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jbwhit/fine-structure-inference | notebooks/2017-03-20-jbw-data-science-methods-paper.ipynb | 1 | 8236878 | null | mit |
pyreaclib/pyreaclib | pynucastro/library/tabular/generate_toki_from_dat.ipynb | 1 | 2061 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"path = \"./\" \n",
"files= os.listdir(path) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def find_header(file):\n",
" f = open(file,\"r\")\n",
" data = f.readlines() # data is a list. each element is a line of \"A23_Ne_F.dat\"\n",
" f.close()\n",
" \n",
" inde = [] \n",
" for i in range(len(data)):\n",
" if data[i][0]==\"1\": # if the line start as \"1\" , this is not a header line any more\n",
" break \n",
" return i\n",
" \n",
" \n",
"for file in files:\n",
" if file.split(\".\")[-1]==\"dat\":\n",
" \n",
" a = file.split(\"_\")[0]\n",
" b = a.split(\"-\")\n",
" n1 = b[0][0:2]\n",
" n2 = b[1][0:2]\n",
" e1 = b[0][2:len(b[0])]\n",
" e2 = b[1][2:len(b[1])]\n",
" \n",
" header = find_header(file)\n",
" rate_file = open(e1+n1+\"--\"+e2+n2+\"-toki\",\"w\") \n",
" rate_file.write(\"t\\n \"+e1+n1+\" \"+e2+n2+\"\\n\"+file+\"\\n\"+str(header)+\"\\n152\\n39\") \n",
" rate_file.close()"
]
},
{
"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": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
HugoGuillen/nb2py | tutorial.ipynb | 1 | 3073 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# nb2py tutorial\n",
"\n",
"All the examples are generated from this notebook and located in the folder `tutorial_files`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import nb2py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exporting marked cells\n",
"The `dump` function automatically exports cells starting with `#~`, but this behaviour could be changed with the parameter `marker`.\n",
"\n",
"The markers are removed from the output file."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"#~\n",
"#This is a cell example with the standard marker\n",
"a=2\n",
"b=3\n",
"print(a+b)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb2py.dump('tutorial.ipynb','tutorial_files/standard.py') "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"21\n"
]
}
],
"source": [
"#please export this cell\n",
"#This is a cell with a custom comment as marker\n",
"x=10\n",
"y=11\n",
"print(x+y)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb2py.dump('tutorial.ipynb','tutorial_files/custom.py',marker='please export this cell')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Exporting cells by indices\n",
"You can also export cells using a list of indices.\n",
"Consider that the cells will be written regardless of their type."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb2py.dump_indices('tutorial.ipynb',\n",
" 'tutorial_files/indices.py',\n",
" indices=[3,5])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nb2py.dump_indices('tutorial.ipynb',\n",
" 'tutorial_files/markdown.md',\n",
" indices=[0,2,7])"
]
}
],
"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 |
sysid/nbs | tw_template.ipynb | 1 | 30632 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Template"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Navigation\n",
"[Variables/Functions](#Variables/Functions-^)\n",
"[Data](#Data-^)\n",
"[Model](#Model-^)\n",
"[Training](#Training-^)\n",
"[Predictions](#Predictions-^)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables/Functions [^](#Navigation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from IPython.core.debugger import Tracer\n",
"#Tracer()()\n",
"\n",
"import os, sys, time\n",
"\n",
"### prevent the dying jupyter notebook\n",
"stdout = sys.stdout\n",
"#sys.stdout = sys.__stdout__ # did not work to restoure print -> console\n",
"#sys.stdout = open('keras_output.txt', 'a+')\n",
"#sys.stdout = stdout\n",
"\n",
"import utils\n",
"import importlib\n",
"importlib.reload(utils)\n",
"\n",
"#Allow relative imports to directories above cwd/\n",
"sys.path.insert(1, os.path.join(sys.path[0], '..'))\n",
"\n",
"#import modules\n",
"from utils import *\n",
"\n",
"%matplotlib inline\n",
"\n",
"np.random.seed(42)\n",
"\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.metrics import mean_squared_error\n",
"\n",
"%pwd #Verify we are in the right directory\n",
"!uname -a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# argparse\n",
"import argparse\n",
"parser = argparse.ArgumentParser(description=\"programpurpose\")\n",
"parser.add_argument(\"-s\", \"--sample\", help=\"run on sample\", action=\"store_true\")\n",
"args = parser.parse_args([\"-s\"])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Create references to important directories we will use over and over\n",
"current_dir = os.getcwd()\n",
"dataname = 'airline'\n",
"dpath = \"{}/data/{}/\".format(current_dir, dataname)\n",
"#h5Path = '{}weights_TF.h5'.format(dpath)\n",
"#h5PathModel = '{}model_TF.h5'.format(dpath)\n",
"#h5Path, h5PathModel\n",
"#\n",
"#modelType = 'dense'\n",
"\n",
"seq_len = 40"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data [^](#Navigation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%mkdir -p $dpath"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"zipname = 'xxx.zip'\n",
"!wget -P $dpath https://datamarket.com/data/set/22u3/international-airline-passengers-monthly-totals-in-thousands-jan-49-dec-60#\n",
"!unzip -d $dpath data/power/household_power_consumption.zip"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 144 entries, 0 to 143\n",
"Data columns (total 1 columns):\n",
"International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60 144 non-null int64\n",
"dtypes: int64(1)\n",
"memory usage: 1.2 KB\n"
]
},
{
"data": {
"text/plain": [
"(144, 1)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"ename": "NameError",
"evalue": "name 'MinMaxScaler' 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-3-647b899e5858>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# normalize the dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'MinMaxScaler' is not defined"
]
}
],
"source": [
"# read data\n",
"df = pd.read_csv(dpath+'international-airline-passengers.csv', sep=';', usecols=[1], engine='python', skipfooter=3)\n",
"df.info()\n",
"ts = df.values\n",
"ts = ts.astype('float32')\n",
"ts.shape\n",
"\n",
"# normalize the dataset\n",
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
"ts = scaler.fit_transform(ts)\n",
"\n",
"plot1(ts)"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Correlation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"autocorrelation_plot2d(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"autocorrelation_plot_all(df.values[:,0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"def autocorrelation_plotPD_all(series):\n",
" # solid: 95% dashed: 99% confidence interval for correlation values\n",
" from pandas.tools.plotting import autocorrelation_plot\n",
" autocorrelation_plot(series)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"print(test_corr(df))\n",
"autocorrelation_plotPD_all(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"df.values[:,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"test_stationary(df.values[:,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> Loading data...\n",
"Data shape: (103, 41, 1) Train: (93, 41, 1) Test: (10, 41, 1)\n"
]
},
{
"data": {
"text/plain": [
"((93, 40, 1), (93, 1), (10, 40, 1), (10, 1))"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([ 112., 118., 132., 129., 121., 135., 148., 148., 136.,\n",
" 119., 104., 118., 115., 126., 141., 135., 125., 149.,\n",
" 170., 170., 158., 133., 114., 140., 145., 150., 178.,\n",
" 163., 172., 178., 199., 199., 184., 162., 146., 166.,\n",
" 171., 180., 193., 181.], dtype=float32)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"array([ 118., 132., 129., 121., 135., 148., 148., 136., 119.,\n",
" 104., 118., 115., 126., 141., 135., 125., 149., 170.,\n",
" 170., 158., 133., 114., 140., 145., 150., 178., 163.,\n",
" 172., 178., 199., 199., 184., 162., 146., 166., 171.,\n",
" 180., 193., 181., 183.], dtype=float32)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def load_data(ts, seq_len, normalise_window, split=0.9, shuffle=False):\n",
" print('> Loading data...')\n",
" sequence_length = seq_len + 1\n",
" result = []\n",
" \n",
" # create gliding window\n",
" for index in range(len(ts) - sequence_length):\n",
" result.append(ts[index: index + sequence_length])\n",
" \n",
" if normalise_window:\n",
" result = normalise_windows(result)\n",
"\n",
" result = np.array(result)\n",
" #print(result[:4, :])\n",
" \n",
" row = round(split * result.shape[0])\n",
" train = result[:row, :]\n",
" test = result[row:, :]\n",
" print(\"Data shape: \", result.shape, \"Train: \", train.shape, \"Test: \", test.shape)\n",
" \n",
" if shuffle:\n",
" np.random.shuffle(train)\n",
" \n",
" x_train = train[:, :-1]\n",
" y_train = train[:, -1]\n",
" x_test = test[:, :-1]\n",
" y_test = test[:, -1]\n",
"\n",
" x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
" x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1)) \n",
"\n",
" return [x_train, y_train, x_test, y_test]\n",
"\n",
"def normalise_windows(window_data):\n",
" normalised_data = []\n",
" for window in window_data:\n",
" normalised_window = [((float(p) / float(window[0])) - 1) for p in window]\n",
" normalised_data.append(normalised_window)\n",
" return normalised_data\n",
"\n",
"\n",
"#X_train, y_train, X_test, y_test = load_data(ts, seq_len, False)\n",
"X_train, y_train, X_test, y_test = load_data(ts, seq_len, False)\n",
"X_train.shape, y_train.shape, X_test.shape, y_test.shape\n",
"X_train[0,:seq_len, 0]\n",
"X_train[1,:seq_len, 0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plot2(X_train[0], y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"if m.classtype == 'dense':\n",
" print(\"Squeezing X_train\")\n",
" X_train = np.squeeze(X_train)\n",
" X_test = np.squeeze(X_test)\n",
"elif modelType == 'ltsm':\n",
" pass\n",
"else:\n",
" raise ValueError('Wrong model type: ', modelType)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model [^](#Navigation)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Abtract Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class BaseModel():\n",
" \n",
" def __init__(self, layers, verbose=1):\n",
" #self.FILE_PATH = 'http://www.platform.ai/models/'\n",
" self.modelPath = dpath+self.get_classname()+'.h5'\n",
" self.checkpoint = ModelCheckpoint(filepath= dpath + 'checkpoint-{epoch:02d}-{loss:.3f}-{val_loss:.3f}.hdf5')\n",
" #self.csvLogger = CSVLogger(dpath+'trainingLog.csv', separator=';', append=True)\n",
" self.verbose = verbose\n",
" \n",
" @classmethod #classmethod always gets class as parameter\n",
" def get_classname(cls):\n",
" return cls.__name__\n",
" \n",
" def save(self):\n",
" self.model.save(self.modelPath)\n",
" print(\"model saved to: \", self.modelPath)\n",
" \n",
" def load(self):\n",
" self.model = load_model(modelPath)\n",
" print(\"model loaded from: \", self.modelPath)\n",
" \n",
" def train(self, X_train, y_train, epochs=1, batch_size=64, val_split=0.05, verbose=1):\n",
" global_start_time = time.time()\n",
" try:\n",
" history = self.model.fit(\n",
" X_train, y_train,\n",
" batch_size=batch_size,\n",
" nb_epoch=epochs,\n",
" validation_split=val_split,\n",
" verbose=verbose,\n",
" #callbacks=[self.checkpoint, self.csvLogger]) #BUG: csv logger not rerun capable\n",
" callbacks=[self.checkpoint])\n",
" except KeyboardInterrupt:\n",
" print('Training duration (s) : ', time.time() - global_start_time)\n",
" return self.model, history\n",
"\n",
" print('Training duration (s) : ', time.time() - global_start_time)\n",
" return self.model, history"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"### Model Sequential"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"def preprocess(x):\n",
" return x\n",
"\n",
"class BaseModel():\n",
" \n",
" classtype = 'dense' # class variable\n",
" classname = 'BaseModel'\n",
" def __init__(self, layers):\n",
" #self.FILE_PATH = 'http://www.platform.ai/models/'\n",
" self.modelPath = dpath+self.classname+'.h5'\n",
" self.checkpoint = ModelCheckpoint(filepath= dpath + 'checkpoint-{epoch:02d}-{loss:.3f}-{val_loss:.3f}.hdf5')\n",
" self.csvLogger = CSVLogger(dpath+'trainingLog.csv', separator=';', append=True)\n",
" self.create(layers)\n",
"\n",
" def create(self, layers):\n",
" print('> Create Model')\n",
" start = time.time()\n",
" \n",
" model = self.model = Sequential()\n",
"\n",
" model.add(Dense(\n",
" input_dim=layers[0],\n",
" output_dim=layers[1],\n",
" activation='relu'))\n",
"\n",
" model.add(Dense(\n",
" output_dim=layers[2],\n",
" activation='relu'))\n",
"\n",
" model.add(Dense(\n",
" output_dim=layers[3],\n",
" activation='linear'))\n",
"\n",
" model.compile(optimizer='adam', loss='mse', metrics=['mse', 'mape'])\n",
" print(\"Compilation Time : \", time.time() - start)\n",
" \n",
" def save(self):\n",
" self.model.save(modelPath)\n",
" print(\"model saved to: \", self.modelPath)\n",
"\n",
" def load(self):\n",
" self.model = load_model(modelPath)\n",
" print(\"model loaded from: \", self.modelPath)\n",
" \n",
" def train(self, X_train, y_train, epochs=1, batch_size=64, val_split=0.05, verbose=1):\n",
" global_start_time = time.time()\n",
" try:\n",
" history = self.model.fit(\n",
" X_train, y_train,\n",
" batch_size=batch_size,\n",
" nb_epoch=epochs,\n",
" validation_split=0.05,\n",
" verbose=verbose,\n",
" callbacks=[self.checkpoint, self.csvLogger])\n",
" except KeyboardInterrupt:\n",
" print('Training duration (s) : ', time.time() - global_start_time)\n",
" return self.model, history\n",
"\n",
" print('Training duration (s) : ', time.time() - global_start_time)\n",
" return self.model, history\n",
" \n",
"m = BaseModel([seq_len, seq_len+1, (seq_len+1)*2, 1]) #bias added\n",
"m.classtype\n",
"m.modelPath\n",
"m.model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Functional"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Dense"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class BaselineModel(BaseModel):\n",
" \n",
" classtype = 'dense' # class variable\n",
" #classname = 'BaselineModel'\n",
" \n",
" def __init__(self, layers):\n",
" super(BaselineModel, self).__init__(layers)\n",
" #self.FILE_PATH = 'http://www.platform.ai/models/'\n",
" #self.modelPath = dpath+self.classname+'.h5'\n",
" self.create(layers)\n",
" \n",
" def __call__(self, layers):\n",
" self.__init(layers, verbose=0)\n",
"\n",
" def create(self, layers):\n",
" print('> Create Model', self.get_classname(), ' type: ', self.classtype)\n",
" start = time.time()\n",
" \n",
" inputs = Input(shape=(layers[0],))\n",
" \n",
" x = Dense(layers[1], activation='relu')(inputs)\n",
" x = Dense(layers[2], activation='relu')(x)\n",
" preds = Dense(layers[3], activation='linear')(x)\n",
" \n",
" self.model = Model(input=inputs, output=preds)\n",
" self.model.compile(optimizer='adam', loss='mse', metrics=['mse', 'mape'])\n",
" print(\"Compilation Time : \", time.time() - start)\n",
" \n",
" \n",
"m = BaselineModel([seq_len, seq_len+1, (seq_len+1)*2, 1]) #bias added\n",
"m.classtype\n",
"m.modelPath\n",
"m.model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class DenseModel(BaseModel):\n",
" \"\"\"take the seq_len as feature input and predict one output\"\"\"\n",
" \n",
" classtype = 'dense' # class variable\n",
" \n",
" def __init__(self, layers):\n",
" super(DenseModel, self).__init__(layers)\n",
" self.create(layers)\n",
"\n",
" def create(self, layers):\n",
" if verbose: print('> Create Model', self.get_classname(), ' type: ', self.classtype)\n",
" start = time.time()\n",
" \n",
" inputs = Input(shape=(layers[0],))\n",
" \n",
" x = Dense(layers[1], activation='relu')(inputs)\n",
" x = Dropout(0.25)(x)\n",
" x = Dense(layers[2], activation='relu')(x)\n",
" x = Dropout(0.25)(x)\n",
" preds = Dense(layers[3], activation='linear')(x)\n",
" \n",
" self.model = Model(input=inputs, output=preds)\n",
" \n",
" self.model.compile(optimizer='adam', loss='mse', metrics=['mse', 'mape'])\n",
" if verbose: print(\"Compilation Time : \", time.time() - start)\n",
" \n",
" \n",
"m = DenseModel([seq_len, 500, 250, 1]) #bias added\n",
"m.model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%time\n",
"#%%capture output\n",
"#sys.stdout = open('keras_output.txt', 'a+')\n",
"#m.compile()\n",
"model, hist = m.train(X_train, y_train, epochs=1, verbose=1)\n",
"#sys.stdout = stdout\n",
"hist\n",
"\n",
"#m.save()"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"#### LTSM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"hidden": true
},
"outputs": [],
"source": [
"class BaseLTSM(BaseModel):\n",
" \n",
" classtype = 'ltsm' # class variable\n",
" classname = 'BaseLTSM'\n",
" \n",
" def __init__(self, layers):\n",
" super(BaseLTSM, self).__init__(layers)\n",
" self.create(layers)\n",
"\n",
" def create(self, layers):\n",
" print('> Create Model', self.get_classname(), ' type: ', self.classtype)\n",
" start = time.time()\n",
" \n",
" inputs = Input(shape=(seq_len, layers[0]))\n",
" \n",
" x = LSTM(layers[1], activation='relu', return_sequences=True)(inputs)\n",
" #x = Dropout(0.1)(x)\n",
" x = LSTM(layers[2], activation='relu', return_sequences=False)(x)\n",
" #x = Dropout(0.1)(x)\n",
" preds = Dense(layers[3], activation='linear')(x)\n",
" \n",
" self.model = Model(input=inputs, output=preds)\n",
" self.model.compile(optimizer='rmsprop', loss='mse', metrics=['mse'])\n",
" print(\"Compilation Time : \", time.time() - start)\n",
" \n",
"#m = BaseLTSM([1, seq_len+1, (seq_len+1)*2, 1]) #bias added\n",
"m = BaseLTSM([1, 50, 50, 1]) #bias added\n",
"m.modelPath\n",
"m.model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training [^](#Navigation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%time\n",
"%%capture output\n",
"#sys.stdout = open('keras_output.txt', 'a+')\n",
"#m.compile()\n",
"model, hist = m.train(X_train, y_train, epochs=1, verbose=1)\n",
"#sys.stdout = stdout\n",
"hist\n",
"\n",
"#m.save()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"output.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from keras.models import load_model\n",
"# returns a compiled model\n",
"# identical to the previous one\n",
"model = load_model(h5PathModel)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Grid Search\n",
"https://github.com/fchollet/keras/blob/master/examples/mnist_sklearn_wrapper.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"from keras.wrappers.scikit_learn import KerasClassifier, KerasRegressor\n",
"\n",
"def _model():\n",
" return m.model\n",
"model = KerasRegressor(build_fn=_model, verbose=1)\n",
"# __call__\n",
"#model = KerasRegressor(build_fn=m, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%capture output\n",
"grid = GridSearchCV(model,\n",
" param_grid={'nb_epoch': [1],\n",
" 'batch_size': [10]},\n",
" #scoring='mse',\n",
" scoring='neg_mean_squared_error',\n",
" n_jobs=1)\n",
"grid_result = grid.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"output.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# summarize results\n",
"# The unified scoring API always maximizes the score, so scores which need to be minimized are negated in order for the unified scoring API to work correctly. The score that is returned is therefore negated when it is a score that should be minimized and left positive if it is a score that should be maximized.\n",
"print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
"means = grid_result.cv_results_['mean_test_score']\n",
"stds = grid_result.cv_results_['std_test_score']\n",
"params = grid_result.cv_results_['params']\n",
"for mean, stdev, param in zip(means, stds, params):\n",
" print(\"%f (%f) with: %r\" % (mean, stdev, param))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# validator.best_estimator_ returns sklearn-wrapped version of best model.\n",
"# validator.best_estimator_.model returns the (unwrapped) keras model\n",
"best_model = grid.best_estimator_.model\n",
"metric_names = best_model.metrics_names\n",
"metric_values = best_model.evaluate(X_test, y_test)\n",
"for metric, value in zip(metric_names, metric_values):\n",
" print(metric, ': ', value)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"grid_result.best_score_\n",
"grid_result.best_params_\n",
"grid_result.cv_results_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predictions [^](#Navigation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"seq_len, X_test.shape, y_test.shape\n",
"X_test[0, :10], y_test[0]\n",
"X_test[1, :10], y_test[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def predict_sequence_full(model, data, window_size):\n",
" #Shift the window by 1 new prediction each time, re-run predictions on new window\n",
" curr_frame = data[0]\n",
" predicted = []\n",
" \n",
" # loop over entire testdata\n",
" for i in range(len(data)):\n",
" if m.classtype == 'ltsm':\n",
" predicted.append(model.predict(curr_frame[np.newaxis,:,:])[0,0]) #get element from shape(1,1,:)\n",
" elif m.classtype == 'dense':\n",
" predicted.append(model.predict(curr_frame[np.newaxis,:])[0][0]) #get element from shape(1,1)\n",
" else:\n",
" raise ValueError('Wrong model type: ', modelType)\n",
" \n",
" curr_frame = curr_frame[1:] #move window\n",
" #Tracer()()\n",
" curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0) #fill frame with prediction\n",
" return predicted"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%time\n",
"length = len(X_test)\n",
"if m.classtype == 'ltsm':\n",
" predicted = predict_sequence_full(model, X_test[:length], seq_len)\n",
"elif m.classtype == 'dense':\n",
" predicted = predict_sequence_full(model, np.squeeze(X_test[:length]), seq_len)\n",
"else:\n",
" raise ValueError('Wrong model type: ', modelType)\n",
"#predicted = predict_sequence_full(model, np.squeeze(X_test[:length]), seq_len)\n",
"print(len(predicted), predicted)\n",
"plot_result(y_test, predicted, length)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_test.shape\n",
"y_test\n",
"np.array(predicted)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def evaluate(X, y, verbose=0):\n",
" # gives the metrics, defined during compile\n",
" for n in zip(model.metrics_names, model.evaluate(X, y, verbose=verbose)):\n",
" print(\"{}:\\t{}\".format(n[0], n[1]))\n",
"evaluate(X_test, y_test, 1) "
]
},
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"cell_type": "code",
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| mit |
shafeeqr2/python | Ping Pong Game/Ping Pong Game.ipynb | 1 | 9302 | {
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"ad"
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"source": [
"#pingpong\n",
"\n",
"from vpython import *\n",
"import math\n",
"import random\n",
"import string\n",
"\n",
"scene.width = scene.height = 800\n",
"scene.title = \"Ping Pong\"\n",
"scene.forward = vector(0,-0.25,-1)\n",
"scene.background = vector(.96,.96,.96)\n",
"center = (0,0,0)\n",
"stopper = 1\n",
"score = 0\n",
"sentence = \"\"\n",
"computer = box (pos = vector(0,0,-3), color = color.green, size = vector(1.5, 0.05, 0.30))\n",
"player = box (pos = vector(0,0,3), color = color.blue, size = vector(1.5, 0.05, 0.30))\n",
"ball = sphere(pos = vector(0,2,0), radius = 0.1, color = color.red)\n",
"glass = box(pos = vector(0,1.5,0), color = color.blue, size = vector(3,3,6), opacity = 0.1)\n",
"scoreboard = text (text = sentence, align='center', depth = -0.3, color = color.blue, height = 1,\n",
" pos = vector(0,1.2,0))\n",
"miniscoreboard = label(pos=(0,3.8,0), text =\"\", color = color.black)\n",
"\n",
"ball.velocity = vector (0,0,0.65)\n",
"dt = 0\n",
"g = -0.2\n",
"\n",
"right_text = text(text=\"right\", align='center', depth = -0.3, color = color.green)\n",
"left_text = text(text=\"left\", align='center', depth = -0.1, color = color.green)\n",
"gmover = text(text=\"GAME OVER\", align='center', depth = -0.1, color = color.red)\n",
"\n",
"gmover.visible = False\n",
"right_text.visible = False\n",
"left_text.visible = False\n",
"scoreboard.visible = False\n",
"\n",
"while True:\n",
" print('hi')\n",
" dt = dt + 0.002\n",
" score = score + dt*500*stopper\n",
" miniscoreboard.text = \"SCORE: %d\" %score\n",
" ball.velocity.y = ball.velocity.y + g*dt\n",
" ball.pos.x = ball.pos.x + ball.velocity.x*dt\n",
" ball.pos.y = ball.pos.y + ball.velocity.y*dt\n",
" ball.pos.z = ball.pos.z + ball.velocity.z*dt\n",
"\n",
" plminx = player.pos.x - 0.75\n",
" plmaxx = player.pos.x + 0.75\n",
"\n",
" plminz = player.pos.z - 0.15\n",
"\n",
" cpminz = computer.pos.z + 0.15\n",
" cpmaxz = computer.pos.z - 0.15\n",
"\n",
" computer.pos.x = ball.pos.x\n",
"\n",
" if scene.kb.keys:\n",
" key = scene.kb.getkey()\n",
" if key == \"q\" or key == \"Q\":\n",
"## right_text.visible = False\n",
"## left_text.vislbe = True\n",
" if player.pos.x > -1:\n",
" player.pos.x = player.pos.x - 1*stopper\n",
" \n",
" if key == \"w\" or key == \"W\":\n",
"## left_text.visible = False\n",
"## right_text.visible = True\n",
" if player.pos.x < 1:\n",
" player.pos.x = player.pos.x + 1*stopper\n",
"\n",
"## 0.075 comes from the radius of the ball + height of slider above ground .i.e. 0.0\n",
"\n",
" if (plminz <= ball.pos.z):\n",
" if (plminx <= ball.pos.x):\n",
" if (ball.pos.x <= plmaxx):\n",
" if (ball.pos.y <= 0.075):\n",
"\n",
" ball.pos.z = 2.85\n",
" ball.pos.y = 0.076\n",
" ball.velocity.x = random.random()\n",
" ball.velocity.y = ball.velocity.y*(-1)\n",
" ball.velocity.z = ball.velocity.z*(-1)\n",
" dt = 0.45\n",
" score = score + 100\n",
"\n",
" if (cpminz >= ball.pos.z):\n",
" if (ball.pos.y < 0.075):\n",
"\n",
" ball.pos.z = -2.85\n",
" ball.pos.y = 0.076\n",
" ball.velocity.x = random.random()\n",
" ball.velocity.y = ball.velocity.y*(-1)\n",
" ball.velocity.z = ball.velocity.z*(-1)\n",
" dt = 0.45\n",
"\n",
" if (ball.pos.x >= 1):\n",
" ball.pos.x = 1\n",
" ball.velocity.x = ball.velocity.x*(-1)\n",
"\n",
" if (ball.pos.x <= -1):\n",
" ball.pos.x = -1\n",
" ball.velocity.x = ball.velocity.x*(-1)\n",
"\n",
" if (ball.pos.y <= 0):\n",
" ball.pos.y = 0\n",
" ball.velocity.x = 0\n",
" ball.velocity.y = 0\n",
" ball.velocity.z = 0\n",
" stopper = 0\n",
" gmover.visible = True\n",
" scoreboard.text = \"YOUR SCORE IS %d\" %score\n",
" miniscoreboard.visible = False\n",
" scoreboard.visible = True\n",
" right_text.visilbe = False\n",
" left_text.visible = False\n",
" glass.visible = False\n",
" \n",
" rate(18)"
]
},
{
"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": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\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": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
zhouqifanbdh/liupengyuan.github.io | chapter2/homework/computer/3-29/201611680288.ipynb | 27 | 6693 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入第1个整数，以回车结束。1\n",
"请输入第2个整数，以回车结束。2\n",
"请输入第3个整数，以回车结束。3\n",
"最终的结果是： 9\n"
]
}
],
"source": [
"def compute_fact(end):\n",
" i = 1\n",
" fac_n = 1\n",
"\n",
" while i < end:\n",
" i = i + 1\n",
" fac_n = i*fac_n \n",
"\n",
" return fac_n\n",
"\n",
"m = int(input('请输入第1个整数，以回车结束。'))\n",
"n = int(input('请输入第2个整数，以回车结束。'))\n",
"k = int(input('请输入第3个整数，以回车结束。'))\n",
"\n",
"print('最终的结果是：', compute_fact(m) + compute_fact(n) + compute_fact(k))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"最终结果是： 3.140592653839794\n",
"最终结果是： 3.1415826535897198\n"
]
}
],
"source": [
"def compute_sum(end):\n",
" i = 0\n",
" total_n = 0\n",
"\n",
" while i < end:\n",
" i = i + 1\n",
" total_n = total_n + 1*(-1)**(i+1)/(2*i-1)\n",
"\n",
" return total_n\n",
"\n",
"\n",
"print('最终结果是：', 4*compute_sum(1000) )\n",
"print('最终结果是：', 4*compute_sum(100000) )"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"输入您出生的月份3\n",
"输入您出生的日期4\n",
"您是双鱼座 ！\n"
]
}
],
"source": [
"def c_tions(m,d):\n",
" if m==1:\n",
" if d>=1 and d<=19:\n",
" print('您是摩羯座 ！')\n",
" else: \n",
" print('您是水瓶座 ！')\n",
" elif m==2:\n",
" if d>=1 and d<=18:\n",
" print('您是水瓶座 ！')\n",
" else:\n",
" print('您是双鱼座 ！')\n",
" elif m==3:\n",
" if d>=1 and d<=20:\n",
" print('您是双鱼座 ！')\n",
" else:\n",
" print('您是白羊座 ！')\n",
" elif m==4:\n",
" if d>=1 and d<=19:\n",
" print('您是白羊座 ！')\n",
" else:\n",
" print('您是金牛座 ！')\n",
" elif m==5:\n",
" if d>=1 and d<=20:\n",
" print('您是金牛座 ！')\n",
" else:\n",
" print('您是双子座 ！')\n",
" elif m==6:\n",
" if d>=1 and d<=21:\n",
" print('您是双子座 ！')\n",
" else:\n",
" print('您是巨蟹座 ！')\n",
" elif m==7:\n",
" if d>=1 and d<=22:\n",
" print('您是巨蟹座 ！')\n",
" else:\n",
" print('您是狮子座 ！')\n",
" elif m==8:\n",
" if d>=1 and d<=22:\n",
" print('您是狮子座 ！')\n",
" else:\n",
" print('您是处女座 ！')\n",
" elif m==9:\n",
" if d>=1 and d<=22:\n",
" print('您是处女座 ！')\n",
" else:\n",
" print('您是天秤座 ！')\n",
" elif m==10:\n",
" if d>=1 and d<=23:\n",
" print('您是天秤座 ！')\n",
" else:\n",
" print('您是天蝎座 ！')\n",
" elif m==11:\n",
" if d>=1 and d<=24:\n",
" print('您是天蝎座 ！')\n",
" else:\n",
" print('您是射手座 ！')\n",
" else:\n",
" if d>=1 and d<=22:\n",
" print('您是射手座 ！')\n",
" else:\n",
" print('您是摩羯座 ！')\n",
"\n",
"\n",
"m=int(input('输入您出生的月份'))\n",
"d=int(input('输入您出生的日期'))\n",
"c_tions(m,d)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入一个可数英文单词的单数形式，以回车结束book\n",
"books\n"
]
}
],
"source": [
"def n_change(a):\n",
" if a.endswith('y'):\n",
" print(a[:-1],'ies'sep='',)\n",
" elif a[:-1]in 'sx' or a[:-2]in ['sh','ch']:\n",
" print (a,'es',sep='')\n",
" elif a.endswith('an'):\n",
" print (a[:-2],'en',sep='')\n",
" else:\n",
" print (a,'s',sep='')\n",
"\n",
"a=input('请输入一个可数英文单词的单数形式，以回车结束')\n",
"n_change(a)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入第1个整数，以回车结束。1\n",
"请输入第2个整数，以回车结束。5\n",
"请输入第3个整数，以回车结束。2\n",
"最终的和是： 9\n"
]
}
],
"source": [
"def compute_sum(m,n,k):\n",
" total_mn = m\n",
"\n",
" while m < n:\n",
" m = m + k\n",
" total_mn = total_mn + m\n",
"\n",
" return total_mn\n",
"\n",
"m = int(input('请输入第1个整数，以回车结束。'))\n",
"n = int(input('请输入第2个整数，以回车结束。'))\n",
"k = int(input('请输入第3个整数，以回车结束。'))\n",
"\n",
"print('最终的和是：', compute_sum(m,n,k) )"
]
}
],
"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 |
explosion/thinc | examples/04_configure_gpu_memory.ipynb | 1 | 4399 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using a single memory pool for Cupy and PyTorch or TensorFlow\n",
"\n",
"Requesting memory from a GPU device directly is expensive, so most deep learning libraries will over-allocate, and maintain an internal pool of memory they will keep a hold of, instead of returning it back to the device. This means the libraries don't by default play well together: they all expect to be the single consumer of the GPU memory, so they hog it selfishly. If you use two frameworks together, you can get unexpected out-of-memory errors.\n",
"\n",
"Thinc's internal models use cupy for GPU operations, and cupy offers a nice solution for this problem. You can provide cupy with a custom memory allocation function, which allows us to route cupy's memory requests via another library. This avoids the memory problem when you use PyTorch and cupy together, or when you use cupy and Tensorflow together. We don't yet have a similar solution for using PyTorch and Tensorflow together, however.\n",
"\n",
"To start with, we call the `require_gpu()` function, which tells Thinc and PyTorch to allocate on GPU."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install \"thinc>=8.0.0\" torch \"tensorflow>=2.0\" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from thinc.api import require_gpu\n",
"\n",
"require_gpu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We then call `use_pytorch_for_gpu_memory()` to set up the allocation strategy. Now when `cupy` tries to request GPU memory, it will do so by asking PyTorch, rather than asking the GPU directly."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from thinc.api import use_pytorch_for_gpu_memory\n",
"\n",
"use_pytorch_for_gpu_memory()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To test that it's working, we make a little function that allocates an array using cupy, and prints its size, along with the current size of PyTorch's memory pool. Notice the over-allocation: PyTorch grabs a *much* bigger chunk of memory than just our little array. That's why we need to have only one memory pool."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cupy \n",
"import torch.cuda\n",
"\n",
"def allocate_cupy_tensor(size):\n",
" array = cupy.zeros((size,), dtype=\"f\")\n",
" print(array.size, torch.cuda.max_memory_allocated())\n",
" return array\n",
"allocate_cupy_tensor(16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also see that even when we free the tensor, the memory isn't immediately released. On the other hand, we don't need to resize the memory pool when we make a second small allocation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow\n",
"\n",
"with tensorflow.device('/device:GPU:0'):\n",
" arr = allocate_cupy_tensor(1000)\n",
" arr = None\n",
" arr = allocate_cupy_tensor(1000)\n",
" arr = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we make a huge allocation, we'll have to resize the pool though. Let's make sure the pool resizes properly, and that memory is freed when the tensors are removed."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"arr = allocate_cupy_tensor(1000)\n",
"for _ in range(100):\n",
" arr2 = allocate_cupy_tensor(900000)"
]
}
],
"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": "3.8.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
maxhutch/thesis-notebooks | Playing With yt.ipynb | 1 | 6930 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Figure 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start by loading some boiler plate: matplotlib, numpy, scipy, json, functools, and a convenience class."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"matplotlib.rcParams['figure.figsize'] = (10.0, 16.0)\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from scipy.interpolate import interp1d, InterpolatedUnivariateSpline\n",
"from scipy.optimize import bisect\n",
"import json\n",
"from functools import partial\n",
"class Foo: pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And some more specialized dependencies:\n",
" 1. ``Slict`` provides a convenient slice-able dictionary interface\n",
" 2. ``Chest`` is an out-of-core dictionary that we'll hook directly to a globus remote using...\n",
" 3. ``glopen`` is an open-like context manager for remote globus files"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from chest import Chest\n",
"from slict import CachedSlict\n",
"from glopen import glopen, glopen_many"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Configuration for this figure."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"config = Foo()\n",
"config.name = \"HighAspect/HA_visc/HA_visc\"\n",
"#config.arch_end = \"alcf#dtn_mira/projects/alpha-nek\"\n",
"config.arch_end = \"maxhutch#alpha-admin/pub/\"\n",
"config.frame = 1\n",
"config.lower = .25\n",
"config.upper = .75"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open a chest located on a remote globus endpoint and load a remote json configuration file."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c = Chest(path = \"{:s}-results\".format(config.name),\n",
" open = partial(glopen, endpoint=config.arch_end),\n",
" open_many = partial(glopen_many, endpoint=config.arch_end))\n",
"sc = CachedSlict(c)\n",
"with glopen(\n",
" \"{:s}.json\".format(config.name), mode='r',\n",
" endpoint = config.arch_end,\n",
" ) as f:\n",
" params = json.load(f)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to grab all the data for the selected frame."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"T = sc[:,'H'].keys()[config.frame]\n",
"frame = sc[T,:]\n",
"c.prefetch(frame.full_keys())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'frame' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-4-75d0baf2092d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0myt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#test = frame['t_yz'] + 1.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtest\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m't_yz'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m data = dict(\n\u001b[0;32m 5\u001b[0m \u001b[0mdensity\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"g/cm**3\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'frame' is not defined"
]
}
],
"source": [
"import yt\n",
"#test = frame['t_yz'] + 1.\n",
"test = np.tile(frame['t_yz'].transpose(),(1,1,1)).transpose() + 1.\n",
"data = dict(\n",
" density = (test, \"g/cm**3\")\n",
")\n",
"bbox = np.array([[params['root_mesh'][1], params['extent_mesh'][1]],\n",
" [params['root_mesh'][2], params['extent_mesh'][2]],\n",
" [0., 1.]])\n",
"#bbox = np.array([[params['root_mesh'][1], params['extent_mesh'][1]],\n",
"# [params['root_mesh'][2], params['extent_mesh'][2]]])\n",
"ds = yt.load_uniform_grid(data, test.shape, bbox=bbox, periodicity=(False,True,False), length_unit=\"m\")\n",
"\n",
"slc = yt.SlicePlot(ds, \"z\", \"density\",\n",
" width=(1,16))\n",
"slc.set_buff_size((14336,448))\n",
"#slc.pan((.25,7))\n",
"slc.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"sl = ds.slice(\"z\", 0).to_frb((1., 'm'), (128,128), height=(32.,'m'))\n",
"plt.imshow(sl['density'].d)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.imshow(test[:,7000:7500,0].transpose())\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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
BayesianTestsML/tutorial | Python/Bsigntest.ipynb | 1 | 169298 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayesian Sign Test\n",
"\n",
"Module `signtest` in `bayesiantests` computes the probabilities that, based on the measured performance, one model is better than another or vice versa or they are within the region of practical equivalence.\n",
"\n",
"This notebook demonstrates the use of the module.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will load the classification accuracies of the naive Bayesian classifier and AODE on 54 UCI datasets from the file `Data/accuracy_nbc_aode.csv`. For simplicity, we will skip the header row and the column with data set names."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"scores = np.loadtxt('Data/accuracy_nbc_aode.csv', delimiter=',', skiprows=1, usecols=(1, 2))\n",
"names = (\"NBC\", \"AODE\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Functions in the module accept the following arguments.\n",
"\n",
"- `x`: a 2-d array with scores of two models (each row corresponding to a data set) or a vector of differences.\n",
"- `rope`: the region of practical equivalence. We consider two classifiers equivalent if the difference in their performance is smaller than `rope`. \n",
"- `prior_strength`: the prior strength for the Dirichlet distribution. Default is 1.\n",
