DPg4cO8LxR+Du3AFacv68Ss5f\ncnYoP//cRvl/0O4D3N6Y3wQcPtPKW7duHdnJDTOzpW6UxX5elXuPPfZYrByLaGPuAC1tzB2gpY25\nA7S0MXeAFjbmDtDSxtwBFt3ILr2U9AzgrIg4Ks2vBrZFxNsb6/hQ3sxsAea69HKUxX5H4LvAkcAP\ngXXACRFxw0gCmJn12MjaOBFxv6TXAlcAOwAfcqE3MxuNsfoErZmZLY6x+wStpLMlfUvSeklXS9ov\nd6b5kPSPkm5Iz+Ezkh6VO9N8SPpjSd+R9ICk38udZxglf1hP0nmStki6PneWhZC0n6S1aZ/5tqRT\nc2eaD0m7SLom1ZsNkt6aO9N8SdpB0rWSPj/bemNX7IF3RMRTI2IFcAlwZu5A83Ql8KSIeCpwE7A6\nc575uh54CfDl3EGGsQQ+rPdh6uylug84PSKeBDwDeE1Jr39E/Ao4ItWbpwBHSHp25ljzdRqwgTmu\neBy7Yh8R9zZmdwN+nCvLQkTEVRGxLc1eA+ybM898RcSNEXFT7hzzUPSH9SLiK8DW3DkWKiLuiIj1\nafrnwA3A3nlTzU9E/CJN7kx9PvGnGePMi6R9gT8EPkj9jW8zGrtiDyDpzZJuA14FvC13nhZOBC7N\nHWKJm+7DevtkytJrkiaAp1Ef5BRD0kMkrQe2AGsjYkPuTPPwbuANwLa5VsxS7CVdJen6aW5HA0TE\nGyNif2AN9ZMZK3PlT+u8Efj3iLgwY9RpDZO/IL7CYAxI2g34FHBaOsIvRkRsS22cfYHnSprMHGko\nkv4IuDMirmWOo3oY7SdoHxQRLxhy1QsZwyPjufJLWkn9p9WRIwk0T/N4/UuwGWiexN+P+ujeRkTS\nTsCngY9GxCW58yxURNwj6QvAf6KMr8F8JnCMpD8EdgEeKemCiHjldCuPXRtH0vLG7LHAtbmyLISk\no6j/rDo2nfwp2ZxHC2PgG8BySROSdgaOAz6XOVNvqP6ffj4EbIiIc3LnmS9Jj5a0LE3vCryAQmpO\nRJwREftFxIHA8cCXZir0MIbFHnhraimsByaB12fOM1/vpT6xfFW6HOrc3IHmQ9JLJN1OfWXFFyRd\nljvTbCI4Q4whAAAAdklEQVTifmDqw3obgE+U9GE9SRcBXwMOknS7pD/JnWmengW8nPoqlmvTraSr\ni/YCvpTqzTXA5yPi6syZFmrWlqY/VGVm1gPjeGRvZmYdc7E3M+sBF3szsx5wsTcz6wEXezOzHnCx\nNzPrARd7M7MecLE3M+uB/w+vOVpp9+JCbAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112629290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"pd.Series(norm_dp() for _ in range(10000)).hist()\n",
"_=plt.title(\"Histogram of Samples from norm_dp\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And here is a histogram for samples drawn from `norm_hdp`, our second sampler."
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4w+8YZ/hm+XGGb2Zm47jhNyj/DLFou4Ba5X78PL78ueGbmXWEM/yOcYZvlh9n+GZmNo4b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\nTf8SuAc4sN2qhis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"text/plain": [
"<matplotlib.figure.Figure at 0x11137fed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.Series(norm_hdp() for _ in range(10000)).hist()\n",
"_=plt.title(\"Histogram of Samples from norm_hdp\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The second plot doesn't look very much like the first! The level to which a sample from a Dirichlet process approximates the base distribution is a function of the dispersion parameter $\\alpha$. Because I set $\\alpha=10$ (which is relatively small), the approximation is fairly course. In terms of memoization, a small $\\alpha$ value means the stochastic memoizer will more frequently reuse values already seen instead of drawing new ones.\n",
"\n",
"This nesting procedure, where a sample from one Dirichlet process is fed into another Dirichlet process as a base distribution, is more than just a curiousity. It is known as a [Hierarchical Dirichlet Process, and it plays an important role in the study of Bayesian Nonparametrics](http://www.cs.berkeley.edu/~jordan/papers/hdp.pdf) (more on this in a future post). \n",
"\n",
"Without the stochastic memoization framework, constructing a sampler for a hierarchical Dirichlet process is a daunting task. We want to be able to draw samples from a distribution drawn from the second level Dirichlet process. However, to be able to do that, we need to be able to draw samples from a distribution sampled from a _base distribution of the second-level Dirichlet process_: this base distribution is a _distribution drawn from the first-level Dirichlet process_.\n",
"\n",
"Though it appeared that we would need to be able to fully construct the first level sample (by drawing a countably infinite number of samples from the first-level base distribution). However, stochastic memoization allows us to only construct the first distribution just-in-time as it is needed at the second-level.\n",
"\n",
"We can define a Python class to encapsulate the Hierarchical Dirichlet Process as a base class of the Dirichlet process."
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class HierarchicalDirichletProcessSample(DirichletProcessSample):\n",
" def __init__(self, base_measure, alpha1, alpha2):\n",
" first_level_dp = DirichletProcessSample(base_measure, alpha1)\n",
" self.second_level_dp = DirichletProcessSample(first_level_dp, alpha2)\n",
"\n",
" def __call__(self):\n",
" return self.second_level_dp()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the Hierarchical DP is a Dirichlet Process inside of Dirichlet process, we must provide it with both a first and second level $\\alpha$ value."
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"norm_hdp = HierarchicalDirichletProcessSample(base_measure, alpha1=10, alpha2=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can sample directly from the probability distribution drawn from the Hierarchical Dirichlet Process."
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Lm5frWJ+N4enpaXLT9feQJI0CVzZySO2mSTodICLOTtOWA2cCdwIrImL/NP5E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\nqwU3SGZmVgv/H1K5zy49GGkDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1116b9750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.Series(norm_hdp() for _ in range(10000)).hist()\n",
"_=plt.title(\"Histogram of samples from distribution drawn from Hierarchical DP\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`norm_hdp` is not equivalent to the Hierarchical Dirichlet Process; it samples from a _single distribution_ sampled from this HDP. Each time we instantiate the `norm_hdp` variable, we are getting a sampler for a unique distribution. Below we sample five times and get five different distributions."
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hTejCCy/kmGPezg03TL+tHXaA1772Ney5555Tb2sR19hdQ5qHtlxDmrZ5qiF5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\n7s1wQ3wCjDQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1126956d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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DRnDOqjlntXLJmYsmTvs+n+JXubuvf0HdWczMrD1y+E+gsjM/P990hKFyyAjOWTXnrFYu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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a840590>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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ktXdIUfzndVW/pj+44j4zM9tKTMJ/ApW12dnZpiN0cabh5JgJ8szlTMPJMdMkqf0vNYxD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\nC/8fDGLl0WUzPJkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112939c90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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yDampTu4Ay5TZTqXm6uQOMEQnd4AhOrkDtFqJQ3YeBDczexAqrUMK4POSLpL0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b3ac790>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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hAWeqJsdMkGcuZ6omx0zjpPYOKYr/vK7br+lf1uUxMzPbSYzDfwKVtdnZ2aYjLOBM1eSY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\n/wFa3DXPGzw0ngAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1126ff750>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x112a2da50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in range(5):\n",
" norm_hdp = HierarchicalDirichletProcessSample(base_measure, alpha1=10, alpha2=10)\n",
" _=pd.Series(norm_hdp() for _ in range(100)).hist()\n",
" _=plt.title(\"Histogram of samples from distribution drawn from Hierarchical DP\")\n",
" _=plt.figure()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a later post, I will discuss how these tools are applied in the realm of Bayesian nonparametrics."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
txoof/automate_rsync | automate_rsync_v2.ipynb | 1 | 9007 | {
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%reload_ext autoreload"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"import ArgConfigParse\n",
"import logging\n",
"import logging.config\n",
"import constants\n",
"import sys\n",
"from os import chdir\n",
"import shutil\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def main():\n",
" #### CONSTANTS ####\n",
" # pull the absolute path from the constants file that resides in the root of the project\n",
" absPath = constants.absPath\n",
" # change the working directory - simplifies all the other path work later\n",
" chdir(absPath)\n",
" \n",
" version = constants.version\n",
" app_name = constants.app_name\n",
" app_long_name = constants.app_long_name\n",
" \n",
" # set the base log level\n",
" loglevel = 50\n",
" \n",
" ## CONFIGURATION FILES ##\n",
" # logging configuration file\n",
" logging_cfg = constants.logging_cfg\n",
" \n",
" # default base configuration file\n",
" base_cfg = ArgConfigParse.fullPath(constants.default_cfg_name)\n",
" user_cfg = ArgConfigParse.fullPath(constants.user_cfg)\n",
" \n",
" # Setup logging\n",
" logging.getLogger(__name__)\n",
" logging.config.fileConfig(logging_cfg)\n",
" logging.root.setLevel(loglevel)\n",
" \n",
" # record updates to config file\n",
" update_config = False\n",
" \n",
" args = ArgConfigParse.CmdArgs()\n",
" args.add_argument('-c', '--config', dest='config', ignore_none=True, default=None,\n",
" metavar='cfg.ini',\n",
" help='Use specified configuration file')\n",
" args.add_argument('-v', action='count', default=0, dest='verbose', ignore_none=True,\n",
" help='increase verbosity of logging by adding additional \\'-v\\'')\n",
" args.add_argument('-V', '--version', ignore_none=True, dest='version', action='store_true',\n",
" help='display version number and exit')\n",
" args.add_argument('-i', '--interactive', ignore_none=True, dest='interactive', action='store_true',\n",
" help='interactively add rsync jobs to config file')\n",
"\n",
" # handle basic command line arguments\n",
" args.parse_args()\n",
" \n",
" print(args.options)\n",
" if args.options.version:\n",
" print(f'{app_name} version {version}')\n",
" sys.exit(0)\n",
" \n",
" logging.root.setLevel(loglevel-args.options.verbose*10)\n",
" \n",
" \n",
" logging.root.setLevel(10)\n",
" \n",
" logging.debug(f'log level set to {logging.root.getEffectiveLevel()}')\n",
" \n",
" # use the config from the command line\n",
" if args.options.config:\n",
" user_cfg = ArgConfigParse.fullPath(args.options.config)\n",
" elif not user_cfg.exists():\n",
" # create the config file directory\n",
" user_cfg.parent.mkdir(parents=True, exist_ok=True)\n",
" update_config = True\n",
" \n",
" config_file_list = [base_cfg, user_cfg]\n",
" \n",
" config_file = ArgConfigParse.ConfigFile(config_file_list)\n",
" config_file.parse_config()\n",
" \n",
" # prompt user to create configuration file\n",
" config = ArgConfigParse.merge_dict(config_file.config_dict, args.nested_opts_dict)\n",
" \n",
" \n",
" \n",
" if update_config:\n",
" logging.info(f'updating configuration file: {user_cfg}')\n",
" ArgConfigParse.write(config, user_cfg)\n",
" \n",
" \n",
" return config, config_file, args"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Namespace(config=None, interactive=False, verbose=0, version=False)\n",
"21:11:21 <ipython-input-22-e5555c6d0fa1>:main:55:DEBUG - log level set to 10\n",
"21:11:21 ArgConfigParse:config_files:175:INFO - processing config files: [PosixPath('/Users/aaronciuffo/Documents/src/automate_rsync/automate_rsync.ini')]\n",
"21:11:21 ArgConfigParse:config_files:177:WARNING - config files not found: [PosixPath('/Users/aaronciuffo/.config/com.txoof.automate_rsync/automate_rsync.ini')]\n",
"21:11:21 <ipython-input-22-e5555c6d0fa1>:main:76:INFO - updating configuration file: /Users/aaronciuffo/.config/com.txoof.automate_rsync/automate_rsync.ini\n",
"21:11:21 ArgConfigParse:write:48:DEBUG - adding %BaseConfig to config\n",
"21:11:21 ArgConfigParse:write:48:DEBUG - adding %sshOptions to config\n",
"21:11:21 ArgConfigParse:write:46:DEBUG - skipping: __cmd_line\n",
"21:11:21 ArgConfigParse:write:51:DEBUG - writing configuration to /Users/aaronciuffo/.config/com.txoof.automate_rsync/automate_rsync.ini\n"
]
}
],
"source": [
"if __name__ == \"__main__\":\n",
" c, cf, a = main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"a.nested_opts_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"o.parent"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c = ArgConfigParse.ConfigFile(['./automate_rsync.ini'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c.parse_config()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"re.split('\\s{0,},\\s{0,}', c.config_dict['%BaseConfig']['rsyncoptions'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = '-a, -z ,-v,-h'\n",
"re.split('\\s{0,},\\s{0,}', s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p = ArgConfigParse.fullPath('./foo/bar/text.txt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p.parent.mkdir(parents=True, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"p.parents.mkdir(parents=True, exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"o.nested_opts_dict"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sys import argv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"argv"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"argv.pop()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sys.argv.append(\"-vvvv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"argv.append('-c')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"argv.append('foo.ini')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "automate_rsync-d-A0v8x-",
"language": "python",
"name": "automate_rsync-d-a0v8x-"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| lgpl-3.0 |
grehujt/SmallPythonProjects | image_processing/image_diff.ipynb | 1 | 639726 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import cv2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7d3c400>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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xovg4NjPe0vmOUhtLCOaoe4bXml1/pZFCRLSipVJc7fe04J3gN8HLyvN6EMMt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QmldPDdSaDagCRF9Pp++szZd0ActmXtQow6Zr2fda4dEGcUSRFermWWf96w5RWLJZrSSAdi9V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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x819ce10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"im = plt.imread('testim3.png')\n",
"plt.imshow(im)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"float32 (384, 691, 3)\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f8af60>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mceOrT8BvOENARBlRog882BUWKeSuumyMkfEMg7iLMgKhfLrn5sdi0f/7zPwH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\ndHSsOLrQd3R0dKw4/g/TXn6IBlwtMAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x4db9978>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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YAEgsPgl2PmjVg6zB/PWdMPZFUEtx8pnpCZMP+GIS2ql4FrSulcvl4pUFjn8r\njukudeXSLhBk7+xEdh3ygtntOjxb8/cZo7tuNALAxCXVYL9eefHiNda18sk3PsOv//pvY5Z4/Y03\nYmEl9ggOuRaERGsbiAelnEssKsNaQ+Ne1HmPl8X5l+T8glc9YXuRb23wiDh0E0RtDfzWzEKtERJT\njG33e0lUI1N623sjFb9GbSPmlURAM1LyBp4Wkk0zsIAAJHnHtyz1qO5Oy0KPJGPfm/NGsV129XI/\nBVy2NxcH5GgCKykfQcwwdLTjeYBDkW3fOZ0Xb+NP+bimya2omGOiEuvNDG2NHGtHhwemPYhuv88+\nxychubdryGNXXrzxgrfe/iaGxHOS2Gi9U7X3sDRpTo7m7I16ino/SrZjHUwtu3h+EMnV5o1qwY/N\nDdchSIc/Z2UkEYB9Tfnr9d6OKrf3Qaq+eaR47p7pz6BqeEE+O8R9js0emLZ31pzZe0dwdMF/MJF0\nhKowFEf9hl6kkJBORd+0PZgKHTWlJJ9Ps4Kf8e7DjI9NRh+V01HKlJIPPwzgUNjMxqnZREVMModW\n0jHhD8OoGLn4Tt1CD6+mB8kiRnQEKrVkRPHgEOTv8mjxL3U5gv227U4SB7E6JZSTOJ6QkSTPbPfN\nM+rD2sE0kkyDnPnLf+k/5C/87M/yU3/6X+flw5WtN0YoXXIpDFOGmTcfqU/KWsqhrdUxeHj1wLP7\nO8cjdXB3PvsmmjNmAx39YPfVPGDnlLAgNj258IVcUj6wzYSEz4jeiPBosGm9sW9bqIKUy+UhsuAU\nbem+LTnOKSzFIa9SEq9evuR3P/8FIHM6n1lPlWVNN8lZybQ+UDwRSChFEjY6fdvoQfB5ZpiPTGvO\njb43aizC6SujpkcVBKGaisyu93aQXCLwmc98hhfPnx0Kk1xyQBeJUjNtNJa10PvOtl0BJdewIOi7\nqzv20KaXaIs3ZT2d6G1nC/mfhTQUI5KQeL+cPUCZV3weWI22b3TteJY///NsuKQFzG0EfFMaDAYP\n11c+h09nz7K7kpI3zi118Wa3UAd59+w4qliXsHqD0g028btnMceH+s9L9p6Kkguf+fSn+exn/xSn\n05nRBsnAhleLM1AZAaUYlBwkcchfcy4HZzR9XmbStW0bre2cTqfYCG6qH5DYqH1tlvxIjmkawZ1D\nLj03kTwbr2JeOL9H+APJoSzKUw+vBi6J55AsjxG9GXJIOntsirUWr1piVUxI1YyISZMEd7HDuq4h\nGzUkGRJHFwyPAAAgAElEQVQ9OVO2+WHHxyLQO5RhASsM1y9H09S+Oy5X8q09fTZUuTRsctFTdmWs\n4bsyeg9DK4cZeuvUid8ZzpJFtj3UF5IkoVvn7u4eEC8XI6MigkTOmet1Cz2wGziBT8YxBhZZy9yh\nLaAS75zrrsqY2eKjCfL5z/8OLq90/LLvzXHReOgle6B4dr53CAtCG+5EVqnlsIHIxVvPr9eNUov7\ny0hCJMeG4cqAWrJnnkXCLC2RHl3bxCID3D7wRFWHFkRdAZBLDRjNXLd9VCApZGUOEZXsfiif/NQb\njtGf73jYrpRcOMWGKtFAZsNhgrWUaMARkuN6iEHNlZoLXRvDwrNIfR5lHO7KtWAEhh4S2ZQyXUdA\nfjeoxmagLDkULX5/e+vsuxN+OpRn9/ecTuvRRXq5XDifTtSyeOWyd4d3smfy8igDq6UgeHdnLr6B\nr8viizqSiJT8GU2YzcloTxwkO4FZ6k0lk0OPv4buf8pJc6hSUhb62FnWyquHV6zVq52Myy29I3Zn\n26/UUo9mqDSTq94ZbVAkUcPs7rSuR9ATYhOf8yU+5xiDb3/nHX7zt36bfXNPnf6orwR8vpXQn09O\nwgO3369923zziP4RAZbVE71cUnTCT7gzBc+noWRx3X+KDYrJJZm/XkqZEXyDhp0CWHBmzsfFLTjm\nvVeDkQCZS0S7usTU5am+mqevjSdTHL+nqi46wa1GtlA+5Yhv4PMvh7FhiwY2X3yCSKw/gEeJ7A8b\nH4tAL0iUJeFBgR2dhXNH7tGkM2WE4IuQA6OeD8PYo8x0XTdOeEnyQGLeWNQCZ9tHw3LCRJCcvS08\nZy7bFbWBCbTRPcgUd4lUcxmeB3V8weYoveOBmIUSJiZF13D4i+zZF5B/T9UJT9PBL//f/4RvfPVN\nMsqp3ioJz5y8H+Dlq1ekLLTRosTzDsXLtnPdNxR/X0d0wy/EgXG/5lATJcIGQhJ7C+nhEGxXbAQi\nrINmAQ8k3zxMjVKmHDbF94Z7mMDRuTcX0BiD995/j6+//TYP2wNSje+8+w45r3zh97/E3d09yymT\nF5/k29bZeveOzZToKqS8gGRICuK6/mEdNeWUi/sitUE2XEVVFizJYYeRSjmgoGQuO8ziuKuTptlh\nobqwtY7kipF4661v8+533qPk6l3UrTFa5/Lq4nySEsZhe1SGxeW46pn3NOOSIFFb84Bbc2UM35Re\nXV8hCZo1ltOKRnYuST2TE6ONhoVkeNgO0c1dloWyntnHYLs6Ibrrzta2I8HJJBapiML9/cJXv/Zl\nTvdn9n3QhvrrZnf31DFIFFcQxdpKqYAIl30PmeQeMlqHfcjTfCtI7gTLaWVX5b3336XrFTKeVIkw\nzCmHvHjjomfGnm3PwFlqIdoGsSQMcZB2qSsGbHuj5BpwoMNf3gGbwcQbyJI3T7WmSCre9CjJG83U\nocW6VLbuHfJuZOeBeTRXC0055KwAZ1NUay6/ntYlpRS26+6JiLiCxmFEIy+rg5oJ9m33RAiHM2sp\nh6pQADGjd8HU4wgk70TWEBF0yGXxzyl/wqZm/zJjkgszI/HWad+xWjDmszT0HdLJ2KOzLEg+4u+R\nn0VLdOyCUbIjcH93h47Bua6OB6q6h033bF9Dmz07+txa161fAdq+37pHzQ7pYA6Yxp3swrNHjWWp\nLvED18gGSYpqlIFOtt7f33u2Xytqs+tvOubJgUMefESQaa43XjitJ8ymDrdTcor+guHZ8bqg3BRF\ns9FIQ68/hqsOvDyVAyLL0Ywy4YTem2e9ActIkoPzuG5OYkoSrpcrIom//Tf/Jv/l3/nPub9beOdb\n7/Da/Sf4/Od+lzdef508obPuZfCpLoh6xeFCn0EWdwlM2bPWvTnEUkqlhwmVlMKmjbyuroQKGMrU\nyMFTlJQZuOXwVG0UyajupOTqnJyEmiElZe8b52cnJAsqynJa2HuP7N/5m6GDdXFuoA1vNss1/EpC\nWZFKZtrLOjTh2eHQEUlODmKXg+jPZQkXxgKSGSroSPTuskYQtusV7TvnZUEZlJIO7HomSobDbNO/\n5/7ZM7773XcpJXG5dGpaKfXsDTrZSNkOUzBPkhz6qNmJ1lzSQYYKUFM+moy8aisuAsgSG4BE0HU9\nuCR345RQxkx1CjCB9/nWUZmPwx21t0bfe6wZO9ZEXSrChD+m1t0lvLU6pp9z9v6WsNUuObNdt+jl\ncCXe5EM8iTNam70jN15Gx3S8TEyX1skbHv5TATHVUujTgdZgWd3y3AI29v6dWx+Ir3NDdVq6cKjD\nRKDWG3QzRvvQ8fXjEejnU34UWKdcawb9w0I32PDDBnXfHgXBcLJME3Z4JPuSFEimhTLFrRXaNjFe\nCf/s6LIN7/u5mxNl2FRGeGdkKANmMAmJnpqrXZycUmZ33d2d63rX8wmJ6sDE8c3T6ewYqJnbICDR\nOFU9O1gcznKXzkpKwt3dHRrStmV14tYhB7/mdXF7glodUpCSD0JXHsm6cnZbZh2d2SlpwjF5cy6H\nIimHaVXOmdP5FF2Bjr1Pv6IaQaX3wfnO7WnfevstvvLVL/Frv/4b/Fv/5r/NH3zpD/nkJ3/CcdEl\nh5zON+NhgxJWxT4fEgQOPwNAj82ntZ3R9PDurqWybRfqsnr2PtURIbPVCM5uIRCckHonZcn1ppSS\nDClx9+wZXZWug/vnz3l1vbBtl8MRdcpat817NXz9Pgq2ELiqX+Pd+ewcVJ3Ooyvrsob6KTYfBbEU\n0tcANc3ncMqZupwoxXFplws7SZqOatKO63PZYToSBkR4/uwZrtK60tvgvfdecVrv3GE1GbV4YMth\nqeAQhvehqLs7O/GsRq0L14ChRGBvke1nT1RyqZRyQiTTh3lqGoG9jZtyZq71yNYOHimFidxsVMo5\nI1mOjWY28xH3G/Nqb4lEMUeXPcEfIRLNTn4/3N5hd8I6eUNhrdWT6cDZp0e+K6qiCzckpzdyPx8q\nmClMmBXOhAPnJlHDs370TglNvmSX1E741Kv4m18OsRamEnB2Dn/Y8bEI9Af0gt/Ym2zolnUKQRbi\nsIfv8Bbt7BJ44o1MnN18DpFEh1lyiMZw7TKR6U0NriCHp/VSa0ipNDpgOQ4SeUwKTduEdV3prTmR\nEvDSVPiUUtChx6ETe9sP/HVCG4/JwRESwVJdfWBm4S8z1Qr1UPtw7PiTKOII3JO5GKM7/4BDR+Ts\nYTmC2uQxJswFoN1hsCiSmBYLKcmhA27NDdjmYnIXTSfD96h4vDop/MGX/oDf+/wX+Hf/nX+PX/vV\n3+K1F5+gLhlJsfCzHJp+L1WdAIY5wWORp0TKhXU9xXzJEAZnsxX9fH/vcr6uhwVz3xvLvG8xD6Y1\n8X7dUOuM0Rwu0AE6qCkxtg0ZSjLh1XvvU0msdaHURF0KkojOUAdcbkZWzuc4qerVx1KX6IuIrsaj\nouosdWW/7CRzSWnXzrqeSalyCtM+r7gaqrtr5cOAy/TGgXgn8nrgtzM4Iu79Y+YNPkutfm5Bgfdf\nvuIP/uAPuVtfcD49p9SFumRa28jZTQMl/JxyypzW5ZBkznMONHxzailO5KqSgkjFwkRPzSHSIPan\nomdyW2MMtus1gm87rBomi+V/dznnJNZnx7WasgZvIDbly7eqdPaNTMI+TTECdpgOluqHAD08PITH\nlB2Vytw0J1qgQ0O+qYeKbzrMPkbOZ9Bvez+07zoG++YOutd9c8FBeAgpRms759PJu/z1JhywSGg9\nXtyaRz/M+FgE+ltCb+Fu5/CKqh32pZN4nax52zaSeMBKwZ7PDBy72c4mcWJkqi5ma3YfAw23Obi5\nP3ordpwcFUxMzr7Tl1Loe49DR4Sa80HEbpfLI+I1PNwDKjLTaNxyd0fPADeXdD6SlAounXM709nN\nOw/y4MgoPDMjNoDwvIg9bm6U86CQknNIJpWlVq9i9gaBDU6DptH9wIXz6ewl8HBDrVnRqLo8cEJj\n0y7aTcLieEI41BjrevITpoBPvvEGb371y3z2p/4MX/gXX+T5sxexWKZqxPmWlG+HrzjW65rj1lwp\ntUWvw8Roe9djIZzO5wMHbb15I04KN8p9R3Jh23t0UTv2b6F5X+uKDjuUF6pKiwxyzU7GL7mwpOJ6\nfbzxZ8KMU2t/O+puSgi9inTvfneedA2AV25q/jpqhvXO3ensiY253PPhckFK5WHbWFYPvN2mv7pF\nVSPBHyWu2yU2Zj/lae87e2u0cXNDne6KKWWWdSXlwem8UHLhX3zxi9SygBilGvfPTiBTMeJQRiqR\nPsTG6n7w2WXv8b0RtgMS1Y0rVjJlNmdFT4YGb4XhXjylcnc6x9r2JM29ZcJiuJY4WMcPFTo08B48\nuF6voQjSI45cp4FZ2JLPDWY2Sjm85sKB0Ro1J85RqY44WGRq9XME5DSRg+xBfN+unE/r0UcwG55G\n+NHMzdCMI1k7351DUZUPOKwu1buoawnb9kzJLqvVWHuCuG9SbEAfdvxIgV5EviQivyUi/4+I/Ep8\n7w0R+d9F5Pfj6+s/7HVc9WJIym7sFAHCtanTFGgcu7uLzBe27qdQNXXP9zYGJD+SryVlYFhO7NoZ\nyUslbe6XUZYKS+aqPUq4fBBns4RsNihLZZiX7m108lJQcR+Mh31jHx2SwyKWBMuuWnnYr87eS6J1\nPzmrmzKS+5qfz6dbRp48C/CTo65HSWmqbGFXOrOWlH0RT/JrjSP3DGMtFd1dWZRKpqNs2rAGS125\nXq7s28YnX3vB2K7sox+yTQ3C8qFf2a2z3K2oiF9vl9iAFhKZnKrDKWZYSYfG15Xkgmpkf1l448Vr\n/N7v/B7/0V/5T/mVX/tNXn/jDe5fnDAZlKVQq5f1NXnTDKOx5Eyqib1fGdqpa6b13b1aTMmmLAmy\nGEsSkiX2S2O/drSB7kalslZh9CvP7lcSA9PdDzJpG3nfEe2IDSjQTVAye1colbKeMCns6u6m19HZ\nze2F1RKSClkymULWTFb3UjER51YQtwswWFTJyUDUM/KxU4ElmvNMjLQWNu20lpwMHcZSEn1/RdId\nGZ3zUllSoojAwN0im3eMt2un1JXLNI5TOC1nl/mp8zqZzFAn7q0YPUPOJ2rJ1NXVV//8d7/IfhWW\n+pySz+S8QPb78rBd2PuVS3uAHEG0VJAccE4BMqopekU6uQimjVIMtR3FmxY1EYFLyTWhgjc3qpIX\nN8mb9hmiGeuht1/qYYjW1ZVYzj/kIMUHqS4Mg0Z4aImToLPSbq0xJJPKwpKqcyN1IdeVfdfD3K4u\nhZRBuvsdYW6A1lS59k4OMj7Xlct1Zx9+Gl63cRwV6Y1oGnJVT1RulbyirdG3RraC/29hH4NdlWFC\nUzym1IVunUEnrQnwJsYPO/5VZPR/xcz+nJn9dPz97wP/2Mz+LPCP4+8//EIO+IVDWzyPrvMbvB6l\nz4iy7rSeOK+nYxc9OsnMECXKvXTAERPbysXJIjHPoIlSb56f6k0wg9Ny4nK5YCHZq3GqDeGG6cfo\nLUwjLwkpmo5xdLFOt8KJ859WP0FmqB6dbpN4Vh3RkDQOPPVxiVrDBtntZZ303a7bgU+OMUilcN02\n7xBN3mY/oSaXfVW++/5Lby5K5eAVclQbGqoc7YqFWRl4luzNQ8bWNkRw9UXvhzZ8ObkSZxLmpWTe\nefcd/vrP/TX+r3/yTxlj5xOfeIGZspTiuHOOYwRxX5TZxVpyARP3FOp6YKSSMkONHk1bfVZ9STif\nV9eFl4Qk/DSidY3u3lAemTDw044kuzKpd/VFjZ/NmgCxcLKMexMJq+Op6se9mbjFgyb1xrPR/Wcl\nSPQxXOn0CKtPKSO5+Fm2wRMJ7hM0CbcUTWutT+4j0Udg9SLHWbwpZ8fTUZZa4lm4sd39s/twSkyH\n97pOvX1y76Yc82ti3S9ePOP5i3u++947vPnmV/nGN9+i7Z22NfreONV72iVhY+XldzvXh8H7775i\nu+xIyu5RYx1kuM89XnEiKQ4YWeIcHKO3EYoWYEpm4XBczSlzvTw4hGojRAfRYQ2hAksHJDMPKppw\n5TwZbfpleWXgstZb06AG1g8QzUnJyev9ekWHQy7TGNEw9r2RRbg7nw441Kv9fMCgIjeC2dTlmnbc\nZ69SpwPobX33cADtIYXGyRDzysiCL0tkTsuZ3i0a1z7c+Cigm78F/IP48z8A/vYP+4Wj/Tg5wbOu\nizPgEF1wfriwdwA6Rg3ui/Pq5UvPxHs/zmAseZ6Z6lnnUhdv3R7KGBYZpwUU4ovLVI9u13mIgsj0\n+c5HsBx6OxEmh51BDtXLHk1WQwenwEMd+A7P78DyEAtIyU9q2kO2toRVwSSkJyZ9YIwx2San4Aeo\nRAmnflTisi7u5hkElIYLp6kixbHq09ld/CYfUT/QARqefCmIv1JYl4LpCKXNRk5CH43TaTnIYM+U\nptuexaHmie+8+y5f+eo3+Po3v86LF68dNsh769R1YW/dG2ySe6q4ksef0/RXB47W9KmHlxTNO0uN\nM2Y9mM7qzzsZvaXcIZ4IAzmTUmFd14M4TUkQG+Hv7geLm1l4vbgCIonEAhTKklAbx+Ena/R9tNGQ\n7IQm4jpvjazTHRXnQSMSXJHMHAQzCX9335x7wEjT658gt+XoZHU1VcqFy/UKKbnSR+343nJ2+Gxv\n4bc0bp3BvTXQub44MOBl8TNy7853CPDuu+9xXu94/uw11tOJF6/ds9TM/f2JWpw03bdB33fG3tHe\nOC2VmhKlrCEtdjnudt2Zh5wv1RujlmVhnsPaAwYFMB08e/acbd+p2RMS7/L2JbUs3nPQWw+J5S14\n6+iHsGLGlimcmEq9+d/Evyek4m5KnljO4yYl+neSeEA3OM6VmBwi5vDSEh3ZKd020SjXPTkLOwxv\nkhtIKeRSXecm+NGjsS4nZGxmjNbQ3v3wmdbC/+oxG/CDx48a6A34P0TkV0XkF+J7nzazr8efvwF8\n+sO8iog3D/jO2kOvegukk1AKjumQQ061ibvX7fTW4tzYiZ3eLAT8PE/PkU6nEzn7uY3zoYMH4hrH\njE171cMDZ4wjg3dPHMcB58Eek4hz87B2bF7g8rZ5ug7mG9ro0+xEDoIox6T29+vHZ8g5Hc1R08dm\nHqY+g5BiXMMZcl28+qnFtbyK47N5qezbzrBxOID6wezTSgKH0CbOqOoHkSc58M9SCqfTySuUOED9\nOMyjlGNz/NVf+zV+9i/+LG9+5au88fq/xmnJ1Jw4LZW788k3P3ObXG+dH34QdnICPUv2bkrJ3pJv\nt3MI5te9NQw3xiox+WdW53a/j1RYUTXp8OrhRoB7Rmk6rdVcu5yTOCQR3M4883NEo5iffOYHzBuO\nM4+u9DZC1hkHvERwr7Ue3ka9jYMEnydVraeF63BfplMth7x0zmGDw8dJBJf6mrqXUbhzihltu7KU\njHUPDueTe+ts++ZNc8DpdHZlTvBIM6FSdRfNlDPremZZVt56+21a69zfFUg7uTRevLbwxqfOPHtR\nuL/3oyi7dvamGHHo/GiBv/t5tAaR8Ex1Uqz5IGznYd06Bj1kvzkFRINRcj02inmuwJROeg+LRdPS\nxK9nt3OPJCloXfHaTJiP3o6gP9d+XFTIhj3z3naXYs4GQv+aIvEZBz+XoyN3xqDJBagOtu0amL1P\naSdzg0Mw867xOG8YOOZ6LgU12K49umcfu2/+8PGjBvr/wMz+HPCfAP+1iPylx/9o9ljH8cEhIr8g\nIr8iIr8ycekZKI/W5VCGYDfDpNZGyKESYUZxHA02JZJTWuUeLsuhUyWMrXKuXC6bQwSpHpn9bFGf\nCo/Jrh8n0egIN8MJZWRKrR7kQxLlJbuieGYxSdN992tO0R7uGWul1gUiqxwxWeapMjo01ENyBP+p\nAFjXJSZDKIFiUhPBa2/Ne1QAEyecXLkhkPyAZwky97SuUeL6vTSNRhUzWvi7zDMq19XhqO3qh5W0\nOOgbQpfcfBO57lf+6l/9y7z55pu89da3ePHidRC/z1Np4SqkfGzkWUocqK0eNAPKclI7fP7x4NBj\nsy250LYW58AOUG/88TNTfcHOe++HVbsfkBPJTu6NKLOnxhtC+11qHCjPkY334ccU5pzpbWbdwloq\n07pjqTdIbD7HWou37Aecs9S5YXswLynRd++0lewSwJySH/EXihmXoNqxebS208OvJqXklWh2b5XZ\nOVmm31N12G1vV99UR5jEjdtxenPdueS0g3iXdl0LL1+9x1e+9k1MM6Wu7HtHpFBS5u75yvluDd8c\n4/KwU0uQk5Osz+7c6VDF3Nz1VoWHYZxERuv9ItFEJYQ44eaeedO2+L2YZygciq2wvZCICZ5YhdeS\nTh8hhx5tKoJs+mVJBHKHXS6XB0x8bbinfkguw64h53wo8oBIJnyjEm6OtqVU53FM3YNHiMNs7LA/\nGCHfnmq8x5YPS11DPOJWdh/U9/zg8SMFejP7anx9C/gfgZ8Bvikin4nJ8xngrT/md3/RzH7azH7a\nD7tIocX101Nm8HDq3pUrDs/kD6hRVBW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H+OjzH8WyZAQ/IEYH57MOXgPWsmAcAw6HWaGf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/xjUH39jZ5ZS55GsNNZGL2xogTdRkLZi9BpWLlQk8tjxalpUe3uJoGdG7G8Jh5mfC8RMdL7d4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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x67ce860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"im2 = cv2.GaussianBlur(im, (15, 15), 0)\n",
"print(im2.dtype, im2.shape)\n",
"plt.imshow(im2)\n",
"im3 = im - im2\n",
"rmin, rmax = np.min(im3, axis=(0, 1)), np.max(im3, axis=(0, 1))\n",
"im3 = (im3 - rmin) / (rmax - rmin)\n",
"plt.figure()\n",
"plt.imshow(im3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"uint8 uint8\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f39cc0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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i1/OwRXkEZk+B4BbcQGi0Q3SNfg6rSG1x3NIsaImh51glkWRaDGnjGNuC9gYj\nQB4yYkC0yxWjeMFmtbE38qhzYAgX/oTjWwL05XCQvAL2jQ++Lnr1mFJpZqcE+Weu1IeQuMb03YFV\nEIIBk7EVYM7XAuo8f3MugEhhrGNzIsJBy8DVMgmkETa5Aff6PpUN68x+9FcKbLG2s6/LZwqtEZ50\nG/jkodVQQTj6Uqm2EN8DgC6rbYZ6fMUXP7zHlz+8x8PDE87ns7tBDCT66g8HcT+8ckerd5IGUmgS\nSyqpiRtr8qlStKypKEV7PNGHoIXSurgXsgucW8WiaZrtVIHlZAXgidsSqOpR2TBSJhxkAqBKRWLj\nkSduEyzuukqbJNIDxzzw78Qu7euKLr4Jr6S6thUVS4K2vKxYlxXr2rEsCz7//IjD3S5CL5lig8Ct\nSHZOQLcFXEmXKFLGG9tlWm2cK5QudZqhRQ7LPJMa7oiqZFLO0yVUN6Ft5gt8nPzS4GRuUTXfCxBk\nQbij3SO8pMhMxl/G0Mm27jxX0wJkPc0bly4m8bh6CTkAaKmFPKjtY+j9Wsl83fF7AnoR+ecAHmD5\nMhdV/VMi8kcA/E0AvwjgnwP4JVX98pvK4sCMGC3XAC8EoDzRhL5MRCkdTmEIKy5NPBOoVoQkBSJ3\nvI+ClO9bKVuGV5qDjYMnm7rGxKagXbtook4+0FtXzDXIbxKhehvI5MUjCBhHHWa0t6mXfu/+OZSV\nZDsEiG3q9NuLNH+yD3zxy8tZO5ZlxenljMf7Z3z15QPu759wOp3d/+3b5emu6Z7EWBmTngwKDuAt\nsT7HTAvWKlWkYmqAGXGM5LETWyhE8SFLRczxCQVRJl06U6n6+Lumi0KogOLZRyiQHSIpxL7NwGvU\nH4g0Cr2j6WqWRFg7BhISm2Y9ZFM8UsQhWbtiXVZQClAeYOIGhIWweubLy+WCl5c73L0/YH+YsdtN\nxu7FcyK5LFBpTJPF79sDZzI4YOA/lPZwvyCFzeVMUORXStiulF85TuoJ2NomxzuaRyER6FM5UI5Z\nvp+dafSdbGk3th/RQQJPVaCQ5s8hQOgpu0aynhwZkq36dCs2qjXWk8kAWyool03ljuRQaFT6EnD2\noxy/H4z+P1TV3ymffxXAP1DVXxORX/XPf+kbS5EUgoyP1eEzB6jwRncRVCbvgFufphRMO0HeXrjo\ncb3z9Spk7Cp1alUayEVekDkUheL3IxMiLQr/emvBHnh9a4yKwXhf5H3z+bQUeK1a0u8p0UOcebUP\nzGfIXuQrB+D8AAAgAElEQVR/Pg5eJERy52xauakDIcmE/JzuLP1yuuD56RX3Xz3i4cMjTq+vWJcF\nWp7bmo/6W62mwk0/GXMdM99vEFNVcqAECNPentXrm1PUgb73RHJQ2UqZpNXkSeZGGdz8FCVURW0K\nHnm9CoIFtgLi2culrzPaRn1SN2dxdQ9BFzJEZ5gl576BjMlSd189wakDmGSOecQ/5pgxV9qK8/mC\nw/Me+8MOx6O97vc77HZqm8ic5UMU6wpPzwvMs0A6ME0Y9n8kIRjnD6enpbreEC3lswUk/gMXblke\nXZUkJG5RobhwZFhZLreXOt+9nv4gla6eVpvZQwn2nnMmB9FxSgTxsCxeUAklZWJD0BAMfszPQ9I1\numDLWpmM6bQ/5fhxuG7+HIA/4+//KoB/iE8BerAPxwZc+cRBNpRan6GYYeZISlF0bEtQ3+54HcqP\nQSDZqODbhvNu1S/89YVpJGujWG73BEiYuVluC3dMHfRbdY6is9IB6hW7YpMN+yXuJ9mPKL+H+RFw\nF38SZViZ1WxVqLPDBa8vJzzeP+H+wwNenl6wXC6WVthBvHsopbofms/GLPtQ86jKEYByIjqqtKi+\nr21IXmh+fs+/kh3mPxNgy0QmXG/n0vCZ2RcR8f3iTLBshOS3KU/hgtLhO43+Zd/n3oDKCAflE4vF\nHGTGvJuS6K4we/e4/y5oMpU7+fCqR36dF/SuOF9W7F4vOJ0W3N0d8O6d9YmILdZaaK/zTO4aXtdc\nL2ktQicpP/V+Ut+X+ZLA5nMg5ozHxiuG/EJ5uoO7y2srJI5yc3Oel8+2r8ba2f1hKhElU8ed4+C/\ndfZ/mnBUS+P6WrmG9xP1dsXF4zXDIZaiQl2x/gg4/3sGegXw90VkBfA/qur3Afy8qv6W//4vAfz8\nj1YcgDLoW/fNdgvUR4sAgV+ik4fuFwRjHhRCYXsE6K1bp76v390sw38PIkk2PbBGsusbZfkldcH3\nSuH4zElXTfZQo0DV5EyScJd9xbqBZCUUqt1iC/IsP1Pz0mzV3rEuC06nM54eX/DhwyOeHp5xOZ+h\n6+JgxHh5C6UEF4DdXbEd4UgdsBF+gl22jbs+2UImRnOQ93PG9quHh7gHv7hhria4FFOcfanwkW5o\nzL6vRYk6EFRFG1aJn2yK0gC9FSsmFwCLvRHAwz6r/WKVtmRi9tH6aEVflOloIG1GhAgO3aDQZbVn\nq3fFsvoiOdcFRDHNe+ymhnk2l0PvirWbMrksLh8RxpxWF5enrRvKe6GLIwlHyqPN2SaWbz/lLqsd\nFrQzemkTthFL2xh2vlYFYBaC+02aAN1jYNxN230RnFY553OkKxbJkFfkLOeomOXbfP4SXyaQ2Sss\nRPk2uiliN4UT2x/BRf97Bvp/X1V/U0R+DsD/KiL/11A1VRWpXrI8RORXAPwKALy7O/Bb0Bxiawfz\nRIujRspg1Tj4wggQ53g3lwRTFQ6DVQiZ/4Zvb7R//e62VWAVsM1Edp/YdGQz3q9x9hYgr5syyyQo\nf3zM27iAa82pDoFW/SuUTD5xgqwxmCYvFISzR5FJ0EQ/yiBCITkorauZ/6/PJ9w/POHh/gmn1xNW\nf/wfXQX0zVNRoLhrlEy9jIQYaqVCioncoe7GmYRRG0CAvG+82k7ykZ33AB+7Vx8makY5pKK0jTf2\ne/PqBnhH+cVKYisEPrF7KlUk4wUSCAnx3QvIcFo4gLm/ui6cexiowB4wQjO/d0VfFndDZobHuLuX\nb7pjhS6uhPsK7QvWZcG6HG0zHxRt2uOw3xlAdcXlsqKvivPFsGua6EohaTG5snUiC35kuuLoa01p\nsGuSJufUlujPJERk9FwQbzHXUk6vWfA1ayYypLuTgC7ack5RhDilXQgGOUMF+5HRJwEoi7E6RiJR\ndJgSgWMuTZny6ZOP3xPQq+pv+usPRORvAfjTAH5bRH5BVX9LRH4BwA8+cu33AXwfAH72e98Zul+B\nzOdhJ+ckKB2fGp/CnxE0NT9LsN56hygnv00GzRXx8n0IVC1Lxv8cYISLvFU7i0T9430IsBiQogiV\nC0H11ce5vJRlNQm2SOCAYuM+yDeh5CoqCZmqBtg30voIL77lx6AQOvR1W4A9u2/+4atHPD+94HK5\nxCYoixzh+1A1Wao5+R0nNfujjD+VmxH9Mj7NZQCKda07bLPJjDkxxVjcKFLncDJOjeemJrjAhsxU\neDB9l9IAK+8v3xodzJahm1TWPJdWS3QtP0vUK8JfHdOYqjY2i8GVFPWLqqdosHb23iFrB6RnvDd4\n27Ta4kEjahkyX/uK5bL4Q2AuEaXT2ozdYY95EnQ0rLrgsna0RbCbJ2C23EBWL1vWZGYD491sm8TE\nJ0BCPBZeYWy+yHCKcHP+YnMuggbaGEPPPhw2ekWb/V1fk0Q2Vx517ldLU9JC66KlJM2p5YXFmgKq\nRQGY9eHK0AuNCCXKGUqdqam6P8D8D2JnrIi8B9BU9cHf/8cA/lsAfwfAXwTwa/76tz+xPJ8whirD\ndnJIiKDE9xqAHhqyRL+IbCQiZ7mPPwtPsB1YeTk/B3v0v1cXjTHtCW2isLk5Fq4IrsRnmxVlACtw\ni8TKfCYto7kmpTneZrIXwHOF52YT4UQKQWvkPuDSa1oPvijcHSwwEKLSn/UdR8Ysod4V69Jxfr3g\n8f4ZTw/POJ/O9ozXbtlOInxSMzAWoECXP/dbS2agi8lHkA7W1RpkmpytIhZgqVjsaDCFanWNqa5l\nDL1Nld3XJerRNYcoRykHUXvSdLE2d4c6B4wB0AX5HQE+XgbO7QBS/PmexoEnWH/ZmkQwQHanKyFG\nOEEsZlzdMuA6SYQ5rhK7c9duUTnrYovsBvQr2jThrgPzYR9rNMvaIWdgP8/Y77zPmjm2GM00AWj+\nNDlf1026TdLhkso1Z45FTmlTREGOKJfigMn0wChzVXLMQ46pAJqiaYela0ZErGUART4JC96m3qim\nlfwx8CHwJaaslPWFxIwIR+UvEXmVVnslugqP9/8DCq/8eQB/yztwBvDXVfV/EZF/BODXReSXAfwL\nAL/0KYWRFX3sO3L67Y44AKiZJoFkA1u3yna369f52wu1G4SkAn181wyYp2nGvNujtR0gzXzV6wW9\nX0Y2TG1VyEXdaJOJm4pmEF9gdKISjyZ0Ru/IV/hJESzJxGJRf4JJ3iGvCqWAOOfmYhbydwAeytex\nnBa8PJ3w+PCE15ezu2x63rNEDIgrB+j4Vxdj63Ansy2gMLXwfbbWLIdKV6x2s6HbUzlxYhFpcXUM\n96kHXTh1EjooX21zYTZNf4A6+OBvNaAFGX1tf/QrB5T389QHzCo5KCsioTpAUmuM9VbmUerNo72K\nalJmuETct6/I+eBplk9i8ebT1DDPOyzLiuPdEdNuRl8Vfek4rx3necLhYDl0wkKlHKsx+9n3iVDB\nZTuAcXWTirble1TXDxdftwETvpy7Selb966kPDTPpDlBVezpbZDIBty5UxsA8/lQQAXiXggu46cF\nIF6XaSCTpogsHJZx/eLKIjFGyzzlGEJ89+2PsBr7uwZ6Vf1nAP7dG9//EMCf/V2Waa/+OUwXfsdM\ncpz9Aw6WTpTNZ9xezLwF+h8DenZ+As8G7F07T9MB83SAtIa+LhBRf1ISGVjyQFYvdrUSYFHu19I0\nNSsv4Sl5gvcfcrLXI6wTZ+6lg4MNQRiGGJ2dJxLQwj9MliN5Ciwx2+W84PX1jMeHZzw9mstGe07g\n8BWXKBg7LIa+8OEb41JM8zLOrTVMzTJx7ucJU2tYlg65pE4d5CJYloxdRUbsrJFpCUZQcKXnDFzJ\nqJ1ccNenU8JhpADx2EgEENPii06sla1PloqQU0Xmby/KV0fFYISIdgZPNzDXlWPo6amhltVSWZ/w\nNoUii7UREXOJrYp1Ubw8veJyPuN8OmO33wMyYVnsuss0Y72zOtRotGoR2YZAr6PWxWdj9BqB5Uny\nzAWmfv3kEF+BPstnnetcr+Hb9H8zRzz3Iago7PlRtKI9ZNXB3qyEdLuZdZ19FlihGk5c7oxmXZTx\n/mXEQ2lERluXvRTRxL9b7OQjx7dmZyxX9SuLT6Dmir/kKv6GXcZrAa8t89+y+/rdFvBzB+11OdsO\nHnaKQiAyucbtxpwkc/GwbtHWqLeXW/CVX9tmFdbH7hHhwUV4S0PdvPcaCRdVUziCV1TabgWliY/x\nGTZb62d43+GLsGc8Pj7jw4dHvL6eDBQczDq0uFJo6pLJen4bZ7XXVlnGPef+Awn31tQE+92Mw24H\nAbAuAKN7WFtOuNH6cx4+zKR8e2ttQsoZEos5bAMMJYv1FKpL/SGR9EWoZcA0ZVJuLlGrorw5v2nx\nVJ7jMqslvrqNysmwyn/r4o8gXAJ8fLulKYJOGeNvVHzWb71bJM5l6ejPJ4h0nF5OmPc7TJOB/TTv\ncJ4sYynU3ZqGl9tOzAgpLXtnvAc6N2ZEPSQA1nKSpSKTANMC+jHWUu43gmiMqwLQadhpn5wuFdZA\nRQQRYlulhM9hFi+3cQzKPKSMj9ufRrk3YlSUCyQ2brWfxDTF2+MKpGN7tRQfvHfYholXoL/ltmlF\nWVADh+Igg8GoGMTZq6TvZFREaht/1vWC5uFdw4MogMx1UoST97TXolwCxNInnQJdeVphpXpdZjAM\nYUSyVx/iLkxJNkjB0yLIHxkPJ5nBLvq64nK+4Pn5FfcfHvH4+Izz+WI7YHtl6jnTDc/o7ihsleXe\nYizFj8vJMrWG/TTjuN9hP++wrktJjpZ9L6XXEpypUMe9DbSbxu/yXUOCzeA2cd+pxI4hLsgzht/7\noissTxNJgrNZwWAJgErA69lgZEfVn/RE8lAUeIKDRMiu9bMDhq5uWTT7E6qjbovgquDmnRh7H5Ou\ntsh9Pl/AtSPRjmm2J3K1acY07XG4e4fDbo++AtO0w/FwBEShywJd1wg/TXYvIf8c3w7x7RbFKhLA\nl2gN2GNMGbTg6lx8uVcSUEfFJ1dAT7TWBtuJ3JlTKAQyRK/VNzJusAxyRnxRjzIKwhJnAZ5pU2vh\nlG0BAH++sxaxoItv+gkEevp8tQBK8GRGUkQn0YR3nRsgX4SngmZRAthqeUFEp/CeKXoJqWaaUcsi\nyhBOEhEH+hOAbonSVNH7giEJFdLtoRvQ4l3N15zJnWIiplxFRUXM3AzkpfUOLddyM5EJTwc8C25R\ncJqTLVClskxcgz0Yq6yWKfH0esbTwzMe7p/w+nrCsizo/YJ4+hPcL116leA+uI3cnM41Bdcnysax