"- `prior_place`: the region into which the prior is placed. Default is `bayesiantests.ROPE`, the other options are `bayesiantests.LEFT` and `bayesiantests.RIGHT`.\n",
"- `nsamples`: the number of Monte Carlo samples used to approximate the posterior.\n",
"- `names`: the names of the two classifiers; if `x` is a vector of differences, positive values mean that the second (right) model had a higher score."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Summarizing probabilities\n",
"\n",
"Function `signtest(x, rope, prior_strength=1, prior_place=ROPE, nsamples=50000, verbose=False, names=('C1', 'C2'))` computes the Bayesian sign test and returns the probabilities that the difference (the score of the first classifier minus the score of the first) is negative, within rope or positive."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0 0.70922 0.29078\n"
]
}
],
"source": [
"import bayesiantests as bt\n",
"left, within, right = bt.signtest(scores, rope=0.01)\n",
"print(left, within, right)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first value (`left`) is the probability that the first classifier (the left column of `x`) has a higher score than the second (or that the differences are negative, if `x` is given as a vector).\n",
"\n",
"In the above case, the right (AODE) performs worse than naive Bayes with a probability of 0.29, and they are practically equivalent with a probability of 0.71.\n",
"\n",
"If we add arguments `verbose` and `names`, the function also prints out the probabilities."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P(NBC > AODE) = 0.0, P(rope) = 0.70744, P(AODE > NBC) = 0.29256\n"
]
}
],
"source": [
"left, within, right = bt.signtest(scores, rope=0.01, verbose=True, names=names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The posterior distribution can be plotted out:\n",
"1. using the function `signtest_MC(x, rope, prior_strength=1, prior_place=ROPE, nsamples=50000)` we generate the samples of the posterior\n",
"2. using the function `plot_posterior(samples,names=('C1', 'C2'))` we then plot the posterior in the probability simplex"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
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lsFmE0iE2GXQZ/oYT+WhTKdRTKcoTlSrJfHKVmf0WTIAKC+53z8fk67DWEhZy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\nyTFK/iIiIjlGyV9ERCTH/D/IVN79daUYnAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efde6ed25f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['figure.facecolor'] = 'black'\n",
"\n",
"samples = bt.signtest_MC(scores, rope=0.01)\n",
"\n",
"fig = bt.plot_posterior(samples,names)\n",
"plt.savefig('triangle.png',facecolor=\"black\")\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Checking sensitivity to the prior\n",
"\n",
"To check the effect of the prior, let us a put a greater prior on the left."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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coEzAXd1zeePih1nYVuKG++r45QkxTiT4C/E8fOUYWNsHX7m63i2ZPFRuFjo3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\nhCgwEvyFEEKIAvP/RvQ6NlCkpgkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efde6fa0e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"samples = bt.signtest_MC(scores, rope=0.01, prior_strength=1, prior_place=bt.LEFT)\n",
"fig = bt.plot_posterior(samples,names)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"... and on the right"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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9HevsDQJA50pO22Uh89/yOPt93SHqWT08X5AQ24D0/IV4A9I+XHUmfPwaCfxD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\npQqMBn+llFKqwGjwV0oppQrM/wNqLrwnzsHCWAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efde2071320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"samples = bt.signtest_MC(scores, rope=0.01, prior_strength=1, prior_place=bt.RIGHT)\n",
"fig = bt.plot_posterior(samples,names)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The prior with a strength of `1` has negligible effect. Only a much stronger prior on the left would shift the probabilities toward NBC:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8wUE48SB/vS30upDYw+K6vko1FJcFMBEEWXKLH4R8D4ntTyPsXEjU8Vr8eEPU\nuYjcuvnFyoSlsmgN/O+v7AbQ/b8xEJIQo4EU+RHiLfgu3H8BfOnndqPfWKTrZqBrpthRsuPjTtwb\nd9we9vx+mMWpn41ONRGufpqw/RXcxm3jkXzeJn4VJ2gT2sRvojdnwiBufBAZ7AZC1+7yD3IDMweO\nB+magee6yXiFQIFn6wzYKf7QnjRIVUPKPt6p3QpduxXKS6OAYMUT6Joptntg9Xjclp1xW3cl6lla\n0kJA/1wIp+8H1cmx+9+HGJtk5C/EW/jK0TB/Bfz8yXJHMnzCVf+0ZXeViz/jCFD214JONhG2vxyv\n6Su8aYfYDwQ6/rWhHJuI3UG/RoyJPwDY59gZADNoaSBCxSNyox3sjn9nYN1fxTMFUWBnDcJ4W5LS\n9sNAGG8edN14RiGeKdAuyquyvQeynXgzjkYn6sgtuA9/2iG2kBCgvGoMq0r23hkDZ8yFJ74Ov3sa\nFo5s40EhNpkkfyH+g12mxQ1dvlTuSIaf6V8DXnVxRJ979W5Mtgt/mxMAZY/QJeoGHp/pJL/0L7bs\nb5zM8VO28k2YxSb+mNaQqINkOj7SZyBRZdf80w32ofmsXeuPAggCqB9nH9dnGwKRrLJLDI5n9w7k\nM3bWIAqKO/5NkMX0rwUMOlkflyPuJfvyz4qnEkymvRiWP+s4lF9D9uU7Icxs8nv32iq4/Ndw/dlw\n8Ddl+l+MDrLbX4gN8By46Vz4ws9gZUe5oxkh+R6y827HZDvwZxxl1/e9uG1uZEfcUda+GcpP48/4\nEGgH07+GsHdFPDqP1/KVslPyNbYoEI5rE7fWGNfDOPHjCmv69kkQBkAEnm/3BhSWCAqPUSreMxAv\nL4QDHZWUl8Zp2Y3ETmfZZQET4W9zIrpua/zZJ+DP/jD+NicOPD5RB04S5Xib/dZddb8tAHXuQZt9\nKSFGhKz5C7EBXz4aalPwxTvLHckIi3K4E94NToJo7Uvo+hn2eJ+Xtl34nIR9XFUjJKshyKO0j3ar\n41F5ADXNdmkg22NH5V4aMl12tJ+otsk7jAv6JNO2AmCiamDav3ESoKCrzVYANMZeJ99vX8NP2eRe\nWEoYdNZfN0yHRA12OcEmdZ0eZ+NWGuWmbXXA/tWEa18iXPsi5Df/7KYBHn8Vbv2EXSLqrIDqj2J0\nk5G/EOvZYQp8+v1w7o3ljqQ8svNuJzvvNkyuy3bWg4FNfRCPvt3i8TrAjsQLHBeIKK75aw1RZEfY\nWtulBSKiWPmyAAAgAElEQVT7Xe3a+7RGOY7dD1A4xx/mBjb8DS7mo+JjfvHz4jsH9gvYgAYe7vgM\nbmGi/Br7lzBbksRfMG8ZfP/3cN1ZJbukEMNGRv5CDOJo+O3/wLd+U7mtW1WqmcQ2J6D8OlS6CeWn\nibqWESx/HKdua5t4c/Gau3ZssteeXYdP1tjvuZ4d1SsF+X7UuK3tFH2+v7hpTyWr7dn+wqg8kYR0\nLcp1SValCZVnn+P6UNcCXgLVOB7lJe3u/0SNnTHI9ManB+ITAVEEmLjCoBtvClSY/jaC5X/Dnbg3\nyq8j6lpU8vfuiVdt57/IwNOvl/zyQpSMjPyFGOQLR8DaHrixtM3gRhU7Re7gNM1BJetBu5h8T7yR\nryAeSSs1aCe/iUf2hfvjaXwDxksA9ghgPB8QT/M7dsSv7W5/5flordGOW7wMjovSLsrxiiWG0c7A\njn8Vx1OoL4DZYHxRlLM/g9Jxz4LSCyM4fa5t+TypcVheQoiSkJG/ELFtJ8HcM+GI70FXf7mjKTHt\nk9ju4zhN2xO2vbDeN4vp2HKTOE3bxVPveYgidFUzUe9KnOY5Nqm7CYoJ1oTxVD9Q12pH88bYs/1R\nBDUNqL7uuBiQQvs+fmMz1c0NRAa0q9Guj59MkEh45DrbiZTG9PeQamnFr6klzGVJNTXhuC5BGNkT\nAb3r7L6C+onQ3wWuj2oYj0rHiT0MbIzJGlsyOOgnXPpXonWvEq5d/z0ondVdkPRt69+fPT5sLyPE\nZpGjfkJgl6xvPAe++ktYsrbc0QwDpW09/rhjXrFxzuT9cRq3AwXBsscI255DOfE6f+HMmrajZ2/q\ngQM7+gvn7ZWyyT6MBvYCDFmfJ556z4BS8Qhfo1wPx/NQKmsH7Y6KZwCIp+1tTQDH94uNc5TrokMz\nUCcgigv+uN5ALQE3PgpYmI0wFE8U6GQjiXdfGC892A8sweKHCdc8j92jUDqX/xr+eSl8/D1w699K\nemkhSkJG/kIA538QtmqBz95a7kiGiQkI214kbHs2XgO3GdVp2t5O7aMwmTZUohZv2vsx2Xizn+PF\nlfWMHe0nq+yIurrRJlCl47K98ZKAdkBrvPoG/IZWwsJ6fqoOMj2kG+pJNTbT35sh25MhVZMmyAU4\nrkMilUA7DrkAEqkkdePHk80G5PMR2vPIdHWTD0IaWhoxjkOIi06m8WtqiPr77AePnnY7C2CM/SCQ\n67MbB4O4HkBhn0KxqmAKb9K+RD3LIOhj8MbAzREZ+Ptr8NNPwG2PQc+mlxEQYljIyF9UvFnj4cIP\nwV4Xj/ECLWEGlWrBn3UsUdcb5F//A/lF99vmPShMrgtn3B4AGBOiCrvqCwrr5xBP85uBAj/Fx9j/\n0Y6Dch1wPVRg7PE+E9nRfrxEEEURjtYYDI6jcbRDGIUorVHKXsPGAo4TNwaKwHE1hVMESvvoQiIv\nnv0P43oDhdMJhX0B6wlyxQZB3vQPoNwE2RduhqA0az7PvAFzH4Ifnw7HXFmSSwpRMjLyFxVNK7j3\nfPjRg/DA8+WOZvipRB1O07YQZgnb58fr+lnbinfqQbgtOxKs/BdO/cyB43R2Lt5eIAog22cr8kUB\npGpRTZNwG1oxqTo78jYRyvNR2iHs7YRsP2hFomUcfnU1blwOuLqhBu1qGhpriYw9adEyro76xmrc\nRIKOtg5Q4HkejuuSrqtmwtRWIqMIgoAw0we5Pry6OvJ4tiaAn4ZUnZ2hcN2B2YlCGeDCBwUvAY5L\nuPoZop5lOLXTQGnCNc/FpYlL8ynwsflw0ZGwrhdeWFqSSwpREjLyFxXt04fYkeUP/1TuSEaG6V1B\n9uU7SMw6jsRO54AJCVY+hTtx7+Jj3HG7xw822BG2svX+jYnvM/a+0K7xK8dBua5d949r+yvtDH08\nCkdrtOtgjEFphXbiEb6rUNi1fMfRaK3QWYMxBl04PYBBOy6u56KyQ8/8K+KTAgr7YcWx+xpMGA6a\nuRg0i1H4MwpxW3ejMC0QrPo3Tv0M3In7Eix9xBYA2ky5wO7+/81/w0Mv2s2AQmwJZOQvKtbWrXDr\nJ+HI70N76Wq9bLmKZ97Bbd2lWBDHZNrQVRPiB8XldP0UJt9tG/n4KYrn5jHFXfu4dvRscv1E61ZC\npo/kpK2JwgDH90nW15OoqyUXOLROn0wYGkxkE38UhpgoYvLURro7+8lk8mhHk65KUFebpLOjn5q6\nKqprUqRrUlRVJ6mtt6WGe3syNDZWUdtcj/JSpGrT9Pf2Y1CkGhpxq6oJwggV5OPKgJniqQU7kxE3\nERq8MTHKoasngleDclNEvSswvStL8rYvXwfNNXDye+Cuv5fkkkJsNhn5i4qklG3EcvmvbWOWsc6d\nfABO07bkF/6OqHsx2Zd+SvFMfBTgNG4LbmpgtK603QgY5QCfDY6ctRo4ORCGKO2iXRelFErFo/q4\nCp8T1/xX8XNVXJnPcbT9QKBUcdTvOApjsDMAji7OAGitivV7tKMhMmjXHVjzV/bEAEqjlMaoQbEW\n6wAMMuhrExmU46BTTeQW/QHTWdr+vJfcA898E47bE+7+R0kvLcQmkeQvKtK5B0HSsw1ZKkKheY12\nbDW+QsEeJ0li+9PBBHYd3Bi7mz/IgYnIL/w9evxuODVT7Hp5osruokfZGQCtUIkUTn0LYaaPIIzw\nGloJMhl62trxq6qpbaqjvyeD4zo4rkNdfZrm5hSZTMCK5Z3U1KWobajCcRS+79DXl2Pc+GrmTK7n\ntcXrWLqqlyAI6OnswU/41NancT2HRMLF8xzWtvWQSCTo7+0mzOdtfIXNicnq+ChgvOYfBJDptn9X\nDpgsJtMRn3iw/EnvIbt+8tdesbmRXQJ5Z/sCsnnb+vfuz8LDL9lCUkKUk1T4ExVnn9lw2Qn2l3E0\nlnf3DxbX4FeJOhI7n4c382h7/6Ad/VHP8vi++DlK421zHE7N5IHHxn8qrVCua0f42DP4hZMA2rHr\n9MYYIhOh4937Azdw4w8CdpIhXueP/7RH8xWeq4szBRhDFEaY+Ho67iWgnXjErwr7Big+3v4Idhag\n2JtgvdMLwdoXUYmagecU6gVAsTGQN/2DJHY4E5Ueh/JrSOxwRtzq+J154lW480m467Pv+KlClJys\n+YuKc+/50FILE+rhxWXQ1l3uiIafrp+JTjWhUi0o7WLCLNHaFyEKCNuex2ndGe3X2XXxVC04vj0j\n77h2vdxPQ02jnTkIcqgohDDAq6nGraohn8mD1kRhhJfwaWhtYOK0cTQ2VbNm1Tpq6mtoHV9DU3Ma\nrTWdnTkmtVZRV5/CcRWtDSkmNldTlXQxCtJJj5XtfeTCiCgy5PIR1XVVNLfU0NCYRilFJhPQ3ZUl\nnw2oqa9G+QkaW+tJphKEkSJVX0+yro5c23JMX6ed0TCRrUlQPw4yveiq1kHJPj6+GOXQ1ZPwph5A\n1LMMXT0ZlagjXPcKRHmclh0hyhO2bfzxEK3go/vCOQfZSpLzV8JLy4bn31qIjSHT/qKinHmAPf01\n+dPwiffBI1+xR/z+91fwamn2d22RgsUPEiz7G/6sY8HxCRb9sfg9p3GO7W0fBkBEsORvuFP3H3iy\nMXGlPgcVT3WbQbv4C+v3xSQK6HidXysTHwCwI3vHcdC6sJM/nkFQCq0du9aPLu4XsK+g7Ogee9bf\n1gNQmPi+wsSNivcfaMchisI4XGcg/sF/Do61cIyxMANiAK8KXVddbAiUX/hb23wotJV6si/eNNDN\n8G1oBSfuDRcfA2u64NwbIIjgjk/Bn1+wRwCFKIdCFwwhxrxJjfD0ZXDgZfBifOa6JmW7sH3uMLj/\nWfshYMFY3QDoJOy6tYpb7hoDJsRp3hF38n72Mdp2xzOZdSjl28doB9L1kEih+jrBTdhfGo4LqRrb\ncCffR2rCZPo6e6lprEU7mkTCpaGphv7+HL7v4roOQRDi+w71dQkSnsuK1b1su1UDVSkXrRRhZOjo\nzbF8TQ8zJtVRnXTpzQT0ZwN2nlpLEBkef2UtmXxIJhvQ1ZUln4+IQkNPRw+u7+AnfHK5PF3tXUSZ\nPvDiDYt9XZi+Lvv3/i47mxHaGQtMBJme4lFFtEvUucgmfhPFyT/7H9/a9SkFx+8FXzvWJviv/RL+\nPOjk4A9Osf/tnX5t6f55hXgnZM1fVIy5Z8IP/jiQ+AG6++Gb98HM8+3I/4lLbI3/6S1lC3NYuFMO\nJrHDGeiaqaA1iZ3OIbHjGYAibHsek+sdMjJWbtXAk5UaWiFPrX9fYYxOcV0fBi7nOE5xrb9wv1OY\nLRg0K1B4jI4fqJWtDeA49ua7GtfR8V6+wusMvF4huCg+HWDiNXyltJ2diF8DrQsvHP+98ENZ+UX3\nk33pNnTtVPztT8Hb+ggSO5yOKh6HBAr9D9ajlN3R/+zl8PkPwOdvhfd8fWjiB7jo57DfHDhs5w1e\nRohhJ2v+oiKc8l54/w5w6rUb3uSXDeDRl2HuX2DbiXDd2TB7PDy3GDr7Rj7eknASxV3uum46OtVM\n1PEqTuse6FQjEBfD6VlGlO3EaZgFJsLk+1GOF5/nZ2CjnNIoPzVQGz/dYNf/u9ugr4e88lCu3SRn\njMH1HLTjEAQGz9O4rmZCc5pxTWl8z8EYxfbTG8gGEf3ZAE8rJtQlmNKYJJlM0N6TZXZzimpXMW95\nNz25iGyoeG7eamqrfBpqkwRhxJrVHTS31DBtqwbSVQkSSQ/Pd+2HnHQa13fxEl68J8EeK0xNmUWo\nXLvUkc/ZM//JanBcdN00wpX/srUQohzkeu2a/9qXId+DM34v/K0Ph6AP07/GvkUKjtkD7vg07D0T\nvvQLuOhOWLB6w/80+RCeXwI3nG3bR2c3bhVBiJKRkb8Y8ybUw3c/YiutBeFbP7azD75+D8w6H1Z0\nwL++AdeeCVObRybWUlHpVhLbn4438ygAgiUPkX3hBqKuNwAzsNhnIpxxe+DPOqpY9Edpf2ANvDhC\nj9f641uhWI5SOr6jMMqOz/EX1s8HxxTf5WiNE6+5O1qj45kEowqjfXvW3677K9z43H9YKDio1aB9\nBAOzBIURvh58esBx7I7/QkzK9hJQ2hnaq0BR/JCj3DTOuN1sQ6AwsF0L/aqBtsWFY4Pxe3Lk7va/\nky8fDV++C/a8GP7w7Nv/G/3lJfj9s/C9j739Y4UoNVnzF2PevefDs2/A1+5+589trIbzP2B7s//i\n73aJYGl76WMsNdvA5ziinmXkF/5m6DedBIntPo4J8+QW3EdimxOhpgVQ0NteXPNGaaiqj0fHGZv0\nvCRke2H8LJxkEu1oojAkymSgr4Mpu+5EPoBkyiOd9kinXBpqU1QlXdK+Sy4IMUDSc+juz6OU4qjt\nWlm4LsPSrn4w8PzCtRy++0Ta+gIm1Pg0pF3a+/IsWJulOmE/bPTlIxpSLo1ph9fbs8xf0cVWLTUs\nXdNDe0+W3p4Mq1Z00dBUw9L5r4PrYcKIKJ+3v/C62uxpBkP8ocfYn1G7djNfXBnQ5HswJoeunUZu\n/j2YzkX2PdQeR+yc55Lj7Ka+S+6BX//rnf871aTg+W/BWdfBgy9sxj+4EO+QjPzFmPaRfWBGK1x2\n36Y9v70HvnIXbPM/dlbgmW/C1afCxIbSxllS2sNkO8i+cMObE79y7HIACuWlbeKHeBRbGNEWHjt4\n9L9edT9Mca1eaydOoPEofPDIPB7J6/XW8wfvDYgMeLqwfg9BaGxOjmcDADxXx9ewJwSc+Hy/52o8\nN76Wwt4Pg04gqIFZCFWIWw/0LVCF+9ev/qeJMmtRTgLtNxAs/wf+Vh/Aad6RD+wM/7gkz2UnwDfu\nhd2/smmJH+yek3Ouh+vOguoNbyMQYljImr8Ys1pr4b7z4cSrYcnazbtWf85u2rrpUdhzht08OKnR\nziiUrVe7VzVQda7A8Ulsfypuy86Eq59m/Yk9f85HbeMeE9mRvTEEa19E+/V2JKywo183Yae7c/12\nVzzG1gDwk3FN/AizbhV+yifV0IB2XPy6ero7+0hVJQnDiK6ufnp6c2jPIRdGrFrXRy6I6O7LsWJN\nL9tMriWbj5jTWk02itAObNua5vS9t+KVtn58FxylyEcm7vqnmFbvkwvs5sJ1fTkWt2dJei5Tm9M8\n9uxKuvryJBIuyaRtAlRXl6CjI0NNQy3V9bX4VWn8VBKnqpYglyv2McDxBur/+2nbmdBN2w9LQNS9\nlEN2CLjlw//m8J3yXHYffPZWeHn55v8zLlgNO06BA7eD3z+z+dcTYmPIyF+MWdecBjc9Ak8tLN01\n13TBBXfAdhfYTVsvfBuuOBnG1ZXuNTaGO+VAEtudgm6YPfQbxtjEXmjCU3xC2ib7KABjyM7/BSbf\nC9rBbdnJPkZpm/h13MlvEBUnwcEVAUFhgrA4iteFRkEmKo6mBx+xV9iNgCo+0qfjNfswMjiDXi7h\nOrjaJvvBVQGVsa9v1/Pj58YvoFWhJ4CtK1CcJUDhuE5xxqBwSkBpHVf/Y72fKxo0GwAow8FbLeXR\nT/6b77z3Aa5+bl92/8kh3P109ZCyAZvrv2+3ewcO2K501xTircjIX4xJx+0JJ70bPvJD22m21Hqz\ntjjQT/8G75kNc8+yVQOfeQP6Nv44+CbT1RPRVeOJ1s3HZDsGvmEiwo4FhCufAuz5dFU1gcQ2J+BU\nTwE3iXISRGtfwmmYhXLiDn1K2VGw4w30Acj3Q20Lydbx6HQNkZ+GVB1UNaCqaiGKSDc2kG6ow/c9\nklUJejp76F6zlpbmKiZOacIAq1Z1k82G1NcmaapLsv3EWg7crpnefMTanhwd+YCWap/JtUl6cwH3\nvbiag7ZuYkZ9FZNrU3T05XlhZQ+1SZf5q/pZ3Z1jx9Zq5rRUMbkhyTZNaerTDol0gv58SEONz/i6\nFCvW9hNGBj/pk+nP4TqKVFWCbG+WnrY23NoGov5eCPIDpX3DfNzXIOSAqcu44bA/ccw2C7ny2f34\n3J/fy4ttLWCwLZDDLKavNEUhsnlb9e/Hp8P1D9sPlkIMJxn5izGnqRquPsXW7s/m3/7xm2NlB3z+\nNtjhQvBdmPcd+PZHbAvX4RQsf5zsc3OJChvQYu60Q0ls+zF0w0xI1JPY7hT8rQ6zR/jCTHG63516\ncHFK2+S6AWUX3wcP1QffCuv+he558YeFKAxRDNxXGHEH+bC4477YXK+QXw34jj1z7yg7O5APIjvh\nEM8EFM7vKyDhaMJCRcA4hGwYxTMDAzMErlb4nkMYYesBFN8V+/0oiuyeg+KNuD9BHF+8T+C9k5fy\npxN+ydXv+zPXzduNXW88iV+8MocwCMm++FPCjlcBgxlc9EdpuwyzGX73NDz+KnzznbcNEOIdk93+\nYsy5/VP2mN7/3D7yrz2pES460s46zP0LfO93dtPgSFDpcfizjrNf5Htti94wh4ny5F6+E0w+PgVw\nLFH3ElSqCZVstN38sn0D+wASKUhUQ18HOB66ZQrGS9qOeCYkUVtHti+Dn0wQhgEYSKZTVNVVEYYh\n06Y1EASGTDYk4TvMmFzLyo5+Gqp8qpIezdU+k+sT+K7ioOmt/N+jC2mt89iqKU215zCttgqtwI/b\nAPfnAjqyAd35PH1ByLq+PFW+bQqUjwwNKZd1mYBsPuK11X0s7+inPxeSzQX09gbk8yEd7T30dvfj\nupqa+iqWvfI6teNa6Vmzhig04Prs0/wKX93uPqZUrePyp97LHfPmEEZgetag3JSdGTEh2edutNX+\nzMDw3J91HCrdSu7VX2H6Nr1OdEOVXUo64Qfw2PzN+s9BiLckI38xphy5O+wxHb56V3lef1k7fPpm\n2PXL9hf5/O/BN463fx92UR5MSNS9GJPvBhOSe/WX5ObdZjfwuVWYoJfsq/egayaj/JqBITnYv3up\ngRK3tjUfJk7wZvAZ/uLm/7iSXvFrRRSZ4vn7KLJr/I62Z/aNGagVoOPrVPkO2XxcR6Bwdl8VugjY\n2QQ3PjHgFK4Vv2ShYFNhdsHRdpOg7QwYVwNUKu4poO1eg3hGAmPQrsO7mxbw2/2v4vq9fsrPFuzO\nznd9ltvm70Jo7GNUorChQ4Fy8aYfhvKqh7z1JuiP91ps3lTTul741M22ymTK36xLCfGWZOQvxoyG\nKntm+qRr4G+vlDsaa1qzLf5yzB7woz/BlfdDx3BWDFR6oAgNtmmNqpqAP/Nohv7fXcU16z1omgId\nK+0ufq3tmrefgnQddK6CunH2a7DFcdI1EBkSqSSu78aFdzRewiOfzVNdU0VNXYJUysVxNDVpjxPf\nNYlnlnazrCNDc7XHDhOraesL2X9qI+uyObqyeXxHk9CaxV0Ztm5I42lNY8qnqcqnKunQ3pMnCCMe\nW7KW19dlyYWGpKdxtWJ1Vw7HUWTyIY0pl+qEw6L2fhrSPqu7MkSRoa0jw/KVXfT35th223Hkn/st\nX5z1S2bXrOJbT+/PbasOJehsx3SstB+WtBMn9HDg5njF7oDB8icIO16DfPcG3vvNc8en7QmVC+4o\nyeWEeBMZ+Ysx48qT4e5/bjmJH+CNNnuOe8+v2iWBV6+Ai4+F2tQwveCQ5BP/Pcrb+00Y/1moa1/Y\nwY/9u9ZxZz9swRuITwgMTG+bQdc3xmCiQlU/UxxlhyYijMvoKmWjCCJDwrNd+fKB3ekfRYZcGI/v\nld0L4Mbr7oWtB0EU2aqAhR3/SuE7Do4eGPXruBSBnRmwSwGOo4p7DAqr/26823/H9Hz+t/p8btnz\nx9y3ZBd2/MVnuWnergTKHfiZi7MfNhiT6bDVATPrij+/07Q9ie1ORtVMtUssJUr8AJ+5BU7e1x4r\nFWI4yG5/MSZ8cBc4731w3P9tmTulO/rg1/+Ge/4JR+0O15wKCQ+efgNyw13XPegjXPUMUcd8dP0M\nlPbA9ezoNsxDXydUN2CH8I4d3boJm/zcxMB1lAITohJpu85flSLoXocbZhk/fRK5XES2L0MymUBp\nRSYT0tOTJx9EbD2hhuWdWdK+g+c5tPUGTK73aE0neWF1D2+syzKuNsVe0+pZ3tXPNk21zF/XzZLu\nDC+t6WZZZ4Y9ptVz//xVTKtNsfvEenxXsbo3x4zGBK6jaK5y2WtKPWEQ8eSidXT2ZpnQmEY7msUr\nu2nue4avjruKMyb8lj/mjuS8p8/mX91zyOskJggw61bYn71Qz8BP2hkP10eFIWAgzNn3L35flePj\n1M/Abd2ZsPP1+Cjl5n8I6MvB4rVw1cfhhoeH58SKqGwy8hejXm0KfnIGnHndyByz2xwLVtk2rvtc\nArPGw2vftxsEh7u6m7fVIfhzPhonp+DNu/qjIE7uhRHvoH0AMKQokH2uwRiDdj3CfIBWhfG13VU/\n+Ax8FBkyuRDPUQTxTEEUGRSKwET4jq3e15sNcB0NCkJj7IkA7Ig+E0YoFL6jiIDIGKo8hzAyxbP7\nBkUujEj7TvFrMEwx8/hm40VcPflr/K17d/b5+w+4r+9oIidp9wVoDdqxdQRgYEakOEMy0N9AedXF\n90Ql6+1sQJiFME9i9vH4hYqJJXDX32HeMrj4mJJdUogiWfMXo951Z9nR/idvKnck79zsCfaX+/t3\ngCvuh2sesDUESs2duC9O847kF/4GZ8p+6PS4uJe9axO769iuRyayyc9P2S53YE8DeCnwEjbv9Xfj\n1jUyZetxoDTZbEgUGXq7eklVpVBKMXt6IztOr6epyuUfizpZuqqHA3ZoJTCK3lxEla+Z3pygJ2eY\n2ZAiF9riOlWuw7pcnp1bGnhoURszG9PUJRwMhpktNbTUJljVkeHZlZ305UN8rejOh2xdW8XKvn7+\nuqCD+rRLc9pj3suPc0rqBrb3XuTu3Cnc2f4Blq7KsWLZWvyEj+u6OK5Dpj9DpqeXsK8f48a77Lrb\nINM7UAGwMz7PX/igVPx0Y8gv+iNR/xoS25xI1L+G/IJNrCW9Aa218Ny34IPfgX+/XrLLCiEjfzG6\nvX9He7vwznJHsmnmr4CTfwQHXAa7TIMFV8L/HA7pxNs/9y1p3+7mB1SyydYFmHcruEl0daEvfeG8\nuwblDR3ZF2vhY6fBC1X7Bs0E5HNBsVveQMl/O7LvzwTkQ+Kz+bb6Xn8+wonLBURKxWUFDLnA4Ayq\n1oexa/0pT8fPUUQoG4axZ/iTriYydu+AKZb/1VQlHOoyL3Nkx6e5vObzvBDszH8F9/K78COEOmmr\n/cU9BGw1wHjPQuHcf+FnKXT7M1Gxg2HxNmhaw2Ta8aYfhtO8HblX7ypp4gdY3QXn3wY3nQueU9JL\niwona/5i1KpOwv0XwLk32OnR0aytG+7+B9z/LHxsX/jeR+2GtmcXv30b4iGURiUb8Gcegzt+D5ST\nxJt6MCrZiDd5P5za6QMlgCHeBBgns6p6SNbYkW4iBSg74nVccFxcP0Gquop8JkvUvopA+wTGIZ/L\n07G6nf41q1GJJFtt3UQ+grbODF35iOVtvWw3rQmlNa+v7sFXMKslxcOvrGNVV5ZHX17NpNoU+2zV\nxPTmKrr7A15u72ZabZrmtE82jFjVl+MfSzuo93wcpejLhbhK4zsONb5HaAxB1wucHH2L9/b/iH+q\nQ/he/9fRkw5gVXfEG8u7yAcRyZStYOh6HlEYobQi29VJvrMDE4YoE0HnGnRdE6Sq7WbHnna7ATJd\nb6semsKHIQVhgErWoNPjcZp3IOpZBvnSFnZ4fond0zJrPDw8r6SXFhVMRv5i1Pr2SbbZzgPPlzuS\n0nlxqS3wcui3Yd/Z8NoV8F+HQtLbuOd70w7B3+ak+CuFrtsaAKd+a1vwJ+hfr3pf4ZlmUD+AQSuB\nyoEwhCjCRNHAbIB2CPN5WzVv0MjYRBHZbICOu/Pp+Gx/Nm+XBnzPoT8f4joa14mr8rma9t4c+TDC\niUfcWinCKCKIInu2P672F0SRLdnr2JE/QFXmVXZefB4fXPYR2pM78MT2f+ex1JlkTNK+pqPxPYd8\nXHVwSNcCA47nUZgFMYDRGhPE5/W1HjQDEhb3AwSrbBs/lai1FRKjwN7C4dl08v/snXeYXVW5/z9r\nl9ygfnwAACAASURBVNPmTJ/0TigmSBGkXEAQC2IXBLt4QZpe9arXiwURsWDBrvxUqgLX3kVsCCIt\nBCkhoaSTnukzp++y1vr9sfbMZJIQQsjkJJP1eZ7zZGafffZ+zzmT/e71lu978Q3wvpfDEbPG5PCW\n/RC78rfsk7x0Pnzi9fCmb469hG896CrALx4wNzfnnmxudEJpIgE7qvwW6TachonEGxfgNk43K1Et\nEX4DOqoQb74ft2Uuo0bYCsxqXyko90EUmBsDPw21IrRPh1QWrRRRrEk1NJBubSesVKkOFqj29qLK\nBZzGNhpbm2hsziIcQVNDiqZcinIoeXxpNzWlaG5I09GcpRIpfNflpLktNOUzzGzP4jmCSk2S812O\nmtZCMYhY1lsmVJoDWxuY1ZIl47k82VugEMZsXP8YB6//NC8euIqVuVfQOf8GrllxIHesLuG7LnMn\n5gBobUjRXwrZ3FWkOFhlytRmqtWYwd5BcvkscawIw9joG0RVUpNmoKVEBxXj8P00eCnzvOuhgwHc\nptmoYBDhZRBKEiy+FjW4Co02dRQq3K1/D8WaiQ596a1w/V0jbY4Wy65iV/6WfY5cGq4736yGBsdS\nMGcvYNEaOOOb8Iavw2mHwfKvw/teYeYIbA/ZuZDgsWsgGgTHw8l2EK/9B6AQ6Wb8Wa9iS43+4Ry2\nSiYBDrX2abNCFo6X9PyLobF8pu9d6y10AkaU/5RKHCkQRtJ0DwrwfRcZa4IoRiZ3L1prAqnIeIJq\nJAml6enXGiKpjF6/K6jFcni70tAareGYDf/NecW3s0HP4t55D7K49QOUVZYJ+RSxVKhkXw1Ipcn4\nLqmUh4zlcH2C4zrEUYzruWY2QTIDQMsYHAcxlPcXwjh/YeYfCL/ZTBfMtCTPZXBnnELq0HNIv+Ad\npOe9E9znW7SxLT/6F3QOwsdfv9sPbdkPsSt/yz7HVe+A7iJcdWu9LdlzbBqAn94Pdy+FC081ksGV\nEB5bu8Uq0E0jUk0gayA83PZDEV4WVelEBwM42YkjhWxCmF5/EpW/OByZ6JdthMZ2RKkvUQH0EH7a\nDMEpDSDXPUmcbYU4JtvcRK6tjXRLKw3NDfgpn+JgFa0F1SBm9bpBpNIcNKeNSW05KmFMXymkGkgi\npdlcCHnhlDyreqqs7q/i+zAhl2ZJd4ElXSUObm9kYj5FfxAyUW8gv/QSDu/8AhtyJ/PH1m+yNnsy\nqwZj/vFkLxvLIa7nMG9Knnza4aE1g0xoSrO6u8KMjgby+TSlULF2dR+lwbKp9K8EOK5DKuUT1Wpk\nJkwh7NmIl2s0lf86KYhMpI6JA3OT5DjgZZJUCajiOpyGCaAlOiojexbvVtGfIe56Em68EG5bZMZL\nWyy7il35W/YpTjwYzjoWPnxzvS2pD/9eBa/7Gpz9HSMZvOzrcP6pplMvfchbSb3gbYhsBzroN3lp\nFaHDAnLTAwSLrzFpAFkz4eygQrj0d4kevRh2ZKhkZU7SKy9j0xaohdH3d1yIQjQQh9Hwin/L6vko\njE0lvtbJ1D5JJBWeK1Bao5Ppf1prBqoRuZRDNZJEUhstAMB3BYNBTD5azxHr/of5S15G2ZvC3w66\njyc6PkooTCuiEIKUJ4ilIowVQaxIew4OZtXvOYJqKEl7AtdzcFwzP8B1HBzXQcYKx3VNNENrhOuh\n43C03kEUjlT+A8PDBZKfvYlHgXYgDgif/L+Rz3I3s74PLv2FuQFw7dXb8jywK3/LPkM2Zar7P3IL\nPLqm3tbUlw398H/3wsKV8IFXwuVnQqEqeaKng7BrMagQXd6I7Hp4VPW5P+0kcNNoFRl1urYXoKMq\nwvXNDUEcAnpkyl++xVS5ByUo9ZpVsJdGhFUAZBShlSTVkGNg/SYyjXky2TSVcg0Zx0yf0cpBM1pY\nu7lIPusTRAoQZFMuzbkUKzcWUI7LibPbmT8pz0AtIuUKuqohnZtWcMLAlziq63KKzSdznfcV5ORX\nc/+GkIa0h0DQVQrpLEZMac0wvSVNMZSs76+xpq+Gk2j+B7FiTWeRzsEaDbkUM6a2MFiWvPzEAyjX\nYoqFKuViFSeVprG9iUA6qIEeGOiGoDys5T+c9lDSdEnEWxSbaLNNVbvR5c2gIkRuopmuuJt5+Gl4\ny/EwsRnus5P/LLuIvXe07DN87ix4aDX8/qF6W7L3cP9yOO3L8O7vw9sOXsKic2/iPf9R2u6q0J16\noslFK4nsWWI2CmGm1oktetmHFe5UUgsgTdW/TvrgMaFwoZSZrBeZ1b9wHGQsTQeAKwjDGClNlb/n\nCqqBxBFGnc8EF4zefxBLOksBvnCQGtLBBl7aexmXq7dS1E3cMuUO1ky7lMhtNfl7T1AOY7xEz19g\nVvhBLPGdEc2BoUjE0JRBrTVxrJJBRFAsBeRyPsJz0DqJXChlpgp6fnIMNfw5GenBaETrQIiRlr90\nIyoq4OSnknrBO/BmvoLUQW/GnXDEmHzv518L//taOGTKs+9rsWwPu/K37BMcdyB88Wx4/dehunsL\nqccF63rhpntMROQjp8Mn3wj9FViyLglcezlSs02xn47LuG0HJ84Lo2EPptgv25jUANQgnTdOUMXm\nBiCTN33wSiImTh+W/PUbGsjkG8i3tVAt1fB8HykluVyKhnyGrr4q5UqEFtA/WENLyGY9itWIae0N\nPLm6HyUgrm3guIGrOL7nMnqyR/E99SVu7TuOtJ/n4I4GDmjN4giHB9cN0tbg010MKdYk/zG7mUfW\nFeirxCzfWEAqaMr5KA2DlYg16wepBTGDA1WKhRoSqJRjVq7uohZowiAkDmMcR5BvaaJcqqB7N6Bl\nlFQf1ozT91IQVpIbAI2OKsjeJ3GaZ0AcINwsOiqBrKGqXTiZNmTv4+hwcLd/34MVKIdw2ZtMIaAt\n/rc8V+zK37LXk/ZNjvNDN0Hv7tVPGXf86yk49Ytw4fWmMPCJr8LbTwBHh+hgAF3pRA6sGnHoW4r8\naDUy1W9o+p8yPf64QythUwtAnEgDo03ofyjf7whkLHFd01cPZvIfQBybPnsplTmV0oSxYlq2nzfL\nL/Oh4huISHFF6g/8s/ESQr+dtO/QUw4Jpenvl0rTnPUo1mJyKZdaLFFAyjNzAFK+QxhJtEo6EjSk\nUyZq4XkucWxmBDiOwHUdpDS2ktQtyFga3YJ0A0n9v/k8/GTE73CHhIkIeFOORscVhgoAhPBAuHgT\njiB6+jZUce2Yfdffv90MhfrQq8bsFJZxjNX2t+z1XPlWOHgynPXteluybyFyE3jZgQNccaaktUHw\nuV9LfvEA+AeeZfLRgA4GEQ0TzAtkksNOZU0le1gGdKJsVzMO309Dvg2EINPaTtzXiRCQmToL13Wp\nlqv4aZ+GfI6mliyNjSmk1GSzPqtX9jBxUiMNDSkGBiqEm1Zx2Yv+wWvyf+VB9w3ck7uAVeVmHCFY\nsbFAJu3Rmk8zpSVNRz5FECk6ixEzWtOUQkkUa6qhpLcU0NGU4cUz8tzxZA++75JxHRYu7aFUCjnh\nyKl0DVYolEPCUBLHilIpolyoEMdxMpKY4aLFcncPIpVBlwag1I+OqknvfqLpH9cYWTdpExFAQFBG\nR0VEpg2UJFz6szFZ9W/J3Emw4Ao4/nIzNMpi2Vnsyt+yV3P0HDjvlH1zaE89cfLTSR10FneHZ/Hy\nX57DJfeczodf4/HYV3ze/KICIrnnF+kmkOGI4p/rm97B4Up2scUAID0sbSu0QoUhpDKoIEAnjlMA\nSkqiMCSO5VDjAFIq0mmPoBrRJPr5n4nXce+JH0Nrydlrr+P7xQ9RdicMiw56rkCgUUpRiyQyWcm7\nAmqRREmj/mf20URSMVCJacq4lGoxKU/ge0YnoFKL8BzHRAJI6gQEOG7i9KXRTx66CRCug45j49Qd\nZ6QeYkTecDgCoOKyqYdAoypdiEwrAHHXw2Pu+ME4/C/8Dq6/YKQUwWLZGWzO37LXkvLgtkvgsl/C\nghX1tmYfwfFx8lPRYRG3ZS6quA5kjVWFdq757TLWlCfyqZev531HLaazlGZZXxumh12ZmwA/ZZxd\nrWT+bWqHdEMy3S6Zb59rha6nUaV+VBwhGprAzyAcQXXTWuIgwvF8KpWQvu5BOiY2s3pFDzM7Yi7u\nuIVPtX2F9Wo2Vwx8hgfd0ynJLH3FkJUbi/QVQ3AEsYI5k5qY3ZGlXItYtK5AoRpz5MwmVvfWKNRi\n1m4usWpzCcczrXyPrR3g8ZX9NGV9mppMmN7PePi+S2d3mb7+KnEkyWRcNm/sx/U9St29BNUaOA6p\ntEdxcxdUCmglEXEImQZE21SE65nPplYeKfLT2lT1p7IQVNAOiHQzOioTr7kdp2EyOtwqT+V4OPlp\nRhJ4N/HgStPumfHhwVW77bCWcY51/pa9ls+cYS5ol/6i3pbsO/hzX4837UTjgLoeRvU+jhpYiex5\nDHfS0azJnsZ193ewodLKpSc9woUvepzOcgPL+lqQxadxshPMSnZIyMZNjZ5vDwg/g4iqida/QHgp\nnEwOx3VQQQ0tHITj4GUyxFHMrI6Yizp+wpdmfIM14VQ+uekT3BW/irLK4TgCpRUqWdlrrcnnfLTW\n+J4g5bp4jqCrEOB7Lu0NPuVQEsYm0jBYCsnlUmbyn9LEkUJJzeT2LMVaTBgpM2cgVtRqkihW5HI+\nxULNFO/HEXEQIjyPVDpNrVJJ2h2HOvs0IpWGsGbqIeLQ1EEMRU68DLJvqZmUGFYQjkO85m+4E4/G\nm3qC0VmobB75fua8Gm/6SSAD0xK4G9DAvcvgpvfBLx+AgXGuemnZPVjnb9krOWIWfPvdRtCmVKu3\nNfsOwm/AyU7AaZqGO/FI1ODq4V5z4Xi4zTNBRawoTuP6RfPZXG7g0yc8yHlHPElnMIHlfS3Gm6Rz\niZythGrJrPwBaiVEvs1M/VPK1AFoUFIRdW+iec5BxLgI12dKc8gn5v+Zz036Kn3eDC545GJuWn40\nxTiH47p0dRYpV2JSKRfXEcRSE4YxpUrE5s1lVj7dS2chYENfFS01rY0pFi7tpXuwxkApoLOnQko4\nHDyzhQ09ZV44q41ASgKlWN9dZnNXmVI55InF6+jrKyOlxvNcarWYgd5CMr7XRUUhslYllJpccxPR\n+hX4E6Ya914togd7jeMvdhtBJMcDx0OFgyAcnBlHQ64F1fU48fp7IKog0Di5ScieJehwRIpPeFmc\nhknI7kWjtj9fhgphP/pq0/VhsTwb1vlb9jo8F/70MfjC7+GepfW2Zh/A8XEaTKhflzehBlcj0q0g\nBLLz0UTBD3QwgOx8CPw8Tn4qCMGyvlauX3Qo3dUcn3nJw7znsKfYWM6xYrBtWOkPJOCAn4WwatoB\nVaIBICMTBk/nIA7xc3ka3SIfnfsHrj70/9EZT+CCRy7iDvlaeippwiBGCMg3ZqjVYlzXwfOcYfXc\nKFKmCj823QbC0aQzPlpDU2OaUjlEJd0DSkEYxkydmKdUjUj7LlIpKsmkpyCQZn5ALUQpheu6uJ6L\n6wlqlWC4kl9JiQoC8H1cz0WWC0bHwPfRtWrS2uck7zWpjXAchJtDpHKmNkArHL8Jt3ku7oTDidbf\ng9y8YBsHryudyM6HdqvjH+KBFfD+V5pZCg+t3u2Ht4wzrPO37HV88o3Q2gCX/LTeluwtCJzGGck4\n3m314v3Zp+NNOQ7Q4PikDjoD4TcSPv7jkRGzbmq4FkBXNuNNONxU9GsN6QaWduW5bvER9IcNXHHi\n/bzrBYtZX2pkVbHDRAJyzZBpMNX+lUGjANjYDoikHiBHc1uGD874Fde9+Hp6omYuXnQx1y0+jEHd\nTCqbNVK7nkdYC+lc30u1XGXytHbSaY/OjQPUKiEdHXkOmN4MnsMhB7SzaUMfg4M1UukUPX0VVi3b\nRCqTJooUE9tyzJjWTOdAlQnNWZ7eXKCzt0IUaeJYU6tFlAs1apUaWiqiKKZcKFPsLzFh2gQToo9i\n4lih4whdGiCqVvEnzkAKH1UqjIj6qMh0OYTJkCMNZi6CZyYhVgqgYjQakW7AyXVAUExy+3umoUpr\no/h30/vgZwugUN0jp7Xso1jnb9mrOHQ6fP9ceP3X7MVrCLfjcPxZr8TJtqMGlm/zvHDTONkOnPwU\n3PZ56Fo/OuhH9T01vI9/0Jl4E19kcvnCRYclnGw7WoYIP2OK/XB4qq+DaxcfSbEq+PxLFvC2Q55g\nfbGJ1YNNpthPq5GWQMcF16eRAT56+N386Pgb6A0aeO8/z+Qna06i5E0grFZxXI9U2gcEKlEAjJKZ\nANl8FtdxUEpTq4WksynyOZ9KLUIAUSgpFGvk8lnThleq4qc8/JSPENDU4FOuSXxXUK5GRLHJ+QtH\nEMeaKIyRsYkAaEweX0pJpiGDkhopJTKKUVFshvYkK36URiuZjDfG6CEIEtXDOLkhSJx/XEWHBeJ1\n/0RXe3Eap6DjEK/jcNDRbsvt7wzdRfA9+OBpcMu9e+y0ln0Q2+dv2WtwHbj/s3DNnXDdnfW2Zu9B\n5CaTmn0acmAFcfdjo7T6t8SbdRpObhLh8l9DbKq+RLYDp2kW3uTjkja9GJwUALLvSdwpR5tQ/pYy\nthrTLdDQyllHbOLSI2+nt5rl8/efwJ2rJ5oiQCHItzfz/hc+wAfm380/Ns3jygXHsjycQ7Z9ArHU\nyNIgutSPdn1Sk6aTyqSo9ffjuA4NHe00tTSwftVmsvks02a0IaVmoL9KOptCSRNezzemCWoRQRDR\n0JhhzfKNdEzuIJPzKQ4UqZQC8q15Mpk0G5etpmXyRMIgQgjNlJkT2Lyhn1w+h5SScqFMFEQm+hCG\n5BpzVAplVGnArNodDwa7YNIBUO43tQ5goh0yHhFDKvUyHPEYSnt4KZBy+HPUYQEhHKK1t6NKGxGZ\ndhACXe0Zs7+T4b8DFx64Ar77N6P+Z7FsD7vyt+w1/O9rYWqrGdxj2YKohKp04c94KV7LQcjuRdvd\nzZ/+EoTfgBpcZYr8vBzpee/AyU8HQA6uRNf6EF4WVIjsWWxkfody+0Oja4VA+Dm0cHh8cCrXPPpC\ngkhw5cn3cObBK+ms5HjTgcu56RW/oRr7XHDv27h+1Sn0FQDHRQgHHMeI5tTK4Lo4qSzCcQCNDCO8\nTIZUOkW1XENrU4iXy6UoF0Ncb2TCHho836FWichkU1TKNRzXNfr7GqrlgFQmZdT7ggAFuL5PUA1o\naslTrQTDPtus8qXJ6ydqgzgmGkEUIhwPEQWIVAahNWJofK/rJZ+Rhjg2gkcAOkYFBYRrRvtqFSAc\n33x+bpro6b+YKYpOivT8d+O2H5qM+pVj+MdiZBoWrICbLoaf3AdFWzBr2Q7W+Vv2Cg6ZAtddYLT7\nB22r0rZohds0C1Vc+4ySsSLdjHA842CUWZG6+engeMaxOS7Ryt8juxclNxAqeV0jQibqfY6HyWkn\nq9igik7nWdI7kWseORSpBNee/jdefcAaXvLH/+LaR15Ib19kpv4JB8r9qP7NKOGbmGJUA6VQ6Tyy\nUkZ4PngpgkpAuVBBJ5K6SpsCPcdzh8V2GpszFAsV4liRbUgT1GJa2htpbWugUgqo1SLSmRTpbIaW\nlizlmiTflDdtg0pTLFTwfJ84jqmWq7i+S745TzqTRitNUAuYNK0DZERYqUIcoB0XUew1ffsyNqv5\noGweDS1QGTCr/ETVTzhG9jhadaspvMy0mS9DxcSd/wYVDn93xFVk7xL2RLC1cxAa0nDhy+Cn94/5\n6Sz7IFbhz1J3HAE3XgSX/xrWjH1UdN8krhAu/RnxhrufeZf1dxEu/dlwyB8tCVf8hmj5r9BBP3Jg\nBSLdgtN8AE7TbFIHn43bNo9o+e+INt0Hjp/ktJOxvmDSAFEIwkFqh1uemM+Mq88F4Mne9mTwT6L8\nhx5RxKsWRvQB0CafjkBHptofGRudAK0QQhCHMTJWxHFsZgVISVAJcISp1tfK5PLjMCIMYlxXoKVE\nSoWMJGEQ4bkOQbWG6zooqdBSoZQyCn5aIyOJjGOUkiafryTlgQKu7418iFqjRaIGOKTjT/KIQ+P0\nldziOQWOi9M+38ghJ9vjrkcgSoR8XB+Rm4DIdpjPeA9x5e9hZju888Q9dkrLPoT37LtYLGPLf58O\nQQQ/+Ee9LRmf6LBA+JRpnUgf+T7AMU6u2gNoM4muomBIw95JJUVtANrktV3PTP0LSlRlctkY2Gy6\nANIZowjoZUx4fGhKYKl3JE9e6EG3TDKOuFI2zr9UQhf7ENMPIdIaJY1cb1wtM3P+XNbddy+qcQJe\nvpk4lLgudC16iuaD5hOEEVpphBCEtZAozOL6LoXNXWbwkFboSoGGuQdR6OxCOx7Ccaj29kFQhHQe\nLSMGejZCVENoZW53tB5d0Cgcc4OTbUoK/zSkcmZbLYRMEwBuy0HmnHEFkW7GaZqB7HzQHEdGqOJ6\nc2MwdOw9QCTh3Gvgz5fA7UtMNMBiGcKu/C115cBJ8Kk3wvnXjfgJy9gh+5eblkFAZNqIO/+NyE8D\nHOKN95nWNMEWQvFbSP8mIWxTG5A8F4fGSUqZTAQUI/nxLVfPWkNYQ2uFljKJELgm1VAtoaRERkY9\nT0UhtXKFVHMrOqyiZIyUEi0EIp0hLAwaZy1NFb+SZkUfByFuykcpsxpXUhFVq7i+NzRzD9zEoYvk\nd8cFxLDu/8g2zHuCkWE+Q7K+SUrFrOLN+9Q6RodFonV3QVQ24kqASDXhNM4gWvVHopV/YCjVsqd4\n5GlTPPv/zt2jp7XsA9icv6VuCAG//Qj88A748/Zr2Cy7E2FW7PGG+3AbZ6BlhNdxGG7Lgbjt84lW\n34bw80biV0UjTl4lTlDp4Tu0T5/4IF+47xgz8U8IE/KOgkQS2DMPscXku1oJp20ylPqgVjQCO5k8\n/qTpyCBAD3Sii33I4iAiLOO1Tqbc0w1BDa00sfAIKwEimyfatBpSGdxsFhGHhN2bCYOIYNM6lJdG\nOx46DI3AT6FIpqWNhuZGqBWR1Yqx2fGg0G06HZDgZ8zqPqpB+wxzUxMFpvYhGWdMpsHsHwXG+Wfy\nRtzITyHKfQg/Z4YAZzvMe1eS1AGvxm2fjy5vGhNhn53h3uVw6ZugpwiPr6+LCZa9ELvyt9SN/3ql\nae/77l/rbcn4QjRMGR7ZuyXejFPxZ78Kb8bJiGw7TqbFPBHXUKX14OfQWkJcQW8RnlaVTnRQABUA\neiQqICMj9iPM6tkUCepE+S8pGFTKOETXR5cLJnUgFVrGaBmhgqo5nuubmwzHMSUClRJOKoPGQUfB\nSF2B1pBpQFZKyChpzxNJx3Iqja6VkzS9O7zaj2sVomrVFBsO6fIPVfF7nrkRUfHQp2fek+MZW5Qa\nUfWLQ/RQQERG5iZAxuhiJ7hpUBFOywHmKG4af/Zp4GZAhaigfzd/yztPEMG5PzRy2R2NdTPDspdh\nc/6WujBnAlx+Jpx4hbmuW3YTXo7UgW8CIFh83bC0L34ed8J8QKAGViHcNMJvBBWbG4H8dFKHnI3w\n8qYIDzBeuIYqb8ZREuFPNyvaVNYcM5UzK/4tR/76afNcWDE/p3PoSgGRaUQPdEK+1UzHK/VDtUhc\nzhi54DBJRaSzeB2TKQ0U0XEirOOmzA1ArYDOd0C2Cd23kUhKczwvZWzwUlDqQ0ehOWayYg9LRcJi\nsup2XES535w/24g/eTayUoJqCR3VTGHiYKfZV7iQ8iGuocMKQhrhIWQAjo8eWGc6LFSEliGqsAaR\nagLhoiqduK7pCIg3Lxyer1AvFq6Em++B7/0nvO27dTXFspdgnb+lLlx3AXzlj7BsU70tGWfImhnj\nq6IRxw8QV1GFdWhZQw0sH1YKTB30ZgB0XEakm7cqvBDgZY0y4BBaoYccmXCGUwnmd2Hy/kMV8nEE\nKXMcLSOzkg4r5qZhaH+ddAK4vmmni0J0GJgVukrqC6LAjM1VGh2Hyeo/D6Hpw9dDAjwCk4ePapBu\nQLguyBihpHHk2lT566G8fhwiy4PmZyWNzcJJPoOhDgYH3DQi7aKrRtxHNEw0E/wyrUZNUQY4uYm4\nHS80UROt8fJTiFbdatoy3QxOy0FGf2GMe/x3xGd+BY9eCWe8GH7777qZYdlLsM7fsse58GWmB/kb\nt9XbknGIVqbnfJvtkmj5b7bZLEsb8XKTEH4eXelGyxCnYQrIEC1rRrBGuMbJuylTze4lzttxAG/0\nvIGoBkNCuo6btPx55t+h3LksmpC73iKFkAjn6FoJuX4QnW8zNw+ua/7tXWta6tomo0uDeO2TkP2b\n0eufMsfNJPHsyiCkszhI3EwD8YYVaC+N8Hx0kIzr9dLQOhm6VqPCqklRqHikhS+TA5nceAxV/AeR\niZRohY6KyRQiU9kvtEwKCV10EJrVv4wg3QwlD3/my3CaZhNvWoDseni3fM27Qi2C866FX34I7noK\n+rYvFGnZT7A5f8seZUY7fOFs04Jkw/31x81PTX4SqEoXcvNCdK2PaM1fidf/C9n7JHg5dFxF1/pQ\n/cuSOQAk43zlFs4/yd0nKnfDuXKtGO4C0Gq4FgAwP8eRcbLJzYDWjEQQlEYkNw1aKXSSHpDVMiKT\nH3HOQ1MGXQ/iEBVUTf2Ca7QLTIdBcg6ZqPR56W3VDbWAKDb3L0oaJ662uLmREUK7COEi0i3mNcJF\nV/vQwQAiZW5CdDiIP+0luBOPRA6uRkclVMmEuUTDFETDlDH4Np+d+5bBLxaY/L9l/8Y6f8se5drz\n4Zt/hic31NuScYqXw2k9JCnCAyc/HdEw+Rl3l31PooNBdDCAGliBKm0gXPozdFjEP+C1eJOPQYcF\nZP9SZGE17uRjjLY/gIyI19+DLnUmGvfJytkZkglOThIn1fGOuQHQUc3cQARlo6FfGYRqETzPVM97\nqZGOgbCCDsrDmni6NIAe7ET1bkSWCujGDnPssGxUBlVSdFjsI964Gp3KGN2BoGQcvnBMOqTQY86T\nbhhJjzgukFTxo0FJdLkXqoMmYqC1ee/aTE8klTU2AiLVjHBzgDCfV2k9utaLKq7FbZ5juijy4ZUQ\nFAAAIABJREFUU8BNkzrwTaYuw03v5i9/57j0l3D8gfC6Fz37vpbxiw37W/YY555iqo2v+lO9LRm/\n+LNegZOfTuymkYMr8ee+HoDgsWvNUJ+tkL1PIHufQGQnIPyG4e06LJnaATdj8tlt883zWo8cR9aQ\nfcvwpp2YOF3QwaAJeyOS6n3jMHW1E2QGkWlCpPNJmH+ohVAN5+BNMaE20QBIugeGbiqS3Lxwzc1D\nFBjHPjSuz7xg5HdNcpzk1kFJhtc7WpnzDe2PGglFBSVzAK0Rfj55Lk6kj4dOYyIe0cZ7cFKtOLnJ\nid5BgPAyeBOOJFr1J3SlC9n3FMLPm89ThqiBVcnnFz7n73d3UAmMrsYt74e7P27ltPdXbJ+/ZY8w\ntRV+9SF487dg80C9rRnHaIWTbjLa/VEJJ9OBqnSZ6vOWA9C1AZzWA43zkiMTX9Lz343begi62oUO\nBgGFKqzBn3YSIBBuClXegA4KyNJaPvPKtXzh/v/AybQisu2gInSlKykaVOiohJZVhJsGL2XC5MI1\n+XThQFhLUgTuSLRAJM5dxehKP8LPmS4AGY+E5cNKIjlcM+8hrBnHnGs2kQThbNWKWDavGUolaJkU\n9cmkw0CDcNBxbeT+QcYj5xRiK2EjjCJiXENXe9GFdciuR/AmHYXwc8Tr7jT9/K4PUQVd60PXepG9\njw/LLqvBlajBlWP/t7AD1vTA3EnwuiPhD/UrQ7DUERv2t+wRfvheuPp2WLyu3paMb9TAcsJlv0TX\n+szKdO3t6PImUrNfhTfjZXjTTsKf+Qr8Oa8d/brSBhAO3oxTcNvmg5vGm3IcINBxgK50oYrrEV4W\nr/0w85pqLyoYGHaO0cb7UIW1JvftN+A0TjWONSyho4rZNwrZYplucvxxLdEGSAbpKGkcfxyMVN/L\neKReQCY1AjLeQo1vaNsWq2lNslp3AGWK8nTyhDayx0P5fDG0qh9KXQz9DEYRMVH3G5JKFikjhuTP\neiXerFcm0sahqZvoehiBwJt2Iu6U43FaDhr9nvcSPv5TOHU+vOrweltiqQc27G8Zc951EsxogzO/\nWW9L9j+8SUfhTjwaVemESieyfyki0zISek6I1t9Fak7OqP7NeCmiYRKy7ymc7ESijffhth6MN+X4\npD7AiMQ72XacbDu62o9INeJPPRHRNG3EKcsIrSUilQccM0o4EcYxY3J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ai8WyL/G1P0FL\nztQKWfZurPO3jOJ/XwtdBbjxrnpbYrFY9jWkgnOvgSvfAtPb6m2NZUdY528ZZv40I9d5oQ33WyyW\nXeTx9fCdv8IP31tvSyw7wjp/CwCuY8R8Pv1LM7jDYrFYdpUv/xGmtMA5L6m3JZZnwjp/C2BW/OUA\nrr2z3pZYLJZ9nVia8P9Vbzc3AZa9D+v8LRw8xQzoOP9ao9hlsVgsz5dFa+AH/4Dvn1dvSyzbwzr/\n/RxHmHD/Fb8xgzosFotld/HF38PcifD2E+ptiWVrrPPfz/ngq0yF7tV/r7clFotlvBHGJvz/zXfB\nxKZ6W2PZEuv892PmToJPvwnee40N91sslrHh36tM6/DV59bbEsuWWOe/nyKEGcRx5e/NYA6LxWIZ\nKz77Gzh0Gpx1bL0tsQxhnf9+yvteYYb3fPsv9bbEYrGMd4LIaP9/5xzoaKy3NRawzn+/ZPYEuOLN\n5j+jsuF+i8WyB1iwAn5yv7kBsNQf6/z3Q649H666FZZuqrclFotlf+KyX8LRc+CNR9fbEot1/vsZ\n558KTVn4+m31tsRisexvVEN477Vw9X9Ca0O9rdm/sc5/P2J6mxm4cd41pr3PYrFY9jT3LIVfP2ja\n/yz1wzr//Yhrzodv/9UM3rBYLJZ68cmfw0teAK85st6W7L9Y57+f8J6TYXIzfOWP9bbEYrHs71QC\nE/7/wXkmDWnZ81jnvx8wpQW++jajtBXLeltjsVgs8M8n4NZH4OvvrLcl+yfW+e8H/OA8M2Bj0Zp6\nW2KxWCwjfPxn8MrDzMOyZ7HOf5zzjhNgzkT4wu/qbYnFYrGMpliFC6+Da94L+Uy9rdm/sM5/HDOp\nGb7xLjj3hxDZcL/FYtkL+dti+Mfj8NW319uS/Qvr/McxV/8n3HAXPLS63pZYLBbLM/M//wevexGc\nOr/eluw/WOc/Tjn7OJg3Da74Tb0tsVgslh0zWIGLb4DrLoCGdL2t2T+wzn8c0tFo9LPPu8YM1LBY\nLJa9ndseNQJAV7613pbsH1jnPw757nvglnvhgRX1tsRisVh2ng/fDG8+Bk46pN6WjH+s8x9nvOnF\ncNRsM0DDYrFY9iX6y/BfP4IbLoRsqt7WjG+s8x9HtDaYIr/zroGaDfdbLJZ9kN8/BP9eBZ87q96W\njG+s8x9HfOvd8KuFcO+yeltisVgsu84HfwzvPAGOO7DeloxfrPMfJ7zmSJMn++TP622JxWKxPD96\nS/Chm+DGCyHt19ua8Yl1/uOA5hz88L1mUEYlqLc1FovF8vz51UJ4fANcfma9LRmfWOc/Dvj6O+GP\nD5tBGRaLxTJe+K8b4bxT4Og59bZk/GGd/z7OaYfByw+FS35ab0ssFotl99JVgI/eAjdeBCmv3taM\nL6zz34dpzMI158OF10OpVm9rLBaLZffzk/tgdRdc+sZ6WzK+sM5/H+arb4e/LzYPi8ViGa9cfANc\n/HI4Yla9LRk/WOe/j3LqfHjtkWYghsVisYxnNg3AJT8z1f+eW29rxgc2i7IPMr0N7rgUFq6Ej7y6\n3tZY9ldsFbZlTyIEzJkAH301fPXWeluz72NX/vsg2RT0FM0gDIvFYtkf0Bq+9Rd4ZE29LRkfCEDX\n2wiLxWKxWCx7Drvyt1gsFotlP8M6f4vFYrFY9jOs87dYLBaLZT/DOn+LxWKxWPYzrPO3WCwWi2U/\nwzp/i8VisVj2M6zzt1gsFotlP8M6f4vFYrFY9jOs87dYLBaLZT/DOv86c8aLYdPVkPZHtq3+Fsib\n4bNv3nb/z59tntuSOy8124Ye5Rvgia/CpW8CfztDMNrzcOVbYclXoHg9lK6HRV+CL74FJjaZfTI+\nbPwenHXs7nuvFovFsifZ3vV1iOltEN8MtR9BW/6Zj3HwFPjRRbDuu2bfdd+FH18MB03edt8bLxq5\nDsc3Q/815jp77flw3IHb7n/KvNHX7q0fjVmz35GzzHV6etuufArbxw72qSOuA196qxlSEUQj23Ui\nuPzh0+E7f4W+0ujXbU+PedFauOh683Mubf6oLj/DOPP/vmlkv3nT4G+fMOf4zl/h36vN9qNmw4Wn\nwgumwpu/BbUIvnKruUn47b9Bqt31ri0Wi2Xseabr6xDvPsno2/suvP0/4Oq/b7vPyw+F338Ulm6C\nT/4cVnfBnInm2vzwF+GN34A7Hh/9mq4CvOHr5ueGDLxgCrzrJLjvcvjSH+DTv9z2PB+8CR5cue32\nUs38++ga+PsSs/g794fP6WPYIdo+6vM48xh09UZ0c2709lXfRN95KbpyI/qqd4x+7vNno+XNo7fd\neSn6rsu2Pf5N70Nvunrkd9dBP/FV9NKvodvz2+7vCPSrjxj5vSVn7Dvr2Pp/VvZhH/ZhH8/l8UzX\n16HHU1ehF30Jvfpb6IWf2/b5tjy6+wfouz+D9t3Rz6U89D2Xo7u+j25tGNl+40Xotd/Z/vm+8S5z\n7T7jxSPbTplntp06/9nfz+lHoIMfoye37J7Px4b9x4DLzzQhm0Onm9G7pethw/e2DeOffyr8eREM\nVrY9xro++P7t8P5XwJSWXbOjWB09+/qMF8MhU+ATP4Pe0rb7K23sGWKgAn9dbOy0WCyWvYHdcX09\n7kATtv/x3XDzPXD0HBMVHfX6l0Jbg4mcRnL0c2EMH77ZpFB39vp4yU+hcxA+vItj2P++GApV+M+T\nd+31W2Od/xjyu4/A3xab0NBP7oPL3gSfSWagpzw45QVw99JtXycEaA1X/h5iBZed8eznEoAjTKir\nMQuvfRG84wT4+YKRfV55mDnebYue8TDbcPdTxs7t1Q5YLBZLvdjV6yvAe15iroX/dy/cdLfZds5J\no/d5+Qth0wA8/PT2j/HvVcaZv2z+ztkbS7jjCTj2AHON3xLX2fbhbLWPVHD/cjj98J0737Nhc/5j\nyDV3wlW3mp//8Tg0ZeF/XgPf/DPMm2qK6hatfebX95bgW3+Gj7/eHGd19zPve+LBEN00etsfHoaP\n3jLy+4x26C5sP//1TDyyxvxHOmoOPLBi519nsVgsY8muXl9THrz1ePOazkHzWLDC5OU/9QuGa65m\ntMHTPTu2YU3vcyvCW9trzt+eh57iyPa/fnzbfZeshyM+OXrbo2vgY6/d+fPtCOv8x5BfLBj9+88X\nmFDSYTNGquq7C9u+buiPD+Brt8H7XwlXnAXnfP+Zz/XoGjj/OvNz2oPDZ8Jnz4RffsjcGQ+x9R3n\nszFk39RdTD1YLBbLWLCr19c3HAUtuZEVP5jw//fPhVe80ITXx4qhy++W13iA9/8IFm5V8FcNt319\nd9Fc31sboL/8/Gyxzn8M6Rzc/u9TW0Zy8UG842MUq6Za9cq3mErRZ6IUwCNPj/y+YIWJHPzig3Da\nYSY8tq4XXnGoaXvZ2dX/0B9gNrVz+1ssFsueYFevr+85GSoh/PNJaM6ZbX9bbPL655w04vzX95m6\ngh0xu8NER3eWGe3Gpr6tHPeyTaOv38/Eltfj5+v8bc5/DJm81Wp5UrP5d0P/SMFda8OzH+e7fzXt\nI184e9s7xh3xxAbz7+Ezzb9/X2xySa85YuePMdT/umWIymKxWOrNrlxfJzbBqw6DXAo2fBf6fmge\nK79h6prOeDHkM2bf25fA5GZTDLg9jjnAHG/rVr9nwndNZGHBiud2Hd+StuT97I7rsXX+Y8hbjhv9\n+9v+A4o1WLwOntpoth0w8dmPU4vgC7+DNx1t/uB2lsNnmH+7kz+U3zxo+lW/8naTc9oa14HXHDl6\n25wJ5t+lm3b+vBaLxTLW7Mr19Z0nmuvcxTfAS784+vHhW8yK+uzkuNf906yuv32OydNvSdqHb73b\n3GRcd+fo557JsX/17TCh0dQk7CpzJpq6gfBZIsY7gw37jyHnnwqOY6pCX3U4vPcUuPzXRrihVIM1\nPXDcXPjpfaNft728/LV3mkKP0w7b/rmasnDsXPPalGcc/2VnmNDVbx40+ygNZ34L/v5JePRL8O2/\nwEOrzXNHzIQLX2aiBbc9OnLc4w40x1jzLIUvFovFsifZlevre14Cq7rM9XRr7lkKl7zOhP5vvMuI\nq739avjth+H+z8I3/wJPd8PsCfCR043y3xnfNC3RW5L2R67FuZRpr37XSXD8gfD538EfH9723POn\nmVTE1jy2dnTu/7i58K+ndunj2gbr/MeQN34Dvvce04IyUDFf/Bd+N/L8zxeYu8wPbyXXu707x1jC\nZ39tZCa3flpjnP39nzW/R9Lk93/zIHz+t6ZuYIinNpoK0o+91vSLfvZM80e6bDP8aqG5IdiS170I\nfrZVYY3FYrHUm+d6fT1ilikGvOxX2z+e1vCjf8EnXg8zO2Btj0mVHv1p+NQb4ctvhY5Gs9r/x+Pw\njqu3jYhqbVb393/WXJfLgVk83bsMPnIzPLhq2/0BvnPOduwBjrlspBZgeptJ4V76i+f2Oe2Iuisx\njbfH5Wca1SYhdrzfnAno8MfoEw6uv83bexw7Fx3dhJ47qf622Id92Id9wPi5vj7XxyWv+//t3bFN\nw0AAQNGjiMIA9IxDxSrUNLACbJABWIBlImrEEFAYOpBiMHKU/94AluXi/t1ZZ4/3/cNy1/POf0Uv\nb2PsPmeax+j2epoJ71/XvhOAeY59fJ1juxnj5mqM+x92LX5D/P/B19TqEHdP0/nO82/+OrWm7WY6\nwrLkFhPAX53C+DrX5cUYj8/TFwmXcjYOf44AwAmw8geAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeA\nGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY\n8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8Qf4NfgiAAAA\nW0lEQVSAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEH\ngBjxB4AY8QeAGPEHgBjxB4AY8QeAGPEHgBjxB4AY8QeAmA8U+m+BxeIqXQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efde206a588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"samples = bt.signtest_MC(scores, rope=0.01, prior_strength=10, prior_place=bt.LEFT)\n",
"fig = bt.plot_posterior(samples,names)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Auxiliary functions\n",
"\n",
"\n",
"The function `signtest_MC(x, rope, prior_strength=1, prior_place=ROPE, nsamples=50000)` computes the posterior for the given input parameters. The result is returned as a 2d-array with `nsamples` rows and three columns representing the probabilities $p(-\\infty, `rope`), p[-`rope`, `rope`], p(`rope`, \\infty)$. Call `signtest_MC` directly to obtain a sample of the posterior.\n",
"\n",
"The posterior is plotted by `plot_simplex(points, names=('C1', 'C2'))`, where `points` is a sample returned by `signtest_MC`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n",
"\n",
"`@ARTICLE{bayesiantests2016,\n",
" author = {{Benavoli}, A. and {Corani}, G. and {Demsar}, J. and {Zaffalon}, M.},\n",
" title = \"{Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis}\",\n",
" journal = {ArXiv e-prints},\n",
" archivePrefix = \"arXiv\",\n",
" eprint = {1606.04316},\n",
" url={https://arxiv.org/abs/1606.04316},\n",
" year = 2016,\n",
" month = jun\n",
"}`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
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| gpl-3.0 |
vzg100/Post-Translational-Modification-Prediction | .ipynb_checkpoints/Phosphorylation Sequence Tests -Bagging -dbptm+ELM-VectorAvr.-phos_stripped-checkpoint.ipynb | 1 | 46171 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Template for test"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mark/anaconda3/lib/python3.6/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": [
"from pred import Predictor\n",
"from pred import sequence_vector\n",
"from pred import chemical_vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.\n",
"\n",
"Included is N Phosphorylation however no benchmarks are available, yet. \n",
"\n",
"\n",
"Training data is from phospho.elm and benchmarks are from dbptm. \n",
"\n",
"Note: SMOTEEN seems to preform best"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [1, 2, 16, 10, 13, 20, 10, 11, 1, 1, 18, 18, 12, -0.3692307692307692, 32.93076923076923, 0.0]\n",
"Finished working with Data\n",
"Training Data Points: 200363\n",
"Test Data Points: 50091\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.15450690442535608\n",
"Specificity : 0.9454687729251234\n",
"Accuracy: 0.8002435567267573\n",
"ROC 0.549987838675\n",
"TP 1421 FP 2230 TN 38664 FN 7776\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.79996806 0.79940508 0.7992853 0.79918147 0.79778399]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.23771462403789223\n",
"Specificity : 0.9309787737735336\n",
"Accuracy: 0.895439714697625\n",
"ROC 0.584346698906\n",
"TP 803 FP 4315 TN 58202 FN 2575\n",
"\n",
"\n",
"\n",
"None\n",
"x pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [10, 1, 10, 10, 18, 12, 10, 10, 11, 3, 11, 14, 5, 0.16923076923076927, 81.67692307692309, 0.07692307692307693]\n",
"Finished working with Data\n",
"Training Data Points: 200363\n",
"Test Data Points: 50091\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.1441500381637771\n",
"Specificity : 0.9467741935483871\n",
"Accuracy: 0.7998243197380767\n",
"ROC 0.545462115856\n",
"TP 1322 FP 2178 TN 38742 FN 7849\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.80012776 0.80080254 0.79940508 0.79994011 0.79846277]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.25636471284783896\n",
"Specificity : 0.9372170769550682\n",
"Accuracy: 0.9023142878822369\n",
"ROC 0.596790894901\n",
"TP 866 FP 3925 TN 58592 FN 2512\n",
"\n",
"\n",
"\n",
"None\n",
"y ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [18, 11, 3, 1, 15, 3, 10, 5, 3, 2, 7, 19, 2, 0.21538461538461537, 0.48461538461538456, 0.15384615384615385]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 331728\n",
"Test Data Points: 82932\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.8049394443125149\n",
"Specificity : 0.9312870510998971\n",
"Accuracy: 0.8671321082332514\n",
"ROC 0.868113247706\n",
"TP 33896 FP 2805 TN 38017 FN 8214\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.52008248 0.90747842 0.93661072 0.93544108 0.93811723]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.19330965068087627\n",