\nXRMEE3bTDof9AYfdDlNrltu+L0WJFIkq/UNXRwPzBPkEQvreWR+6HBhpI+yv6nrxnZW2Ssc+Iw8D\nmL8n/PkEMI04IMsgXNMws81uaU3TjN08QwFLa325WP0ir4NmO7oXTvLjXWe/G9hrl9hh2tjc8scw\n21S8/nSw8wW9qy2oiqJdXF4xYZov6Co4Hvfoqtgfjvjs88+hfcXp5RkLPGJEIpoelXTFXC3KJaBe\n2P8aQB9WGmdqS8Cvu9+3GzFD1JSyYOPaJo3nbhtoczWkLMsLIE1t41LBm7C8/B4V7EWMlPjoY4gW\nY92d2MVDR8QWwxPjLdcNyeCnHt8aoOdRAReAgSsTJUkKRdWw9fwwjzb+MGmM520gax8YeRKJAMcK\n+sP9yqRhtjoCJOPEI7FVuGdQlE29mRUUOyc5+Ew5UBQW448BmG9YBJEFMKV2rGRVdjylIR7+Pbgz\nANAtENcF8I0bOXJiNazrYr55T0H8/PSCy/mMtV9gKXA1AE5qmxWwCBxvaFGsplfLesqgKgXqceDT\nNGG/2xubn2bEw8S1e77xWulcvjSi6EAvGgAe/ceFNA2sLAqHp+kgR1QM2hXijxskgFOR1J29IX2u\nNMyj45CyWTzgVfO8w2effQ5pgoeHB1sDiXqMSiTr7DDuStkr6W4aSVCExH+5jMjzvdd6g0rH0hV9\nXS3NhO/UtCkgmOYVEMHr3R7L5QLxekMnLBeLwAKVJSPZJN2LlK1Y/1DaQVzH0vB9TzeAXt0aDuD0\nSK1g8JXlU+Y9QsvGd0JjGgefyxHFBYYJKDs7BphBEU05M7hc498j3WEq3MgYcUcw9m45omvoJF2+\nCkXvNve1yuonHN8KoOeC1+D7BcCHDiTcJkON8+TG+42iq6z+qmsEYR0M58YUke3JMVij/76u4Bt4\n5bNZx+ssRDkZDCHG3EqFfW3aYHudeA+E8rrR5Ku+qecEU3W2tG3leE+7H8/IcUpluC62Qer5+RXP\nTy/O5i/+lCyFiCXD6qrhQ147pwtBMiefSAWA7OPAPt8QM00N+3nC3WHGfp6wmwSrPzhowKkYugoI\nqXxi7QTj1BnWUDbH9Z4C9ln+HvAf8oJYi6ojosLyil/+lj9O1DYizROmacI8z7aHggy+MHcCGP1r\nN+tLF0+ArMd192LBCEdZo13xBwCThRH3XpSU71R+ft7h8fEBj48PePduj3lu8Yxd4ygNu3kHg7Ye\n8yHH2zSVJRorsuF9acDpi7HKfhaLnKlx8AzJHXo0iZj9FVkr7Wsx/oreBZG6w8uRqEzKVq2jJRL0\ndbqSZrkT5KWhyYRQtLRA6OaNNQsbA3vucI96f+rxrQB6HuPiZIK8fW6xEJvntejo6n/nsVUcqMIb\nf3AFkPcOoBfPZTL48erETYAcfP+1HP8cDzRGIZkVQBh61rJOI+/7un6r1vV4vqsd9+1auxq2i6ze\nN/7Z0iYgrRz+ouM1NFXNbXPB89MJLy9nLJcFa7fc6RaJNGNqswup4rycobqgo9t2gzULt+FssY9g\nmPhIMGsN2E2C437CcZ5wmBt204Sz6BhGWxQ4y8/J2CCN4Y2lTeX8Wwux4dIoym+0EAmIsPI3Q5jn\n+z9au5Zsu6wP+HmqHZfLGU9Pj2it4XI5b+pmbe6SIGBuwMmSzN16MJc6dk2UNrckdXsiEHmINF0Y\nAgN6EhBV2LNM5YSX5yd8+PIDfviv7rCbgHfvjybfasnRjvsDPjveQXrHcjmFgoj+gSuPWIhPELV4\nfMEkDM/kAJq7RhuZ/ATujhgsL+Q4Z0SYk5fWoS2Bnow+bqPcF9MjU24vssZ6BtN3wjQ522cjFNWK\nIba5TVWibqzO6TziuP7EAf2WTw6sMVw3MgCsXYfY6rwF1ii7gn0okM2fm1e8L4H+VqTHdeUrM8/7\n3Po81E1usKybmF7YtFXE3guAMuFi3WBb1ADa4szamAaQ1CP5uWQB5e1tIDTRW5YVr6czXl5OOJ8u\nWOsmJTUw2E177OZdsJHLuqYQO3qKK1bGw9eModxB6tCCXRMc5gl3+xnH3YTDfsY8zdAL4vomDSvy\ncWtU3hB4tkSENVNtvTpWBILhuzpkVQlxkLxbO9SfiLWxTKLnyngScWMjmZYz4cpAsSxnPD6tXq+Q\nCqtLK64Br/M0maK9LAsgzMYY2g9E+0KIMzIGNRqI2ojWhte7I9w87JfIdfR6wsOHB/zOD/bYTRO0\nfwd37w7Y73bY7Xb47LPP8DPf+S6W0yueHxXL5RJ1Z6QJYozG+WSxGZnyLNyiaPYkOceOVlw32SrK\nc53bCvVHFTZp6NIBls/uagLpTjTsC1vP9vrEfSkkxBZihJYNjyiWfcu9AJFWfBte6WPFfmBqj089\nvhVAD8A0XNrm9m/tZf6ymYQCiaRD/Fz92jzIwgK8ZfwFoDVAf7QJjnpFBsXA71DCFsk02sgetky/\nNDc2cVgNKKgEHjKBTOsAaMkICPDBxx3C8Oyc9sL4/DQRWf9ZDYS44YMLWoQ7Qg9ZGsciJlQMi+By\nWfD8csL9h0fc33tI5WIZEFU9SmNZscgF8AduXy4XqD9NqusKUcU8zdhNE6aJqF/GXhXdc8lPAszT\njLv9Du+PB7w7HPD5u4NFdUCw9DU30UiztTiN0bc2Kds3CFYRtRILs2X04gpRshwA4RvPLguO61kd\nN0SFN2N6Br/j9V6CHi4MkoPeF++XYn36HafW0DxnRe/dF8S7K18XjBCgDu0tlItMHlff7FkC3Zlr\nRqb7ztAS4gl0aBfURU8ooKvi8rrgoT9ivaw4n844nU74oz/3R/C9732O/WGP959/hs/evcPzuuI8\nTRbyGY+PTLVuUUw5Bs0BtBUVwEf0KZlxpC8oaz1lF3CM84A5tvegyrj9+c50949DgXFZyeaZMmDE\n+6w1rqvB/PY+jwM30CCyA/eHBJaRZdnizaDk1K3zefpJdd34gl0wojLRyUbtq41LRHKL8sC0QllU\npeFlNz9D6tk+cQvIF5yLazF8lKhv1vvaqhiayYpWhlcW6BLg/A4FgLoLCVf5CfRkVN0F0IpwMGpm\nvmaZKFewUnyXLLe2k9Ep1dUFCDoUS+94fnnF8/MLzqcz1mX1zWG8kz8vVjsuEH/E3YquFnoqvgA2\nt4bDfo95aljVdm/SV63qk0WAWRr284zjfo/jfo/Dfo/j8Yj9bmdb75Ghd1xAzoV2Kla2B/65Ktts\nNZlrsP0w3+uOXQIEojA+eUgEsZ1dnZHTfQf61WNxcOxxqe+8v+lHJru2cuoWfUS76MutzE9jDMX7\nqfkzcVteD7gf2S0Mv4cMTN4VA+src1S9Sr72jsv5jOcnRZsa5nnGbjfjcNjjO59bvV5eX3A+naBr\n9zBEU3XN5ZzrdCGhbpGId3AohCLDWoA9xpQhReLKg1O3xu6reuoIrlX0cq2TSi8vd8VaPTiD41GC\nLq8mt5mYkPKGAPcGcH5WkGL7BpDnLx3SGibJR6h+0/HtAHqpr3SZXIdBbv2nBaezQwiMm/PJRiVP\nS2VwoyrbL2INoCiQyvDpVdwuKg9FlbaQndF/F0ZYMCW2XRFb7hJyAgAICSp1S7SGohKy+lgZSJZg\nwh4i6nUa+y1cJqU/mT2zryuWZcHryyteX0+4XBbbO4AMJ+VuzGW1zVC2IFsWkzSzRFJ5TtIwPFzB\nd202adjtZhwPBu53xyPu7o7Y7/eYpoZ17Z7kixPX7RIBanRH3dtgFU1OXaZi1CeXjOvhqqEI4a21\nHOuzNlwZJrdqulpKL4jn5MmSJXI/FZst60rgUyt7O35jnUdFngrN3EbhR/Onf9FI8RaG8uWCJUpY\nZ6hEr4w9B1ihJ8XT4zOmuWG/azgedvjs3Ts8HZ+A3QX9fIpFWqs3wlcNpKKOUEVvgwDm9oCUuWX9\np5HUzF4yhLqSmbpD1kfB13i0lflStKA4YxdRoPWYy2QiiRMxBf0UuoEIKIyfd9dzIzGROsQxV0fX\noK8dbtI6fN3x7QB6IIUViN4JE3dg8TJcE9N6APHxmvi8ma6K2wtxZFyDoojC00zM8st9hybJ1Z9u\nfudrRPKU+ltlKKXZdttYkwCfE4y+vhpal4yVD4WgmHOHIU3gqlC11HGrYFlRVcVyWfH6csbp9Yx1\nudhDRHyjWCpAAL5rM1LmMv5cO5gaund7hBsEHtZoDAvdWPo8Tdjvd/4gDAP5w2GPabat6jTJB8Ut\n4uF/NvGHGHW2KfWiRZxUOSgjxk1Xw2KhnxsZMlXhebGMzccWZuTY+XlAhl9S3qY2Q9ztotCQNTBl\nhPLaAuJKkCRgfQzoNwd/83jRSEnhn6UzC6aMY5kFIIDTZTadIvZz146ODpxOeLxXfDELjvsZ744H\nHKYZeP/es1jyotg+x2a6MgGChrNOJDIkRXR7cPxk7Itg+F63Gu4rAmgXW4R1oB/aq9HFObHgdYt0\nnDlPNfp1rEPiQDmPxUppV5HiUEbYhOd+hFDeOr4dQK+b1/pD9kAObMxK62Sl5qbZ5EI3xOVGp5QO\npFCU+46TfDyGsiLyp7ozxtetmynvae8yqmIEU9ncSyFDeNm4QdvSwwKMZ5YQIYqRwkzN2M330RZu\n2lm/A4bEUAzpW84LTi8nLOcL+nJBXy+2WQlUIlwwZlIzLh8zR7zvOWgGwl0L/3VQE3go3m52kL/D\nu7s7HA977PaTRyCkb5cLsaZAgdammCz2Oo61ALkRyhpn32rWAZv+oKGFOL/+IOkm0NrTOUm1yIC7\nYtHahN28g6jislzQe/fHRALQ1VxzOQCguulcIIzt/5uqkrQEe/XIpCalHIK8tdP0k8ZjCvNZwKOy\nH5SKEwlln/m9tAPL5YJXdDx8BXw5NxwPe+ynGYfJFtSZ04nzUX0tgAGndFu2sQYxh+3+NrfMhRk0\nLvLYkMQ0yJgts7SniYBP2I0vBRHu2NHRIFDp1iexW9/bDhJID112GQQ2hE9JvnxOu5Ksi8xbXArF\nh2tc+abjWwH0oakK26yGNFTBlBcj2EumRagatm4/rp3L+ccOKvhPgOBBZTJE4pBVb78Ln6xclbM9\npAwsBZpHPuRbInzs2pLJ67WwGDLXqfnCEVMJ2E2tG4UbNnUEoJbl14kTfV/KyO/tCUTn8wXnVwup\ntCdFLYCuyTjITsPfDTBVsYGbZUGcprRGbA5qgLyI+XePhyPevXuHd+/ucDjsMe9mby9i4qCMS3MS\n0DwfTlf12P4iE4zrRtntCrg7TXwC0jWSY1UlNY7oq5TdIW6+7PRVmFUWue6FdS/OZIX9njTWY9zT\nomB0jirQe8O0cdtyDOqiY2sN+90O8zRh7Ys9QQrmaoHQPen3YUilwB/wk+sRjSHPlHfqRj6kJlJf\n+NOqzite0PFhEuz3Oxz3e3z3s88wyx1kv8PE2HEfx03LgyIQfPkdQmYRlu54TrXONPtZs08BjcXV\ngZbV72J8+qBtYl4VS68LdZbGvbVITBA2KuChPYj28GM1JNRDK/UnL+qGiFtYbAFjAIBvH1ct4W5+\nghQmbyySxY7KIK4ZNGH6x2gR1PuOzBxDj6cQSLCFgb2Xz1FGAVVObtaNWSzDwJRsU/gYqxkb6xgO\nDlUfZOvKbRIafFZkg6oPUWo/FMtjc/S1YzkvHkWz2rNgey8J1QCt/nhBMD2C6tQapnnGVHbekgWy\nns0XasnkD/s99rvZcqv4mK1uDUSrxYBIeY9psq37sQCabWWOdEvspZjbhN1kVsBlWXBeLsgHYniP\nMQcsvTQAACAASURBVASUE7bn3gRubQctGZdKLiRq7NouJMcV4mU5m2trXYoS0FQiCe0ozaWJMCws\nWj8XmFTuP5jw/nDEYb/Hsi44Xc44Lxcsvfuzdd2Z50+torVUGSVEgEkinzoZ/+BeYo57yVpfsODp\nCdh9OePueMTP/ezPYr+z5zigTTH3tSXrJb21B53bx+Ys39aSLRqIiDwAtdsAqeYK8IvEBiYjBIre\ni81AslhVq01C/72MiKbVkFaXl63qe1kSPAZMCfTHUFfKMeevOttV6LCm8SnHtwTorRE0v9hhkesl\nzkGARmwIAQbmOxhd3okc2HDr8DeqisrAP9J39bqBNBSAv+nLxkZZ1O89Ex7BZpomZ6jFt1pxnQBc\nK0G2Dvq+PQ7HGx254MgnQmOI59k2kB/YcOmGMIlRmJX72BkquS4L+rp6yGSaChHdotHhoDUhMKU2\n+0YqDhRdGWRurQl2+x2Od0e8f3eHu8MB+3mHqU2Y2uTuilwwpmJoHHjvt8mBe11XLEqJ4FpLNk5a\nsxjv4xHzNOPldAJenrDCFBjDIRPoE3xKMYVdWmgdI4EgDb01f2g3ILG5yfcXLBeodksJRPdH7CL2\nUah9GbKQcdcEB0q+UDa8jnNruNvt8e54BwXw/PqKp9cn6PmElWkAZPL5Ygvjpqg9Hkay/fGABFYN\nVDId6qkocg0AWJaO0+sJjw+P+Or4Fe7v73E87jDNkz30eposvBPACub+yT9TJfZoRXsI+zT8Ztre\nCBATwEmxemMtwZWRwAmWJocLElCxXSQsmobua2M2bgNosCtUo/dHXyRczl03MxeSUD5MyVXLOQ0+\n7nS5Tby+7vjWAH24SoCbYHtlJvtBMOHgjKy9bgHaXn8blD9at3iPEJaPATu/2yqAyLNRBpVlTa1h\nN8+Y5tn8hEPWR5RyvIxQWkXLK9UZn9xzrWCo4PK1KtNUlNu2XbF6dUa/LPEwCy5IKtuq8Dpx/Gi+\n2lgbWNMfSennrFWIu3WOhz3ev3+Hu7sjdp64rHmuG9ZOXKEx94pV22K8VZnJ0lxGQIJrjItme9vU\nMO12mGSCtLM/oKMo3kqzUL73sQgZgM3jSRpmmSyr6TSht4YVjG33yBsHET6IG6X4WxbVdqEw3qq5\nSRLs7U0+GIXrFg27NkHaBN0rzpcTznICiaK5nC36R6XZLuaQWd/M5psVqTfF749Ohch01D1q3Dtw\nuQAvzy/48NUHfPnVV7h7d8S83wGtYfbyXfUVK6l0KmjR1H0f2f4Rd72+dR6yBLUnXbGjYzdzcKgy\nvoNJkOzLPAyuTispjT+vEFMKa3cX8maxt9CragfyMZCVwNZF2U89vhHoReSvAPjPAPxAVf8d/+6P\nAPibAH4RwD8H8Euq+qX/9l8D+GWYQv6vVPXvfUpFhvER+sI2ddlka1MAfKxaVQT5vEjEhAyABjbg\ndrPNVn4s3rn1UOysb1IPX++v97zWrti4oaO1hnmahmsq2LPNXKgSN5mHRSWqf7bjqqLXbY9+0c04\nlN+VZWOM0V6XJS0tKt2oe/Gq0mXFBSrP0Gl4zGRS3udwsBHBfp7x7u6I93fHiLNnX5VG24MzfPo0\nVKDrWD2Jlt2K4Z9lx3EvbAqKS1/x/PqKpsDr+eS7StUnvxVFlcZmhTKNfrATBc1zuuwxTzNWARb1\n7IgeNkqpir73frDR0mEcDYiGKR/tGNZVyFabYJIZMgGrRzXFWPmazjxNmJu7lrhm0RLwY+drEBd3\nQmhUw29rfmPGowf4l3FStXqcz2c8PT3hiy++wPv377A/HiDThH0TTNgBzVh3L+0BktlSsZIZs++E\nfPeWILMWWuVUxu+tI83tV6NmNKNeurtiutjeFu5focuxBRmwSnBfQI8xsu+6c41Q6IJwe4p42hKX\nj6Zebuf4/WjHpzD6/wnA/wDgr5XvfhXAP1DVXxORX/XPf0lE/i0Afx7Avw3gXwfw90Xk31TVFd9w\nED6rL3tgmsJ80x8HqvG7IgDq04ETcsNWt9dHVEIB+ajkR4TnlhK5Xc+xjVrO27qlaJWrjje+Yi0u\n9AGQEXmzqdegDrOuGZXxseb5ImplLGqpidd1DTAvxmoqJAIVrY1q6UTZ3iDVsELQkGz+nUfYTC3G\nJetPDFaPtbcot+SE6fqwCZU54It0+avV+7IsWBdbb1iWFR2WjTEyNHp9I8pEYLOwOwtmRJF3aFip\nHlm0ritWtTDUuuOzjj0HepAP9qXXObxwW1DzhogI9vs93h3usN/tcL6c8fL6GmPdfO1iXqcAFSit\no9GVSvCMaBXf+UwZjrpvHqTO+g+bkhRYseJ0OuHLL77E3bt32B0PkHmCtoYdGmRqw+5TAj4Br8Me\nzXcDr0FRZfqUSrjCxeMzhHUfrmPflr4WcYkJl4zpRG0CFUvSp7GnQMAILoJ7Z5JDMH5eANgTtASZ\nlpiuSyMRmYbZOtP7nSlGfgS0/0agV9X/TUR+cfP1nwPwZ/z9XwXwDwH8Jf/+b6jqCcD/KyL/FMCf\nBvC/f+1N2KmFMVRQIchvk1WFDxEYfgv2WQHlShBuKYfb342CQKWTgDWAT5yTMbsD8ItE02p9VRWr\n27V8HiQnNoUP4GQHSLksLLG5yOZuxQTmojSLnbt1K9Wjsvcamhd9qMbW1zW31ndnSTRXFRrbxalk\nLKy8RT81EV+ENatFmpjf3e83zxPujgccjwfsdnMqMMlp2sp4NKF1xDBT9cgPyb4ss6PaBE1oenNT\nF4DebZGXfY5Ij1XkMMGX6wV1A45CjcEuFyzasari4k9yimfAVnkLhZRgGSUFAShzgwpng/ZTm3DY\n7fD5+/f43ne/h/d373A+n/HFV1/h9fQacjtPE9ap2YK1rx/M84z9ziyQ3le8nl7RL2cwF3pYNNJt\n/bQ1anZUIQtrk4wfCf6qwOWy4MP9A3b/6ncw73eQaQZkwl1rmGUHmRr1f5CItH/KX8zzJE7iydxk\nmiAyBdmoriDKEI3gJhLRMgNgcM7wv9Lfxu4zog9IV06H72Y3JgAoV2q4eG1zmIFVXLTVbnPH9mDl\nA9Ub4A8m655i5NOR/nfro/95Vf0tf/8vYQ8KB4A/BuD/KOf9hn93dYjIrwD4FQB4d3dEphT1wfO4\ncWNDgqBTg0LAhvnbQXMuOCzP3YDbMGE+AnoArtwvgrGs3HKfCgjExpL7xjltAHS9X03kRLM7EiIx\noiKYpNiGnJI7w/4m0J/nEh+/cIqplDrcaHMQHqtItlkT7BQUtu7uWL4vbiaptzel26RszRJg9sU3\nLmbO84zdNNu4q1oInqc3mCb6Eojykek9+l48L1AwITWTV9XYPh+w0YNiF8bm1WTOoJCjcdTjs5S7\nU3Mng2/JzARQ7bj4/oICg2jOfqMVJABhI+WobKf0CPZ1vGz897sZn79/h+995zv43uef47P377H2\njtYEX93fx6L/NDfs+ozDfo+74xEHKA6HI94d32Pe7XC5nPHhXrCsi63H+DqHQsFMkdFXkbcHEQln\nJMB3kBGs1eZn74qXlxO++OFXaPMObd5j2u0guxnHydJQG5vV3Ljl41xDkYNAOf2332e0iUDvY6cu\nNmGegAQ8+o5uSISyrsNAHk6ZYbAykJE4PuY+31d4iC8aujbEPnBllF9LRagk7VYXe4yNFhJDoLfc\nT9q3Ffz48XtejFVVFZFPv2Ne930A3weAn/2Z7yo3teSR2pqifIVdY3nl0nJtlHmDuapegd3HwL4C\n4y03DX3PAfiMmb6VNrmy41ofrxNZSeyIFcm4dxeiwPL4I1xpKAb2QYCL5Fm1TtV1JLwX2ZkziLyg\n+bNfucvVgd4nDg2HZHdWj7CC/P5zE8zTFDtH52nC/nDAbppsIXrt2HmWw8mDw61/NmFlKq7gCJQE\nXbjZm53LhzmEomN/wUFC4Jth7L09oEkBpYLKKB22pAkiGiOYIbhdjVZO/c8ORuIACAZHEAkfrOd/\nqn75q4mm9Y3db/Jw1M/evcNnd0cc95avH22H733nOxBYpsh5EuzmyVLK4zPsDjMgDcfjEYfDHaZp\nwvPLC17Or2jPdH0UMiLwBGstolgoZy3yK5WFbFOz0VcK4HJZ8fz0ii9/+BX2hwOO7+6wuzug7Wcc\nZlsspgJn9sfWbGF7EnE3HcfUErpJmwzkuQmr9iklJOYw8qFcoKIkMbvuc26RiBw20aJQ++Dk7AKo\n2j6Mpla3Ls3y0kPsedJUiCC42zX09zNggGBPBdRXvU47/TXH7xbof1tEfkFVf0tEfgHAD/z73wTw\nJ8p5f9y/+/rDme+Isb5cJrjJ2u2Ua9AN183XAHu4I8p33wT2ZKEVJrOcEbi3vvha33wd7xHuESS7\no9uJ7xvSBaG3+qMc6cpisVUR+j8byybcUPAt/b5ipHRlALC9gQ7u7E/rjOwk6AaRNBhza2IgP0+Y\nmpm+zUH+7u4IgeB8OkMgFoXUmoVvLqsVa/M5GM4kDZPM6P5g8iXCPW0nbn1IOjVjLqBqELsgdSjK\n6sZxvd6QnTpsuAnXF/taUzNzjEFZ1SgzxmbL6ZVWUSUXWV4SHVun2M8TjvsdDr4RSdAxScP74x5N\nP8P5fAYg2O8sqdb+sMP7/h4KiWe/dqaD2A5nGVd1ORh/l1hXIktNBewD4PXtq+KCC56fn/Hhwz0+\nv3/A++9+jv3xYBvi5hlNfL+EA/3cGuZm8jNB44lO5hXwkM8il9xxbT72DTm70deU4xax/IVlDTZe\nCxlSttmc8pkKIsbLwwS0BcPnps4cOQqMlxlrdh2rWt8F61+1JA/85uN3C/R/B8BfBPBr/vq3y/d/\nXUT+e9hi7J8E8H9+Uoke0z0AY93otAG2CuwD0yYDvgLe61e+3+5+/To3zrYO8f4GcN8shyy/MjRV\njBWOU1NpbMoFykKSCyOZSK2LWRWITVh8raqmLj4H8xY+TMHM8ZURNN5fNNLjSThRcdKjYo/CJvbc\nBLtpwrzLtAWCjv1+j7u7Aw77vcW5Z8Vwfn3F5fUV3Vm+ubU0NkRP0jBNO3TtWNYFl3XB68nBPnK7\nZz+yssm9rhXxuHD48ckUIyNydV7IGsvxfGGi6iZ7tnG8jjJZfip6Yii7yEyNeIFa/8xTwzw17KaG\nSYAJimlumN8dcdlNWJYV8yxo8wygYe02zt1BZlkXnC/ncNkMzJYyArNIOnRoc+fzc4dDb/CvfAbt\ny/Mznp6ecDqdsCwr1tV80fPUYu1mag1Tc7AXwaQ9NkuR1dM/ng9KIWM3qyzYMZU/QnKRi6FR4+xr\ngcXl06oh84ADvefmby23RmngUvMNfExkRgwYZU3re81d5b0T/E021t9v142I/M+whdd/TUR+A8B/\nAwP4XxeRXwbwLwD8klf2H4vIrwP4JwAWAP/Fp0TcgM2VQgoDtKMeHxXw/JyCBvm4i+KW0tiec/v3\n6/ICWD9BSYQvsTJ7B3mtZSIBaNABIohnyQ4FIwSOrqNg500CFJtS1K/nW13oG01cy+mukuBOKVbR\noQ8alYODUpSpignOMg97tLmh9xVLV0xTw93xgLvjAdM021OG/N7r5YKX1Xbd2gO/NZ/uA2f0U8M0\n7X0beo+HfdAvXycSRMLXq+i+89OtQGvkVceQCJSOSlnw8bBVZgBr9mE9P8ogLkRxA3SWgdbNfUfi\nYmW2sO6yNMqOxg7lqQl2U8Nu8qghadjPDYfdhPP5YtFN84y1C1TXiG5Ze8dysV3P67pe+asD+ABs\n+XwwaVCmWW0SgcpeBRYCu+J0PuP1xdIWr+uKZemY1w6dYLny24RpamYRthabJVv40208u7PgwjHS\ntSLsavMgSC8xYwVgCUi5E+caVKWUyTMNiFvOnTq2wrEkDoxysAX3+rmTLKgHQnS6dj7t+JSom7/w\nkZ/+7EfO/8sA/vKnV8GvQ+LVAIgBbh8HagJlijoLHNntrWsJiKX+V+cwVI6pRhtTilbQVkU8FOQj\nriE+DYsM8BZbvFIRIR8VCMZzKzjr5rehjGx01PEW4JMVRjkt858YhsR2jngSVFXI8YQett83gx0P\nBxz2OygU576gNfFslHc47PYAgEWAqZlpy7w90hrazqOrfGJNAkyTuRnmaee5f6zOL6cLLmvH+XIu\n6xUkEUkK6JYYT2CeG2T/FAVMZUHFZuliPVKipWIBSkZLllWcqllejkmLIZZQTFCNYRcfyJqQK/51\nBegCiGW54PX0isvlHXA8Zrz8NGGa/elbq0UDrQropWe52tHXBefTK04vr/bkJ+1OMEwOyHzTMZZk\naHBBIJVpawJP62lZkZFypl2xXlacXk84vZ58w6BiXTrWaUWfZkyTA7TPI3HAIAUhPpvHkfcuwyuF\nzEXnkymPUU2jRz/nRACwuOISOuFAk8AMF2E4eMoUH0TiHMGOKNOr0rkgXJm9vWfkai/K4FOPb8nO\n2NHtogXcK1MGcqJW0Iyea8mFczqn4uBxK0zt5vf19yHCheGUEgplQAzW1bWXIH3mYNqDUscAVbJ3\nyTjezLXNiZEgQ6YgSJAbC4YxYB0B3arMKCdWeWSNA+PirYzGu09UYA/9Tl9jnMpzYREDu6nhsNth\nv58xTQ2XdYGIbYa6u7vD4XDAPE3oXdGaPfR6aoLDbudKuAcL5ISZmrkk5qmZJSAS7oXp+RXPry9o\nLw2yatQNUe9qVXlbWwPXhAimCezZtwKuDcAXAwVdGnpjLpMMmawPcgYw5jkCZZhgmZEV3JSjoVQ3\n4CBwgHBAkvJ1MyV1uiy4f3zGPM1ez89weP/esn4e95j9sY4PTw94OZ2xrkBrHU3FFtqXBa+vLzi9\nvmC9XBKQJd6mMgIQ8RiKUDi2kC2AtFhAFRELW4UBuZQy+tpxOV1wPp2xXFbLH+TPPJgnk53euuWj\naUE1IBC35roHCbCOuZbVxBe6pcaqUw3lQvow0A7YPLm7SikbY5MUgOW7Nz4UN4lJ82SFLfCjypV2\ne/i4WQQ2BtotOtg3GoObu10Xx67zTzm+FUBv+NfKhwT9LcgPf56dENTAen1ONZcqy857y6YuEiMe\nbIUMHAqGk0kBeYFt1a/3ZSw4/YahlFywI3WqVSiafrMOtCQBwPO4sN08UagghRNAAoAlTGSEub9p\ndrFkUmkNOYD86gQVj8P2yJg2We4ZAbDoAsGKnT8e8G5/sA1P82zsRhuOuyMOd0ccjvbgEDLMeZ4h\nUBx2lvSKj3dc19X2GSgXYt1POwnabFE53cIcsJs9nUSbINKTLYV1leF54RfNLke4RRrDA22Muqqn\nX/DcRMXSDCUNUzg2VHaPq+d7Uhaa4P+n7l1CbuvW9KDnHWPMy1rru+z9/6dyLKsKqpM0EhvaScdO\ngV0h2Amx4QULy0YwCGnk0lEIBWloiSAIJRENGGOBgkGEYAQRITGoCJqkE4hChfKcU3X+f1++b601\n5xjjtfFexphzrf3/+5SdXfOc7/++vda8jDnGO973ee8pJnluZbdBQxlKqdKwxR3fSggWZcISbASw\n2qS1hg6BsJaKdx9fcFmvWPIV0zzgn3j8IX7u668wzxMAwuVywfl6QVqrFBYLA2LOWCiI2SRYnX9u\n47Zdoai2I1V0VLpprUhEEjo7Sv0g1szYfL2ilFVr7QNlzbheLnj9+Irr+YLycALlPiJF8jZiKFIX\niiT2n1haUlYWu77ZyptJNWjORkW1shvKeBPbWGXtwO09/N17JK/z4CCAxBQa0XhJS9KCgpKo+SLk\neSS2F832bvuuVhW03opT7PHC+Mnt9fbzuccXweiB7ST1DL5n8n0/VtuwRI28jBm70kbU/u7I8Z7g\n2Eff7Kdwg6qxjdwx5Lux2UNLJvRmJyInHFhSVMf4N2Pb4ovN4XRhzNuf/4nrWLZJf3+p+74VeDKU\nW22HyJi/IHoTsjEQUgoYxoSUEmpOgviDRHikKAlP0yhhkzbWlCTKZpxmDGnQgmOytilGBDCGYUBK\nSZOQfCRiw2eLpKjIlRCZfVMyQxuVmOpsZYy372pFzowJuzXWNSRAwiolk3ZLQcar+7XlDdEYYzC6\ndRTZ02hlIKpAUKOIqPdWQyYix4o1Z+SS5X7wuAWQhZswpMwBSBzQtW7Q/jhPePP2Lb76wVd4fH7C\nkCLyWrDmLEg7JoyakZpyRoorCBHLmvHycsbH81kqk1oBLqPBO+bHjTnQ4toVeccQMQwDapFIqkLt\nXmaeKFmEwLKuKLUgIqHUgrVkhBJFqwsFoUTtj2srJ3i7uu265eVYGQfmBq6soF8xDdUwjgHDxlVu\nTJm2/6y7VSUzbcIFm6171HIdXU3am0PooZlz9z8AK+I3AfAH1nTTbZpmqLzDbG7P3zuC9Ft0+Mzv\ndy+6xj6/Nx5fu46B769VNcIXtjHq3XBMRf2eZ978QLcWwQUFw/fcnTGr1uCMWcmoI8C77/odxz2h\nJ+nz8iNMeUCJWZp4Q2Lk53HEYZwxjoO+s9h5x2HAOI4YrNQwNbNWTBFE0BLEcn9mllh6hhJ7BWpB\nhqj+ZW2CEgzUkkHVopBkAYyp29itHWIvhDuKgW32Ddjo1vOWDuD04fO298/oxi1aqbKgoHJE5LD5\nHiQmIaIBiSoiCAsgzF6fr8okKBDGNOAwHTCkATlnvJ4vKLkijQlPj0f83Nc/hz/0wz+E57fPOMyT\noMwk2sI0HZALgRER0whmxrqsGIcrKgMfPr5geHnBRSNvHEB9H5PpzScaKWLZ1C0kk/19bL6KNjRf\n8ypZ1/pdqRW5VDBJaYpA1auUBpjQM7+BABth8lZxsuVF5Fq1lAL5OEO36g7sP3HYngyaMBY7/sDY\nrrn1QhANogk0bOavf2BnznH0rnWlSmP03xcRtj++EEbfM7eApt3cR+HmADT1acvAW11zN7zZfLgD\ntZeuQhZms95j4maKCRvE3JgDe80QQ4mu7untiRXcVPbQOh8SujBJoF3fvzc1W6Ahd311mCAzRt4Q\nSZvD1jB8L0CwMWlAUed+DO1Z8GfFGBFTFHQ+DhjHAet1Qc2rxjvLBgBzF0nDfp0xcusuZSVlU4zS\nDcs2SRBGX63nbC3ydy2CXEtFrdlpIhBJXXXbCCrv+vcHWoVH8G0QoFHePQ3JTT+uLRhiFT+JJPaQ\nOxsrs/sV5HGyqc0MVXJRp3LSUgJt7FGjZAhJfS0VuVZJxFHeMKcBz4+P+Pr5GYdpxnVd8e27D7he\nVhwOM37w1Rv8/A9+Dm8eH6Xt4pB0riLmAzDNM87nBbkCKYkArlPFMFxQKuP04QPG9+8QlguKRRV1\nc9mHotrhGET3CKvpbV0WbTVZte1kHzYie8ZKX+dl1UqecO2n1AouRu9a2wjNV2LbQUweEs/KKgxB\npIFRgvoDVWX0EnpqzLY5mYH2Rz9K1WrM8hA6U+AdVO+lSrC/T/8PLbtRe0beQE3WUiP+fU/fn3l8\nIYwergr14LKhMfv3llGZygeT3TtGz90mtIbjMSSEYJmWWr4WxohsYWwEnRnDiEEZayvrbkw+IaVm\nnqjVWud1CE//Y2xWbLmKAkDoE8P2ZitQL/VJE8yavc/+oi5+r0ezMi4XU5v3a39/AvHr/2TSVfAE\nkqSVGDEM8u4EcahxrajEuK4LSs0Y1ogxJalXQ1HD4ljL5Wp2YwBIi0T5mtsPA1yrqPSXC/IqzMIK\nPF2XiwiWlJBiBJMyUkAZpwlrc8qpeYOaat9moTkIN6unn1EnGEjn3Ryg0udWUGQh3ZBanjB0mz0q\nPUrInCD7QCSljEnrFqmBNsSgNV8SgAouGd6EhwjTNAmjf/MGx2lCqQXHccTLywXzNOHrt2/w/PAg\n/hGo65ECKEaEQcL0Pr6ecb1mlAoMw4hxmgAKOOcVh8MBaRiE3irpfuti5gkODnwGjT+647NKe8lc\nUYo6za0wFyxYQN655oz1umC5Li2kNqpxxjaQ/iJtakIUPUDA1krMNyxhwSoUKtkaib0bRF0tGqjz\neLs1esbv1RMUBbjQD0Hq09/sWUP+5Bea41fxZWPoxerXNIZeKiNnRi6MXKrkkug5Log+8/gyGL1O\nnGKDDRoNwYiIdr/tjF6FJmduwB6NB8Q0IKURMQ4AE0pdUbBojZamDrPjbDnENmpU0u7valqHPgG4\niaChHQjBkuFaQ/GhG+/9RdsIPggy9HoZ3fT5XKARPLEBirA5k/z9TJu5h2nt+R3H5YY2SBmraTLR\nugNBw9tqxjVnEIAxRBzGEUSzNJjQRCZBZNzqeAQAUZikCyeNvFhzxvn1FR8/fMBF46xFWDKWZQEB\nzuhjShpiJ6ai1g+WvLJlYRGv8IqCHRLVQ9Lct5EV5AvKYkIICUwBIbBUo+xuwIoQ1drRrodoPKRh\nmVVNFt5hLNhKsp4ryU9AAhNDAlIYTIQhStjqYZpwmAYAA4YgjUUAwhgjCCw28VwQU/LonwrC6/mC\nb7/9Fu8/vuLD6yuWUvD0+AQKAYXVs7PRbMwJ2BzbrvmIytIVuDNHNwMsCW/yVtwEhjPnBpDWJQui\nX4XRhyjP6aW/7J+qgpW9nJ8JGgdZWt/A+u/KKaTjlDGLdqTr1NvFfRPYfzR/QQEZkwaRBNLw3r6y\nKhx0EghdUJIKIt1LHYp37g9j/qxJbEBWs5eVLf+DyeiBG2ZDpu46MfXbRM9RdN2k6y2z1xNV8kak\nOCKlEUSEXCROO+eiTJ4aCPd7yi2ELWiqEbUxUveMXo29q1Y5MKe2+dX8016lCbKwf68gRZH6iB0X\nKHfUQ98Y1ARAj1AtmuHz7fYd89GImzQMCDEgpKC/EyIqai4oVXIUVwbCumqRqYQ08uaO/XsTkRee\nqopu8rJiuV6xXC+4nM94PZ+x5IzCIlRyFpQrqfIBKUmIJsxEp3NMzK3QGbSsA7GEduta2ww1x2fn\nKAeaucnmtJ9bR/y95iTPZzJkx0BRpgHytoKVGbkWXY/odFJVGEKZSkoJDKh5ChqCaBnDGgk0DiAm\n5CwmrvV6wfX8ius4ysimAISINVe8Xi74eD7j3fv3ePfhI959+IDHp2eM84zrsuDdxw9Yc96CHl7M\nhwAAIABJREFUFm4MyfZm0Ho0ABA1U7ayaTVbxg+/uglXo+laxZxlzL7WigTS0Mw7JKlzRzet9drf\n+9wYJnbNt7YWHyqn2Ne+5zmmdfOdfSa5FNJS0fazsCAxOzqT31ScNKfqrfNVzDOd45WtUmxb644N\nfdbxRTB62ySbz+7+e3ueMedbCviOKTCmEgIiS1nWoIW6/P7G4HdNvym0Lexj0mfX2jDFTTjdnTHd\nvB+2pY83tnTXBDY3+NTr3dz/RhB0N9qomrvP+g2y1ZQAQJyw4zQhpCSZizFiGCZp8swFoRZFxAGV\nCZelgGLGONeWK3EvGY6gYWdC2FbrJoAwxQROAwiEpVYs1Wr8CVNEKci1IBX2blT+bqpZ2SaxuOfG\nj9tmt/mlAFA1Zo7OH2N+IjUJQRKBRGgzQBU1SJZk6OvoKxrmUm6owpA9ALFlWQoBsfqtNARTK37m\nWrEsGa8vZ7wezhhDkDBJSBZyUKop6xXnjy8IIOQ1YzysoDTgfLni5fUV1+WK67rguqz49sMHpJ/+\nHtI4gQGcLxfJoCVlxjaVShtWPhwwzZkkkxpiD4f2EbbewabasL+9MX8NdwWhVMa6arXMyq4t96VD\nejQrEVh70NcxalOndI4lX0v2ewEDWmQMbPtNxkam0ehYibDdg9x/R+5r6nmEs3Vub9oDQuMV7W/L\nhNXPWL/Xz1sHss9H88AXwujbQSDaRoj0yL5nxL7Qn+DptwxMaoDXuiJXsaNKdQa18/F26no1fosI\n2jf2uVRvRGfaMMndExyaJPZ3aItOHTFuZ6QbEzsG9y8cIeztDuieo/PFZIaxZk6w8+47YF1s3J3f\nqGGSMUVQFFMOmFAzgWpBJalTQhSxZCHSpVTkaiYJzS7tzF7yLgTSyBSKAA8JXEagVgwUcJhnLKXi\nUgper1e8ni9u9jPUlIJkgQYKTU1miXipMGQeAWg98N30k46NDVGbuSqKtpCixHCDmpAGAqhW9Z8Y\n06qKeqMKFLXZR0iP3d2ci6ZRkasyAirKfFgZqGoDJJpuLRXLsuJyueI6JAQM3oUraQQT14rz6xlZ\nwyXHeQbFAa+XC7755hu8ns9Yc9ZG6BnlclZBHNSEsK1i0oS+7dG4oRVzihObEC6orD4pNuFqTJjb\nTbUgGTMJgs2lAaZucVybtet8X6jo2JOr7sV+b1ofY+okPVcgdsmFromwiaT2/M0jyOglNK2tF0Ro\ne7Sh9ib8eoa/dbY2e704a1UhvEM333d8OYx+h2KFiMKGye92oi+0OYP2P/3BYNSakbPa9jTjsqq6\n7CgezQm3QXiKBq2sCbBl4K3H62aIm/sBmuijzq2SC0ptdUQktn2LnpuDp8GJXuRsqR7dP/qNSP4p\nMzdUCd44Itv7NCHg89eZmhyDBYmhH6dRzDjjACLpixqIwSuQkkR40ComsjRYU+/Q0Pzu+ZZsBgCI\nEnc/pIRxGlCPB2eE52VB/PDRnesEeNXKlBKGlES4XBfkwihcUEsGUDFE6c1r6WculNGhVtUUgjoV\nI3Wt96JogmDhT5HFDMgaMx0QId001cSkGp+bhJQZCrOvnXNfnsz6jtYRy8x7IAICaQ1/wjSOOM4z\nxiR+p1pUW4qWKK5Ms15xvS4AvYJCRGHg5fXcMXoJZyxVInuKj4UEkdeGLJ249f4mdMz2Xp2+AECi\n6DZtB9uCd6A4+A8TqSnL+hywFC6zn544uYVoVpueTvu1cFDcADkbY+iau5Mgfe7GS43JE6wsMTcT\nr33ujL69piP3atVfd0Kdt6i+GHJXza7UXgBUbGPpf4ZCN/hCGL0tlBzbDNO2piqV0ejPnYz6932T\nRy9RxWFWwKBKrVyqEqqPxTaViZwebRv661TIavfgbZGv3iTVImuClF+NCVesqGv1LjWkv02VNFfO\nJjUfxgRpgzIMQYK2dd9pD282SKBH7HRzWq/+3thXBcBiGCMeHk84nY54/XiW6IogiUYIjCFqKGYk\n1AKkIUiYX9LoJws/6zYvAVJfh9xyKsx+HgHTogCMrxdhSLXZ042RpjSAiHC9ruDS+gpxzaqBqSYX\nCEE7GdlPr91Z4SwZEm20EIJFTOl91CbLZOUUlG5KFzeujIkI0qeVtOco2GnbfP5enEtz320FSD3X\ngSKO84S3j094Pp20loxmEJPQUXWG0EDJkjNeLhecl1WiOdiqVrYIE0Z1RsS1otSi3cS2VSxdx1Sz\nZq3wOjUgaKs9Aluy46bsgfooeoKiqMwerTerajnWqjHYPkBjwD0QtDUxftGjfb+I2aKdYXvMonKq\ngjnpUcBOE40+tVezhQaT0cn2cA1C1920A328ixrWtbb17mvalKqBAww349yahb//+CIYvSMV29jO\ntLGR/h5g3zMGagz1ViPwv1wVF8BUxUPO/QDkYOueo84axrYGuAmQHim3zbtjquhMK2jSO5eii1tb\nyBYAD39ks4f20TLU07J/tnmkI2ERXJ6G3kspalukF4aufdwIgl4Y2DDMAQzEFPH0/ICn50e8f/cB\n65ohLValy8+QNMs1BtQqmbTTNIhZIYZNglx7jWbykvUMgJbaDUHKTYDF9LKuK8qyaiOPFgFCANai\nYYsxgSggRWClilrF7BQHiekPMarGxwAUXZtqr+ORCJwOTHT0JcKHVcg204TVdiHKEjHBEkopWhUQ\ntIoks/gVPNtR18CqgUozimZKkOcBIRIiMYYYMY0TgIq8Vo3OkMVScA1maJZmY5BBaw1d14zrmsFF\n3sFswC1Zx8owdHV7iJyeK1UEsDT8IE3h5+xCz0wyBhlg7+nAxUiTIJ2hBiAMKjBVGwe0t0CVhh13\ntPbNnnSqJWeqzrC5CVUvM6wMHhCHPbhn3LZL27qHoFE2Tbo0EEXNgS+VP3U+mR3duxCFMXtj8uyK\nC3e3LdzW42c12wBfCKMntLogVsSr9QJFY2DOlPrFvF3wm/t3SEyATmfrRz/p7E0UJBFqjzR7N0vH\nJLHHw92m9H/DQgpQr1dQiJ0tH7vFC5vrbt/OmHkTNP7vLraedO5I66eAe5VPBZk/30fp77bXiEKX\nFFaKZFZyAA6HA56envDw+A6X8xVLXi3oGUTaZCQmcBVTzjhKQ4leMPcMZOtMk6GK4NDIkxhlJeYJ\n6+mIfFnAWcseaIOKWgu4FgnfjIQQI8ADxkTgWoTxaBQQKaNHKZ6Y5ZiQSLSRyl6eYO//kbID2r+2\nc9iHQAhDRApiq19LxZJl80PnexpHhCCRQ8u6Sh32TWfTiBC0zxCTMwWbs+v1gpfzC46HGWNKaiZi\nUNAoESYvORwCYZ4kUupIAakCIWVclhXny4JlLS6kPA69Q5Ebf5IeFuYagpjkYghg7RJWdC8Fn019\nK9I9V9V0qHHARIQQE+IwIQ0TYhru1FuymeHdbpT5uOEFn2ANuoPg4cJo8fyNydJNQbtmsun2hmn1\nXEGQQnsppY05123uctouOWrLuA3Jmx3/u8793OOLYPRAK2rW0BPdVLBsVeMag3XJvYW2N6jbN+An\nZULnSPX7bdH89uxGWP3GbygPTZVUYkKwaoRlwwhvx98Q05aZo70Lbc9tSL5TMxV5hBhBmsAj3ckq\n3H6wY+6fOoRhJxyPR4zDiPPlgpfzK3jNSGnA8XTA09MjXj+ekZesCKSqA0mdsilgGIOWPrCEr7B5\n9q3JrrM2MYk6regvhYDjPKGejihLloxcEFAZuQAlBwyJUPUZQxgRaABXcQhXgpgUYtRNCmS2DSZT\n07J2+YYmN/TAGslRxaYPqEN4GBAoojDjvGbQ5YIrQxupFDAY4zBiGgbEEHHBCubsm1wXXEOEGuKX\nkELGy/WCH3/7U+RacDocEENEignjmDAMI4CAvCw4LxeUsiLzhPkwA0GiclIE5mnGOF5wXldwLQ1F\nd8y9Of15o1EzSy35HIr4YixDOUancYaGtHKruMj9xJFqoNrZahhnTPMR4zQhpaE5OcM2E7mnGw9/\n3O1JRQntWTs+aaBOkur2TLTXCzpAaAy3g922t2IkBQdpM3fGA9z56vtDHa/m+1ABWGvpmq+U5iP5\nfTL7z2k88p8A+OcB/JiZ/yn97N8B8K8D+Ime9heZ+b/T7/4CgF+FeKL+DDP/ze8dRY/i7G1dBbfY\n9bYQe5R9j9lvzRK3TLQ/7qz/Bm3KOdxZL8jVP2Z2IdUYs5mdyBdVzAGhqd747vj1TzL5T13TCURD\nK9ZfM8UEClIDxgkTLZLiHjrqx2BJMSEEjOOIaZqQS0G4BEVi8vnj0wM+fnjB5eWC83WRsrNrwTgO\nGAZgHCKGMSGmsGWWKhTvqePyfjpKrZnChVFJbNcpEI7TsBFgrKWJpyGg8ABKkjQ0xCDJUqWAcpFY\ndKjACYSiz845S3y6a+I6O3foa7tG7GONRJiGhMfTCeM4Yc0V715fxTFaKjKLs/F1uSIlSSib5xnS\nbo6w5lVQnOF38pqVUmq3SpOVy7pi/fAer9cLDuOE43TA88MTfnB4wOnhCYES8PoRP333Ae9fPuI1\nX/FYCoZxFG2GCOMgTdhflwVLycpUttEgBoD0ZW/ePRdxPHNSVJ4SQtGyBQZ0avP0EFF3nwhSk00a\nRkyzCCPx5YhpTRKTwu1G1fH4onR7pKfpPjrNeUPb0kJfVX1lnZCw9YQyaTG7WClq0wBk3Zmtnk/B\nskhSn5luTDB4fPze0YpdeKXXtxFAINpBM+99nxVjf3wOov9PAfyHAP7q7vN/n5n/3f4DIvqjAP4U\ngD8GaSX4t4joj/D3dJkitAp+3HY9xDljqjJv+6Q6E77P+G6YfMdMoNqBA0W9n42G9tRk137ieVI7\nQ76r1SJ6tmqfIUQhLt24/kznKAAI3WVb0w1hs7nstD69foNg+jkB0FSiJqzsIVbPfnOnHlVD1PTL\n5YKcM67LomUeZEzDMOB4OuHx8QEfvv2I88ur+CD0GTFFDKPa5jttXF8Zbfa3M9x/LhulojABLPVS\nyrqC1xWoBZb9WqkiBmAcEzgGpCITPwRoKQUgVPZm4Z6/EJqPpKjWVX3htmMS27/EyBM6kw0xqIp9\nehoGHA8zjtMB1yx2+GVZ1dwBrHnFZVk8UmNIEr00lGYP58LdWqswBwNBBHRmRl4lLPLl9YLTdMUw\nzEjjhIfHNwgh4lxWLFzw7cePCK/AZVlxOp0wH44Y0ogQI6Z5xiEvuJYVS17gvqq7h0IJK8RWRVPN\nKAAFJE2eoqrhNjcoVE06JCV8KSQgJEHzw4D5MOFwGDGMQ1cUzCGg/O7NSDvN9CYcmjtK6jcTtiBn\n87bdbfdmzf45rGYyKfhGqLVgzYx1XZ1RtxBKQ/a9IG0C1U069syNYKhSJqFYa8x7nOjTx+d0mPqf\niOiXP/N+fwLAX2fmK4B/RET/EMAfB/C3P+tqsuxBQZHcZ6JueDw1DiGD9O9Cr97tmX2PfNGcXAga\nMugL6tuq3cdR3W7I+kHlNvmBgXvlJWXxaEszRoQaqlehjlgOzUTTPbvf8ECLKrB36q2hVQdWckEl\ntVvv0E9/LxuOF6Ng07IEkZWS8fr6CiJy1VPKEouJYj4ccHp8wOF4wMf3H1DrgGmMUo54GBBj6uLl\nlclCyx8QtuGWnfDqhbVHgeSMdbliXRbQKoXUkiYzhRgw0oDIQCgFMUsIbSJCzRk5W2x6H0obEEkd\n1FGZf5Z2h1JKofkNRDXXkM9xQBoklDPFhJgIqAXL9YJ1yTi/noHCoBgxJYmSqWqbDpB6QOfrglqB\necwaiQStm8QAiRnKzDjuj0HzuwjdMFYuuFDGWhkUB8RhQq0F13XF6+WMl8sZtahNflnwsGY8Pj6L\ntpYixnGUfgAOJjqa24AJ23JSxkHQsGb3aqBBW+cdbOq1JPOTqPlMylcPOBxGzPPgvYWNtiXaBapN\n32q5G+2Khbjuaao+JsU6tmsE5LPvKdOnTJg5f1Ang5XyAJMk1QUtTrJn7hoayRX6m7uOIjqZFXJf\n7ang6QWA1+Dh6tawJr8+8/j/Y6P/N4noXwbwvwL4s8z8DYBfAPB3unN+Wz/7zMMWrFvcIPHJxkVt\nswHwl93bs20T39y5Y25NSItQMZRhMctW48SrZNrR8281yfQq4MY26I+jJj/cvlo9/rsfpY3LrrZY\nBZN3/eKa+uvQe2f2YahPgA21NOoxR10b6y2Opm6+bFMYGnUOQAC07s04jTgcDzg9HPF6msFDwGFM\n2tFIqzPC6vtYYlxjnjaKjQlOv7OwSXs/Y3iBAigN0kQiCSKUhB1IGO11AV+lFs6YAsoStDbOVviH\nfg5tzSOhonh4pjEDq0aZovTAnaYRh2nSVokJgRgvL6/43d/9Bu/fvcc7+oD5cMA4jRhiwDwMrhVU\nbS5yXa7IeUUMSZ8h2kIKCQhVmEfdoePa5sNomUFYc8HL+Yz3Hz5gzSu+efctPr6+4Kr9X5f8iuu6\n4rKuqJBw36JMum7sycbQCdtiLUYBWmufi+xXS0Cz4lu6Z7mjPQNOQcNaSU0yQR2Y8zzgcBgwTQlD\nsozi4DS5adhj797R6kaD9y+24KoXYPtCudbovC9rbAzaCF62nYU8WrhoE7oCHO0cS4Lq7rOZw25+\nPHlLfnmDGwooqA5gzYz8s9jqf7+M/j8C8Jf0zf8SgH8PwL/2s9yAiH4NwK8BwMPDSRcUW0ZKtu+s\nEBOU4NQupuHPvJs4YOuIRXdL9v/ShggIpOFbLTrHmb0yGrPL05bSfAFaswt7NrsKZiVTjeELQdXN\nOD1Rh9Sxx43BC/5rYWG3gsUW3kqycXsWOqbdMW/LHWj1G5vZqsmQFiYoiSKtdCxDa7hogwViIDBj\nTBGnwwwMCWOKSFoHp+0m6HranJqza0u4e4bfz3cMETRIUS/r7pMsKkgZfdUoh1WLaY3jgEIB8XzW\nYchW79egz9ANzAiVwcGScai7DoAy+2kccZpnnA6Ttj+UDMvX8Yz3/B6vlzOWWnDkozT6CIQxJoRR\nir8t64pcsth0c5E51yxcCtKww2L0Y5c8w9Q58gwPhIDz9Yof/fjHuJyvYFT89Ntv8HI+IxepPwQU\nrFdGZgBBcjoYwFoyrppc5gxeGRs4ONYlRU3MjICAIUQJrYyDlGbQEsQcWuMXmHZIQXoCW0kDRBAi\nQhAH8uE44nicMBmi1yzfGBsouo3Q2vrSyEAhGs1sgQw64GDXs++VFvYI8Sso+nJMZX9wJ2RVywgE\n75h1G6rcReAoRG87Ojhd9Q5Xy9mIMXalErbz8DnH74vRM/OPfPhE/zGA/1b/+Y8B/FJ36i/qZ/fu\n8ZsAfhMAfvjDH3AfdYOO0caYpH5KHAAiMBfksqLWVexi1GzarMjVFhrYbtzG7AhW8KqqGkaOXMgr\nKW4YvXE+21Od9hA1fthr1jtDACoZASnBqOTfqJlE/r2ZReDEbDU+TFXXVwiNuDcMiG1jtu/MQdwE\ngc2Xvpvmh7Z5apFHErcuXaAYjDVniRapOtcM5MsVL+/e45uffoP333yL5eNHzIkQokRMIOpTG/B0\ngWIShbv36N+p/84YDIGASNp2UNu4aZXODTNhxlAZgzLPeR5RU8L5fEZ4PQOZ9DpqwkE3kzS1YERb\nCmJB+kHMB0XFbowBQwgYo8TET4Mw5ESEeRjw9PCIYRol1LAWrLlAWh4CQwx4PMxYh0EYfe+IkzhJ\npa8kDmVLyKrVozEaApfxFK549+EdXj6+4OH4gMPpiMuqReCKVaMkRJY48DUzzpczlrwia1JUybWL\nv7f9pcZOBwCsfQYSpmnEPB1BacJlueLlzKh59f1hETMWTdfvEyKJ0JnnEc9vHvD1D97gzVfPOB7F\niT1O0m5yGKTH7bIsWNe1Y3K0/bXT5MVpLSBuH0EndCJ+BhjSNgHgdMnd+d31itzbTt+Kkm4L2s50\nW7zlJPTdwKzOjoOfntlr7khlKTVdSO6xKRvyPcfvi9ET0c8z8+/oP/8FAP+X/v03APw1IvoNiDP2\nDwP4u59zz6AedVWOlBebNEtacTKgcgYAby7MquYCnaS/O2YTIsp0fCV2KFJPllrpVoJBv9zZzqDo\nP0ZtFWahd9xJ9I1QVwGzccQ2NRCGRGTdm7XHEK/NC/XvuH3bdltDB+o/6JEYTGCRox4HhYq+7N4e\n6WKvws3kwwyUdcU3P/ldfPuT38X7d++xnC+oNXfZixIt42oItXEF2+x31uv2zdq5brLy9dppvYZu\nibw2DcCSB6ARQhKLv7iztReaVpbY6uW7ULWNvtEUyedN3kdKPeQsdDqlUdBoADIXnK8rrmUFF5a8\nACKtB5QQTRj2FQ31GVBNgNkqGObGLNRxK7HrYioYwoBxnDGxdZHVSWEWbSFETMOE4/GE67JgfRW/\nR7Y6Ko7o9ZfO92a3EHmNozSNoJRAZZUa8DCaUyEKaFcnfR+dy5gihmnA8eGA5zePeH5+wvF4xDCO\nSHHAOE44HSXUMmdpKm9Ozs3RgSnrHmbvcY++rH/BlAawNjuptWwZtAolqCxoIKuFe4KigxijC9kn\nDZ1XNdlU0wB0Uht72AKcEAihaq18ktWzfsIcVDDQTcjIdx6fE175XwD4FQA/IKLfBvBvA/gVIvqn\n9Z3+bwD/hr7U3yOi3wLw9wFkAH/6+yJubp6HXiobkxVyF4YuBF+1cYATU9v72/t0kp6ckzkb7Bj/\nFiF7VEVfUK1n3KzZidQqEzpyRu0YKXShAO7ijC3Uou7G7G+rtLNJdm7T0rMmRx2k4/KWaqbhmHrK\nxiy317BTcat1T6ZlVG16oOe0kD+57nq94ic/+hHe/eT3kNX2bcW3rM43KYO5DY91jnyDhppbZifS\nbGJCs+lv/hAI6fcMKSCOUWvHk4eIDkNyxmmSrNcO7VFyTb8pu0VSrZu7H3NIllIRKIizliNCJHAk\njMOCDy8veD1fkYsg5FKluURVhkH2HvquFQCXov47KTKWS8FSVglr7JBiYUlgGsKImIKaioI7NW3M\n4nydcDgcEeOARRO2RIC0teonn9m0wAZEQGJmXPIVXDKW9Ypi9aM0ai6GJEvCVVs8WjkQwjiOODwc\n8PbtE776+i0enx8xTROShlWmlDCOI8ZRSosvy7LRhrfaqtGzAS747/YS5DSYYsQ8zyjrKgEL0uTR\n72HSm3QjmgUhEAFaMA8IKNxprPqsPnZeyIXBWjPTfLECmILMa+XuHQICCRAQOhNzqWWdMITGb318\nnz4+J+rmX7zz8V/5jvN/HcCvf/YI9sdORZeY3oxaFx2uebobqm5qYCOAHq23iIngvMAaKgSI+cb+\nFyCp4ilIe7KekYOhdnw0REld/1G9Z4sOMOFguysCGrsMM9GgWenQhtyZMkx42Pxs5L+eazZGnRQn\nuG6zuoDqIk12zxZC22sb0HZ92/mUDS7lHM6vr7iczwiV1cQj33On4fQadXMWqyj0Me3glw1u8zFv\nx6e/3ZnqITwQRMZSdqHWhsSGQaNlUgTn4o1JvEwtGoAIJJm1tehKmeqjTUOE/mTOWN9JGI20WCQi\nrCUDBAxjwmEeEQcR9C8vZyyr2MSXUqH12BxV9s43q+tu9cit/osV2GIWs01lWYNhHDBptFPmIn4W\nrUFkvQTGacI4zqCQMK8LztcLKK+bCReU3Oi0RZMJ9ZRavfJlrurQ1b7BUhIhyrN97RShBkHzx9MB\nb94+4+sffOUmGwurFOEitdhzzlLuQiNatiYa9lUzZm5bjrp5NHo2GmwaEfu7tk3s6EFoHuxZumZy\n4k1RYmX0wDa5CZ12pv/2ujY6dBtLC7eU+7XibApExJsLgiQgDuOIzz2+kMzYZlf3rUaGeCtKEZuc\ndDGSrDFGVYBCd+4GX6O9U9ZWPwCyMVUamyM1UtAqhWiMnqgJCZfS6srriAjy8eaZVYWDqd+sMfbu\nmFXo2swo8iyCObLMLNXYurYkbQTW/2bfknCCNVOL3Ui/ct7v6L69yT7u2XqsbpyiAGKUqA1Q66xl\nqrkLqxv5sR15N3W+WazqYD8Os523peyECPnsbe5u5j8A7jQeFCGmlKQ5igELamjPmkpXkqgHDg31\ny7oKY2X06FbWOMQq/XRLEtMZGJWLOGEPI8IQfT1fPooZRnpeS8XC3j7bz5Mxgra0YdMti5VOx2HE\naT7iMB8kszSvXr4CRI6ULdx1HAbJjh1GXJeLaMto69XG42xHhBEDeS3OpIqF/2nBNdEmxNlKxGCW\nPRSolbh+fvOIr3/ua3z99Vd4eHjEOImpi3Xt13XB6/kVcYmaiLTc1Hsx7cBBUm8e2UygDJohiDqv\nGWecgdJMQUa/Tscd7zd/mm8eEyQ2R/qIfcmIHt1bJA6rUK8dkvfvdspUu8byVoSWpT3k5x1fCKOX\no6lb8m9HflzV+ZrVJ2krF7wWTh+G19+v98pvUGXHHyMHdxIJo1enEQTxN1elWZ0bqnXUrdLaNQON\nvrFeogFSwa+qcJGa6HDiByzzsS8NcMsKHYFzeyFyStRzupraOpEw+77dBywhu4aibeM6crAZtmin\nDjn2gnMcRjw+P+H1/QdcPryglgKqwly9d49uQmZgK3RhH/oYfHxoa9kzd3u+m+v0QgOamzU3ZmhI\nTJn+MDS0u6qjdqv96byyCHvxA+m9tNYPSM0oKqgrtAqlaWtgNQSogNR3H5LkE6wHaaYSAYzLinhZ\ngcuCy7K6g1UyJrF9H48+0zl1+69I0yFFnA4nPJ4eMY2zCkulXQpukiml4Hq9Yl1WDOMgyWxDy3Mw\noX7j9ETLAiW9T3WbsdGvJD9RkB7BpHWKSTuAxRgwTiNOj0e8/fot3n71Fo9Pj5jmSTuDibZQVVuo\n57OPZV+9kTrC6ZXYng62lGVrzSi1YFkkcc7DkGlDiM38omviJk2LPqOukg+RJ0nuAcr2fm0vbWvh\n9GWI901JeLMWzOx+oM85vgxG3yPF3cLY0VSYrnwtbe3uG2bv6phJ5HbO9vakKrqgvQhqTL5j+Cbl\njSPZPS2uV1Rruxd58wkCQCyOFSZh9IUh9j4d2xa7tR8jBsCYGhyROG5tcK9t6P69lfqIyVqQAAAg\nAElEQVR74dkwfBNM5i+tYC+Tu7dZ74UoCAhDwpsffI31uuCnlXH9+NoiV5iBKEjOQ093a3p/pW8/\n7QXELfPBhsmznmNMQGK9jZEHt/umJIxN93H/on53iWMmcGCEmDCOE5gIa16aWq5MP1dGKGK+yCVL\nmKEyh5CarTwQYRwHTOMAqhUpJgARa6lYNeHIOk31jTc8VJEIVBXF67gDSder00FKIDwcH5DigMu6\nYFmyItbGQK7XK5iBcZhxwulGgN8I1A0dwOlQPiBYq04KUStQJoCSMveojF4KxI1jktpIz49489Ub\nPD0/Yj7Mvh7WxAdsfR62pr8tYcBwFtz52mmdjfD775v5tVTpXdx6NJgY0BIYFn5kmpSVgNb96OZJ\nB45NwNzWpukmsdv4W3s+o8+cbZdthUXN+dYh/R3Hl8Ho9dgwU/SEFtr3jkxwM7ntPsLCGjuze3fP\nUuRlTC1QY+xRfwK6KBvq7qGU3xtc2MIfTVCQ9dAUBs8hoCIIiraiYorYZB3JCSxgL+861u5024RZ\n/96aDniPVeqwjZF2G7VDIvsrW+RH95n9JkHrj2/e+FC++dFPcP74IgxK7crMVRJzCF7KtvUB3j3V\naLubb9sAN34YXcemv1C7HtYubvvuDIDU3JQGSeKSUFF/4TYMMnpjTciKOMwzEANezxIDb88slbEu\nUup31RBAYdYSlWXRPwYGUkwYYkIOi/gXfACNDkzrNGQPqo1uQZCuVfI7kDQhOR0fcDwcEWLEWjJe\nz2ecz69aesHi7y3lnvH+wzusdQWIPGzRaKvNC6FZi9sct7kVh2tMg2a6RhBFMMxGb7WSIoYUMR8n\nnB5PeHrzjIenJ8yHgxR/22VNN2LnhpoNtDjY68+307t/6/s60vfxd8hbmSpBE6AIKMReXbOycBP5\nvxp6XVjoWmGzo+4ezXSzHWsvEDp2Dmf66nvpQ2krV2nV+JnHF8XoYQtIgJhltqi9V9NcC+iROnTh\nNHlniwLImYc/B3C1PDqjt3hbbNOQqSF0P5Q4ggXdOyGIoIDGv5Pa5hlBelQKdG630TBRtod1zL5H\n4sIPTOgB28gIcqRhk7S3s99sUItA6GMTAX+oCSHb7GZ62YQYEjBME56+eqtaB6Hix7i+vAJVEIdk\nAgO1BgSNNHDTFEF6oirC2XgrNkydfGh7Ex23ifM5dJRFpPMknYQKDGEnb2zu2pWhQeqSVwBluCz2\n/SEipIR1XbFmiygyUwiQQ0ZZJWYdqsWklKSImHWGqkCI8nw+A1eteXPNKzIX5G5zt6x4YRDWtCOo\nGcZCi71iJBHWUlAuZ1yXBS8vLzhfzsg5e/Eso4NSC14vL7jkK0BSzK2U3OjLfnRSJZnQLtc/FMnH\nNCCmERS1SbYZPjVYgULAMEgvgtPDEY/PT3h8+4z5eJTOZJolK6U6uOvTsKNNpRk3CXaLvtVauys2\ngNpKCisJ13a9oX7Zo2pegaQyad1mEMSc55tTrxGzXdPC9mPYNgHv2iOSdazqHbe9yaa0H4ipsJmO\n/oAxetvUpibeJDk5XzHOy/4hYcu/jAkJA2+bv8eOeyTZ0DxgkY+9ucYLK3mVSq1voYg/WDgftTGQ\n2u6knkcXb9swvDicKiMEbs5Vgsfue+MQJ+DG7OxkD/1To+kGWRDAtYoJxS/E5v233LxjlruD+893\nEJ9CwDBOeHz7FqWI7fCbXFCuF/2+bQxl4f7bsBoze7MPG/x+s8gcNGbfVPTtOMUU1eN8eVaFCJ0Y\nAuLQzDfFkntYhLw5yq1uujMXpY9IQIpRwhzXjHWVEskIDNYiZzEmMLFGlyTEFMGQht5i609I44zM\n7/B6XfByvuC6rNLGr7a2cjbvPa+y5taBBa2yzmSuBefliqIlG3LOWJYFuWTtNNVQrJm4csmoZfVq\nmEZuam1XsgjKYELHTPV2mhEbotrlY8TW30TSICVFTLOUs354fsLT22c8PEkyGWnW9A6WOKE3xqx7\nzPsubtfeyd54SUdpLbLm9nz7295J9lKfAW4hoXA6Mv3dS7/Z9U6Su2ibjQmnUaodXsSutgYjkrxW\nXEDv8f7PcnwRjB5oi2OhjL15rT9nc/733M8ZW3c+cSMC06GiOuw8ysZQJbVnOmOx1QyCzBre7YWV\nMHgO3XVkVXSgfUWBUKuogLZqPf9lISOigH4y3PYMi56IShjlLmMEbvhy2+j9bxVCrPPC3XxvTJ32\nDJVM/kQChnHE4/MzLi+veP3wgtdF7NhWt8Wacbtg0nHxnRiijWlmQwjsmsV3Hv076D36zSb9bgcM\nacBCWTe1igki7LITmnalIW5DigAnXJcVKWntnCRtE9MQQSkBlT2rkaI0mkGR0hcxDAgpYSmM18uC\n87IIEge8YNxdXwTaOGu1xuPaYzUXXLGghNzR3c3lflj0Sy4FmVkyXZV2zQ8WQNLEROnXTKtbRp+k\nzo1ncJu2JubLaMXKjgc8Pj/g+e0zHp8ecTgeEFLUvWEOSjhjd62KmyN0E3XV92fZa6xkum9bU59P\n3cdG6x1Hl8gWvcwdwGiM3bQAlr6GYBZHvAkEn5eOxrdk2QG9DetWbaI2M41FYVkzcEleVPoHuTb3\nOccXx+gBbAh8G0WxQ+Of+Bsd+t7fn0yp5C1iJxKijjAi314niFM4nmkKDdFv+9Vacgob8kRj9IZc\nhHEYC+YNY+sdY3snmXwGIFgFxVFimVfgbm7aTqi1e9z5N/W2+vuEelt7v3uUhi7OxxPG+YBzegHn\nFQBp9EV7J+aemfZzcbtm3avcjI1Alm55c77d0dakRWuwZ1xLxyx/Qqd19e+l2JBI1WXGNCaMw4CP\nL69Y1hVpGBARRZOMESlG75ZG5ghieYIXMssZHy9XvCwrFi0o1jP5Nse39MFctf6NZEo6IMkAgpiL\nkobfha5cht0DgBYRO2BdV1y0GYujYEIDPKqRWUQRIYBZzTOkGmuwXq8WLSZ7MA4JwzTgcJzx8PSA\n5zfPeHx+wvF0QhpH3RdGnlUa1nATsrd0ulmZ3RcGWPTfNhYDeTZ3Fs3E3andzc3BbvwfyuwBgCqp\nzd/Cu6MDnh4Y9Gt1f48D/VV9lE2tQKlALtYgnTspA7/fz4LrvxBG3zFIPe4xmT3Dt4gJ2IYwxN0x\ndZt3v46FgCMBwcIQNRrEVFUjiv0zt/dhT43fj8kZvb1bl40HAAEBMYq9ODA0EeLetOzRrM4NAEvF\nZKG5hiKccFSYcI8buo1AOziuuuctu93O/34gdhuyP0JAGAakcUIaBsn+tHlAe59t7JDdz5BOr000\nFHZPy+tVeCbe33EjkOSXIXoRlDHF9v46Bi8ux22DNY1OkPBhnhCSvF8ukjA0JNn0IQbtLGWmBi1g\nx0CkiLwUnM+v+Pb9R3z74QXn6yJNULiF3d36ILox3UyZfCaZsepzihFDEgfnmrMwiypNvpkl+Sel\nhON8wBoTai5Avcrr9tngMCEKyRLVuHgpYKShhQpiKlPLvjbz2DTieDrg8ekBz2+e8PTmGceHI8ZZ\nmop4ghsM1VuoaL+47d0tO9VaS27pYbtH+7/pztzZ/JEhcbS5F1u97OE+ViwQW11FEeChdxTfP6xE\nBbq92R990+9apY1gVsFbCmsujlxIfj3d7oXvOL4MRi/ccvuRloq9h+RvLjfULf9ooY1Ee1q5g9yV\nkLusSBfR9v3NcFuFSxMe/TgaA1PxQU1/JtI6OgiyXZgQapVKhB1RusAmn6DN88HWeNjQQCO4TViW\nMidWxOxx53aCGqQbAWGDjre2zR2T6efLvyWAIkIaENKIEDMI5siyZ9v//HH+190tbnPS/tPRA3fv\ngw6e7eiJRM2W/SJjlhpKSWrbdLvPR2eoGuYIl+elEDBPI8b5gFwqPr68elmCqtmylpwlTr0KYgkQ\n4FpwvS745tv3+MnvfYMPL69Yc+1QbTe/bl7o16YHF7v+tSroxQEstW4YFRQihnHB5XrFZblIeQbL\njh0nhBCRtdbLPmSvyRFB8oESQpBwUEbwaENfF90bYpMfcXw44unpEW/ePOHp+QmnxyOmw4iQrAyI\n/hCA2pmE7mmxO9pgD3UkZ4I2F1ZAjXZ7ikBerkLmtbpJxCA8cytRLDdsGhlXnXdA6NrAFqij/4bQ\nfW+yZpWQCAhj1g4qAPchlVqRi9ro2UjaUvh6Ivl8Tv9lMHo0ov4UY9+o5Ly1MbsNnFkKEAl/3TKA\njvUKc1bCxQ6xQxgBK/NHz1AAzXLlzTjJmFePQmmjH3R3YGP/Grsv1S8bobTxCtE3xkx6T1P9pILh\nHqVQ999OXpCVGdB/qzoYfFJuZsk3eAusb4RGLpy36EleTroGud3WHkQWmrZdFReNnWBuygb5715D\n275vc0h6WQXc0lJjhlaMTvqqxhhMXjRGqoNg1kgskNTLAWFIEdOYME8D6uMR4Irz+SxFwbroFjeh\nEkm3qlxweb3g3bfv8Xs//Rbfvv+AnEubZ27RJqyUQmwUg40A9vnaTYWYpEaM44x5PiAEwjRXXJYz\nKgFLWcGMxuRjRIRETqWcUfjimpNZHYTmIogSIg3C6C3YQAmKSEw7UQXINI84PRzx9OYRT2+e8fT0\niNPpiEmRvPbZ8fesbKvYEv97wW9RZ3aY+UXKHRtRN4qQjPaguRtqigqQshVWXgGA6FoZnpFqJkR9\nsEXEdPq50yshNCc2mmm2UaSBrvt71EGUDa6wF4SrLIzeAJxFOzVz06128l3HF8Po5fg0k79njzem\n7SYfIo+dbvOgJhVjyAxsGLAy/FtbcM8g0K7Xc/qJ/qS2YZ9zh876sffvQKGzTbYzmBt6S0nKBVet\nW24lB+6Ng+/8ZWOysQT0QrO/Xp+t1zeDyH000YSuOvhCqwvvc+aC4bsORTrGZQyJfcdh998NXZHo\n7WZgNEEcY8QwDBLfXtfdVLW1t5sHSMmHUZ24KUpzcukgVLGuK7Iy+1IKUhSGUrkirxkvL6/49tv3\n+Oabd/jw4QXLkpVhsCO/1sCvsQtzllOTHM7X9oIsxoRxmjBPMw6HI0IIWNYFy3pt7DNITkBUBzGz\nxLinmLCGqM6/Ng9S7ymBgjD5QJq96puBxNwTxcE9zzMeH494fPOAx7dPeHh8xPF4xDQN6nzdzXNn\nk/ctwxZn0glpNFpDx/D6KGND75vw6g7Vm7/YnlejhNZyzWAUf+/mGzE66mFSk4RS3qJn9J/mB71J\nrjcV6Zur78+eAXikDd+/x89yfDGM3kv37hEYmrOzR/A9s9eTYUnYDdWZLU/qkwOs1fP04P6P3lTx\nCfuXoZvdx7YZG4Az4rUF09KiYICbykr6boGlFsqN4G+PRYxRqy4OWJfFe0feOzaEoAzYUf1uk7Tp\na6zcyLY934jed7aPS/Yp+8dEQIqEFKVeUAkE4s5MhiZc/FZ35rpn1L2mZKh7Ozvbd/jURtu/f1A7\ndUwRec3urHWmQOROOEDs2kMaMA6jtPojdWgeZpRS8fLyCq6MZVmlaJrGea95xevrGe/ef8D7Dx9x\nuS4oaicPrI0kuNHR3XW9p+GSMG2LvoghYhonzLOg+WmawCz15i9XabvYp9yvefGqiVXNICEk1JJd\n4BIkWiqlASEO0t+VopeFNvNICAFp1MiapxPePD/i8fkBx6cTpsNB5iy28ExjaaEnVfDm/Y0GXED5\nnienOxNU6K6R4nLGEBnkCVPk4/XOcCGAqpYmQbcF2fh7bz4zeqzqH7B8CzPf3Ad/jS7bWN3PxzIH\nVjOw1eyHFbj19/SM4d/H8UUw+qYaNmnW2+j9vN3fZrLpPtSSBASzjUcNQYwhgK0YGgCqynyqCQQT\nKnfS/qvwZ+JPCIAeXXRaAO/P2RA5meXDv95v8oZuusXuwu7uaTrfJe2dkSmKAt/H6j2Gl/O3ZRFa\ntUlq53fjCAGIQdRdM41tG5sY4wY+FSG2dUZyqyC6mZ8OdW3uuxUQ/fkuqriF68Ug9VhkI3YhmBS0\nOQQDsZVO8OqKuqYpREzTjJIrrteLRLFcLtI4hIHz5YKPLy94fb1Iv9oojDOzZNaHwI5MbQf0a2v2\n3L2DMXSmvKBCaJpnzFrHBwCu1yteX19xOV+8YTUA/zt0Jka3BYcEU6yChk/GNEjzH4uugZ6g9X/S\nkHA4znh8fsTzm0c8PZ5wPM4YjzOS1/83DawRfr9Ce7RKjNbwxz5D4w03tN6BbtcH/N8KXjp0bwOi\nEFCDZK87mPD7KWhTyWBFCiXqCR6RdUNnew0ECma7VLDuxbXxiWSTBwYSETgElKCJcyZ0Otr+WZD9\nF8HoAUIMgwJ6lcDY8nBmjce2aoSmogms6ULAIOxFkXzSzvIxBtSSUSm7rRXVygU45IWZeRxVaJRC\nLbKcNUgZ42Yn7Rh3h/dbKKU5iXWhgzZEBmnGaHGUacze5gTdnyUXnF/PHRLe+vq3amFPDH7G5jmO\njTrmaKqy29oBb4W5FwhSIEIO6xgEEEJgpBSkfSAxuEq9dCbSDNi+jZw+lyw2Xse8X/xu/BWErWwg\n9I3YfX46DcUGaoxGMg917JpRGkLwOPL+ulCBwhUpBczzLHbmcQIRoVZBxTlX1FIkESsEKYGwXPEa\nhImWNWPJklQ1DAMmEAqT0IJKOkuzb5FgMucOALo9TSTmMW+MA+sEJv8uteDl5QUfP37E+XzG+4/v\ncVkuHnfPLGV/S1bHaxWJG0KSLNc4yP6JEoI6TKPY85PVsolAjFJKIk0YLLLm8YiHpxPmecQwSJlm\ny3qV/BRdFptjK+AFKA2Yxq683ciQDIzB932j725uDL8oHfvcUROgsud6hl89Ei9QAEXSKpHcrUFw\n+iN09WsCdyaipi1A38EYfguVtQYxUE1brimmLTD8M4oizAoTCipKkXmq+k4C+PDZx+c0HvklAH8V\nwA91rn6Tmf8DIvoKwH8J4JchzUf+JEuDcBDRXwDwqwAKgD/DzH/zu58B7QnJzuw3Uo+t1EAraNYz\nKkf2HZoWTK/30rKgRj1dmTL/bfYx0qJp1m2+9tjWTReNoZiQcDu/j0PUR7e7V4nX9ldTAjBp3RaN\nut/6rpYo0XGsxu+3q83dGFsnqJZktbWVEG5KqxkCqlvm2W7bazWdsHXJoAhvlPDCkjO4FFhHHtOy\nxIavRiFH9rQZnd+S+jXwnaVzxt1cyL8bMwi7G+m81yqaHRsDMURvKN3jl2SbEzCkhHmepAGG1scp\nueB6WbCuWevIVHAuqKWilozMGyOilgmWol3iSJd6/iElkBbXai49nQ8SxmWGuiYojVG1NbYyvlyl\npDdBGqGXkp0GZArEf5JiBJeCXLObOWAxaUFq80zzjPl0xDTPntwUY0IcBoRBNYh5xvF0wOE4YRwE\nPEUVElH7DQfq1tUZMSnzYq0wYIyRYFDJ6MHopC2ngZRmojGrnM+g3otNI4cAHzFTiQZH2tYPAJKa\nYjioGYbbQ5spyLJhe8d/b07eApT9/jRBhqACBU07k1LYQt61e3ERhEWi8lg7bPH3h3X2x+cg+gzg\nzzLz/05EjwD+NyL67wH8qwD+B2b+y0T05wH8eQB/joj+KIA/BeCPQdoJ/i0i+iP8nZ2mpNuL8L+q\nG1fRJzeXjLJkY396JTkqMiQoxKwkVEXaSJ9OW7x++8HvCf8E7uAJ3eK5o2dvEnBSNJTbMuVEUVPC\nFUgBgnjqs7ZEq95mzPhYW/ztM/QwFNMxQv0YrqS6fRLtRBd07Z06udM+M56/oyP2/9pGtPtQtyOl\nRds4TUijJHMt57PMTlCUqM7amOxHk2/UPAayjeXBU93LdvO+kwiVtwk/baTt9XsBaeQg6+oeOh9n\nqRW5FkQirdk+CbNWprouK5bL1Rm9MJvqnchqlSQ7IpIMUQpAZCRIw/SUW+PrOAxaLbHVVOnVdQJ1\nrTXjlh4ggjmjAHxFzRkxSis+gD3Uk0CStxEI46D+nusC1Iv4DKj1QAghYRhnHB8e8fjmGYfjESFF\nqfZIEJQ/zzg+nDBOI0Ztig6WKpkxJtGUSKJowoZhWqqceUaNCvUzWxNqjNJNKt2atX3ZARwmbGrt\n7GjaKMKaF9kXQdeI2canJRDMpIJt3A27FmkUZ0Cv3XPji7p36PN79E8htLDOrnWZZZeDCOuatZjm\n50P6z+kw9TsAfkf//kBE/wDALwD4EwB+RU/7zwD8jwD+nH7+15n5CuAfEdE/BPDHAfztTz2DCNpU\nBBBJLBzPpGjPYE21ceOuCe+wq9qoE9w72MBdbK2pyDuG2fsJ/Lrub+9kBOqYY3PGsKlhZKFZOpZA\nIK7gQqiloAJYa9XOQtV7E++fCSixkTDBFt6oTJu6WPeuFs5WrW2/+3vz9j/bNemEaZvo7rzOvNPW\nSHF0iJjmA+bTA9Iw4LUU1DVLklqI6kCU0r1DihjnCWnQLNUQ0dtmWCFOHx3UN0a2DW9M3PdIJ/Q2\n763f18qtExAamwGJgzWNg3Q1ylKh0jJNxWRTsa7Zq1Tm1SKg5N7WZ5RALdbbyYAQqja6cYAjTl/W\nDk3OhJoOJ6xEmXxQpu2x3zZ+jbtGZYl3HwJCHDByAQVtXwfWjNhZ5ruSdIQqBWbypBC8McjhdMLj\n8zNODw8IWi6h1IyUIubDhOPxgKg9eaXnKiOlqPkJ+0TEzqHYralR2Q77+pz14dTd5VtqdRxgpq9O\n0+9Iwfd4rz25uUWZfK1Sbba2gIdm2kSv2Hf37PZtuM/kbyJmqH12q5mjVazUfTMMEu1UqqzXts/o\ndx8/k42eiH4ZwD8D4H8B8ENuDcL/X4hpBxAh8He6y35bP/uee9tEBWyCzHyB5buG8boF7dE4dTfs\nJlFs/A0FAB0b4G5DObNvKLc553pJYnZlIyZVQ1la7IlZxmJzNU5bn1PAKCy231yljrkjS+8oxTfh\nk70N0/7dftuYt4KpEVA7t2GRPS7cr4U9R67v6daAy94RK5suYJhmHB8eMB1PSO8/Iq8FYkITAi1g\nlBJQSkAFY6jSxzUlIJBkTN6Or5PK/ae6Dr3MqlXjpjsmsd94taqtWmOhpewyEJI4FytY6/O0e1QL\npcyrZJxqa7t9BJOZWIyhwemE4abIEDBEaY9Z0cxzuTSm4rPd3dPpU5mBrSsRoXKVukJReuNGjX6K\nJYoJA9pqULtLDWlAHQBQlm5bwZyrA8Z5wuF0xPHhhOPjSaptgpHzIsXyIoFrRllV6w5AHMRsF2PU\nXg6t5owxfYswsb23gxA3mmRbt+1e2B/2vH6P2A37Z4jQFOBkVSnbCU0IOXPX35U7ztTd/x5V7gMD\nej60H7qBQ0+w0ueIicaNdk7jro3emYNPHZ/N6InoAcB/BeDfYub3OwbEdNNN+Hvv92sAfg0Anp+f\nPOIAZC8O2xN2vn5JHVNUpuwGQP3tY2sTYtdv8cO9v4G+E8/Nwd1A7RlKHJXhdaJbP0+GayhVTDtV\nkY25DZwRtPn8TpWvN61sGXnv4W+M37pEGTHBmX2HyrEl1j3j6sflJg9ljjfjpYA4DJhPJxwfHnE+\nfsTKDK4rNslmIYADgYN2ZKpVy0EEtZ/rU6gJFtqsb2MU+uDtb32pzfjINjohA5qUUhVHkDgOU1QN\nkbw7VikFa16R86CIfkXOZTffveCFNzwxAS+o2xg9IYWIHMRsl5hRYkTQ9fEiddSR3IYIerzfEYbO\nrfkUhiFhGBKmskpQh5fAldOHNIAogRZxGDMBaUgY5wnH0xGnhwecHk44Ppy0fj+jlhk1S//WWoS+\nQwpIY1TNzExAOm7u/Q66lsrtzf/T72fSNTIfjr2pVfO0V+3X1fnDBsV350P9BPYMWJQzdXPZNOaN\nNoyGrs0+D72fjOE+r9gy9x2j932PzeIaeHJmb+fXKm0UGZ6UZ76Fzzk+i9ET0QBh8v85M//X+vGP\niOjnmfl3iOjnAfxYP//HAH6pu/wX9bP9JPwmgN8EgF/8hX+SyerDcbPE91VS3J7n6EYdUvqjA+2S\nONRZslPZWQu/e7s9UuFhRONMqGkEJjDsdMfDLnygjIoVpRepKWJkIwVtBOGb+trnDfikKELGZsj3\n1sNnZqNZ3DmsdVsIpIxJSZddRHUI455DaYeO2se783bPjQnj4YjD4xPm0wegFnCOACQ6JWhBMQ6k\nSFqRaogdI1dNiaUmO6BWnW4DO/rSuXDtju4jLRMMpFmTtuFYL4pdXXcCNJlKMhSX64IhJjAz8lq0\njV63SqpFbuepF7JwX4xlRZvHyaI+rCm2v76urb6V9y7YoCEYc9Q6N4E0FFR6wR6mSdZaTUPX9SqN\nSKrY0hMHUMjAckUlsb8fH044PT7i4fEBx+MR8yxlCzwWvA6oRcroMgiUCNHKGnTaVdPMjYnB18sR\ndmf6aIxYooikt20QQavNwTVopgE+pxT9ETbiQsYG0JuJHOOTlqHtmL0M1GiPNEuVWyN2o0FLVzWB\nwC1HwX67A3dTjdTFSdOsycywrUx1rkWcrix0U6xtZKlwpPiZx+dE3RCAvwLgHzDzb3Rf/Q0A/wqA\nv6y//5vu879GRL8Bccb+YQB/9/ueEyxVnuX1K8SmvUVr5PVgjJisp6uAfXJ1UL5rqc4Ct0nreHca\nQEdwVt/C0G4DBpZjDRFEbCYWuU9lKSW61qqNI7oKhPpspgpGl/1KoiZDnS+sNLapyc5soUM3app1\n7RG7/G2jgz3CFkIXfaIHxX2jcZmzHZvvGLprRfrZp5zTIJYsyXnC4fEB08Mj1uWKQiw1X4iktG1K\n3pTCmq/30U/GvaqhNqWPAO13q0zQGoOgDVXi0/uxKSJzE4/OC1lhPEgSTEzBkXiMCYdDkkzkUrAu\nGZe4IIA0M5kbMuugZvuTlU6tlC3remmYcBfZZEMPap7xGuwMyfVg1lo5bGR3wyykrEPU7lmSPzKO\nI8Y0IEXxga0l47xccbkuuC4rUKUhTkgZSBIRNB8PeHx+xvObZ5wejpimhOQCqEmUFET7kddo2geD\nna4EzTvKUv5ErgmaNmzBB1IuXEDckBJOxxOmecJyveLjy0dcr1fkoiUmek3HJLu5/WEAACAASURB\nVItx+65mPRldu2Zh6J18vRg6ngpwtStkvaSaZDPj+B6w+sVOd2ZBqC4MeqJsjJ+7c3U9DRDoZVLF\ntOsbDPJQZ4tKCvt99x3H5yD6fxbAvwTg/ySi/0M/+4sQBv9bRPSrAP4fAH9SX+bvEdFvAfj7kIid\nP/3dETf6Ej79UGZqbMc4YEPbtnkkVLGT5brYpFqBOWDlU4YXIFKB3cfXyknKDKpGa3cAbWv3a+Nh\nUlt7KVhyRuYKw/IuzY0PaGgd62YJqta7sCE0ddXV9k8g6u5oPN7QU6PwygwuWcqral1rK7fcpfI6\nOrrFwJ9+ntnnXch08pMAxCFhOh4xPZxwfn1BrRmc4RurMiMgeAljFyp+f2NjbVjtM/KPjeEZ+m14\nCa7t7HD2RouxDSiaT3PySiZyQl4zzmfp0rRcFwkc4LZR7aeNyG+9eQeZ46C+g9qd2znkWAMToAzH\n0GuP3gwZy0AF6gRx7I6DlCCYplGYfkw4HE6Y5xkUCZkLDqXgui54PV9wvWSAA3LJiMsIDgEPjw94\n+4Ov8fbtGxxOs5ps5Hl9aSVm1S480awJN1sijz8z1Itm+rM67oHhVV9TjEjabjKlAdM44jDNCCBc\nL1dkyuCgPXV7Ut2q3A74+oxsH3MH4qoxdx1UE97Qz+29G3gzX0nfFc4Ye08Tbbm2tvlmj2+1951g\n/PctzQIGrOB0+rnH50Td/M/YTml//HOfuObXAfz6zzIQ2myTJjUDOgbvu92oyLEMYIipGtKDlweW\nBSBHSHdGvHlFc6SSEe52oFqO1Zi5dA1aapHGEbVIKzIAfTuyamGDuglkowcEmMloj6TbZrF/947i\n73LINMajsfxF2QKj01juH/f8A+0Zja0KT97dpdtrDCDEiGGeMR2PGA4HlHXRXsu1Y37dhjCmxmaq\nYQdp/gAi/9zNNt2YbxzYd95xO4fmbNt+F0LANE8YhwFrWL1b05ozELWFX3e9MxjrNtZvVN5qZLa2\nYsbrGk2YJkqEEKL0Gq6m7d0KFtkCrUTBPI84zjNOxyMO8xHDOCGNE6bjEYeHE8KQUAOjMGPNGePr\nGeeXKwBCLgWHvCKkhOe3b/D1D77G6XTUaJ3SrdOeiXU/+rmh+gaI7MwGyroZB5EkIaYYMcSIqD1w\nAxFyzrher97P1p3cILdR2+d+d1uLjg4s0MGt/rt9w44AIeYRKy3cuPN27vWozJKAGbq16QSCPhx8\nhxBduOyagffajdyvzWt7lz+QmbG2/LKJDV02vKPfUiMUQNCu/fi/7YYVjRHZBrHGCiYNbYNzd6qq\nl1QbE9owEwIoNMRYwViyovmSOy95vxmofUYdSu1f3q7YMFqHCjdMdYMkOwHQf9/f380L3LYZdeqt\nXUO4dej2f/fEa3PYm5Z6FRRESGPCcJiR5hnhcgaXCtQMRu1gdednUH7A/qdpaA0E+FV33r33yTBh\no95uN+i2DriNxc5JKWnafhdBA3HKSj9g8musIxMCIcSW7aoyTbM/RVN0B5y+R+/ks/FHnQsm9h6q\nstlvN3YI0nt2Phzx8HjC0+mE4zRjGEaM4wGH4wPmh0dMD0eEcdDqiIy4rKgUENME0oJ6lSuGacJX\nX32Ft1+9RUwRy3LB9XqWktgdc7a68HDAs6U3Q54UzP/QXduBcVIkH0jyDGJQRq/h9euyCpN3p7lp\nMOK0r7XCzKx7Jn/3BzZmod1NY25l7IxdBIyF4HZM3iK1UKReUbAIu144OOE5ASqabyYc853shYjl\nTLA2GXaNiBso/lmOL4TRMzYeFnaO1B09g++uZMC1YIGZOn3bE21aiYHYITCFS9395BoPW8OWALx8\nsflDuGLJBWsWNG/XUc+cHdVs39lUOGuC3o9185Kd0HNGWustQfWv0yF3FyxOSAR4oaduc3yCgLYy\npJ8vFRn9PLNyWD0vJEmhT+MISgM4LBrWZsj8lkmgH7M904BV91a347w3fnKyaswczjgkYa26YNpU\ntGRtmF2LM3vr5WkDjdoiMUT9ScHRPLP4bqgwUJqmUT0BZ4viKAREZi0hzeqPkvmvpBFTnhciwiSl\nAdN8xMPzM968ecZpnjHGhBQHzPMRx8cnzKcT4jSBUlAmlhUHidYSYvLn/3/UvU2obtuaHvS8Y8z5\n/ay11zm37q1LKCsFaZg0EhtJJx07ogj2gjZCbKhgYdkIBsFGfjoRQkEELbEhQomNCIZY+INFiAQj\nigQqCUZETdIJVEXq1rn3/O5z9l5rfd+cc4zXxvs75vr2uftAIbvmvd/Za31r/ow5fp73eX/H4XTE\n/cMrHE4H6b7VQoYH3Su9RxqlHYslLdEgW3TauoA7k203rkoFU6nJbFN8zKzkswGvlSfxIoG78c8f\n3+tZGfIw33V9BMAGmHYDZA4hEG/uE1Gyo5sZanUN7erz+1UMv6/PSz8rvYtihVSBlQQ761uDRfs9\ndkz76ccHAvRxyLuYo8aYof6NjfklBtjJ08GdtJoK5roOfCd7f4g+JzpbTyVK3R6DDht4xzDy8Ket\nBchn+3BBPNOdtwkk7bzOLI5nq//SuzuWjYUQkLY4JCT0GvrGQNHskLEerGel7WSUS+8Xqq8sgsH8\nwQAoZ9mGF2q0fyKKPtmELUWdgxK3jULgpkwdgPlNBrkLFcrpfd1Uwd793pWm6TFTuh4eZ541RdHq\nRL3uW9NwR3mw7CN7wOF4RK1F2HiTkgaAlemAL9pSJQt4nmfUSTbHhtfwkUgNWfn60VIEQUzIAbYA\nqKRsl8y5LiF8viFFa+gta08Fx5M4Tn/mBz/ARw8PONSKSoR5PuB894Dzq1eYdANuhkSFrdsmRc3A\nKtRm39N3mid0brheL+jMWNZF5rYnD40x5i+1qZi3ZnOvpTjQdyV1XQedpgkTFcxFYvsn7WOb0/6v\nr2XSTdGFcMV8iMgtf37+pLU0yodwwloSHTdj8jfAlGMugYFOtqWg+Q9lXExAW9NDzInQM6SwSpis\neFWMJxEkIq2L47qz7D/cCV4j6LscHw7QE0kESk+DmhFAvoBJR/szSsHWozpi10UbWia5cuDWfA41\nbsA7b0soFqOggS9Ac0RlJ4uBaLTUnmEROhabLWczbBA1qaSzBvfuBJQ+M/b/0Cp6UIdWet2B5Bsw\n+l6DcO3A25YYkBFnh3yyIQj3Z8QMq83YFk96d6ufY5Egs9lei0zyXiScjVj7hT1HMTc9nMZ5kHyw\noo84R0HBwF7CXZmbAlC8GTN7PX+7JVl9+oOweRHszU072T7ceweKsNHD8ShZoNMkoaE+TwiyAxF8\n3AoXcFUSYCaLUlBt0XdGrwRCteYCJA7/zhDTYGteI6XWGR9/72fw/Z/9Wfzghz+Lu7uzCwzZ/OPO\nQb6DsbUNy7rgulyxtQ1UC+phUpBXFk0d63rF1lYAQGsWs50pjE92H4/sYzMGXYtkE89T0fUpPonC\nXTXfokBfMRcTCDIPu68gubcRHxuwolniiaqMyWS6+9aYtGXMJx867zkA1CqW9s4+lsKm4y19DSkb\nJfbIUlkvRqb8KSVd15GJWTjozQSk5TQQG5NLpJpse2lbQ95Q6N95fCBAn5yMFNUjyRE6QCcYrPxs\nzhC2AZObDLyZdEIWZUgeu7wHDbwE/VIMPNg36nCrAwBQQSFJwzRGb2zSW0AKRCbmnRkngNZvWB2V\nnsVrL94ZXCKywy0vpkVQBrtgQQwD+jhYLx5ZO7tAjHtYf4Sky/IzA7P/lqg2a60Xsb1ahcsE6UmA\nkgrNiJTKpoKxrfE081Hke0TbmLuH/tnzCiClAmzvVArQlTo2kL919rDKTcHVGKZVvJzmGfUwa1mH\nonXQLcpKBTmCoUn9eV3QtYo9ukhNHSsJgSJRSLD5rt3RAZQ2afkMAYfj8Ywf/PCH+MEPf4iPv/cx\njscjSiUPTySq4EoaFbbhel3wfH3Gum2SOVsnMTUVM3eGvTjGZocmDqTJhzaY3pKWon06TWKegULX\n1LuGgBegiLnGQN41hTQXHaSpgLlpHgp5oTxWtkuAm0GrhmlWgpM7J0Rp0bGDeUIBNqI2fhdOUJtL\nQtq82CKZqS1+hr6nhl3EJiXJhON8XwVkh9QLEvNsaJPTJDt7yUY29GJdf9vxgQC9HDsy6sDBw7fF\nz7ZBYw/ajfO8HzUe2+5FBK/BIV+9tEybDc8HDrIQimZwCniyD8JUKjpTZM4ltm/380lJewhDHus0\nByOj0sIDxW6YZMWLm+wB8aXUH2zCJlwzSXvZG3FdSFswA9VlUbrGB0VOcjYLjNoaycKNXZOGVu7M\nOaM2Et8Zx5TsRNO45N1UALhElqNBAbx3d/haNc1SCjaN7gCL2t439bskkEchVN2wxCpJYujLgft6\nPwYQQItUaalhYqBMmA4HqQpZq8j2bgXvJFKmTrP0QZXdxu7u7vG9n/k+7l894HA8ybVTUROQmHpa\nkxIP26rRK9vqCWoOyhTAI8EIPeYjj3sB5AijW4f9PUeOFLW/E9QW7htqE1gT12qN0h/uu9I1ZWZX\nKfMt6yJ8aBKm7BqS1eopstkQAV5kLs+tHB1jfgAbpzzPh/k3jiZIBQppLufeR2DP2/ePOWNzpI3f\nd7+e0rOK5Xyw8v3vQOk/EKBXW1i5oVrpxDH1yZY622Sk4hLZmFwYE0Zen4+RkWRQkX8LUUwwKG6R\nmh4Q8bcEhoRVFy97YItTmBfB6o4TFQkbZJtYZtcbhY1pIcby85tI2KcVdoNrKvtuiwilYBAGgjla\nxYRYjEQsinHCmS191AL4RuSOX6FgX6w/5QHabHK2updG9I77yeUE2yAkA31eUPJ+5lNISVIsQNN6\n0+u0P9WPUEnLKmuI5k2Hl2oApIAfYaDWi6NGNY6D/N3MDCJgJtQuwuZ4kugkMtamWxIyEyYARBXT\nccbxfMbpfMbpdMbd/QNKnbB1Rt82UFfNaZOia61tUmJC7c21TihTdXPN+xxurnRy9W6QGaKzCLDw\nTynExikGwDQgNbFoFJPVW/dwSOquIWualJIfOPgRoOzd7PRFWbbuP8uZXIUT3Pxvtz77aJy9UzYE\nNyWTjY11FlpJwzUBwzF/3/V8J2QIkrdtm/tqXjC4n3J8EEDPux9evIKzpAzY+jNHhExmTFIyYQRQ\nE+yd2e3kY0IH4j4YbWz2RAP+QlDHW1Tk6yy20F4kLrbZzu9FRb7VX9d2D0ufxmfEf4Mh5Q6RLqH4\nO+ATYnCQpe9Nou3F4PCON+bPELLI6txMUrJ3DpVVz7N7iTCMQtAE6GbIMlAWajc69kJrAMx8pu9t\nc4Ei2azr5hGx0ALs7TrSjrCYZSLyevCuhusze+vD5jOpI6JMcBV21XoHSh20j6yBCNjDhZoJOSKJ\n2JnnSfwspUjlThh/qZhmsbUTpGTENB9wvDvj/Ooe57s7HA8n1DKpaUirGrKAQL9ccX18RGsr6umA\nMh/E1DRNoJL3Vn5X9AbtwGs8whmosJ0APsxv8O9K+l74mJgy2HfJCqHv85MANpNPYatUIBvY2H31\nfCMTRU2kBZSyzJXJ2bAgnuWAriGsvb9k3PFzT++YgVvfK62trm6xFFwm2swNMN8LGCmclwWLRG9t\nTWzzW+u3HcXfcnwQQA8o+CZWLWNUokMRKozIdalymWuNZFMEC1fwUbXv2FBaV90LTXRUDxwg2e6f\nNDyvF2+ATlDGLxEOhcXaBqJQ7x2cAlgU5lwjCfYc+klm4fGuo6AzNu6WfJvktrigzindZ7SoNmR9\nIQDOL9lCWtMuZndCNAvhFxBJEKZl5XXT7lQAPEOZiGOnIRsLAF3LJhiVy1qXtYupw+okWbtM7fdh\n61I4DSzP2bYN27o60wfiOY7V6T9Svlc33aiS5WxCo5JThvHtFYRhws3yPnROHA4zqE5oILCaAGuV\nonBS50WSoSatHz9pHfjD8YjDfIBs+6f7GaiW23WD8uvjE7Z1wQHA6SjXUK0aptlAVLyiJUAxDiWV\noiAbUfZzjWva3M1zQlmFbpjuCKdXEIZtCFP/uimCbSbp+lBhzdqeyhKi0pH2a2DorBpJiZoANJw3\nrQ8D9gywDrr2MS1YyzuzR7a6plioq1ZC2pecgD2RFb+PhWxqaTQXFFkADI3wc0wAiBCI/Iv3PT4Y\noHeb9m6REUxaQhY5Ahzt2IOfTcXOTd0giYAqDPVugMRQc6XdzDt2MFNAB8UnkpldZCLl5Bxi1j1m\nRXU0ucsKPIWLJ8AwM7h0gCQywRyEDupEw/sG2IcDzVkQpX50/mgCo3iRqHkWByIz+05H3zo2sL1f\nZVSYWev3GBjQMGbxZL22FNmoYj7giaokTbGwclcOyOKyg7UbIzMbq4sSZU5NgcWzi5OmkYWo1Yjh\n9K6dO9Z1Qds2HA+TtxW7n+x5IEKdK+rBWLEKLQ9tj/HZL1wbjQB7ARIC4XQ84m4+oh7vwNMsNfqP\nB5zOZw3znGRbOdUKWasW9s64LgtK2UIQQebterni7Tdf4+n1N+DeUOcD6vcqpvmIxlbzZ9JSDuTv\ny7r5C5h0y8YQ3t0IBZPvk2o8IoM9WPqZURTsu/xebZEl7ZQZbBt8OAmSe1mlSTPZMrM4mD2kMrRg\nww0rYjuYWDg+Nv7Ghi1ypXtoJXxsrMZN93pEBKLqN2UNjuAuUXNm3rIxzj6NF+aamBEe1ulA3hr6\n1l1Qyt979GXWOL5DVbMPB+g5Uvbld/k+AA/DMrRsQzv2DoyQqD0BEvw+loQ/XqUPzqYPA9uUHTf8\nLbXz9osJS947sYQhdQRGmKM1ncPQkMsM2hGZMPRPunOiocG29Nx5nnE8nkBEWJcVjZv3vYNVyW3g\ndLvRaTecc+P97ZxSKg7HA46nE+o0YVuXEEbG2DSsNLCDdShCuNLYhbCt/2zDDKA7O3SgN2bUOBgm\nyVZ+rTVvH403lut79KXsn5qdr8o6dTN5M2UMJWsVrArDwcB0DsFT2eDjeD7h/NHHmM/3qPOM+TBj\nPh5lQw8it1tbfklrDU03RVnWRd5B5wH3juVywfL0jG1dQRCG37fmG94A0HrxFvSYpaNhPzvpYdNo\nYb6gW2Nt/wG61vJxhoqYfzmwAFAQ0/52wuZPCDMO7O9WDsDNJLo2UtFPB1UTsrfANrHk3FYD1hxW\ne8sfYfeVZF1ORfjUaZzecW/z9zLR3YRMJO7Jv83NNy6E8jvYO32HWPoPBuiBBORs/3ICER367CSh\nmEBjZEDcyOcfcZJ/toclKaDcBqqXDdyBtV23+y5eYH+5TMq9urhfOLEgMh0MkH/hlXfwHx/p55m9\nz4FXoGZoqrGp/UJ2ppJ57ihgbvkFxvcgHI9H3D28wuntG4nTXhetPS/gTJ3S1YndGW0kAJYtkclT\nKaA6Kwvb0LcNbHZqu8wmQYeG8EHLHgjg2TMMzOId499i2+zl/kWA5C0wyL3hGimxR2DJWBTM84zT\nnZR0nuaDb8IttnTou4Twss1BSq1YFnh2tVW95NbQ1k3eqFa0zliXBXVdQRbpUyyHIZLoFJZG0NW5\nZWvJujO/nYGyzcHKYVLhNMffucKYIwsYeWNPRuRB2Fh4vI6eD69nr7cKgFXt0OLi9/b2weHaZcP4\nDPT52P9OYHTTSHWe2y53hilZSORn2fahzEBrNGZpt8jd8P2k1d/XWwgkMwO97/FhAT2/lOABQOQT\n3sqAms0+H6NaSJncppPgq9jAzsDKuLOxSmuXT1hm3FrS+295+C7YsjOHNBllsfGgjiq2BnO8IWAG\nOznBo4QYAbz2ZJtg67oquBX45hb6d0eSniJVrJUcS3Ugv6lN+z7OgF8PM+4/fsDHyxWdG54fH9HX\nRXacgtVNia3sTP3Ni0iiUYC2dTU/FNTDjNPpDCJgWy64Pj9ivTTd+QlDKVvi2OOAIaaAmiJPcj/b\n+1AKEbScCpO/47yKQAC/NjNlmDCR74o6GS0mv06yb+w0H8JRnQSQTz8VeKUQaJZObtvmYNxbw3Zd\n0FpDOcyYzkfQcUZj8U/M0+SkyISgzUR/E4polvRlzP2dYA8NbJx3Ow6kbDfdJx+2Xm8srhdaJId5\nsyiRkPfYASy/ZPMjkx9t3wL0ETWXcycyBjj50HXBliBlb37jmszqJexVbO2tBWP3chw9TExNP9Fm\nzdPYs7qfcnw4QK8NjzDBDGwdvo2mnmvSPM55x22HtSZqlTl5/bkwpq3z2W6YFq2cFTbXQXsIRA72\n7cvHLrB2sttJs1YytJnDPg3VRiiVKzYW5fsTc3oGK8Pg9ILapm7x1LiAqGqVPm9wTFQz4YxSbxCC\nLk8RgAGomSUVsLIrqRYc7+7wvR/+ANNhxvPjW2zXq2ZeNkfOWtXZaSGAmuFoNup163h+vuJ6XQEG\n5uOMVx89YJ4K1uszHr+e8fj6Na7PT+hbB3WOUFkaQdPC/qAA80Kps1C9MulmJEWZL/l8sXYHqMXC\nDtAbgTsfRBBgV2c1WzkPRBhgnhf6g15Lvo9taw1tXbFeFyyXK5iB6XzC+eMH1INs1G5x+w7SToll\njrjh1EkInKXb8wyk36VZCvgW/USkVeb+du+o2En+fDvV6tFac16aDMn71V/BzjMNhONhQbDiZ7t3\nUyd935KmzQjrAeJnEOmG72JRba4Fs9hhfPzswQH4zcwx3MU31oDeRsGSI26soJr7Fay/1OTDv+ei\nbixzhfMESvY9WyAxDxzsY6uM7LRUZm4VBgkAJbWfY9oFb81Mjg1fA6Md2NK/gxCgdBd4W4yTmST2\nQAQ7OUksYyl2vf/J7pUyfqFCwgHW0TfYjZ9tYQOQ2i0bGETiALb+tRA38t/tfrm/R6BBVrRNKhgw\nUGJtmsxUasX5/hUOhyO2731PQL412UlHd7+SnZEOqFYbxw95t2XZ8ObtM96+ecS6rmI7PxxwOB0w\nHw+gItEwjRn96QLuWwrhtI/UJzHhIW+gZal1YnlCUZ2AOoGpoFsyvRZle6nhxHzYa1T5NZgojU8C\nP4s6UoLhNlj2HFu/mc0fIt14B8Lst3VD2xrKNOH06h7nh1eoh4Pu6DVr0h45uy6FhhIP0Pm53xh7\n4EXpe7iAk7eRMEf9QN1LJl0V1M3n0KG7Jvno2kTT+0pn+VMzWNs0tDXq5qWBuYf2bO802sqFLW8N\n2DZW04iVJw6Aho6FLDHVJnqUZfAMAwn29wg7Nlt80iQM6LvZ35v4i3pq/xjfn30IgSN2v/c93meH\nqV8A8F9CNv9mAL/KzP8JEf37AP4tAJ/pqX+Bmf+GXvPnAfwigAbgzzDz3/z2p6SOBcPKDu8djQG6\nSZo7U9JzDBRN0tMwhaTjNStSbjWai0bWYQzeED+5LLWtImCcSu8AxWE+sQkKkEcy1dzqFRdmtw/r\nC9sswRcHdCKkHboGAdFZSi07ZTcBt2tPBvRkEgtan89NvzsjDPCQLpQ66+VYMR+O7vTbesOm5odp\nmnCYtKwAYVgozECdOhgVvQGPj0/ofcN6XaWA1lRwON/hTp2VbRMVGT3iEyxczwrhWVQXMtsm+3uy\nZ2c27+Py0nQIwOuIW78TALbvSFEwR+rAZ0nqRva/B41wyBu6PdtIeu+gWnG+O+P+owccz2egis1f\n+jRCWy3IwciBvI+ByWiKGUD/htnGvwlVzxPmTGj1zrqBiWb7KtBaHxTvrPSeDtjjR7ABYJZyEUYK\nGFDmbMw4ZcljBF2rRJo/g7kmCdqQLoIppMJXgqg0XBLmbxISMbTZ3tWfzWiNHej3pqXBxNTDNi/4\npX3yu+yM3QD8e8z8fxDRA4C/T0T/k/7tP2bm/zCfTER/GMCfAvBHIFsJ/i0i+kP8U3aZ8sPYKOUI\nD11nCdw9Gmbn4QYSO8iseHf03nVLOkbVsKnbDllO8bPsJQzygnS+xQG+AHw7NSmPEDVQ7KXYr+Uh\nNMydrtGpPvCD3doEHUKImRrpbIRs85b9W7EvUPZ3V80ggballUvMvU7mxC45A5KVaM2PCwmXhImM\nnWX3FgCyTavskiRlc2P88vjWWnE6HdC2M1rfcHnuWNdNbcozaqk4ns5Y7u5xfXpGX9ao5U5pLqW4\nfnuQW7uUSArYWzEqEwgMDAG70XdZUL5g9An4CxGG3SmTILMxyICW338Aec7x3+qwnSacP3olzt1X\nr1CmKlcUKZtQp0ltxC2Axe7tRGF8xvA+A8gbAMosiMJh+Z205IQ6KkmJjm+2AQEu26jbCBrZHH4R\nrTKGPpPWsJ/nScbqekXTDNKoPEtje3r3qCsH/CZA3xPowkaeTfJYX1ggHKW4eP2YvT61MVh6977p\nXUjItoaj1o6b/gOW4AKJTE5mqfc83meHqU8AfKI/vyGifwTg57/lkj8B4K8x8xXAbxLRPwbwxwH8\nxvs0aN9+mezChPMiytEu+2vMoGA/2wQ2543VqGFwJrU33p1c/dMHB/PKA6NsyL8hY0sa+8w6sRUY\nfdL5vUjKnRYgavnA93K1AjfBZGIjDGeJBqbGhRIAZD+AC4aumaPDu4+L3J12WmiLGeh9g286cWOs\nmBMzs9Hh/OONGdoZFeRCxYT0jRERYTAVnO8OaP0MZpakJ4gJo5YCTIzj8Yj5cMBSCjo1v5/lO3hJ\nBvuPGul9qN81KfKr7dSa0fSW+j/97icm4NgvamAMYx2uNf6vY22gx5Cyyaf7OxABh+MRZZpgpgdC\n1PQBpM5+7GtsYZ/KVm0+YQT4TL7c3FPYBZlB/p4124e6PIXzLODoQ/LnwCPF9p8MivZ78RIj0feD\n8GTLeO0vwbOpczSBvfmurLXEGHw45ssoxFJy2NpmQA/yjUMIo5CJGkQK9FuYYPZj7u+r/eqmKI/3\nf+cUfXF8Jxs9Ef0BAH8MwN+F7CX77xDRvw7gf4ew/q8gQuDvpMt+G98uGACESuITzyZYiarufOPN\ngs0mCazr1lkjlJ0l8M8RK6M6mJ0pwZQZDOoACsMiAW1SG9D7ote5W4qYKghi/9s0pM9Z8LuxJLSF\nJD0ytjLLhMkaRH6XbDqwiZII6wBqSBOMwKhS32FYKN7HWrtDokUKuLHU2VeQdAAAIABJREFUU9mN\nSQay1Lhgt8kUZJs2e1idM8yX7wVIcbnDXHF3dwIz4+lJioxJSVzZyLqdzjieTrhME5oXKjMgyVU+\nATO7e+cQqSM2a5AYTIQxJkOvjow39ZuNh5Q5Dp1wD4it9cE3EaQgmub33An1Mk043lkZBZaa8q1L\ndi2K5w1kJuvas2lixhnSvYfyv7BM7NsoY20EhNy03lHVLi0htDIjRdONsuKWLe19NoRD7kwbOmes\nv1trWBbJJ1jXbbBdu2OzG5NvQ+y8bS4i5TASq9d7m4XTs259HrHkfmid4pFYsYeYGriz3tfaIJ8I\ntczHTaDX5xreiDC6OQQ3j/cGeiJ6BeC/BfDvMvM3RPSfAfhLkBH/SwD+IwD/5ne43y8B+CUA+N7H\nH7u9zEr9Ouj2DituBLMhGuuA2OfZKaBSV495NuZ/w5Rg12shMshvqqqRZ+pl1gMddLZ68trpsgGJ\nhQXZiimoJHVQ3BbYIpzRFpc5xOx/XrX6phCg+DDgYabG0hkJzpPo4VhApiKbl8wWMnOYcXr3MG+N\nN2fvIzBQqkR7TNOERhLKGMyd4+He5QFWPlJEYQ9lHrd31PFxQZxS6Zm7siWSjFY+iUAHYVagl4ii\ne1zOd3g6zFgvz4lDFicHVuvbw/rI2qYJWGaX342FMXfryqwHZcKQOmAQWlSgJgwzbXT1KWygaYJl\nXTvY6i064AFNrKGiZpXy0NwqCVLXpyc8vX2DvnUczmecXxFQqpdc7gr6JvjINJuYSP4uA2mw9aNj\nbf1GpMV4vYok+fxp3FCaZsZqFrlU5IQzZ+i8ayhRLdSCCFKb7OeuF/QuxURWXpUtR0apkaqBzTO7\nGaR3KNjaOexgzMbDiNKmRWm+kuQLyBSJucnomkQn4C47Zu40iRuaRV4y2Wog19p7mO9E++/9fbHv\nB/RENENA/r9i5v9OJhr/JP39Pwfw1/XXHwH4hXT579fvhoOZfxXArwLAz//cP8Vbi5dmsNazRlKf\nMiMzxoUBUEjVcnJVTsHDVC6bQEAK1ySAFahgCzgWt+0rakdJD7UqlpsmM1iKOLRq3tY7in7f2pZq\n0lAU4aIAC8mCTbBh64nIAmSs72DTQd4g7PTWY+5opLRAkARBZ9i+Z2HzNAttdmB1dA6FWwRhGQgd\np37VjvZfBy0s/2x2SOaoQ57+nrGVkMVXvGUtFacjYZpmuV63daul4HA84ng8abkHArdEBLLA1I84\nWRWQWSl+ibyKuCpiZXzckG6LAMh4ARMKan5zAJXX4dbAm+xkxV0ScQLmg5uwj4/8p0PS75Faydyx\nLSuev3mDp69eSz39hw2lzqBp9qSq0JrCtu4WGxsL7Y/88zA2Bvb6bpa0VbwePun80hpDXTbPYNdk\nLI0/8mUEuAHiLlykB+DZ+7FqRFwya0YqSpazSU2oJ5epCorG8PPH67v7m1jXkheBc9MRA9xFuJGC\nfArvYmbw1r0vM9iPWrf5vOD9y6pxWHSNyDzZsUy6hLzd73u8T9QNAfgvAPwjZv6V9P3PsdjvAeBf\nBvD/6M+/DuCvEtGvQJyxfxDA3/u2Z5itUaS/lSVlMCvYu1SVo5SiKeSi8Fv1PplXtlvOmNZOBKmH\nTgB7MTQFTFPlbCnZqqVQrSVGT0qhmtFbgL5j65t6ziF/M67YACqbTHZmdeQqLA8qvvRCZk8x9pTO\n0e/iz6FtGFsCD3gWnvmXKkJWN00wyN6aqj4TJ3t7mBtaY9mKTjfoeBHPuytPcetw4ZkAJJvQXpgM\nsu07sUgJx5ScgOfnZyzL4gAg8fhzaAvaD6FhULB4ofIOToJ3NqeUPCCIhn/vSL93Y8aR9wTu3TzW\nPoJahyfmoe24FeOTBK8JRdicCOLBDHBjLM9XXN48YXl80m0QCw7HMw6nM+gwB5BDNQvT5gxYVE3Y\n8SjY07wNCmBFNVybd6KlVjfv2fwvDIn+IVKzQ3Mh0omAxrpyKGrOd6fm1luuyTqIEoBUA2b46FV5\nfmWlJAtTwSAx7URtP5K26Xu5UxUSf180JNakto0LNSU7FjfPGqufgB66Vm9NGhuHEFw92m1C8tuX\n2HC8D6P/ZwH8awD+byL6P/W7vwDgXyWiP6rN/C0A/7Y28B8Q0a8B+IeQiJ0/ze8RcSN9EjapvbPP\ngN7MDHIe+bWmt7oWkNRnB08VCAzSKomQScaajZZUUfjiDI957IVK0V50bD1Smm2RkgGuqndADqsc\n64CzLv4OYIwd3/WRgqCYet49yhnAgQCnmz4OHgWM702pzlreNcdUSqmVjkEA78+xZ+efgZdRBgOT\nHwTgePP97+6YJqkLT6qdtK2BiNWHrQ7xZJf3J9h8SILDxsi0rRvy8cXhgQE7weltvHG+7BPLLojD\nZiwbaeT37RomaF1jpi0OCWV/QNs2LJcLrherdUNoy4Ll+RnbdUHVXayyFpCjWAz0VbYjaKOsmb1Q\nBlhldbxloYJ5miUfopQUJij3tFpK+3nQmEO7MJDPpYFV07QrDC+kPaTAmKJ0eIxJ1y5KYB92cDEb\n97SOOQRBinazawGWZKkG3Q9XgxuS9CCzzXc5103T/twE+Emryf4DcRCzx/FzjuD73QyvZOa/jdvT\n/W98yzW/DOCX37sVSMz0xvemCuWQKltX4jjRSQhCKYxe9lAaAwyw5buoRLSAQR5V2syUkG3wcXQI\n6+3d4rTN7hodVtBdA3AUcXN0MFixBY6TUt6VXnyYE2v3F4Q/+1v72JlyGYHDn6cM3Vm8oN9e8Lbe\n3Hk6RABhbNce0MdFZ0DWkSOJ9m2+9bP3B4yJscbST1iui5Qg3qSIV4eG4SZW7OzdGqxzAqRApOGX\nJT3nBfmgHaHYgb3+MBCXUoqYbgrADdj6Jlph61q2wanrtx/+HLcViABeVlyeL1iWFaiy2TnKhG3d\nsF6vmE9H30T7xTN0rZEyZPa2A+YDMFI1muTGNhXdROXh1QOmOmFZrnh8fMTWNvCygGrBpg7JDk7l\npjXdnxFmVo3wKnSDuCGYrmHEWEO+B3nMLNrmjEa+DLHqRlI6m1HpRtcrHqCjU9dNAsOvZc8oCsbO\n6NPcH004cC0BybqRTUkG9F21O3dkv+fxYWTG6iFMXN1jumgcoBUQJURMnWStw8OXuoCue7gpCp35\nvSiAOAnepBHcEDU6uTuQnIdwYWGmXc4X2IJgqIAgzZYrcMeNnU3kD2GSZ9zSRvJm0gI82W4pAP1t\n9PMFwDqNfXleeo1M/HyMQiFm7//hOal3gwXtAHD3zL3Gsf99LwAHLU3bWKeKAx8ABparLRwRagaI\ntzQQAEhnx33TsRe2+fv8b2qgZ04ObTbQAqnGoVULtxXbtqG2JmWJdV74ayZNEvrKxlrl7wC3juV6\nxXJdQEQ4PjzgfH8n8dfNCmGxO/xtcxWfjUnwm18k+l3rBKUXknHT0e4dnUJon45nfPTwEQ6HAx4f\nH3G9yqbk67ap70Pbrghp/W4lrEkXECnQ8w0iYGAuy1tA0U08WdByAk+OdxodoubEjfU5jp0rMzAw\nZm7o1LxcA/U4p/j4Zd/jSHZGoLf1MeYN5IJsAu6GX/QiWufbjg8C6PNkllC7MMl4SnLnUM1bkw0g\nqmwsvMosTsJBgUxt8paSTACaZ90WaLykTCSldMHOAsi8nUiMwdR+J3HkZ2VrrWkJBBKJ3+uQnQky\nwWCaQNzH+0Qn+aybUUifafanRryEsHkJ9rZQrZ97l0T0bPpgHQivtpr+Z7v1ZFAgZXiZ3fmoKdiH\nEBiF275tt8xK9v0LAE2HxVDbw0vR2i+QqIj1esU8zShlAtE6CEa2sdFz7Y39zW1hv4PF28/5X+sD\nAyZSdNgLqeE9oSaHdUFbpD4+1K9Afj+bH+wCxAfThC4DXUtJlFJw9/AR7j9+wOn+DlvvWNeGaTpE\nkhjFiDklll6FmCntK/YS3S/QD7pGCkFyQcTWzgq+tqH1NEn2amviz7I5b1qVrSfSNRvzKUCwEEVp\n8ayNDMAdBCYpBGLjbsqCm2Xk9qFYmLFmnaoy33MfwaxYSiqZ0bkB3FRhZw+wKIoLTfu157HLH/cr\n2DDY36KgmRkSIvBsJL/ve3wQQA/kwU3xBjaAbLmn8FhgZsZ8OGCeqrCk1uRf3a4PnWXXIRKoMq+i\naQyApS9D6n80VubH3h5bXMOg2NWsoMfkrMo6/paKHySXfcGw3sgmVCo7P1xrIH84HDDNk5grWsOK\nqN8hr8z6jgYRuT07Rm01Ob59WPwzTKl3XObA6U9PFyfmndn9vq/e9Z29x2BLJt082k4nCf3UHVZx\nPp9xPJ0xzwes18UZEAMOGiHMg8kTIqnKIkneZVrag7wBOwE7QNZ38bho0igKJSdtQ9+k6iR160Ny\noZWhWB/sfe2PIGCaJ9x/9IDDPON0f4d6mDGBMTclG5ow5WNKtiY6Ojdwl+dHhYZYC3k+2E1irhG4\nENrWsK4brtcLnp4esW0rnp6esFyv4tfhFqajAi825/PD761C1NYZoEl+aay0H72bHQS7n98V7EUQ\nqunDo2x0PHpUjMxb+LlfxNsmEUG+7SAzUMJTyCwb9HQQuFNqTwL55JBtndGS9zILrGgL/GNhoU4c\nfs8BPUcUBkEHN00uk+LGEI3VTwrAhQik5pxCxe3zgw047UyQ7aVsi02dRjBmnaSszxRD+MSE3C6+\nY235Gfb7HhSyhBZ/bWa2ca7tUzpNEyatny5qskQ2NC3Ji7QoLOZXGBN8MexNKIPtH3hpAuqSIOZM\nCvvn7Cab/s1IWwjEeNdvY+nvNIXcOCc9EuH0ZKAQpnnC4XTC8XzGdDyCLhfLHw8fiQEIYkcjc+SX\n1C/ZVp/H5KZAz7+n3jFtFMjMLc7tvUstfd2cXKT+eA+90Y3nyRmlFhzvzjidT1pKQhyvVQFZcNsE\nr6huVvjPbdldgg72SdOhrWF4B5CEeIrvQd5xXRe8ffMW3KV20bIseHp6knpGxBF+mQVsBrD0TvJs\nShpe8YCM0fQx9lYmaPG3eP+hQKCycSgQ5y6yS+whlASJDBx2y2WMQDNTr910MMu0l4xe5oJpGgxz\nMtt7vgzPfL/jgwB6hkRxFJLYaJIvZWceNSOY6SUzunWVJIlaZ9nncgfAwzNai/DGYQbbvUknkNn8\nm8bThrpIpbidrBnIw0whmWWodFfhsWeCRe31+prDdfZvOF6BUcXGyBTkJUbmAYvUiPh47PrulrnE\n1Hmi0EKMne5NGHmiFSvN6ue+PCe/601b+zuOW8IzCyl7nH+oSNIQA9M843A+yW5N84SOBuYWLAmQ\ncHm9Tnwg4gfykr4Y1vHg93mXX+GWAMhjapphKbFw29awXJ5RLxfU40nq/ThJyEPPwzNM0xBiRKBj\n1JF3pyaSGSQ6NtrPOQIkRiv/an1Lu7Hdp+8TEbZtw5u3b/D4+OgaOrOYeMo0SR+rs9uIXF4I/m6Z\noUCiecxPNTpYg0RkEAywNJNKkbj9BNpVI7K4dDSSUMp8z26BCamdpFYCoimBuDhn3cRGY1sGsFcH\nbe+MtvUXGMDMUb7YtBG2JK+xlv77Hh8E0Bujp1Lh9NP/NsyA4RpuknVHLFu9scbcdxRQiUQMWzBu\nqw4tHXuR4JUHGYDGyFraDFdGA2k9jKaD8G6g8jokaUK+UPX9O2M4+3uGBrOpeYpIS9JadcbgjWEm\nUmYut4qJm2O694Ngu9kbC88AfwvA/Mp9H+j1zrRMI8hmuZePfyEgMphZ7+RnyZwpw/uMMlw22p5q\nRS0FjQpsX1o71wR49lf494nZF/Pz5A/gWwnmZ74L/F1rKkpiuoayMgFdsmO35Yp5XVHnI6zSnUP1\nTnAayHvIMTpaT7YACidzYM3Yf3JelnxpDNOcZaQaQT4nOrYtnmVgShAWuvGm7y5CopZqDY95NZiE\nEtHByyMDOCewe1dIcvRXjvtP3+tzaingUlGrNWHHmHfja+tCchsJ4Ca+gKbOU1EZ4AqQ4YS1XbWC\nofAaRiITZmP93bJif08DPRCSDj05B+U/e2iQBd9V0gHdjTW2IIONEFHYRRVNzDYtt2fswwlioVew\nF9Q2Uwyh9A40i+NtqkGMZgfm25BWVP30iU4ad0+UJlRSZ7tk1S6LDLg5X/vWsLXNyyq8jPrY/Zz+\nHAxK+2PHjO19WF5GmpoWfkxMuQdBWZdqTDtbg94v+oPcrrMbXZfx5ABgz5b69llj0yPFgGfALxB7\n6VSqVG0sEyptkCgRBfsM3kUYttQnkluVYrXVw/Hs/nOKtuba+5mnxD6y9n32P6Ux0S5j2x1qWTAd\nN/Edmc8p9Z0IsDoAhMzzFK2Whn5/7ImEz4f0uWXiK4Uw18nt/LJDWZCAUqKchyxA9nUwcjdhtCYQ\n3PntIm1ci9ZaA7aSVRwAewBnztFIQV7sfmHmsz4VgjcVlvXOPYVcDzQKaeKq8BJB39zMknJdXVBI\nA4aNvTUSx05mQGouDVpAttmbeW0fLvp+xwcB9CbMO9jjV81UYwCtcll3TooSt8LQO3oBrParx4vs\nVH1mZeh9mHXItcEt3CAmRIoqKcb+pF6KZbpl5csZHSUTSDF2aHVArLZ5JlE6iciJHASEpU82td1K\npUZpt9lVM8gP9kPrWJ/YoyGC0pLymiUmAFzYRrbiy4FTcLf+cYB+OQFjsYys2xaosX7baGIYO2at\nb5RyDtI9TDjZ5fa3UqTYWZ0PqNOMum4orDVWzLiq41MKUAthMrBS4K8O7jaeCeTtQQwPn8umDant\nHu3tsDmIyKYs2uiiRGdZ0a4L+nlFmSaAq0sKZrhpadLKlFbMCypEzEG9Z+PSvzd5sgwjS60aAZ2G\niMyB36eUosXjhPpuDSiloXPzcS3WB2xBbybRdBWHrIaJfytqR6xagz3P+i0lVnE3LVtfisUuPjD1\nNB/GV42VZkLJisyR7uVrETMCLQWddJ2n7GQZNsWZKsStU4EUAiywqBmLfnTylJy9VjmzNzVtERzY\nX5qfutTk4dEZ+3sO6AGzhUmsbl5Ezo51opKyRlZ7oaRTd5QupX5B8IWaD6uhw5xNKsodO0PSaiDA\nCgaYIunK+5Pc9FG5YqrVw6BuUieE+l9KwVRFfZVNCfBCQNi9R8efqned0brE7aaLfMBDKIVm4X/T\ntlgjjXU6+GYaimA8YcNVn5NrDuwLldI1P832boCuPeMgnrs5hMaLiweNYO/kHp5HGq2igrlojftS\nKzx9HjbP9JxCqIVQq5gBrXCamys8f8HGNfVzam9uRykW5kcKtHqm9YPGcEqRMwGcbVuxXi+YFokW\nIjN33OhL+7Q2bqAd3RAjbz8YGeY0pzMAgkhqGVF37SPLNMrn2RcO2kj3UUAl8iKEA5Pmcc3kPnTn\nt553y9wpz4x6SXn4R7BERK9ojk3uH8o3BCu7VzJZZZ1e14a+pRIp1vzOGs03dmXvkbPgeyFwBHoI\ngMOZuqAPOxFwDYAja9cSyRih2fyeA3pmYGsdpgJnkB4mMAmzLESoGhZQQbBMBV+4KSkpq3I2GQdA\n0sE3lRzEWtGQVfnX84AorAYZ31oqahFTU0uDow3XbdUE3Kci//rG3CwToOtErqWgKuOvCiq2pdyW\ndoc3m2Nm0tZATv31bVL/llM2Ik7yuIRgeIckG551E3S1cbk9dt677pV/zjZxlT4v3gMYnaT+t64+\njVp8D1rm4vMIYJQqdnz/qM0bNYpzmQZW0r2H9ySSrOahj/d9DQSzjf7MAkMIT8O2LNiWBdNpA9VJ\nyuE6QIzju++rfb/kMYA2YexGci2Ke/d5ND7DAhTEB2Bcw2zlwoihZUTIY/XF2arams8vchkXRCHa\nbfVxqgFqAuZsM2claJ2j+iOQ+qbHx3xpe3PU0DtsJr0u7L5UzPMMBrBqtJaYls3UJT91JYngID2m\nfYFzlMxoT7fCal3NQ43DPOMagOKWZO8aucK3vse7jg8D6MHYuClzHGNZgwmQL9BKhF4qUEnMML25\naQeZPVAKw1ITS+6aSJLQyVgEzC3SRxiDnmwUWL3wouoSKsUuRbnj3ZZqAE4CIrVo+VsdfAnAKZhL\nwVQkfLKU4qVOe5fogE1CKGC1zHUeKWOnIdHpFghYm5zOAy+B4QbIO+sZCLPROJIJv2dd6WdjVcN4\n7wD/FhveH2aycdB4B4N1GzVHZqGwMw03BKMmo1mdCPNcMVfCVAqmonmO6tgvSoNp/wyEFkPAiyzY\neNdg/bfayjBnpSIwd/RtRbte0JYTME0A1cHskm20L7S3XdvGh5qD3TQrQqFqLEb9RVannqKUsoFR\n7yAL/9R2tNYcnJpMSIDF+X1zNJXyWqayyD/y8S1KiIq904vLjSUn8E6kLvxjsU5USg4s/2WUkd3P\nauuIOceSsKQPuifSFWgoZVM+zsm8lO43jBdsbYgDPpdMbl0+Hik1ZMjCmf++ve97fBBAD4bUyWZG\ns4Kixp4t0UmBvkIWJCYtC0BVEzzUVt07mDcwA6XEwEuHKnArZhuDsYlCbEwZOrChqkU9DvhIFkTU\nA3EIBZsIonmQ2nnNcxATwQSYZPgWzLUG0ENwtDWdZMbmlSTJNoWZJUe7vVtvMT4Kc9JA34FgIpTi\nrq2t9t8gVQj6x35GPp9T+6xfvDYL3j1RyU4emjYKk8EGbWDbrbaJ7EG7XBc8PT1jWa9gsCSbTSUc\nrIUE6KeCqRDmKs41IRxwgW59E/0wMitrh/0b0RAU/WhkAraZN0uyTSMAlthHEjmzrVgvF9TTFZgn\nFByAtINSvP/tjGKT5aM5S6LHgvuaaUq11iIvzSTVSdn6gM0GLswSzOjK1IVtBtBz71I5tDC4Vn1/\nfaDeuzfp34GQFIC0Mizl8U/RJRnUzYYhHNyIRBCKYc4B8NS4xIQ5zcE4n72xvQPLYklNzZOqMhYB\n1jeSfSXtHOPco8SCzSf5wUC+ab351rVMhYK8mXoss97MOpTm3e89oAe0SluTjiMbVESZAD26bs1H\nhVE7o5FKWZZloqVlwGiRKMLhOPUIF7LO6q52M4d7ktN/PDmGkFgCVMOQTzVUcCEAAe9krrFb2h3M\nFj+Xqvb7MNsYHHIBChffAq7QJKYFImxtw7KuUgyri0bDZIwkFpMfxqKxY/O+4IwRjww07iHKtjnC\nTckx1mrnBMhDpc/Qa8MxLkg46ymJnXk7TYvYgxgDm5aEWNYFy/WK6+WC6+WK58sVy+WK3hoKMeoU\nG2PUWqQ+zlQwFwjg21gZwwaNqfm+eFvsO6p969PipvkKAFMk4hnQmOwkGWt0oPcNm1acpGlCZUgZ\nh0Kxj62Or0w7GQz2cRtBz8behkm4UxGR45nk8CiAUib0TrIhSmtKiGVdti7gJAKA/e9kN+4shb5a\n0/wK9aWVGgBopkmovblJiGMH0EuVbTVJouVsRyifo4COhf6InpmTM34TeJ7rqJPLo2uN4JmGwBaA\nIee0LrVsWmP0jTWfJpmySEoyeInzXG9+94EmZfpQqNXCNg+R66ytolUw4p2NKNjP45p4v+ODAHqG\nMPrWo3g/K2haoSCXt9QlzdhYeu/oRTb6sOyCQozCjM7VQzXFMQJXlWGA6C0IgLPkEmMWJplJN6Lw\nxQlhIhJyCV9oBI2OoIKpVE3UgBc4s7JS4m8IgDetYVABfQISap1AteJwOACFsFyvAjZbbJySO1Ww\nMSbFYCox2pd+9uxAQBlgMBdO7L0nVduexQyNIMmGpdQY3UjhxeRkHurV2LNCO0p94k0ezRPrsuDy\n/IzHp0c8Pr7B89MTrpcLtmXFqqYG832YQ7xofP00T+i9gqcKKhNKlV2qpJaSlj6AgqkDfVOtoeG6\nbtg0x6Eru30J8pT+G991M6GQzCn3/jdG31Zsz8+gWjG1rglU4iAstTrgk5kCUxnmdx4+aW33NZ13\nYIlksYlNAmSbmSqY0ZzkGFONNH2Zo6TMUzm0akwE1WprRy2ybwBXA3qWDWFU8BQGGhWPwvHyHjf6\n1ACP06JzIeqx5kDvKgAt25R5V/JgLH8AwwmGRsUk866zaxL4YIbsoay6hSZZDiDPESnTQd5ud8Qq\nbMmEN/NPNjPt3jv93l/Ms3cfHwTQg1NRfmhNDpP6HHZpAoWX2/odBgImyYFGHbWG+YRh90EgMbqb\nWKoCot2LYCpWAE0psuuQVaxEArpsEvFJbo9xScxSWAkACott1IE3aRIJ5N3klBoeNuLRtJL7EoA7\neW9J/czyzC764hyJjQv2wNnGON7LJmfxNvUhGAE6hgB7FcKBafqEjdA2A3vPkCDvUT+/NTHRfPn5\nF3j91Zf4+uuv8fT0FpfLM9ZlQd+am1HMyVprcTtwncThdnc64HQ44Hw64HQ64XQ64Xg8Sm0hte1X\nD4vV0VEn4Lw2rNsmn3XFtq7jxtuMncajmlYGZTenFTXJSQnddbmgPxHaKkX8LPqnTtImmirqPEMy\nfQrMlh/ym4d/Q0gSzARR1IRjcezEDBRCb9JCqSsprLzbOk3rzdgRc0dLAlo0PglN7RDtgGsHtSZR\nTe7TMDWjowFSv2nbzPaEvckO1v0cc89t8zb7EtALyMeGIW5OsRLFLbQGVvxhf6f8SeZTWPNEszNP\nxlggLSVEQbNabb24ibHv3snY/Li+YpqMBAecz/z244MAeobFqkpHzlPFYT6glIJlWWTglc65Z96Z\nKbReTfe9KGGVKQmiYvXE6Nme2KXaYUmRABTRFLHdW1osRK7eeratLlmbivl8c1a5EFGBQqVqeDSh\n2vmdZQe7zHAxTubWYqcaBrAsUnfdbXepP/dMXp6hk8jeMf3tljP1ltlnb/cHUoalC6KXIJPbNSwY\nVvXbBGexsFdycIk9bVWQK8Bfr1e8efMG/+9v/ha++OILPD6+wbJcsW2r1I3pPPRDlHqOn+dpwul0\nxPEw43g84HQ84Xw+4XQ+43w64Xw64e54xPlwxDzPqFNFrYRKVSBwOmDqHYfWsG4r1nXBuqxYV9kD\ndmvKWnm/aOFGcKIgC77gewNWnTdbQ6+T2NOJ0DRklOaKejyCpkkTdHKYAAAgAElEQVT2mq0VpVQ1\nl9xi9y+1KQFzUgeo2YABVmFddcpvrcnm9gC4sRWGRYjlAESbjG5qAMBabz8XiKvqj5KjgNHReJWY\n9EEO7tv9cn7mdTJ+TNEfAbZptJBt1J3t64YVXaN6kOayzV3TliXxS6N/fAOiyHhtFmap/TMIg3Tf\n7INwc83u2IP+76qNnohOAP43AEc9/79h5r9IRN8H8F8D+AOQHab+JDN/pdf8eQC/CKAB+DPM/De/\n7RnMEkJo06LzWHsdgKqARSMjaqSqewdpR9q+jcIvBNI1PCnv8mzJLMSS/mhJGtm0kesVGdC7/Vaf\nCYyTLX4RYG0QiQ+KXe7DRCRqbWtdnazvAN50/7ZuklTDYjqwzNwspPZ9mw/LIdiz/ReTRvthUB8T\n2L9kWqIxFTdHjPczQZQXIMCx8bJrSMllTYQca2sL5KIA//r1a3z++ef4J7/5W3j7zRus24LelRSo\nTdV5I+X9Ccb6/s+XC6Yqm5bUacJhPuBwPAjI353xcD7jo7t73N3dCeM/zjgeJgWqCXMVP4xpCNtx\nw7pu2NYV69qxLRu2dfNNLozQBPkNjh9J2goGqyJVmYAijL3b/CgEOl5B84wyz8A0oU6yfWKt1Usd\nI433ftwLSXBDJYSewQymigNN6MxY1wXX6wLeZM1U1SxsTpg5J88VW8tWoLCxhmaqBiFmnI5JNRWG\n+lg7gdRGb5qzvYPPNDXBWgfmOTm0gcPEZLWfYhNw2xQ8wh9tf+hcZyaAO5mSEPNX/s+DuUgYvIV+\nqkC5oYXsPzk6J6+vTOKQ/va7baO/AvjnmfktySbhf5uI/kcA/wqA/5mZ/zIR/TkAfw7AnyWiPwzg\nTwH4I5A9Y/8WEf0h/pbtBFklor1Eaw2Xy0Wy1rSOp9hXK6YyeT0NO987XF+8qyebPFqAPd3YgLwY\nsLIIAEYAfU7WsEPmnaQ4ZxOKm5Z4VKXsnYgJha00w6gGdjLpLvV6WBeAP5A1IczaRITODauWPrCJ\nZ3ZkfTDytOjcNcrDWDyDSCJPbHesccLY5BrZg9lNh3HbsXYJzoPuDSq3Ir9TLEzvJjbwj1feH6a1\nCbuSefH669f49NNP8emnn+KLz7/AF599LgXuuIdigGRhS3NFAJ9eLK6tVJS1gWgBlYtkyU4TjocZ\n5+MRr05n3N/f4f7+Dh/d3+Hh/g7n8xnn850watUSZHJJOOfhcEDbOralYVlW2eVpXbFuK7YU4w3X\nCvV/pCbC3oUJM4MqQ6hCAbE6C8HoywLUCTRXBfwD6uGA6XAQc4/6I8QsZGadmL+2rqRyCAuKglDn\nivP5jGma8PT0hG+++Qa2yxoBPieZAS7WjxZ5NsaDR8ldi0cvoCrrpQBa3Y0lCmmXD8Gm7SBrmhhC\nsG1sOU2sEeQtU7V7GYLeNJwxraP4ew+gd60g5kvM1WDgPf/rQqV7nH9PTuF3aSJ6x2D2+3VgBPM7\nALwd77OVIAN4q7/O+mEAfwLAP6ff/xUA/yuAP6vf/zVmvgL4TSL6xwD+OIDfePdDgJwv3FrDRR1o\nBRodYY5NZTXQndBRyDf9JksnZwm3lFpR4fiAmgSkMqssLUvvN5YkTCmYkB1unuFgKXtb2V44dAXW\nSiTRHLVia6I2OvyIGiJJMdAMVCTg7ohNINjsjBbSZYx4x8ZtLrBc351dqxZUjC2zV1B0G3iaSwXK\nfGMyDKz8JkKLqiL3YGN2CG3KNn7Rk9nP0PERiavWY+W6mhV8uTzjq9df4pMff4If//jH+PLLL/H2\n7Vtclqu3RS3Kg1bhVk/WctUmQJlRAa+xpNIeaB0boHXVr3h6fMI30zc4HGacTkc8nE/4+NUrPDy8\nwve//wOcz3eYjwcpWaDjaKBfqKKWCdM8Y1M7/rquuK4LaFnQF6nAis6gbsl6MfgGWARGJxYioHsq\ncO/YlhUdV2AqoKkA04RyOGI+HjEdjpgOB6n1U6VkAqqYngpLQbVS5VOJQL1DN9/FPE24u7vD8XgE\nEeFyuWDbNhAaSicwE3qXMt9bFwFrriSG2vohGkDRtcDN5pASMZBH2hWqqIBr1mYCoqzVJ5Nity0A\nfS7ps518hHnGALj35vuvCmtHSrpqHgkzaJ0MZftjdr2jNpr+PRIaw9H6UsNgxgj6iI878m1dJfB3\n/DGzK77b8V42eiKqAP4+gH8awH/KzH+XiH4fM3+ip/wYwO/Tn38ewN9Jl/+2fre/5y8B+CUAOJ/v\nrDAGAAUm7SgqFRIGVtWOKK9s0GqXWSU5y16VhWAqVwZ61hrckNheWfVD28xMk73aZlAw25ydZ2of\n5VFznZJ1sdsWiDMKdaxqphoqAdptlVVxLxHW55EB4TQysDK7vjZoYMsA66