"Specificity : 0.9196378585024874\n",
"Accuracy: 0.8824038242658775\n",
"ROC 0.556473754592\n",
"TP 653 FP 5024 TN 57493 FN 2725\n",
"\n",
"\n",
"\n",
"None\n",
"x ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [3, 2, 17, 3, 19, 2, 10, 17, 16, 9, 10, 13, 8, -0.6615384615384616, 53.67692307692308, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 331646\n",
"Test Data Points: 82912\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.8055343875023814\n",
"Specificity : 0.9273949169110459\n",
"Accuracy: 0.8656768622153609\n",
"ROC 0.866464652207\n",
"TP 33826 FP 2971 TN 37949 FN 8166\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.51964107 0.90494741 0.93722184 0.93751131 0.93792139]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.2847838957963292\n",
"Specificity : 0.9203736583649247\n",
"Accuracy: 0.8877911829425601\n",
"ROC 0.602578777081\n",
"TP 962 FP 4978 TN 57539 FN 2416\n",
"\n",
"\n",
"\n",
"None\n",
"y SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [2, 11, 3, 20, 18, 5, 10, 19, 10, 19, 19, 20, 12, -0.46153846153846145, 4.176923076923081, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 305876\n",
"Test Data Points: 76470\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.8097830639082\n",
"Specificity : 0.9536073598504415\n",
"Accuracy: 0.8862429710997777\n",
"ROC 0.881695211879\n",
"TP 29004 FP 1886 TN 38767 FN 6813\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.56215509 0.94004106 0.95558985 0.95514522 0.95540677]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.21551213735938426\n",
"Specificity : 0.9368811683222164\n",
"Accuracy: 0.8999013582214128\n",
"ROC 0.576196652841\n",
"TP 728 FP 3946 TN 58571 FN 2650\n",
"\n",
"\n",
"\n",
"None\n",
"x SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [13, 18, 3, 19, 7, 11, 10, 18, 11, 10, 10, 16, 9, -1.4153846153846155, 39.13076923076924, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 305822\n",
"Test Data Points: 76456\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.8111776110595971\n",
"Specificity : 0.9568600582376979\n",
"Accuracy: 0.8890472951763105\n",
"ROC 0.884018834649\n",
"TP 28869 FP 1763 TN 39104 FN 6720\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.56117818 0.94031861 0.95564711 0.95613106 0.95505853]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.1950858496151569\n",
"Specificity : 0.9318425388294383\n",
"Accuracy: 0.8940739054556491\n",
"ROC 0.563464194222\n",
"TP 659 FP 4261 TN 58256 FN 2719\n",
"\n",
"\n",
"\n",
"None\n",
"y random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [7, 12, 3, 9, 19, 11, 10, 10, 18, 12, 5, 11, 7, -0.6230769230769231, 61.961538461538474, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 73804\n",
"Test Data Points: 18452\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5995934959349594\n",
"Specificity : 0.7205623901581723\n",
"Accuracy: 0.6592781270323\n",
"ROC 0.660077943047\n",
"TP 5605 FP 2544 TN 6560 FN 3743\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.65770648 0.65759809 0.64627141 0.65859079 0.65376694]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.6551213735938425\n",
"Specificity : 0.6128253115152679\n",
"Accuracy: 0.6149935503452463\n",
"ROC 0.633973342555\n",
"TP 2213 FP 24205 TN 38312 FN 1165\n",
"\n",
"\n",
"\n",
"None\n",
"x random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [10, 11, 8, 8, 8, 17, 10, 11, 1, 10, 0, 0, 0, -2.1, 198.60000000000002, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 73804\n",
"Test Data Points: 18452\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.600556268720582\n",
"Specificity : 0.7126537785588752\n",
"Accuracy: 0.6558638629958812\n",
"ROC 0.65660502364\n",
"TP 5614 FP 2616 TN 6488 FN 3734\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.65673098 0.65304574 0.65743551 0.65756098 0.65913279]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.5361160449970397\n",
"Specificity : 0.6255898395636387\n",
"Accuracy: 0.6210031110099401\n",
"ROC 0.58085294228\n",
"TP 1811 FP 23407 TN 39110 FN 1567\n",
"\n",
"\n",
"\n",
"None\n",
"y ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [10, 10, 4, 11, 1, 1, 10, 20, 11, 12, 10, 10, 2, -0.06153846153846161, 96.96923076923079, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 143742\n",
"Test Data Points: 35936\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.30112212659331083\n",
"Specificity : 0.9248047239974586\n",
"Accuracy: 0.7654997773820125\n",
"ROC 0.612963425295\n",
"TP 2764 FP 2012 TN 24745 FN 6415\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.76819902 0.7660285 0.76769813 0.76449144 0.76766384]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.30076968620485495\n",
"Specificity : 0.8593662523793528\n",
"Accuracy: 0.8307307079444571\n",
"ROC 0.580067969292\n",
"TP 1016 FP 8792 TN 53725 FN 2362\n",
"\n",
"\n",
"\n",
"None\n",
"x ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [20, 7, 3, 3, 20, 7, 10, 11, 10, 10, 3, 8, 13, -0.06153846153846153, 91.20769230769231, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 143737\n",
"Test Data Points: 35935\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.3179738562091503\n",
"Specificity : 0.9247991029714072\n",
"Accuracy: 0.7697787672185891\n",
"ROC 0.62138647959\n",
"TP 2919 FP 2012 TN 24743 FN 6261\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.77047447 0.76744121 0.7666342 0.76935493 0.7723541 ]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.3359976317347543\n",
"Specificity : 0.8684357854663531\n",
"Accuracy: 0.8411412094999621\n",
"ROC 0.602216708601\n",
"TP 1135 FP 8225 TN 54292 FN 2243\n",
"\n",
"\n",
"\n",
"None\n",
"y near_miss\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [12, 5, 18, 9, 13, 18, 10, 17, 9, 13, 9, 7, 13, -0.4769230769230769, 80.94615384615385, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 73804\n",
"Test Data Points: 18452\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5902866923406076\n",
"Specificity : 0.6827768014059754\n",
"Accuracy: 0.6359202254498157\n",
"ROC 0.636531746873\n",
"TP 5518 FP 2888 TN 6216 FN 3830\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.52904834 0.66962931 0.65689356 0.63804878 0.62493225]\n",
"Number of data points in benchmark 65895\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Benchmark Results \n",
"Sensitivity: 0.6699230313795145\n",
"Specificity : 0.4445830734040341\n",
"Accuracy: 0.4561347598452083\n",
"ROC 0.557253052392\n",
"TP 2263 FP 34723 TN 27794 FN 1115\n",
"\n",
"\n",
"\n",
"None\n",
"x near_miss\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [14, 1, 17, 7, 10, 20, 10, 3, 3, 1, 2, 3, 8, 0.05384615384615386, 27.77692307692308, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 73804\n",
"Test Data Points: 18452\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5943517329910141\n",
"Specificity : 0.6690465729349736\n",
"Accuracy: 0.6312052893995231\n",
"ROC 0.631699152963\n",
"TP 5556 FP 3013 TN 6091 FN 3792\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.5294819 0.65721873 0.65976588 0.6400542 0.62617886]\n",
"Number of data points in benchmark 65895\n",
"Benchmark Results \n",
"Sensitivity: 0.7282415630550622\n",
"Specificity : 0.44519090807300415\n",
"Accuracy: 0.45970103953258973\n",
"ROC 0.586716235564\n",
"TP 2460 FP 34685 TN 27832 FN 918\n",
"\n",
"\n",
"\n",
"None\n"
]
}
],
"source": [
"par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n",
"for i in par:\n",
" print(\"y\", i)\n",
" y = Predictor()\n",
" y.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n",
" y.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=0)\n",
" y.supervised_training(\"bagging\")\n",
" y.benchmark(\"Data/Benchmarks/phos_stripped.csv\", \"S\")\n",
" del y\n",
" print(\"x\", i)\n",
" x = Predictor()\n",
" x.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n",
" x.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=1)\n",
" x.supervised_training(\"bagging\")\n",
" x.benchmark(\"Data/Benchmarks/phos_stripped.csv\", \"S\")\n",
" del x\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y Phosphorylation "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [12, 12, 1, 10, 8, 9, 15, 10, 2, 10, 1, 10, 11, -0.2846153846153846, 66.13076923076923, 0.07692307692307693]\n",
"Finished working with Data\n",
"Training Data Points: 10099\n",
"Test Data Points: 2525\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.028846153846153848\n",
"Specificity : 0.9991738950846758\n",
"Accuracy: 0.9592079207920792\n",
"ROC 0.514010024465\n",
"TP 3 FP 2 TN 2419 FN 101\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.95486936 0.95326733 0.95524752 0.95562599 0.95721078]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9980437186357064\n",
"Accuracy: 0.9980046699214604\n",
"ROC 0.986826737367\n",
"TP 40 FP 46 TN 23468 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"x pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [8, 9, 14, 11, 9, 1, 15, 17, 12, 20, 15, 20, 11, -1.2230769230769232, 53.43076923076925, 0.15384615384615385]\n",
"Finished working with Data\n",
"Training Data Points: 10099\n",
"Test Data Points: 2525\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.08270676691729323\n",
"Specificity : 0.9987458193979933\n",
"Accuracy: 0.9504950495049505\n",
"ROC 0.540726293158\n",
"TP 11 FP 3 TN 2389 FN 122\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.95486936 0.95524752 0.95485149 0.95364501 0.9548336 ]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9965127158288679\n",
"Accuracy: 0.996476331988962\n",
"ROC 0.986061235963\n",
"TP 40 FP 82 TN 23432 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"y ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [5, 4, 17, 7, 3, 6, 15, 18, 5, 12, 2, 1, 2, 0.35384615384615375, -2.976923076923078, 0.3076923076923077]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 19352\n",
"Test Data Points: 4838\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9494780793319415\n",
"Specificity : 0.9950880065493246\n",
"Accuracy: 0.9725093013642001\n",
"ROC 0.972283042941\n",
"TP 2274 FP 12 TN 2431 FN 121\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.8545154 0.98016119 0.98222406 0.97374406 0.97808559]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9965977715403589\n",
"Accuracy: 0.9965612396518786\n",
"ROC 0.986103763819\n",
"TP 40 FP 80 TN 23434 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"x ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [3, 17, 9, 15, 17, 2, 15, 15, 6, 17, 9, 10, 10, -1.6384615384615389, 42.18461538461539, 0.3076923076923077]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 19352\n",
"Test Data Points: 4838\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.943609022556391\n",
"Specificity : 0.9950900163666121\n",
"Accuracy: 0.9696155436130632\n",
"ROC 0.969349519462\n",
"TP 2259 FP 12 TN 2432 FN 135\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.86567473 0.97974788 0.97850351 0.97705189 0.97891255]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9964276601173769\n",
"Accuracy: 0.9963914243260454\n",
"ROC 0.986018708107\n",
"TP 40 FP 84 TN 23430 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"y SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [9, 18, 20, 2, 14, 2, 15, 3, 2, 18, 3, 15, 13, 0.4769230769230769, 36.5, 0.15384615384615385]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 19226\n",
"Test Data Points: 4807\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9408033826638478\n",
"Specificity : 0.9905814905814906\n",
"Accuracy: 0.9660911171208654\n",
"ROC 0.965692436623\n",
"TP 2225 FP 23 TN 2419 FN 140\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.89244851 0.9854379 0.98647805 0.98605909 0.98605909]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9944713787530832\n",
"Accuracy: 0.9944385480789641\n",
"ROC 0.985040567425\n",
"TP 40 FP 130 TN 23384 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"x SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [9, 7, 8, 17, 3, 13, 15, 8, 1, 17, 12, 3, 8, -0.9461538461538465, 52.492307692307705, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 19228\n",
"Test Data Points: 4808\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9444681759931653\n",
"Specificity : 0.9914876368058371\n",
"Accuracy: 0.9685940099833611\n",
"ROC 0.9679779064\n",
"TP 2211 FP 21 TN 2446 FN 130\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.89663062 0.98585691 0.98793677 0.98231377 0.98730753]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9946414901760653\n",
"Accuracy: 0.9946083634047973\n",
"ROC 0.985125623137\n",
"TP 40 FP 126 TN 23388 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"y random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [10, 13, 5, 15, 12, 12, 15, 5, 12, 12, 5, 11, 5, 2.1153846153846154, 58.13923076923078, 0.46153846153846156]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 945\n",
"Test Data Points: 237\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.4406779661016949\n",
"Specificity : 0.7478991596638656\n",
"Accuracy: 0.5949367088607594\n",
"ROC 0.594288562883\n",
"TP 52 FP 30 TN 89 FN 66\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.53781513 0.55508475 0.53389831 0.56355932 0.56779661]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 1.0\n",
"Specificity : 0.6756825720847155\n",
"Accuracy: 0.6762470812990873\n",
"ROC 0.837841286042\n",
"TP 41 FP 7626 TN 15888 FN 0\n",
"\n",
"\n",
"\n",
"None\n",
"x random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [17, 17, 9, 6, 2, 7, 15, 4, 9, 12, 16, 9, 7, -1.9076923076923076, 63.96923076923077, 0.15384615384615385]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 945\n",
"Test Data Points: 237\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.4745762711864407\n",
"Specificity : 0.6722689075630253\n",
"Accuracy: 0.5738396624472574\n",
"ROC 0.573422589375\n",
"TP 56 FP 39 TN 80 FN 62\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.6092437 0.61864407 0.55932203 0.61864407 0.56355932]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.664625329590882\n",
"Accuracy: 0.6651666312884738\n",
"ROC 0.820117542844\n",
"TP 40 FP 7886 TN 15628 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"y ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [12, 20, 3, 10, 12, 17, 15, 11, 11, 9, 12, 10, 13, 0.46923076923076923, 34.57692307692308, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 9226\n",
"Test Data Points: 2307\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.05217391304347826\n",
"Specificity : 0.9995437956204379\n",
"Accuracy: 0.9523190290420459\n",
"ROC 0.525858854332\n",
"TP 6 FP 1 TN 2191 FN 109\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.95103986 0.95015171 0.952732 0.9518647 0.94882914]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9982988857701794\n",
"Accuracy: 0.9982593929102102\n",
"ROC 0.986954320934\n",
"TP 40 FP 40 TN 23474 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"x ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [5, 9, 2, 4, 5, 1, 15, 7, 2, 7, 5, 1, 3, 0.33076923076923076, 2.7, 0.3076923076923077]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 9232\n",
"Test Data Points: 2309\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.08695652173913043\n",
"Specificity : 0.9990884229717412\n",
"Accuracy: 0.9536595928973581\n",
"ROC 0.543022472355\n",
"TP 10 FP 2 TN 2192 FN 105\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.95062798 0.94887348 0.94930676 0.95060659 0.95147314]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 0.975609756097561\n",
"Specificity : 0.9964276601173769\n",
"Accuracy: 0.9963914243260454\n",
"ROC 0.986018708107\n",
"TP 40 FP 84 TN 23430 FN 1\n",
"\n",
"\n",
"\n",
"None\n",
"y near_miss\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [13, 10, 9, 3, 2, 17, 15, 10, 18, 10, 9, 12, 12, -0.015384615384615467, 33.06153846153847, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 945\n",
"Test Data Points: 237\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5677966101694916\n",
"Specificity : 0.7058823529411765\n",
"Accuracy: 0.6371308016877637\n",
"ROC 0.636839481555\n",
"TP 67 FP 35 TN 84 FN 51\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.66386555 0.69491525 0.59745763 0.59322034 0.63559322]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 1.0\n",
"Specificity : 0.38895976864846477\n",
"Accuracy: 0.39002334960730206\n",
"ROC 0.694479884324\n",
"TP 41 FP 14368 TN 9146 FN 0\n",
"\n",
"\n",
"\n",
"None\n",
"x near_miss\n",
"Loading Data\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [16, 11, 10, 11, 2, 9, 15, 12, 9, 12, 3, 8, 9, -0.6538461538461541, 110.33846153846154, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 945\n",
"Test Data Points: 237\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5254237288135594\n",
"Specificity : 0.6890756302521008\n",
"Accuracy: 0.6075949367088608\n",
"ROC 0.607249679533\n",
"TP 62 FP 37 TN 82 FN 56\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.65966387 0.66525424 0.68220339 0.61016949 0.6440678 ]\n",
"Number of data points in benchmark 23555\n",
"Benchmark Results \n",
"Sensitivity: 1.0\n",
"Specificity : 0.4367610785064217\n",
"Accuracy: 0.437741456166419\n",
"ROC 0.718380539253\n",
"TP 41 FP 13244 TN 10270 FN 0\n",
"\n",
"\n",
"\n",
"None\n"
]
}
],
"source": [
"par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n",
"for i in par:\n",
" print(\"y\", i)\n",
" y = Predictor()\n",
" y.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n",
" y.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=0)\n",
" y.supervised_training(\"bagging\")\n",
" y.benchmark(\"Data/Benchmarks/phos_stripped.csv\", \"Y\")\n",
" del y\n",
" print(\"x\", i)\n",
" x = Predictor()\n",
" x.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n",
" x.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=1)\n",
" x.supervised_training(\"bagging\")\n",
" x.benchmark(\"Data/Benchmarks/phos_stripped.csv\", \"Y\")\n",
" del x\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"T Phosphorylation "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [12, 9, 19, 12, 20, 1, 13, 8, 16, 3, 20, 0, 0, 0.10909090909090913, 3.445454545454546, 0.0]\n",
"Finished working with Data\n",
"Training Data Points: 66323\n",
"Test Data Points: 16581\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.10776699029126213\n",
"Specificity : 0.9600474390334297\n",
"Accuracy: 0.8012182618659912\n",
"ROC 0.533907214662\n",
"TP 333 FP 539 TN 12952 FN 2757\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.80400434 0.80430587 0.80723764 0.80735826 0.80772014]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.259529602595296\n",
"Specificity : 0.9512871798180528\n",
"Accuracy: 0.9334171380683008\n",
"ROC 0.605408391207\n",
"TP 320 FP 2265 TN 44232 FN 913\n",
"\n",
"\n",
"\n",
"None\n",
"x pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [1, 10, 20, 20, 20, 9, 20, 20, 20, 1, 19, 10, 18, -1.3153846153846156, 32.330769230769235, 0.0]\n",
"Finished working with Data\n",
"Training Data Points: 66323\n",
"Test Data Points: 16581\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.11469780219780219\n",
"Specificity : 0.960421391469749\n",
"Accuracy: 0.8118931306917556\n",
"ROC 0.537559596834\n",
"TP 334 FP 541 TN 13128 FN 2578\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.80316005 0.80141117 0.80259349 0.80464415 0.80548854]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.22141119221411193\n",
"Specificity : 0.959932898896703\n",
"Accuracy: 0.9408548082966688\n",
"ROC 0.590672045555\n",
"TP 273 FP 1863 TN 44634 FN 960\n",
"\n",
"\n",
"\n",
"None\n",
"y ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [20, 4, 7, 13, 8, 4, 10, 20, 17, 20, 8, 17, 14, -0.9230769230769231, 30.7, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 109785\n",
"Test Data Points: 27447\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.7862885857860732\n",
"Specificity : 0.947399570910705\n",
"Accuracy: 0.8656319452034831\n",
"ROC 0.866844078348\n",
"TP 10953 FP 711 TN 12806 FN 2977\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.52074908 0.91427114 0.94494644 0.94695038 0.94662246]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.1678832116788321\n",
"Specificity : 0.9488999290276792\n",
"Accuracy: 0.9287240729101194\n",
"ROC 0.558391570353\n",
"TP 207 FP 2376 TN 44121 FN 1026\n",
"\n",
"\n",
"\n",
"None\n",
"x ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [3, 17, 13, 18, 18, 12, 20, 10, 11, 9, 5, 20, 10, -0.33076923076923076, 64.68461538461538, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 109778\n",
"Test Data Points: 27445\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.7966882649388048\n",
"Specificity : 0.949760236075249\n",
"Accuracy: 0.8722900346146839\n",
"ROC 0.873224250507\n",
"TP 11066 FP 681 TN 12874 FN 2824\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.52014866 0.91860084 0.94545256 0.94126221 0.94425011]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.28791565287915655\n",
"Specificity : 0.9440394003914231\n",
"Accuracy: 0.9270898805782527\n",
"ROC 0.615977526635\n",
"TP 355 FP 2602 TN 43895 FN 878\n",
"\n",
"\n",
"\n",
"None\n",
"y SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [14, 1, 11, 17, 20, 11, 20, 12, 8, 16, 18, 20, 4, -0.9076923076923076, 15.115384615384615, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 102928\n",
"Test Data Points: 25732\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.8027764087399376\n",
"Specificity : 0.9590647588139843\n",
"Accuracy: 0.8851235815327219\n",
"ROC 0.880920583777\n",
"TP 9773 FP 555 TN 13003 FN 2401\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.55691136 0.94882058 0.95449246 0.95495706 0.95192569]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.19302514193025141\n",
"Specificity : 0.9552659311353421\n",
"Accuracy: 0.9355751099937146\n",
"ROC 0.574145536533\n",
"TP 238 FP 2080 TN 44417 FN 995\n",
"\n",
"\n",
"\n",
"None\n",
"x SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [2, 11, 2, 3, 12, 13, 20, 20, 8, 14, 9, 13, 14, 1.2000000000000002, 42.07692307692308, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 102917\n",
"Test Data Points: 25730\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.8066076405968191\n",
"Specificity : 0.9601684895063554\n",
"Accuracy: 0.8873688301593471\n",
"ROC 0.883388065052\n",
"TP 9839 FP 539 TN 12993 FN 2359\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.55825884 0.94869802 0.95565315 0.95557542 0.95444829]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.19302514193025141\n",
"Specificity : 0.9537174441361809\n",
"Accuracy: 0.9340666247642991\n",
"ROC 0.573371293033\n",
"TP 238 FP 2152 TN 44345 FN 995\n",
"\n",
"\n",
"\n",
"None\n",
"y random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [9, 9, 9, 5, 9, 18, 20, 17, 7, 18, 16, 2, 17, -2.5538461538461537, 52.85384615384616, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 24059\n",
"Test Data Points: 6015\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5328947368421053\n",
"Specificity : 0.7015126050420168\n",
"Accuracy: 0.6162926018287614\n",
"ROC 0.617203670942\n",
"TP 1620 FP 888 TN 2087 FN 1420\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.61635638 0.62101064 0.63801131 0.61988693 0.61822414]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.6853203568532036\n",
"Specificity : 0.6500204314256833\n",
"Accuracy: 0.6509323276765138\n",
"ROC 0.667670394139\n",
"TP 845 FP 16273 TN 30224 FN 388\n",
"\n",
"\n",
"\n",
"None\n",
"x random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [13, 3, 3, 7, 19, 7, 20, 12, 3, 1, 10, 13, 18, 0.6076923076923079, 10.984615384615386, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 24059\n",
"Test Data Points: 6015\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5342105263157895\n",
"Specificity : 0.7210084033613445\n",
"Accuracy: 0.6266001662510391\n",
"ROC 0.627609464839\n",
"TP 1624 FP 830 TN 2145 FN 1416\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.625 0.61768617 0.63318923 0.61739275 0.6130695 ]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.6536901865369019\n",
"Specificity : 0.6277609308127406\n",
"Accuracy: 0.628430756337733\n",
"ROC 0.640725558675\n",
"TP 806 FP 17308 TN 29189 FN 427\n",
"\n",
"\n",
"\n",
"None\n",
"y ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [11, 1, 2, 9, 9, 8, 20, 2, 12, 2, 13, 20, 10, -0.04615384615384599, 42.5923076923077, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 47213\n",
"Test Data Points: 11804\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.25221540558963873\n",
"Specificity : 0.936076662908681\n",
"Accuracy: 0.7660962385631989\n",
"ROC 0.594146034249\n",
"TP 740 FP 567 TN 8303 FN 2194\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.75559132 0.75770925 0.75734983 0.75599424 0.75768872]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.23763179237631793\n",
"Specificity : 0.9154138976708175\n",
"Accuracy: 0.8979048816258118\n",
"ROC 0.576522845024\n",
"TP 293 FP 3933 TN 42564 FN 940\n",
"\n",
"\n",
"\n",
"None\n",
"x ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [7, 9, 2, 13, 12, 7, 20, 2, 7, 2, 3, 12, 9, 0.2076923076923077, 0.48461538461538456, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 47274\n",
"Test Data Points: 11819\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.23231292517006802\n",
"Specificity : 0.9321995720238766\n",
"Accuracy: 0.7581013622133852\n",
"ROC 0.582256248597\n",
"TP 683 FP 602 TN 8277 FN 2257\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.7571912 0.75894746 0.75241158 0.75638856 0.75672703]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.2733171127331711\n",
"Specificity : 0.9084887197023463\n",
"Accuracy: 0.8920804525455688\n",
"ROC 0.590902916218\n",
"TP 337 FP 4255 TN 42242 FN 896\n",
"\n",
"\n",
"\n",
"None\n",
"y near_miss\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [3, 18, 10, 19, 10, 2, 20, 19, 9, 20, 13, 11, 19, -0.9230769230769229, 16.030769230769234, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 24059\n",
"Test Data Points: 6015\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5394736842105263\n",
"Specificity : 0.6769747899159664\n",
"Accuracy: 0.6074812967581047\n",
"ROC 0.608224237063\n",
"TP 1640 FP 961 TN 2014 FN 1400\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.54521277 0.6512633 0.62071832 0.60508813 0.59161955]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.7112733171127331\n",
"Specificity : 0.4847624577929759\n",
"Accuracy: 0.4906138696836371\n",
"ROC 0.598017887453\n",
"TP 877 FP 23957 TN 22540 FN 356\n",
"\n",
"\n",
"\n",
"None\n",
"x near_miss\n",
"Loading Data\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [9, 1, 3, 10, 20, 19, 20, 9, 1, 9, 9, 8, 7, -1.853846153846154, 46.57692307692309, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 24059\n",
"Test Data Points: 6015\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.5434210526315789\n",
"Specificity : 0.6796638655462185\n",
"Accuracy: 0.6108063175394847\n",
"ROC 0.611542459089\n",
"TP 1652 FP 953 TN 2022 FN 1388\n",
"\n",
"\n",
"\n",
"None\n",
"Cross: Validation: [ 0.53889628 0.64544548 0.62254739 0.6107416 0.60392418]\n",
"Number of data points in benchmark 47730\n",
"Benchmark Results \n",
"Sensitivity: 0.7120843471208435\n",
"Specificity : 0.49618254941179\n",
"Accuracy: 0.5017598994343181\n",
"ROC 0.604133448266\n",
"TP 878 FP 23426 TN 23071 FN 355\n",
"\n",
"\n",
"\n",
"None\n"
]
}
],
"source": [
"par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n",
"for i in par:\n",
" print(\"y\", i)\n",
" y = Predictor()\n",
" y.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n",
" y.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=0)\n",
" y.supervised_training(\"bagging\")\n",
" y.benchmark(\"Data/Benchmarks/phos_stripped.csv\", \"T\")\n",
" del y\n",
" print(\"x\", i)\n",
" x = Predictor()\n",
" x.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n",
" x.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=1)\n",
" x.supervised_training(\"bagging\")\n",
" x.benchmark(\"Data/Benchmarks/phos_stripped.csv\", \"T\")\n",
" del x\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
spatchcock/models | advection_diffusion/advection_diffusion_decay_source_crank_nicolson.ipynb | 1 | 3771 | {
"metadata": {
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"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ \\frac {\\partial C}{dt} = D \\frac {\\partial^2 C}{dx^2} - w \\frac {\\partial C}{dx} - \\lambda C + P $$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Discretise the equation\n",
"\n",
"$$\\frac {C^{t+1}_{x} - C^{t}_{x}}{\\Delta t} = \\frac {D}{2} \\Bigg(\\frac {C^{t+1}_{x+1} - 2C^{t+1}_{x} + C^{t+1}_{x-1}}{\\Delta x^2} + \\frac {C^{t}_{x+1} - 2C^{t}_{x} + C^{t}_{x-1}}{\\Delta x^2} \\Bigg) -\\frac {w}{2} \\Bigg( \\frac {C^{t+1}_{x+1} - C^{t+1}_{x-1}}{2 \\Delta x} + \\frac {C^{t}_{x+1} - C^{t}_{x-1}}{2 \\Delta x} \\Bigg) - \\frac{\\lambda}{2} \\Big( C^{t+1}_{x} + C^t_{x} \\Big) + P^t_x$$\n",
"\n",
"$$C^{t+1}_{x} = C^{t}_{x} + \\frac {D \\Delta t}{2 \\Delta x^2} \\Bigg( C^{t+1}_{x+1} - 2C^{t+1}_{x} + C^{t+1}_{x-1} + C^{t}_{x+1} - 2C^{t}_{x} + C^{t}_{x-1} \\Bigg) - \\frac {w \\Delta t}{4\\Delta x} \\Bigg(C^{t+1}_{x+1} - C^{t+1}_{x-1} + C^{t}_{x+1} - C^{t}_{x-1} \\Bigg) - \\frac{\\lambda \\Delta t}{2} \\Big( C^{t+1}_{x} + C^t_{x} \\Big) + \\Delta t P^t_x$$\n",
"\n",
"if $\\frac {w \\Delta t}{4\\Delta x} = \\sigma$, $\\frac {D \\Delta t}{2 \\Delta x^2} = \\rho$ and $\\frac{\\lambda \\Delta t}{2} = \\mu$\n",
"\n",
"$$C^{t+1}_{x} = C^{t}_{x} + \\rho C^{t+1}_{x+1} - 2\\rho C^{t+1}_{x} + \\rho C^{t+1}_{x-1} + \\rho C^{t}_{x+1} - 2\\rho C^{t}_{x} + \\rho C^{t}_{x-1} - \\sigma C^{t+1}_{x+1} + \\sigma C^{t+1}_{x-1} - \\sigma C^{t}_{x+1} + \\sigma C^{t}_{x-1} - \\mu C^{t+1}_{x} - \\mu C^t_{x} + \\Delta t P^t_x$$\n",
"\n",
"and rearranging,\n",
"\n",
"$$-(\\sigma + \\rho)C^{t+1}_{x-1} + (1 + 2\\rho + \\mu)C^{t+1}_{x} + (\\sigma - \\rho)C^{t+1}_{x+1} = (\\sigma + \\rho) C^{t}_{x-1} + (1 - 2\\rho - \\mu)C^{t}_{x} + (\\rho - \\sigma) C^{t}_{x+1} + \\Delta t P^t_x$$\n",
"\n",
"gives the discretised equation for $x > 1, x < L$.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$-(\\sigma + \\rho)C^{t+1}_{-1} + (1 + 2\\rho + \\mu)C^{t+1}_{0} + (\\sigma - \\rho)C^{t+1}_{1} = (\\sigma + \\rho) C^{t}_{-1} + (1 - 2\\rho - \\mu)C^{t}_{0} + (\\rho - \\sigma) C^{t}_{1} + \\Delta t P^t_x$$\n",
"\n",
"$$-(\\sigma + \\rho)C^{t+1}_{0} + (1 + 2\\rho + \\mu)C^{t+1}_{0} + (\\sigma - \\rho)C^{t+1}_{1} = (\\sigma + \\rho)C^{t}_{0} + (1 - 2\\rho - \\mu)C^{t}_{0} + (\\rho - \\sigma) C^{t}_{1} + \\Delta t P^t_x$$\n",
"\n",
"$$(1 - \\sigma + \\rho + \\mu)C^{t+1}_{0} + (\\sigma - \\rho)C^{t+1}_{1} = (1 + \\sigma - \\rho - \\mu)C^{t}_{0} + (\\rho - \\sigma) C^{t}_{1} + \\Delta t P^t_x$$\n",
"\n",
"\n",
"\n",
"$$-(\\sigma + \\rho)C^{t+1}_{L-2} + (1 + 2\\rho + \\mu)C^{t+1}_{L-1} + (\\sigma - \\rho)C^{t+1}_{L} = (\\sigma + \\rho) C^{t}_{L-2} + (1 - 2\\rho - \\mu)C^{t}_{L-1} + (\\rho - \\sigma) C^{t}_{L} + \\Delta t P^t_x$$\n",
"\n",
"$$-(\\sigma + \\rho)C^{t+1}_{L-2} + (1 + 2\\rho + \\mu)C^{t+1}_{L-1} + (\\sigma - \\rho)C^{t+1}_{L-1} = (\\sigma + \\rho)C^{t}_{L-2} + (1 - 2\\rho - \\mu)C^{t}_{L-1} + (\\rho - \\sigma) C^{t}_{L-1} + \\Delta t P^t_x$$\n",
"\n",
"$$-(\\sigma + \\rho)C^{t+1}_{L-2} + (1 + \\sigma + \\rho +\\mu)C^{t+1}_{L-1} = (\\sigma + \\rho)C^{t}_{L-2} + (1 - \\sigma - \\rho - \\mu)C^{t}_{L-1} + \\Delta t P^t_x$$\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | unlicense |
cchuang2009/DyLabIPy | DiffEq/0-Basics.ipynb | 1 | 118747 | {
"metadata": {
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{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Differential Equations Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goals\n",
"\n",
"1. Solutions: Theoretical computation\n",
"2. Visualization\n",
"3. Properties of Equations: Stability, time behavior\n",
"4. Machine learning: Iuput equations, output numerical scheme"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider \n",
"\\begin{eqnarray}\n",
" \\frac{d y}{d x} &=& x-y,\\\\\n",
" y(x_0)&=&y_0\n",
"\\end{eqnarray} "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solution is sum of general solution, $y_g$ which satisfies $y_g'+y_g=0$, and special solution, $y_s$ which satisfies $y_s'+y_s=x$ since\n",
"\\begin{eqnarray}\n",
" y'&=&(y_g+y_s)' \\\\\n",
" &= &y_g'+y_s'=-y_g+x-y_s=x-y \n",
" \\end{eqnarray}\n",
"\n",
"1. $\\frac{d}{d x} =D$ implies $ (D+1)y=x$.\n",
"2. $y_g$ satisfies $ (D+1)y_g=0$.\n",
" \\begin{eqnarray}\n",
" (D+1)y_g&=&0 \\\\\n",
" (r+1)=0 &\\to &r=-1 \\\\\n",
" y_g &= & C_1 \\exp(-1\\times x)=C_1 \\exp(-x)\n",
" \\end{eqnarray} \n",
" \n",
"3. $y_s$ can be calculated by the follows:\n",
"\\begin{eqnarray}\n",
" (D+1)y_s&=&x \\\\\n",
" y_s &= &\\frac{1}{1+D} x \\\\\n",
" &= & (1-D+D^2-D^3+\\cdots) x \\\\\n",
" &= & x-1\n",
" \\end{eqnarray}\n",
"\n",
"4. $y(x_0)=y_0$ implies $y(x)=e^{x_0} (y_0+1-x_0) e^{-x}+x-1$:\n",
"\\begin{eqnarray}\n",
" y(x_0)_s&=& y_0 \\\\\n",
" &=& y_g(x_0)+y_s(x_0) \\\\\n",
" &=& C_1 \\exp(-x_0)+x_0-1 \\\\\n",
" &\\to & C_1=\\exp(x_0)(y_0+1-x_0) |\n",
" \\end{eqnarray}\n",
" \n",
"##Conclusion\n",
"$$ y(x)=e^{x_0} (y_0+1-x_0) e^{-x}+x-1$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from numpy import *\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def f(x,y): # DE is y'=f(x,y) for \n",
" return x-y # this function f\n",
"\n",
"def yexact(x,x0,y0): # exact solution y(x) that satisfies y(x0)=y0\n",
" y=y0*exp(-x + x0) + (x*exp(x) - x0*exp(x0) - exp(x) + exp(x0))*exp(-x)\n",
" return y"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig=plt.figure(figsize=(5,5)) # fig is figure variable\n",
"ax=fig.add_subplot(1,1,1) # in 1x1 array of subplots, ax is subplot 1\n",
"\n",
"#\t\t\t\t\t I. Slope Field\n",
"\n",
"xmesh=linspace(-4.,4.,9); ymesh=linspace(-4.,4.,9) # x and y mesh values\n",
"\n",
"# plot mini-tangent for\n",
"# y'=f(x,y) at each mesh point\n",
"for xp in xmesh:\n",
" for yp in ymesh:\n",
" m=f(xp,yp)\n",
" h=0.25/sqrt(1.+m**2)\n",
" #ax.plot([xp-h,xp+h],[yp-m*h,yp+m*h],'b')\n",
" ax.arrow(xp,yp,2*h,2*m*h, head_width=0.2, head_length=0.2, fc='r', ec='r') \n",
" \n",
"#\t\t\t\t\tII. Dot at given (x0,y0) and solution curve through (x0,y0)\n",
"x0=-4.; y0=3.5\n",
"ax.plot([x0],[y0],'mo') # 'm'agenta d'o't\n",
"X=linspace(-4.,4.,101) # X[0]=-4., .., X[100]=4.\n",
"Y=yexact(X,x0,y0)\n",
"ax.plot(X,Y,'m',linewidth=2) # lines joining points (X[i],Y[i]), 'm'agenta\n",
"\n",
"ax.set_xlabel('x'); ax.set_ylabel('y')\n",
"ax.grid(True) # add a grid to the plot\n",
"ax.set_aspect(1.) # require that (scaling factor for y)/(scaling factor for x), is 1.\n",
"plt.title(\"Slope Field for y'=x-y and\\nsolution curve through ({0:g},{1:g})\".format(x0,y0))\n",
"#plt.savefig(\"306ch1_slope_field_soln_1.3.5.png\")\n",
"#plt.show() # to display immediately"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"<matplotlib.text.Text at 0x107929610>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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zzzU0ilYzjsHUN5SDBplWJO6KWik6yhUVwyjSUYyj7+YF+ZwPxIlTandURWmX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\n",
"text": [
"<matplotlib.figure.Figure at 0x107539bd0>"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Symbol DE solver"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Solve $y'=x-y, y(0)=y_0$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import Symbol , dsolve , Function , Derivative , Eq, lambdify, symbols,solve"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x=Symbol('x')\n",
"x0,y0=symbols('x0 y0')\n",
"y=Function('y')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 34
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f_=Derivative(y(x),x)-x+y(x)\n",
"sol=dsolve(f_,y(x))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print sol\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"y(x) == (C1 + (x - 1)*exp(x))*exp(-x)\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yy=sol.rhs\n",
"C1=Symbol('C1')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"z= lambdify(x,sol.rhs-y0)\n",
"z(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"1.0*C1 - y0 - 1.0"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c=solve(z(0),C1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ysol=yy.subs({C1:c[0]})\n",
"print ysol"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(y0 + (x - 1)*exp(x) + 1.0)*exp(-x)\n"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Steps for Scheme Auto-generated\n",
"\n",
"1. SymPy -> theoretical solution\n",
"2. NumPy -> numerical computation\n",
"3. Matplotlib -> visualization"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def ode1(f,x0,y0):\n",
" sol=dsolve(f,y(x))\n",
" \n",
" yy=sol.rhs\n",
" C1=Symbol('C1')\n",
" z= lambdify(x,sol.rhs-y0)\n",
" c=solve(z(x0),C1)\n",
" ysol=yy.subs({C1:c[0]})\n",
" return ysol"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ysol=ode1(f_,0,1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print ysol"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"((x - 1)*exp(x) + 2.0)*exp(-x)\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"z=lambdify(x,ysol)\n",
"z(1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"0.7357588823428847"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t=linspace(-4.,4.,101) "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def makepic(t,f_,xin,yin):\n",
" yy=np.array([])\n",
" ysol=ode1(f_,xin,yin)\n",
" z=lambdify(x,ysol)\n",
" for t1 in t: \n",
" yy=append(yy,z(t1))\n",
" plt.plot(t,yy,'r--')\n",
" \n",
"def yexact(t,f_,xmesh,ymesh): \n",
" plt.figure(figsize=(5,5)) \n",
" plt.xlim(-4,4)\n",
" plt.ylim(-4,4)\n",
" for xp in xmesh:\n",
" for yp in ymesh:\n",
" makepic(t,f_,xp,yp)\n",
" yy=np.array([])\n",
" ysol=ode1(f_,0,1)\n",
" z=lambdify(x,ysol) \n",
" for t1 in t: \n",
" yy=append(yy,z(t1)) \n",
" plt.plot(t,yy,'b')\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xmesh=linspace(-4.,4.,9); \n",
"ymesh=linspace(-4.,4.,9);\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"yy=yexact(t,f_,xmesh,ymesh)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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aKEVHIw8xvRpTTAxyDE+eVG+fPRs5mhkRGCkpYGcMGJA15uX8+XgWRYsyt26N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EonTXrCjNyyZa6fUjV+PHRbjwpZWTAMr72mHYpttSJUqVkTi8PRErfZsxHKVKuGRV6qFHgr0VxR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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x117a82550>"
]
}
],
"prompt_number": 68
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Notes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. If $x-y=0$, $y'=0$ implies $ (x_0,y_0)=(x_0,x_0) $ being equilibrium.\n",
"2. If $ x_0 >y_0$, $y'(x_0^+)>0 \\to y$ increaseing and $y\\sim x (\\to \\infty)$ as $x\\to \\infty$\n",
"3. If $ x_0 <y_0$, $y'(x_0^+)<0 \\to y$ decreasing and then increasing ( since $C_1\\exp(-x)<x$ eventually and referece above graph) and $y\\to \\infty$ as $x\\to -\\infty$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Question:\n",
" \n",
" Explain the results in above note from the theoretical solution of $y(x)$. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
Avnerus/socialbonds | Eyelink test.ipynb | 1 | 4661 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Volumes/Store/Avner/Projects/socialbonds/venv/lib/python2.7/site-packages/numexpr-2.5.2-py2.7-macosx-10.11-x86_64.egg/numexpr/cpuinfo.py:76: UserWarning: [Errno 2] No such file or directory\n",
" stacklevel=stacklevel + 1):\n",
"/Volumes/Store/Avner/Projects/socialbonds/venv/lib/python2.7/site-packages/cili-0.5.3-py2.7.egg/cili/util.py:252: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n"
]
}
],
"source": [
"from cili.util import *\n",
"#samps, events = load_eyelink_dataset(\"/home/avnerus/Code/eegtest/EyeLink/pilotexp_eye.asc\")\n",
"#samps, events = load_eyelink_dataset(\"/Volumes/Store/Avner/POUYAN-BRAIN/POUYAN.asc\")\n",
"samps, events = load_eyelink_dataset(\"/Volumes/Store/Avner/MATTI-BRAIN/MATTI.asc\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" name b_num state\n",
"5606473 BUTTON 1 1\n",
"5606523 BUTTON 1 0\n",
"8761209 BUTTON 1 1\n",
"8761259 BUTTON 1 0\n"
]
}
],
"source": [
"buttons = events.BUTTON\n",
"print(buttons)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_r 885.8\n",
"y_r 649.6\n",
"pup_r 251\n",
"input 0\n",
"samp_warns ...\n",
"targ_x 5257\n",
"targ_y 4769\n",
"targ_dist 715.2\n",
"remote_warns ....F........\n",
"Name: 5606476, dtype: object\n"
]
}
],
"source": [
"print samps.loc[5606476]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Int64Index([5606473, 8761209], dtype='int64')\n"
]
}
],
"source": [
"from cili.extract import extract_event_ranges\n",
"#print(samps)\n",
"\n",
"\n",
"#FIRST INDEX WORK AROUND\n",
"#pouyanMs = (48 * 60 + 30) * 1000 + 160\n",
"#print(\"Total Pouyan time: %dms\" % (pouyanMs))\n",
"#pouyan_start = indexes[0] - pouyanMs\n",
"#pouyan_samples = samps.loc[pouyan_start:indexes[0]][['x_r','y_r']] # 1521526\n",
"\n",
"indexes = buttons[(buttons.state == 1)].index\n",
"print(indexes)\n",
"\n",
"matti_samples = samps.loc[indexes[0]:indexes[1]][['x_r','y_r']] # 1521526\n",
"\n",
"#interesting_samples = samps.loc[indexes[0]:indexes[1]][['x_r','y_r']] # 1521526\n",
"\n",
"\n",
"#print(samps.loc[indexes[0]:indexes[1]])\n",
"#print(interesting_samples)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Chunk it in SQL\n",
"import sqlite3\n",
"conn = sqlite3.connect('matti_eyedata.db')\n",
"c = conn.cursor()\n",
"c.execute('DROP TABLE data')\n",
"c.execute('CREATE TABLE data(onset int primary key not null,x_r real,y_r real)')\n",
"matti_samples.to_sql('data', conn, if_exists='replace')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"conn.commit()\n",
"conn.close()"
]
},
{
"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 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
}
| gpl-3.0 |
thehyve/transmart-api-training | oauth.ipynb | 1 | 155599 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Examples of interaction with TranSMART RESTful API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's connect to the api."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"········\n"
]
}
],
"source": [
"import getpass\n",
"from transmart_api import TransmartApi\n",
"\n",
"api = TransmartApi(host = 'http://localhost:8080', user = 'admin', password = getpass.getpass())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The below output has not the best data shape."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{u'label': u'\\\\Public Studies\\\\GSE8581\\\\Biomarker Data\\\\GPL570\\\\',\n",
" u'subject': {u'age': 65,\n",
" u'birthDate': None,\n",
" u'deathDate': None,\n",
" u'id': 1000384597,\n",
" u'inTrialId': u'GSE8581GSM210006',\n",
" u'maritalStatus': None,\n",
" u'race': u'Afro American',\n",
" u'religion': None,\n",
" u'sex': u'FEMALE',\n",
" u'trial': u'GSE8581'},\n",
" u'value': None},\n",
" {u'label': u'\\\\Public Studies\\\\GSE8581\\\\Endpoints\\\\Diagnosis\\\\',\n",
" u'subject': {u'age': 65,\n",
" u'birthDate': None,\n",
" u'deathDate': None,\n",
" u'id': 1000384597,\n",
" u'inTrialId': u'GSE8581GSM210006',\n",
" u'maritalStatus': None,\n",
" u'race': u'Afro American',\n",
" u'religion': None,\n",
" u'sex': u'FEMALE',\n",
" u'trial': u'GSE8581'},\n",
" u'value': u'non-small cell adenocarcinoma'},\n",
" {u'label': u'\\\\Public Studies\\\\GSE8581\\\\Endpoints\\\\FEV1\\\\',\n",
" u'subject': {u'age': 65,\n",
" u'birthDate': None,\n",
" u'deathDate': None,\n",
" u'id': 1000384597,\n",
" u'inTrialId': u'GSE8581GSM210006',\n",
" u'maritalStatus': None,\n",
" u'race': u'Afro American',\n",
" u'religion': None,\n",
" u'sex': u'FEMALE',\n",
" u'trial': u'GSE8581'},\n",
" u'value': 1.41},\n",
" {u'label': u'\\\\Public Studies\\\\GSE8581\\\\Endpoints\\\\Forced Expiratory Volume Ratio\\\\',\n",
" u'subject': {u'age': 65,\n",
" u'birthDate': None,\n",
" u'deathDate': None,\n",
" u'id': 1000384597,\n",
" u'inTrialId': u'GSE8581GSM210006',\n",
" u'maritalStatus': None,\n",
" u'race': u'Afro American',\n",
" u'religion': None,\n",
" u'sex': u'FEMALE',\n",
" u'trial': u'GSE8581'},\n",
" u'value': 51.0},\n",
" {u'label': u'\\\\Public Studies\\\\GSE8581\\\\Subjects\\\\Age\\\\',\n",
" u'subject': {u'age': 65,\n",
" u'birthDate': None,\n",
" u'deathDate': None,\n",
" u'id': 1000384597,\n",
" u'inTrialId': u'GSE8581GSM210006',\n",
" u'maritalStatus': None,\n",
" u'race': u'Afro American',\n",
" u'religion': None,\n",
" u'sex': u'FEMALE',\n",
" u'trial': u'GSE8581'},\n",
" u'value': 65.0}]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"observations = api.get_observations(study = 'GSE8581')\n",
"observations[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We would like to use pandas for flatten above json output."
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>label</th>\n",
" <th>subject.age</th>\n",
" <th>subject.birthDate</th>\n",
" <th>subject.deathDate</th>\n",
" <th>subject.id</th>\n",
" <th>subject.inTrialId</th>\n",
" <th>subject.maritalStatus</th>\n",
" <th>subject.race</th>\n",
" <th>subject.religion</th>\n",
" <th>subject.sex</th>\n",
" <th>subject.trial</th>\n",
" <th>value</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>1.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Forced Expir...</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Age\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Organism\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Homo sapiens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Race\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Afro American</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Sex\\</td>\n",
" <td>65</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384597</td>\n",
" <td>GSE8581GSM210006</td>\n",
" <td>None</td>\n",
" <td>Afro American</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>1.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Forced Expir...</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Age\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Organism\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Homo sapiens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Race\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Caucasian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Sex\\</td>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384598</td>\n",
" <td>GSE8581GSM212788</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>inflammation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>4.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Forced Expir...</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Age\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>control</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Organism\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Homo sapiens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Race\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Caucasian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Sex\\</td>\n",
" <td>55</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384599</td>\n",
" <td>GSE8581GSM212067</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>MALE</td>\n",
" <td>GSE8581</td>\n",
" <td>male</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",
" </tr>\n",
" <tr>\n",
" <th>550</th>\n",
" <td>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>551</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" </tr>\n",
" <tr>\n",
" <th>552</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>1.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>553</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Forced Expir...</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Age\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>555</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>556</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>not specified</td>\n",
" </tr>\n",
" <tr>\n",
" <th>557</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Organism\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Homo sapiens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Race\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Caucasian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Sex\\</td>\n",
" <td>61</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384652</td>\n",
" <td>GSE8581GSM212809</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Forced Expir...</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Age\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>control</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Organism\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Homo sapiens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Race\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Caucasian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>569</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Sex\\</td>\n",
" <td>57</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384653</td>\n",
" <td>GSE8581GSM210196</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>570</th>\n",
" <td>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>571</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" </tr>\n",
" <tr>\n",
" <th>572</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>2.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>573</th>\n",
" <td>\\Public Studies\\GSE8581\\Endpoints\\Forced Expir...</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>574</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Age\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>575</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>576</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>control</td>\n",
" </tr>\n",
" <tr>\n",
" <th>577</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Organism\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Homo sapiens</td>\n",
" </tr>\n",
" <tr>\n",
" <th>578</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Race\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>Caucasian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>579</th>\n",
" <td>\\Public Studies\\GSE8581\\Subjects\\Sex\\</td>\n",
" <td>54</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>1000384654</td>\n",
" <td>GSE8581GSM212790</td>\n",
" <td>None</td>\n",
" <td>Caucasian</td>\n",
" <td>None</td>\n",
" <td>FEMALE</td>\n",
" <td>GSE8581</td>\n",
" <td>female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>580 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" label subject.age \\\n",
"0 \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ 65 \n",
"1 \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ 65 \n",
"2 \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ 65 \n",
"3 \\Public Studies\\GSE8581\\Endpoints\\Forced Expir... 65 \n",
"4 \\Public Studies\\GSE8581\\Subjects\\Age\\ 65 \n",
"5 \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ 65 \n",
"6 \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ 65 \n",
"7 \\Public Studies\\GSE8581\\Subjects\\Organism\\ 65 \n",
"8 \\Public Studies\\GSE8581\\Subjects\\Race\\ 65 \n",
"9 \\Public Studies\\GSE8581\\Subjects\\Sex\\ 65 \n",
"10 \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ 77 \n",
"11 \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ 77 \n",
"12 \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ 77 \n",
"13 \\Public Studies\\GSE8581\\Endpoints\\Forced Expir... 77 \n",
"14 \\Public Studies\\GSE8581\\Subjects\\Age\\ 77 \n",
"15 \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ 77 \n",
"16 \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ 77 \n",
"17 \\Public Studies\\GSE8581\\Subjects\\Organism\\ 77 \n",
"18 \\Public Studies\\GSE8581\\Subjects\\Race\\ 77 \n",
"19 \\Public Studies\\GSE8581\\Subjects\\Sex\\ 77 \n",
"20 \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ 55 \n",
"21 \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ 55 \n",
"22 \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ 55 \n",
"23 \\Public Studies\\GSE8581\\Endpoints\\Forced Expir... 55 \n",
"24 \\Public Studies\\GSE8581\\Subjects\\Age\\ 55 \n",
"25 \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ 55 \n",
"26 \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ 55 \n",
"27 \\Public Studies\\GSE8581\\Subjects\\Organism\\ 55 \n",
"28 \\Public Studies\\GSE8581\\Subjects\\Race\\ 55 \n",
"29 \\Public Studies\\GSE8581\\Subjects\\Sex\\ 55 \n",
".. ... ... \n",
"550 \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ 61 \n",
"551 \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ 61 \n",
"552 \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ 61 \n",
"553 \\Public Studies\\GSE8581\\Endpoints\\Forced Expir... 61 \n",
"554 \\Public Studies\\GSE8581\\Subjects\\Age\\ 61 \n",
"555 \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ 61 \n",
"556 \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ 61 \n",
"557 \\Public Studies\\GSE8581\\Subjects\\Organism\\ 61 \n",
"558 \\Public Studies\\GSE8581\\Subjects\\Race\\ 61 \n",
"559 \\Public Studies\\GSE8581\\Subjects\\Sex\\ 61 \n",
"560 \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ 57 \n",
"561 \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ 57 \n",
"562 \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ 57 \n",
"563 \\Public Studies\\GSE8581\\Endpoints\\Forced Expir... 57 \n",
"564 \\Public Studies\\GSE8581\\Subjects\\Age\\ 57 \n",
"565 \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ 57 \n",
"566 \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ 57 \n",
"567 \\Public Studies\\GSE8581\\Subjects\\Organism\\ 57 \n",
"568 \\Public Studies\\GSE8581\\Subjects\\Race\\ 57 \n",
"569 \\Public Studies\\GSE8581\\Subjects\\Sex\\ 57 \n",
"570 \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ 54 \n",
"571 \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ 54 \n",
"572 \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ 54 \n",
"573 \\Public Studies\\GSE8581\\Endpoints\\Forced Expir... 54 \n",
"574 \\Public Studies\\GSE8581\\Subjects\\Age\\ 54 \n",
"575 \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ 54 \n",
"576 \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ 54 \n",
"577 \\Public Studies\\GSE8581\\Subjects\\Organism\\ 54 \n",
"578 \\Public Studies\\GSE8581\\Subjects\\Race\\ 54 \n",
"579 \\Public Studies\\GSE8581\\Subjects\\Sex\\ 54 \n",
"\n",
" subject.birthDate subject.deathDate subject.id subject.inTrialId \\\n",
"0 None None 1000384597 GSE8581GSM210006 \n",
"1 None None 1000384597 GSE8581GSM210006 \n",
"2 None None 1000384597 GSE8581GSM210006 \n",
"3 None None 1000384597 GSE8581GSM210006 \n",
"4 None None 1000384597 GSE8581GSM210006 \n",
"5 None None 1000384597 GSE8581GSM210006 \n",
"6 None None 1000384597 GSE8581GSM210006 \n",
"7 None None 1000384597 GSE8581GSM210006 \n",
"8 None None 1000384597 GSE8581GSM210006 \n",
"9 None None 1000384597 GSE8581GSM210006 \n",
"10 None None 1000384598 GSE8581GSM212788 \n",
"11 None None 1000384598 GSE8581GSM212788 \n",
"12 None None 1000384598 GSE8581GSM212788 \n",
"13 None None 1000384598 GSE8581GSM212788 \n",
"14 None None 1000384598 GSE8581GSM212788 \n",
"15 None None 1000384598 GSE8581GSM212788 \n",
"16 None None 1000384598 GSE8581GSM212788 \n",
"17 None None 1000384598 GSE8581GSM212788 \n",
"18 None None 1000384598 GSE8581GSM212788 \n",
"19 None None 1000384598 GSE8581GSM212788 \n",
"20 None None 1000384599 GSE8581GSM212067 \n",
"21 None None 1000384599 GSE8581GSM212067 \n",
"22 None None 1000384599 GSE8581GSM212067 \n",
"23 None None 1000384599 GSE8581GSM212067 \n",
"24 None None 1000384599 GSE8581GSM212067 \n",
"25 None None 1000384599 GSE8581GSM212067 \n",
"26 None None 1000384599 GSE8581GSM212067 \n",
"27 None None 1000384599 GSE8581GSM212067 \n",
"28 None None 1000384599 GSE8581GSM212067 \n",
"29 None None 1000384599 GSE8581GSM212067 \n",
".. ... ... ... ... \n",
"550 None None 1000384652 GSE8581GSM212809 \n",
"551 None None 1000384652 GSE8581GSM212809 \n",
"552 None None 1000384652 GSE8581GSM212809 \n",
"553 None None 1000384652 GSE8581GSM212809 \n",
"554 None None 1000384652 GSE8581GSM212809 \n",
"555 None None 1000384652 GSE8581GSM212809 \n",
"556 None None 1000384652 GSE8581GSM212809 \n",
"557 None None 1000384652 GSE8581GSM212809 \n",
"558 None None 1000384652 GSE8581GSM212809 \n",
"559 None None 1000384652 GSE8581GSM212809 \n",
"560 None None 1000384653 GSE8581GSM210196 \n",
"561 None None 1000384653 GSE8581GSM210196 \n",
"562 None None 1000384653 GSE8581GSM210196 \n",
"563 None None 1000384653 GSE8581GSM210196 \n",
"564 None None 1000384653 GSE8581GSM210196 \n",
"565 None None 1000384653 GSE8581GSM210196 \n",
"566 None None 1000384653 GSE8581GSM210196 \n",
"567 None None 1000384653 GSE8581GSM210196 \n",
"568 None None 1000384653 GSE8581GSM210196 \n",
"569 None None 1000384653 GSE8581GSM210196 \n",
"570 None None 1000384654 GSE8581GSM212790 \n",
"571 None None 1000384654 GSE8581GSM212790 \n",
"572 None None 1000384654 GSE8581GSM212790 \n",
"573 None None 1000384654 GSE8581GSM212790 \n",
"574 None None 1000384654 GSE8581GSM212790 \n",
"575 None None 1000384654 GSE8581GSM212790 \n",
"576 None None 1000384654 GSE8581GSM212790 \n",
"577 None None 1000384654 GSE8581GSM212790 \n",
"578 None None 1000384654 GSE8581GSM212790 \n",
"579 None None 1000384654 GSE8581GSM212790 \n",
"\n",
" subject.maritalStatus subject.race subject.religion subject.sex \\\n",
"0 None Afro American None FEMALE \n",
"1 None Afro American None FEMALE \n",
"2 None Afro American None FEMALE \n",
"3 None Afro American None FEMALE \n",
"4 None Afro American None FEMALE \n",
"5 None Afro American None FEMALE \n",
"6 None Afro American None FEMALE \n",
"7 None Afro American None FEMALE \n",
"8 None Afro American None FEMALE \n",
"9 None Afro American None FEMALE \n",
"10 None Caucasian None FEMALE \n",
"11 None Caucasian None FEMALE \n",
"12 None Caucasian None FEMALE \n",
"13 None Caucasian None FEMALE \n",
"14 None Caucasian None FEMALE \n",
"15 None Caucasian None FEMALE \n",
"16 None Caucasian None FEMALE \n",
"17 None Caucasian None FEMALE \n",
"18 None Caucasian None FEMALE \n",
"19 None Caucasian None FEMALE \n",
"20 None Caucasian None MALE \n",
"21 None Caucasian None MALE \n",
"22 None Caucasian None MALE \n",
"23 None Caucasian None MALE \n",
"24 None Caucasian None MALE \n",
"25 None Caucasian None MALE \n",
"26 None Caucasian None MALE \n",
"27 None Caucasian None MALE \n",
"28 None Caucasian None MALE \n",
"29 None Caucasian None MALE \n",
".. ... ... ... ... \n",
"550 None Caucasian None FEMALE \n",
"551 None Caucasian None FEMALE \n",
"552 None Caucasian None FEMALE \n",
"553 None Caucasian None FEMALE \n",
"554 None Caucasian None FEMALE \n",
"555 None Caucasian None FEMALE \n",
"556 None Caucasian None FEMALE \n",
"557 None Caucasian None FEMALE \n",
"558 None Caucasian None FEMALE \n",
"559 None Caucasian None FEMALE \n",
"560 None Caucasian None FEMALE \n",
"561 None Caucasian None FEMALE \n",
"562 None Caucasian None FEMALE \n",
"563 None Caucasian None FEMALE \n",
"564 None Caucasian None FEMALE \n",
"565 None Caucasian None FEMALE \n",
"566 None Caucasian None FEMALE \n",
"567 None Caucasian None FEMALE \n",
"568 None Caucasian None FEMALE \n",
"569 None Caucasian None FEMALE \n",
"570 None Caucasian None FEMALE \n",
"571 None Caucasian None FEMALE \n",
"572 None Caucasian None FEMALE \n",
"573 None Caucasian None FEMALE \n",
"574 None Caucasian None FEMALE \n",
"575 None Caucasian None FEMALE \n",
"576 None Caucasian None FEMALE \n",
"577 None Caucasian None FEMALE \n",
"578 None Caucasian None FEMALE \n",
"579 None Caucasian None FEMALE \n",
"\n",
" subject.trial value \n",
"0 GSE8581 None \n",
"1 GSE8581 non-small cell adenocarcinoma \n",
"2 GSE8581 1.41 \n",
"3 GSE8581 51 \n",
"4 GSE8581 65 \n",
"5 GSE8581 66 \n",
"6 GSE8581 chronic obstructive pulmonary disease \n",
"7 GSE8581 Homo sapiens \n",
"8 GSE8581 Afro American \n",
"9 GSE8581 female \n",
"10 GSE8581 E \n",
"11 GSE8581 non-small cell squamous cell carcinoma \n",
"12 GSE8581 1.29 \n",
"13 GSE8581 53 \n",
"14 GSE8581 77 \n",
"15 GSE8581 67 \n",
"16 GSE8581 chronic obstructive pulmonary disease \n",
"17 GSE8581 Homo sapiens \n",
"18 GSE8581 Caucasian \n",
"19 GSE8581 female \n",
"20 GSE8581 E \n",
"21 GSE8581 inflammation \n",
"22 GSE8581 4.04 \n",
"23 GSE8581 79 \n",
"24 GSE8581 55 \n",
"25 GSE8581 69 \n",
"26 GSE8581 control \n",
"27 GSE8581 Homo sapiens \n",
"28 GSE8581 Caucasian \n",
"29 GSE8581 male \n",
".. ... ... \n",
"550 GSE8581 E \n",
"551 GSE8581 non-small cell adenocarcinoma \n",
"552 GSE8581 1.28 \n",
"553 GSE8581 89 \n",
"554 GSE8581 61 \n",
"555 GSE8581 65 \n",
"556 GSE8581 not specified \n",
"557 GSE8581 Homo sapiens \n",
"558 GSE8581 Caucasian \n",
"559 GSE8581 female \n",
"560 GSE8581 E \n",
"561 GSE8581 non-small cell adenocarcinoma \n",
"562 GSE8581 2.3 \n",
"563 GSE8581 74 \n",
"564 GSE8581 57 \n",
"565 GSE8581 65 \n",
"566 GSE8581 control \n",
"567 GSE8581 Homo sapiens \n",
"568 GSE8581 Caucasian \n",
"569 GSE8581 female \n",
"570 GSE8581 E \n",
"571 GSE8581 non-small cell squamous cell carcinoma \n",
"572 GSE8581 2.83 \n",
"573 GSE8581 75 \n",
"574 GSE8581 54 \n",
"575 GSE8581 75 \n",
"576 GSE8581 control \n",
"577 GSE8581 Homo sapiens \n",
"578 GSE8581 Caucasian \n",
"579 GSE8581 female \n",
"\n",
"[580 rows x 12 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"from pandas.io.json import json_normalize\n",
"df = json_normalize(observations)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>label</th>\n",
" <th>\\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Endpoints\\FEV1\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Endpoints\\Forced Expiratory Volume Ratio\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Subjects\\Age\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Subjects\\Height (inch)\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Subjects\\Lung Disease\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Subjects\\Organism\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Subjects\\Race\\</th>\n",
" <th>\\Public Studies\\GSE8581\\Subjects\\Sex\\</th>\n",
" </tr>\n",
" <tr>\n",
" <th>subject.inTrialId</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>GSE8581GSM210004</th>\n",
" <td>None</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.54</td>\n",
" <td>58</td>\n",
" <td>63</td>\n",
" <td>72</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210005</th>\n",
" <td>None</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.69</td>\n",
" <td>83.66</td>\n",
" <td>84</td>\n",
" <td>60</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Afro American</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210006</th>\n",
" <td>None</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.41</td>\n",
" <td>51</td>\n",
" <td>65</td>\n",
" <td>66</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Afro American</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210007</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.51</td>\n",
" <td>80.96</td>\n",
" <td>46</td>\n",
" <td>66</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210008</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.64</td>\n",
" <td>57</td>\n",
" <td>53</td>\n",
" <td>65</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210009</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.72</td>\n",
" <td>74</td>\n",
" <td>53</td>\n",
" <td>64</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210010</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.45</td>\n",
" <td>73</td>\n",
" <td>77</td>\n",
" <td>63</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210011</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>1.87</td>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>72</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210012</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.76</td>\n",
" <td>70.58</td>\n",
" <td>61</td>\n",
" <td>69</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210014</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.98</td>\n",
" <td>78</td>\n",
" <td>71</td>\n",
" <td>63</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210015</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.59</td>\n",
" <td>74</td>\n",
" <td>68</td>\n",
" <td>65</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210071</th>\n",
" <td>E</td>\n",
" <td>emphysema</td>\n",
" <td>1.16</td>\n",
" <td>43</td>\n",
" <td>68</td>\n",
" <td>67</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210087</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.59</td>\n",
" <td>74</td>\n",
" <td>68</td>\n",
" <td>65</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210090</th>\n",
" <td>E</td>\n",
" <td>hematoma</td>\n",
" <td>4.08</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>72</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210188</th>\n",
" <td>E</td>\n",
" <td>metastatic non-small cell adenocarcinoma</td>\n",
" <td>1.2</td>\n",
" <td>72</td>\n",
" <td>65</td>\n",
" <td>63</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210192</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.31</td>\n",
" <td>75</td>\n",
" <td>50</td>\n",
" <td>65</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210193</th>\n",
" <td>E</td>\n",
" <td>Unknown</td>\n",
" <td>1.63</td>\n",
" <td>80.78</td>\n",
" <td>67</td>\n",
" <td>66</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210194</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>0.52</td>\n",
" <td>58</td>\n",
" <td>56</td>\n",
" <td>60</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210196</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.3</td>\n",
" <td>74</td>\n",
" <td>57</td>\n",
" <td>65</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210978</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.53</td>\n",
" <td>74</td>\n",
" <td>73</td>\n",
" <td>66</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210979</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.17</td>\n",
" <td>76</td>\n",
" <td>55</td>\n",
" <td>63</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210992</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>0.9</td>\n",
" <td>55.9</td>\n",
" <td>70</td>\n",
" <td>71</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210993</th>\n",
" <td>E</td>\n",
" <td>giant bullae</td>\n",
" <td>0.4</td>\n",
" <td>45</td>\n",
" <td>61</td>\n",
" <td>73</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM210994</th>\n",
" <td>E</td>\n",
" <td>carcinoid</td>\n",
" <td>1.69</td>\n",
" <td>76</td>\n",
" <td>64</td>\n",
" <td>63</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM211007</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>1.7</td>\n",
" <td>59</td>\n",
" <td>61</td>\n",
" <td>66</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM211008</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>3.66</td>\n",
" <td>72</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM211009</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.64</td>\n",
" <td>41</td>\n",
" <td>75</td>\n",
" <td>71</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM211010</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.44</td>\n",
" <td>86</td>\n",
" <td>61</td>\n",
" <td>60</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM211865</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.13</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>67</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM211872</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.61</td>\n",
" <td>70</td>\n",
" <td>74</td>\n",
" <td>70</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212067</th>\n",
" <td>E</td>\n",
" <td>inflammation</td>\n",
" <td>4.04</td>\n",
" <td>79</td>\n",
" <td>55</td>\n",
" <td>69</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212068</th>\n",
" <td>E</td>\n",
" <td>metastatic renal cell carcinoma</td>\n",
" <td>1.99</td>\n",
" <td>85</td>\n",
" <td>78</td>\n",
" <td>63</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212069</th>\n",
" <td>E</td>\n",
" <td>no malignancy</td>\n",
" <td>1.6</td>\n",
" <td>77</td>\n",
" <td>81</td>\n",
" <td>66</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212070</th>\n",
" <td>E</td>\n",
" <td>carcinoid</td>\n",
" <td>3.7</td>\n",
" <td>81.86</td>\n",
" <td>40</td>\n",
" <td>67</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212074</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.06</td>\n",
" <td>54</td>\n",
" <td>64</td>\n",
" <td>65</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212075</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>1.3</td>\n",
" <td>78</td>\n",
" <td>79</td>\n",
" <td>63</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Afro American</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212787</th>\n",
" <td>E</td>\n",
" <td>NSC-Mixed</td>\n",
" <td>3.66</td>\n",
" <td>78</td>\n",
" <td>78</td>\n",
" <td>70</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212788</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>1.29</td>\n",
" <td>53</td>\n",
" <td>77</td>\n",
" <td>67</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212789</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>1.94</td>\n",
" <td>86</td>\n",
" <td>62</td>\n",
" <td>62</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212790</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.83</td>\n",
" <td>75</td>\n",
" <td>54</td>\n",
" <td>75</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212809</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.28</td>\n",
" <td>89</td>\n",
" <td>61</td>\n",
" <td>65</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212810</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>0.88</td>\n",
" <td>52</td>\n",
" <td>52</td>\n",
" <td>66</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212811</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>1.37</td>\n",
" <td>82</td>\n",
" <td>77</td>\n",
" <td>58</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212848</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>0.55</td>\n",
" <td>44</td>\n",
" <td>59</td>\n",
" <td>58</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212849</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.73</td>\n",
" <td>80</td>\n",
" <td>39</td>\n",
" <td>73</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212850</th>\n",
" <td>E</td>\n",
" <td>carcinoid</td>\n",
" <td>2.32</td>\n",
" <td>76</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212852</th>\n",
" <td>E</td>\n",
" <td>Giant Cell Tumor</td>\n",
" <td>0.99</td>\n",
" <td>63</td>\n",
" <td>72</td>\n",
" <td>60</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212853</th>\n",
" <td>E</td>\n",
" <td>lymphoma</td>\n",
" <td>2.42</td>\n",
" <td>80</td>\n",
" <td>71</td>\n",
" <td>66</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212854</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.26</td>\n",
" <td>83</td>\n",
" <td>64</td>\n",
" <td>66</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM212855</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.43</td>\n",
" <td>69</td>\n",
" <td>73</td>\n",
" <td>68</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213017</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.12</td>\n",
" <td>73</td>\n",
" <td>79</td>\n",
" <td>69</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213018</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.34</td>\n",
" <td>81</td>\n",
" <td>82</td>\n",
" <td>68</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213019</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>1.7</td>\n",
" <td>72</td>\n",
" <td>71</td>\n",
" <td>71</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213020</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>0.67</td>\n",
" <td>42</td>\n",
" <td>75</td>\n",
" <td>63</td>\n",
" <td>chronic obstructive pulmonary disease</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213034</th>\n",
" <td>E</td>\n",
" <td>non-small cell adenocarcinoma</td>\n",
" <td>2.56</td>\n",
" <td>61</td>\n",
" <td>70</td>\n",
" <td>69</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213035</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>3.11</td>\n",
" <td>73</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213036</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.68</td>\n",
" <td>71</td>\n",
" <td>59</td>\n",
" <td>69</td>\n",
" <td>control</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GSE8581GSM213037</th>\n",
" <td>E</td>\n",
" <td>non-small cell squamous cell carcinoma</td>\n",
" <td>2.25</td>\n",
" <td>72</td>\n",
" <td>77</td>\n",
" <td>66</td>\n",
" <td>not specified</td>\n",
" <td>Homo sapiens</td>\n",
" <td>Caucasian</td>\n",
" <td>male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"label \\Public Studies\\GSE8581\\Biomarker Data\\GPL570\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 None \n",
"GSE8581GSM210005 None \n",
"GSE8581GSM210006 None \n",
"GSE8581GSM210007 E \n",
"GSE8581GSM210008 E \n",
"GSE8581GSM210009 E \n",
"GSE8581GSM210010 E \n",
"GSE8581GSM210011 E \n",
"GSE8581GSM210012 E \n",
"GSE8581GSM210014 E \n",
"GSE8581GSM210015 E \n",
"GSE8581GSM210071 E \n",
"GSE8581GSM210087 E \n",
"GSE8581GSM210090 E \n",
"GSE8581GSM210188 E \n",
"GSE8581GSM210192 E \n",
"GSE8581GSM210193 E \n",
"GSE8581GSM210194 E \n",
"GSE8581GSM210196 E \n",
"GSE8581GSM210978 E \n",
"GSE8581GSM210979 E \n",
"GSE8581GSM210992 E \n",
"GSE8581GSM210993 E \n",
"GSE8581GSM210994 E \n",
"GSE8581GSM211007 E \n",
"GSE8581GSM211008 E \n",
"GSE8581GSM211009 E \n",
"GSE8581GSM211010 E \n",
"GSE8581GSM211865 E \n",
"GSE8581GSM211872 E \n",
"GSE8581GSM212067 E \n",
"GSE8581GSM212068 E \n",
"GSE8581GSM212069 E \n",
"GSE8581GSM212070 E \n",
"GSE8581GSM212074 E \n",
"GSE8581GSM212075 E \n",
"GSE8581GSM212787 E \n",
"GSE8581GSM212788 E \n",
"GSE8581GSM212789 E \n",
"GSE8581GSM212790 E \n",
"GSE8581GSM212809 E \n",
"GSE8581GSM212810 E \n",
"GSE8581GSM212811 E \n",
"GSE8581GSM212848 E \n",
"GSE8581GSM212849 E \n",
"GSE8581GSM212850 E \n",
"GSE8581GSM212852 E \n",
"GSE8581GSM212853 E \n",
"GSE8581GSM212854 E \n",
"GSE8581GSM212855 E \n",
"GSE8581GSM213017 E \n",
"GSE8581GSM213018 E \n",
"GSE8581GSM213019 E \n",
"GSE8581GSM213020 E \n",
"GSE8581GSM213034 E \n",
"GSE8581GSM213035 E \n",
"GSE8581GSM213036 E \n",
"GSE8581GSM213037 E \n",
"\n",
"label \\Public Studies\\GSE8581\\Endpoints\\Diagnosis\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 non-small cell squamous cell carcinoma \n",
"GSE8581GSM210005 non-small cell adenocarcinoma \n",
"GSE8581GSM210006 non-small cell adenocarcinoma \n",
"GSE8581GSM210007 non-small cell adenocarcinoma \n",
"GSE8581GSM210008 non-small cell adenocarcinoma \n",
"GSE8581GSM210009 non-small cell squamous cell carcinoma \n",
"GSE8581GSM210010 non-small cell adenocarcinoma \n",
"GSE8581GSM210011 non-small cell squamous cell carcinoma \n",
"GSE8581GSM210012 non-small cell adenocarcinoma \n",
"GSE8581GSM210014 non-small cell adenocarcinoma \n",
"GSE8581GSM210015 non-small cell adenocarcinoma \n",
"GSE8581GSM210071 emphysema \n",
"GSE8581GSM210087 non-small cell adenocarcinoma \n",
"GSE8581GSM210090 hematoma \n",
"GSE8581GSM210188 metastatic non-small cell adenocarcinoma \n",
"GSE8581GSM210192 non-small cell adenocarcinoma \n",
"GSE8581GSM210193 Unknown \n",
"GSE8581GSM210194 non-small cell squamous cell carcinoma \n",
"GSE8581GSM210196 non-small cell adenocarcinoma \n",
"GSE8581GSM210978 non-small cell adenocarcinoma \n",
"GSE8581GSM210979 non-small cell adenocarcinoma \n",
"GSE8581GSM210992 non-small cell squamous cell carcinoma \n",
"GSE8581GSM210993 giant bullae \n",
"GSE8581GSM210994 carcinoid \n",
"GSE8581GSM211007 non-small cell squamous cell carcinoma \n",
"GSE8581GSM211008 non-small cell adenocarcinoma \n",
"GSE8581GSM211009 non-small cell adenocarcinoma \n",
"GSE8581GSM211010 non-small cell adenocarcinoma \n",
"GSE8581GSM211865 non-small cell squamous cell carcinoma \n",
"GSE8581GSM211872 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212067 inflammation \n",
"GSE8581GSM212068 metastatic renal cell carcinoma \n",
"GSE8581GSM212069 no malignancy \n",
"GSE8581GSM212070 carcinoid \n",
"GSE8581GSM212074 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212075 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212787 NSC-Mixed \n",
"GSE8581GSM212788 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212789 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212790 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212809 non-small cell adenocarcinoma \n",
"GSE8581GSM212810 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212811 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212848 non-small cell squamous cell carcinoma \n",
"GSE8581GSM212849 non-small cell adenocarcinoma \n",
"GSE8581GSM212850 carcinoid \n",
"GSE8581GSM212852 Giant Cell Tumor \n",
"GSE8581GSM212853 lymphoma \n",
"GSE8581GSM212854 non-small cell adenocarcinoma \n",
"GSE8581GSM212855 non-small cell adenocarcinoma \n",
"GSE8581GSM213017 non-small cell adenocarcinoma \n",
"GSE8581GSM213018 non-small cell adenocarcinoma \n",
"GSE8581GSM213019 non-small cell adenocarcinoma \n",
"GSE8581GSM213020 non-small cell adenocarcinoma \n",