bY1ucIY0YxgZWuVy0i\ntB5L+EoLTO2bvq2iaT+wCSn2Tb2V3tvMMtE1OmtVKKdSCmxlB+yVxM/x9PyML774HL/zyY/wySef\n4KvXr3G5PGPbpC/Nqe02YDV/5A6xp1h8uAEQwCgs9ZLMtANoOWGrsb6tuFwvePv0iDdTwVdfHXF/\nd4ev37zBw8NHePXwgPPdHQ6no5gjiu3kVFCqvD8p05/mGXWbMc8HlHIRB+66Yuv6LhwlcIQBN8nL\nAQNaoI2q1H8XjXUDb0JsuBBQJyyHA+bjCfNJyjTPxxOmaQbmCYQZTBMKWEC/ECoo+Z3k2LZtKLVt\nu5sJ0BdYDRbr0wZ4lIzdiFR9dsGumoPCvWvZXbib15Cxi6PsxniEXd3WPw0smJ2ly8hLqRJzvAqj\n5x0YmwkqbxJuAB+mGPaEMYufB5LZx5l/BFEYwA+arD0PsU5MIL3rnTOJ9CX0nsd7Ab2aXf4oEX0P\nwH9PRP/M7u9M9K7dON95z18F8KsA8DM/83222GyPNAKQVcyyCx976YiwjDiTfjJZvHSN/ivzQRa9\nq+5C5/ft82dk22YBhfMMlvUWKq3xVdEclJmLuAYVdpuoAL0l4uukycXWYueTQQW1kC1K7cpAn1mA\naB1ADr2z9+899ZQJnATcRf0hok1FzPPeROXXOJDHhE0nOchyusz+EpzFh1JlQMfWNjw9PuGzzz7D\nb//ot/HJJ5/g9devxUmvpgJLuS+IuUIq+EEKPOagdMGq/hsQLJ29dEhIoO4sRK7hKDBJLVpsa8f1\nIqD/+PSEu7s7vHp4wKuPPsZHHz3g7v4ep+NJnLdlGhipMf2D1b0vBdsiG3cvy4plXUC8YdNQXAnL\nZTAaWgeoFRBVz6imQkBjcGvOKhsYKBXL8YDD8Yz5fMLxfI/5eEI/HoBDQ6WjkAlIMbdKRbWp4jrW\n5XLBVXeGArPkcjALoHNKkiLy62x6UJoX1pfEmvijQNm7ecFY68vA+8kmybvs0NmhKUNNvpUfkEiY\n/mxA3DTahttIchxo3W5v5kIF+t0aZGmEzF1idd7GNfvdoWKJ7uzyoc+6acZXZl7baZV8VzYPfMeo\nG2Z+TUT/C4B/CcBPiOjnmPkTIvo5AJ/qaT8C8Avpst+v373zIDVt2CtbR5KnI42vlsOSYiMCdhYZ\nEl4mkbF+n4gQEHGbfo+a1nsBsgf7XPXOJ6FSxAJSVhIhk8TKLrYm+9WWXVq9/t0orKt/Bp6qWjYz\n1ajw2os5Zo6Y5t0EuXnYROsFRW2spHu7UbWaQla+wSrs7YSh/jtVcUISEbZ1RVsX9Ga2zCScFUJM\npth3xnryGDRmbFvD4+NbfPrpp/jRj36E3/nkE7z++mss6wK/EVLWcQJneTjAPYteCpuUz5XwRnRl\nBGn0ULoJ9ui3zoyNGa2t2LY3ePv0jK/fvMXp62/w8OoBH338ER5ePeD+/h7n0xnzNEf9otRxpRQc\nDgfM5tCdVtRrxbIsKBth24DNek3Ht6GBaJPkKlQtyFaEJJjNua1oDGzLFe26YL1esV0XHE9ntPMJ\nfD6BuGEqALeThGdWgEgyzg2ZrAS2bd85OHSdPUfUmRUwc5+X/Y/EwYpSfG1nE4uTNA6f0a3Irz0o\nZzImWeg3woSNjfcA+m3r4MYx/hzgPppXFMD34Mzs2gDUL6Rczj/s77Qz1ajQ8fvs2rsXbO/qh+/i\niAXeL+rmhwBWBfkzgH8RwH8A4NcB/BsA/rL++z/oJb8O4K8S0a9AnLF/EMDf+9ZnAJhKVTbfdbME\nGQavFaKA2HZ2cW3j6FRMIOo2Ef28+MpbME4kO3IYVKGCOonwcZu2/VcH0SwS++dLGrUUS7JqiSBI\nYlgpfi7TbhDTAmBt8F6qm50xvRDcZIIXyooCXnpHd9iyRo9SJHENV72M2iAinE4n3N3dodaC56dn\nPD1KKN1gEkIIZ283YkykzR29i8lkax2Pj4/49LPP8Du//SP8+Cc/wTdv3mJZG7oxbh+DCFEk7PpO\nU3hsONyU4CanEADJWyB9zCY4oR/2Kyy3qG8Na+u4rhserwvePj7h62/e4NWrV3h4eMDDq1d4uH+F\n8+kkJpuhBoxGn1RjckomakVtFetaUbeKVTeYsfr2UnaATM4J0JcC6hI3szFkUx69Zts62rqiXa5Y\nL0e06xl9uYLahrv5ICG+82GI1IkxCTC1uZjXR4jE0DItisZ+tzlg8wfp3rJuwzNxaw3mqJrcJjMT\nifO66N4RcV9WZ6iVMdi2DU37kjUKLwIqct5DYvMDq9+HREKtAawEIFWZTH/3f7uVd4nEKBOK+3fG\nrp9u/fxdjvdh9D8H4K+onb4A+DVm/utE9BsAfo2IfhHAPwHwJ7Uh/4CIfg3APwSwAfjT/C0RN3ZY\n5EhFQVcHoTnJJIyesBVV//RlaymqNirDtumlCyIneLD0qS4OW6iWTCVbCEJLLxgz7v6vRDjIghKH\nG7cOUhApNrDmUC6QDSNIBnfjDiLNzqwTKld5npYykLBhC1cbNZYYV2OvEg5qzNJtgxrhA5DmFRjI\ndtUghLF7WKpV5mRCbAobhdqqjgdpIwwCLZPXSjuXUnE6nfDw8CDRLL3jcnkWJzmHqcKjMoZ3ssnO\nAMlm2cva8HxZ8OXrr/GTn3yGn/zkJ/j69Wtcnp/FN0BSQ8V5d3FdLd7bFhFpJUIHG31vIjd7CKBr\nU1B8XpgAMZup2JLlxBK956+x9Y61rbhcN7x5+4QvvnotG3SfT3i4fyWA/+oV7u4kPn+ej7ohikbD\naORLIcI0V9R+wNwbWtskcmdtaJqF27YmUTuQmk7id5I5h0KgMoF6A7eObe2gdkVbFqzlCXWacDke\ncDgd8XR3h+35Gee7e0kSOx0xzwfM84xStJ89aqQPnwDeGEPL+WZdY0Y0PBu1henRarNDyzqIbXss\nt/wu1pqBnk1QqKwgMp+cgXx323zbuppt1Fyr807ar0Sy5Th6mbeNjUjBQVycwQbeQViyPd8YvNoI\nEfllYWa+dZgZ1ZIeo5+Hs95x9e3jfaJu/i8Af+zG918A+Bfecc0vA/jl79KQHvYVr1xnZTubgqht\nZAyQCzzqLUw2Gg9vXdKh8bvW6bDJU1xlN+DpdgIH2NtkNODtEKbJRFJmIS34YRjsfhqNAOircaSZ\nS4wBD+yfc7VM/04O25mKqjqcu7Bmc1ojE3q2J8rNpeCbmGWYOni0Xcj7q9xBtRsYYMbbWYi8lYo2\n09i6rnh+fgYAXK/XUPV1EevqQ6R1xb9WQ4W7JOU8PV/x5Vdf48effoZPP/sc33z9Na6XC5qnmpO/\nm4E9CJ7gwujCUK1fFXDI+q9oTBNZGJ9Eu5g5jKXCVtL0enQqQys6qjZi+gMFp2XA7cDruuKyLHh+\nuuCbN29xf3fGw6s7PLx6hfv7B5zPdzgdz6kEhDhumQlcGNQlTn+qjKl29FlyJ9Z1BW2bMNRmUR8K\nbICQn1K8pIUJPtllUOgltw1t3YDe8XR6i9PpjNP5iOPxjOPxhPlw0JIbkf9hJQRkvUb8uZQt0R4z\nQZnYfyQpqQO1JWc+C13aa4t75roH/WyDB8KJam2zto4BDK5nwMT5oOlbBI4VdeuAFy1DMHzu9gwD\n+tQeB3m1victQZvnGGTOX38LTomPL7QnHkyeL4H/248PIzPWWKy9SEaszHCNpKppwTrYMkWJKUWZ\nUMTFJiZZULRSn4IeFS+M5gAHDY9KXnRAVHn2iBmrgmgW+Tg8AocU7Ih0yzEJExSCbTblqpjL0Q/6\njvYdgdKWckmxdqEUCWFm6hrKGlhbrEuZYVtKUSFlDsJY2d9bijbFi5FPQCAWXu8dl+dnd9gt66Ix\n7eNCGnc9Gt+DO7C2DU/PF3z+xVf48Wef47PPv8A3b95gWRTkzQGWJnie+ALJnHYrsn/VbgzZGSzp\nKSG0HLYt2oU84iWfa05aOOBFuQuX6C6cu1Yj7FITZ1nw9PyIN2+PeHX3Fq8eHvFw/4Dvfe97OB6F\n3dv+t1ZywJyc0JK5XCtqZanDo6Ga1+sFbeshj3RszJ+k7AY5VoJZNiNnvuIRjOX5GZf5EYfDAcfj\nCYfzGcfTCbOGZ9apeqSaranMqAebeRKDzcMNu1eAzFqB92tXrbPE/LV2Ir1T/j6bUMTJHmTKQy9Z\nq0MqcbPIO18++l8XRhzv4htzq6klYuGzaSasCyEIG6zUcZi8DNI4zab0LgnofcbxOF7WTldCdmaw\nn3Z8EEAP6NTo0fkExEYPAEQ1TM4gXWCcOj0DGBBxr3K5AhtDQB7CzLuZLJIOLyF9JiQC2ApMy5D4\nQWPZnmClpg9rtDhOo1iZHXJ2g29R10sIOcTgZ1JjPghGqKZ7p5TveJ/7CRaFogxWBZp0h0xeUvVV\nQgkluqM2oEOrJCZvZLbXQhfHVaMzrJ2+tZyrnaM5yvrTFt22afjkV6/x408/xU+++ELs8cuaTD4h\n/L29IBVuSbgCaBSgLQUhoyyzn5vSnj0jlEi0C1VWeBD+PqwiCDLQAHHOMNbK/iAZzOtWcF02PF0W\nvHl6xjd3b3FZFtzf3+F8vsPheMQ0HVIGb5GcB9MViFALUKaKOk9SdpmAdSFsaUzMZtwAy39Km4EE\nQPTe0ZYVvG7YLguWqeIyPWM+PmI6nnA4nXA4HjEfD5jmWcoukOsxnhgkbDWbc5L5ZPhYuYHY59iE\nMrjrFp8vwYt2/e1rZPhZ9AJh5JZhmgDcImLSmrajI4VO+lrqfm7+296xyrtrMnCnWeDzSAin9+CL\nc2HjuHtXA3pnbenv73N8MEBvZgCZz5bUJH8awD2BPNgmWvGJ42Ffse4xTB8dJYYufNK4fSIHeo8V\ndwkbHcusQJAWtkd8UETUAKwF08hLDEOlumGWD5mXcx1r3EiijCzzHEoZMbu5iBJ7tBd0wxWLSLKI\nD2trTJrdEPgEJ/kIWmsWbaio43NbJIA5dncF1ahTY0cGGei9np8u+Or11/jJp5/h0y++wDdvH3Fd\nV/eN2DgBCM2Los3B0FjbavI+96tWcmFoRVSz6ctNiwkPB3tKYB/3998pnLc+mmnC+aLUdna9WeOG\ntTGWdcPz5YrrsmrG7T3u71+pOeekdvIUcUY6D6xcB2u9GwDzNGGdKqalYF2kqNoKgJr4uHwWF7ue\nfHwkkkdKdWxNSjdclwXlcsX09IzD8aBx+EfMh1nKRJTqlSl9Pg9+Ja3L1ILRv5g3YJgztHh/vXT0\n5399vPf/6hwYI2huA30GaLnevt+DfGTTjtuQZlMM767tERRBWUAZWUygnd4hvw8yq0+HkQ571u96\n1M3/P4d0ktNiZV0c60SZxPiBsfOUym9MMZTIAAP3bxs7BTxMrDkTSqFPqicxxCTAam5hZmUBMSjG\nYGMAODZJyQy7W3laQRwiAI3MLytXMod/QJDI97E0808kf5gNUifDfkIAXhfcNA9GSgZLwnOvUoo5\njdzhHaymh31YF4RYR7RfzeCtfRVx0QiDDUskxLKs+Or1a3z62ef4/Isv8ebxUWrss42f0XMKm7O3\nNC0Um0eGi8mEZ3/3+WSM3v5sfzBzlt3TtBmdJ0jfR1ezVtz0Fx7aBZ1DgLruLXO7q4192/D09IS3\nbx9xd/eIV69e4f7+Fe7OYiufpkk1uRB2yirEYX48SmmQWhTwFwnPLAvWFdiwebkEZlZTVszX3DdQ\nUKPOoNaxrhuWZUG9XDAdDpgPUgPoMB0wzVFeIUhXNnv0AQQ9Np3DZs8ITflWNJfPz9zWHTi6OVDF\nDhKQ53ozbGaXvgN2AFbszOayCyZ9F+s7d8T6eSKsoh1GIsd2BNAHvfO5wyPIvzw4feK9hz56j+OD\nAHpmSMEjZ5zQjpLJUEDQwGEBXZRY2F0G2Pb1tOqHJhntvKJ2z7zxg5atkd89dIodyA3oDZwIkOgG\nwFmJDWoGeju360AUZlSGmwuYlG10C/1rYgM1AhcSSu2EDNsr1Hf16eJk7lYLBUAu2ma0w+BbkzMH\nu7bSDmf7Fo8uDA2WDe/3e8l4Qptwld4XqF0WwEzGuiB21HVZ8fbtIz774kt8+eVXePv4iFVLAvi9\nCH5/MIuZKy0mXwC+0GwBBDD0DhTqsjOS/slx3ecCAymBDbBCd/qMVF7DZ4+1A5RIApJEQeoPcwKb\nIJfKhpe+YF0brtcVT89XPD494/7+Ca/uX+H+TuPwjweUqUqWOEXpZikxXEGzZrdOVez38yTnXwpA\nV/C2Bbvm5uZEco0h3pp13YmJUxz+67qhXFfU+Yr5MON4OGI+HDBr8TfPf9Cwkt6z+QQOmkMETLP1\nJqbFkqrIIq2lbDaMcd7NLRcCtmbUPm8AbuTN7O4KsNnWnmvSZ6IntyR/D79PMz+NaoJOIm0d7rVm\nFQw2JawUCJtmmQnRmGsA1SZsDQ7fvefxYQA9pHql70gjX4Z6RWqjdueSDq7adySJUIt75RK/CAC2\n+HCT4Kzag6nzObrG2mSC5kVc+4tBDHY3nEuEXoqHd9pzJMpEnVFdo0QqULjCUGicmNYeWYRWuz8X\nSyoUm1ePPgHlxcQOWnlBeV9aXynr7Mb+vm3cfDGwqs1xfwdiFgC1psguRR3LuuLt0xO+fP0aX3z1\nGm+ensRcYwCRGTklJ11aT3F0bUNiWKT5GBriySrhmPI7qb3WWCPI3z/2BInMSwm/BTwDL/cxSzST\n1WoXARpCwzTSPCbMUmO1aQngRZ22z5crnp6e8eruCff39zjd3eF4PkskzDR5IAAbuQFpiWJgKiTh\nU7ohCZcCXhbwuqGpbdzGqRjxsDXlAotDsEuCLloTLW5bFqzzKmGYxyPOrWOap5SsZEAom+FId3EC\neElYslIEUrG1odruWb6+o5y0j3dmKRyGs6IRRnugdyGz82ndsrN73H0y70hxg+ATdi+Lz8/aaphl\nxoMMZ/xvOpcTmdObx+xImoCYf2Liu1hhQ7P3Oz4IoAdbnGpBMe8R4B3pHnpLcNCkIkvnyZLdJ0Zi\nA9Y58TiVrmRqbNj2/Bz73x7QI+jaD1OvjeGaZmLCRF/Rwvv1l+YLTFCgix3Xnm3Ch2n08vdsM0xt\n1gW/1+YyExhkEPLU2Qszu7aDe9ESyhHTm/vb7ZAJ3G3TEmdjWmIAzOhbx7KteHx6wpevv8ZnX36F\nb94+4rKuUvDNmJb3cRq5sF2odqJMqptG4xgr7R2EpuQxWB+bdsd+W92EA8KyrB5L9G06iJI2AAcd\nsxcHzwsmKFFAFm2gjC2Z4zp3UGNsvWFdG5ZlweVywdPzM8739zjf3+Pu/k5CHycpkVxLRa+SP2Lm\nMiYFfT5gOhIOpKBfJSSzbRqSCzFFUJ4be2HE5odgCTnsjNbEeb4uK8qyYNsaDocDDlbfhzQmvgfQ\nt0RONgX71ruXCgYYVoLe5lTVncBqzWCaxsGjsKzfTUbtzDIYv5MIILZb+PUZ6Hu3OvKqffYggj09\n99aR14fN1YwXsR6NwNmUymtrH7gQ18kdd/PxPY4PA+izbV1V9OxsydLNBtuKLe3tegZIL5237MBj\ndkJjXbnjjKHmf+Pe5hyMplsaOxF55AkzD5tbuBQm9gJeBt5gYfXs+cCcii3B2WyA/MhW7BDzDfvm\nFGF/Nf8FIABpq9kYMg8T11oYMk9j/7UPxmQZvBwnA1m1MdjfTd29Lgsen57x9Ztv8NXr1/jqm28c\n5DsIvfx/7X1tqG3dddYz1tr73HvzNiatDSUkwUaMQiySFAmKpUilmlYx+i8/1ILV+KP4gYIkFvyg\n9EdFq3+kUFulqLWUaDUERRoNFEEbE5vUfDQ2tYEmpCZBa+65Z3+tNYc/xnjGGHPtfe577xvjOftl\nz5f93n3WXh9zzTnmM54x5phjSqQt6AYIPESQ8eGZRAhcWdnVSb29tZcbgYeqtthDxsePKdSR2SiK\n5SDdWBWA4Y/qm8OJdsqpWki0CbTN/g6uAIT2QnRDrI/Q2eZhpmnC7nDAzX6HR7sttrstHj2y9MgM\nyVxhhUHc+dSar6tALMIaV2usINBhhIwTZJzQJt/s2jdF4eYZthavtBPXFKiGVa2usA4yQfcHHA4G\n9A+urnICWc0aZqNxPssAfo6skm1WTDOVo0fT+7hpqtBRoTqcGMu93HH8SnGZ0P1SS84VJKtfRvRY\nIAajcyT99CWkNCRhSQBKqffFYoRVAKGlcvrc8p4tWf/zTsQC9wXovahTMk6cAAVgFe5+6IG9ftjB\nJ7fG0wT6Ch/UqlLOSzCtdSALsjpGxIH7OslGeP7gG6SMkvMOGm8pxWKwhUIWldFPYAUw+/U5SdSz\nighVlHx2BTebYCMgwgeirzkIhcc6miUyKJ/p7Je/V8si5jUWgA8HDyooVRwOE7bbHa5vbvD4+gn+\nz+PHuH5yjZvtDpMCM8zNVQEWzm7STDWQDR0lzZSCprWXiksXQ8pKK4rLnmMT5MbSxTZjlxZKQCBu\nMaR1GJ9Yuq/O8JsroLTiNF8mLQQdS40KrfM/Z8yWXEsVhzZjN03Y7vfYbnd49GiDF17gJiiP8OjR\nQ4yDmydOEhhy2hzgh5VvXj+MkHGFebSN1FUsERroLmvq00jUpE6TxfZE4LiAGuds0jz9wwG7/R7r\n9ZWlCFEA2iDqRGwcQn4ndx/lwqMcc+J9MDjJaa1hHDw5YHNJP9MAACAASURBVNmIqAJ9149uNZ5i\n9cbk62QrXSJVptM3H6GVzTNglvU09dmn3TXS1QFRi9tLHfMhCPW42jF6H5633A+gp6Z0jdzlewe6\nNnJc6hq9ArsItXqCk016Sj/wO1eFvkg35LPju3/mudkmKSJmFmsmWrOEYENXj+oqiXu0ZqlmNSMC\nABPewfcqrTlIlNaAv4IxIcSEVqQ/ppUEjXunOwOoGTI1XFyCZsHnoC1vt0rWXDdh6ISfAIzcxGGa\nuVhoi8fXT3D9ZIObzQab3Q77abZIlMFXCTNRxqDFepUA2eQ8ZQ4AtMi0YKazblfEVICqcJ9zHrc5\n8AG256qvUxDP+SO+iQrbIEJ6KAHiLh5xN1KVqVJ/iSEaP0eufi6XJOGgVQSPrtKGaW7hLtlut9hu\nt9hsNnjhFS+gza/E1dXaNhcZymR61R9ORmwtwWCAP4xowwxgDxELhtDmTD/YuHocvzBnWlpoBPuW\nymjc7yOtBNoMUY2du4ZxdOuS7jbvR5fL5nIosMCDeVCMAsziO2D5trjDsHDFUgbcndZbm5R9B/DG\nTUaM6Ng5LhddrPwS7LUD+mzWU8xa0Z+2BPk0I9n/iiM48n/rM49py/MA/v0AeqBj8Sn8xT8X/7cX\n7nzO2tzkSybFc2pj0EwiM0Np0No7g5vWjLzpAJ76COoLZxo2uz1GGMDaJtS+E1bETLorRJz5acut\n2AALkXSrJcFTYjNqpjPGYHMYLUK6emUwRAVhZjfrLYzjpm+bnFTzX7Ifsf+pXyPorSUtVgfbrhtc\nYudY6KRNLF7f3ODxkxs8udlgt9vb5g8YsL56CFkr9HAADns07M2dENaUsW+bFB6ijhIDxFhjvGQ4\n3cMWiw/jj6oF13A8ULm1YWkZS5jhE2gE/SHcgYA0azPqR5uf4SKs0sQE4RAmNn0O5qi3K2L11Zlz\ns3DWwzRhv99hu9lgc3OD6bDHo1c8xNXVA6yvVhjHldXYnxMrWSnuBP3RQF9oSc3G8rmRBpSWp9Wr\nqU9Oxn2ZYkR91fI+SUVrQJsxAJbv58pi8AcZ+nfnGC2repXMTtyyHAbICAgGj1CBz9GVTitkJhi0\n1y/uhzLOIzpKQtnEzlMtSVGkGmboctTxNMD2oHz6HBGSuUwhUV+E9U9LgBcClSioKtotzzhV7gXQ\nK2jC2V+VsQvSb9ibTgX0heY43TgDRFqEJNaQx0FzFSTKMwom+pd+ysOEpDBHIcghFwz5DlijfxjB\nwcHCJxE0eF8TppI8inUcRowlf6RlBRzMJ+v1J3c1kDfhaDOBMhm+u1sdtKW8vsZ1NVqmtZzYZu76\nziTumFONPbYc8rudhQp+5eYGj29ucLPdYr8/ADpgtbZNuMerNVQGrPc7DDfX0BtbPZmdwvaW0kEa\nbgppRdAFadYWNxSdLXUIVhlqBHXmalG6BlxExHz33Dhb4MxRaDdUmSkuPFdW9KqBjFMEdd+BtFoL\noQiw4nsAdF/OrWGeDjZZu9vgMO3x6MYSpT14+BBXV1cYxhVGGcNq4b3Tby1GFkRsona1indpIk4m\nNCY8VS1qZmrNwd1IwKzwFANkyswM2QBtWMmAhw8eRP+sxlXIJHsj8MzBOv81mdUGDDqYQme8q8At\nDYTlyjDFPiEgHaT+iLZM2QDYanB7zqw5GWvhk+jO5RhhqWt3emAuilv7a2gtKdJq6hXHCZBnO1VA\nopA+Y7kXQA+ga8zli0Ye9EWD+I+2M1CT8E/DNwisjZy3KOBe77cA9jo5XM8zsCXj9cGscCBlHehu\nSOFl/eumyqxUhrWRmXD9wCKCyCte87abItQIS7N2pOB5aOEwFsHu25jVkKir14PtVOYdln7HPmTN\nwu/2hwM2262x+JsbPN5ssdnvsZ8maFOLGLl6gAeveAFXDx9CxhGr/RYQxTTZNnuNEUl9TydDRh5g\nejp6oXpz2iOfhK+V6RMM/DUTtZX+Z5tY8BDZY92X15glF+gIHJRKvQLhyehhiidTqGo8i+/YKyG/\nfygtP96aJakDME0HzPOMzWaDBw8eWJ6aqyus1lcRmZMuG2sE041DwkzzKBzPdhnujbr6eraEZAeP\nHZ+bMfmIpnF2Os+zh8c2DCK4Wq0xrsbc31jKPFE0laQxVoCVONagQaRU3cU4wLY9LPNRy8nS9NFr\n167xoT++ccIV4aahL5/Wn42HvG8/0YpuTGS/avfbcvw8q9uFhKwSs5NRSC9S7h3QU+P1Pqugv0cA\nxLZlQ9j1A5baNxu+dcvy2QnB8kshg1NQ2Wj4dxXGABjlkGb6ovHVzGfWmUJmLu8SIeRsf8mU043R\nF4/MM9eNP5osMkg6et0YYaqSz7J2KpuolGd1q43rKyldODnADwfzHz95coPrmxtcbza43u2wOUw4\nzEyTAIyjWuTMuMK4NiBQNOyurixMtdvWsfY3wS6ruRTzaDfQdlJkTEbwJyTyUlGm1WWLs8jyi4Xj\nbZ0yqKewPBk6AVoc4O1XJxQ56JnvnCcHm/VrBYitD60i7rLy/mmtRduvVjdYrddYrddYryxdwbDy\nLTg9bbTd1+wTrhwF5ThCDHPFdazAngnqPrEJuzYywxaQhmrsZ5DjWqMJuuIDR8Ly8HsIwEl4G2f2\nt4rY3roDesBt/djpwiALyUvL0/9W9V2l1N/N2Tz9+IKu/hWwbwPaHHu9MuC/t11/zOQXTVXqcmpc\nPq3cD6APIckDS4A6jqMBllKjrpnFUwsso29MM9o59ttxo9boEprhsdjGTgD9h3C/e1uAB/OHhNXg\nHTP4Llo24bSwNlxJ1cx+MxrGocVkcmh1KkNUxlmYhPJdE9BrKCpDLHufn9/DBxwtmoiGEOnakwI3\nz3NMDl5fX9tnayx+N0/Yz2RIxsCmYcA07THPe0zTCgqN9LXMLhgDS1N3RuRSx5xcyRY2H2yQ4kER\nKdEwybCtMOtluIrgCl6K4vD/mefIjluQkLepk40hZEgsbQNIEozNc46HDH0uT7QFWZJ1DmbroEWZ\nVFdimn07+wpW2e8wDB5nvxptj9phjD1sre+HcEnNvqeeWQsK1dkytKrJtbaykjVYrrlwGDZJX3dp\n2qNEfkmry0khSIzX7w/TTLUQWQ1NL75wLfLtqGZQPPs0gP4WRq9cZU5wz4nX2SOCWNkkGD3JPMXK\nU+G1o3NOuTx7dq/lOf11Sxb/NEVzqjzLDlMPAfwcgAd+/ntV9W+IyN8E8GcBfMlP/Wuq+m/8mvcA\n+F5YHMVfUNV/9+JV0egYf5PClvybcODXcEArBpw1hj7qXxrE3SpHi540fo/3JssI32oBTj/H4jpc\nSJuiiZm4Mgu4gbfFKMN8964kLJChTBZLsiv66a0OFks9eBgkY/JRNb8swsTq6wQYNHB1KtlSP9bq\nKNS4zoDWzWUZbGhrMj9OuD6+vsb148e4vr7Gk80Ntoc99vOMA2OSy3LxPYBxfYNxFLQ2QcYVDtOE\nzeYG+/0+8quz/dgX0UOJxVi+iNBXy4EkiJWwQwpPvmtZoGYKrdtOJBQIH2irhflId99QRni+uDvJ\nc98rGTwcUMJFZ/MtlnohScJSLutEameZhCbzfp9tkxWbUFDIPGE4DJBx8FXTzGA6HAG9uPzaJKUx\nfKGictmc59ni76lkoPDIVsQ8lNTQXg8Q8P17qdikZJfVCvLKMXeagLn4hMLj4mQRhDwK4K5R9fbO\nHD8d2JO9qy3YYsKzWTPihmkPguAFkaKs9bKR4F2/l7FUfusVRN/nVRSqFbF0F9VnPkt5Fka/A/Ad\nqnotImsA/1FE/q3/9vdU9e/Uk0XkzQDeCeB3wrYS/ICI/HZ96i5Tmiv7SOnyFwCITZBs0NrxwQGd\nOSRMkOyqoazm7GPthyOGWy2JohPALJAnG9RXYw4qxgZggoKmmIc5XUEO0OOo4dePkMnQRqYQ5tly\nizBrZUPDLJPtsLVaO3tB0FUZRo+yQDd51L+Hu3MctRpaRpDwNeNkuna8bUu7NOEKXBtAh2nCZrvF\n45trfPnLX8bNzQ022y32hwMO6iA/Z5ilqkW5HFrD1CZsthusVitgHDFNLfLYT9PsBku2ObmZaPME\nWARHf48hlRzZdESOOsseVEIxE5ibNHeFcLmO+rUGw5HT3i0phnnS3RADsWD1PNucyTiSCDiowiy0\nOaJg3DIcV6nQmgI6mWyFC5MkJymPYgQKWYl+Ijg5kM2YgXmONlGIp0awiVou5II2CP+FuQXHccR6\ntcb6ag3Miq1usZ8mnz+RdEkVGWZ7yziYy2gcfQP0ERHeqmY1iG9YH9FhPgk+yImxpijvANClI+oT\nyAWUMbuF4shB90xl2jW1yDx5FIxfTiLPO+TYr2o2/+5BHYDWrSJzMZr6ZNHt4KxHeLS0HOq1z8Pm\ngWfbYUoBXPufa/88TZW8A8BPqeoOwK+KyGcAvA3Af3r6gwjyHEtlUDm7BBBoH+RMLPMjcLqBjv1g\nxw1XgU61Zj1sRUBu16YETLJdRssvn92YKcwvSuWTgChIL0O8vvETD5GES+MQJqwJVJrxfCeuUmza\nIMWyjVh7d4j0QM/5A/EtEu3+MakpQJtmi6p5co2vPH6M6ydPsNvtDOTnGRNyC7eMT4YN6jbjcFAc\nmucukiEWsTB1sWgiZwyvanWoxVNTWdqEeYvFbFZRO3+ALRZaudtChwGyWmEQwTQfMGOCOsCRNrOZ\nCSSsSXDRktfFbR5XwM7g3N03eAXUwwojJtvfL9xRcBdNYYlqKsJfhyQo+89AIyO0+IEifPvhxuMT\nxI8NdnfX2gBZPNtBBIM0Z/kVtrJ2zSeVeV8RoO7Fax6iAYO424gJ47x/NPope/kkCBLk4/2zLaTx\nFbydip+e2KDl3nUDEkbbmNwh+pAKYlmSadf6n6quR9Uo68o5mOX5FbS7ZvZj/TOOXK/PWZ7JRy+2\nX+xHAPw2AP9AVX9eRL4LwJ8XkT8F4MMA/oqq/m8ArwPwn8vln/NjL/4cFxquhCODZ3oAO8d/h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zndZ1QT3mlmkW9VP2mW3qvtRqGvuANVkqKzB7o4eP4dvDBvlQGOKxcpvnCXtNoA/G0+iy2WKz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\nuZRLeZmX/wvmTgXoN1uUlgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IsWWS0sc6owN/iIjOsXMO4m+tt82ZMZ//0/k8CA1twDRtzHc6KSLK3loYCm4JUxHWUl/y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4VQQDaH/HAl4AuLIlOe5vrzwOQlmrHDXCmaio+kq5xmJraYeefC3cSgUYPxOG1YuTOWE5hSFtA4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\nvKX7mNNDjQplJHovUJflKizRTIXuqBtW1ojnOAwMzvPTX3zKH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odzXFeZAyuEcrBeCC33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\nglU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2vZjV+3m8R+uQmCDg+JzWGmJK6G2FC81qOXRFChl91Rq6BM0ydHlmrU1De3HcZdFa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\nTaxKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8346438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from PIL import Image\n",
"from PIL.ImageFilter import GaussianBlur\n",
"im4 = Image.open('testim3.png') # RGB 3-channel image\n",
"im5 = im4.filter(GaussianBlur(radius=2))\n",
"im4 = np.asarray(im4.getdata()).reshape(im4.size[::-1]+(3,)).astype('uint8')\n",
"im5 = np.asarray(im5.getdata()).reshape(im5.size[::-1]+(3,)).astype('uint8')\n",
"print(im4.dtype, im5.dtype)\n",
"plt.imshow(im5)\n",
"im6 = im4 - im5\n",
"rmin, rmax = np.min(im6, axis=(0, 1)), np.max(im6, axis=(0, 1))\n",
"im6 = (im6 - rmin) / (rmax - rmin)\n",
"plt.figure()\n",
"plt.imshow(im6)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
samuelsinayoko/kaggle-housing-prices | ols/data_exploration_numerical_features_functional.ipynb | 1 | 30888 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import itertools\n",
"import os\n",
"import sys\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import scipy as sp\n",
"import sklearn as sk\n",
"import sklearn.preprocessing\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import statsmodels.formula.api as smapi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sys.path.insert(1, os.path.join(sys.path[0], '..')) # add parent directory to path\n",
"import samlib"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the cleaner chaining method for transforming the data https://tomaugspurger.github.io/method-chaining.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sale price distribution\n",
"First step is to look at the target sale price for the training data set, i.e. the column we're trying to predict. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"target = pd.read_csv('../data/train_target.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"target.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The sale price is in hte hundreds of thousands, so let's divide the price by 1000 to get more manageable numbers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"target = target / 1000"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.distplot(target);\n",
"plt.title('SalePrice')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import scipy as sp\n",
"sp.stats.skew(target)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sp.stats.skewtest(target)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The distribution is skewed (as demonstrated by the large z-score (and small pvalue) of teh skewtest). It is right skewed (the skew is positive). Skewed distribution are not ideal for linear models, which often assume a normal distribution. One way to correct for right-skewness is to take the log [1,2,3]\n",
"\n",
"- [1] http://fmwww.bc.edu/repec/bocode/t/transint.html \n",
"- [2] https://www.r-statistics.com/2013/05/log-transformations-for-skewed-and-wide-distributions-from-practical-data-science-with-r/\n",
"- [3] Alexandru Papiu's notebook https://www.kaggle.com/apapiu/house-prices-advanced-regression-techniques/regularized-linear-models/commentsnotebook "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We apply the function $x \\rightarrow \\log(1 + x)$ because it is always positive for $x \\geq 0$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"logtarget = np.log1p(target)\n",
"print('skewness of logtarget = ', sp.stats.skew(logtarget)[0])\n",
"print('skewness test of logtarget = ', sp.stats.skewtest(logtarget))\n",
"sns.distplot(logtarget)\n",
"plt.title(r'log(1 + SalePrice)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Merge the training and test datasets for data preparation\n",
"We're going to explore the training dataset and apply some transformations to it (fixing missing values, transforming columns etc). We'll need to apply the same transformations to the test dataset. To make that easy, let's put the training and test datasets into one dataframe. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def read():\n",
" \"\"\"Read training and test data and return a dataframe with ['Dataset','Id'] multi-index\n",
" \"\"\"\n",
" raw_train = pd.read_csv('../data/train_prepared_light.csv')\n",
" raw_test = pd.read_csv('../data/test_prepared_light.csv')\n",
" df = pd.concat([raw_train, raw_test], keys=['train', 'test'])\n",
" df.index.names = 'Dataset', 'Id'\n",
" return df\n",
" \n",
"df = read()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initialize pipeline with raw data. We can always get the data and apply all the transformations in the pipeline by calling `pp()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp = samlib.Pipeline(df.copy()) \n",
"assert pp == df # the pipeline output equals df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select Numerical features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The dataset is wide with 78 features. Create dataframe containing the numerical features only."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.columns, len(df.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've got 3 data types: int, float and object"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.dtypes.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split the data between categorical and numerical features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"is_categorical = (df.dtypes == object)\n",
"is_numerical = ~is_categorical"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum = df.loc[:, is_numerical].copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum.columns, len(dfnum.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've got 36 numerical features. We can use the `describe` method to get some statistics:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But that's a lot of numbers to digest. Better get started plotting! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def select_numerical_features(df):\n",
" return df.loc[:, df.dtypes != object]\n",
"\n",
"pp.append(select_numerical_features)\n",
"# Check the pipline\n",
"pp == dfnum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deal with NaN values "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cols_with_nulls = dfnum.columns[dfnum.isnull().sum() > 0]\n",
"cols_with_nulls"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum[cols_with_nulls].isnull().sum().sort_values(ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on the description, the null values for the `MasVnrArea` should be 0 (no massonry veneer type)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We may want to refine this in the future. Perhaps build a model to predict the missing GarageCars from the other features?\n",
"median_list = 'LotFrontage', 'BsmtFullBath','BsmtHalfBath', 'GarageCars', 'GarageArea', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', 'BsmtUnfSF'\n",
"zero_list = []\n",
"\n",
"def fillnans(dfnum):\n",
" return dfnum.pipe(samlib.fillna, 'median', median_list)\\\n",
" .pipe(samlib.fillna, lambda df: 0, zero_list)\\\n",
" .assign(GarageYrBlt=dfnum.GarageYrBlt.fillna(\n",
" dfnum.YearBuilt[dfnum.GarageYrBlt.isnull()])) # fill with year garage was built\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum = fillnans(dfnum)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Check that we got rid of the nulls\n",
"assert not samlib.has_nulls(dfnum)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp.append(fillnans)\n",
"# Check the pipline\n",
"pp == dfnum"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Order columns in alphabetical order"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def order_columns(df):\n",
" return df.reindex_axis(df.columns.sort_values(), 1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp.append(order_columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp().head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum = pp()\n",
"dfnum.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot violinplots for each feature \n",
"The violin plots give us some idea of the distribution of data for each feature. We can look for things like skewness, non-normality, and the presence of outliers. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.featureplot(dfnum, ncols=6, nrows=6, figsize=(12, 4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Many of the features are higly skewed and some have very long tails. Some have discrete values (`YrSold`, `Fireplaces`).\n",
"\n",
"The features with very long and thin tails, such as `ScreenPorch`, are almost constant (blobs with long tail) as can be seen below"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(1,1, figsize=(4, 4))\n",
"sns.distplot(dfnum.ScreenPorch, ax=ax)\n",
"ax.set_title('Distribution of ScreenPorch')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Drop nearly constant features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def test_nearly_constant(series):\n",
" counts = series.value_counts()\n",
" max_val_count = max(counts)\n",
" other_val_count = counts.drop(counts.argmax()).sum()\n",
" return other_val_count / max_val_count < 0.25\n",
"\n",
"is_nearly_constant = dfnum.apply(test_nearly_constant)\n",
"is_nearly_constant.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dropme = dfnum.columns[is_nearly_constant]\n",
"dropme"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def drop_constant_features(df):\n",
" return df.drop(df.columns[df.apply(test_nearly_constant)], axis=1)\n",
"\n",
"pp.append(drop_constant_features)\n",
"pp == dfnum.drop(dropme, axis=1) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum = dfnum.drop(dropme, axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Log transform the other features if they have a high skewness"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using a log transformation for some of the skewed features should help, as illustrated below. We use the raw data (not the standardized one) because we need positive values for the log function (we'll standardize the transformed variables later)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, axes = plt.subplots(1,2, figsize=(8, 4))\n",
"sns.distplot(dfnum['LotArea'], ax=axes[0])\n",
"sns.distplot(np.log1p(dfnum['LotArea']), ax=axes[1])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use dataframe & series whenever possible for maximum flexibility (see below)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def skewtest(train, sort=True, ascending=True):\n",
" \"\"\"Return dataframe of zfactor and pvalue for skew test\"\"\"\n",
" test = sp.stats.skewtest(train)\n",
" zfactor = test[0]\n",
" pvalue = test[1]\n",
" df = pd.DataFrame(dict(zfactor=zfactor, pvalue=pvalue), index=train.columns)\n",
" if sort:\n",
" return df.sort_values(by='zfactor', ascending=ascending)\n",
" else:\n",
" return df\n",
"\n",
"skewtest(dfnum).head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def is_skewed(train, min_zfactor=10, plot=False):\n",
" \"\"\"Return series of booleans indicating whether a column is skewed or not.\n",
" \"\"\"\n",
" sk = skewtest(train)\n",
" if plot:\n",
" plt.figure(1)\n",
" plt.title('Z-factor distribution from skewtest')\n",
" plt.xlabel('Z-factor')\n",
" sns.distplot(sk.zfactor)\n",
" plt.figure(2)\n",
" sk.zfactor.plot(kind='barh')\n",
" plt.title('Z-factor for skewtest')\n",
" return sk.zfactor > min_zfactor"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"is_skewed(dfnum, min_zfactor=10, plot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's apply a log1p transform to all these and plot the distributions again"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def transform_skewed_colums(dfnum):\n",
" \"\"\"\n",
" dfnum: dataframe to transform\n",
" dropme: columns to drop\n",
" is_skewed: iterable of length dfnum.columns indicating if a column is skewed\n",
" \"\"\"\n",
" dfnum2 = dfnum.copy()\n",
" skewed_colz = is_skewed(dfnum)\n",
" dfnum2.loc[:, skewed_colz] = dfnum2.loc[:, skewed_colz].apply(np.log1p)\n",
" return dfnum2\n",
"\n",
"pp.append(transform_skewed_colums)\n",
"\n",
"# the transformed dataset has fewer columns and we only want those\n",
"dfnum2 = pp()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum2.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"is_skewed(dfnum2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sorted(sp.stats.skewtest(dfnum2)[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"zfactors2 = sp.stats.skewtest(dfnum2)[0]\n",
"pd.Series(data=zfactors2, index=dfnum2.columns)[is_skewed(dfnum)].sort_values().plot(kind='barh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now our originally skewed features look more symmetric. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check that the distributions are less skewed"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"skewed = is_skewed(dfnum)\n",
"skewed.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"samlib.featureplot(dfnum2.loc[:, skewed], nrows=3, ncols=6, figsize=(10,3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.featureplot(dfnum2.loc[:, ~skewed], nrows=2, ncols=5, figsize=(10, 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Save transformed numerical data\n",
"Use the storage magic to communicate between notebooks. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dfnum2.to_csv('transformed_dataset_dfnum2.csv', index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlations\n",
"We're now in a good position to identify the key numerical features. Those should be hightly correlated with the sale price."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def correlation(train, target_t):\n",
" corr = pd.DataFrame(data=train.apply(lambda feature: sp.stats.pearsonr(feature, target_t['SalePrice'])), \n",
" columns=['pearsonr'])\n",
" corr = corr.assign(correlation=corr.applymap(lambda x: x[0]),\n",
" pvalue=corr.applymap(lambda x: x[1]))\n",
" corr = corr.drop('pearsonr', axis=1)\n",
" return corr.sort_values('pvalue', ascending=False)['correlation']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"correlation(dfnum2.loc['train', :], logtarget).plot(kind='barh')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort columns in dfnum2 by correlation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sort_columns_by_correlation(dfnum2, target_t=logtarget):\n",
" corr = correlation(dfnum2.loc['train',:], target_t)\n",
" return dfnum2.reindex_axis(corr[::-1].index, axis=1)\n",
"\n",
"#sort_columns_by_correlation(dfnum2)\n",
"pp.append(sort_columns_by_correlation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp().head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pp().to_csv('transformed_numerical_dataset.csv', index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Scatter plots\n",
"The correlation can be high even if there is no linear relationship between a feature and the target, so we should check the scatter plots."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train = pp().loc['train'].assign(target=logtarget)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.featureplot2(train, ncols=4, size=3, aspect=1.0, plotfunc=sns.regplot, y=\"target\", data=train)\n",
"#(train.iloc[:, :-1], ncols=4, nrows=7, plotfunc=scatter, figsize=(12,3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some features have some sort of bi-modal distribution with a lots of 0 values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cols_with_zeros = ['OpenPorchSF', 'MasVnrArea', 'TotalBsmtSF', 'WoodDeckSF', 'BsmtUnfSF', 'BsmtFinSF1', '2ndFlrSF']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"not_oktrain = train.loc[:, cols_with_zeros + [\"target\"]]\n",
"samlib.featureplot2(not_oktrain, ncols=4, size=3, aspect=1.0, \n",
" plotfunc=sns.regplot, y=\"target\", data=not_oktrain)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dealing with the zeros\n",
"Let's recompute the correlations after dropping the zeros."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"notok = not_oktrain[not_oktrain != 0].drop('target', axis=1)\n",
"#correlation(notok, logtarget)\n",
"notok.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def correlation2(train, target=logtarget['SalePrice'], ignorena=True):\n",
" \n",
" def corr(series):\n",
" if ignorena:\n",
" mask = ~series.isnull()\n",
" return sp.stats.pearsonr(series[mask], target[mask])\n",
" \n",
" df = pd.DataFrame(data=train.apply(corr), columns=['pearsonr'])\n",
" return df.assign(pearson=df.applymap(lambda x: x[0]), pvalue=df.applymap(lambda x: x[1])).drop('pearsonr', axis=1)\n",
"\n",
"notok_corrs = correlation2(notok).sort_values('pearson')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"corrs_with_zeros = correlation(not_oktrain, logtarget).reindex(notok_corrs.index)\n",
"corrs_without_zeros = notok_corrs['pearson']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"pd.DataFrame(dict(with_zeros=corrs_with_zeros, no_zeros=corrs_without_zeros)).plot(kind='barh')\n",
"plt.title('Effect of removing zeros on correlation')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"not_oktrain2 = not_oktrain.reindex_axis(notok_corrs.index[::-1], axis=1);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def regplot_dropzeros(data=not_oktrain, drop_zeros=False, **kwargs):\n",
" col = data.columns[0]\n",
" if drop_zeros:\n",
" mask = data[col] != 0\n",
" xt = data[mask].squeeze()\n",
" yt = logtarget[mask].squeeze()\n",
" else:\n",
" xt = data.squeeze()\n",
" yt = logtarget.squeeze()\n",