"GSE8581GSM213034 non-small cell adenocarcinoma \n",
"GSE8581GSM213035 non-small cell squamous cell carcinoma \n",
"GSE8581GSM213036 non-small cell squamous cell carcinoma \n",
"GSE8581GSM213037 non-small cell squamous cell carcinoma \n",
"\n",
"label \\Public Studies\\GSE8581\\Endpoints\\FEV1\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 2.54 \n",
"GSE8581GSM210005 1.69 \n",
"GSE8581GSM210006 1.41 \n",
"GSE8581GSM210007 2.51 \n",
"GSE8581GSM210008 1.64 \n",
"GSE8581GSM210009 2.72 \n",
"GSE8581GSM210010 1.45 \n",
"GSE8581GSM210011 1.87 \n",
"GSE8581GSM210012 2.76 \n",
"GSE8581GSM210014 1.98 \n",
"GSE8581GSM210015 2.59 \n",
"GSE8581GSM210071 1.16 \n",
"GSE8581GSM210087 2.59 \n",
"GSE8581GSM210090 4.08 \n",
"GSE8581GSM210188 1.2 \n",
"GSE8581GSM210192 2.31 \n",
"GSE8581GSM210193 1.63 \n",
"GSE8581GSM210194 0.52 \n",
"GSE8581GSM210196 2.3 \n",
"GSE8581GSM210978 1.53 \n",
"GSE8581GSM210979 2.17 \n",
"GSE8581GSM210992 0.9 \n",
"GSE8581GSM210993 0.4 \n",
"GSE8581GSM210994 1.69 \n",
"GSE8581GSM211007 1.7 \n",
"GSE8581GSM211008 3.66 \n",
"GSE8581GSM211009 1.64 \n",
"GSE8581GSM211010 1.44 \n",
"GSE8581GSM211865 2.13 \n",
"GSE8581GSM211872 2.61 \n",
"GSE8581GSM212067 4.04 \n",
"GSE8581GSM212068 1.99 \n",
"GSE8581GSM212069 1.6 \n",
"GSE8581GSM212070 3.7 \n",
"GSE8581GSM212074 2.06 \n",
"GSE8581GSM212075 1.3 \n",
"GSE8581GSM212787 3.66 \n",
"GSE8581GSM212788 1.29 \n",
"GSE8581GSM212789 1.94 \n",
"GSE8581GSM212790 2.83 \n",
"GSE8581GSM212809 1.28 \n",
"GSE8581GSM212810 0.88 \n",
"GSE8581GSM212811 1.37 \n",
"GSE8581GSM212848 0.55 \n",
"GSE8581GSM212849 2.73 \n",
"GSE8581GSM212850 2.32 \n",
"GSE8581GSM212852 0.99 \n",
"GSE8581GSM212853 2.42 \n",
"GSE8581GSM212854 2.26 \n",
"GSE8581GSM212855 2.43 \n",
"GSE8581GSM213017 2.12 \n",
"GSE8581GSM213018 2.34 \n",
"GSE8581GSM213019 1.7 \n",
"GSE8581GSM213020 0.67 \n",
"GSE8581GSM213034 2.56 \n",
"GSE8581GSM213035 3.11 \n",
"GSE8581GSM213036 2.68 \n",
"GSE8581GSM213037 2.25 \n",
"\n",
"label \\Public Studies\\GSE8581\\Endpoints\\Forced Expiratory Volume Ratio\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 58 \n",
"GSE8581GSM210005 83.66 \n",
"GSE8581GSM210006 51 \n",
"GSE8581GSM210007 80.96 \n",
"GSE8581GSM210008 57 \n",
"GSE8581GSM210009 74 \n",
"GSE8581GSM210010 73 \n",
"GSE8581GSM210011 56 \n",
"GSE8581GSM210012 70.58 \n",
"GSE8581GSM210014 78 \n",
"GSE8581GSM210015 74 \n",
"GSE8581GSM210071 43 \n",
"GSE8581GSM210087 74 \n",
"GSE8581GSM210090 68 \n",
"GSE8581GSM210188 72 \n",
"GSE8581GSM210192 75 \n",
"GSE8581GSM210193 80.78 \n",
"GSE8581GSM210194 58 \n",
"GSE8581GSM210196 74 \n",
"GSE8581GSM210978 74 \n",
"GSE8581GSM210979 76 \n",
"GSE8581GSM210992 55.9 \n",
"GSE8581GSM210993 45 \n",
"GSE8581GSM210994 76 \n",
"GSE8581GSM211007 59 \n",
"GSE8581GSM211008 72 \n",
"GSE8581GSM211009 41 \n",
"GSE8581GSM211010 86 \n",
"GSE8581GSM211865 71 \n",
"GSE8581GSM211872 70 \n",
"GSE8581GSM212067 79 \n",
"GSE8581GSM212068 85 \n",
"GSE8581GSM212069 77 \n",
"GSE8581GSM212070 81.86 \n",
"GSE8581GSM212074 54 \n",
"GSE8581GSM212075 78 \n",
"GSE8581GSM212787 78 \n",
"GSE8581GSM212788 53 \n",
"GSE8581GSM212789 86 \n",
"GSE8581GSM212790 75 \n",
"GSE8581GSM212809 89 \n",
"GSE8581GSM212810 52 \n",
"GSE8581GSM212811 82 \n",
"GSE8581GSM212848 44 \n",
"GSE8581GSM212849 80 \n",
"GSE8581GSM212850 76 \n",
"GSE8581GSM212852 63 \n",
"GSE8581GSM212853 80 \n",
"GSE8581GSM212854 83 \n",
"GSE8581GSM212855 69 \n",
"GSE8581GSM213017 73 \n",
"GSE8581GSM213018 81 \n",
"GSE8581GSM213019 72 \n",
"GSE8581GSM213020 42 \n",
"GSE8581GSM213034 61 \n",
"GSE8581GSM213035 73 \n",
"GSE8581GSM213036 71 \n",
"GSE8581GSM213037 72 \n",
"\n",
"label \\Public Studies\\GSE8581\\Subjects\\Age\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 63 \n",
"GSE8581GSM210005 84 \n",
"GSE8581GSM210006 65 \n",
"GSE8581GSM210007 46 \n",
"GSE8581GSM210008 53 \n",
"GSE8581GSM210009 53 \n",
"GSE8581GSM210010 77 \n",
"GSE8581GSM210011 56 \n",
"GSE8581GSM210012 61 \n",
"GSE8581GSM210014 71 \n",
"GSE8581GSM210015 68 \n",
"GSE8581GSM210071 68 \n",
"GSE8581GSM210087 68 \n",
"GSE8581GSM210090 50 \n",
"GSE8581GSM210188 65 \n",
"GSE8581GSM210192 50 \n",
"GSE8581GSM210193 67 \n",
"GSE8581GSM210194 56 \n",
"GSE8581GSM210196 57 \n",
"GSE8581GSM210978 73 \n",
"GSE8581GSM210979 55 \n",
"GSE8581GSM210992 70 \n",
"GSE8581GSM210993 61 \n",
"GSE8581GSM210994 64 \n",
"GSE8581GSM211007 61 \n",
"GSE8581GSM211008 71 \n",
"GSE8581GSM211009 75 \n",
"GSE8581GSM211010 61 \n",
"GSE8581GSM211865 69 \n",
"GSE8581GSM211872 74 \n",
"GSE8581GSM212067 55 \n",
"GSE8581GSM212068 78 \n",
"GSE8581GSM212069 81 \n",
"GSE8581GSM212070 40 \n",
"GSE8581GSM212074 64 \n",
"GSE8581GSM212075 79 \n",
"GSE8581GSM212787 78 \n",
"GSE8581GSM212788 77 \n",
"GSE8581GSM212789 62 \n",
"GSE8581GSM212790 54 \n",
"GSE8581GSM212809 61 \n",
"GSE8581GSM212810 52 \n",
"GSE8581GSM212811 77 \n",
"GSE8581GSM212848 59 \n",
"GSE8581GSM212849 39 \n",
"GSE8581GSM212850 71 \n",
"GSE8581GSM212852 72 \n",
"GSE8581GSM212853 71 \n",
"GSE8581GSM212854 64 \n",
"GSE8581GSM212855 73 \n",
"GSE8581GSM213017 79 \n",
"GSE8581GSM213018 82 \n",
"GSE8581GSM213019 71 \n",
"GSE8581GSM213020 75 \n",
"GSE8581GSM213034 70 \n",
"GSE8581GSM213035 67 \n",
"GSE8581GSM213036 59 \n",
"GSE8581GSM213037 77 \n",
"\n",
"label \\Public Studies\\GSE8581\\Subjects\\Height (inch)\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 72 \n",
"GSE8581GSM210005 60 \n",
"GSE8581GSM210006 66 \n",
"GSE8581GSM210007 66 \n",
"GSE8581GSM210008 65 \n",
"GSE8581GSM210009 64 \n",
"GSE8581GSM210010 63 \n",
"GSE8581GSM210011 72 \n",
"GSE8581GSM210012 69 \n",
"GSE8581GSM210014 63 \n",
"GSE8581GSM210015 65 \n",
"GSE8581GSM210071 67 \n",
"GSE8581GSM210087 65 \n",
"GSE8581GSM210090 72 \n",
"GSE8581GSM210188 63 \n",
"GSE8581GSM210192 65 \n",
"GSE8581GSM210193 66 \n",
"GSE8581GSM210194 60 \n",
"GSE8581GSM210196 65 \n",
"GSE8581GSM210978 66 \n",
"GSE8581GSM210979 63 \n",
"GSE8581GSM210992 71 \n",
"GSE8581GSM210993 73 \n",
"GSE8581GSM210994 63 \n",
"GSE8581GSM211007 66 \n",
"GSE8581GSM211008 71 \n",
"GSE8581GSM211009 71 \n",
"GSE8581GSM211010 60 \n",
"GSE8581GSM211865 67 \n",
"GSE8581GSM211872 70 \n",
"GSE8581GSM212067 69 \n",
"GSE8581GSM212068 63 \n",
"GSE8581GSM212069 66 \n",
"GSE8581GSM212070 67 \n",
"GSE8581GSM212074 65 \n",
"GSE8581GSM212075 63 \n",
"GSE8581GSM212787 70 \n",
"GSE8581GSM212788 67 \n",
"GSE8581GSM212789 62 \n",
"GSE8581GSM212790 75 \n",
"GSE8581GSM212809 65 \n",
"GSE8581GSM212810 66 \n",
"GSE8581GSM212811 58 \n",
"GSE8581GSM212848 58 \n",
"GSE8581GSM212849 73 \n",
"GSE8581GSM212850 69 \n",
"GSE8581GSM212852 60 \n",
"GSE8581GSM212853 66 \n",
"GSE8581GSM212854 66 \n",
"GSE8581GSM212855 68 \n",
"GSE8581GSM213017 69 \n",
"GSE8581GSM213018 68 \n",
"GSE8581GSM213019 71 \n",
"GSE8581GSM213020 63 \n",
"GSE8581GSM213034 69 \n",
"GSE8581GSM213035 71 \n",
"GSE8581GSM213036 69 \n",
"GSE8581GSM213037 66 \n",
"\n",
"label \\Public Studies\\GSE8581\\Subjects\\Lung Disease\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 chronic obstructive pulmonary disease \n",
"GSE8581GSM210005 control \n",
"GSE8581GSM210006 chronic obstructive pulmonary disease \n",
"GSE8581GSM210007 not specified \n",
"GSE8581GSM210008 chronic obstructive pulmonary disease \n",
"GSE8581GSM210009 control \n",
"GSE8581GSM210010 not specified \n",
"GSE8581GSM210011 chronic obstructive pulmonary disease \n",
"GSE8581GSM210012 not specified \n",
"GSE8581GSM210014 control \n",
"GSE8581GSM210015 control \n",
"GSE8581GSM210071 chronic obstructive pulmonary disease \n",
"GSE8581GSM210087 control \n",
"GSE8581GSM210090 not specified \n",
"GSE8581GSM210188 not specified \n",
"GSE8581GSM210192 control \n",
"GSE8581GSM210193 not specified \n",
"GSE8581GSM210194 chronic obstructive pulmonary disease \n",
"GSE8581GSM210196 control \n",
"GSE8581GSM210978 not specified \n",
"GSE8581GSM210979 control \n",
"GSE8581GSM210992 chronic obstructive pulmonary disease \n",
"GSE8581GSM210993 chronic obstructive pulmonary disease \n",
"GSE8581GSM210994 not specified \n",
"GSE8581GSM211007 chronic obstructive pulmonary disease \n",
"GSE8581GSM211008 control \n",
"GSE8581GSM211009 chronic obstructive pulmonary disease \n",
"GSE8581GSM211010 not specified \n",
"GSE8581GSM211865 not specified \n",
"GSE8581GSM211872 not specified \n",
"GSE8581GSM212067 control \n",
"GSE8581GSM212068 control \n",
"GSE8581GSM212069 not specified \n",
"GSE8581GSM212070 control \n",
"GSE8581GSM212074 chronic obstructive pulmonary disease \n",
"GSE8581GSM212075 chronic obstructive pulmonary disease \n",
"GSE8581GSM212787 control \n",
"GSE8581GSM212788 chronic obstructive pulmonary disease \n",
"GSE8581GSM212789 control \n",
"GSE8581GSM212790 control \n",
"GSE8581GSM212809 not specified \n",
"GSE8581GSM212810 chronic obstructive pulmonary disease \n",
"GSE8581GSM212811 control \n",
"GSE8581GSM212848 chronic obstructive pulmonary disease \n",
"GSE8581GSM212849 not specified \n",
"GSE8581GSM212850 not specified \n",
"GSE8581GSM212852 chronic obstructive pulmonary disease \n",
"GSE8581GSM212853 control \n",
"GSE8581GSM212854 not specified \n",
"GSE8581GSM212855 not specified \n",
"GSE8581GSM213017 not specified \n",
"GSE8581GSM213018 not specified \n",
"GSE8581GSM213019 not specified \n",
"GSE8581GSM213020 chronic obstructive pulmonary disease \n",
"GSE8581GSM213034 not specified \n",
"GSE8581GSM213035 control \n",
"GSE8581GSM213036 control \n",
"GSE8581GSM213037 not specified \n",
"\n",
"label \\Public Studies\\GSE8581\\Subjects\\Organism\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 Homo sapiens \n",
"GSE8581GSM210005 Homo sapiens \n",
"GSE8581GSM210006 Homo sapiens \n",
"GSE8581GSM210007 Homo sapiens \n",
"GSE8581GSM210008 Homo sapiens \n",
"GSE8581GSM210009 Homo sapiens \n",
"GSE8581GSM210010 Homo sapiens \n",
"GSE8581GSM210011 Homo sapiens \n",
"GSE8581GSM210012 Homo sapiens \n",
"GSE8581GSM210014 Homo sapiens \n",
"GSE8581GSM210015 Homo sapiens \n",
"GSE8581GSM210071 Homo sapiens \n",
"GSE8581GSM210087 Homo sapiens \n",
"GSE8581GSM210090 Homo sapiens \n",
"GSE8581GSM210188 Homo sapiens \n",
"GSE8581GSM210192 Homo sapiens \n",
"GSE8581GSM210193 Homo sapiens \n",
"GSE8581GSM210194 Homo sapiens \n",
"GSE8581GSM210196 Homo sapiens \n",
"GSE8581GSM210978 Homo sapiens \n",
"GSE8581GSM210979 Homo sapiens \n",
"GSE8581GSM210992 Homo sapiens \n",
"GSE8581GSM210993 Homo sapiens \n",
"GSE8581GSM210994 Homo sapiens \n",
"GSE8581GSM211007 Homo sapiens \n",
"GSE8581GSM211008 Homo sapiens \n",
"GSE8581GSM211009 Homo sapiens \n",
"GSE8581GSM211010 Homo sapiens \n",
"GSE8581GSM211865 Homo sapiens \n",
"GSE8581GSM211872 Homo sapiens \n",
"GSE8581GSM212067 Homo sapiens \n",
"GSE8581GSM212068 Homo sapiens \n",
"GSE8581GSM212069 Homo sapiens \n",
"GSE8581GSM212070 Homo sapiens \n",
"GSE8581GSM212074 Homo sapiens \n",
"GSE8581GSM212075 Homo sapiens \n",
"GSE8581GSM212787 Homo sapiens \n",
"GSE8581GSM212788 Homo sapiens \n",
"GSE8581GSM212789 Homo sapiens \n",
"GSE8581GSM212790 Homo sapiens \n",
"GSE8581GSM212809 Homo sapiens \n",
"GSE8581GSM212810 Homo sapiens \n",
"GSE8581GSM212811 Homo sapiens \n",
"GSE8581GSM212848 Homo sapiens \n",
"GSE8581GSM212849 Homo sapiens \n",
"GSE8581GSM212850 Homo sapiens \n",
"GSE8581GSM212852 Homo sapiens \n",
"GSE8581GSM212853 Homo sapiens \n",
"GSE8581GSM212854 Homo sapiens \n",
"GSE8581GSM212855 Homo sapiens \n",
"GSE8581GSM213017 Homo sapiens \n",
"GSE8581GSM213018 Homo sapiens \n",
"GSE8581GSM213019 Homo sapiens \n",
"GSE8581GSM213020 Homo sapiens \n",
"GSE8581GSM213034 Homo sapiens \n",
"GSE8581GSM213035 Homo sapiens \n",
"GSE8581GSM213036 Homo sapiens \n",
"GSE8581GSM213037 Homo sapiens \n",
"\n",
"label \\Public Studies\\GSE8581\\Subjects\\Race\\ \\\n",
"subject.inTrialId \n",
"GSE8581GSM210004 Caucasian \n",
"GSE8581GSM210005 Afro American \n",
"GSE8581GSM210006 Afro American \n",
"GSE8581GSM210007 Caucasian \n",
"GSE8581GSM210008 Caucasian \n",
"GSE8581GSM210009 Caucasian \n",
"GSE8581GSM210010 Caucasian \n",
"GSE8581GSM210011 Caucasian \n",
"GSE8581GSM210012 Caucasian \n",
"GSE8581GSM210014 Caucasian \n",
"GSE8581GSM210015 Caucasian \n",
"GSE8581GSM210071 Caucasian \n",
"GSE8581GSM210087 Caucasian \n",
"GSE8581GSM210090 Caucasian \n",
"GSE8581GSM210188 Caucasian \n",
"GSE8581GSM210192 Caucasian \n",
"GSE8581GSM210193 Caucasian \n",
"GSE8581GSM210194 Caucasian \n",
"GSE8581GSM210196 Caucasian \n",
"GSE8581GSM210978 Caucasian \n",
"GSE8581GSM210979 Caucasian \n",
"GSE8581GSM210992 Caucasian \n",
"GSE8581GSM210993 Caucasian \n",
"GSE8581GSM210994 Caucasian \n",
"GSE8581GSM211007 Caucasian \n",
"GSE8581GSM211008 Caucasian \n",
"GSE8581GSM211009 Caucasian \n",
"GSE8581GSM211010 Caucasian \n",
"GSE8581GSM211865 Caucasian \n",
"GSE8581GSM211872 Caucasian \n",
"GSE8581GSM212067 Caucasian \n",
"GSE8581GSM212068 Caucasian \n",
"GSE8581GSM212069 Caucasian \n",
"GSE8581GSM212070 Caucasian \n",
"GSE8581GSM212074 Caucasian \n",
"GSE8581GSM212075 Afro American \n",
"GSE8581GSM212787 Caucasian \n",
"GSE8581GSM212788 Caucasian \n",
"GSE8581GSM212789 Caucasian \n",
"GSE8581GSM212790 Caucasian \n",
"GSE8581GSM212809 Caucasian \n",
"GSE8581GSM212810 Caucasian \n",
"GSE8581GSM212811 Caucasian \n",
"GSE8581GSM212848 Caucasian \n",
"GSE8581GSM212849 Caucasian \n",
"GSE8581GSM212850 Caucasian \n",
"GSE8581GSM212852 Caucasian \n",
"GSE8581GSM212853 Caucasian \n",
"GSE8581GSM212854 Caucasian \n",
"GSE8581GSM212855 Caucasian \n",
"GSE8581GSM213017 Caucasian \n",
"GSE8581GSM213018 Caucasian \n",
"GSE8581GSM213019 Caucasian \n",
"GSE8581GSM213020 Caucasian \n",
"GSE8581GSM213034 Caucasian \n",
"GSE8581GSM213035 Caucasian \n",
"GSE8581GSM213036 Caucasian \n",
"GSE8581GSM213037 Caucasian \n",
"\n",
"label \\Public Studies\\GSE8581\\Subjects\\Sex\\ \n",
"subject.inTrialId \n",
"GSE8581GSM210004 male \n",
"GSE8581GSM210005 female \n",
"GSE8581GSM210006 female \n",
"GSE8581GSM210007 male \n",
"GSE8581GSM210008 female \n",
"GSE8581GSM210009 female \n",
"GSE8581GSM210010 female \n",
"GSE8581GSM210011 male \n",
"GSE8581GSM210012 male \n",
"GSE8581GSM210014 female \n",
"GSE8581GSM210015 male \n",
"GSE8581GSM210071 male \n",
"GSE8581GSM210087 female \n",
"GSE8581GSM210090 male \n",
"GSE8581GSM210188 female \n",
"GSE8581GSM210192 female \n",
"GSE8581GSM210193 female \n",
"GSE8581GSM210194 female \n",
"GSE8581GSM210196 female \n",
"GSE8581GSM210978 female \n",
"GSE8581GSM210979 female \n",
"GSE8581GSM210992 male \n",
"GSE8581GSM210993 male \n",
"GSE8581GSM210994 female \n",
"GSE8581GSM211007 male \n",
"GSE8581GSM211008 male \n",
"GSE8581GSM211009 male \n",
"GSE8581GSM211010 female \n",
"GSE8581GSM211865 male \n",
"GSE8581GSM211872 male \n",
"GSE8581GSM212067 male \n",
"GSE8581GSM212068 female \n",
"GSE8581GSM212069 female \n",
"GSE8581GSM212070 male \n",
"GSE8581GSM212074 male \n",
"GSE8581GSM212075 female \n",
"GSE8581GSM212787 male \n",
"GSE8581GSM212788 female \n",
"GSE8581GSM212789 female \n",
"GSE8581GSM212790 female \n",
"GSE8581GSM212809 female \n",
"GSE8581GSM212810 female \n",
"GSE8581GSM212811 female \n",
"GSE8581GSM212848 female \n",
"GSE8581GSM212849 male \n",
"GSE8581GSM212850 male \n",
"GSE8581GSM212852 female \n",
"GSE8581GSM212853 female \n",
"GSE8581GSM212854 male \n",
"GSE8581GSM212855 male \n",
"GSE8581GSM213017 male \n",
"GSE8581GSM213018 male \n",
"GSE8581GSM213019 female \n",
"GSE8581GSM213020 male \n",
"GSE8581GSM213034 male \n",
"GSE8581GSM213035 male \n",
"GSE8581GSM213036 female \n",
"GSE8581GSM213037 male "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfp = df.pivot(index = 'subject.inTrialId', columns = 'label', values = 'value')\n",
"dfp"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'data.frame':\t58 obs. of 10 variables:\n",
" $ X.Public.Studies.GSE8581.Biomarker.Data.GPL570. : Factor w/ 2 levels \"E\",\"None\": 2 2 2 1 1 1 1 1 1 1 ...\n",
" $ X.Public.Studies.GSE8581.Endpoints.Diagnosis. : Factor w/ 14 levels \"carcinoid\",\"emphysema\",..: 12 11 11 11 11 12 11 12 11 11 ...\n",
" $ X.Public.Studies.GSE8581.Endpoints.FEV1. : Factor w/ 53 levels \"0.4\",\"0.52\",\"0.55\",..: 40 21 14 39 20 45 16 23 47 25 ...\n",
" $ X.Public.Studies.GSE8581.Endpoints.Forced.Expiratory.Volume.Ratio.: Factor w/ 40 levels \"41.0\",\"42.0\",..: 13 37 6 32 12 24 23 11 20 28 ...\n",
" $ X.Public.Studies.GSE8581.Subjects.Age. : Factor w/ 31 levels \"39.0\",\"40.0\",..: 14 31 16 3 6 6 26 9 12 21 ...\n",
" $ X.Public.Studies.GSE8581.Subjects.Height..inch.. : Factor w/ 15 levels \"58.0\",\"60.0\",..: 13 2 7 7 6 5 4 13 10 4 ...\n",
" $ X.Public.Studies.GSE8581.Subjects.Lung.Disease. : Factor w/ 3 levels \"chronic obstructive pulmonary disease\",..: 1 2 1 3 1 2 3 1 3 2 ...\n",
" $ X.Public.Studies.GSE8581.Subjects.Organism. : Factor w/ 1 level \"Homo sapiens\": 1 1 1 1 1 1 1 1 1 1 ...\n",
" $ X.Public.Studies.GSE8581.Subjects.Race. : Factor w/ 2 levels \"Afro American\",..: 2 1 1 2 2 2 2 2 2 2 ...\n",
" $ X.Public.Studies.GSE8581.Subjects.Sex. : Factor w/ 2 levels \"female\",\"male\": 2 1 1 2 1 1 1 2 2 1 ...\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Wb8Dcg9eOe5o5aYvRIh2SEDrmVVAtWK7JNvv919Z1lQSVkEVZ6WG+UKDwIR1W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\ngx5YngAAAABJRU5ErkJggg==\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R -i dfp\n",
"\n",
"plot(c(dfp$X.Public.Studies.GSE8581.Subjects.Age), c(dfp$X.Public.Studies.GSE8581.Subjects.Height..inch..))\n",
"\n",
"str(dfp)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"ename": "MemoryError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-2-bf1385ab0859>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m(\u001b[0m\u001b[0mhdHeader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdRows\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mapi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_hd_node_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstudy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'GSE8581'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Lung'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/vagrant/workspace/transmart_api.py\u001b[0m in \u001b[0;36mget_hd_node_data\u001b[1;34m(self, study, node_name, genes)\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[0mhd_node_data_url\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhd_node_data_url\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 78\u001b[0m \u001b[1;34m'&'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0murllib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlencode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'dataConstraints'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'genes'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'names'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mgenes\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[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m \u001b[0mhd_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_protobuf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhd_node_data_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_access_token\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[0m\u001b[0;32m 80\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mhd_data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/vagrant/workspace/transmart_api.py\u001b[0m in \u001b[0;36m_get_protobuf\u001b[1;34m(self, url, access_token)\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Authorization'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Bearer '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0maccess_token\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 51\u001b[0m \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0murllib2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mRequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 52\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_protobuf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murllib2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\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[0m\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_access_token\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/vagrant/workspace/transmart_api.py\u001b[0m in \u001b[0;36m_parse_protobuf\u001b[1;34m(self, proto)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_parse_protobuf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mproto\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[0mhdHeader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mHighDimHeader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mposition\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdecoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_DecodeVarint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mproto\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mMemoryError\u001b[0m: "
]
}
],
"source": [
"(hdHeader, hdRows) = api.get_hd_node_data(study = 'GSE8581', node_name = 'Lung')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#1 - double\n",
"#2 - string\n",
"[(x.name, x.type) for x in hdHeader.columnSpec]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"hdDataDic = {row.label: row.value[1].doubleValue for row in hdRows}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pandas import DataFrame\n",
"hdDataDic['patientId'] = [assay.patientId for assay in hdHeader.assay]\n",
"assayIds = [assay.assayId for assay in hdHeader.assay]\n",
"DataFrame(data=hdDataDic, index = assayIds)"
]
}
],
"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 |
leriomaggio/deep-learning-keras-tensorflow | 0. Preamble.ipynb | 1 | 9380 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deep Learning Tutorial with Keras and Tensorflow"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div>\n",
" <img style=\"text-align: left\" src=\"imgs/keras-tensorflow-logo.jpg\" width=\"40%\" />\n",
"<div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the Materials"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"imgs/github.jpg\" />\n",
"```shell\n",
"\n",
"git clone https://github.com/leriomaggio/deep-learning-keras-tensorflow.git\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Outline at a glance\n",
"\n",
"- **Part I**: **Introduction**\n",
"\n",
" - Intro to Artificial Neural Networks\n",
" - Perceptron and MLP \n",
" - naive pure-Python implementation\n",
" - fast forward, sgd, backprop\n",
" \n",
" - Introduction to Deep Learning Frameworks\n",
" - Intro to Theano\n",
" - Intro to Tensorflow\n",
" - Intro to Keras\n",
" - Overview and main features\n",
" - Overview of the `core` layers\n",
" - Multi-Layer Perceptron and Fully Connected\n",
" - Examples with `keras.models.Sequential` and `Dense`\n",
" - Keras Backend\n",
" \n",
"- **Part II**: **Supervised Learning **\n",
" \n",
" - Fully Connected Networks and Embeddings\n",
" - Intro to MNIST Dataset\n",
" - Hidden Leayer Representation and Embeddings\n",
" \n",
" - Convolutional Neural Networks\n",
" - meaning of convolutional filters\n",
" - examples from ImageNet \n",
" - Visualising ConvNets \n",
"\n",
" - Advanced CNN\n",
" - Dropout\n",
" - MaxPooling\n",
" - Batch Normalisation\n",
"\n",
" - HandsOn: MNIST Dataset\n",
" - FC and MNIST\n",
" - CNN and MNIST\n",
" \n",
" - Deep Convolutiona Neural Networks with Keras (ref: `keras.applications`)\n",
" - VGG16\n",
" - VGG19\n",
" - ResNet50\n",
" - Transfer Learning and FineTuning\n",
" - Hyperparameters Optimisation \n",
" \n",
"- **Part III**: **Unsupervised Learning**\n",
"\n",
" - AutoEncoders and Embeddings\n",
"\t- AutoEncoders and MNIST\n",
" \t- word2vec and doc2vec (gensim) with `keras.datasets`\n",
" - word2vec and CNN\n",
" \n",
"- **Part IV**: **Recurrent Neural Networks**\n",
" - Recurrent Neural Network in Keras \n",
" - `SimpleRNN`, `LSTM`, `GRU`\n",
" - LSTM for Sentence Generation\n",
"\t\t\n",
"- **PartV**: **Additional Materials**: \n",
" - Custom Layers in Keras \n",
" - Multi modal Network Topologies with Keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Requirements"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial requires the following packages:\n",
"\n",
"- Python version 3.5\n",
" - Python 3.4 should be fine as well\n",
" - likely Python 2.7 would be also fine, but *who knows*? :P\n",
" \n",
"- `numpy` version 1.10 or later: http://www.numpy.org/\n",
"- `scipy` version 0.16 or later: http://www.scipy.org/\n",
"- `matplotlib` version 1.4 or later: http://matplotlib.org/\n",
"- `pandas` version 0.16 or later: http://pandas.pydata.org\n",
"- `scikit-learn` version 0.15 or later: http://scikit-learn.org\n",
"- `keras` version 2.0 or later: http://keras.io\n",
"- `tensorflow` version 1.0 or later: https://www.tensorflow.org\n",
"- `ipython`/`jupyter` version 4.0 or later, with notebook support\n",
"\n",
"(Optional but recommended):\n",
"\n",
"- `pyyaml`\n",
"- `hdf5` and `h5py` (required if you use model saving/loading functions in keras)\n",
"- **NVIDIA cuDNN** if you have NVIDIA GPUs on your machines.\n",
" [https://developer.nvidia.com/rdp/cudnn-download]()\n",
"\n",
"The easiest way to get (most) these is to use an all-in-one installer such as [Anaconda](http://www.continuum.io/downloads) from Continuum. These are available for multiple architectures."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Python Version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'm currently running this tutorial with **Python 3** on **Anaconda**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python 3.5.2\r\n"
]
}
],
"source": [
"!python --version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Configure Keras with tensorflow\n",
"\n",
"1) Create the `keras.json` (if it does not exist):\n",
"\n",
"```shell\n",
"touch $HOME/.keras/keras.json\n",
"```\n",
"\n",
"2) Copy the following content into the file:\n",
"\n",
"```\n",
"{\n",
" \"epsilon\": 1e-07,\n",
" \"backend\": \"tensorflow\",\n",
" \"floatx\": \"float32\",\n",
" \"image_data_format\": \"channels_last\"\n",
"}\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\r\n",
"\t\"epsilon\": 1e-07,\r\n",
"\t\"backend\": \"tensorflow\",\r\n",
"\t\"floatx\": \"float32\",\r\n",
"\t\"image_data_format\": \"channels_last\"\r\n",
"}"
]
}
],
"source": [
"!cat ~/.keras/keras.json"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test if everything is up&running"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Check import"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy as sp\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Check installeded Versions"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy: 1.11.1\n",
"scipy: 0.18.0\n",
"matplotlib: 1.5.2\n",
"iPython: 5.1.0\n",
"scikit-learn: 0.18\n"
]
}
],
"source": [
"import numpy\n",
"print('numpy:', numpy.__version__)\n",
"\n",
"import scipy\n",
"print('scipy:', scipy.__version__)\n",
"\n",
"import matplotlib\n",
"print('matplotlib:', matplotlib.__version__)\n",
"\n",
"import IPython\n",
"print('iPython:', IPython.__version__)\n",
"\n",
"import sklearn\n",
"print('scikit-learn:', sklearn.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"keras: 2.0.2\n",
"Theano: 0.9.0\n",
"Tensorflow: 1.0.1\n"
]
}
],
"source": [
"import keras\n",
"print('keras: ', keras.__version__)\n",
"\n",
"# optional\n",
"import theano\n",
"print('Theano: ', theano.__version__)\n",
"\n",
"import tensorflow as tf\n",
"print('Tensorflow: ', tf.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<h1 style=\"text-align: center;\">If everything worked till down here, you're ready to start!</h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n"
]
}
],
"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 |
JWarmenhoven/DBDA-python | Notebooks/Chapter 12.ipynb | 1 | 461647 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Chapter 12 - Bayesian Approaches to Testing a Point (\"Null\") Hypothesis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- [12.2.2 - Are different groups equal or not?](#12.2.2---Are-different-groups-equal-or-not?)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import pymc3 as pm\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
"\n",
"import theano.tensor as tt\n",
"from matplotlib import gridspec\n",
"\n",
"%matplotlib inline\n",
"plt.style.use('seaborn-white')\n",
"\n",
"color = '#87ceeb'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pandas 0.23.4\n",
"numpy 1.15.0\n",
"pymc3 3.5\n",
"matplotlib 2.2.3\n",
"seaborn 0.9.0\n",
"theano 1.0.2\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -p pandas,numpy,pymc3,matplotlib,seaborn,theano"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Data\n",
"Using *R*, I executed lines 18-63 from the script `OneOddGroupModelComp2E.R` to generate the exact same data used in the book. The script can be downloaded from the book's website. After executing the lines, the List object `dataList` in *R* contains five elements:\n",
" 1. `nCond`: A scalar value (4) representing the number of conditions (background music types).\n",
" 2. `nSubj`: A scalar value (80) representing the number of subjects.\n",
" 3. `CondOfSubj`: A vector representing the condition (1, 2, 3 or 4) of a subject during a test.\n",
" 4. `nTrlOfSubj`: A vector with the number of trials/words per subject (20 for all subjects).\n",
" 5. `nCorrOfSubj`: A vector with number of correct recalls per subject.\n",
" \n",
"I exported the last three elements of `dataList` to a csv file using the following command in *R*: \n",
"`write.csv(data.frame(dataList[c(3:5)]), file='background_music.csv', row.names=FALSE)`"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 80 entries, 0 to 79\n",
"Data columns (total 4 columns):\n",
"CondOfSubj 80 non-null category\n",
"nTrlOfSubj 80 non-null int64\n",
"nCorrOfSubj 80 non-null int64\n",
"CondText 80 non-null object\n",
"dtypes: category(1), int64(2), object(1)\n",
"memory usage: 2.1+ KB\n"
]
}
],
"source": [
"df = pd.read_csv('data/background_music.csv', dtype={'CondOfSubj':'category'})\n",
"\n",
"# Mapping the condition descriptions to the condition codes. Just for illustrative purposes.\n",
"bgmusic = {0:'Das Kruschke', 1:'Mozart', 2:'Bach', 3:'Beethoven'}\n",
"df['CondText'] = df.CondOfSubj.cat.codes.map(bgmusic)\n",
"\n",
"cond_idx = df.CondOfSubj.cat.codes.values\n",
"cond_codes = df.CondOfSubj.cat.categories\n",
"nCond = cond_codes.size\n",
"\n",
"nSubj = df.index.size\n",
"\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"\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>CondOfSubj</th>\n",
" <th>nTrlOfSubj</th>\n",
" <th>nCorrOfSubj</th>\n",
" <th>CondText</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>8</td>\n",
" <td>Das Kruschke</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>7</td>\n",
" <td>Das Kruschke</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>20</td>\n",
" <td>8</td>\n",
" <td>Das Kruschke</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>Mozart</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>Mozart</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>9</td>\n",
" <td>Mozart</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>11</td>\n",
" <td>Bach</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>Bach</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>3</td>\n",
" <td>20</td>\n",
" <td>11</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>Beethoven</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>Beethoven</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>4</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>Beethoven</td>\n",
" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" CondOfSubj nTrlOfSubj nCorrOfSubj CondText\n",
"0 1 20 8 Das Kruschke\n",
"1 1 20 7 Das Kruschke\n",
"2 1 20 8 Das Kruschke\n",
"20 2 20 9 Mozart\n",
"21 2 20 12 Mozart\n",
"22 2 20 9 Mozart\n",
"40 3 20 11 Bach\n",
"41 3 20 6 Bach\n",
"42 3 20 11 Bach\n",
"60 4 20 6 Beethoven\n",
"61 4 20 12 Beethoven\n",
"62 4 20 12 Beethoven"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('CondOfSubj').head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 12.2.2 - Are different groups equal or not?\n",
"Given the data, how credible is it that the 4 types of background music influence the ability to recall words \n",
"**differently**?"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CondText\n",
"Das Kruschke 8.0\n",
"Mozart 10.0\n",
"Bach 10.2\n",
"Beethoven 10.4\n",
"Name: nCorrOfSubj, dtype: float64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The means as mentioned in section 12.2.2\n",
"df.groupby('CondText', sort=False)['nCorrOfSubj'].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: in contrast to the *R* output in the book, the parameters in PyMC3 (like $\\omega$ and model index) are indexed starting with 0.\n",
"\n",
"\n",
"Model 0 = condition specific $\\omega_c$ \n",
"Model 1 = same $\\omega$ for all conditions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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],
"source": [
"with pm.Model() as model_1:\n",
" # constants\n",
" aP, bP = 1., 1.\n",
" \n",
" # Pseudo- and true priors for model 1. \n",
" a0 = tt.as_tensor([.48*500, aP]) \n",
" b0 = tt.as_tensor([(1-.48)*500, bP])\n",
" \n",
" # True and pseudopriors for model 0\n",
" a = tt.as_tensor(np.c_[np.tile(aP, 4), [(.40*125), (.50*125), (.51*125), (.52*125)]])\n",
" b = tt.as_tensor(np.c_[np.tile(bP, 4), [(1-.40)*125, (1-.50)*125, (1-.51)*125, (1-.52)*125]]) \n",
" \n",
" # Prior on model index [0,1]\n",
" m_idx = pm.Categorical('m_idx', np.asarray([.5, .5]))\n",
" \n",
" # Priors on concentration parameters\n",
" kappa_minus2 = pm.Gamma('kappa_minus2', 2.618, 0.0809, shape=nCond)\n",
" kappa = pm.Deterministic('kappa', kappa_minus2 +2)\n",
" \n",
" # omega0 \n",
" omega0 = pm.Beta('omega0', a0[m_idx], b0[m_idx]) \n",
" \n",
" # omega (condition specific)\n",
" omega = pm.Beta('omega', a[:,m_idx], b[:,m_idx], shape=nCond)\n",
" \n",
" # Use condition specific omega when m_idx = 0, else omega0\n",
" aBeta = pm.math.switch(pm.math.eq(m_idx, 0), omega * (kappa-2)+1, omega0 * (kappa-2)+1)\n",
" bBeta = pm.math.switch(pm.math.eq(m_idx, 0), (1-omega) * (kappa-2)+1, (1-omega0) * (kappa-2)+1)\n",
"\n",
" # Theta\n",
" theta = pm.Beta('theta', aBeta[cond_idx], bBeta[cond_idx], shape=nSubj)\n",
" \n",
" # Likelihood\n",
" y = pm.Binomial('y', n=df.nTrlOfSubj.values, p=theta, observed=df.nCorrOfSubj)\n",
"\n",
"pm.model_to_graphviz(model_1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Multiprocess sampling (2 chains in 2 jobs)\n",
"CompoundStep\n",
">BinaryGibbsMetropolis: [m_idx]\n",
">NUTS: [theta, omega, omega0, kappa_minus2]\n",
"Sampling 2 chains: 100%|██████████| 11000/11000 [00:33<00:00, 330.01draws/s]\n",
"The number of effective samples is smaller than 10% for some parameters.\n"
]
}
],
"source": [
"with model_1:\n",
" trace1 = pm.sample(5000, target_accept=.95) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 864x864 with 12 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pm.traceplot(trace1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Figure 12.5"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 7 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"\n",
"# Define gridspec\n",
"gs = gridspec.GridSpec(3, 3)\n",
"ax1 = plt.subplot(gs[0,0])\n",
"ax2 = plt.subplot(gs[0,1])\n",
"ax3 = plt.subplot(gs[0,2])\n",
"ax4 = plt.subplot(gs[1,0])\n",
"ax5 = plt.subplot(gs[1,1])\n",
"ax6 = plt.subplot(gs[1,2])\n",
"ax7 = plt.subplot(gs[2,:])\n",
"\n",
"# Group the first six axes in a list for easier access in loop below\n",
"axes = [ax1, ax2, ax3, ax4, ax5, ax6]\n",
"# Differences of posteriors to be displayed: omega x - omega y\n",
"x = [0,0,0,1,1,2]\n",
"y = [1,2,3,2,3,3]\n",
"\n",
"# Plot histograms\n",
"for ax, a, b in zip(axes, x, y):\n",
" diff = trace1['omega'][:,a]-trace1['omega'][:,b]\n",
" pm.plot_posterior(diff, ref_val=0, point_estimate='mode', color=color, ax=ax)\n",
" ax.set_xlabel('$\\omega_{}$ - $\\omega_{}$'.format(a,b), fontdict={'size':18})\n",
" ax.xaxis.set_ticks([-.2, -.1, 0.0, 0.1, 0.2])\n",
"\n",
"# Plot trace values of model index (0, 1)\n",
"ax7.plot(np.arange(1, len(trace1['m_idx'])+1),trace1['m_idx'], color=color, linewidth=4)\n",
"ax7.set_xlabel('Step in Markov chain', fontdict={'size':14})\n",
"ax7.set_ylabel('Model Index (0, 1)', fontdict={'size':14})\n",
"ax7.set_ylim(-0.05,1.05)\n",
"\n",
"fig.tight_layout()"
]
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
unnikrishnankgs/va | venv/lib/python3.5/site-packages/nbconvert/tests/files/notebook2.ipynb | 10 | 125437 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NumPy and Matplotlib examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First import NumPy and Matplotlib:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"module://ipykernel.pylab.backend_inline\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"print(matplotlib.backends.backend)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import set_matplotlib_formats\n",
"set_matplotlib_formats('png', 'pdf')\n",
"matplotlib.rcParams['figure.figsize'] = (2,1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{matplotlib.figure.Figure: <function IPython.core.pylabtools.<lambda>>}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ip.display_formatter.formatters['application/pdf'].type_printers"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we show some very basic examples of how they can be used."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a = np.random.uniform(size=(100,100))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100, 100)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"evs = np.linalg.eigvals(a)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(100,)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evs.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Here is a very long heading that pandoc will wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap and wrap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a cell that has both text and PNG output:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([97, 2, 0, 0, 0, 0, 0, 0, 0, 1]),\n",
" array([ -2.59479443, 2.67371141, 7.94221725, 13.21072308,\n",
" 18.47922892, 23.74773476, 29.0162406 , 34.28474644,\n",
" 39.55325228, 44.82175812, 50.09026395]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10b0ecf10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(evs.real)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```python\n",
"import math \n",
"import numpy as np\n",
"def matrix_calc(L=None):\n",
" \"\"\"docstring\"\"\"\n",
" if L is None:",
" L = np.random.random(size=(1,1))",
" raise Exception(\"message\")\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This string contains a ~~strikethrough~~, struckthrough. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This string contains a `'` single tick inside backticks, leading to a ``\\textquotesingle`` in latex."