" sns.regplot(xt, yt, **kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**With the zeros**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.featureplot(not_oktrain2, ncols=4, nrows=2, figsize=(12, 3), plotfunc=regplot_dropzeros, drop_zeros=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Without the zeros **"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.featureplot(not_oktrain2, ncols=4, nrows=2, figsize=(12, 3), plotfunc=regplot_dropzeros, drop_zeros=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data imputation\n",
"Impute the zero values from the rest of the dataset for the \"not ok\" columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from statsmodels.imputation import mice"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pp()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Replace zeros by NaNs\n",
"df[df.loc[:, cols_with_zeros] == 0] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"df = df.rename_axis({'1stFlrSF':'FrstFlrSF', '2ndFlrSF':'SndFlrSF'}, axis=1)\n",
"df.loc['train','SalePrice'] = logtarget.values\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.has_nulls(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp = mice.MICEData(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp.update_all()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp.data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cols = ['OpenPorchSF', 'MasVnrArea', 'TotalBsmtSF', 'WoodDeckSF', 'BsmtUnfSF', 'BsmtFinSF1', 'SndFlrSF','SalePrice']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imputed_notok = imp.data.loc[:, cols]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imputed_notok.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"samlib.featureplot2(imputed_notok, ncols=4, size=3, aspect=1.0, \n",
" plotfunc=sns.regplot, y=\"SalePrice\", data=imputed_notok)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp.data.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp.data.index=df.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp.data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"imp.data.reindex_like(df).to_csv('transformed_numerical_dataset_imputed.csv', index=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
dividiti/gemmbench | script/SGEMM_NT/explore-n-lws.ipynb | 1 | 7370 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import ck.kernel as ck\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import json\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"Collective Knowledge: v%s\" % ck.version({})['version_str']\n",
"print \"pandas: v%s\" % pd.__version__\n",
"print \"NumPy: v%s\" % np.version.version\n",
"print \"Matplotlib: v%s\" % matplotlib.__version__\n",
"print \"JSON: v%s\" % json.__version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dataset = 'SGEMM_NT'\n",
"data_uoa = dataset + '-explore-n-lws'\n",
"module_uoa = 'experiment'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r=ck.access({'action':'list_points', 'module_uoa':module_uoa, 'data_uoa':data_uoa})\n",
"if r['return']>0:\n",
" print (\"Error: %s\" % r['error'])\n",
" exit(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_list = []\n",
"index_list = []\n",
"\n",
"for point in r['points']:\n",
" with open(os.path.join(r['path'], 'ckp-%s.flat.json' % point)) as point_file:\n",
" point_data = json.load(point_file)\n",
" # Data. \n",
" Gflops_per_s = point_data.get(\"##characteristics#run#run_time_state#EXECUTION#Gflops/s#all\")\n",
" #match = point_data.get(\"##characteristics#run#run_time_state#RESULTS#match#all\")\n",
" data_list.append(Gflops_per_s) # + match)\n",
" # Index.\n",
" cl_file = point_data.get(\"##characteristics#run#run_time_state#METADATA#file#all_unique\")[0]\n",
" lws_j = point_data.get(\"##characteristics#run#run_time_state#EXECUTION#lws_j#all_unique\")[0]\n",
" lws_i = point_data.get(\"##characteristics#run#run_time_state#EXECUTION#lws_i#all_unique\")[0]\n",
" matrix_order = point_data.get(\"##characteristics#run#run_time_state#CMD_LINE_ARGS#matrix_order#all_unique\")[0]\n",
" index_list.append((cl_file, lws_j * lws_i, lws_j, lws_i, matrix_order))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_repetitions = 4\n",
"#ci = pd.MultiIndex.from_arrays([range(num_repetitions)*2, ['Gflops/s']*num_repetitions + ['Match?']*num_repetitions])\n",
"ci = pd.MultiIndex.from_arrays([range(num_repetitions)])\n",
"mi = pd.MultiIndex.from_tuples(names=['OpenCL program', 'LWS_jxi', 'LWS_j', 'LWS_i', 'Matrix order'], tuples=index_list)\n",
"df = pd.DataFrame(data=data_list, index=mi, columns=ci)\n",
"df.index = df.index.droplevel('OpenCL program') # not interested in as it's fixed here\n",
"df['mean'] = df[range(num_repetitions)].mean(axis=1)\n",
"df['std'] = df[range(num_repetitions)].std(axis=1)\n",
"df.sortlevel('LWS_jxi')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import re\n",
"# # left paren, whitespace, number, whitespace, comma, whitespace, number, whitespace, right paren\n",
"# r = '\\(\\s*(?P<lws_j>\\d+)\\s*,\\s*(?P<lws_i>\\d+)\\s*\\)'\n",
"# f = df.index.to_series().values\n",
"# [ m.groups() for t in f for m in [re.match(r, t[0])] ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ymax = np.int64(df['mean'].max() + df['std'].max())\n",
"plot = df['mean'] \\\n",
" .unstack(level='Matrix order') \\\n",
" .plot(yerr=df['std'].unstack(level='Matrix order'),\n",
" title='Gflops/s vs Local work size',\n",
" kind='bar', figsize=(12,10), colormap=matplotlib.cm.autumn,\n",
" ylim=(0, ymax), yticks=range(ymax))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# plot = mean \\\n",
"# .unstack(level='Local work size') \\\n",
"# .plot(yerr=std.unstack(level='Local work size'),\n",
"# title='Gflops/s vs Matrix order',\n",
"# kind='bar', figsize=(12,8), colormap=matplotlib.cm.autumn,\n",
"# ylim=(0, ymax), yticks=range(ymax))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dump results for paper"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_repetitions = 4\n",
"mi_tex = pd.MultiIndex.from_tuples(tuples=index_list)\n",
"df_tex = pd.DataFrame(data=data_list, index=mi_tex)\n",
"df_tex.index = df_tex.index.droplevel(0) # not interested in as it's fixed here\n",
"df_tex = df_tex.sortlevel(0) # 'LWS_jxi'\n",
"df_tex['mean'] = df_tex[range(num_repetitions)].mean(axis=1)\n",
"df_tex['std'] = df_tex[range(num_repetitions)].std(axis=1)\n",
"df_tex = df_tex.loc[[16,32,64]]\n",
"df_tex"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('%s_tmp.tex' % data_uoa, 'w') as tex_file:\n",
" tex_file.write(df_tex.to_latex())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot.get_figure().savefig('%s_tmp.pdf' % data_uoa)\n",
"plot.get_figure().savefig('%s_tmp.png' % data_uoa)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
poppy-project/community-notebooks | tutorials-education/Documentation Snap! & Poppy/Decouvrir Snap! pour Poppy - Ressources.ipynb | 2 | 2730 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"../Images/Poppy_Bandeau_Snap.png\" alt=\"Logo Poppy + Snap!\" style=\"height: 130px;\"/>\n",
"# Découvrir Snap! en vidéos "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"### Voir sur YouTube : \n",
"\n",
"* [(YouTube) Vidéo 1 : Découverte de Snap!](https://www.youtube.com/watch?v=MbyYEPeo9mw&feature=youtu.be)\n",
"* [(YouTube) Vidéo 2 : Snap! pour _Poppy_ ](https://www.youtube.com/watch?v=-SGL4hd_sBo)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# Vidéo 1 : Découverte de Snap!"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<iframe width=\"720\" height=\"480\"\n",
"src=\"http://www.youtube.com/embed/MbyYEPeo9mw\">\n",
"</iframe>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML(\"\"\"\n",
"<iframe width=\"720\" height=\"480\"\n",
"src=\"http://www.youtube.com/embed/MbyYEPeo9mw\">\n",
"</iframe>\n",
"\"\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# Vidéo 2 : Snap! pour _Poppy_"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<iframe width=\"720\" height=\"480\"\n",
"src=\"http://www.youtube.com/embed/-SGL4hd_sBo\">\n",
"</iframe>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import HTML\n",
"HTML(\"\"\"\n",
"<iframe width=\"720\" height=\"480\"\n",
"src=\"http://www.youtube.com/embed/-SGL4hd_sBo\">\n",
"</iframe>\n",
"\"\"\")\n",
"\n",
"\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| lgpl-3.0 |
BinRoot/TensorFlow-Book | ch02_basics/Concept09_queue.ipynb | 1 | 10062 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ch `02`: Concept `09`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Using Queues"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you have a lot of training data, you probably don't want to load it all into memory at once. The QueueRunner in TensorFlow is a tool to efficiently employ a queue data-structure in a multi-threaded way."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will be running multiple threads, so let's figure out the number of CPUs:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import multiprocessing\n",
"NUM_THREADS = multiprocessing.cpu_count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate some fake data to work with:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xs = np.random.randn(100, 3)\n",
"ys = np.random.randint(0, 2, size=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a couple concrete examples of our data:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Input [ 1.46034759 0.71462742 0.73288402] ---> Output 0\n",
"Input [ 1.1537654 -0.09128405 0.08036941] ---> Output 1\n",
"Input [-0.61164559 -0.19188485 0.06064167] ---> Output 0\n",
"Input [ 0.1007337 0.34815357 0.24346031] ---> Output 0\n",
"Input [-1.25581117 1.44738085 1.15035257] ---> Output 0\n"
]
}
],
"source": [
"xs_and_ys = zip(xs, ys)\n",
"for _ in range(5):\n",
" x, y = next(xs_and_ys)\n",
" print('Input {} ---> Output {}'.format(x, y))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a queue:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"queue = tf.FIFOQueue(capacity=1000, dtypes=[tf.float32, tf.int32])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up the enqueue and dequeue ops:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"enqueue_op = queue.enqueue_many([xs, ys])\n",
"x_op, y_op = queue.dequeue()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a QueueRunner:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"qr = tf.train.QueueRunner(queue, [enqueue_op] * 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that all variables and ops have been defined, let's get started with a session:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create threads for the QueueRunner:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"coord = tf.train.Coordinator()\n",
"enqueue_threads = qr.create_threads(sess, coord=coord, start=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test out dequeueing:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 1.46034753 0.71462744 0.73288405] 0\n",
"[ 1.15376544 -0.09128405 0.08036941] 1\n",
"[-0.61164558 -0.19188486 0.06064167] 0\n",
"[ 0.1007337 0.34815356 0.24346031] 0\n",
"[-1.25581121 1.4473809 1.1503526 ] 0\n",
"[ 0.60369009 -0.87942719 -1.37121975] 1\n",
"[ 1.30641925 1.55316997 1.01789773] 0\n",
"[ 0.0575242 0.59463078 0.47600508] 1\n",
"[-1.22782397 -0.86792755 1.37459588] 1\n",
"[-0.27896652 0.51645088 1.36873603] 0\n",
"[-0.34542757 0.79360306 0.32000065] 0\n",
"[-0.46792462 -0.31817994 0.91739392] 0\n",
"[ 0.24787657 0.83848852 1.16125166] 0\n",
"[-0.46220389 -0.09412029 -0.9981451 ] 1\n",
"[ 0.06739734 -1.08405316 -0.3582162 ] 1\n",
"[-1.2644819 -0.27479929 1.15882337] 1\n",
"[-0.68015367 -0.10199564 1.4274267 ] 0\n",
"[-0.48884565 -0.39484504 0.1496018 ] 1\n",
"[ 1.48414564 -0.43943462 -0.12646018] 0\n",
"[ 0.49450573 0.42091215 -0.17693481] 0\n",
"[ 0.02265234 0.99832052 0.26808155] 1\n",
"[-0.94086462 1.67000341 0.92434174] 1\n",
"[-0.50961769 -0.39044595 -0.5737586 ] 0\n",
"[-0.95702702 0.61196166 -0.86487901] 1\n",
"[-0.6125344 -0.30916786 -1.06602347] 1\n",
"[-1.91383719 0.26860073 0.50380921] 1\n",
"[-0.14638679 0.11614402 1.36613548] 1\n",
"[-0.56817967 1.4221288 0.99365205] 0\n",
"[-0.04597072 0.43875724 -0.4809106 ] 0\n",
"[-0.2000681 -0.2384561 0.06599616] 0\n",
"[ 0.5862993 0.85386461 0.82285357] 1\n",
"[ 1.64371336 -0.46838599 0.22755136] 0\n",
"[ 0.21683638 -0.96399426 1.78278649] 1\n",
"[ 0.03778305 2.49208736 0.07467758] 0\n",
"[-1.48958826 -0.11699235 0.98281074] 1\n",
"[-0.27623582 -0.41658697 -0.89554274] 0\n",
"[-1.64742625 1.83507264 -0.76936585] 0\n",
"[-1.5386405 0.14272654 0.17047048] 1\n",
"[ 0.63654041 1.75451732 -1.14198494] 0\n",
"[-0.57061732 0.11121389 1.39394116] 1\n",
"[ 1.94736981 -0.36588097 0.54801333] 1\n",
"[-0.56976408 -1.36990237 -0.9922803 ] 1\n",
"[-2.47653961 1.19603479 -0.3038739 ] 0\n",
"[-0.76740891 -0.49611184 0.47167206] 0\n",
"[ 1.62004089 0.13268068 0.28845155] 0\n",
"[-0.91749012 -0.30151108 -0.08271972] 0\n",
"[-0.21053326 -0.16114895 -0.52424961] 1\n",
"[ 0.19968066 0.2387522 2.0314014 ] 0\n",
"[-0.29072183 0.53720349 -0.38972732] 0\n",
"[-0.85891634 -0.26684314 -1.91741192] 1\n",
"[-2.07077003 1.97488022 -0.92741841] 0\n",
"[ 2.37270904 2.19385314 -0.29643178] 0\n",
"[-0.18054648 -0.1651988 1.70858753] 1\n",
"[-0.27851281 -0.13095042 0.30613536] 1\n",
"[-0.13653868 -0.14431253 1.3018136 ] 1\n",
"[-1.79938364 0.26698261 -0.3283855 ] 0\n",
"[-0.43491617 -0.8737886 -0.48871836] 1\n",
"[-0.27275884 0.08004636 -0.34334385] 0\n",
"[-0.06538768 -0.47280514 -1.82918119] 0\n",
"[ 1.72329473 0.6359638 1.53474641] 0\n",
"[ 0.88200653 0.87051851 0.17676826] 1\n",
"[-2.22127795 -0.39812142 0.69118947] 0\n",
"[-0.90146214 0.23153968 -1.07890677] 0\n",
"[-0.66513097 -0.74897975 -1.9886812 ] 0\n",
"[ 0.95217085 -0.1361241 -0.81558466] 1\n",
"[ 0.97319698 0.10349847 1.78010297] 0\n",
"[ 0.54321396 1.10134006 -1.03641176] 1\n",
"[ 0.46445891 0.56387979 0.10383373] 0\n",
"[ 0.22231635 -1.20880091 0.20125042] 1\n",
"[ 0.56338882 -0.76195502 -0.33035895] 0\n",
"[ 0.13885871 0.62347603 0.32560909] 0\n",
"[-0.63413048 0.19185983 1.65251637] 1\n",
"[ 0.81965917 -0.14427175 -0.9943186 ] 0\n",
"[ 1.98786604 -1.38118052 -0.34296793] 0\n",
"[-0.49028778 -0.30242845 0.81718981] 0\n",
"[ 0.48434621 -1.3200016 -0.32307461] 0\n",
"[-0.91041267 -0.34315997 0.71205115] 0\n",
"[ 0.61457998 -0.85814965 0.6939835 ] 0\n",
"[-0.40195578 -1.11846507 -0.19713871] 1\n",
"[-0.47889531 -0.75685191 1.68955612] 1\n",
"[ 1.51117146 -2.23529124 1.13895822] 0\n",
"[-0.00831293 -0.50950557 0.08648733] 1\n",
"[-0.47011089 1.04781067 -0.05893843] 1\n",
"[-0.34855339 -0.5695411 -0.12196264] 1\n",
"[-0.47251806 -0.49479187 0.27609721] 0\n",
"[-2.04546118 -0.16185458 1.42348552] 0\n",
"[-0.67136103 -0.16650072 0.3609505 ] 0\n",
"[ 1.22566068 1.18665588 -1.87292075] 0\n",
"[-0.80474126 -0.1114784 0.00531922] 1\n",
"[ 0.62691861 -3.26328206 -0.39003551] 0\n",
"[-0.77470082 -1.23692167 -1.55790484] 0\n",
"[-0.49005547 -0.19645052 -0.21566501] 1\n",
"[-0.44095206 -0.13273652 -0.59810853] 0\n",
"[-0.9750855 -0.46043435 0.06064714] 1\n",
"[-0.181191 -0.12452056 0.23064452] 1\n",
"[-0.34818363 -1.13179028 1.20628965] 0\n",
"[-1.58196092 -1.3506341 -2.05767131] 1\n",
"[-1.66225421 -0.43541616 1.55258 ] 0\n",
"[-0.12949325 -0.15456693 0.04389611] 0\n",
"[ 0.24592777 0.11407969 -0.31221709] 1\n"
]
}
],
"source": [
"for _ in range(100):\n",
" if coord.should_stop():\n",
" break\n",
" x, y = sess.run([x_op, y_op])\n",
" print(x, y)\n",
"coord.request_stop()\n",
"coord.join(enqueue_threads)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
luca-heltai/ePICURE | applications/collocation_laplace_using_bsplines.ipynb | 1 | 17666 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
"%pylab inline\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from utilities import *\n",
"from interfaces import *\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linfty error: "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.73333694995e-33\n"
]
},
{
"data": {
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z8U/bUTIkoEt/f+wZYq5MZHaLwbajKKV8RJPwCtye+DCt3nrLdpQMCejSbz5t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\nlAog/w+yF0IYC+p+5gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107bc6450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"p = 2 # Degree\n",
"n = 5 # Knots Boundaries\n",
"# knots = np.r_[p*[0], linspace(0,1,n), p*[1]] # Make an open knot vector\n",
"knots = np.r_[(p-1)*[0], linspace(0,1,n), (p-1)*[1]] # Make an uniform vector, such that end points are zero\n",
"vs = BsplineVectorSpace(p, knots)\n",
"\n",
"# Collocation points\n",
"x = linspace(0,1,vs.n_dofs+2)[1:-1]\n",
"\n",
"A = -interpolation_matrix(vs, x, 2) # Second derivatives\n",
"\n",
"# Laplace equation using Collocation approach...\n",
"\n",
"# A*u = -u''(x) = f(x) \n",
"f = lambda x: 1\n",
"exact = lambda x: x*(1-x)/2\n",
"\n",
"cu = squeeze(np.linalg.solve(A, f(x)))\n",
"\n",
"s = linspace(0,1,1000)\n",
"u = vs.element(cu)\n",
"\n",
"plot(s, u(s))\n",
"plot(s, exact(s))\n",
"\n",
"print 'Linfty error: ', max(abs(u(s)-exact(s))**2)"
]
}
],
"metadata": {
"name": "",
"signature": "sha256:12a660f55ad7c81c72f63842b4fc2a3983580d25fe6e5ee8c2f76ab92f18a0ae"
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
cbentivoglio/neurolearn_clone | scripts/Neurolearn_Test_Script.ipynb | 1 | 210484 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Neurolearn Functionality"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Couldn't import dot_parser, loading of dot files will not be possible.\n"
]
}
],
"source": [
"from pyneurovault import api\n",
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import nibabel as nb\n",
"import numpy as np\n",
"import pickle\n",
"from nltools.analysis import Predict, apply_mask, Roc\n",
"%matplotlib inline\n",
"\n",
"outfolder = \"/Users/lukechang/Downloads/nv_tmp\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Download Pain Images"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting NeuroVault collections meta data...\n",
"Found 238 results.\n",
"Extracting NeuroVault images meta data...\n",
"Found 6302 results.\n",
"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=1000\n",
"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=2000\n",
"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=3000\n",
"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=4000\n",
"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=5000\n",
"Retrieving http://neurovault.org/api/images/?format=json&limit=1000&offset=6000\n",
"NeuroVault Object (nv) Includes <nv.images><nv.collections>\n",
"Elapsed: 172.62 seconds\n"
]
}
],
"source": [
"tic = time.time() #Start Timer\n",
"\n",
"# Pain Collection\n",
"collection = 504\n",
"\n",
"# Will extract all collections and images in one query to work from\n",
"nv = api.NeuroVault()\n",
"\n",
"# Download all images to file\n",
"standard = os.path.join(os.path.dirname(api.__file__),'data','MNI152_T1_2mm_brain.nii.gz')\n",
"nv.download_images(dest_dir = outfolder,target=standard, collection_ids=[collection],resample=False)\n",
"\n",
"# Create Variables\n",
"collection_data = nv.get_images_df().ix[nv.get_images_df().collection_id == collection,:].reset_index()\n",
"img_index = sorted((e,i) for i,e in enumerate(collection_data.file))\n",
"index = [x[1] for x in img_index]\n",
"img_file = [x[0] for x in img_index]\n",
"\n",
"dat = nb.funcs.concat_images([os.path.join(outfolder,'original',str(x) + '.nii.gz') for x in collection_data.image_id[index]])\n",
"holdout = [int(x.split('_')[-2]) for x in img_file]\n",
"heat_level = [x.split('_')[-1].split('.')[0] for x in img_file]\n",
"Y_dict = {'High':3,'Medium':2,'Low':1}\n",
"Y = np.array([Y_dict[x] for x in heat_level])\n",
"\n",
"# Pickle for later use\n",
"# Saving the objects:\n",
"with open(os.path.join(outfolder,'Pain_Data.pkl'), 'w') as f:\n",
" pickle.dump([dat,holdout,Y], f)\n",
"\n",
"print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load Pickled Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Load Pickled File - Elapsed: 24.17 seconds\n"
]
}
],
"source": [
"tic = time.time() #Start Timer\n",
"\n",
"# Getting back the objects:\n",
"with open(os.path.join(outfolder,'Pain_Data.pkl')) as f:\n",
" dat, holdout, Y = pickle.load(f)\n",
"print 'Load Pickled File - Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run Prediction"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"overall Root Mean Squared Error: 0.98\n",
"overall Correlation: 0.55\n",
"Total Elapsed: 7.30 seconds\n"
]
},
{
"data": {
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wAO985zvx4IMP4u67744KgOCEE07A0NAQjjnmmOzYvHnzUFtbixNOOCE79v73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IUhZadEZuDmZx+bhasgXZ+lEcNbUq8jEfj6dQmjLvWw6I2JYjGo7j+wXLyPe0Y3oAxPp6sLb1u1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/p1hCnkOqHrfapOBkwc7mSJXf0l4FfD9VvvdZgY6/feSm4Tw/oxLqYjtDmE3uGAe1+oGYs8PN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\nBsLAYhL9tvS4w4su53M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"text/plain": [
"<matplotlib.figure.Figure at 0x110558690>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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fKD+uk9GJ/5j279U2wMzagUvxo+5xwLnOub5qXy9kKpTfeFElt2nUNm88Fv9x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\ntkW+JUqxxpqyFGtQgdnMXg1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"text/plain": [
"<matplotlib.figure.Figure at 0x13581b750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tic = time.time() #Start Timer\n",
"\n",
"## Test Prediction with kfold xVal\n",
"# SVR\n",
"# negvneu = Predict(dat,Y,algorithm='svr',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5}, **{'kernel':\"linear\"})\n",
"# negvneu = Predict(dat,Y,algorithm='svr',subject_id = holdout, output_dir=outfolder, cv_dict = {'loso':holdout}, **{'kernel':\"linear\"})\n",
"# negvneu.predict()\n",
"# print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\n",
"\n",
"# Ridge\n",
"negvneu = Predict(dat,Y,algorithm='ridge',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5})\n",
"negvneu.predict()\n",
"\n",
"# # Principal Components Regression\n",
"# negvneu = Predict(dat,Y,algorithm='pcr',subject_id = holdout, output_dir=outfolder, cv_dict = {'kfolds':5})\n",
"# negvneu.predict()\n",
"print 'Total Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Apply Mask"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed: 44.62 seconds\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python2.7/site-packages/nilearn/signal.py:50: UserWarning: Standardization of 3D signal has been requested but would lead to zero values. Skipping.\n",
" warnings.warn('Standardization of 3D signal has been requested but '\n"
]
},
{
"data": {
"image/png": 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JOqkunGkz2Vwn8tZTEr9pc9N3ADZ5b+qcREKsFMmEjKi+qIljofU5DnYKs4tr8Dzgf5ls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"text/plain": [
"<matplotlib.figure.Figure at 0x107ce42d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tic = time.time() #Start Timer\n",
"\n",
"# Load data using nibabel\n",
"pines = nb.load(os.path.join(outfolder, 'ridge_weightmap.nii.gz'))\n",
"\n",
"pexpd = apply_mask(data=dat, weight_map=pines, output_dir=outfolder, method='dot_product', save_output=True)\n",
"pexpc = apply_mask(data=dat, weight_map=pines, output_dir=outfolder, method='correlation', save_output=True)\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.plot(pexpd)\n",
"plt.title('Pattern Expression')\n",
"plt.ylabel('Dot Product')\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(pexpc)\n",
"plt.xlabel('Subject')\n",
"plt.ylabel('Correlation')\n",
"\n",
"print 'Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## ROC Analysis"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------------------\n",
".:ROC Analysis Summary:.\n",
"------------------------\n",
"Accuracy: 0.84\n",
"Accuracy SE: 0.11\n",
"Accuracy p-value: 0.00\n",
"Sensitivity: 0.93\n",
"Specificity: 0.75\n",
"AUC: 0.88\n",
"PPV: 0.79\n",
"------------------------\n",
"[ 1. 1. 1. ..., 0.03571429 0.03571429\n",
" 0.03571429]\n",
"Total Elapsed: 1.30 seconds\n"
]
},
{
"data": {
"image/png": 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8W0T+y7uKiBRSbyeLadP6PCOOF+cViohIkRUpWbyeVxgiIkXX28ni9T75YVql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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a539710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tic = time.time() #Start Timer\n",
"\n",
"# Create Variables\n",
"negvneu.yfit\n",
"include = (negvneu.Y==3) | (negvneu.Y==1)\n",
"input_values = negvneu.yfit[include]\n",
"binary_outcome = negvneu.Y[include]\n",
"binary_outcome = binary_outcome==3\n",
"\n",
"# Single-Interval\n",
"roc = Roc(input_values=input_values, binary_outcome=binary_outcome)\n",
"roc.plot()\n",
"roc.summary()\n",
"\n",
"print roc.tpr\n",
"roc.accuracy\n",
"\n",
"# # Forced Choice \n",
"# roc_fc = Roc(input_values=input_values, binary_outcome=binary_outcome, forced_choice=True)\n",
"# roc_fc.plot()\n",
"# roc_fc.summary()\n",
"\n",
"print 'Total Elapsed: %.2f seconds' % (time.time() - tic) #Stop timer"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<nltools.analysis.Predict instance at 0x107dfd9e0>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from nilearn.input_data import NiftiMasker\n",
"mask = nb.load('/Users/lukechang/Github/neurolearn/nltools/resources/MNI152_T1_2mm_brain_mask_dil.nii.gz')\n",
"nifti_masker = NiftiMasker(mask_img=mask)\n",
"pcr = nb.load(os.path.join(outfolder,'pcr_weightmap.nii.gz'))\n",
"ridge = nb.load(os.path.join(outfolder,'ridge_weightmap.nii.gz'))\n",
"pcr_mask = nifti_masker.fit_transform(pcr)\n",
"ridge_mask = nifti_masker.fit_transform(ridge)\n",
"np.corrcoef(pcr_mask,ridge_mask)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Univariate Regression\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tensorflow/docs-l10n | site/ja/tutorials/estimator/boosted_trees.ipynb | 1 | 21926 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "7765UFHoyGx6"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "KVtTDrUNyL7x"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xPYxZMrWyA0N"
},
"source": [
"# Estimators を使用するブースティング木"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p_vOREjRx-Y0"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td> <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\"><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/tutorials/estimator/boosted_trees.ipynb\">TensorFlow.org で表示</a> </td>\n",
" <td> <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/tutorials/estimator/boosted_trees.ipynb\">Google Colab で実行</a> </td>\n",
" <td> <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\"><a target=\"_blank\" href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/tutorials/estimator/boosted_trees.ipynb\">GitHubでソースを表示</a> </td>\n",
" <td> <img src=\"https://www.tensorflow.org/images/download_logo_32px.png\"><a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/estimator/boosted_trees.ipynb\">ノートブックをダウンロード</a> </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6gWdn5lrlkhR"
},
"source": [
"> 警告: 新しいコードには Estimators は推奨されません。Estimators は `v1.Session` スタイルのコードを実行しますが、これは正しく記述するのはより難しく、特に TF 2 コードと組み合わせると予期しない動作をする可能性があります。Estimators は、[互換性保証] (https://tensorflow.org/guide/versions) の対象となりますが、セキュリティの脆弱性以外の修正は行われません。詳細については、[移行ガイド](https://tensorflow.org/guide/migrate)を参照してください。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qNW3c_rop5J8"
},
"source": [
"**注意**: 多くの最先端の決定フォレストアルゴリズムの最新の Keras ベースの実装は、[TensorFlow 決定フォレスト](https://tensorflow.org/decision_forests)から利用できます。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dW3r7qVxzqN5"
},
"source": [
"このチュートリアルは、`tf.estimator`API で決定木を使用する勾配ブースティングモデルのエンドツーエンドのウォークスルーです。ブースティング木モデルは、回帰と分類の両方のための最も一般的かつ効果的な機械学習アプローチの 1 つです。これは、複数（10 以上、100 以上、あるいは 1000 以上の場合も考えられます）の木モデルからの予測値を結合するアンサンブル手法です。\n",
"\n",
"最小限のハイパーパラメータ調整で優れたパフォーマンスを実現できるため、ブースティング木モデルは多くの機械学習実践者に人気があります。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eylrTPAN3rJV"
},
"source": [
"## Titanic データセットを読み込む\n",
"\n",
"Titanic データセットを使用します。ここでの目標は、性別、年齢、クラスなど与えられた特徴から（やや悪趣味ではありますが）乗船者の生存を予測することです。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KuhAiPfZ3rJW"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from IPython.display import clear_output\n",
"from matplotlib import pyplot as plt\n",
"\n",
"# Load dataset.\n",
"dftrain = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')\n",
"dfeval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')\n",
"y_train = dftrain.pop('survived')\n",
"y_eval = dfeval.pop('survived')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NFtnFm1T0kMf"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"tf.random.set_seed(123)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3ioodHdVJVdA"
},
"source": [
"データセットはトレーニングセットと評価セットで構成されています。\n",
"\n",
"- `dftrain`と`y_train`は *トレーニングセット*です — モデルが学習に使用するデータです。\n",
"- モデルは*評価セット*、`dfeval`、`y_eval`に対してテストされます。\n",
"\n",
"トレーニングには以下の特徴を使用します。\n",
"\n",
"<table>\n",
" <tr>\n",
" <th>特徴名</th>\n",
" <th>説明</th>\n",
" </tr>\n",
" <tr>\n",
" <td>sex</td>\n",
" <td>乗船者の性別</td>\n",
" </tr>\n",
" <tr>\n",
" <td>age</td>\n",
" <td>乗船者の年齢</td>\n",
" </tr>\n",
" <tr>\n",
" <td>n_siblings_spouses</td>\n",
" <td>同乗する兄弟姉妹および配偶者</td>\n",
" </tr>\n",
" <tr>\n",
" <td>parch</td>\n",
" <td>同乗する両親および子供</td>\n",
" </tr>\n",
" <tr>\n",
" <td>fare</td>\n",
" <td>運賃</td>\n",
" </tr>\n",
" <tr>\n",
" <td>class</td>\n",
" <td>船室のクラス</td>\n",
" </tr>\n",
" <tr>\n",
" <td>deck</td>\n",
" <td>搭乗デッキ</td>\n",
" </tr>\n",
" <tr>\n",
" <td>embark_town</td>\n",
" <td>乗船者の乗船地</td>\n",
" </tr>\n",
" <tr>\n",
" <td>alone</td>\n",
" <td>一人旅か否か</td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AoPiWsJALr-k"
},
"source": [
"## データを検証する"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "slcat1yzmzw5"
},
"source": [
"まず最初に、データの一部をプレビューして、トレーニングセットの要約統計を作成します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "15PLelXBlxEW"
},
"outputs": [],
"source": [
"dftrain.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "j2hiM4ETmqP0"
},
"outputs": [],
"source": [
"dftrain.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-IR0e8V-LyJ4"
},
"source": [
"トレーニングセットと評価セットには、それぞれ 627 個と 264 個の例があります。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "_1NwYqGwDjFf"
},
"outputs": [],
"source": [
"dftrain.shape[0], dfeval.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "28UFJ4KSMK3V"
},
"source": [
"乗船者の大半は 20 代から 30 代です。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CaVDmZtuDfux"
},
"outputs": [],
"source": [
"dftrain.age.hist(bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1pifWiCoMbR5"
},
"source": [
"男性の乗船者数は女性の乗船者数の約 2 倍です。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-WazAq30MO5J"
},
"outputs": [],
"source": [
"dftrain.sex.value_counts().plot(kind='barh')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7_XkxrpmmVU_"
},
"source": [
"乗船者の大半は「3 等」の船室クラスを利用していました。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zZ3PvVy4l4gI"
},
"outputs": [],
"source": [
"dftrain['class'].value_counts().plot(kind='barh')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HM5SlwlxmZMT"
},
"source": [
"大半の乗船者はサウサンプトンから乗船しています。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "RVTSrdr4mZaC"
},
"outputs": [],
"source": [
"dftrain['embark_town'].value_counts().plot(kind='barh')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "aTn1niLPob3x"
},
"source": [
"女性は男性よりも生存する確率がはるかに高く、これは明らかにモデルの予測特徴です。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Eh3KW5oYkaNS"
},
"outputs": [],
"source": [
"pd.concat([dftrain, y_train], axis=1).groupby('sex').survived.mean().plot(kind='barh').set_xlabel('% survive')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "krkRHuMp3rJn"
},
"source": [
"## 特徴量カラムを作成して関数を入力する\n",
"\n",
"勾配ブースティング Estimator は数値特徴とカテゴリ特徴の両方を利用します。特徴量カラムは、全ての TensorFlow Estimator と機能し、その目的はモデリングに使用される特徴を定義することにあります。さらに、One-Hot エンコーディング、正規化、バケット化などいくつかの特徴量エンジニアリング機能を提供します。このチュートリアルでは、`CATEGORICAL_COLUMNS`のフィールドはカテゴリカラムから One-Hot エンコーディングされたカラム（[インジケータカラム](https://www.tensorflow.org/api_docs/python/tf/feature_column/indicator_column)）に変換されます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "upaNWxcF3rJn"
},
"outputs": [],
"source": [
"CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',\n",