]
}
],
"metadata": {
"signature": "sha256:9fffd84e69e3d9b8aee7b4cde2099ca5d4158a45391698b191f94fabaf394b41"
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
idaholab/moose | modules/phase_field/test/tests/KKS_system/two_phase_lagrange_multiplier.ipynb | 1 | 92456 | {
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"id": "b3fc536a-38f8-4442-9e7b-ec9532386feb",
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"plt.rcParams.update({'font.size': 18})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ff78a571-50a3-4dbc-a6f9-d2171d579e1c",
"metadata": {},
"outputs": [],
"source": [
"Ft = pd.read_csv('two_phase_out_c_0036.csv')\n",
"Fm = pd.read_csv('lagrange_multiplier_out_c_0036.csv')\n",
"Fa = pd.read_csv('auxkernel_out_c_0040.csv')\n",
"Fn = pd.read_csv('nonlinear_out_c_0040.csv')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "04f1d274-abc5-47c7-bac7-2039cf6586fe",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(15,12))\n",
"ax = fig.gca()\n",
"ax.plot(Ft['x'], Ft['c'], 'b-', label=\"two phase\", linewidth=5, alpha= 0.25)\n",
"ax.plot(Fm['x'], Fm['c'], 'r--', label=\"multi phase\")\n",
"ax.plot(Fa['x'], Fa['c'], 'go', label=\"auxkernel\")\n",
"ax.plot(Fn['x'], Fn['c'], 'y.', label=\"nonlinear\")\n",
"ax.set_xlabel('position')\n",
"ax.set_ylabel('concentration')\n",
"ax.legend(loc='lower right')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "95c049e3-51a8-4505-bf71-745ae5a7a080",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Fn1 = pd.read_csv('nonlinear_out_c_0001.csv')\n",
"\n",
"fig = plt.figure(figsize=(15,12))\n",
"ax = fig.gca()\n",
"ax.plot(Fn1['x'], Fn1['c1'], 'r-', label=\"c1\")\n",
"ax.plot(Fn1['x'], Fn1['c2'], 'b-', label=\"c2\")\n",
"ax.set_xlabel('position')\n",
"ax.set_ylabel('concentration')\n",
"ax.legend(loc='lower right')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ca417293-b0df-4d33-90cb-95f0dd7b44cf",
"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": "3.10.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| lgpl-2.1 |
khalido/nd101 | mnist_keras_cnn.ipynb | 1 | 72745 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Keras Version: 2.0.4\n",
"Tensorflow Version 1.1.0\n"
]
}
],
"source": [
"import keras\n",
"print(f\"Keras Version: {keras.__version__}\")\n",
"import tensorflow as tf\n",
"print(f\"Tensorflow Version {tf.__version__}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Keras](https://keras.io/) is a high level wrapper (API) for Tensorflow and Theano which aims to make them easier to use. Tensorflow gets quite verbose and there is a lot of detail to handle, which Keras trys to abstract away to sane defaults, while allowing the option to tinker with the tensors where wanted.\n",
"\n",
"# the data\n",
"\n",
"To get a feel for Keras, I'm seeing how it goes with MNIST. \n",
"\n",
"Keras already has some [datasets included](https://keras.io/datasets/), so using the ever popular mnist:\n",
"\n",
"> ** MNIST database of handwritten digits**\n",
"\n",
"> Dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"from keras.datasets import mnist\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Checking the data:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Shapes x_train: (60000, 28, 28), y_train: (60000,), x_test: (10000, 28, 28), y_test: (10000,)'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f\"Shapes x_train: {x_train.shape}, y_train: {y_train.shape}, x_test: {x_test.shape}, y_test: {y_test.shape}\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The train and test images are `28x28` sized images, which we need to reshape into a 1d vector to make our super simple NN deal with. \n",
"\n",
"Now, it's a good idea to always eyeball the data, so here goes:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0, 255)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# min to max values in x_train\n",
"x_train.min(), x_train.max()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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IiEB2djbi4uLg5+eH3NxchISEIDw8HIsWLXJKG86ePYtXX30VW7ZsQdeuXZ3ymiZy9bk4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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120209240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"fig, axes = plt.subplots(3,3, figsize=(5,5))\n",
"\n",
"for i, ax in enumerate(axes.flatten()):\n",
" ax.imshow(x_train[i])\n",
" ax.set_title(f\"Class {y_train[i]}\")\n",
" ax.set_xticks([]) , ax.set_yticks([]) "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([5, 0, 4, 1, 9, 2, 1, 3, 1], dtype=uint8)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_train[:9]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ok, we've seen the data, but we need to preprocess it into a neural net friendly shape. \n",
"\n",
"## preprocessing the data\n",
"\n",
"The image data is 60K `28x28` images, and the image test data is 10K `28x28` images. Unlike a simple NN, a convultional net can use the spatial data, so no need to flatten out the `28x28` image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We still need to normalize the data:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train = x_train.astype('float32') / 255\n",
"X_test = x_test.astype('float32') / 255\n",
"X_train.min(), X_train.max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Keras expects images to have a depth - which generally with images means they have different colors, like RGB etc. the MNIST data is greyscale and thus doesn't have a depth, but we need to assign a 1 since Keras needs the depth specified. We know the image is `28x28`, so below we are just adding a 1:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(60000, 28, 28)\n",
"(60000, 28, 28, 1)\n"
]
}
],
"source": [
"print(x_train.shape)\n",
"X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n",
"X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n",
"print(X_train.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Moving on to the image labels:\n",
"\n",
"the image labels are stored as a simple numpy array, with each entry telling us what number each corresponding drawing is. Since our NN will spit out a prediction of the likelyhood of what number the drawing is, our NN will work better with the y data [one hot encoded](https://www.quora.com/What-is-one-hot-encoding-and-when-is-it-used-in-data-science)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Existing image labels\n",
"y_train: [5 0 4 1 9 2 1 3 1 4] | y_test: [7 2 1 0 4 1 4 9 5 9]\n",
"Y_Train encoded: [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
"Y_test encoded: [ 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n"
]
}
],
"source": [
"print(\"Existing image labels\")\n",
"print(f\"y_train: {y_train[:10]} | y_test: {y_test[:10]}\")\n",
"\n",
"from keras.utils import np_utils\n",
"Y_train = np_utils.to_categorical(y_train)\n",
"Y_test = np_utils.to_categorical(y_test)\n",
"\n",
"print(f\"Y_Train encoded: {Y_train[0]}\")\n",
"print(f\"Y_test encoded: {Y_test[0]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"so now our data is all ready to go!\n",
"\n",
"# convultional neural net"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 57000 samples, validate on 3000 samples\n",
"Epoch 1/5\n",
"57000/57000 [==============================] - 84s - loss: 0.2561 - acc: 0.9225 - val_loss: 0.1235 - val_acc: 0.9613\n",
"Epoch 2/5\n",
"57000/57000 [==============================] - 97s - loss: 0.0761 - acc: 0.9763 - val_loss: 0.0527 - val_acc: 0.9877\n",
"Epoch 3/5\n",
"57000/57000 [==============================] - 101s - loss: 0.0515 - acc: 0.9838 - val_loss: 0.0452 - val_acc: 0.9893\n",
"Epoch 4/5\n",
"57000/57000 [==============================] - 106s - loss: 0.0400 - acc: 0.9877 - val_loss: 0.0568 - val_acc: 0.9873\n",
"Epoch 5/5\n",
"57000/57000 [==============================] - 93s - loss: 0.0316 - acc: 0.9900 - val_loss: 0.0441 - val_acc: 0.9903\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation, Dropout, Flatten\n",
"from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
"\n",
"from keras.callbacks import EarlyStopping\n",
"early_stopping = EarlyStopping(monitor='val_loss', patience=2, verbose=1)\n",
"\n",
"model = Sequential()\n",
"\n",
"model.add(Conv2D(16,(2,2), input_shape=(28,28,1), activation='relu'))\n",
"model.add(Dropout(0.05))\n",
"\n",
"model.add(Conv2D(32, (2, 2), activation='relu'))\n",
"model.add(MaxPooling2D(pool_size=(2,2)))\n",
"model.add(Dropout(0.05))\n",
"\n",
"# the weights from the Conv2D layer have to be made 1D for the Dense layer\n",
"model.add(Flatten())\n",
"\n",
"model.add(Dense(64))\n",
"model.add(Activation('relu'))\n",
"model.add(Dropout(0.05))\n",
"\n",
"model.add(Dense(10))\n",
"model.add(Activation('softmax'))\n",
"\n",
"model.compile(optimizer='rmsprop',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"# we can either use part of the training set as validation data or provide a validation set\n",
"history = model.fit(X_train, Y_train, epochs=5, batch_size=128, shuffle=True, \n",
" validation_split=0.05, callbacks=[early_stopping])\n",
"\n",
"#model.fit(X_train, Y_train, epochs=10, batch_size=128, shuffle=True, validation_data=(X_test,Y_test))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 9984/10000 [============================>.] - ETA: 0s"
]
},
{
"data": {
"text/plain": [
"[0.040342996675986793, 0.98729999999999996]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(X_test, Y_test, batch_size=256)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"and viola, this pretty simple CNN gets over 98% accuracy!"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_17 (Conv2D) (None, 27, 27, 16) 80 \n",
"_________________________________________________________________\n",
"dropout_23 (Dropout) (None, 27, 27, 16) 0 \n",
"_________________________________________________________________\n",
"conv2d_18 (Conv2D) (None, 26, 26, 32) 2080 \n",
"_________________________________________________________________\n",
"max_pooling2d_8 (MaxPooling2 (None, 13, 13, 32) 0 \n",
"_________________________________________________________________\n",
"dropout_24 (Dropout) (None, 13, 13, 32) 0 \n",
"_________________________________________________________________\n",
"flatten_6 (Flatten) (None, 5408) 0 \n",
"_________________________________________________________________\n",
"dense_13 (Dense) (None, 64) 346176 \n",
"_________________________________________________________________\n",
"activation_18 (Activation) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dropout_25 (Dropout) (None, 64) 0 \n",
"_________________________________________________________________\n",
"dense_14 (Dense) (None, 10) 650 \n",
"_________________________________________________________________\n",
"activation_19 (Activation) (None, 10) 0 \n",
"=================================================================\n",
"Total params: 348,986\n",
"Trainable params: 348,986\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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X/rp27crOnTtITh7JgQOZhIaGUVxcTGbmXp577h/U1dUxZcp4hgwZRUrKDyxYsAiAP/1p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\n4CMiIiIiIgpT/z++20EHgNjCXgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1295e8eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(1,2,figsize=(15,5))\n",
"\n",
"acc = axs[0]\n",
"acc.plot(history.history['val_acc'])\n",
"acc.plot(history.history['acc'])\n",
"acc.legend(['val_acc', 'acc'])\n",
"acc.set_title('Model Accuracy')\n",
"acc.set_ylabel('Accuracy')\n",
"acc.set_xlabel('Epoch')\n",
"\n",
"loss = axs[1]\n",
"loss.plot(history.history['val_loss'])\n",
"loss.plot(history.history['loss'])\n",
"loss.legend(['val_loss', 'loss'])\n",
"loss.set_title('Model Loss')\n",
"loss.set_ylabel('Loss')\n",
"loss.set_xlabel('Epoch')\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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
zrhans/python | topicos/Estacoes-ATMOS-2011.ipynb | 1 | 248460 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Analise e Tratamento Basico (Triagem) de dados\n",
"Analises por Hans. 2015\n",
"\n",
"* 2012 (10sec)\n",
"* 2013 (10sec)\n",
"* 2014 (10sec ate 1Min seguinte)\n",
"* 2015 (1Min)\n",
"\n",
"----"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.4.2 (default, Oct 8 2014, 13:08:17) \n",
"[GCC 4.9.1]\n",
"1.9.2\n"
]
}
],
"source": [
"import sys\n",
"import numpy as np\n",
"import pandas as pd\n",
"print(sys.version) # Versao do python - Opcional\n",
"print(np.__version__) # VErsao do modulo numpy - Opcional\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import datetime\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#?pd.date_range\n",
"#rng = pd.date_range('1/1/2011', periods=90, freq='10mS')\n",
"#rng"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dados Importados OK\n"
]
}
],
"source": [
"# Carregando dados no dataframe df_dados a partir do arquivo .csv em servidor remoto.\n",
"#df_dados = pd.read_csv('http://fortran-zrhans.c9.io/csdapy/sr311-2014.csv', index_col=None)\n",
"\n",
"#Dados local\n",
"#df_dados = pd.read_csv('../dados/sr311-2011.csv', index_col=None,parse_dates=['Timestamp'])\n",
"df_dados = pd.read_csv('sr311-2011.csv', index_col=None,parse_dates=['Timestamp'])\n",
"\n",
"\n",
"print(\"Dados Importados OK\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['Unnamed: 0',\n",
" 'Timestamp',\n",
" 'Rain_mm',\n",
" 'AirTCmin',\n",
" 'WS_msmin',\n",
" 'AirTCmax',\n",
" 'WS_msmax',\n",
" 'AirTCsd',\n",
" 'WS_mssd',\n",
" 'BP_mbar',\n",
" 'T108_C',\n",
" 'RH',\n",
" 'AirTC',\n",
" 'SlrW',\n",
" 'WindDirs',\n",
" 'WS_msa',\n",
" 'WindDir_SD1_WVT',\n",
" 'WindDir_D1_WVT',\n",
" 'WS_ms_S_WVT',\n",
" 'PanelTemperature',\n",
" 'Status',\n",
" 'Battery']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Verificanco o nome das colunas\n",
"df_dados.columns.tolist()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Timestamp</th>\n",
" <th>Rain_mm</th>\n",
" <th>AirTCmin</th>\n",
" <th>WS_msmin</th>\n",
" <th>AirTCmax</th>\n",
" <th>WS_msmax</th>\n",
" <th>AirTCsd</th>\n",
" <th>WS_mssd</th>\n",
" <th>BP_mbar</th>\n",
" <th>...</th>\n",
" <th>AirTC</th>\n",
" <th>SlrW</th>\n",
" <th>WindDirs</th>\n",
" <th>WS_msa</th>\n",
" <th>WindDir_SD1_WVT</th>\n",
" <th>WindDir_D1_WVT</th>\n",
" <th>WS_ms_S_WVT</th>\n",
" <th>PanelTemperature</th>\n",
" <th>Status</th>\n",
" <th>Battery</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2011-12-01 15:15:00</td>\n",
" <td>0</td>\n",
" <td>19.09</td>\n",
" <td>5.45</td>\n",
" <td>19.23</td>\n",
" <td>7.7</td>\n",
" <td>0.044</td>\n",
" <td>0.675</td>\n",
" <td>953</td>\n",
" <td>...</td>\n",
" <td>19.17</td>\n",
" <td>893</td>\n",
" <td>63.72</td>\n",
" <td>6.725</td>\n",
" <td>6.472</td>\n",
" <td>63.01</td>\n",
" <td>6.725</td>\n",
" <td>23.04</td>\n",
" <td>0</td>\n",
" <td>13.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2011-12-01 15:15:10</td>\n",
" <td>0</td>\n",
" <td>19.09</td>\n",
" <td>3.95</td>\n",
" <td>19.26</td>\n",
" <td>6.2</td>\n",
" <td>0.052</td>\n",
" <td>0.654</td>\n",
" <td>953</td>\n",
" <td>...</td>\n",
" <td>19.16</td>\n",
" <td>892</td>\n",
" <td>70.01</td>\n",
" <td>5.300</td>\n",
" <td>4.752</td>\n",
" <td>73.68</td>\n",
" <td>5.300</td>\n",
" <td>23.09</td>\n",
" <td>0</td>\n",
" <td>13.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2011-12-01 15:15:20</td>\n",
" <td>0</td>\n",
" <td>19.13</td>\n",
" <td>4.70</td>\n",
" <td>19.26</td>\n",
" <td>6.2</td>\n",
" <td>0.043</td>\n",
" <td>0.654</td>\n",
" <td>953</td>\n",
" <td>...</td>\n",
" <td>19.17</td>\n",
" <td>889</td>\n",
" <td>78.31</td>\n",
" <td>5.300</td>\n",
" <td>6.596</td>\n",
" <td>79.16</td>\n",
" <td>5.300</td>\n",
" <td>23.15</td>\n",
" <td>0</td>\n",
" <td>13.91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Timestamp Rain_mm AirTCmin WS_msmin AirTCmax \\\n",
"0 0 2011-12-01 15:15:00 0 19.09 5.45 19.23 \n",
"1 1 2011-12-01 15:15:10 0 19.09 3.95 19.26 \n",
"2 2 2011-12-01 15:15:20 0 19.13 4.70 19.26 \n",
"\n",
" WS_msmax AirTCsd WS_mssd BP_mbar ... AirTC SlrW WindDirs \\\n",
"0 7.7 0.044 0.675 953 ... 19.17 893 63.72 \n",
"1 6.2 0.052 0.654 953 ... 19.16 892 70.01 \n",
"2 6.2 0.043 0.654 953 ... 19.17 889 78.31 \n",
"\n",
" WS_msa WindDir_SD1_WVT WindDir_D1_WVT WS_ms_S_WVT PanelTemperature \\\n",
"0 6.725 6.472 63.01 6.725 23.04 \n",
"1 5.300 4.752 73.68 5.300 23.09 \n",
"2 5.300 6.596 79.16 5.300 23.15 \n",
"\n",
" Status Battery \n",
"0 0 13.89 \n",
"1 0 13.86 \n",
"2 0 13.91 \n",
"\n",
"[3 rows x 22 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dados.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#removendo a primeira coluna, e ajustando a coluna Timestamp para ser o indice\n",
"del(df_dados['Unnamed: 0'])\n",
"df_dados.set_index('Timestamp', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Selecionando apenas algumas colunas de interesse\n",
"df_dados = df_dados[['AirTC', 'RH', 'Rain_mm']]\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AirTC</th>\n",
" <th>RH</th>\n",
" <th>Rain_mm</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Timestamp</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-12-01 15:15:00</th>\n",
" <td>19.17</td>\n",
" <td>34.59</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01 15:15:10</th>\n",
" <td>19.16</td>\n",
" <td>34.18</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01 15:15:20</th>\n",
" <td>19.17</td>\n",
" <td>35.65</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01 15:15:30</th>\n",
" <td>19.14</td>\n",
" <td>34.93</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-01 15:15:40</th>\n",
" <td>19.14</td>\n",
" <td>36.30</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AirTC RH Rain_mm\n",
"Timestamp \n",
"2011-12-01 15:15:00 19.17 34.59 0\n",
"2011-12-01 15:15:10 19.16 34.18 0\n",
"2011-12-01 15:15:20 19.17 35.65 0\n",
"2011-12-01 15:15:30 19.14 34.93 0\n",
"2011-12-01 15:15:40 19.14 36.30 0"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#df_dados = df_dados.dropna()\n",
"df_dados.head()\n",
"\n",
"#s_chuva = df_dados.Rain_mm"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#s_chuva.cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x7f3fb22fea90>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PZpZwkn+VSri4h9RPqhiMfaaiAKqb+zxOP/bvkfFBDbrfly3/ixQyH88CBPxy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\nyaeqq7DXRcwTKKdddiMS5Pr6DPzcGM67Z5oGYVToGtC0x36ERkWn9c0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MTge0PSQvhnSi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\nJEmSJEmSJAHw/wFP5CUpE0gf7QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3fb0ace208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,8))\n",
"\n",
"plt.subplot(2, 1, 1)\n",
"plt.title(\"Dados Brutos\")\n",
"df_dados.AirTC.plot()\n",
"df_dados.RH.plot()\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"df_dados.Rain_mm.plot()\n",
"#plt.savefig('figs/nome-da-figura.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ajustando o dominio temporal da serie de dados\n",
"----"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Indice 2011 criado OK\n"
]
},
{
"data": {
"text/plain": [
"(Empty DataFrame\n",
" Columns: []\n",
" Index: [2011-01-01 00:00:00, 2011-01-01 00:01:00], Empty DataFrame\n",
" Columns: []\n",
" Index: [2011-12-31 23:58:00, 2011-12-31 23:59:00])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#df_dados.index.min(), df_dados.index.max(), \n",
"## (Timestamp('2015-01-01 00:00:00'), Timestamp('2015-05-29 10:00:00'))\n",
"\n",
"# Criando um novo dominio continuo com base no inicio e fim da serie de dados original\n",
"#d = pd.DataFrame(index=pd.date_range(pd.datetime(2015,1,1), pd.datetime(2015,5,29), freq='Min'))\n",
"d = pd.DataFrame(index=pd.date_range(pd.datetime(2011,1,1), pd.datetime(2011,12,31,23,59,00), freq='Min'))\n",
" \n",
"print(\"Indice 2011 criado OK\")\n",
"d.head(2),d.tail(2)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Junçao OK\n"
]
}
],
"source": [
"# Unindo os dois DataFrames pela esquerda (o que não houver em d será substituído por NaN\n",
"ndf_dados = d.join(df_dados)\n",
"#ndf_dados.fillna(0) #Substitui valor NaN por 0\n",
"print(\"Junçao OK\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x7f3f9dd01ef0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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XBFAiIiIiIiIyyvbWAQq5Z1bPzM6AemZFRERERGSFqmuonzmDNj45wzw11DsW6z+a5/fM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\nkiRJkiRJkiRJkiRJkiRJkiRJkiSNz/8FHHSezZJboAEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3fad6aa8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16,15))\n",
"\n",
"#Grafico Temperatura\n",
"plt.subplot(3, 1, 1)\n",
"plt.title('Dados Brutos Reindexados')\n",
"plt.ylabel('Graus')\n",
"plt.xlabel('')\n",
"ndf_dados.AirTC.plot(legend=True)\n",
"\n",
"#Grafico Umidade\n",
"plt.subplot(3, 1, 2)\n",
"#plt.title('Dados Brutos Reindexados')\n",
"plt.xlabel('')\n",
"plt.ylabel('%')\n",
"ndf_dados.RH.plot(legend=True)\n",
"\n",
"#Grafico Chuva\n",
"plt.subplot(3, 1, 3)\n",
"#plt.title('Dados Brutos Reindexados')\n",
"plt.xlabel('')\n",
"plt.ylabel('mm')\n",
"ndf_dados.Rain_mm.plot(legend=True)\n",
"\n",
"#plt.savefig('figs/nome-da-figura.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Em busca dos GAPs"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AirTC</th>\n",
" <th>RH</th>\n",
" <th>Rain_mm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01 00:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:01:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:02:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:03:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:04:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:05:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:06:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:07:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:08:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:09:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:10:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:11:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:12:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:13:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:14:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:16:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:17:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:18:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:19:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:20:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:21:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:22:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:23:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:24:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:25:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:26:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:27:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:28:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-01 00:29:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:15:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:21:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:22:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:23:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:24:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:25:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:31:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:32:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:33:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:34:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 14:35:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 21:48:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:16:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:17:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:18:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:19:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:20:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:26:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:27:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:28:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:29:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-28 23:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-29 17:37:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-30 23:56:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-30 23:57:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-30 23:58:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-30 23:59:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31 00:00:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31 00:10:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-12-31 07:30:00</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>482326 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" AirTC RH Rain_mm\n",
"2011-01-01 00:00:00 NaN NaN NaN\n",
"2011-01-01 00:01:00 NaN NaN NaN\n",
"2011-01-01 00:02:00 NaN NaN NaN\n",
"2011-01-01 00:03:00 NaN NaN NaN\n",
"2011-01-01 00:04:00 NaN NaN NaN\n",
"2011-01-01 00:05:00 NaN NaN NaN\n",
"2011-01-01 00:06:00 NaN NaN NaN\n",
"2011-01-01 00:07:00 NaN NaN NaN\n",
"2011-01-01 00:08:00 NaN NaN NaN\n",
"2011-01-01 00:09:00 NaN NaN NaN\n",
"2011-01-01 00:10:00 NaN NaN NaN\n",
"2011-01-01 00:11:00 NaN NaN NaN\n",
"2011-01-01 00:12:00 NaN NaN NaN\n",
"2011-01-01 00:13:00 NaN NaN NaN\n",
"2011-01-01 00:14:00 NaN NaN NaN\n",
"2011-01-01 00:15:00 NaN NaN NaN\n",
"2011-01-01 00:16:00 NaN NaN NaN\n",
"2011-01-01 00:17:00 NaN NaN NaN\n",
"2011-01-01 00:18:00 NaN NaN NaN\n",
"2011-01-01 00:19:00 NaN NaN NaN\n",
"2011-01-01 00:20:00 NaN NaN NaN\n",
"2011-01-01 00:21:00 NaN NaN NaN\n",
"2011-01-01 00:22:00 NaN NaN NaN\n",
"2011-01-01 00:23:00 NaN NaN NaN\n",
"2011-01-01 00:24:00 NaN NaN NaN\n",
"2011-01-01 00:25:00 NaN NaN NaN\n",
"2011-01-01 00:26:00 NaN NaN NaN\n",
"2011-01-01 00:27:00 NaN NaN NaN\n",
"2011-01-01 00:28:00 NaN NaN NaN\n",
"2011-01-01 00:29:00 NaN NaN NaN\n",
"... ... .. ...\n",
"2011-12-28 14:15:00 NaN NaN NaN\n",
"2011-12-28 14:21:00 NaN NaN NaN\n",
"2011-12-28 14:22:00 NaN NaN NaN\n",
"2011-12-28 14:23:00 NaN NaN NaN\n",
"2011-12-28 14:24:00 NaN NaN NaN\n",
"2011-12-28 14:25:00 NaN NaN NaN\n",
"2011-12-28 14:31:00 NaN NaN NaN\n",
"2011-12-28 14:32:00 NaN NaN NaN\n",
"2011-12-28 14:33:00 NaN NaN NaN\n",
"2011-12-28 14:34:00 NaN NaN NaN\n",
"2011-12-28 14:35:00 NaN NaN NaN\n",
"2011-12-28 21:48:00 NaN NaN NaN\n",
"2011-12-28 23:16:00 NaN NaN NaN\n",
"2011-12-28 23:17:00 NaN NaN NaN\n",
"2011-12-28 23:18:00 NaN NaN NaN\n",
"2011-12-28 23:19:00 NaN NaN NaN\n",
"2011-12-28 23:20:00 NaN NaN NaN\n",
"2011-12-28 23:26:00 NaN NaN NaN\n",
"2011-12-28 23:27:00 NaN NaN NaN\n",
"2011-12-28 23:28:00 NaN NaN NaN\n",
"2011-12-28 23:29:00 NaN NaN NaN\n",
"2011-12-28 23:30:00 NaN NaN NaN\n",
"2011-12-29 17:37:00 NaN NaN NaN\n",
"2011-12-30 23:56:00 NaN NaN NaN\n",
"2011-12-30 23:57:00 NaN NaN NaN\n",
"2011-12-30 23:58:00 NaN NaN NaN\n",
"2011-12-30 23:59:00 NaN NaN NaN\n",
"2011-12-31 00:00:00 NaN NaN NaN\n",
"2011-12-31 00:10:00 NaN NaN NaN\n",
"2011-12-31 07:30:00 NaN NaN NaN\n",
"\n",
"[482326 rows x 3 columns]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#numpy.all(numpy.isnan(data_list))\n",
"# np.any(np.isnan(ndf_dados)) Se returnar True eh porque algum valor NaN foi encontrado\n",
"\n",
"# Mostra onde os dados Possuem valor NaN\n",
"ndf_dados[np.isnan(ndf_dados.Rain_mm)]\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"129822"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.count_nonzero(~np.isnan(ndf_dados))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"43274\n",
"43274\n",
"43274\n"
]
}
],
"source": [
"def numberOfNonNans(data):\n",
" count = 0\n",
" for i in data:\n",
" if not np.isnan(i):\n",
" count += 1\n",
" \n",
" return count\n",
"\n",
"print(numberOfNonNans(ndf_dados.AirTC))\n",
"print(numberOfNonNans(ndf_dados.RH))\n",
"print(numberOfNonNans(ndf_dados.Rain_mm))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ndf_dados.head()\n",
"\n",
"#Exportando para um novo arquivo\n",
"ndf_dados.to_csv('sao_roque_2011-AirTC-RH-Rain.csv',na_rep='NaN')\n",
"# TODO: Este arquivo nao possui mais gaps no dominio temporal (as imagens foram ajustadas para o valor NaN), portanto pode-se pular a etapa reindexar caso seja utilizado no futuro."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Analises Diarias"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_dados_diarios = ndf_dados[['AirTC','RH']] .resample('D', how='mean')\n",
"chuva = ndf_dados.Rain_mm.resample('D', how='sum')\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AirTC</th>\n",
" <th>RH</th>\n",
" <th>Acum_Chuva</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2011-01-01</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-02</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-03</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-04</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2011-01-05</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AirTC RH Acum_Chuva\n",
"2011-01-01 NaN NaN NaN\n",
"2011-01-02 NaN NaN NaN\n",
"2011-01-03 NaN NaN NaN\n",
"2011-01-04 NaN NaN NaN\n",
"2011-01-05 NaN NaN NaN"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dados_diarios['Acum_Chuva'] = chuva\n",
"df_dados_diarios.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x7f377bfdc5f8>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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J8mt/RaotetS4ui+sPJJ6N9McSR12kSoiIiIiIiK9ORi7hSXA2cAzyXha16My1gOubPPa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\n1m7xsP8+txoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f378ae8e630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Mostrando tudo\n",
"plt.figure(figsize=(16,8))\n",
"plt.subplot(2,1,1)\n",
"plt.title('Media Diaria 2015')\n",
"plt.xlabel(\"\")\n",
"plt.ylabel(\"mm, Graus, %\")\n",
"df_dados_diarios.AirTC.plot(legend=True)\n",
"df_dados_diarios.RH.plot(legend=True)\n",
"df_dados_diarios.Acum_Chuva.plot(legend=True)\n",
"\n",
"plt.subplot(2,1,2)\n",
"acumulado = df_dados_diarios.Acum_Chuva.cumsum()\n",
"plt.xlabel(\"Data\")\n",
"plt.ylabel(\"mm\")\n",
"acumulado.plot(legend=True)\n",
"\n",
"#plt.savefig('figs/nome-da-figura.png')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes.AxesSubplot at 0x7f377abc3780>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AdNKqA0AnrToAdNCqA0Anu+p8ZmQBAABgDRyRBQAAmC9HZAEAAJgvRRbG06oDQCetOgB0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f377abc30f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Histograma do Acumulado Diario\n",
"plt.figure(figsize=(16,8))\n",
"plt.xlabel(\"Acorrencia\")\n",
"plt.ylabel(\"mm\")\n",
"df_dados_diarios.Acum_Chuva.plot(kind='hist', orientation='horizontal', alpha=.75,legend=True)\n",
"\n",
"#plt.savefig('figs/nome-da-figura.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quais dias o Acumulado de chuva foi superior a 20mm em 2011 ?"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Series([], Freq: D, Name: Acum_Chuva, dtype: float64)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quais dias o Acumulado de chuva foi superior a 20mm em 2015?\n",
"df_dados_diarios.Acum_Chuva[df_dados_diarios.Acum_Chuva > 20.]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantos dias o Acumulado de chuva foi superior a 20mm em 2011 ?"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Quantos dias o Acumulado de chuva foi superior a 20mm em 2015?\n",
"df_dados_diarios.Acum_Chuva[df_dados_diarios.Acum_Chuva > 20.].count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estatistica geral do DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"( AirTC RH Rain_mm\n",
" count 43274.000000 43274.000000 43274.000000\n",
" mean 20.058216 69.170374 0.000282\n",
" std 4.553391 19.981468 0.008455\n",
" min 8.760000 13.720000 0.000000\n",
" 25% 16.840000 56.180000 0.000000\n",
" 50% 19.350000 74.890000 0.000000\n",
" 75% 22.770000 85.000000 0.000000\n",
" max 34.290000 96.300000 0.254000,\n",
" AirTC RH Acum_Chuva\n",
" count 31.000000 31.000000 31.000000\n",
" mean 20.007347 68.593343 0.393290\n",
" std 2.503228 11.317924 0.758716\n",
" min 16.245928 43.038158 0.000000\n",
" 25% 18.284633 60.139178 0.000000\n",
" 50% 19.413023 68.780313 0.000000\n",
" 75% 21.465977 77.770182 0.381000\n",
" max 25.485402 87.066553 2.286000)"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ndf_dados.describe(), df_dados_diarios.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"# Atividade\n",
"## Refazer os procedimentos para os anos de 2012 ate 2014.\n",
"\n",
"* Dados disponiveis em http://fortran-zrhans.c9.io/csdapy/\n",
"* Aplicar os tratamentos e mostrar tambem os graficos para temperatura maxima, minima, velocidade do vento e radiacao solar.\n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Resultados apos executar os procedimentos para cada ano a partir de 2012.\n",
"----\n",
"\n",
"```bash\n",
"\n",
"hans@hasus:~/Dropbox/workspace/spyder/spyderprj01$ python3 ATMOS-Anuais.py -i 2012; python3 ATMOS-Anuais.py -i 201\n",
"3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"1.9.2\n",
"Analisando ano 2012\n",
"Dados 2012 Importados OK\n",
"Gerando Graficos Brutos - 2012\n",
"Indice criado OK\n",
"Junção OK\n",
"Gerando Graficos Brutos Reindexados - 2012\n",
"Gerando Graficos Media Diaria - 2012\n",
"Gerando Graficos Acumulado de Chuva - 2012\n",
"Gerando Graficos Histograma Acumulado diario de Chuva - 2012\n",
"2012-01-01 41.402\n",
"2012-01-13 24.638\n",
"2012-02-05 45.466\n",
"2012-06-17 30.480\n",
"2012-07-06 62.992\n",
"2012-07-24 37.592\n",
"2012-08-26 28.956\n",
"2012-09-09 20.320\n",
"2012-09-10 52.578\n",
"2012-09-16 31.242\n",
"2012-09-18 35.814\n",
"2012-09-19 27.432\n",
"2012-10-07 20.320\n",
"2012-10-22 24.638\n",
"2012-11-23 26.924\n",
"2012-12-27 43.180\n",
"2012-12-28 29.972\n",
"Name: Acum_Chuva, dtype: float64\n",
"17\n",
"\n",
"________\n",
"FINALIZADO!!\n",
"________\n",
"3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"1.9.2\n",
"Analisando ano 2013\n",
"Dados 2013 Importados OK\n",
"Gerando Graficos Brutos - 2013\n",
"Indice criado OK\n",
"Junção OK\n",
"Gerando Graficos Brutos Reindexados - 2013\n",
"Gerando Graficos Media Diaria - 2013\n",
"Gerando Graficos Acumulado de Chuva - 2013\n",
"Gerando Graficos Histograma Acumulado diario de Chuva - 2013\n",
"2013-02-25 29.718\n",
"2013-03-09 22.352\n",
"2013-03-12 44.958\n",
"2013-03-20 34.798\n",
"2013-04-04 25.908\n",
"2013-04-12 26.924\n",
"2013-05-19 41.402\n",
"2013-05-29 20.828\n",
"2013-06-01 20.828\n",
"2013-06-28 24.638\n",
"2013-06-29 20.828\n",
"2013-07-07 27.178\n",
"2013-08-09 28.702\n",
"2013-08-13 22.860\n",
"2013-08-23 25.654\n",
"2013-08-24 61.722\n",
"2013-08-25 34.036\n",
"2013-08-26 22.860\n",
"2013-09-20 51.816\n",
"2013-09-21 44.704\n",
"2013-10-21 27.940\n",
"2013-11-11 53.340\n",
"2013-11-15 20.320\n",
"2013-11-20 23.368\n",
"2013-11-21 20.828\n",
"2013-12-05 22.098\n",
"Name: Acum_Chuva, dtype: float64\n",
"26\n",
"\n",
"________\n",
"FINALIZADO!!\n",
"________\n",
"\n",
"3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"1.9.2\n",
"Analisando ano 2014\n",
"Dados 2014 Importados OK\n",
"Gerando Graficos Brutos - 2014\n",
"Indice criado OK\n",
"Junção OK\n",
"Gerando Graficos Brutos Reindexados - 2014\n",
"Gerando Graficos Media Diaria - 2014\n",
"Gerando Graficos Acumulado de Chuva - 2014\n",
"Gerando Graficos Histograma Acumulado diario de Chuva - 2014\n",
"2014-01-03 23.622\n",
"2014-01-10 21.844\n",
"2014-02-12 20.574\n",
"2014-02-25 22.352\n",
"2014-03-17 22.352\n",
"2014-03-31 24.130\n",
"2014-04-08 49.784\n",
"2014-04-11 44.196\n",
"2014-05-21 37.338\n",
"2014-05-31 37.846\n",
"2014-06-05 53.594\n",
"2014-06-13 37.084\n",
"2014-06-24 25.908\n",
"2014-06-26 30.226\n",
"2014-07-03 23.876\n",
"2014-07-23 34.798\n",
"2014-08-31 25.146\n",
"2014-09-02 25.654\n",
"2014-09-06 29.972\n",
"2014-09-11 27.686\n",
"2014-09-26 25.146\n",
"2014-10-13 37.338\n",
"2014-10-17 46.990\n",
"2014-10-19 23.368\n",
"2014-12-12 44.704\n",
"2014-12-20 33.274\n",
"2014-12-21 35.306\n",
"2014-12-27 30.226\n",
"Name: Acum_Chuva, dtype: float64\n",
"28\n",
"\n",
"________\n",
"FINALIZADO!!\n",
"________\n",
"\n",
"3.4.3 |Anaconda 2.2.0 (64-bit)| (default, Mar 6 2015, 12:03:53) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"1.9.2\n",
"Analisando ano 2015\n",
"Dados 2015 Importados OK\n",
"Gerando Graficos Brutos - 2015\n",
"Indice criado OK\n",
"Junção OK\n",
"Gerando Graficos Brutos Reindexados - 2015\n",
"Gerando Graficos Media Diaria - 2015\n",
"Gerando Graficos Acumulado de Chuva - 2015\n",
"Gerando Graficos Histograma Acumulado diario de Chuva - 2015\n",
"2015-01-28 29.718\n",
"2015-02-09 21.590\n",
"2015-02-21 70.104\n",
"2015-03-09 23.368\n",
"2015-03-30 21.844\n",
"2015-04-05 28.448\n",
"2015-04-20 44.450\n",
"Name: Acum_Chuva, dtype: float64\n",
"7\n",
"\n",
"________\n",
"FINALIZADO!!\n",
"________\n",
"\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"Elaborado por Hans Rogerio Zimermann para o curso *FSC878 - Topicos Especiais II* PPGMET - UFSM."