" 'embark_town', 'alone']\n",
"NUMERIC_COLUMNS = ['age', 'fare']\n",
"\n",
"def one_hot_cat_column(feature_name, vocab):\n",
" return tf.feature_column.indicator_column(\n",
" tf.feature_column.categorical_column_with_vocabulary_list(feature_name,\n",
" vocab))\n",
"feature_columns = []\n",
"for feature_name in CATEGORICAL_COLUMNS:\n",
" # Need to one-hot encode categorical features.\n",
" vocabulary = dftrain[feature_name].unique()\n",
" feature_columns.append(one_hot_cat_column(feature_name, vocabulary))\n",
"\n",
"for feature_name in NUMERIC_COLUMNS:\n",
" feature_columns.append(tf.feature_column.numeric_column(feature_name,\n",
" dtype=tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "74GNtFpStSAz"
},
"source": [
"特徴量カラムが生成する変換は表示することができます。例えば、`indicator_column`を単一の例で使用した場合の出力は次のようになります。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Eaq79D9FtmF8"
},
"outputs": [],
"source": [
"example = dict(dftrain.head(1))\n",
"class_fc = tf.feature_column.indicator_column(tf.feature_column.categorical_column_with_vocabulary_list('class', ('First', 'Second', 'Third')))\n",
"print('Feature value: \"{}\"'.format(example['class'].iloc[0]))\n",
"print('One-hot encoded: ', tf.keras.layers.DenseFeatures([class_fc])(example).numpy())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YbCUn3nCusC3"
},
"source": [
"さらに、特徴量カラムの変換を全てまとめて表示することができます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "omIYcsVws3g0"
},
"outputs": [],
"source": [
"tf.keras.layers.DenseFeatures(feature_columns)(example).numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-UOlROp33rJo"
},
"source": [
"次に、入力関数を作成する必要があります。これらはトレーニングと推論の両方のためにデータをモデルに読み込む方法を指定します。[ `tf.data`](https://www.tensorflow.org/api_docs/python/tf/data) API の`from_tensor_slices`メソッドを使用して Pandas から直接データを読み取ります。これは小規模でインメモリのデータセットに適しています。大規模のデータセットの場合は、多様なファイル形式（[csv](https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_csv_dataset)を含む）をサポートする tf.data API を使用すると、メモリに収まりきれないデータセットも処理することができます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "9dquwCQB3rJp"
},
"outputs": [],
"source": [
"# Use entire batch since this is such a small dataset.\n",
"NUM_EXAMPLES = len(y_train)\n",
"\n",
"def make_input_fn(X, y, n_epochs=None, shuffle=True):\n",
" def input_fn():\n",
" dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))\n",
" if shuffle:\n",
" dataset = dataset.shuffle(NUM_EXAMPLES)\n",
" # For training, cycle thru dataset as many times as need (n_epochs=None).\n",
" dataset = dataset.repeat(n_epochs)\n",
" # In memory training doesn't use batching.\n",
" dataset = dataset.batch(NUM_EXAMPLES)\n",
" return dataset\n",
" return input_fn\n",
"\n",
"# Training and evaluation input functions.\n",
"train_input_fn = make_input_fn(dftrain, y_train)\n",
"eval_input_fn = make_input_fn(dfeval, y_eval, shuffle=False, n_epochs=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HttfNNlN3rJr"
},
"source": [
"## モデルをトレーニングして評価する\n",
"\n",
"以下のステップで行います。\n",
"\n",
"1. 特徴とハイパーパラメータを指定してモデルを初期化する。\n",
"2. `train_input_fn`を使用してモデルにトレーニングデータを与え、`train`関数を使用してモデルをトレーニングする。\n",
"3. 評価セット（この例では`dfeval` DataFrame）を使用してモデルのパフォーマンスを評価する。予測値が`y_eval`配列のラベルと一致することを確認する。\n",
"\n",
"ブースティング木モデルをトレーニングする前に、まず線形分類器（ロジスティック回帰モデル）をトレーニングしてみましょう。ベンチマークを確立するには、より単純なモデルから始めるのがベストプラクティスです。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JPOGpmmq3rJr"
},
"outputs": [],
"source": [
"linear_est = tf.estimator.LinearClassifier(feature_columns)\n",
"\n",
"# Train model.\n",
"linear_est.train(train_input_fn, max_steps=100)\n",
"\n",
"# Evaluation.\n",
"result = linear_est.evaluate(eval_input_fn)\n",
"clear_output()\n",
"print(pd.Series(result))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BarkNXwA3rJu"
},
"source": [
"次に、ブースティング木モデルをトレーニングしてみましょう。ブースティング木では、回帰（`BoostedTreesRegressor`）と分類（`BoostedTreesClassifier`）をサポートします。目標は、生存か非生存かのクラスを予測することなので、`BoostedTreesClassifier`を使用します。\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tgEzMtlw3rJu"
},
"outputs": [],
"source": [
"# Since data fits into memory, use entire dataset per layer. It will be faster.\n",
"# Above one batch is defined as the entire dataset.\n",
"n_batches = 1\n",
"est = tf.estimator.BoostedTreesClassifier(feature_columns,\n",
" n_batches_per_layer=n_batches)\n",
"\n",
"# The model will stop training once the specified number of trees is built, not\n",
"# based on the number of steps.\n",
"est.train(train_input_fn, max_steps=100)\n",
"\n",
"# Eval.\n",
"result = est.evaluate(eval_input_fn)\n",
"clear_output()\n",
"print(pd.Series(result))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hEflwznXvuMP"
},
"source": [
"このトレーニングモデルを使用して、評価セットからある乗船者に予測を立てることができます。TensorFlow モデルは、バッチ、コレクション、または例に対してまとめて予測を立てられるように最適化されています。以前は、`eval_input_fn` は評価セット全体を使って定義されていました。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "6zmIjTr73rJ4"
},
"outputs": [],
"source": [
"pred_dicts = list(est.predict(eval_input_fn))\n",
"probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])\n",
"\n",
"probs.plot(kind='hist', bins=20, title='predicted probabilities')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mBUaNN1BzJHG"
},
"source": [
"最後に、結果の受信者操作特性（ROC）を見てみましょう。真陽性率と偽陽性率間のトレードオフに関し、より明確な予想を得ることができます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NzxghvVz3rJ6"
},
"outputs": [],
"source": [
"from sklearn.metrics import roc_curve\n",
"\n",
"fpr, tpr, _ = roc_curve(y_eval, probs)\n",
"plt.plot(fpr, tpr)\n",
"plt.title('ROC curve')\n",
"plt.xlabel('false positive rate')\n",
"plt.ylabel('true positive rate')\n",
"plt.xlim(0,)\n",
"plt.ylim(0,)\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "boosted_trees.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
sarathid/Learning | Deep_learning_ND/Week 1/dlnd-your-first-network/DLND-your-first-network/dlnd-your-first-neural-network.ipynb | 2 | 337782 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Your first neural network\n",
"\n",
"In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load and prepare the data\n",
"\n",
"A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = 'Bike-Sharing-Dataset/hour.csv'\n",
"\n",
"rides = pd.read_csv(data_path)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>instant</th>\n",
" <th>dteday</th>\n",
" <th>season</th>\n",
" <th>yr</th>\n",
" <th>mnth</th>\n",
" <th>hr</th>\n",
" <th>holiday</th>\n",
" <th>weekday</th>\n",
" <th>workingday</th>\n",
" <th>weathersit</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.22</td>\n",
" <td>0.2727</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.22</td>\n",
" <td>0.2727</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0.24</td>\n",
" <td>0.2879</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instant dteday season yr mnth hr holiday weekday workingday \\\n",
"0 1 2011-01-01 1 0 1 0 0 6 0 \n",
"1 2 2011-01-01 1 0 1 1 0 6 0 \n",
"2 3 2011-01-01 1 0 1 2 0 6 0 \n",
"3 4 2011-01-01 1 0 1 3 0 6 0 \n",
"4 5 2011-01-01 1 0 1 4 0 6 0 \n",
"\n",
" weathersit temp atemp hum windspeed casual registered cnt \n",
"0 1 0.24 0.2879 0.81 0.0 3 13 16 \n",
"1 1 0.22 0.2727 0.80 0.0 8 32 40 \n",
"2 1 0.22 0.2727 0.80 0.0 5 27 32 \n",
"3 1 0.24 0.2879 0.75 0.0 3 10 13 \n",
"4 1 0.24 0.2879 0.75 0.0 0 1 1 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rides.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking out the data\n",
"\n",
"This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the `cnt` column. You can see the first few rows of the data above.\n",
"\n",
"Below is a plot showing the number of bike riders over the first 10 days in the data set. You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x66b7c50>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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znnQ08RbFhQo+FXxCiB3bCvQspBh0RQghQbBbdPJJ0fn6XadGt+9evARfs55dRAW/z0FX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IBSn8HaWr+QMbbb6j8Amz28DldMcAq+twKfFsqiFVrRyFTw62PRqZ1ZTBV86r/eurRLaEFQiXZu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\ndm5i60+ePo1b7rwPT9d4rZRMvmgMfErEI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/nu3u/efPfc55xjJeIC38zMzMysRFzgm5mZ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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x81bd970>"
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 380
}
},
"output_type": "display_data"
}
],
"source": [
"rides[:24*10].plot(x='dteday', y='cnt')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dummy variables\n",
"Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to `get_dummies()`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>yr</th>\n",
" <th>holiday</th>\n",
" <th>temp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" <th>season_1</th>\n",
" <th>season_2</th>\n",
" <th>...</th>\n",
" <th>hr_21</th>\n",
" <th>hr_22</th>\n",
" <th>hr_23</th>\n",
" <th>weekday_0</th>\n",
" <th>weekday_1</th>\n",
" <th>weekday_2</th>\n",
" <th>weekday_3</th>\n",
" <th>weekday_4</th>\n",
" <th>weekday_5</th>\n",
" <th>weekday_6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>13</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.24</td>\n",
" <td>0.75</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 59 columns</p>\n",
"</div>"
],
"text/plain": [
" yr holiday temp hum windspeed casual registered cnt season_1 \\\n",
"0 0 0 0.24 0.81 0.0 3 13 16 1 \n",
"1 0 0 0.22 0.80 0.0 8 32 40 1 \n",
"2 0 0 0.22 0.80 0.0 5 27 32 1 \n",
"3 0 0 0.24 0.75 0.0 3 10 13 1 \n",
"4 0 0 0.24 0.75 0.0 0 1 1 1 \n",
"\n",
" season_2 ... hr_21 hr_22 hr_23 weekday_0 weekday_1 weekday_2 \\\n",
"0 0 ... 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 \n",
"2 0 ... 0 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 0 \n",
"\n",
" weekday_3 weekday_4 weekday_5 weekday_6 \n",
"0 0 0 0 1 \n",
"1 0 0 0 1 \n",
"2 0 0 0 1 \n",
"3 0 0 0 1 \n",
"4 0 0 0 1 \n",
"\n",
"[5 rows x 59 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']\n",
"for each in dummy_fields:\n",
" dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)\n",
" rides = pd.concat([rides, dummies], axis=1)\n",
"\n",
"fields_to_drop = ['instant', 'dteday', 'season', 'weathersit', \n",
" 'weekday', 'atemp', 'mnth', 'workingday', 'hr']\n",
"data = rides.drop(fields_to_drop, axis=1)\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scaling target variables\n",
"To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.\n",
"\n",
"The scaling factors are saved so we can go backwards when we use the network for predictions."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']\n",
"# Store scalings in a dictionary so we can convert back later\n",
"scaled_features = {}\n",
"for each in quant_features:\n",
" mean, std = data[each].mean(), data[each].std()\n",
" scaled_features[each] = [mean, std]\n",
" data.loc[:, each] = (data[each] - mean)/std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Splitting the data into training, testing, and validation sets\n",
"\n",
"We'll save the last 21 days of the data to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Save the last 21 days \n",
"test_data = data[-21*24:]\n",
"data = data[:-21*24]\n",
"\n",
"# Separate the data into features and targets\n",
"target_fields = ['cnt', 'casual', 'registered']\n",
"features, targets = data.drop(target_fields, axis=1), data[target_fields]\n",
"test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Hold out the last 60 days of the remaining data as a validation set\n",
"train_features, train_targets = features[:-60*24], targets[:-60*24]\n",
"val_features, val_targets = features[-60*24:], targets[-60*24:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Time to build the network\n",
"\n",
"Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.\n",
"\n",
"The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called *forward propagation*.\n",
"\n",
"We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called *backpropagation*.\n",
"\n",
"> **Hint:** You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.\n",
"\n",
"Below, you have these tasks:\n",
"1. Implement the sigmoid function to use as the activation function. Set `self.activation_function` in `__init__` to your sigmoid function.\n",
"2. Implement the forward pass in the `train` method.\n",
"3. Implement the backpropagation algorithm in the `train` method, including calculating the output error.\n",
"4. Implement the forward pass in the `run` method.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class NeuralNetwork(object):\n",
" def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n",
" # Set number of nodes in input, hidden and output layers.\n",
" self.input_nodes = input_nodes\n",
" self.hidden_nodes = hidden_nodes\n",
" self.output_nodes = output_nodes\n",
"\n",
" # Initialize weights\n",
" self.weights_input_to_hidden = np.random.normal(0.0, self.hidden_nodes**-0.5, \n",
" (self.hidden_nodes, self.input_nodes))\n",
"\n",
" self.weights_hidden_to_output = np.random.normal(0.0, self.output_nodes**-0.5, \n",
" (self.output_nodes, self.hidden_nodes))\n",
" self.lr = learning_rate\n",
" \n",
" #### Set this to your implemented sigmoid function ####\n",
" # Activation function is the sigmoid function\n",
" self.activation_function = lambda x: 1.0/(1+ np.exp(-x))\n",
" self.activation_function2 = lambda x: x\n",
" \n",
" def sigmoid(self, x):\n",
" return 1 / (1 + np.exp(-x))\n",
" \n",
" def train(self, inputs_list, targets_list):\n",
" # Convert inputs list to 2d array\n",
" inputs = np.array(inputs_list, ndmin=2).T\n",
"# print('Inputs')\n",
"# print(inputs)\n",
"# L = inputs_list.size\n",
" targets = np.array(targets_list, ndmin=2).T\n",
" \n",
" #### Implement the forward pass here ####\n",
" ### Forward pass ###\n",
" # TODO: Hidden layer\n",
" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs) # signals into hidden layer\n",
" hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer\n",
" \n",
" # TODO: Output layer\n",
" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)# signals into final output layer\n",
" final_outputs = final_inputs # signals from final output layer\n",
" \n",
" #### Implement the backward pass here ####\n",
" ### Backward pass ###\n",
" \n",
" # TODO: Output error\n",
" # Derivative of out layer function is 1.\n",
" output_errors = (targets - final_outputs) * 1\n",
" # Output layer error is the difference between desired target and actual output.\n",
" \n",
" # TODO: Backpropagated error\n",
" hidden_errors = np.dot(self.weights_hidden_to_output.T, output_errors)\n",
" # errors propagated to the hidden layer\n",
" hidden_grad = hidden_outputs * (1 - hidden_outputs)\n",
" # hidden layer gradients\n",
" \n",
" # TODO: Update the weights\n",
" self.weights_hidden_to_output += self.lr * np.dot(output_errors, hidden_outputs.T)\n",
" # update hidden-to-output weights with gradient descent step\n",
" self.weights_input_to_hidden += self.lr * np.dot((hidden_errors * hidden_grad), inputs.T)\n",
" # update input-to-hidden weights with gradient descent step\n",
" \n",
" \n",
" def run(self, inputs_list):\n",
" # Run a forward pass through the network\n",
" inputs = np.array(inputs_list, ndmin=2).T\n",
" \n",
" #### Implement the forward pass here ####\n",
" # TODO: Hidden layer\n",
" hidden_inputs = np.dot(self.weights_input_to_hidden, inputs)\n",
" hidden_outputs = self.activation_function(hidden_inputs)# signals from hidden layer\n",
" \n",
" # TODO: Output layer\n",
" final_inputs = np.dot(self.weights_hidden_to_output, hidden_outputs)\n",
" final_outputs = final_inputs# signals from final output layer \n",
" \n",
" return final_outputs"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def MSE(y, Y):\n",
" return np.mean((y-Y)**2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the network\n",
"\n",
"Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.\n",
"\n",
"You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.\n",
"\n",
"### Choose the number of epochs\n",
"This is the number of times the dataset will pass through the network, each time updating the weights. As the number of epochs increases, the network becomes better and better at predicting the targets in the training set. You'll need to choose enough epochs to train the network well but not too many or you'll be overfitting.\n",
"\n",
"### Choose the learning rate\n",
"This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.\n",
"\n",
"### Choose the number of hidden nodes\n",
"The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: 99.9% ... Training loss: 0.042 ... Validation loss: 0.130"
]
}
],
"source": [
"import sys\n",
"\n",
"### Set the hyperparameters here ###\n",
"epochs = 5000 #100\n",
"learning_rate = 0.1 #0.1\n",
"hidden_nodes = 50 #2\n",
"output_nodes = 1\n",
"\n",
"N_i = train_features.shape[1]\n",
"network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)\n",
"\n",
"losses = {'train':[], 'validation':[]}\n",
"for e in range(epochs):\n",
" # Go through a random batch of 128 records from the training data set\n",
" batch = np.random.choice(train_features.index, size=128)\n",
" for record, target in zip(train_features.ix[batch].values, \n",
" train_targets.ix[batch]['cnt']):\n",
" network.train(record, target)\n",
" \n",
" # Printing out the training progress\n",
" train_loss = MSE(network.run(train_features), train_targets['cnt'].values)\n",
" val_loss = MSE(network.run(val_features), val_targets['cnt'].values)\n",
" sys.stdout.write(\"\\rProgress: \" + str(100 * e/float(epochs))[:4] \\\n",
" + \"% ... Training loss: \" + str(train_loss)[:5] \\\n",
" + \" ... Validation loss: \" + str(val_loss)[:5])\n",
" \n",
" losses['train'].append(train_loss)\n",
" losses['validation'].append(val_loss)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-0.021852061436678137, 0.5)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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CJPdEpGLyhXWTwPMLFda9SCeYiohIFKpXrw7A/v37k9wTkYrJ99ryvdYSQWFd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HfyEgrKqnAyvcCq8zCpXyvZ44sI8cext8ZbtS17UbBuAwrWCSGEEFJfJOeUoFV4MPxlJrAzdDqj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5b91630>"
]
},
"metadata": {
"image/png": {
"height": 252,
"width": 373
}
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(losses['train'], label='Training loss')\n",
"plt.plot(losses['validation'], label='Validation loss')\n",
"plt.legend()\n",
"plt.ylim(ymax=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Check out your predictions\n",
"\n",
"Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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V51pQJ8srN8Uh2lsjPN5fTMt8jL95ZtXI7zc+scLGkLR4x+a6MVX8J4nBaYW+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UqnwOGdted8wDx5/X3Tvu8FobTb6nDAvrBJuyI50jGCrd8OT0s6Jp6nrHCQNtjje/W0W0F0YlY9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\n8MSi3lK4RBBdX6Wd2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TingrQnJgexY2smF8LemAiaJ3e3uB82wbSoFsc46EDaAeBpn7n+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tJz1ojwGPce6nJI/nPJ9DbUweb5cF2v3tnKGgfeuJQttcbfdy57RT0F6uPF5nRCfu2wpmwcnQ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YVaN+1xxur5KII6a4yTIXzyxPreB0RLBp3DDv5x2t+x/+1rzXoib6uvNe/vW+74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\nqqqqqqqqjfY/XN8bcsqoAGAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x5bb6490>"
]
},
"metadata": {
"image/png": {
"height": 272,
"width": 502
}
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(8,4))\n",
"\n",
"mean, std = scaled_features['cnt']\n",
"predictions = network.run(test_features)*std + mean\n",
"ax.plot(predictions[0], label='Prediction')\n",
"ax.plot((test_targets['cnt']*std + mean).values, label='Data')\n",
"ax.set_xlim(right=len(predictions))\n",
"ax.legend()\n",
"\n",
"dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])\n",
"dates = dates.apply(lambda d: d.strftime('%b %d'))\n",
"ax.set_xticks(np.arange(len(dates))[12::24])\n",
"_ = ax.set_xticklabels(dates[12::24], rotation=45)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Thinking about your results\n",
" \n",
"Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?\n",
"\n",
"> **Note:** You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter\n",
"\n",
"#### Your answer below"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Unit tests\n",
"\n",
"Run these unit tests to check the correctness of your network implementation. These tests must all be successful to pass the project."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
".....\n",
"----------------------------------------------------------------------\n",
"Ran 5 tests in 0.021s\n",
"\n",
"OK\n"
]
},
{
"data": {
"text/plain": [
"<unittest.runner.TextTestResult run=5 errors=0 failures=0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import unittest\n",
"\n",
"inputs = [0.5, -0.2, 0.1]\n",
"targets = [0.4]\n",
"test_w_i_h = np.array([[0.1, 0.4, -0.3], \n",
" [-0.2, 0.5, 0.2]])\n",
"test_w_h_o = np.array([[0.3, -0.1]])\n",
"\n",
"class TestMethods(unittest.TestCase):\n",
" \n",
" ##########\n",
" # Unit tests for data loading\n",
" ##########\n",
" \n",
" def test_data_path(self):\n",
" # Test that file path to dataset has been unaltered\n",
" self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')\n",
" \n",
" def test_data_loaded(self):\n",
" # Test that data frame loaded\n",
" self.assertTrue(isinstance(rides, pd.DataFrame))\n",
" \n",
" ##########\n",
" # Unit tests for network functionality\n",
" ##########\n",
"\n",
" def test_activation(self):\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" # Test that the activation function is a sigmoid\n",
" self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))\n",
"\n",
" def test_train(self):\n",
" # Test that weights are updated correctly on training\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" network.weights_input_to_hidden = test_w_i_h.copy()\n",
" network.weights_hidden_to_output = test_w_h_o.copy()\n",
" \n",
" network.train(inputs, targets)\n",
" self.assertTrue(np.allclose(network.weights_hidden_to_output, \n",
" np.array([[ 0.37275328, -0.03172939]])))\n",
" self.assertTrue(np.allclose(network.weights_input_to_hidden,\n",
" np.array([[ 0.10562014, 0.39775194, -0.29887597],\n",
" [-0.20185996, 0.50074398, 0.19962801]])))\n",
"\n",
" def test_run(self):\n",
" # Test correctness of run method\n",
" network = NeuralNetwork(3, 2, 1, 0.5)\n",
" network.weights_input_to_hidden = test_w_i_h.copy()\n",
" network.weights_hidden_to_output = test_w_h_o.copy()\n",
"\n",
" self.assertTrue(np.allclose(network.run(inputs), 0.09998924))\n",
"\n",
"suite = unittest.TestLoader().loadTestsFromModule(TestMethods())\n",
"unittest.TextTestRunner().run(suite)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
D-X-Y/NATS-Bench | notebooks/create-query-sss.ipynb | 1 | 7842 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-29 08:23:08] Try to use the default NATS-Bench (size) path from fast_mode=True and path=None.\n",
"[2021-03-29 08:23:08] Create NATS-Bench (size) done with 0/32768 architectures avaliable.\n",
"\n",
"API create done: NATSsize(0/32768 architectures, fast_mode=True, file=None)\n",
"\n",
"[2021-03-29 08:23:08] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=12, and is_random=True.\n",
"[2021-03-29 08:23:08] Call query_index_by_arch with arch=1234\n",
"[2021-03-29 08:23:08] Call clear_params with archive_root=/Users/xuanyidong/.torch/NATS-sss-v1_0-50262-simple and index=1234\n",
"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\n",
" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\n",
" 'performance of the CIFAR-10 test set after training on the '\n",
" 'train+valid sets by 12 epochs. The per-time and total-time '\n",
" 'indicate the per epoch and total time costs, respectively.',\n",
" 'test-accuracy': 83.87,\n",
" 'test-all-time': 8.31445026397705,\n",
" 'test-loss': 0.4872739363670349,\n",
" 'test-per-time': 0.6928708553314209,\n",
" 'train-accuracy': 85.74,\n",
" 'train-all-time': 69.73253917694092,\n",
" 'train-loss': 0.4183172229385376,\n",
" 'train-per-time': 5.811044931411743}\n"
]
}
],
"source": [
"from nats_bench import create\n",
"from pprint import pprint\n",
"\n",
"# Create the API instance for the size search space in NATS\n",
"api = create(None, 'sss', fast_mode=True, verbose=True)\n",
"print('\\nAPI create done: {:}\\n'.format(api))\n",
"\n",
"\n",
"info = api.get_more_info(1234, 'cifar10')\n",
"pprint(info)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-29 08:23:12] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=90, and is_random=True.\n",
"[2021-03-29 08:23:12] Call query_index_by_arch with arch=1234\n",
"[2021-03-29 08:23:12] Call _prepare_info with index=1234 skip because it is in arch2infos_dict\n",
"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\n",
" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\n",
" 'performance of the CIFAR-10 test set after training on the '\n",
" 'train+valid sets by 90 epochs. The per-time and total-time '\n",
" 'indicate the per epoch and total time costs, respectively.',\n",
" 'test-accuracy': 89.4,\n",
" 'test-all-time': 62.35837697982788,\n",
" 'test-loss': 0.3388326271057129,\n",
" 'test-per-time': 0.6928708553314209,\n",
" 'train-accuracy': 95.206,\n",
" 'train-all-time': 522.9940438270569,\n",
" 'train-loss': 0.14320597895622253,\n",
" 'train-per-time': 5.811044931411743}\n"
]
}
],
"source": [
"info = api.get_more_info(1234, 'cifar10', hp='90')\n",
"pprint(info)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-29 08:23:15] Call the get_cost_info function with index=12, dataset=cifar10, and hp=12.\n",
"[2021-03-29 08:23:15] Call clear_params with archive_root=/Users/xuanyidong/.torch/NATS-sss-v1_0-50262-simple and index=12\n",
"Call query_meta_info_by_index with arch_index=12, hp=12\n",
"[2021-03-29 08:23:15] Call _prepare_info with index=12 skip because it is in arch2infos_dict\n",
"{'T-ori-test@epoch': 0.6709375381469727,\n",
" 'T-ori-test@total': 8.051250457763672,\n",
" 'T-train@epoch': 5.539922475814819,\n",
" 'T-train@total': 66.47906970977783,\n",
" 'flops': 7.991706,\n",
" 'latency': 0.014862352974560795,\n",
" 'params': 0.067378}\n"
]
}
],
"source": [
"# Query the flops, params, latency. info is a dict.\n",
"info = api.get_cost_info(12, 'cifar10')\n",
"pprint(info)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-29 08:23:17] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=12, and is_random=True.\n",
"[2021-03-29 08:23:17] Call query_index_by_arch with arch=1234\n",
"[2021-03-29 08:23:17] Call _prepare_info with index=1234 skip because it is in arch2infos_dict\n",
"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\n",
" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\n",
" 'performance of the CIFAR-10 test set after training on the '\n",
" 'train+valid sets by 12 epochs. The per-time and total-time '\n",
" 'indicate the per epoch and total time costs, respectively.',\n",
" 'test-accuracy': 84.28,\n",
" 'test-all-time': 8.31445026397705,\n",
" 'test-loss': 0.46498328766822816,\n",
" 'test-per-time': 0.6928708553314209,\n",
" 'train-accuracy': 86.004,\n",
" 'train-all-time': 69.73253917694092,\n",
" 'train-loss': 0.405061281375885,\n",
" 'train-per-time': 5.811044931411743}\n"
]
}
],
"source": [
"info = api.get_more_info(1234, 'cifar10', hp='12', is_random=True)\n",
"pprint(info)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2021-03-29 08:23:20] Call the get_more_info function with index=1234, dataset=cifar10, iepoch=None, hp=12, and is_random=True.\n",
"[2021-03-29 08:23:20] Call query_index_by_arch with arch=1234\n",
"[2021-03-29 08:23:20] Call _prepare_info with index=1234 skip because it is in arch2infos_dict\n",
"{'comment': 'In this dict, train-loss/accuracy/time is the metric on the '\n",
" 'train+valid sets of CIFAR-10. The test-loss/accuracy/time is the '\n",
" 'performance of the CIFAR-10 test set after training on the '\n",
" 'train+valid sets by 12 epochs. The per-time and total-time '\n",
" 'indicate the per epoch and total time costs, respectively.',\n",
" 'test-accuracy': 83.87,\n",
" 'test-all-time': 8.31445026397705,\n",
" 'test-loss': 0.4872739363670349,\n",
" 'test-per-time': 0.6928708553314209,\n",
" 'train-accuracy': 85.74,\n",
" 'train-all-time': 69.73253917694092,\n",
" 'train-loss': 0.4183172229385376,\n",
" 'train-per-time': 5.811044931411743}\n"
]
}
],
"source": [
"# The same code as above, but return the different performance because we set is_random=True\n",
"info = api.get_more_info(1234, 'cifar10', hp='12', is_random=True)\n",
"pprint(info)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
drphilmarshall/StatisticalMethods | lessons/mcmc_diagnostics.ipynb | 1 | 13536 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# MCMC Diagnostics\n",
"\n",
"Goals:\n",
"* Learn how to determine whether a Markov chain can reliably be used for inference"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## References\n",
"\n",
"* Gelman 11.4-11.5\n",
"* [Kravtsov notebook on convergence](http://nbviewer.ipython.org/url/astro.uchicago.edu/%7Eandrey/classes/150404_mayacamas/mcmc_convergence.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Diagnostics\n",
"\n",
"To be useful to us, a chain must\n",
"1. have converged to the posterior distribution\n",
"2. provide enough effectively independent samples to characterize it"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"What would make us confident of convergence?\n",
"* Is the chain stationary?\n",
"* Do independent chains started from overdispersed positions find the same solution?"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"How do we guess the number of independent samples?\n",
"* Check how well the chain appears to exploring the distribution.\n",
"* Compare the autocorrelation length scale with the chain length."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"There are numerical estimates that can help with this, but **they are not a substitute for human visual inspection**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Common misuses of/misconceptions about convergence\n",
"\n",
"Convergence does **not** mean\n",
"* that parameters are \"well constrained\" by the data\n",
"* that the autocorrelation length is small\n",
"* that there are not occasional excursions beyond a locus in parameter space"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Convergence tests\n",
"* Inspection! There is no substitute.\n",
"* Gelman-Rubin statistic"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"* How stationary does each sequence appear? \n",
"* Are all chains sampling the same PDF?\n",
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_sandbox_ab.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_sandbox_a.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_sandbox_b.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>\n",
"\n",
"Conservatively, we might remove the first $\\sim500$ steps based on this."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"#### Gelman-Rubin convergence statistic\n",
"\n",
"This approach tests the similarlity of independent chains intended to sample the same PDF. To be meaningful, they should start from different locations and burn-in should be removed.\n",
"\n",
"For a given parameter, $\\theta$, the $R$ statistic compares the variance across chains with the variance within a chain. Intuitively, if the chains are random-walking in very different places, i.e. not sampling the same distribution, $R$ will be large.\n",
"\n",
"We'd like to see $R\\approx 1$ (e.g. $R<1.1$ is often used)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"In detail, given chains $J=1,\\ldots,m$, each of length $n$,\n",
"\n",
"* Let $B=\\frac{n}{m-1} \\sum_j \\left(\\bar{\\theta}_j - \\bar{\\theta}\\right)^2$, where $\\bar{\\theta_j}$ is the average $\\theta$ for chain $j$ and $\\bar{\\theta}$ is the global average. This is proportional to the variance of the individual-chain averages for $\\theta$.\n",
"\n",
"* Let $W=\\frac{1}{m}\\sum_j s_j^2$, where $s_j^2$ is the estimated variance of $\\theta$ within chain $j$. This is the average of the individual-chain variances for $\\theta$.\n",
"\n",
"* Let $V=\\frac{n-1}{n}W + \\frac{1}{n}B$. This is an estimate for the overall variance of $\\theta$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Finally, $R=\\sqrt{\\frac{V}{W}}$.\n",