]
}
],
"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": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
kingb12/languagemodelRNN | old_comparisons/noingX_compared.ipynb | 1 | 397776 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comparing Encoder-Decoders Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Architecture"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"encdec_noing6_200_512_04drb \n",
"\n",
"Encoder: \n",
"nn.Sequential {\n",
" [input -> (1) -> (2) -> (3) -> (4) -> output]\n",
" (1): nn.LookupTable\n",
" (2): nn.Dropout(0.400000)\n",
" (3): nn.LSTM(200 -> 512)\n",
" (4): nn.Dropout(0.400000)\n",
"}\n",
"Decoder: \n",
"nn.gModule\n",
"\n",
"encdec_noing10_200_512_04drb \n",
"\n",
"Encoder: \n",
"nn.Sequential {\n",
" [input -> (1) -> (2) -> (3) -> (4) -> output]\n",
" (1): nn.LookupTable\n",
" (2): nn.Dropout(0.400000)\n",
" (3): nn.LSTM(200 -> 512)\n",
" (4): nn.Dropout(0.400000)\n",
"}\n",
"Decoder: \n",
"nn.gModule\n",
"\n",
"encdec_noing15_200_512_04drb \n",
"\n",
"Encoder: \n",
"nn.Sequential {\n",
" [input -> (1) -> (2) -> (3) -> (4) -> output]\n",
" (1): nn.LookupTable\n",
" (2): nn.Dropout(0.400000)\n",
" (3): nn.LSTM(200 -> 512)\n",
" (4): nn.Dropout(0.400000)\n",
"}\n",
"Decoder: \n",
"nn.gModule\n",
"\n",
"encdec_noing23_200_512_04drb \n",
"\n",
"Encoder: \n",
"nn.Sequential {\n",
" [input -> (1) -> (2) -> (3) -> (4) -> output]\n",
" (1): nn.LookupTable\n",
" (2): nn.Dropout(0.400000)\n",
" (3): nn.LSTM(200 -> 512)\n",
" (4): nn.Dropout(0.400000)\n",
"}\n",
"Decoder: \n",
"nn.gModule\n"
]
}
],
"source": [
"report_files = [\"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_200_512_04drb/encdec_noing6_200_512_04drb.json\", \"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drb/encdec_noing10_200_512_04drb.json\", \"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing15_200_512_04drb/encdec_noing15_200_512_04drb.json\", \"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_200_512_04drb/encdec_noing23_200_512_04drb.json\"]\n",
"log_files = [\"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing6_200_512_04drb/encdec_noing6_200_512_04drb_logs.json\", \"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing10_200_512_04drb/encdec_noing10_200_512_04drb_logs.json\", \"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing15_200_512_04drb/encdec_noing15_200_512_04drb_logs.json\", \"/Users/bking/IdeaProjects/LanguageModelRNN/experiment_results/encdec_noing23_200_512_04drb/encdec_noing23_200_512_04drb_logs.json\"]\n",
"reports = []\n",
"logs = []\n",
"import json\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"for report_file in report_files:\n",
" with open(report_file) as f:\n",
" reports.append((report_file.split('/')[-1].split('.json')[0], json.loads(f.read())))\n",
"for log_file in log_files:\n",
" with open(log_file) as f:\n",
" logs.append((log_file.split('/')[-1].split('.json')[0], json.loads(f.read())))\n",
" \n",
"for report_name, report in reports:\n",
" print '\\n', report_name, '\\n'\n",
" print 'Encoder: \\n', report['architecture']['encoder']\n",
" print 'Decoder: \\n', report['architecture']['decoder']\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perplexity on Each Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Train Perplexity</b></td><td><b>Valid Perplexity</b></td><td><b>Test Perplexity</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>2.30555069546</td><td>755.270092669</td><td>857.640092497</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>2.36730175651</td><td>715.007688611</td><td>868.108036068</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>2.27752305377</td><td>706.426656909</td><td>805.596650048</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>2.22041950514</td><td>740.070604729</td><td>893.207220523</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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YYVuSJEmSpBgzbEuSJEmSFGOGbUmSJEmSYsywLUmSJElS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b59ca90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"from IPython.display import HTML, display\n",
"\n",
"def display_table(data):\n",
" display(HTML(\n",
" u'<table><tr>{}</tr></table>'.format(\n",
" u'</tr><tr>'.join(\n",
" u'<td>{}</td>'.format('</td><td>'.join(unicode(_) for _ in row)) for row in data)\n",
" )\n",
" ))\n",
"\n",
"def bar_chart(data):\n",
" n_groups = len(data)\n",
" \n",
" train_perps = [d[1] for d in data]\n",
" valid_perps = [d[2] for d in data]\n",
" test_perps = [d[3] for d in data]\n",
" \n",
" fig, ax = plt.subplots(figsize=(10,8))\n",
" \n",
" index = np.arange(n_groups)\n",
" bar_width = 0.3\n",
"\n",
" opacity = 0.4\n",
" error_config = {'ecolor': '0.3'}\n",
"\n",
" train_bars = plt.bar(index, train_perps, bar_width,\n",
" alpha=opacity,\n",
" color='b',\n",
" error_kw=error_config,\n",
" label='Training Perplexity')\n",
"\n",
" valid_bars = plt.bar(index + bar_width, valid_perps, bar_width,\n",
" alpha=opacity,\n",
" color='r',\n",
" error_kw=error_config,\n",
" label='Valid Perplexity')\n",
" test_bars = plt.bar(index + 2*bar_width, test_perps, bar_width,\n",
" alpha=opacity,\n",
" color='g',\n",
" error_kw=error_config,\n",
" label='Test Perplexity')\n",
"\n",
" plt.xlabel('Model')\n",
" plt.ylabel('Scores')\n",
" plt.title('Perplexity by Model and Dataset')\n",
" plt.xticks(index + bar_width / 3, [d[0] for d in data])\n",
" plt.legend()\n",
"\n",
" plt.tight_layout()\n",
" plt.show()\n",
"\n",
"data = [['<b>Model</b>', '<b>Train Perplexity</b>', '<b>Valid Perplexity</b>', '<b>Test Perplexity</b>']]\n",
"\n",
"for rname, report in reports:\n",
" data.append([rname, report['train_perplexity'], report['valid_perplexity'], report['test_perplexity']])\n",
"\n",
"display_table(data)\n",
"bar_chart(data[1:])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Loss vs. Epoch"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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TrVs3GBkZ4ccff1Q58FCcg6/OiUdgYCAYYxVOSerRowcAKEWri4qKEizXqlUL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\niCK7BFBCCCEuPZ1Ox5NPPlndzahSf8Z9EleelJQUdDod77zzj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+YaDAbuu+8+cnNz+eab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ONZPOxHmTp3Pe5OEQqqoqDb4GXt72DK+samf3Fhd7a6awd+03+EfqLtCkMeYEcRV0UlRUz+zy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\ncgvccQf8+79DZuYfl04XQghxVZEZKCGEENesoaEhDh8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SoVFGden0kBD1vqk5cyA3FzRX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"text/plain": [
"<matplotlib.figure.Figure at 0x10b4be410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.figure(figsize=(10, 8))\n",
"for rname, l in logs:\n",
" for k in l.keys():\n",
" plt.plot(l[k][0], l[k][1], label=str(k) + ' ' + rname + ' (train)')\n",
" plt.plot(l[k][0], l[k][2], label=str(k) + ' ' + rname + ' (valid)')\n",
"plt.title('Loss v. Epoch')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Loss')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Perplexity vs. Epoch"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BqakpJBIJjh07plJefn4+Vq5cCT8/P9jb28PMzAyenp5YvXo1ysrKqlRHbc91RVu3boVE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\nPI5sjCs2xonNzZrNjZq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"text/plain": [
"<matplotlib.figure.Figure at 0x10ac3a390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.figure(figsize=(10, 8))\n",
"for rname, l in logs:\n",
" for k in l.keys():\n",
" plt.plot(l[k][0], l[k][3], label=str(k) + ' ' + rname + ' (train)')\n",
" plt.plot(l[k][0], l[k][4], label=str(k) + ' ' + rname + ' (valid)')\n",
"plt.title('Perplexity v. Epoch')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Perplexity')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generations"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def print_sample(sample, best_bleu=None):\n",
" enc_input = ' '.join([w for w in sample['encoder_input'].split(' ') if w != '<pad>'])\n",
" gold = ' '.join([w for w in sample['gold'].split(' ') if w != '<mask>'])\n",
" print('Input: '+ enc_input + '\\n')\n",
" print('Gend: ' + sample['generated'] + '\\n')\n",
" print('True: ' + gold + '\\n')\n",
" if best_bleu is not None:\n",
" cbm = ' '.join([w for w in best_bleu['best_match'].split(' ') if w != '<mask>'])\n",
" print('Closest BLEU Match: ' + cbm + '\\n')\n",
" print('Closest BLEU Score: ' + str(best_bleu['best_score']) + '\\n')\n",
" print('\\n')\n",
" \n",
"def display_sample(sample, best_bleu=None):\n",
" enc_input = ' '.join([w for w in sample['encoder_input'].split(' ') if w != '<pad>'])\n",
" gold = ' '.join([w for w in sample['gold'].split(' ') if w != '<mask>'])\n",
" data = [['<u><b>' + enc_input + '</b></u>', '']]\n",
" data.append(['<b>Generated</b>', sample['generated']])\n",
" data.append(['<b>True</b>',gold])\n",
" if best_bleu is not None:\n",
" cbm = ' '.join([w for w in best_bleu['best_match'].split(' ') if w != '<mask>'])\n",
" data.append(['<b>Closest BLEU Match</b>', cbm])\n",
" data.append(['<b>Closest BLEU Score</b>', str(best_bleu['best_score'])])\n",
" display_table(data)\n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<h3>encdec_noing6_200_512_04drb (train)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> amazing watermelon greek salad with feta</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , salt , and salt . <step> add the milk , egg and vanilla .</td></tr><tr><td><b>True</b></td><td> combine the first 6 ingredients and shape into patties . <step> cook patties in a large skillet over medium - low heat 20 <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>57.2545340067</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> hidden valley pinwheel sandwiches</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> chop your green pepper , red pepper , sweet onion , and carrots up . put your carrots off to the side . <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> chinese broccoli with oyster sauce</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ;</td></tr><tr><td><b>True</b></td><td> 1 . using the back of a spoon , mash raspberries in a bowl ; transfer to <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> hamburger muffins</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add the garlic , and cook in a large skillet over medium heat . add the onion , and cook</td></tr><tr><td><b>True</b></td><td> stir together instant coffee , sugar and water . set aside . <step> in a large mixer bowl , beat cream cheese until light <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>48.9453596219</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> fries ( gluten - free )</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> bacon <step> gnocchi <end> <end> <end> <end> , garlic , and onions into a large pot or until tender . <step> add the milk ,</td></tr><tr><td><b>True</b></td><td> bacon <step> gnocchi <end> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> 2 . ) add the celery , carrots , and onions into a large pot and <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> basic guacamole</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> lima bean <step> main dish <step> salad <end> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> baked beans</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> preheat the oven to 475 degrees c ( 220 degrees c ) . <step> roll out pizza crust and place on <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing10_200_512_04drb (train)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> strawberry shortcakes</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , and salt , mixing well . <step> add the milk , and bread crumbs</td></tr><tr><td><b>True</b></td><td> combine coconut , flour , salt . <step> add milk and vanilla . <step> i find it much easier to use <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>70.1611656261</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> 5 - minute healthy strawberry frozen yogurt</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> preheat oven to 350 <step> brown beef in skillet & add taco seasoning . cook as directed on seasoning packet <step> spray an <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> curry chickpea salad</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> preheat oven to 375 degrees . lightly grease donut pans ( this one is my favorite <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> tomato soup with white beans and pasta</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> preheat oven to 400 . pierce potatoes several times with a fork . transfer to a large baking dish and cook <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> coffee cake in a mug with cinnamon oatmeal struesel topping</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> to make the apple - cranberry relish : peel , core , and chop the apples into 1 / 4 - inch - inch -</td></tr><tr><td><b>True</b></td><td> to make the apple - cranberry relish : peel , core , and chop the apples into 1 / 4 - inch <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> to make the apple - cranberry relish : peel , core , and chop the apples into 1 / 4 - inch <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>83.6360058744</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> grilled pizza with no - cook tomato sauce</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> heat 1 tbsp . oil in a large skillet over medium - high heat . add runner beans and swiss chard <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> amazing watermelon greek salad with feta</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> combine the first 6 ingredients and shape into patties . <step> cook patties in a large skillet over medium - low heat 20 <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing15_200_512_04drb (train)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> mussels with thyme and shallots</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> preparation : place the rice , chicken broth and 1 tablespoon of the olive oil in a <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> chocolate pancakes</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add</td></tr><tr><td><b>True</b></td><td> heat a medium sized skillet to medium heat . when hot , add olive oil and then <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> zaru soba ( cold soba noodles )</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is</td></tr><tr><td><b>True</b></td><td> 1 . preheat oven 350º f . <step> 2 . cut one half the onion into thin slices ; place in a large bowl <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> hamburger muffins</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large bowl , combine the ice , garlic , and cashews in a bowl , combine the chips . <step> add the milk</td></tr><tr><td><b>True</b></td><td> stir together instant coffee , sugar and water . set aside . <step> in a large mixer bowl , beat cream cheese until light <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large bowl , combine the cottage cheese , sour cream , egg , salt , garlic salt and pepper . <step> add cheddar</td></tr><tr><td><b>Closest BLEU Score</b></td><td>30.8264237423</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> pizza zacineti breadsticks</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> using a mixer with a paddle , cream together the butter , sugar and eggs until smooth . add the vanilla and zest and combine</td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> egg sandwiches with wilted spinach</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is</td></tr><tr><td><b>True</b></td><td> 1 . preheat oven to 350 degrees f. and spray donut pan with non - stick cooking spray . <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> mussels with thyme and shallots</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> preparation : place the rice , chicken broth and 1 tablespoon of the olive oil in a <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing23_200_512_04drb (train)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> pumpkin ravioli</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat . add</td></tr><tr><td><b>True</b></td><td> heat oil in a soup pot or dutch oven over moderate heat until hot . add onions and fennel and cook until tender <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> lasagna</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , and salt . <step> add the milk , and 1 . <step> add the</td></tr><tr><td><b>True</b></td><td> whisk together flour , sugar , baking powder , kosher salt , and bacon . store mix <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>51.4184255717</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> bacon - wrapped egg sandwich</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> roll out pizza crust and place on a cooling rack on top</td></tr><tr><td><b>True</b></td><td> preheat oven to 350 . slice and peel the mangoes . place mango flesh in a food processor or blender and puree for 10 <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> rinse the chicken pieces and place them , single layer <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>49.447428999</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> lemon delight mini cheesecake</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> <end> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> tofu dengaku</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> place the split peas in a fine - mesh strainer and rinse thoroughly under cold water . transfer to a large saucepan , add the</td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> fish in creamy sauce with cheese</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> 2 on a greased baking sheet , lay out the crescent seamless dough dough and dough</td></tr><tr><td><b>True</b></td><td> 1 . in a small bowl , combine the miso , tomato paste , chile - garlic paste and a <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> 1 preheat oven to 375 degrees f . <step> 2 on a greased baking sheet , lay out the crescent seamless dough <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>62.2159456296</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> beef barley soup</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> in a medium size bowl whisk together eggs and cajun spice blend and set aside . <step> in a large non stick skillet over medium</td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for rname, report in reports:\n",
" display(HTML('<h3>' + rname + ' (train)</h3>'))\n",
" for i, sample in enumerate(report['train_samples']):\n",
" display_sample(sample, report['best_bleu_matches_train'][i] if 'best_bleu_matches_train' in report else None)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<h3>encdec_noing6_200_512_04drb (valid)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> in a bowl , combine the crabmeat and shrimp with the vodka , lemon peel and juice ; season with salt <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> heat the oil in a large skillet over medium heat . <step> add shallots and saute ' until softened , <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> in a food processor or food mill , grind or mince beans until fine . <step> saute onion in oil until <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> 1 . finely dice the onion and chop all of the mushrooms into halves . <step> 2 . place in a large wok <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> slice the onions into rings and the garlic into discs and place them in the bottom of the slow cooker . <step> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> brown the beef in a large cast iron skillet over medium heat until the fat begins to render . add the onion <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> pulled <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> <end> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
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},
{
"data": {
"text/html": [
"<h3>encdec_noing10_200_512_04drb (valid)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> for dipping sauce , the dipping sauce is supposed to be a little salty because you will be `` dipping '' the <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> preheat oven to 400 ? . <step> arrange prosciutto in a single layer on a baking sheet . bake at 400 ? <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> take the scallops and slice them very thinly . <step> wash the kumquats and remove the seeds . slice very thinly . <step> peel the</td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> in a food processor , pulse 1 bun until fine crumbs form ( you should have about 1 / 4 cup ) <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> cut each potato into 12 wedges . <step> preheat oven to 225c / 475f ° . <step> combine the garlic <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> cook onion , garlic , and ginger in oil with cardamom and curry powder until onion is soft , 8–10 minutes . <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20</td></tr><tr><td><b>True</b></td><td> if desired , slightly mash chickpeas with a fork or potato masher . in a large bowl , combine all salad <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing15_200_512_04drb (valid)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> in a food processor , pulse 1 bun until fine crumbs form ( you should have about 1 / 4 cup ) <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> slice the onions into rings and the garlic into discs and place them in the bottom of the slow cooker . <step> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> in a large food processor , crush oreo cookies into fine crumbs . make sure there are no large chunks <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> pulled <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> melt butter in a large heavy saucepan . <step> add onions , celery , carrots , garlic and sauté until <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> pulled <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> 1 . cook chicken in a pan with a drizzle of olive oil and some s & p and <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> pulled <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> preheat oven and bake french fries as directed on bag . <step> take your flour tortillas and heat <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> line a 6x9 baking dish with aluminum foil . <step> combine the sugar , salt , pepper , and spices . <step> rub <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing23_200_512_04drb (valid)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> sprinkle scallops with salt and pepper . <step> melt 3 tablespoons butter in very large skillet over medium - <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <UNK2> <mask> <mask> <mask> <beg> <mask> <UNK2> <mask> pulled <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> 1 . cook chicken in a pan with a drizzle of olive oil and some s & p and <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> pulled <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> pulled <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> in a medium bowl , stir together the tomatoes , 1 / 4 cup of water , <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> heat oven to 375 ? f . cook chorizo in large ovenproof skillet on medium - high heat <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> in a medium bowl , combine radish , cucumber , cilantro , garlic , lime juice , and <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <mask> <UNK2> <mask> <mask> <mask> <beg> <mask> <UNK2> <mask> pulled <mask> <UNK2> <mask> <UNK2> <mask> <mask> <mask> <mask> <mask> <UNK2> <mask> <UNK2> <mask></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> in a food processor or food mill , grind or mince beans until fine . <step> saute onion in oil until <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2> <UNK2></b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , add all and cook until tender . <step> add the milk , egg , and onions into 1 / 4</td></tr><tr><td><b>True</b></td><td> combine oyster sauce , sugar and 2 tablespoons water in a small bowl . mix well and set aside <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , cook beef , onion , and garlic until meat is browned ; drain . <step> add the <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>26.1585828258</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for rname, report in reports:\n",
" display(HTML('<h3>' + rname + ' (valid)</h3>'))\n",
" for i, sample in enumerate(report['valid_samples']):\n",
" display_sample(sample, report['best_bleu_matches_valid'][i] if 'best_bleu_matches_valid' in report else None)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<h3>encdec_noing6_200_512_04drb (test)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> shupp noodles - noodle omelet</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ;</td></tr><tr><td><b>True</b></td><td> make noodles : beat eggs , add salt and as much flour as can be worked into the eggs to make <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> thai sweet chili sauce</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , corn and cook until tender , 25 until tender</td></tr><tr><td><b>True</b></td><td> throw all the ingredients but the last ( tapioca flour + water ) in a blender . i use my trusty magic bullet . <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>61.8233964096</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> easy paella</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , salt , and salt . <step> add the milk , egg and vanilla .</td></tr><tr><td><b>True</b></td><td> chicken <step> dinner <step> simmer <end> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>57.2545340067</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> easy paella</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , salt , and salt . <step> add the milk , egg and vanilla .</td></tr><tr><td><b>True</b></td><td> chicken <step> dinner <step> simmer <end> <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>57.2545340067</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> caramelized scallops with smoked chili cream</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , corn and cook until tender , 25 until tender</td></tr><tr><td><b>True</b></td><td> 1 . prepare chili cream : mix chipotle powder in lime juice and let it sit and <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>61.8233964096</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> baked sandwiches</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , salt , and salt . <step> add the milk , egg and vanilla .</td></tr><tr><td><b>True</b></td><td> on a greased surface , roll out bread dough into a rectangle . stir together cooked meat , <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>57.2545340067</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> saute ? ed mushrooms</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . <step> cut each apple into thin slices ; place the</td></tr><tr><td><b>True</b></td><td> 1 . cook shiitake mushrooms in a single layer in 1 1 / 2 tbsp . hot oil in a 10 - to <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>54.969636447</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing10_200_512_04drb (test)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> tasty green bean casserole</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> preheat the oven to 325 degrees f ( 165 degrees c ) . <step> fry bacon in a skillet over medium - high <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> perfect porterhouse steak</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large saucepan , combine the ice , garlic , and cashews in a large bowl . add the milk and stir it until</td></tr><tr><td><b>True</b></td><td> place steak on a plate and coat lightly with olive oil . rub steak with meat tenderizer . sprinkle <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large saucepan , combine the water , lentils , carrot , onion , garlic , and bay <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>31.3482066596</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> charbroiled oysters from dragos</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> heat the grill to med - high . <step> melt butter with garlic and pepper in a large <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> mac - n - cheese pizza</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> preheat oven to 400 degrees f ( 200 degrees c ) . spray a 10x15 - inch baking sheet with cooking spray . <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> mediterranean sandwich</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> mix the olive oil and balsamic vinegar . set aside . <step> cook the chicken breast and cut into strips . <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> coconut curried shrimp</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> heat oil in a large skillet over medium heat . add the onion , and cook over medium - low heat for 20 20 minutes</td></tr><tr><td><b>True</b></td><td> in a small , heavy saucepan , bring the rice and 1 1 / 2 cups water to a <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>55.6126655011</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> bbq baby back ribs - pressure cooker</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . add the onion ,</td></tr><tr><td><b>True</b></td><td> place the metal rack in your cooker and add 1 / 2 cup of water ( <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing15_200_512_04drb (test)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> no - bake strawberry cheesecake</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> 1 . beat cream cheese and sugar until smooth . fold in whipped topping . spoon mixture into <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> steak and cheese sandwich</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> heat small amount of oil to medium heat in skillet and add veggies in following order : peppers , mushrooms , onions and <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> greek pizza</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> combine the cornmeal , flour , sugar , baking powder , salt , and salt . <step> add the milk , egg , and vanilla</td></tr><tr><td><b>True</b></td><td> 1 . preheat a pizza stone in a 500 degree oven for at least 30 minutes . place a piece of parchment paper on a</td></tr><tr><td><b>Closest BLEU Match</b></td><td> combine the cornmeal , flour , sugar , mustard , baking powder and salt , mixing well . <step> add the milk , egg <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>57.2545340067</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> roasted pork cuban sandwich</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> mix the roast seasoning blend together and set aside . <step> rinse roast , pat dry , coat <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> applebee 's bourbon street steak</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> in a large bowl , combine the ice , garlic , and cashews in a bowl , combine the chips . <step> add the milk</td></tr><tr><td><b>True</b></td><td> combine bourbon , brown sugar , salt , and pepper over steaks . <step> preheat grill . <step> place steaks on grill and cook <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> in a large bowl , combine the cottage cheese , sour cream , egg , salt , garlic salt and pepper . <step> add cheddar</td></tr><tr><td><b>Closest BLEU Score</b></td><td>30.8264237423</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> baked sandwiches</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large skillet over medium heat . <step> add the onion</td></tr><tr><td><b>True</b></td><td> on a greased surface , roll out bread dough into a rectangle . stir together cooked meat , <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> in a large pot over medium heat cook and stir <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>64.7285941823</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> skillet macaroni and cheese</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is</td></tr><tr><td><b>True</b></td><td> 1 bring a pot of salty water to boil and cook the pasta until nearing al dente , but not full cooked . drain <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<h3>encdec_noing23_200_512_04drb (test)</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> stovetop cheddar mac</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> in a large saucepan of boiling , salted water , cook the macaroni until al dente ; drain in a colander <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> quick and easy white bean salad</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> roll out pizza crust and place on a cooling rack on top</td></tr><tr><td><b>True</b></td><td> 1 after you chop up the onion , squeeze a little lemon juice over it and let it sit while prepping <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> rinse the chicken pieces and place them , single layer <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>49.447428999</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> dutch baby pancake</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> preheat oven to 425 degrees . in a medium cast - iron or ovenproof nonstick skillet , melt 2 tablespoons butter <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> thai peanut zucchini noodles</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . preheat oven to 350 degrees . <step> 2 on a greased baking sheet , lay out the crescent seamless dough dough and dough</td></tr><tr><td><b>True</b></td><td> in a small bowl , combine the peanut butter , lime juice , honey , sesame oil , garlic and chili paste <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> 1 preheat oven to 375 degrees f . <step> 2 on a greased baking sheet , lay out the crescent seamless dough <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>62.2159456296</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> moroccan pot roast with potatoes and carrots</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees f ( 175 degrees c ) . <step> roll out pizza crust and place on a cooling rack on top</td></tr><tr><td><b>True</b></td><td> 1 . coat the inside of a slow cooker with cooking spray . combine tomato paste , broth <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees f ( 175 degrees c ) . <step> rinse the chicken pieces and place them , single layer <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>49.447428999</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> shrimp with green sauce</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> 1 . heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook until tender</td></tr><tr><td><b>True</b></td><td> 1 preheat the oven to 500f ° . in a food processor blitz the garlic . trim <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> heat oil in a large skillet over medium heat . add the onion , corn and soy beans , and cook <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>79.543691219</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table><tr><td><u><b> butternut squash risotto</b></u></td><td></td></tr><tr><td><b>Generated</b></td><td> <beg> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is is is</td></tr><tr><td><b>True</b></td><td> combine 2 cups squash and 2 1 / 2 cups water in a saucepan ; bring to a boil . reduce <end></td></tr><tr><td><b>Closest BLEU Match</b></td><td> preheat oven to 350 degrees . <step> cut up waffles into bite size pieces . think of them as croutons and that is <end></td></tr><tr><td><b>Closest BLEU Score</b></td><td>87.7277201636</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for rname, report in reports:\n",
" display(HTML('<h3>' + rname + ' (test)</h3>'))\n",
" for i, sample in enumerate(report['test_samples']):\n",
" display_sample(sample, report['best_bleu_matches_test'][i] if 'best_bleu_matches_test' in report else None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### BLEU Analysis"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def print_bleu(blue_structs):\n",
" data= [['<b>Model</b>', '<b>Overall Score</b>','<b>1-gram Score</b>','<b>2-gram Score</b>','<b>3-gram Score</b>','<b>4-gram Score</b>']]\n",
" for rname, blue_struct in blue_structs:\n",
" data.append([rname, blue_struct['score'], blue_struct['components']['1'], blue_struct['components']['2'], blue_struct['components']['3'], blue_struct['components']['4']])\n",
" display_table(data)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>3.35</td><td>19.8</td><td>5.7</td><td>1.8</td><td>0.6</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>27.34</td><td>45.6</td><td>28.6</td><td>23.8</td><td>18</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>7.93</td><td>23.1</td><td>10.3</td><td>5.4</td><td>3.1</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>11.14</td><td>28</td><td>14.3</td><td>7.7</td><td>5</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Training Set BLEU Scores\n",
"print_bleu([(rname, report['train_bleu']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>0</td><td>12.1</td><td>2.3</td><td>0</td><td>0</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>0</td><td>17</td><td>3.4</td><td>0.6</td><td>0</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>0</td><td>17.6</td><td>7.4</td><td>1.8</td><td>0</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>0</td><td>23.6</td><td>5.7</td><td>0.6</td><td>0</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Validation Set BLEU Scores\n",
"print_bleu([(rname, report['valid_bleu']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>0</td><td>10.4</td><td>1.1</td><td>0</td><td>0</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>8.06</td><td>30.2</td><td>12.6</td><td>6</td><td>1.9</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>0</td><td>13.2</td><td>1.7</td><td>0</td><td>0</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>0</td><td>12.6</td><td>2.3</td><td>0.6</td><td>0</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Test Set BLEU Scores\n",
"print_bleu([(rname, report['test_bleu']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>1.52</td><td>14.1</td><td>3</td><td>0.6</td><td>0.2</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>13.25</td><td>31</td><td>14.9</td><td>10.1</td><td>6.6</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>4.11</td><td>17.9</td><td>6.5</td><td>2.4</td><td>1</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>5.29</td><td>21.4</td><td>7.4</td><td>3</td><td>1.7</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# All Data BLEU Scores\n",
"print_bleu([(rname, report['combined_bleu']) for (rname, report) in reports])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### N-pairs BLEU Analysis\n",
"\n",
"This analysis randomly samples 1000 pairs of generations/ground truths and treats them as translations, giving their BLEU score. We can expect very low scores in the ground truth and high scores can expose hyper-common generations"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>29.51</td><td>41.2</td><td>28.6</td><td>25.6</td><td>25.1</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>35.39</td><td>46.1</td><td>34.8</td><td>31.6</td><td>31</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>37.67</td><td>48.7</td><td>39.1</td><td>34</td><td>31.1</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>13.59</td><td>33.5</td><td>14.8</td><td>9</td><td>7.6</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Training Set BLEU n-pairs Scores\n",
"print_bleu([(rname, report['n_pairs_bleu_train']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>100</td><td>100</td><td>100</td><td>100</td><td>100</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Validation Set n-pairs BLEU Scores\n",
"print_bleu([(rname, report['n_pairs_bleu_valid']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>25.7</td><td>38.4</td><td>24.5</td><td>21.8</td><td>21.3</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>43.11</td><td>52.3</td><td>44.3</td><td>40.3</td><td>37</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>28.63</td><td>40.3</td><td>30.2</td><td>25</td><td>22.1</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>31.65</td><td>43.7</td><td>31.8</td><td>27.9</td><td>25.9</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Test Set n-pairs BLEU Scores\n",
"print_bleu([(rname, report['n_pairs_bleu_test']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>43.94</td><td>52.6</td><td>42.8</td><td>40.8</td><td>40.6</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>51.97</td><td>59.9</td><td>51.7</td><td>49.3</td><td>47.8</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>51.24</td><td>59.3</td><td>52.2</td><td>48.4</td><td>46</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>30.5</td><td>45</td><td>30.4</td><td>25.8</td><td>24.5</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Combined n-pairs BLEU Scores\n",
"print_bleu([(rname, report['n_pairs_bleu_all']) for (rname, report) in reports])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Overall Score</b></td><td><b>1-gram Score</b></td><td><b>2-gram Score</b></td><td><b>3-gram Score</b></td><td><b>4-gram Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>12.22</td><td>25.4</td><td>13.9</td><td>9.2</td><td>6.8</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>7.59</td><td>25.7</td><td>11</td><td>5.3</td><td>2.2</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>8.81</td><td>26.3</td><td>9.6</td><td>5.9</td><td>4.1</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>11.94</td><td>29.3</td><td>14.1</td><td>8.7</td><td>5.6</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Ground Truth n-pairs BLEU Scores\n",
"print_bleu([(rname, report['n_pairs_bleu_gold']) for (rname, report) in reports])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Alignment Analysis\n",
"\n",
"This analysis computs the average Smith-Waterman alignment score for generations, with the same intuition as N-pairs BLEU, in that we expect low scores in the ground truth and hyper-common generations to raise the scores"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><td><b>Model</b></td><td><b>Average (Train) Generated Score</b></td><td><b>Average (Valid) Generated Score</b></td><td><b>Average (Test) Generated Score</b></td><td><b>Average (All) Generated Score</b></td><td><b>Average (Gold) Score</b></td></tr><tr><td>encdec_noing6_200_512_04drb</td><td>43.7142857143</td><td>133</td><td>36.1904761905</td><td>58.4</td><td>26.080952381</td></tr><tr><td>encdec_noing10_200_512_04drb</td><td>42.380952381</td><td>112</td><td>54.4761904762</td><td>57.8952380952</td><td>20.2</td></tr><tr><td>encdec_noing15_200_512_04drb</td><td>51.380952381</td><td>122</td><td>40.7142857143</td><td>65.219047619</td><td>19.519047619</td></tr><tr><td>encdec_noing23_200_512_04drb</td><td>20.3333333333</td><td>110</td><td>45.1428571429</td><td>37.3619047619</td><td>24.8380952381</td></tr></table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def print_align(reports):\n",
" data= [['<b>Model</b>', '<b>Average (Train) Generated Score</b>','<b>Average (Valid) Generated Score</b>','<b>Average (Test) Generated Score</b>','<b>Average (All) Generated Score</b>', '<b>Average (Gold) Score</b>']]\n",
" for rname, report in reports:\n",
" data.append([rname, report['average_alignment_train'], report['average_alignment_valid'], report['average_alignment_test'], report['average_alignment_all'], report['average_alignment_gold']])\n",
" display_table(data)\n",
"\n",
"print_align(reports)"
]
}
],
"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": 1
}
| mit |
JaviMerino/lisa | ipynb/profiling/kernel_functions_profiling.ipynb | 1 | 64181 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font size=\"9\">Kernel Functions Profiling</font><br>\n",
"<hr>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import logging\n",
"reload(logging)\n",
"logging.basicConfig(\n",
" format='%(asctime)-9s %(levelname)-8s: %(message)s',\n",
" datefmt='%I:%M:%S')\n",
"\n",
"# Enable logging at INFO level\n",
"logging.getLogger().setLevel(logging.INFO)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"# Generate plots inline\n",
"%pylab inline\n",
"\n",
"import json\n",
"import os\n",
"\n",
"import re\n",
"import collections\n",
"import pandas\n",
"\n",
"# Support to tests execution\n",
"from executor import Executor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tests configuration"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Setup a target configuration\n",
"target_conf = {\n",
"\n",
" # Platform and board to target\n",
" \"platform\" : \"linux\",\n",
" \"board\" : \"juno\",\n",
"\n",
" # Login credentials\n",
" \"host\" : \"192.168.0.1\",\n",
" \"username\" : \"root\",\n",
" \"password\" : \"\",\n",
"\n",
" # Local installation path\n",
" \"tftp\" : {\n",
" \"folder\" : \"/var/lib/tftpboot\",\n",
" \"kernel\" : \"kern.bin\",\n",
" \"dtb\" : \"dtb.bin\",\n",
" },\n",
"\n",
" # RTApp calibration values (comment to let LISA do a calibration run)\n",
" \"rtapp-calib\" : {\n",
" \"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355\n",
" },\n",
"\n",
"}\n",
"\n",
"tests_conf = {\n",
" \n",
" # Kernel functions to profile for all the test\n",
" # configurations which have the \"ftrace\" flag enabled\n",
" \"ftrace\" : {\n",
" \"functions\" : [\n",
" \"select_task_rq_fair\",\n",
" \"enqueue_task_fair\",\n",
" \"dequeue_task_fair\",\n",
" ],\n",
" \"buffsize\" : 80 * 1024,\n",
" },\n",
" \n",
" # Platform configurations to test\n",
" \"confs\" : [\n",
" {\n",
" \"tag\" : \"base\",\n",
" \"flags\" : \"ftrace\",\n",
" \"sched_features\" : \"NO_ENERGY_AWARE\",\n",
" \"cpufreq\" : {\n",
" \"governor\" : \"performance\",\n",
" },\n",
" },\n",
" {\n",
" \"tag\" : \"eas\",\n",
" \"flags\" : \"ftrace\",\n",
" \"sched_features\" : \"ENERGY_AWARE\",\n",
" \"cpufreq\" : {\n",
" \"governor\" : \"performance\",\n",
" },\n",
" },\n",
" ],\n",
" \n",
" # Workloads to run (on each platform configuration)\n",
" \"wloads\" : {\n",
" \"rta\" : {\n",
" \"type\" : \"rt-app\",\n",
" \"conf\" : {\n",
" \"class\" : \"profile\",\n",
" \"params\" : {\n",
" \"p20\" : {\n",
" \"kind\" : \"periodic\",\n",
" \"params\" : {\n",
" \"duty_cycle_pct\" : 20,\n",
" },\n",
" \"tasks\" : \"cpus\",\n",
" },\n",
" },\n",
" },\n",
" },\n",
" },\n",
" \n",
" # Number of iterations for each configuration/workload pair\n",
" \"iterations\" : 3,\n",
" \n",
" # Tools to deploy\n",
" \"tools\" : [ \"rt-app\", 'trace-cmd' ],\n",
" \n",
" # Where results are collected\n",
" # NOTE: this folder will be wiped before running the experiments\n",
" \"results_dir\" : \"KernelFunctionsProfilingExample\",\n",
"\n",
" # Modules required by these experiments\n",
" \"exclude_modules\" : [ \"hwmon\" ],\n",
"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"03:43:22 INFO : Target - Loading custom (inline) test configuration\n",
"03:43:22 INFO : Target - Using base path: /home/derkling/Code/lisa\n",
"03:43:22 INFO : Target - Loading custom (inline) target configuration\n",
"03:43:22 INFO : Target - Loading custom (inline) test configuration\n",
"03:43:22 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n",
"03:43:22 INFO : Target - Connecting linux target:\n",
"03:43:22 INFO : Target - username : root\n",
"03:43:22 INFO : Target - host : 192.168.0.1\n",
"03:43:22 INFO : Target - password : \n",
"03:43:26 INFO : Target - Initializing target workdir:\n",
"03:43:26 INFO : Target - /root/devlib-target\n",
"03:43:34 INFO : Target - Topology:\n",
"03:43:34 INFO : Target - [[0, 3, 4, 5], [1, 2]]\n",
"03:43:36 INFO : Platform - Loading default EM:\n",
"03:43:36 INFO : Platform - /home/derkling/Code/lisa/libs/utils/platforms/juno.json\n",
"03:43:38 INFO : FTrace - Enabled tracepoints:\n",
"03:43:38 INFO : FTrace - sched:*\n",
"03:43:38 INFO : FTrace - Kernel functions profiled:\n",
"03:43:38 INFO : FTrace - select_task_rq_fair\n",
"03:43:38 INFO : FTrace - enqueue_task_fair\n",
"03:43:38 INFO : FTrace - dequeue_task_fair\n",
"03:43:38 INFO : EnergyMeter - HWMON module not enabled\n",
"03:43:38 WARNING : EnergyMeter - Energy sampling disabled by configuration\n",
"03:43:38 WARNING : Target - Using configuration provided RTApp calibration\n",
"03:43:38 INFO : Target - Using RT-App calibration values:\n",
"03:43:38 INFO : Target - {\"0\": 358, \"1\": 138, \"2\": 138, \"3\": 357, \"4\": 359, \"5\": 355}\n",
"03:43:38 WARNING : TestEnv - Wipe previous contents of the results folder:\n",
"03:43:38 WARNING : TestEnv - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n",
"03:43:38 INFO : TestEnv - Set results folder to:\n",
"03:43:38 INFO : TestEnv - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n",
"03:43:38 INFO : TestEnv - Experiment results available also in:\n",
"03:43:38 INFO : TestEnv - /home/derkling/Code/lisa/results_latest\n",
"03:43:38 INFO : \n",
"03:43:38 INFO : ################################################################################\n",
"03:43:38 INFO : Executor - Experiments configuration\n",
"03:43:38 INFO : ################################################################################\n",
"03:43:38 INFO : Executor - Configured to run:\n",
"03:43:38 INFO : Executor - 2 targt configurations:\n",
"03:43:38 INFO : Executor - base, eas\n",
"03:43:38 INFO : Executor - 1 workloads (3 iterations each)\n",
"03:43:38 INFO : Executor - rta\n",
"03:43:38 INFO : Executor - Total: 6 experiments\n",
"03:43:38 INFO : Executor - Results will be collected under:\n",
"03:43:38 INFO : Executor - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
]
}
],
"source": [
"# Setup tests executions based on our configuration\n",
"executor = Executor(target_conf, tests_conf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tests execution"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"03:43:38 INFO : \n",
"03:43:38 INFO : ################################################################################\n",
"03:43:38 INFO : Executor - Experiments execution\n",
"03:43:38 INFO : ################################################################################\n",
"03:43:38 INFO : \n",
"03:43:38 INFO : ================================================================================\n",
"03:43:38 INFO : TargetConfig - configuring target for [base] experiments\n",
"03:43:39 INFO : SchedFeatures - Set scheduler feature: NO_ENERGY_AWARE\n",
"03:43:39 INFO : CPUFreq - Configuring all CPUs to use [performance] governor\n",
"03:43:40 INFO : WlGen - Setup new workload rta\n",
"03:43:40 INFO : RTApp - Workload duration defined by longest task\n",
"03:43:40 INFO : RTApp - Default policy: SCHED_OTHER\n",
"03:43:40 INFO : RTApp - ------------------------\n",
"03:43:40 INFO : RTApp - task [task_p20], sched: using default policy\n",
"03:43:40 INFO : RTApp - | calibration CPU: 1\n",
"03:43:40 INFO : RTApp - | loops count: 1\n",
"03:43:40 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
"03:43:40 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
"03:43:40 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
"03:43:41 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"03:43:41 INFO : Executor - Experiment 1/6, [base:rta] 1/3\n",
"03:43:41 WARNING : Executor - FTrace events collection enabled\n",
"03:43:46 INFO : WlGen - Workload execution START:\n",
"03:43:46 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
"03:43:51 INFO : Executor - Collected FTrace binary trace:\n",
"03:43:51 INFO : Executor - <res_dir>/rtapp:base:rta/1/trace.dat\n",
"03:43:52 INFO : Executor - Collected FTrace function profiling:\n",
"03:43:52 INFO : Executor - <res_dir>/rtapp:base:rta/1/trace_stat.json\n",
"03:43:52 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"03:43:52 INFO : Executor - Experiment 2/6, [base:rta] 2/3\n",
"03:43:52 WARNING : Executor - FTrace events collection enabled\n",
"03:43:58 INFO : WlGen - Workload execution START:\n",
"03:43:58 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
"03:44:02 INFO : Executor - Collected FTrace binary trace:\n",
"03:44:02 INFO : Executor - <res_dir>/rtapp:base:rta/2/trace.dat\n",
"03:44:03 INFO : Executor - Collected FTrace function profiling:\n",
"03:44:03 INFO : Executor - <res_dir>/rtapp:base:rta/2/trace_stat.json\n",
"03:44:03 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"03:44:03 INFO : Executor - Experiment 3/6, [base:rta] 3/3\n",
"03:44:03 WARNING : Executor - FTrace events collection enabled\n",
"03:44:09 INFO : WlGen - Workload execution START:\n",
"03:44:09 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
"03:44:14 INFO : Executor - Collected FTrace binary trace:\n",
"03:44:14 INFO : Executor - <res_dir>/rtapp:base:rta/3/trace.dat\n",
"03:44:14 INFO : Executor - Collected FTrace function profiling:\n",
"03:44:14 INFO : Executor - <res_dir>/rtapp:base:rta/3/trace_stat.json\n",
"03:44:14 INFO : \n",
"03:44:14 INFO : ================================================================================\n",
"03:44:14 INFO : TargetConfig - configuring target for [eas] experiments\n",
"03:44:15 INFO : SchedFeatures - Set scheduler feature: ENERGY_AWARE\n",
"03:44:15 INFO : CPUFreq - Configuring all CPUs to use [performance] governor\n",
"03:44:16 INFO : WlGen - Setup new workload rta\n",
"03:44:16 INFO : RTApp - Workload duration defined by longest task\n",
"03:44:16 INFO : RTApp - Default policy: SCHED_OTHER\n",
"03:44:16 INFO : RTApp - ------------------------\n",
"03:44:16 INFO : RTApp - task [task_p20], sched: using default policy\n",
"03:44:16 INFO : RTApp - | calibration CPU: 1\n",
"03:44:16 INFO : RTApp - | loops count: 1\n",
"03:44:16 INFO : RTApp - + phase_000001: duration 1.000000 [s] (10 loops)\n",
"03:44:16 INFO : RTApp - | period 100000 [us], duty_cycle 20 %\n",
"03:44:16 INFO : RTApp - | run_time 20000 [us], sleep_time 80000 [us]\n",
"03:44:16 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"03:44:16 INFO : Executor - Experiment 4/6, [eas:rta] 1/3\n",
"03:44:16 WARNING : Executor - FTrace events collection enabled\n",
"03:44:22 INFO : WlGen - Workload execution START:\n",
"03:44:22 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