"\n",
"Note that this calculation can also be used to track convergence of combinations of parameters, or anything else derived from them."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Correlation tests\n",
"* Inspection! Again, no substitute.\n",
"* Autocorrelation of parameters"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Do subsequent samples look particularly independent?\n",
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_sandbox_a.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The *autocorrelation* of a sequence (**after** removing burn-in), as a function of lag, $k$, is defined thusly:\n",
"\n",
"$\\rho_k = \\frac{\\sum_{i=1}^{n-k}\\left(\\theta_{i} - \\bar{\\theta}\\right)\\left(\\theta_{i+k} - \\bar{\\theta}\\right)}{\\sum_{i=1}^{n-k}\\left(\\theta_{i} - \\bar{\\theta}\\right)^2} = \\frac{\\mathrm{Cov}_i\\left(\\theta_i,\\theta_{i+k}\\right)}{\\mathrm{Var}(\\theta)}$\n",
"\n",
"The larger lag one needs to get a small autocorrelation, the less informative individual samples are.\n",
"\n",
"The `pandas` function `autocorrelation_plot()` may be useful for this."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_sandbox_acf-a.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_sandbox_acf-b.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Note that the positive/negative oscillations basically tell us when the lag is so large compared with the chain length\n",
"that the autocorrelation is too noisy to be meaningful.\n",
"\n",
"We would be justified in thinning the chains by a factor of $\\sim150$, apparently!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Effective number of samples\n",
"\n",
"From $m$ chains of length $n$, we can estimate the effective number of independent samples as\n",
"\n",
"$n_{eff} = \\frac{mn}{1+2\\sum_0^\\infty \\hat{\\rho}_t}$, with\n",
"\n",
"$\\hat{\\rho}_t = 1 - \\frac{V_t}{2V}$, ($V$ as in the Gelman-Rubin calculation)\n",
"\n",
"$V_t = \\frac{1}{m(n-t)} \\sum_{j=0}^m \\sum_{i=t+1}^n (\\theta_{i,j} - \\theta_{i-t,j})^2$\n",
"\n",
"In practice, the sum in $n_{eff}$ is cut off when the estimates $\\hat{\\rho}_t$ become too noisy (see references)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"The example shown turns out to have $n_{eff} \\sim 600$, compared with the $\\sim 250$ samples we would have left if thinning by a factor of 150."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Exercise: can we declare victory?\n",
"\n",
"In each of the following trace plots, different colors show independent chains. For each decide by inspection whether the following are plausible claims:\n",
"1. The chains have converged to the posterior.\n",
"2. The chains have a reasonable number of independent samples."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"<table>\n",
" <tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_convgame_1.png\" width=100%></a></td>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_convgame_2.png\" width=100%></a></td>\n",
" </tr><tr>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_convgame_3.png\" width=100%></a></td>\n",
" <td><a href=\"graphics/montecarlo1.R\"><img src=\"graphics/mc1_convgame_4.png\" width=100%></a></td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Take-away\n",
"There are a handful of useful metrics for assessing convergence of chains and independence of samples. None is foolproof, and visual inspection is a critical check.\n",
"\n",
"Extra reading: [this short discussion](https://www.jstor.org/stable/2685466) provides some interesting context regarding how experts in the field (and the inventors of said metrics) approach the practical side of MCMC."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bonus numerical making-your-life-easier exercise: convergence\n",
"\n",
"Write some code to perform the Gelman-Rubin convergence test. Try it out on\n",
"\n",
"1. multiple chains from the sandbox notebook. Fiddle with the sampler to get chains that do/do not display nice convergence after e.g. 5000 steps.\n",
"\n",
"2. multiple \"chains\" produced from independent sampling, e.g. from the inverse-transform or rejection examples above or one of the examples in previous chunks.\n",
"\n",
"You'll be expected to test convergence from now on, so having a function to do so will be helpful."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Bonus numerical making-your-life-easier exercise: effective samples\n",
"\n",
"Write code to compute the effective number of samples, as described above. Cut off the sum $n_{eff}$ at the point where 2 successive values $\\hat{\\rho}_t$ and $\\hat{\\rho}_{t+1}$ are negative. Try it out on the same two test cases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Megabonus numerical exercise\n",
"\n",
"Modify your code from the exercises to compute $R$ and $n_{eff}$ for the eigenvectors of the covariance of the posterior, instead of for individual parameters themselves. This can be informative when there are strong parameter degeneracies, as in the example above. The eigenvectors can be estimated efficiently from a chain using singular value decomposition."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"livereveal": {
"scroll": true,
"start_slideshow_at": "selected"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
CivicKnowledge/metatab-packages | example.com/example-ipynb/metadata.ipynb | 2 | 3775 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"tags": [
"Title"
]
},
"source": [
"# A Metatab Example Data Package"
]
},
{
"cell_type": "markdown",
"metadata": {
"tags": [
"Description"
]
},
"source": [
"An example data package, from the Metatab tutorial at https://github.com/CivicKnowledge/metatab-py/blob/master/README.rst"
]
},
{
"cell_type": "raw",
"metadata": {
"tags": [
"metadata"
]
},
"source": [
"Name: example_data_package-2017-us-2\n",
"Dataset: example-data-package\n",
"Version: 1\n",
"Space: US\n",
"Time: 2017\n",
"Spatialgrain: None\n",
"Giturl: https://github.com/Metatab/metatab-packages.git\n",
"Identifier: e7466d89-9156-4df6-a171-3102b04ae583\n",
"\n",
"Section: Documentation|Title|Description\n",
"Homepage: http://metatab.org\n",
" .Title: Metatab Home Page\n",
" .Description: Main Metatab home page\n",
"Documentation: https://github.com/CivicKnowledge/metatab-py/blob/master/README.rst\n",
" .Title: Metatab Python Package README\n",
" .Description: The README in the Metatab Githup repo contains the tutorial for generating this package.\n",
"\n",
"Section: Contacts|email\n",
"Origin: example.com\n",
"Creator: Eric Busboom\n",
" .Email: eric@civicknowledge. com\n",
"Wrangler: Eric Busboom\n",
" .Email: eric@civicknowledge. com\n",
"\n",
"Section: Notes\n",
"Note: None"
]
},
{
"cell_type": "raw",
"metadata": {
"tags": [
"resources"
]
},
"source": [
"Section: Resources|Name|StartLine|HeaderLines|Encoding|Description\n",
"Datafile: http://public.source.civicknowledge.com/example.com/sources/test_data.zip#test_data%2Fcsv%2Fsimple-example.csv\n",
" .Name: simple-example\n",
" .Description: Random UUIDs, integers and numbers\n",
"Datafile: metadata.ipynb#df\n",
" .Name: df\n",
" .Description: Random UUIDs, integers and numbers"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import metapack as mp\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import display \n",
"\n",
"%matplotlib inline\n",
"%load_ext metapack.jupyter.magic\n",
"sns.set_context('notebook')\n",
"mp.jupyter.init()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Code goes after metadata, before schema\n",
"\n",
"df = pd.DataFrame({\n",
" 'rand': np.random.randint(0,100,1000)\n",
"})\n"
]
},
{
"cell_type": "raw",
"metadata": {
"tags": [
"schema"
]
},
"source": [
"Section: Schema|AltName|DataType|Description|Datatype\n",
"Table: simple-example\n",
"Table.Column: id\n",
" .Datatype: integer\n",
"Table.Column: uuid\n",
" .Datatype: string\n",
"Table.Column: int\n",
" .Datatype: integer\n",
"Table.Column: float\n",
" .Datatype: number\n",
"Table: df\n",
"Table.Column: rand\n",
" .Datatype: integer"
]
}
],
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
richardotis/matse501 | MATSE501-HW2.ipynb | 1 | 44027 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.integrate import odeint\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# System parameters (determined by system definition)\n",
"# NOTE: These are not necessarily correct for your case\n",
"alpha = 0.5\n",
"beta = 1.4\n",
"gamma = 0.1\n",
"\n",
"\"\"\"\n",
"func accepts a vector of variables l_vec and returns a \n",
"vector of derivatives with respect to t\n",
"here, v is defined as dv/dt\n",
"\"\"\"\n",
"def func(l_vec,t):\n",
" l,v = l_vec\n",
" return [v,(alpha/l)-beta-(gamma*v)]\n",
"\n",
"# Initial conditions (units?)\n",
"# NOTE: These are not necessarily correct for your case\n",
"l_0 = 0.1\n",
"v_0 = 0.1\n",
"\n",
"y0 = [l_0,v_0] # initial conditions vector\n",
"\n",
"t_output = np.arange(0,50,0.1) # get vector of t from 0 to 50, with 0.1 step\n",
"\n",
"y_result = odeint(func,y0,t_output)\n",
"# y_result is the vector of values, starting from y0, integrated according to our constitutive function, func"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot setup, 10 by 6\n",
"fig = plt.figure(figsize=(10,6))\n",
"# 111 is a layout indicator; only need to worry if we have multiple subplots\n",
"ax = fig.add_subplot(111)\n",
"# unpack the values from our solution\n",
"ll,vv = y_result.T\n",
"# plot the figure and set the title and labels\n",
"ax.plot(t_output,ll)\n",
"ax.set_title('Piston Time Evolution')\n",
"ax.set_xlabel('Time (seconds)')\n",
"ax.set_ylabel('Length (m)')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.text.Text at 0x7f8d091d03d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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8WLNmja3O/CrRkaYEnBfxR2oBf1SUPDeqmtA6SVMCelKV+/ZZTzEn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\nREQKMYwRERERKcQwRkRERKTQ/wH0J8l0SzAvcAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f8d0b022550>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
}
],
"metadata": {}
}
]
} | mit |
oscar6echo/ezvis3d | demo_ezvisd3.ipynb | 1 | 2316938 | null | mit |
rdhyee/working-open-data-2014 | notebooks/Day_06_D_Assignment.ipynb | 1 | 2342 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from pandas import DataFrame\n",
"\n",
"import census\n",
"import settings\n",
"import us\n",
"\n",
"from itertools import islice\n",
"\n",
"# instantiate the census object\n",
"\n",
"c=census.Census(settings.CENSUS_KEY)\n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## EXERCISE\n",
"## FILL in with your generator for all census places in the 2010 census \n",
"\n",
"\n",
"def places(variables=\"NAME\"):\n",
" \n",
" # placeholder generator\n",
" # replace with your own code\n",
" for k in []:\n",
" yield k\n",
" \n",
"\n",
"\n",
" \n",
" \n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# use this code to run your code\n",
"# I recommend replacing the None in islice to a small number to make sure you're on \n",
"# the right track\n",
"\n",
"r = list(islice(places(\"NAME,P0010001\"), None))\n",
"places_df = DataFrame(r)\n",
"places_df.P0010001 = places_df.P0010001.astype('int')\n",
"\n",
"places_df['FIPS'] = places_df.apply(lambda s: s['state']+s['place'], axis=1)\n",
"\n",
"print \"number of places\", len(places_df)\n",
"print \"total pop\", places_df.P0010001.sum()\n",
"places_df.head()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# if you've done this correctly, the following asserts should stop complaining\n",
"\n",
"assert places_df.P0010001.sum() == 228457238\n",
"# number of places in 2010 Census\n",
"assert len(places_df) == 29261"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
phaustin/pyman | Book/apdx2/Supporting Materials/FirstNotebook.ipynb | 3 | 14570 | {
"metadata": {
"name": "",
"signature": "sha256:75e9265a392bf07942083d1bd9b6ab568e7dc38cde50cac5bf9542751a9dbfeb"
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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Demo of IPython Notebook"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"David Pine"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"2+3"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 1,
"text": [
"5"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.sin(np.pi/6)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"0.49999999999999994"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot([1,2,3,2,3,4,3,4,5])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"[<matplotlib.lines.Line2D at 0x10cfc8b90>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x10c730dd0>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Calculates time, gallons of gas used, and cost of gasoline for\n",
"# a trip\n",
"\n",
"distance = float(raw_input(\"Input distance of trip in miles: \"))\n",
"mpg = 30. # car mileage\n",
"speed = 60. # average speed\n",
"costPerGallon = 4.10 # price of gas\n",
"\n",
"time = distance/speed\n",
"gallons = distance/mpg\n",
"cost = gallons*costPerGallon\n",
"\n",
"print(\"\\nDuration of trip = {0:0.1f} hours\".format(time))\n",
"print(\"Gasoline used = {0:0.1f} gallons (@ {1:0.0f} mpg)\"\n",
" .format(gallons, mpg))\n",
"print(\"Cost of gasoline = ${0:0.2f} (@ ${1:0.2f}/gallon)\"\n",
" .format(cost, costPerGallon))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"stream": "stdout",
"text": [
"Input distance of trip in miles: 450\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Duration of trip = 7.5 hours\n",
"Gasoline used = 15.0 gallons (@ 30 mpg)\n",
"Cost of gasoline = $61.50 (@ $4.10/gallon)\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The total distance $x$ traveled during a trip can be\n",
"obtained by integrating the velocity $v(t)$ over the\n",
"duration $T$ of the trip:\n",
"\\begin{align}\n",
" x = \\int_0^T v(t)\\, dt\n",
"\\end{align}"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"!cat LiamSelinaData.txt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Date: 2013-09-16\r\n",
"Data taken by Liam and Selena\r\n",
"frequency (Hz) amplitude (mm) amp error (mm)\r\n",
" 0.7500 13.52 0.32\r\n",
" 1.7885 12.11 0.92\r\n",
" 2.8269 14.27 0.73\r\n",
" 3.8654 16.60 2.06\r\n",
" 4.9038 22.91 1.75\r\n",
" 5.9423 35.28 0.91\r\n",
" 6.9808 60.99 0.99\r\n",
" 8.0192 33.38 0.36\r\n",
" 9.0577 17.78 2.32\r\n",
" 10.0962 10.99 0.21\r\n",
" 11.1346 7.47 0.48\r\n",
" 12.1731 6.72 0.51\r\n",
" 13.2115 4.40 0.58\r\n",
" 14.2500 4.07 0.63"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | cc0-1.0 |
dataDogma/Computer-Science | Courses/DAT-208x/DAT208x - Introduction to Python for Data Science.ipynb | 2 | 2172 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"## Course Objective: \n",
"\n",
"-- --\n",
"\n",
"+ Python language funadmentals: basic syntax, variables, and types.\n",
"\n",
"+ Create and manipulate regular Python lists.\n",
"\n",
"+ Use functions andimport packages.\n",
"\n",
"+ Build Numpy array and perform interesting calculations.\n",
"\n",
"+ Create and customize plots on real data.\n",
"\n",
"+ Use control flow and get to know the Pandas data frame.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About:\n",
"-- --\n",
"\n",
"+ Course has 6 modules, each covering Python baiscs.\n",
"+ Starting with basic arithematic and variables.\n",
"+ Data structures -- Python lists, Numpy arrays & Pandas dataframes.\n",
"+ Python functions & Control flow.\n",
"+ Data Visualization.\n",
"+ Working on real datasets.\n",
"\n",
"### Further,\n",
"\n",
"+ Each module contains quiz, lab sessions.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Course Schedule\n",
"-- --\n",
"\n",
"1. It's a self paced course.\n",
"2. By default a deadline of 4 weeks.\n",
"3. Quizzes -- 30% of total grade.\n",
"4. Lab exercise -- 65% of total grade.\n",
"5. Survey -- 5%\n",
"6. To pass -- 70% of total grade\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
statkclee/ThinkStats2 | code/chap09soln-kor.ipynb | 1 | 8689 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"통계적 사고 (2판) 연습문제 ([thinkstats2.com](thinkstats2.com), [think-stat.xwmooc.org](http://think-stat.xwmooc.org))<br>\n",
"Allen Downey / 이광춘(xwMOOC)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function, division\n",
"\n",
"import first\n",
"import hypothesis\n",
"import scatter\n",
"import thinkstats2\n",
"\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 연습문제 9.1\n",
"\n",
"표본크기가 증가함에 따라, 가설검정력은 증가하는데, 효과가 실제하면 좀더 양성임을 의미한다. 반대로, 표본크기가 줄어들면, 검정력은 설사 효과가 실제한다고 하더라도 덜 양성일 것 같다.\n",
"이런 작동방식을 조사하는데, NSFG 데이터에서 다른 일부 데이터를 갖는 검정을 실시한다. `thinkstats2.SampleRows`을 사용해서, 데이터프레임에 임의로 행일부를 선택한다.\n",
"\n",
"표본크기가 감소함에 따라 검정 p-값에는 무슨 일이 일어나는가? 양의 검정을 산출하는 최소 표본크기는 얼마인가?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9148\t0.16\t0.00\t0.00\t0.00\n",
"4574\t0.10\t0.01\t0.00\t0.00\n",
"2287\t0.25\t0.06\t0.00\t0.00\n",
"1143\t0.24\t0.03\t0.39\t0.03\n",
"571\t0.81\t0.00\t0.04\t0.04\n",
"285\t0.57\t0.41\t0.48\t0.83\n",
"142\t0.45\t0.08\t0.60\t0.04\n"
]
}
],
"source": [
"def RunTests(live, iters=1000):\n",
" \"\"\"Runs the tests from Chapter 9 with a subset of the data.\n",
"\n",
" live: DataFrame\n",
" iters: how many iterations to run\n",
" \"\"\"\n",
" n = len(live)\n",
" firsts = live[live.birthord == 1]\n",
" others = live[live.birthord != 1]\n",
"\n",
" # compare pregnancy lengths\n",
" data = firsts.prglngth.values, others.prglngth.values\n",
" ht = hypothesis.DiffMeansPermute(data)\n",
" p1 = ht.PValue(iters=iters)\n",
"\n",
" data = (firsts.totalwgt_lb.dropna().values,\n",
" others.totalwgt_lb.dropna().values)\n",
" ht = hypothesis.DiffMeansPermute(data)\n",
" p2 = ht.PValue(iters=iters)\n",
"\n",
" # test correlation\n",
" live2 = live.dropna(subset=['agepreg', 'totalwgt_lb'])\n",
" data = live2.agepreg.values, live2.totalwgt_lb.values\n",
" ht = hypothesis.CorrelationPermute(data)\n",
" p3 = ht.PValue(iters=iters)\n",
"\n",
" # compare pregnancy lengths (chi-squared)\n",
" data = firsts.prglngth.values, others.prglngth.values\n",
" ht = hypothesis.PregLengthTest(data)\n",
" p4 = ht.PValue(iters=iters)\n",
"\n",
" print('%d\\t%0.2f\\t%0.2f\\t%0.2f\\t%0.2f' % (n, p1, p2, p3, p4))\n",
" \n",
"thinkstats2.RandomSeed(18)\n",
"\n",
"live, firsts, others = first.MakeFrames()\n",
"\n",
"n = len(live)\n",
"for _ in range(7):\n",
" sample = thinkstats2.SampleRows(live, n)\n",
" RunTests(sample)\n",
" n //= 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"test1: 평균 임신기간 차이\n",
"test2: 평균 출생체중 차이\n",
"test3: 산모 연령과 출생체중 상관관계\n",
"test4: 임신기간 카이제곱 검정\n",
"\n",
"n test1 test2 test2 test4 \n",
"9148 →\t0.16 →\t0.00 →\t0.00 →\t0.00 \n",
"4574→\t0.10→\t0.01→\t0.00→\t0.00 \n",
"2287→\t0.25→\t0.06→\t0.00→\t0.00 \n",
"1143→\t0.24→\t0.03→\t0.39→\t0.03 \n",
"571→\t0.81→\t0.00→\t0.04→\t0.04 \n",
"285→\t0.57→\t0.41→\t0.48→\t0.83 \n",
"142→\t0.45→\t0.08→\t0.60→\t0.04\n",
"\n",
"결론: 예상했듯이, 데이터를 제거함에 따라 더큰 표본크기를 갖는 양의 검정은 음이 된다. 하지만, 작은 표본크기에서 조차도 일부 양의 값을 갖는 등 패턴은 일정치 않다."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 연습문제 9.2 \n",
"9.3절처럼, 순열로 귀무가설을 모의시험했다; 즉, 관측된 값을 마치 전체 모집단을 대표하는 것처럼 다루었고, 무작위로 모집단의 구성원을 두 집단에 배정했다.\n",
"대안은 표본을 사용해서 모집단 분포를 추정하고 나서, 분포로부터 임의 표본을 추출하는 것이다. 이런 과정을 재표집(resampling)이라고 부른다. 재표집을 구현하는 몇가지 방식이 있지만, 가장 단순한 것중 하나가 9.10 처럼 관측된 값에서 복원방식으로 표본을 추출하는 것이다.\n",
"\n",
"DiffMeansPermute에서 상속받고, 순열보다 재표집을 구현하는 RunModel을 치환(override)하는 클래스 DiffMeansResample을 작성하시오.\n",
"\n",
"이 모형을 사용해서 임신기간과 출생체중 사이 차이를 검정하시오. 이 모형이 결과에 얼마나 영향을 주는가?"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"means permute preglength\n",
"p-value = 0.1675\n",
"actual = 0.0780372667775\n",
"ts max = 0.225502935911\n",
"\n",
"means permute birthweight\n",
"p-value = 0.0\n",
"actual = 0.124761184535\n",
"ts max = 0.116868397475\n"
]
}
],
"source": [
"class DiffMeansResample(hypothesis.DiffMeansPermute):\n",
" \"\"\"Tests a difference in means using resampling.\"\"\"\n",
" \n",
" def RunModel(self):\n",
" \"\"\"Run the model of the null hypothesis.\n",
"\n",
" returns: simulated data\n",
" \"\"\"\n",
" group1 = np.random.choice(self.pool, self.n, replace=True)\n",
" group2 = np.random.choice(self.pool, self.m, replace=True)\n",
" return group1, group2\n",
" \n",
"\n",
"def RunResampleTest(firsts, others):\n",
" \"\"\"Tests differences in means by resampling.\n",
"\n",
" firsts: DataFrame\n",
" others: DataFrame\n",
" \"\"\"\n",
" data = firsts.prglngth.values, others.prglngth.values\n",
" ht = DiffMeansResample(data)\n",
" p_value = ht.PValue(iters=10000)\n",
" print('\\nmeans permute preglength')\n",
" print('p-value =', p_value)\n",
" print('actual =', ht.actual)\n",
" print('ts max =', ht.MaxTestStat())\n",
"\n",
" data = (firsts.totalwgt_lb.dropna().values,\n",
" others.totalwgt_lb.dropna().values)\n",
" ht = hypothesis.DiffMeansPermute(data)\n",
" p_value = ht.PValue(iters=10000)\n",
" print('\\nmeans permute birthweight')\n",
" print('p-value =', p_value)\n",
" print('actual =', ht.actual)\n",
" print('ts max =', ht.MaxTestStat())\n",
"\n",
"RunResampleTest(firsts, others) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"순열대신에 재표추출을 사용하는 것은 결과에 거의 영향이 없다.\n",
"\n",
"두 모형은 약간 다른 가정에 기반하고 있고, 상기 예제에서 이것 혹은 저것을 선택할 강력한 사유는 없다. 하지만, 일반적으로 p-값은 귀무가설 선택에 달려있다; 서로 다른 모형은 매우 다른 결과를 이끌어낼 수 있다."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
stephenl6705/fluentPy | 8. Object References, Mutability, and Recycling.ipynb | 1 | 30754 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Variables Are Not Boxes"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [1, 2, 3]\n",
"b = a\n",
"a.append(4)\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Gizmo:\n",
" def __init__(self):\n",
" print('Gizmo id: %d' % id(self))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gizmo id: 1095113832488\n"
]
}
],
"source": [
"x = Gizmo()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gizmo id: 1095113832376\n"
]
},
{
"ename": "TypeError",
"evalue": "unsupported operand type(s) for *: 'Gizmo' and 'int'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-0f524f080953>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGizmo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'Gizmo' and 'int'"
]
}
],
"source": [
"y = Gizmo() * 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Identity, Equality, and Aliases"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"charles = {'name': 'Charles L. Dodgson', 'born': 1832}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lewis = charles"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lewis is charles"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1095096406280, 1095096406280)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id(charles), id(lewis)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lewis['balance'] = 950"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'balance': 950, 'born': 1832, 'name': 'Charles L. Dodgson'}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"charles"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"alex = {'name': 'Charles L. Dodgson', 'born': 1832, 'balance':950}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alex == charles"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alex is not charles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Relative Immutability of Tuples"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t1 = (1, 2, [30, 40])\n",
"t2 = (1, 2, [30, 40])\n",
"t1 == t2"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1095111699976"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id(t1[-1])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t1[-1].append(99)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1, 2, [30, 40, 99])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1095111699976"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id(t1[-1])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t1 == t2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Copies Are Shallow by Default"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[3, [55, 44], (7, 8, 9)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l1 = [3, [55, 44], (7, 8, 9)]\n",
"l2 = list(l1)\n",
"l2"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l2 == l1"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l2 is l1"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"l1: [3, [66, 44], (7, 8, 9), 100]\n",
"l2: [3, [66, 44], (7, 8, 9)]\n",
"l1: [3, [66, 44, 33, 22], (7, 8, 9), 100]\n",
"l2: [3, [66, 44, 33, 22], (7, 8, 9, 10, 11)]\n"
]
}
],
"source": [
"l1 = [3, [66, 55, 44], (7, 8, 9)]\n",
"l2 = list(l1)\n",
"l1.append(100)\n",
"l1[1].remove(55)\n",
"print('l1:', l1)\n",
"print('l2:', l2)\n",
"l2[1] += [33, 22]\n",
"l2[2] += (10,11)\n",
"print('l1:', l1)\n",
"print('l2:', l2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deep and Shallow Copies of Arbitrary Objects"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Bus:\n",
" \n",
" def __init__(self, passengers=None):\n",
" if passengers is None:\n",
" self.passengers = []\n",
" else:\n",
" self.passengers = list(passengers)\n",
" \n",
" def pick(self, name):\n",
" self.passengers.append(name)\n",
" \n",
" def drop(self, name):\n",
" self.passengers.remove(name)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import copy"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1095114522184, 1095114522128, 1095114522296)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus1 = Bus(['Alice','Bill', 'Claire', 'David'])\n",
"bus2 = copy.copy(bus1)\n",
"bus3 = copy.deepcopy(bus1)\n",
"id(bus1), id(bus2), id(bus3)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Alice', 'Claire', 'David']"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus1.drop('Bill')\n",
"bus2.passengers"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1095114547784, 1095114547784, 1095114476232)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id(bus1.passengers), id(bus2.passengers), id(bus3.passengers)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Alice', 'Bill', 'Claire', 'David']"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus3.passengers"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[10, 20, [[...], 30]]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = [10, 20]\n",
"b = [a, 30]\n",
"a.append(b)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[10, 20, [[...], 30]]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from copy import deepcopy\n",
"c = deepcopy(a)\n",
"c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Function Parameters as References"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def f(a, b):\n",
" a += b\n",
" return a"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = 1\n",
"y = 2\n",
"f(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1, 2)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x, y"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = [1, 2]\n",
"y = [3, 4]\n",
"f(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"([1, 2, 3, 4], [3, 4])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x, y"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10, 20, 30, 40)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t = (10, 20)\n",
"u = (30, 40)\n",
"f(t, u)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"((10, 20), (30, 40))"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t, u"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mutable Types as Parameter Defaults: Bad Idea"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class HauntedBus(Bus):\n",
" \"\"\"A bus model haunted by ghost passengers\"\"\"\n",
" \n",
" def __init__(self, passengers=[]):\n",
" self.passengers = passengers\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Alice', 'Bill']"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus1 = HauntedBus(['Alice', 'Bill'])\n",
"bus1.passengers"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Bill', 'Charlie']"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus1.pick('Charlie')\n",
"bus1.drop('Alice')\n",
"bus1.passengers"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Carrie']"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus2 = HauntedBus()\n",
"bus2.pick('Carrie')\n",
"bus2.passengers"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Carrie']"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus3 = HauntedBus()\n",
"bus3.passengers"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Carrie', 'Dave']"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus3.pick('Dave')\n",
"bus2.passengers"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus2.passengers is bus3.passengers"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Bill', 'Charlie']"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bus1.passengers"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['__annotations__', '__call__', '__class__', '__closure__', '__code__', '__defaults__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__get__', '__getattribute__', '__globals__', '__gt__', '__hash__', '__init__', '__kwdefaults__', '__le__', '__lt__', '__module__', '__name__', '__ne__', '__new__', '__qualname__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__']\n"
]
}
],
"source": [
"print(dir(HauntedBus.__init__))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(['Carrie', 'Dave'],)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HauntedBus.__init__.__defaults__"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"HauntedBus.__init__.__defaults__[0] is bus2.passengers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defensive Programming with Mutable Parameters"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Twilightbus(Bus):\n",
" \"\"\"A bus model that makes passengers vanish\"\"\"\n",
" \n",
" def __init__(self, passengers=None):\n",
" if passengers is None:\n",
" self.passengers = []\n",
" else:\n",
" self.passengers = passengers"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Sue', 'Maya', 'Diana']"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"basketball_team = ['Sue', 'Tina', 'Maya', 'Diana', 'Pat']\n",
"bus = Twilightbus(basketball_team)\n",
"bus.drop('Tina')\n",
"bus.drop('Pat')\n",
"basketball_team"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# del and Garbage Collection"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import weakref"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = {1, 2, 3}\n",
"s2 = s1\n",
"def bye():\n",
" print('Gone with the wind...')\n",
"ender = weakref.finalize(s1, bye)\n",
"ender.alive"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del s1"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ender.alive"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gone with the wind...\n"
]
}
],
"source": [
"s2 = 'spam'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ender.alive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Weak References"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import weakref"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<weakref at 0x000000CA5EF6DD68; to 'set' at 0x000000CA5DBA6748>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a_set = {0, 1}\n",
"wref = weakref.ref(a_set)\n",
"wref"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{0, 1}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a_set = {2, 3, 4}"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{0, 1}"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref() is None"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref() is None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The WeakValueDictionary Skit"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Cheese:\n",
" \n",
" def __init__(self, kind):\n",
" self.kind = kind\n",
" \n",
" def __repr__(self):\n",
" return 'Cheese(%r)' % self.kind"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import weakref\n",
"stock = weakref.WeakValueDictionary()\n",
"catalog = [Cheese('Red Leicester'), Cheese('Tilsit'), Cheese('Brie'),\n",
" Cheese('Parmesan')]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Brie', 'Parmesan', 'Red Leicester', 'Tilsit']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for cheese in catalog:\n",
" stock[cheese.kind] = cheese\n",
"sorted(stock.keys())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del catalog"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Parmesan']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(stock.keys())"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del cheese"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sorted(stock.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Limitations of Weak References"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class MyList(list):\n",
" \"\"\"list subclass whose instances may be weakly referenced\"\"\"\n",
" \n",
"a_list = MyList(range(10))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wref_to_a_list = weakref.ref(a_list)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref_to_a_list()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del a_list"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref_to_a_list()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref_to_a_list is None"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wref_to_a_list()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tricks Python Plays with Immutables"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t1 = (1, 2, 3)\n",
"t2 = tuple(t1)\n",
"t2 is t1"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t3 = t1[:]\n",
"t3 is t1"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t1 = (1, 2, 3)\n",
"t3 = (1, 2, 3)\n",
"t3 is t1"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = 'ABC'\n",
"s2 = 'ABC'\n",
"s2 is s1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
harishkrao/DSE200x | Final Project/Week-9-ExampleNotebooks/Planck Satellite Data Simulation using pandas.ipynb | 1 | 2584722 | null | mit |
bioe-ml-w18/bioe-ml-winter2018 | homeworks/Week2-Statistics.ipynb | 1 | 156805 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Week 2 - Implementation of Shaffer et al\n",
"\n",
"**Due January 25 at 8 PM**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"# This line tells matplotlib to include plots here\n",
"% matplotlib inline\n",
"import numpy as np # We'll need numpy later\n",
"from scipy.stats import kstest, ttest_ind, ks_2samp, zscore\n",
"import matplotlib.pyplot as plt # This lets us access the pyplot functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (1) Estimation of a sample mean from a normally distributed variable.\n",
"\n",
"Let us assume that a true distribution of a process is described by the normal distribution with $\\mu=5$ and $\\sigma=1$. You have a measurement technique that allows you to sample n points from this distribution. In Matlab this is a random number generator whose numbers will be chosen from the desired normal distribution by using the call `normrnd(mu, sigma, [1, n])`. Sample from this normal distribution from n=1 to 50 (I.e. n=1:50). Create a plot for the standard deviation of the calculated mean from each n when you repeat the sampling 1000 times each. (i.e. You will repeat your n observations 1000 times and will calculate the sample mean for each of the 1000 trials)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"# Initial code here\n",
"\n",
"numRepeats = 1000\n",
"mu, sigma = 5.0, 1.0\n",
"n = 50\n",
"sampleMean = np.empty((n, numRepeats))\n",
"nVec = np.array(range(1, n+1))\n",
"\n",
"for i in range(numRepeats):\n",
" for j in range(n):\n",
" sampleMean[j, i] = np.mean(np.random.normal(loc=mu, scale=sigma, size=(j + 1, )))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (1a) Plot the standard deviation of the estimate of the sample mean versus n. Add a second line which is 1/sqrt(n). Describe what this tells you about the relationship between n and your power to estimate the underlying mean."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d28ba96a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Answer to 1a here\n",
"\n",
"plt.plot(nVec, np.std(sampleMean, axis=1), label='stddev sample mean')\n",
"plt.plot(nVec, 1./np.sqrt(nVec), 'r', label='1/sqrt(n)');\n",
"plt.title('Stdev of mean estimate v. n, 100 trials');\n",
"plt.ylabel('Stdev');\n",
"plt.xlabel('n');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This shows that the standard deviation of the sample mean (also called the standard error) follows a $1/\\sqrt{n}$ relationship."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1b. Plot the boxplot for the sample means for all values n. Using words, interpret what the boxplot view of the 1000 trials for n=1 means and what the trends in the boxplot demonstrate compared to the plot in 1a (I.e. What information do you gain or lose in the two different plotting schemes)?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d28f2add8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Answer to 1b here\n",
"\n",
"plt.boxplot(np.transpose(sampleMean));\n",
"plt.ylabel('Values');\n",
"plt.xlabel('n');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The box plot shows that the values with higher n converge on the true mean (5 in this case). The box plot shows the overall distribution of values in greater detail, while the standard plot is easier to read for a single value plotted over the x-axis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1c. For n=3, plot the histogram of the mean for the 1000 trials. Use the Kolmogorov-Smirnov test to see if this sample distribution is normal (hint you will need to translate this to the standard normal distribution). Report the sample mean and sample standard deviation, the p-value from the test, and whether you would reject the null hypothesis."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8820188948890058\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d267363c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Answer to 1c here\n",
"\n",
"sampleVec = sampleMean[4, :]\n",
"plt.hist(sampleVec)\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Density')\n",
"\n",
"# Normalize sample mean and stdev\n",
"P = kstest(zscore(sampleVec), 'norm')[1]\n",
"\n",
"print(P)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We do not reject the null hypothesis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1d. Repeat 1c but for n=20. What changes when the number of samples increases?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8582959898044679\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d26790780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Answer to 1d here\n",
"\n",
"sampleVec = sampleMean[21, :]\n",
"plt.hist(sampleVec)\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Density')\n",
"\n",
"# Normalize sample mean and stdev\n",
"P = kstest(zscore(sampleVec), 'norm')[1]\n",
"\n",
"print(P)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nothing changes in this case. For both low and high n the sample mean is normally distributed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (2) Weibull distribution. Now we will explore sampling from an alternate distribution type.\n",
"\n",
"#### (2a) Sample the Weibull distribution with parameters a = 1, 1000 times. Plot the histogram of these values. Describe the shape of this histogram in words. Is it anything like the normal distribution?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d2667cc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Answer 2a here\n",
"\n",
"Wvec = np.random.weibull(1, size=(1000, ))\n",
"\n",
"plt.hist(Wvec);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Doesn't look anything like a normal distribution. Much heavier right tail."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (2b) As in problem 1, plot a boxplot of the sample distribution of the Weibull with A=1,B=1 from n=1:50. How does this differ from the plot in 1b and why? Plot the standard deviations of the sample means versus n. Is this any different?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d26b9ff60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Answer 2b here\n",
"\n",
"sampleMean = np.empty((1000, n))\n",
"\n",
"for i in range(sampleMean.shape[0]):\n",
" for j in range(n):\n",
" sampleMean[i, j] = np.mean(np.random.weibull(1.0, size=(j + 1, )))\n",
"\n",
"plt.boxplot(sampleMean);"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d26605208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nVec = np.arange(1, n+1)\n",
"\n",
"plt.plot(nVec, np.std(sampleMean, axis=0), label='stddev sample mean')\n",
"plt.plot(nVec, 1./np.sqrt(nVec), 'r', label='1/sqrt(n)');\n",
"plt.title('Stdev of mean estimate v. n, 1000 trials');\n",
"plt.ylabel('Stdev');\n",
"plt.xlabel('n');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Doesn't differ from 1b."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (2c) For n=3, plot the histogram of the sample means. What is this distribution, is it Weibull or normal? Report your test results."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9306998616154375\n",
"3.849915719555952e-09\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d26947668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sampleVec = sampleMean[2, :]\n",
"plt.hist(sampleVec)\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Density')\n",
"\n",
"# Normalize sample mean and stdev\n",
"Pnorm = kstest(zscore(sampleVec), 'norm')[1]\n",
"Pweib = kstest(sampleVec, 'expon')[1]\n",
"# Scipy and numpy's weibull distributions are different, but a weibull(a=1)\n",
"# is the same as an exponential distribution.\n",
"\n",
"print(Pnorm)\n",
"print(Pweib)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This distribution is closer to normal."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2d. Repeat 2c and 2d for n=20 (don’t include the plots, but do include the test result for normality and explain the impact of the number of samples n, on normality)."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9090854097141294\n",
"4.392930463836819e-12\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d265d3e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sampleVec = sampleMean[19, :]\n",
"plt.hist(sampleVec)\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Density')\n",
"\n",
"# Normalize sample mean and stdev\n",
"Pnorm = kstest(zscore(sampleVec), 'norm')[1]\n",
"Pweib = kstest(zscore(sampleVec), 'expon')[1]\n",
"\n",
"print(Pnorm)\n",
"print(Pweib)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also looks normally distributed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2e. Repeat 2c but with A=10 and B=2 (I.e plot the histogram of the calculated sample means for 1000 trials of n=3). What is this distribution, Weibull or normal? Why does it look different than in 1c?"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"inputHidden": false,
"outputHidden": false
},
"outputs": [],
"source": [
"# Answer to 2f\n",
"\n",
"# The distribution changes shape but the same outcomes, of the sampling distribution\n",
"# morphing to look normally distributed as N increases, hold."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (3) Differential expression . In this problem you will use the two-sample t-test to explore what differential hypothesis testing looks like in known standards and how multiple hypothesis correction effects the number of false positives and negatives from these tests.\n",
"- Distribution 1, normal with mu=1, sigma=1\n",
"- Distribution 2, normal with mu=3, sigma=1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"dOne = lambda n: np.random.normal(loc=1.0, scale=1.0, size=(n, ))\n",
"dTwo = lambda n: np.random.normal(loc=3.0, scale=1.0, size=(n, ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3a. False Negative: Using n=3, perform 100 comparisons of distribution 1 versus distribution 2 with an alpha=0.05. Anytime you fail to reject the hypothesis it is a false negative. Why is this a false negative? Report the number of false negatives from your 100 tests.\n",
"\n",
"Hint: It'd be helpful to define a function that does this for you at this point."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"64\n"
]
}
],
"source": [
"def falseNeg(n=3, nTrial=100, p=0.05):\n",
" compare = np.empty((nTrial, ))\n",
"\n",
" for ii, _ in enumerate(compare):\n",
" compare[ii] = ttest_ind(dOne(n), dTwo(n), equal_var=False)[1]\n",
" \n",
" return sum(compare > p)\n",
"\n",
"print(falseNeg())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In reality these are two distributions, so the test has missed an occasion when we should have rejected the null hypothesis."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3b. False Positives: Using n=3, perform 100 comparisons of distribution 1 versus distribution 1 with an alpha=0.05. Anytime you reject the hypothesis this is a false positive. Why is this a false positive? Report the number of false positives from your 100 tests."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
}
],
"source": [
"def falsePos(n=3, nTrial=100, p=0.05):\n",
" compare = np.empty((nTrial, ))\n",
"\n",
" for ii in range(len(compare)):\n",
" compare[ii] = ttest_ind(dOne(n), dOne(n), equal_var=False)[1]\n",
" \n",
" return sum(compare < p)\n",
"\n",
"print(falsePos())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the same distribution, so the null hypothesis is true."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3c. Repeat 3b but 1000 times. What is the number of false positives? Predict the number of false positives you would get if you compared samples from the same distribution 10,000 times and explain why."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"41\n",
"357\n"
]
}
],
"source": [
"print(falsePos(nTrial=1000))\n",
"print(falsePos(nTrial=10000))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of false positives is p-value * trials, so proportional to the number of trials run."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3d. Now sweep n from 3 to 30 and report the number of false positives and false negatives for each n when you run 100 comparisons. (Provide this in a table format). Please explain the trend you see and interpret its meaning."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d265128d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nVec = np.array(range(3, 31))\n",
"fPos = np.empty(nVec.shape)\n",
"fNeg = np.empty(nVec.shape)\n",
"\n",
"for nn, nItem in enumerate(nVec):\n",
" fPos[nn] = falsePos(n=nItem)\n",
" fNeg[nn] = falseNeg(n=nItem)\n",
" \n",
"plt.plot(nVec, fPos);\n",
"plt.plot(nVec, fNeg);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of false positives is not dependent upon $n$, while the number of false negatives is."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3e. For n=3, suggest how the number of false negatives changes according to sigma for the two distributions and test this. Report your new values and sigma and the number of false negatives in 100 tests."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"86\n"
]
}
],
"source": [
"dThree = lambda n: np.random.normal(loc=3.0, scale=2.0, size=(n, ))\n",
"\n",
"def falseNegB(n=3, nTrial=100):\n",
" compare = np.empty((nTrial, ))\n",
"\n",
" for ii, _ in enumerate(compare):\n",
" compare[ii] = ttest_ind(dOne(n), dThree(n), equal_var=False)[1]\n",
" \n",
" return sum(compare > 0.05)\n",
"\n",
"print(falseNegB())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of false negatives increases with sigma."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (3f) Lastly, perform 3d for p < 0.01 instead of p < 0.05. How does this influence the rate of false positives and negatives? How might you use this when performing many tests?"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d264dfac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nVec = np.array(range(3, 31))\n",
"fPos = np.empty(nVec.shape)\n",
"fNeg = np.empty(nVec.shape)\n",
"\n",
"for nn, nItem in enumerate(nVec):\n",
" fPos[nn] = falsePos(n=nItem, p=0.01)\n",
" fNeg[nn] = falseNeg(n=nItem, p=0.01)\n",
" \n",
"plt.plot(nVec, fPos);\n",
"plt.plot(nVec, fNeg);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This decreases the number of false positives but increases the number of false negatives."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## (5) Shaffer et al"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this excercise we're going to explore some basic concepts of statistics, and use them to build up to some more advanced ideas. To examine these ideas we're going to consider a classic of molecular biology—the [Luria-Delbrück experiment](https://en.wikipedia.org/wiki/Luria–Delbrück_experiment)."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"repOne = np.loadtxt(\"data/wk2/expt_rep1.csv\")\n",
"repTwo = np.loadtxt(\"data/wk2/expt_rep2.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (5a) First, we need to build up a distribution of outcomes for what an experiment would look like if it followed the Luria-Delbruck process.\n",
"Fill in the function below keeping track of normal and mutant cells. Then, make a second function, `CVofNRuns`, that runs the experiment 3000 times. You can assume a culture size of 120000 cells, and mutation rate of 0.0001 per cell per generation. What does the distribution of outcomes look like?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d26432278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Runs the simulation a bunch of times, and looks for how often the fano (cv/mean) comes out to one side\n",
"\n",
"def simLuriaDelbruck(cultureSize, mutationRate):\n",
" nCells, nMuts = 1, 0 # Start with 1 non-resistant cell\n",
"\n",
" for _ in range(np.int(np.floor(np.log2(cultureSize)))): # num of gens\n",
" nCells = 2 * nCells # Double the number of cells, simulating division\n",
" newMuts = np.random.poisson(nCells * mutationRate) # de novo\n",
" nMuts = 2 * nMuts + newMuts # Previous mutants divide and add\n",
" nCells = nCells - newMuts # Non-resistant pop goes down by newMuts\n",
"\n",
" return nMuts\n",
"\n",
"def CVofNRuns(N, cultureSize, mutationRate):\n",
" return np.fromiter((simLuriaDelbruck(cultureSize, mutationRate) for x in range(N)), dtype = np.int)\n",
"\n",
"cvs = CVofNRuns(3000, 120000, 0.0001)\n",
"\n",
"plt.hist(cvs, bins=30);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (5b) Compare the distribution of outcomes between the two replicates of the experiment using the 2-sample KS test. Are they consistent with one another?\n",
"Hint: Each experiment varies slightly in the amount of time it was run. The absolute values of the numbers doesn't matter, so much as the variation of them. You'll need to correct for this by dividing by the mean of the results."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ks_2sampResult(statistic=0.25100240577385735, pvalue=0.19275155911638572)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ks_2samp(repOne/np.mean(repOne), repTwo/np.mean(repTwo))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (5c) Compare the distribution of outcomes between the experiment and model. Are our results consistent with resistance arising through a Luria-Delbruck related process?"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ks_2sampResult(statistic=0.6293875968992249, pvalue=1.244955761027369e-15)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ks_2samp(repOne/np.mean(repOne), cvs/np.mean(cvs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### (5d) We assumed a specific mutation rate and final number of cells. How might you show whether or not these parameters influence our results?"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Could loop across different values of them, to check that you always get the same outcome."
]
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
},
"nteract": {
"version": "0.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
myuuuuun/oyama_seminar2016 | quantecon/An Introductory Example.ipynb | 1 | 94225 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An Introductory Example - QuantEcon(Julia)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31b37d438>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"1-element Array{Any,1}:\n",
" PyObject <matplotlib.lines.Line2D object at 0x31b64ab38>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"using PyPlot\n",
"ts_length = 100\n",
"epsilon_values = randn(ts_length)\n",
"plot(epsilon_values, \"blue\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"linspace(0.0,1.0,11)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#collect(3:10)\n",
"# ここの処理は〜〜〜\n",
"#plot(collect(1:100), epsilon_values, \"blue\")\n",
"\n",
"s = linspace(0, 1, 11)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2x2x2x2 Array{Int64,4}:\n",
"[:, :, 1, 1] =\n",
" 0 0\n",
" 0 0\n",
"\n",
"[:, :, 2, 1] =\n",
" 0 0\n",
" 0 0\n",
"\n",
"[:, :, 1, 2] =\n",
" 0 0\n",
" 0 0\n",
"\n",
"[:, :, 2, 2] =\n",
" 0 0\n",
" 0 0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts_length = 100\n",
"array = zeros(Int64, 2, 2, 2, 2)\n",
"\n",
"#array[:, :, :] = 0\n",
"#array[1, 1, 1] = 21\n",
"\n",
"array"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3-element Array{Any,1}:\n",
" 10 \n",
" \"にゃん\"\n",
" false "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = [10, \"にゃん\", false]\n",
"\n",
"array"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3-element Array{Any,1}:\n",
" 10.2 \n",
" \"aiueo\"\n",
" \"aaaaa\""
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = Array{Any}(3)\n",
"\n",
"array[3] = 20\n",
"array[2] = \"aiueo\"\n",
"array[1] = 10.2\n",
"\n",
"array[3] = \"aaaaa\"\n",
"\n",
"array"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"UTF8String"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = [\"2\", 30, \"🙅\"]\n",
"\n",
"typeof(array[3])"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = [3, 2, \"20\", \"aiueo\", false]\n",
"\n",
"length(array)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = [2 3; 2 1]\n",
"length(array)"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4-element Array{Any,1}:\n",
" 3 \n",
" 2 \n",
" \"20\" \n",
" \"aiueo\""
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = [3, 2, \"20\", \"aiueo\", false]\n",
"pop!(array)\n",
"array"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6-element Array{Any,1}:\n",
" 3 \n",
" 2 \n",
" \"20\" \n",
" \"aiueo\"\n",
" false \n",
" \"added\""
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = [3, 2, \"20\", \"aiueo\", false]\n",
"x = \"added\"\n",
"push!(array, x)\n",
"\n",
"array"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"aiueo\n",
"3\n",
"false\n",
"20.3\n"
]
}
],
"source": [
"array = [\"aiueo\", 3, false, 20.3]\n",
"\n",
"for variable in array\n",
" println(variable)\n",
"end\n"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1: aiueo\n",
"2: 3\n",
"3: false\n"
]
}
],
"source": [
"array = [\"aiueo\", 3, false, 20.3]\n",
"\n",
"for i in 1:3\n",
" println(i, \": \", array[i])\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"100-element Array{Float64,1}:\n",
" -1.98243 \n",
" -0.289045\n",
" 0.548386\n",
" 2.68586 \n",
" -0.826671\n",
" -0.27946 \n",
" 0.833183\n",
" 0.820687\n",
" 1.68391 \n",
" 0.848353\n",
" 1.43303 \n",
" 1.02952 \n",
" -0.138658\n",
" ⋮ \n",
" 0.193556\n",
" 0.376007\n",
" -0.770974\n",
" -0.935151\n",
" 0.798305\n",
" -1.63905 \n",
" 1.50708 \n",
" -1.1761 \n",
" 0.224439\n",
" -0.517274\n",
" -0.944976\n",
" 1.77557 "
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ts_length = 100\n",
"epsilon_values = Array(Float64, ts_length)\n",
"\n",
"for i in 1:ts_length\n",
" epsilon_values[i] = randn()\n",
"end\n",
"\n",
"epsilon_values\n",
"#plot(epsilon_values, \"b-\")"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello foo\n",
"Hello bar\n"
]
}
],
"source": [
"words = [\"foo\", \"bar\"]\n",
"for word in words\n",
" println(\"Hello $(word)\")\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello 1.0\n",
"Hello 6.123233995736766e-17\n",
"Hello 0.5000000000000001\n"
]
}
],
"source": [
"xs = [2pi, pi/2, pi/3]\n",
"for x in xs\n",
" println(\"Hello $(cos(x))\")\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "LoadError",
"evalue": "LoadError: InterruptException:\nwhile loading In[119], in expression starting on line 4",
"output_type": "error",
"traceback": [
"LoadError: InterruptException:\nwhile loading In[119], in expression starting on line 4",
""
]
}
],
"source": [
"s = 0\n",
"i = 1\n",
"\n",
"while true\n",
" s += i\n",
" i += 1\n",
"end\n",
"\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"55"
]
}
],
"source": [
"s = 0\n",
"i = 1\n",
"\n",
"while true\n",
" s += i\n",
" i += 1\n",
" if i > 10\n",
" break\n",
" end\n",
"end\n",
"\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"my_sum (generic function with 1 method)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function my_sum(from, to)\n",
" s = 0\n",
" for i = from:to\n",
" s += i\n",
" end\n",
" return s\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"54"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_sum(2, 10)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using Distributions"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Distributions.Normal(μ=1.0, σ=2.0)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Normal(1, 2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"PyPlot.Figure(PyObject <matplotlib.figure.Figure object at 0x31bff6978>)"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"([5.0,40.0,191.0,710.0,1340.0,1369.0,901.0,346.0,84.0,14.0],[1.079994632701876,1.8331729634194591,2.5863512941370423,3.3395296248546256,4.092707955572209,4.845886286289792,5.599064617007375,6.3522429477249585,7.105421278442542,7.858599609160125,8.611777939877708],Any[PyObject <matplotlib.patches.Rectangle object at 0x31c024470>,PyObject <matplotlib.patches.Rectangle object at 0x31c22a550>,PyObject <matplotlib.patches.Rectangle object at 0x31c239b70>,PyObject <matplotlib.patches.Rectangle object at 0x31c2476d8>,PyObject <matplotlib.patches.Rectangle object at 0x31c247f98>,PyObject <matplotlib.patches.Rectangle object at 0x31c24c208>,PyObject <matplotlib.patches.Rectangle object at 0x31c2409b0>,PyObject <matplotlib.patches.Rectangle object at 0x31c2523c8>,PyObject <matplotlib.patches.Rectangle object at 0x31c24c898>,PyObject <matplotlib.patches.Rectangle object at 0x31c259780>])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function plot_histogram(distribution, n)\n",
" epsilon_values = rand(distribution, n) # n draws from distribution\n",
" plt[:hist](epsilon_values)\n",
"end\n",
"\n",
"lp = Normal(5, 1)\n",
"plot_histogram(lp, 5000)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"100-element Array{Float64,1}:\n",
" 0.404843\n",
" 0.68938 \n",
" 0.143533\n",
" 0.705652\n",
" 0.129699\n",
" 0.321097\n",
" 0.532875\n",
" 0.562169\n",
" 0.199171\n",
" 0.50925 \n",
" 0.844162\n",
" 0.640836\n",
" 0.444534\n",
" ⋮ \n",
" 0.45307 \n",
" 0.540133\n",
" 0.399599\n",
" 0.828188\n",
" 0.862301\n",
" 0.907723\n",
" 0.968492\n",
" 0.919998\n",
" 0.90256 \n",
" 0.867195\n",
" 0.212816\n",
" 0.175562"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rand(100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise1\n",
"\n",
"reduceを使ってみる"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"my_factorial (generic function with 1 method)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_factorial(n) = reduce(*, collect(1:n))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_factorial(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"非整数に対応してみる"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise2"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"my_binomial_rv (generic function with 1 method)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_binomial_rv(n, p) = sum(rand(n) .< p)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n",
"1\n",
"0\n",
"2\n",
"1\n",
"1\n",
"0\n",
"0\n",
"3\n",
"0\n",
"1\n",
"0\n",
"0\n",
"1\n",
"3\n",
"1\n",
"2\n",
"2\n",
"1\n",
"1\n"
]
}
],
"source": [
"for i=1:20\n",
" println(my_binomial_rv(10, 0.1))\n",
"end"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise3"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"montecalro_pi (generic function with 1 method)"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function montecalro_pi(size)\n",
" sample = rand(size, 2)\n",
" inner = 0.0\n",
"\n",
" for (s1, s2) in zip(sample[:, 1], sample[:, 2])\n",
" inner += s1^2 + s2^2 <= 1 ? 1 : 0\n",
" end\n",
" \n",
" return inner * 4 / size\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.141292"
]
}
],
"source": [
"print( montecalro_pi(1000000) )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise4\n",
"\n",
"n: コインを投げる回数, p: 表が出る確率 のとき, x回以上連続で表が出る確率を求める"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"coin (generic function with 1 method)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"function coin(n, x, p)\n",
" total = 2^n\n",
" sucess = 0\n",
" \n",
" for i=x:n\n",
" sucess += factorial(n) / factorial(i) / factorial(n-i)\n",
" end\n",
" \n",
" return sucess / total\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9453125"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"coin(10, 3, 0.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.3",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
JuliaPackageMirrors/KrylovMethods.jl | benchmarks/benchmarkBiCGSTB.ipynb | 1 | 4173 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Benchmark for BiCGSTB\n",
"\n",
"As an example, we solve the curl-curl system arising in electromagnetics\n",
"\n",
"$$\n",
" \\nabla \\times \\nabla \\times u + \\imath w u = 0\n",
"$$\n",
"\n",
"with natural boundary conditions. To generate the discrete PDE, we use a regular mesh and the mimetic finite volume discretization provided by [jInv.Mesh](https://github.com/JuliaInv/jInv.jl)\n",
"\n",
"A good solver for these kinds of problems is BiCGSTAB and we test the implemenation from `KrylovMethods` for growing mesh sizes. If you have another implementation please add it here and make a PR. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using jInv.Mesh\n",
"using KrylovMethods\n",
"using ParSpMatVec\n",
"using Base.Test\n",
"\n",
"Ns = 16*[1,2,3,4] # number of grid points\n",
"nTrials = 10 # number of repititions\n",
"nthreads = 4 # number of threads used to accelerate sparse matvecs\n",
"\n",
"# allocate space for results\n",
"timesKM = zeros(length(Ns),nTrials)\n",
"timesK = zeros(length(Ns),nTrials)\n",
"timesKMOpt = zeros(length(Ns),nTrials)\n",
"timesIS = zeros(length(Ns),nTrials);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"relresKM = 0\n",
"iterKM = 0\n",
"iterKMOpt = 0\n",
"relresKMOpt = 0\n",
"for k=1:length(Ns)\n",
" n = Ns[k]\n",
" \n",
" # build Curl Curl system\n",
" M = getRegularMesh([0 1 0 1 0 1],[n,n,n])\n",
" CURL = getCurlMatrix(M)\n",
" w = 1\n",
" A = CURL'*CURL - 1im*w*speye(size(CURL,2))\n",
" rhs = randn(size(A,2)) + 1im *randn(size(A,2))\n",
" \n",
" if k==1 # warmup\n",
" resKM = KrylovMethods.bicgstb(A,rhs,tol=1e-4,maxIter=2,out=-1);\n",
" end\n",
" for j=1:nTrials\n",
" tic()\n",
" resKM = KrylovMethods.bicgstb(A,rhs,tol=1e-12,maxIter=100,out=-1)\n",
" timesKM[k,j] = toq();\n",
" iterKM = resKM[4]\n",
" relresKM = resKM[3]\n",
"\n",
" tic()\n",
" yt = zeros(Complex128,size(A,1))\n",
" Afun(x) = (yt[:]=Complex128(0.0); ParSpMatVec.Ac_mul_B!( Complex128(1.0), A, x, Complex128(0.0), yt, nthreads); return yt)\n",
" resKMOpt = KrylovMethods.bicgstb(Afun,rhs,tol=1e-12,maxIter=100,out=-1)\n",
" timesKMOpt[k,j] = toq();\n",
" iterKMOpt = resKM[4]\n",
" relresKMOpt = resKMOpt[3] \n",
" @test iterKMOpt==iterKM\n",
"# @test abs(relresKMOpt-relresKM)/relresKM < 1e-1\n",
" end\n",
"println(\"n=$n, KM.jl: $(mean(timesKM[k,:])) KM.jl+ParSpMat:$(mean(timesKMOpt[k,:]))\")\n",
"end\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using PyPlot"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"loglog(Ns,mean(timesKM,2),\"--b\",linewidth=3)\n",
"hold(true)\n",
"loglog(Ns,mean(timesKMOpt,2),\"-b\",linewidth=3)\n",
"xlabel(\"degrees of freedom\")\n",
"ylabel(\"runtime\")\n",
"title(\"Comparison of PCG runtimes for 3D CurlCurl\")\n",
"legend((\"KrylovMethods.jl\",\"KrylovMethods+ParSpMatVec\"),loc=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.4.6-pre",
"language": "julia",
"name": "julia-0.4"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.4.7"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
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