"03:44:28 INFO : Executor - Collected FTrace binary trace:\n",
"03:44:28 INFO : Executor - <res_dir>/rtapp:eas:rta/1/trace.dat\n",
"03:44:28 INFO : Executor - Collected FTrace function profiling:\n",
"03:44:28 INFO : Executor - <res_dir>/rtapp:eas:rta/1/trace_stat.json\n",
"03:44:28 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"03:44:28 INFO : Executor - Experiment 5/6, [eas:rta] 2/3\n",
"03:44:28 WARNING : Executor - FTrace events collection enabled\n",
"03:44:34 INFO : WlGen - Workload execution START:\n",
"03:44:34 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
"03:44:40 INFO : Executor - Collected FTrace binary trace:\n",
"03:44:40 INFO : Executor - <res_dir>/rtapp:eas:rta/2/trace.dat\n",
"03:44:40 INFO : Executor - Collected FTrace function profiling:\n",
"03:44:40 INFO : Executor - <res_dir>/rtapp:eas:rta/2/trace_stat.json\n",
"03:44:40 INFO : ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
"03:44:40 INFO : Executor - Experiment 6/6, [eas:rta] 3/3\n",
"03:44:40 WARNING : Executor - FTrace events collection enabled\n",
"03:44:46 INFO : WlGen - Workload execution START:\n",
"03:44:46 INFO : WlGen - /root/devlib-target/bin/rt-app /root/devlib-target/run_dir/rta_00.json\n",
"03:44:52 INFO : Executor - Collected FTrace binary trace:\n",
"03:44:52 INFO : Executor - <res_dir>/rtapp:eas:rta/3/trace.dat\n",
"03:44:52 INFO : Executor - Collected FTrace function profiling:\n",
"03:44:52 INFO : Executor - <res_dir>/rtapp:eas:rta/3/trace_stat.json\n",
"03:44:52 INFO : \n",
"03:44:52 INFO : ################################################################################\n",
"03:44:52 INFO : Executor - Experiments execution completed\n",
"03:44:52 INFO : ################################################################################\n",
"03:44:52 INFO : Executor - Results available in:\n",
"03:44:52 INFO : Executor - /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
]
}
],
"source": [
"# Execute all the configured test\n",
"executor.run()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
]
}
],
"source": [
"res_dir = \"/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\"\n",
"out_dir = \"/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample/rtapp:eas:rta/2/trace.dat\"\n",
"out_dir.replace(res_dir, \"<res_dir>\")\n",
"print executor.te.res_dir"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"03:44:52 INFO : Content of the output folder /home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34m/home/derkling/Code/lisa/results/KernelFunctionsProfilingExample\u001b[00m\r\n",
"├── \u001b[01;34mrtapp:base:rta\u001b[00m\r\n",
"│ ├── \u001b[01;34m1\u001b[00m\r\n",
"│ │ ├── output.log\r\n",
"│ │ ├── rta_00.json\r\n",
"│ │ ├── rt-app-task_p20-0.log\r\n",
"│ │ ├── trace.dat\r\n",
"│ │ └── trace_stat.json\r\n",
"│ ├── \u001b[01;34m2\u001b[00m\r\n",
"│ │ ├── output.log\r\n",
"│ │ ├── rta_00.json\r\n",
"│ │ ├── rt-app-task_p20-0.log\r\n",
"│ │ ├── trace.dat\r\n",
"│ │ └── trace_stat.json\r\n",
"│ ├── \u001b[01;34m3\u001b[00m\r\n",
"│ │ ├── output.log\r\n",
"│ │ ├── rta_00.json\r\n",
"│ │ ├── rt-app-task_p20-0.log\r\n",
"│ │ ├── trace.dat\r\n",
"│ │ └── trace_stat.json\r\n",
"│ ├── kernel.config\r\n",
"│ ├── kernel.version\r\n",
"│ └── platform.json\r\n",
"└── \u001b[01;34mrtapp:eas:rta\u001b[00m\r\n",
" ├── \u001b[01;34m1\u001b[00m\r\n",
" │ ├── output.log\r\n",
" │ ├── rta_00.json\r\n",
" │ ├── rt-app-task_p20-0.log\r\n",
" │ ├── trace.dat\r\n",
" │ └── trace_stat.json\r\n",
" ├── \u001b[01;34m2\u001b[00m\r\n",
" │ ├── output.log\r\n",
" │ ├── rta_00.json\r\n",
" │ ├── rt-app-task_p20-0.log\r\n",
" │ ├── trace.dat\r\n",
" │ └── trace_stat.json\r\n",
" ├── \u001b[01;34m3\u001b[00m\r\n",
" │ ├── output.log\r\n",
" │ ├── rta_00.json\r\n",
" │ ├── rt-app-task_p20-0.log\r\n",
" │ ├── trace.dat\r\n",
" │ └── trace_stat.json\r\n",
" ├── kernel.config\r\n",
" ├── kernel.version\r\n",
" └── platform.json\r\n",
"\r\n",
"8 directories, 36 files\r\n"
]
}
],
"source": [
"# Check content of the output folder\n",
"res_dir = executor.te.res_dir\n",
"logging.info('Content of the output folder %s', res_dir)\n",
"!tree {res_dir}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load function profiling data"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"def autodict():\n",
" return collections.defaultdict(autodict)\n",
"\n",
"def parse_perf_stat(res_dir):\n",
" TEST_DIR_RE = re.compile(r'.*/([^:]*):([^:]*):([^:]*)')\n",
" profiling_data = autodict()\n",
"\n",
" for test_idx in sorted(os.listdir(res_dir)):\n",
" test_dir = os.path.join(res_dir, test_idx)\n",
" if not os.path.isdir(test_dir):\n",
" continue\n",
" match = TEST_DIR_RE.search(test_dir)\n",
" if not match:\n",
" continue\n",
" wtype = match.group(1)\n",
" tconf = match.group(2)\n",
" wload = match.group(3)\n",
"\n",
" #logging.info('Processing %s:%s:%s', wtype, tconf, wload)\n",
" trace_stat_file = os.path.join(test_dir, '1', 'trace_stat.json')\n",
" if not os.path.isfile(trace_stat_file):\n",
" continue\n",
" with open(trace_stat_file, 'r') as fh:\n",
" data = json.load(fh)\n",
" for cpu_id, cpu_stats in sorted(data.items()):\n",
" for fname in cpu_stats:\n",
" profiling_data[cpu_id][tconf][fname] = cpu_stats[fname]\n",
"\n",
" return profiling_data\n",
" \n",
"profiling_data = parse_perf_stat(res_dir)\n",
"#logging.info(\"Profiling data:\\n%s\", json.dumps(profiling_data, indent=4))\n",
"#profiling_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build Pandas DataFrame from profiling data"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_df(profiling_data):\n",
" cpu_ids = []\n",
" cpu_frames = []\n",
" for cpu_id, cpu_data in sorted(profiling_data.items()):\n",
" cpu_ids.append(cpu_id)\n",
" conf_ids = []\n",
" conf_frames = []\n",
" for conf_id, conf_data in cpu_data.iteritems():\n",
" conf_ids.append(conf_id)\n",
" function_data = pandas.DataFrame.from_dict(conf_data, orient='index')\n",
" conf_frames.append(function_data)\n",
" df = pandas.concat(conf_frames, keys=conf_ids)\n",
" cpu_frames.append(df)\n",
" df = pandas.concat(cpu_frames, keys=cpu_ids)\n",
" #df.head()\n",
" return df\n",
"\n",
"stats_df = get_df(profiling_data)\n",
"#stats_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot profiling data per function and CPU"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_stats(df, fname, axes=None):\n",
" func_data = df.xs(fname, level=2)\n",
" func_stats = func_data.xs(['avg', 's_2'], axis=1)\n",
" #func_stats\n",
" func_avg = func_stats.unstack(level=1)['avg']\n",
" func_std = func_stats.unstack(level=1)['s_2'].apply(numpy.sqrt)\n",
" func_avg.plot(kind='bar', title=fname, yerr=func_std, ax=axes);\n",
"\n",
"#plot_stats(stats_df, 'select_task_rq_fair')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"03:44:53 INFO : Plotting stats for [dequeue_task_fair] function\n",
"03:44:53 INFO : Plotting stats for [enqueue_task_fair] function\n",
"03:44:53 INFO : Plotting stats for [select_task_rq_fair] function\n"
]
},
{
"data": {
"image/png": 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h41/MvlwAAAAWi2qtza6DqlVJVrXWbquqw5J8KcnPJ3lJkvtba+96hPZttjUAAOxJVcXv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\nEEgBAAAYQiAFAABgCIEUAACAIf4f23eMRvyrGDIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd620dcb450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_all_functions(df):\n",
" functions = df.index.get_level_values(2).unique()\n",
" fcount = len(functions)\n",
"\n",
" fig, pltaxes = plt.subplots(fcount, 1, figsize=(16, 8*fcount))\n",
"\n",
" fig_id = 0\n",
" for fname in functions:\n",
" logging.info(\"Plotting stats for [%s] function\", fname)\n",
" if fcount > 1:\n",
" axes = pltaxes[fig_id]\n",
" else:\n",
" axes = pltaxes\n",
" plot_stats(df, fname, axes)\n",
" fig_id = fig_id + 1\n",
" \n",
"plot_all_functions(stats_df)"
]
}
],
"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
}
| apache-2.0 |
probml/pyprobml | notebooks/book2/14/logreg_laplace_demo.ipynb | 1 | 323 | {
"cells": [
{
"cell_type": "markdown",
"id": "d0ef0b1e",
"metadata": {},
"source": [
"Source of this notebook is here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/10/logreg_laplace_demo.ipynb"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 5
}
| mit |
yedivanseven/Commodities | Monthly.ipynb | 1 | 1670342 | null | gpl-3.0 |
mne-tools/mne-tools.github.io | stable/_downloads/09a8b0bb7a57481cdd1f7832f0291ee6/brain.ipynb | 1 | 7332 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Plotting with ``mne.viz.Brain``\n\nIn this example, we'll show how to use :class:`mne.viz.Brain`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Alex Rockhill <[email protected]>\n#\n# License: BSD-3-Clause"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data\n\nIn this example we use the ``sample`` data which is data from a subject\nbeing presented auditory and visual stimuli to display the functionality\nof :class:`mne.viz.Brain` for plotting data on a brain.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os.path as op\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\n\nimport mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nsubjects_dir = op.join(data_path, 'subjects')\nsample_dir = op.join(data_path, 'MEG', 'sample')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add source information\n\nPlot source information.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain_kwargs = dict(alpha=0.1, background='white', cortex='low_contrast')\nbrain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)\n\nstc = mne.read_source_estimate(op.join(sample_dir, 'sample_audvis-meg'))\nstc.crop(0.09, 0.1)\n\nkwargs = dict(fmin=stc.data.min(), fmax=stc.data.max(), alpha=0.25,\n smoothing_steps='nearest', time=stc.times)\nbrain.add_data(stc.lh_data, hemi='lh', vertices=stc.lh_vertno, **kwargs)\nbrain.add_data(stc.rh_data, hemi='rh', vertices=stc.rh_vertno, **kwargs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modify the view of the brain\n\nYou can adjust the view of the brain using ``show_view`` method.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)\nbrain.show_view(azimuth=190, elevation=70, distance=350, focalpoint=(0, 0, 20))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Highlight a region on the brain\n\nIt can be useful to highlight a region of the brain for analyses.\nTo highlight a region on the brain you can use the ``add_label`` method.\nLabels are stored in the Freesurfer label directory from the ``recon-all``\nfor that subject. Labels can also be made following the\n[Freesurfer instructions](https://surfer.nmr.mgh.harvard.edu/fswiki/mri_vol2label)\nHere we will show Brodmann Area 44.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>The MNE sample dataset contains only a subselection of the\n Freesurfer labels created during the ``recon-all``.</p></div>\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)\nbrain.add_label('BA44', hemi='lh', color='green', borders=True)\nbrain.show_view(azimuth=190, elevation=70, distance=350, focalpoint=(0, 0, 20))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Include the head in the image\n\nAdd a head image using the ``add_head`` method.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)\nbrain.add_head(alpha=0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add sensors positions\n\nTo put into context the data that generated the source time course,\nthe sensor positions can be displayed as well.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)\nevoked = mne.read_evokeds(op.join(sample_dir, 'sample_audvis-ave.fif'))[0]\ntrans = mne.read_trans(op.join(sample_dir, 'sample_audvis_raw-trans.fif'))\nbrain.add_sensors(evoked.info, trans)\nbrain.show_view(distance=500) # move back to show sensors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Add current dipoles\n\nDipole modeling as in `tut-dipole-orientations` can be plotted on the\nbrain as well.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"brain = mne.viz.Brain('sample', subjects_dir=subjects_dir, **brain_kwargs)\ndip = mne.read_dipole(op.join(sample_dir, 'sample_audvis_set1.dip'))\ncmap = plt.get_cmap('YlOrRd')\ncolors = [cmap(gof / dip.gof.max()) for gof in dip.gof]\nbrain.add_dipole(dip, trans, colors=colors, scales=list(dip.amplitude * 1e8))\nbrain.show_view(azimuth=-20, elevation=60, distance=300)\nimg = brain.screenshot() # for next section"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a screenshot for exporting the brain image\nAlso, we can a static image of the brain using ``screenshot`` (above),\nwhich will allow us to add a colorbar. This is useful for figures in\npublications.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\nax.imshow(img)\nax.axis('off')\ncax = fig.add_axes([0.9, 0.1, 0.05, 0.8])\nfig.colorbar(mpl.cm.ScalarMappable(\n norm=mpl.colors.Normalize(vmin=0, vmax=dip.gof.max()), cmap=cmap), cax=cax)\nfig.suptitle('Dipole Fits Scaled by Amplitude and Colored by GOF')"
]
}
],
"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": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
timmyshen/Cat_Tube | specs.ipynb | 1 | 3226 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"specs = pd.read_csv('./competition_data/specs.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(21198, 11)\n",
"tube_assembly_id object\n",
"spec1 object\n",
"spec2 object\n",
"spec3 object\n",
"spec4 object\n",
"spec5 object\n",
"spec6 object\n",
"spec7 object\n",
"spec8 object\n",
"spec9 object\n",
"spec10 object\n",
"dtype: object\n",
" tube_assembly_id spec1 spec2 spec3 spec4 spec5 spec6 spec7 \\\n",
"9258 TA-09259 NaN NaN NaN NaN NaN NaN NaN \n",
"6255 TA-06256 SP-0007 SP-0058 SP-0070 SP-0080 NaN NaN NaN \n",
"5653 TA-05654 NaN NaN NaN NaN NaN NaN NaN \n",
"449 TA-00450 SP-0058 SP-0070 NaN NaN NaN NaN NaN \n",
"14694 TA-14695 NaN NaN NaN NaN NaN NaN NaN \n",
"19962 TA-19964 SP-0012 SP-0026 SP-0063 SP-0080 NaN NaN NaN \n",
"12335 TA-12336 NaN NaN NaN NaN NaN NaN NaN \n",
"15664 TA-15665 NaN NaN NaN NaN NaN NaN NaN \n",
"14435 TA-14436 NaN NaN NaN NaN NaN NaN NaN \n",
"15108 TA-15109 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" spec8 spec9 spec10 \n",
"9258 NaN NaN NaN \n",
"6255 NaN NaN NaN \n",
"5653 NaN NaN NaN \n",
"449 NaN NaN NaN \n",
"14694 NaN NaN NaN \n",
"19962 NaN NaN NaN \n",
"12335 NaN NaN NaN \n",
"15664 NaN NaN NaN \n",
"14435 NaN NaN NaN \n",
"15108 NaN NaN NaN \n"
]
}
],
"source": [
"print specs.shape\n",
"print specs.dtypes\n",
"print specs.sample(10, random_state=0)"
]
}
],
"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-2.0 |
GoogleCloudPlatform/tf-estimator-tutorials | 00_Miscellaneous/tfx/tfx-pipelines/01-census-data-analysis.ipynb | 1 | 65125 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Analysis\n",
"The purpose of this phase is to perform exploratory data analysis to understand the nature of your data, as well as the data preprocessing and feature engineering required for your ML task. \n",
"\n",
"The output of this process is a **raw_schema**, which acts as a contract between your model and the incoming input data. This **raw_schema** is used by the data validation step in the ML training pipeline.\n",
"\n",
"1. Analyze the training data and produce statistics.\n",
"2. Generate data schema from the produced statistics.\n",
"3. Configure the schema.\n",
"4. Save the schema for later use."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### UCI Adult Dataset: https://archive.ics.uci.edu/ml/datasets/adult\n",
"Predict whether income exceeds $50K/yr based on census data. Also known as \"Census Income\" dataset."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"ROOT_DIR = '..'\n",
"DATA_DIR = ROOT_DIR + '/data'\n",
"TRAIN_DATA_DIR = DATA_DIR + '/train'\n",
"RAW_SCHEMA_DIR = ROOT_DIR + '/raw_schema'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Copying gs://cloud-samples-data/ml-engine/census/data/adult.data.csv...\n",
"- [1 files][ 3.8 MiB/ 3.8 MiB] \n",
"Operation completed over 1 objects/3.8 MiB. \n",
"Copying gs://cloud-samples-data/ml-engine/census/data/adult.test.csv...\n",
"- [1 files][ 1.9 MiB/ 1.9 MiB] \n",
"Operation completed over 1 objects/1.9 MiB. \n"
]
}
],
"source": [
"!mkdir $DATA_DIR\n",
"!mkdir $TRAIN_DATA_DIR\n",
"!gsutil cp gs://cloud-samples-data/ml-engine/census/data/adult.data.csv $DATA_DIR\n",
"!gsutil cp gs://cloud-samples-data/ml-engine/census/data/adult.test.csv $DATA_DIR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Adding headers to the CSV files as the CsvExampleGen components expect headers..."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"HEADER = ['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
" 'marital_status', 'occupation', 'relationship', 'race', 'gender',\n",
" 'capital_gain', 'capital_loss', 'hours_per_week',\n",
" 'native_country', 'income_bracket']\n",
"\n",
"pd.read_csv(DATA_DIR +\"/adult.data.csv\", names=HEADER).to_csv(TRAIN_DATA_DIR +\"/train-01.csv\", index=False)\n",
"pd.read_csv(DATA_DIR +\"/adult.test.csv\", names=HEADER).to_csv(TRAIN_DATA_DIR +\"/train-02.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 32562 ../data/train/train-01.csv\n",
"age,workclass,fnlwgt,education,education_num,marital_status,occupation,relationship,race,gender,capital_gain,capital_loss,hours_per_week,native_country,income_bracket\n",
"39, State-gov,77516, Bachelors,13, Never-married, Adm-clerical, Not-in-family, White, Male,2174,0,40, United-States, <=50K\n",
"50, Self-emp-not-inc,83311, Bachelors,13, Married-civ-spouse, Exec-managerial, Husband, White, Male,0,0,13, United-States, <=50K\n",
"38, Private,215646, HS-grad,9, Divorced, Handlers-cleaners, Not-in-family, White, Male,0,0,40, United-States, <=50K\n",
"53, Private,234721, 11th,7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male,0,0,40, United-States, <=50K\n",
"28, Private,338409, Bachelors,13, Married-civ-spouse, Prof-specialty, Wife, Black, Female,0,0,40, Cuba, <=50K\n",
"37, Private,284582, Masters,14, Married-civ-spouse, Exec-managerial, Wife, White, Female,0,0,40, United-States, <=50K\n",
"49, Private,160187, 9th,5, Married-spouse-absent, Other-service, Not-in-family, Black, Female,0,0,16, Jamaica, <=50K\n",
"52, Self-emp-not-inc,209642, HS-grad,9, Married-civ-spouse, Exec-managerial, Husband, White, Male,0,0,45, United-States, >50K\n",
"31, Private,45781, Masters,14, Never-married, Prof-specialty, Not-in-family, White, Female,14084,0,50, United-States, >50K\n"
]
}
],
"source": [
"!wc -l $TRAIN_DATA_DIR/train-01.csv\n",
"!head $TRAIN_DATA_DIR/train-01.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tensorflow Data Validation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow_data_validation as tfdv\n",
"\n",
"TARGET_FEATURE_NAME = 'income_bracket'\n",
"WEIGHT_FEATURE_NAME = 'fnlwgt'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Compute Statistics"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:root:Couldn't find python-snappy so the implementation of _TFRecordUtil._masked_crc32c is not as fast as it could be.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /Users/khalidsalama/Technology/GoogleCloud/source-code/tfx-playground/venv/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_gen_lib.py:366: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use eager execution and: \n",
"`tf.data.TFRecordDataset(path)`\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /Users/khalidsalama/Technology/GoogleCloud/source-code/tfx-playground/venv/lib/python3.6/site-packages/tensorflow_data_validation/utils/stats_gen_lib.py:366: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use eager execution and: \n",
"`tf.data.TFRecordDataset(path)`\n"
]
}
],
"source": [
"train_stats = tfdv.generate_statistics_from_csv(\n",
" data_location=TRAIN_DATA_DIR+'/*.csv', \n",
" column_names=None, # CSV data file include header\n",
" stats_options=tfdv.StatsOptions(\n",
" weight_feature=WEIGHT_FEATURE_NAME,\n",
" sample_rate=1.0\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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" facets_iframe.srcdoc = facets_html;\n",
" facets_iframe.id = \"\";\n",
" setTimeout(() => {\n",
" facets_iframe.setAttribute('height', facets_iframe.contentWindow.document.body.offsetHeight + 'px')\n",
" }, 1500)\n",
" </script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tfdv.visualize_statistics(train_stats)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Infer Schema"
]
},
{
"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>Type</th>\n",
" <th>Presence</th>\n",
" <th>Valency</th>\n",
" <th>Domain</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Feature name</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>'age'</th>\n",
" <td>INT</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'capital_gain'</th>\n",
" <td>INT</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'capital_loss'</th>\n",
" <td>INT</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'education'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'education'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'education_num'</th>\n",
" <td>INT</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'fnlwgt'</th>\n",
" <td>INT</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'gender'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'gender'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'hours_per_week'</th>\n",
" <td>INT</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'income_bracket'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'income_bracket'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'marital_status'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'marital_status'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'native_country'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'native_country'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'occupation'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'occupation'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'race'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'race'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'relationship'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'relationship'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'workclass'</th>\n",
" <td>STRING</td>\n",
" <td>required</td>\n",
" <td></td>\n",
" <td>'workclass'</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Type Presence Valency Domain\n",
"Feature name \n",
"'age' INT required -\n",
"'capital_gain' INT required -\n",
"'capital_loss' INT required -\n",
"'education' STRING required 'education'\n",
"'education_num' INT required -\n",
"'fnlwgt' INT required -\n",
"'gender' STRING required 'gender'\n",
"'hours_per_week' INT required -\n",
"'income_bracket' STRING required 'income_bracket'\n",
"'marital_status' STRING required 'marital_status'\n",
"'native_country' STRING required 'native_country'\n",
"'occupation' STRING required 'occupation'\n",
"'race' STRING required 'race'\n",
"'relationship' STRING required 'relationship'\n",
"'workclass' STRING required 'workclass'"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
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" .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>Values</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Domain</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>'education'</th>\n",
" <td>' 10th', ' 11th', ' 12th', ' 1st-4th', ' 5th-6th', ' 7th-8th', ' 9th', ' Assoc-acdm', ' Assoc-voc', ' Bachelors', ' Doctorate', ' HS-grad', ' Masters', ' Preschool', ' Prof-school', ' Some-college'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'gender'</th>\n",
" <td>' Female', ' Male'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'income_bracket'</th>\n",
" <td>' <=50K', ' >50K'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'marital_status'</th>\n",
" <td>' Divorced', ' Married-AF-spouse', ' Married-civ-spouse', ' Married-spouse-absent', ' Never-married', ' Separated', ' Widowed'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'native_country'</th>\n",
" <td>' ?', ' Cambodia', ' Canada', ' China', ' Columbia', ' Cuba', ' Dominican-Republic', ' Ecuador', ' El-Salvador', ' England', ' France', ' Germany', ' Greece', ' Guatemala', ' Haiti', ' Holand-Netherlands', ' Honduras', ' Hong', ' Hungary', ' India', ' Iran', ' Ireland', ' Italy', ' Jamaica', ' Japan', ' Laos', ' Mexico', ' Nicaragua', ' Outlying-US(Guam-USVI-etc)', ' Peru', ' Philippines', ' Poland', ' Portugal', ' Puerto-Rico', ' Scotland', ' South', ' Taiwan', ' Thailand', ' Trinadad&Tobago', ' United-States', ' Vietnam', ' Yugoslavia'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'occupation'</th>\n",
" <td>' ?', ' Adm-clerical', ' Armed-Forces', ' Craft-repair', ' Exec-managerial', ' Farming-fishing', ' Handlers-cleaners', ' Machine-op-inspct', ' Other-service', ' Priv-house-serv', ' Prof-specialty', ' Protective-serv', ' Sales', ' Tech-support', ' Transport-moving'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'race'</th>\n",
" <td>' Amer-Indian-Eskimo', ' Asian-Pac-Islander', ' Black', ' Other', ' White'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'relationship'</th>\n",
" <td>' Husband', ' Not-in-family', ' Other-relative', ' Own-child', ' Unmarried', ' Wife'</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'workclass'</th>\n",
" <td>' ?', ' Federal-gov', ' Local-gov', ' Never-worked', ' Private', ' Self-emp-inc', ' Self-emp-not-inc', ' State-gov', ' Without-pay'</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Values\n",
"Domain \n",
"'education' ' 10th', ' 11th', ' 12th', ' 1st-4th', ' 5th-6th', ' 7th-8th', ' 9th', ' Assoc-acdm', ' Assoc-voc', ' Bachelors', ' Doctorate', ' HS-grad', ' Masters', ' Preschool', ' Prof-school', ' Some-college' \n",
"'gender' ' Female', ' Male' \n",
"'income_bracket' ' <=50K', ' >50K' \n",
"'marital_status' ' Divorced', ' Married-AF-spouse', ' Married-civ-spouse', ' Married-spouse-absent', ' Never-married', ' Separated', ' Widowed' \n",
"'native_country' ' ?', ' Cambodia', ' Canada', ' China', ' Columbia', ' Cuba', ' Dominican-Republic', ' Ecuador', ' El-Salvador', ' England', ' France', ' Germany', ' Greece', ' Guatemala', ' Haiti', ' Holand-Netherlands', ' Honduras', ' Hong', ' Hungary', ' India', ' Iran', ' Ireland', ' Italy', ' Jamaica', ' Japan', ' Laos', ' Mexico', ' Nicaragua', ' Outlying-US(Guam-USVI-etc)', ' Peru', ' Philippines', ' Poland', ' Portugal', ' Puerto-Rico', ' Scotland', ' South', ' Taiwan', ' Thailand', ' Trinadad&Tobago', ' United-States', ' Vietnam', ' Yugoslavia'\n",
"'occupation' ' ?', ' Adm-clerical', ' Armed-Forces', ' Craft-repair', ' Exec-managerial', ' Farming-fishing', ' Handlers-cleaners', ' Machine-op-inspct', ' Other-service', ' Priv-house-serv', ' Prof-specialty', ' Protective-serv', ' Sales', ' Tech-support', ' Transport-moving' \n",
"'race' ' Amer-Indian-Eskimo', ' Asian-Pac-Islander', ' Black', ' Other', ' White' \n",
"'relationship' ' Husband', ' Not-in-family', ' Other-relative', ' Own-child', ' Unmarried', ' Wife' \n",
"'workclass' ' ?', ' Federal-gov', ' Local-gov', ' Never-worked', ' Private', ' Self-emp-inc', ' Self-emp-not-inc', ' State-gov', ' Without-pay' "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"schema = tfdv.infer_schema(statistics=train_stats)\n",
"tfdv.display_schema(schema=schema)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Alter the Schema"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Relax the minimum fraction of values that must come from the domain for feature occupation.\n",
"occupation = tfdv.get_feature(schema, 'occupation')\n",
"occupation.distribution_constraints.min_domain_mass = 0.9\n",
"\n",
"# Add new value to the domain of feature native_country.\n",
"native_country_domain = tfdv.get_domain(schema, 'native_country')\n",
"native_country_domain.value.append('Egypt')\n",
"\n",
"# All features are by default in both TRAINING and SERVING environments.\n",
"schema.default_environment.append('TRAINING')\n",
"schema.default_environment.append('EVALUATION')\n",
"schema.default_environment.append('SERVING')\n",
"\n",
"# Specify that the class feature is not in SERVING environment.\n",
"tfdv.get_feature(schema, TARGET_FEATURE_NAME).not_in_environment.append('SERVING')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Save the Schema"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"if os.path.exists(RAW_SCHEMA_DIR):\n",
" shutil.rmtree(RAW_SCHEMA_DIR)\n",
" \n",
"os.mkdir(RAW_SCHEMA_DIR)\n",
"\n",
"raw_schema_location = os.path.join(RAW_SCHEMA_DIR, 'schema.pbtxt')\n",
"tfdv.write_schema_text(schema, raw_schema_location)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test loading saved schema"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"feature {\n",
" name: \"age\"\n",
" type: INT\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"capital_gain\"\n",
" type: INT\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"capital_loss\"\n",
" type: INT\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"education\"\n",
" type: BYTES\n",
" domain: \"education\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"education_num\"\n",
" type: INT\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"fnlwgt\"\n",
" type: INT\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"gender\"\n",
" type: BYTES\n",
" domain: \"gender\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"hours_per_week\"\n",
" type: INT\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"income_bracket\"\n",
" type: BYTES\n",
" domain: \"income_bracket\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" not_in_environment: \"SERVING\"\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"marital_status\"\n",
" type: BYTES\n",
" domain: \"marital_status\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"native_country\"\n",
" type: BYTES\n",
" domain: \"native_country\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"occupation\"\n",
" type: BYTES\n",
" domain: \"occupation\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" distribution_constraints {\n",
" min_domain_mass: 0.9\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"race\"\n",
" type: BYTES\n",
" domain: \"race\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"relationship\"\n",
" type: BYTES\n",
" domain: \"relationship\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"feature {\n",
" name: \"workclass\"\n",
" type: BYTES\n",
" domain: \"workclass\"\n",
" presence {\n",
" min_fraction: 1.0\n",
" min_count: 1\n",
" }\n",
" shape {\n",
" dim {\n",
" size: 1\n",
" }\n",
" }\n",
"}\n",
"string_domain {\n",
" name: \"education\"\n",
" value: \" 10th\"\n",
" value: \" 11th\"\n",
" value: \" 12th\"\n",
" value: \" 1st-4th\"\n",
" value: \" 5th-6th\"\n",
" value: \" 7th-8th\"\n",
" value: \" 9th\"\n",
" value: \" Assoc-acdm\"\n",
" value: \" Assoc-voc\"\n",
" value: \" Bachelors\"\n",
" value: \" Doctorate\"\n",
" value: \" HS-grad\"\n",
" value: \" Masters\"\n",
" value: \" Preschool\"\n",
" value: \" Prof-school\"\n",
" value: \" Some-college\"\n",
"}\n",
"string_domain {\n",
" name: \"gender\"\n",
" value: \" Female\"\n",
" value: \" Male\"\n",
"}\n",
"string_domain {\n",
" name: \"income_bracket\"\n",
" value: \" <=50K\"\n",
" value: \" >50K\"\n",
"}\n",
"string_domain {\n",
" name: \"marital_status\"\n",
" value: \" Divorced\"\n",
" value: \" Married-AF-spouse\"\n",
" value: \" Married-civ-spouse\"\n",
" value: \" Married-spouse-absent\"\n",
" value: \" Never-married\"\n",
" value: \" Separated\"\n",
" value: \" Widowed\"\n",
"}\n",
"string_domain {\n",
" name: \"native_country\"\n",
" value: \" ?\"\n",
" value: \" Cambodia\"\n",
" value: \" Canada\"\n",
" value: \" China\"\n",
" value: \" Columbia\"\n",
" value: \" Cuba\"\n",
" value: \" Dominican-Republic\"\n",
" value: \" Ecuador\"\n",
" value: \" El-Salvador\"\n",
" value: \" England\"\n",
" value: \" France\"\n",
" value: \" Germany\"\n",
" value: \" Greece\"\n",
" value: \" Guatemala\"\n",
" value: \" Haiti\"\n",
" value: \" Holand-Netherlands\"\n",
" value: \" Honduras\"\n",
" value: \" Hong\"\n",
" value: \" Hungary\"\n",
" value: \" India\"\n",
" value: \" Iran\"\n",
" value: \" Ireland\"\n",
" value: \" Italy\"\n",
" value: \" Jamaica\"\n",
" value: \" Japan\"\n",
" value: \" Laos\"\n",
" value: \" Mexico\"\n",
" value: \" Nicaragua\"\n",
" value: \" Outlying-US(Guam-USVI-etc)\"\n",
" value: \" Peru\"\n",
" value: \" Philippines\"\n",
" value: \" Poland\"\n",
" value: \" Portugal\"\n",
" value: \" Puerto-Rico\"\n",
" value: \" Scotland\"\n",
" value: \" South\"\n",
" value: \" Taiwan\"\n",
" value: \" Thailand\"\n",
" value: \" Trinadad&Tobago\"\n",
" value: \" United-States\"\n",
" value: \" Vietnam\"\n",
" value: \" Yugoslavia\"\n",
" value: \"Egypt\"\n",
"}\n",
"string_domain {\n",
" name: \"occupation\"\n",
" value: \" ?\"\n",
" value: \" Adm-clerical\"\n",
" value: \" Armed-Forces\"\n",
" value: \" Craft-repair\"\n",
" value: \" Exec-managerial\"\n",
" value: \" Farming-fishing\"\n",
" value: \" Handlers-cleaners\"\n",
" value: \" Machine-op-inspct\"\n",
" value: \" Other-service\"\n",
" value: \" Priv-house-serv\"\n",
" value: \" Prof-specialty\"\n",
" value: \" Protective-serv\"\n",
" value: \" Sales\"\n",
" value: \" Tech-support\"\n",
" value: \" Transport-moving\"\n",
"}\n",
"string_domain {\n",
" name: \"race\"\n",
" value: \" Amer-Indian-Eskimo\"\n",
" value: \" Asian-Pac-Islander\"\n",
" value: \" Black\"\n",
" value: \" Other\"\n",
" value: \" White\"\n",
"}\n",
"string_domain {\n",
" name: \"relationship\"\n",
" value: \" Husband\"\n",
" value: \" Not-in-family\"\n",
" value: \" Other-relative\"\n",
" value: \" Own-child\"\n",
" value: \" Unmarried\"\n",
" value: \" Wife\"\n",
"}\n",
"string_domain {\n",
" name: \"workclass\"\n",
" value: \" ?\"\n",
" value: \" Federal-gov\"\n",
" value: \" Local-gov\"\n",
" value: \" Never-worked\"\n",
" value: \" Private\"\n",
" value: \" Self-emp-inc\"\n",
" value: \" Self-emp-not-inc\"\n",
" value: \" State-gov\"\n",
" value: \" Without-pay\"\n",
"}\n",
"default_environment: \"TRAINING\"\n",
"default_environment: \"EVALUATION\"\n",
"default_environment: \"SERVING\""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tfdv.load_schema_text(raw_schema_location)"
]
},
{
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
Caoimhinmg/PmagPy | data_files/Essentials_Examples/Notebooks/essentials_ch_2_template.ipynb | 1 | 18647 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Jupyter Notebook for turning in solutions to the problems in the Essentials of Paleomagnetism Textbook by L. Tauxe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problems in Chapter 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 1a: WRITE YOUR DESCRIPTION HERE"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# You will need these things!\n",
"import numpy as np\n",
"import pandas as pd\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's write a little function to do the conversion. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# the structure of a function is like this:\n",
"def dir2cart(dec,inc,R): # first line starts with 'def', has the name and the input parameters (data)\n",
" # all subsequent lines are indented\n",
" # continue this function here.......\n",
" pass # this line does nothing - replace it with something that does!\n",
" cart=[1.,1.,1.] # obviously this is not what you want.... \n",
" return cart # returns the stuff you calculated (x,y,z) or (n,e,d)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's read in a data file with some geomagnetic field vectors in it."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# read in the data and transpose it to rows of dec, inc, int\n",
"# you have to change the file name to reflect where you put the data.... \n",
"data=np.loadtxt('ps2_prob1_data.txt').transpose() # this line will read in data\n",
" # now send these data to your function.... and print out the x,y,z\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 1b: Read in locations from 10 random spots on Earth and calculate the IGRF vectors at each place. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we have to understand how the function pmag.get_unf() works. To do this, we need to tell the notebook where the pmag module lives, import it and print out the doc string for get_unf(): "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Called with get_unf(N).\n",
" subroutine to retrieve N uniformly distributed directions\n",
" using the way described in Fisher et al. (1987).\n",
" \n"
]
},
{
"data": {
"text/plain": [
"array([[ 48.79862281, -42.7912137 ],\n",
" [ 254.20814435, 29.76836608],\n",
" [ 5.13710564, -30.52215226],\n",
" [ 340.77085585, -17.93811227],\n",
" [ 263.57383863, 8.21116531],\n",
" [ 100.80277122, 1.73260053],\n",
" [ 141.48219414, -1.3747035 ],\n",
" [ 13.56169561, -38.28824633],\n",
" [ 339.67863846, 41.48404555],\n",
" [ 338.27864251, -9.47637191]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pmagpy.pmag as pmag # this makes the PmagPy module pmag.py available to you\n",
"print pmag.get_unf.__doc__\n",
"pmag.get_unf(10) # now you need to assign this to an array variable name and use it in the following."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"use that function to generate a list of random points on the Earth's surface. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# write your code here."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's find out about ipmag.igrf() "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" Prints out Declination, Inclination, Intensity from the IGRF model.\n",
"\n",
" Arguments\n",
" ----------\n",
" input_list : list with format [Date, Altitude, Latitude, Longitude]\n",
" Date must be in format XXXX.XXXX with years and decimals of a year (A.D.)\n",
" \n"
]
}
],
"source": [
"import pmagpy.ipmag as ipmag # this makes the PmagPy module ipmag.py available to you\n",
"print ipmag.igrf.__doc__"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# figure out how to send your places to ipmag.igrf. do the calculation for 2015. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 1c: Take the output from 1b and call ``dir2cart''. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 2: \n",
"Take the output from Problem 1band plot as an equal area projection (first by hand and then with ipmag functions). The ipmag functions call pmagplotlib and use matplotlib, so these will have to be imported as well. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# this line lets you make plots inside the notebook:\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RwKyFPak/w7FKeO5AP2cofg9/TlnP7/BzZ+GSn4NLPr/F6UPJ07/hWCU8d6CfMxS/hz+n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\nm5k1mEvezKzBXPJmZg32/wF6T4pEsF1CuAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109acc890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ipmag.plot_net(1) # make an equal angle net\n",
"# figure out how to use ipmag.plot_di() and plot the points. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 3"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# code it up here!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's use the pmag function dia_vgp. First let's figure out what it does:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" converts declination, inclination, alpha95 to VGP, dp, dm\n",
" takes input as (Decs, Incs, a95, Site latitudes, Site Longitudes). \n",
" These can be lists or individual values.\n",
" \n"
]
}
],
"source": [
"print pmag.dia_vgp.__doc__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can use it to convert our directions to VGPs. Note that alpha95 is required but is not given so supply a zero in its place. Note also that westward longitudes are indicated by minus signs..."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# you figure it out."
]
},
{
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-3-clause |
martinjrobins/hobo | examples/optimisation/xnes.ipynb | 1 | 26900 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optimisation: xNES\n",
"\n",
"This example shows you how to run a global optimisation with [xNES](http://pints.readthedocs.io/en/latest/optimisers/xnes.html).\n",
"\n",
"For a more elaborate example of an optimisation, see: [basic optimisation example](./first-example.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Minimising error measure\n",
"using Exponential Natural Evolution Strategy (xNES)\n",
"Running in sequential mode.\n",
"Population size: 6\n",
"Iter. Eval. Best Time m:s\n",
"0 6 2780827 0:00.0\n",
"1 12 2780827 0:00.0\n",
"2 18 1869945 0:00.0\n",
"3 24 1455345 0:00.0\n",
"20 126 90555.84 0:00.1\n",
"40 246 90353.97 0:00.1\n",
"60 366 90353.9 0:00.2\n",
"80 486 90353.9 0:00.2\n",
"100 606 90353.9 0:00.3\n",
"120 726 90353.9 0:00.4\n",
"140 846 90353.9 0:00.4\n",
"160 966 90353.9 0:00.5\n",
"180 1086 90353.9 0:00.6\n",
"200 1206 90353.9 0:00.6\n",
"220 1326 90353.9 0:00.7\n",
"240 1446 90353.9 0:00.7\n",
"260 1566 90353.9 0:00.8\n",
"280 1686 90353.9 0:00.9\n",
"300 1806 90353.9 0:01.0\n",
"320 1926 90353.9 0:01.1\n",
"340 2046 90353.9 0:01.1\n",
"360 2166 90353.9 0:01.1\n",
"380 2286 90353.9 0:01.2\n",
"400 2406 90353.9 0:01.3\n",
"420 2526 90353.9 0:01.3\n",
"440 2646 90353.9 0:01.4\n",
"460 2766 90353.9 0:01.5\n",
"480 2886 90353.9 0:01.5\n",
"500 3006 90353.9 0:01.6\n",
"520 3126 90353.9 0:01.6\n",
"540 3246 90353.9 0:01.7\n",
"548 3288 90353.9 0:01.7\n",
"Halting: No significant change for 200 iterations.\n",
"Score at true solution: \n",
"91162.4460941\n",
"Found solution: True parameters:\n",
" 1.49542284064299764e-02 1.49999999999999994e-02\n",
" 5.00898298576238972e+02 5.00000000000000000e+02\n"
]
},
{
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa8feaacac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pints\n",
"import pints.toy as toy\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load a forward model\n",
"model = toy.LogisticModel()\n",
"\n",
"# Create some toy data\n",
"real_parameters = [0.015, 500]\n",
"times = np.linspace(0, 1000, 1000)\n",
"values = model.simulate(real_parameters, times)\n",
"\n",
"# Add noise\n",
"values += np.random.normal(0, 10, values.shape)\n",
"\n",
"# Create an object with links to the model and time series\n",
"problem = pints.SingleOutputProblem(model, times, values)\n",
"\n",
"# Select a score function\n",
"score = pints.SumOfSquaresError(problem)\n",
"\n",
"# Select some boundaries\n",
"boundaries = pints.RectangularBoundaries([0, 400], [0.03, 600])\n",
"\n",
"# Perform an optimization with boundaries and hints\n",
"x0 = 0.011, 400\n",
"sigma0 = [0.01, 100]\n",
"found_parameters, found_value = pints.optimise(\n",
" score,\n",
" x0,\n",
" sigma0,\n",
" boundaries,\n",
" method=pints.XNES,\n",
" )\n",
"\n",
"# Show score of true solution\n",
"print('Score at true solution: ')\n",
"print(score(real_parameters))\n",
"\n",
"# Compare parameters with original\n",
"print('Found solution: True parameters:' )\n",
"for k, x in enumerate(found_parameters):\n",
" print(pints.strfloat(x) + ' ' + pints.strfloat(real_parameters[k]))\n",
"\n",
"# Show quality of fit\n",
"plt.figure()\n",
"plt.xlabel('Time')\n",
"plt.ylabel('Value')\n",
"plt.plot(times, values, label='Nosiy data')\n",
"plt.plot(times, problem.evaluate(found_parameters), label='Fit')\n",
"plt.legend()\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"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
}
| bsd-3-clause |
bukosabino/btctrading | EDA.ipynb | 1 | 11713156 | null | mit |
ellisonbg/ipyleaflet | examples/Primitives.ipynb | 1 | 10321 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"\n",
"from ipyleaflet import (\n",
" Map,\n",
" Marker, MarkerCluster,\n",
" TileLayer, ImageOverlay,\n",
" Polyline, Polygon, Rectangle, Circle, CircleMarker,\n",
" Popup,\n",
" GeoJSON,\n",
" DrawControl,\n",
" basemaps\n",
")\n",
"\n",
"from ipywidgets import HTML"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"center = [34.6252978589571, -77.34580993652344]\n",
"zoom = 10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = Map(center=center, zoom=zoom)\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.interact(zoom=(5,10,1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.clear_layers()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.add_layer(basemaps.Esri.DeLorme)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(m.center)\n",
"print(m.zoom)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Marker"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mark = Marker(location=m.center)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m += mark"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mark.interact(opacity=(0.0,1.0,0.01))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Popup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"html_widget = HTML(\n",
" value=\"\"\"\n",
" <div>Some html with a list of items\n",
" <ul class='list-group'>\n",
" <li class='list-group-item'>Item A</li>\n",
" <li class='list-group-item'>Item B</li>\n",
" <li class='list-group-item'>Item C</li>\n",
" </ul></div>\"\"\",\n",
" placeholder='',\n",
" description='',\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mark.popup = html_widget"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Marker Cluster"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When we have many markers on a map, it is helpful to cluster them together at high zoom levels. First, we create a small grid of markers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xscale = 5\n",
"yscale = 10\n",
"\n",
"x = [m.center[0] + i * xscale * .05 for i in (-1,0,1)]\n",
"y = [m.center[1] + i * yscale * .05 for i in (-1,0,1)]\n",
"\n",
"from itertools import product\n",
"locations = product(x, y)\n",
"markers = [Marker(location=loc) for loc in locations]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we add them all to a `MarkerCluster`, which automatically clusters them at appropriate zoom levels."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"marker_cluster = MarkerCluster(markers = markers)\n",
"m += marker_cluster"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.remove_layer(marker_cluster)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Image Overlay"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"io = ImageOverlay(url='http://ipython.org/_static/IPy_header.png', bounds=m.bounds)\n",
"m.add_layer(io)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.bounds"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.remove_layer(io)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Polyline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pl = Polyline(locations=m.bounds_polygon)\n",
"m += pl"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pl.fill_color = '#F00'\n",
"pl.fill_opacity = 1.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m -= pl"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Polygon"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pg = Polygon(locations=m.bounds_polygon, weight=3,\n",
" color='#F00', opacity=0.8, fill_opacity=0.8,\n",
" fill_color='#0F0')\n",
"m += pg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m -= pg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rectangle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"r = Rectangle(bounds=m.bounds, weight=10, fill_opacity=0.0)\n",
"m += r"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m -= r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Circle"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c = Circle(location=m.center)\n",
"m.add_layer(c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c.interact(weight=(0,10,1), opacity=(0.0,1.0,0.01))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c.model_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.remove_layer(c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.layers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c2 = Circle(location=m.center, radius=30, weight=1,\n",
" color='#F00', opacity=1.0, fill_opacity=1.0,\n",
" fill_color='#0F0')\n",
"m.add_layer(c2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c2.model_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.remove_layer(c2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c2.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CircleMarker"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cm = CircleMarker(location=m.center, radius=30, weight=2,\n",
" color='#F00', opacity=1.0, fill_opacity=1.0,\n",
" fill_color='#0F0')\n",
"m.add_layer(cm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cm.model_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.remove_layer(cm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cm.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiple Circles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"circles = []\n",
"for pos in m.bounds_polygon:\n",
" c = Circle(location=pos, radius=1000)\n",
" circles.append(c)\n",
" m.add_layer(c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for c in circles:\n",
" m.remove_layer(c)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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"
},
"widgets": {
"state": {},
"version": "1.0.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
csaladenes/csaladenes.github.io | test/eis-metadata-validation/Planon metadata validation4.ipynb | 2 | 539131 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EIS metadata validation script\n",
"Used to validate Planon output with spreadsheet input"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Data import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read data. There are two datasets: Planon and Master. The latter is the EIS data nomencalture that was created. Master is made up of two subsets: loggers and meters. Loggers are sometimes called controllers and meters are sometimes called sensors. In rare cases meters or sensors are also called channels."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"planon=pd.read_excel('EIS Assets.xlsx',index_col = 'Code')\n",
"master_loggerscontrollers = pd.read_csv('LoggersControllers.csv', index_col = 'Asset Code')\n",
"master_meterssensors = pd.read_csv('MetersSensors.csv', encoding = 'macroman', index_col = 'Asset Code')\n",
"planon['Code']=planon.index\n",
"master_loggerscontrollers['Code']=master_loggerscontrollers.index\n",
"master_meterssensors['Code']=master_meterssensors.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unify index, caps everything and strip of trailing spaces."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"planon.index=[str(i).upper().strip() for i in planon.index]\n",
"master_loggerscontrollers.index=[str(i).upper().strip() for i in master_loggerscontrollers.index]\n",
"master_meterssensors.index=[str(i).upper().strip() for i in master_meterssensors.index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop duplicates (shouldn't be any)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"planon.drop_duplicates(inplace=True)\n",
"master_loggerscontrollers.drop_duplicates(inplace=True)\n",
"master_meterssensors.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Split Planon import into loggers and meters \n",
"Drop duplicates (shouldn't be any)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\pandas\\util\\decorators.py:91: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" return func(*args, **kwargs)\n"
]
}
],
"source": [
"# Split the Planon file into 2, one for loggers & controllers, and one for meters & sensors.\n",
"planon_loggerscontrollers = planon.loc[(planon['Classification Group'] == 'EN.EN4 BMS Controller') | (planon['Classification Group'] == 'EN.EN1 Data Logger')]\n",
"planon_meterssensors = planon.loc[(planon['Classification Group'] == 'EN.EN2 Energy Meter') | (planon['Classification Group'] == 'EN.EN3 Energy Sensor')]\n",
"planon_loggerscontrollers.drop_duplicates(inplace=True)\n",
"planon_meterssensors.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Index unique? show number of duplicates in index"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(planon_loggerscontrollers.index[planon_loggerscontrollers.index.duplicated()])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3089"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(planon_meterssensors.index[planon_meterssensors.index.duplicated()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Meters are not unique. This is becasue of the spaces served. This is ok for now, we will deal with duplicates at the comparison stage. Same is true for loggers - in the unlikely event that there are duplicates in the future."
]
},
{
"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>BuildingNo.</th>\n",
" <th>Building</th>\n",
" <th>Locations.Space.Space number</th>\n",
" <th>Space Name</th>\n",
" <th>Additional Location Info</th>\n",
" <th>Description</th>\n",
" <th>Classification Group</th>\n",
" <th>Record</th>\n",
" <th>HVAC Ref</th>\n",
" <th>Element Description</th>\n",
" <th>...</th>\n",
" <th>Logger SIM</th>\n",
" <th>Meter Pulse Value</th>\n",
" <th>Meter Units</th>\n",
" <th>Meter Capacity</th>\n",
" <th>Network Point ID</th>\n",
" <th>Tenant Meter.Name</th>\n",
" <th>Fiscal Meter.Name</th>\n",
" <th>EIS Space.Space number</th>\n",
" <th>Utility Type.Name</th>\n",
" <th>Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000</td>\n",
" <td>Alexandra Park</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>AP000-L01/M001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000</td>\n",
" <td>Alexandra Park</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>AP000-L02/M001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M002</th>\n",
" <td>AP000</td>\n",
" <td>Alexandra Park</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Alex Park Main</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>AP000-L02/M002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" BuildingNo. Building Locations.Space.Space number \\\n",
"AP000-L01/M001 AP000 Alexandra Park NaN \n",
"AP000-L02/M001 AP000 Alexandra Park NaN \n",
"AP000-L02/M002 AP000 Alexandra Park NaN \n",
"\n",
" Space Name Additional Location Info Description \\\n",
"AP000-L01/M001 NaN NaN NaN \n",
"AP000-L02/M001 NaN NaN Graduate House 1-11 \n",
"AP000-L02/M002 NaN NaN Alex Park Main \n",
"\n",
" Classification Group Record HVAC Ref Element Description \\\n",
"AP000-L01/M001 EN.EN2 Energy Meter NaN NaN NaN \n",
"AP000-L02/M001 EN.EN2 Energy Meter NaN NaN NaN \n",
"AP000-L02/M002 EN.EN2 Energy Meter NaN NaN NaN \n",
"\n",
" ... Logger SIM Meter Pulse Value Meter Units \\\n",
"AP000-L01/M001 ... NaN NaN NaN \n",
"AP000-L02/M001 ... NaN NaN NaN \n",
"AP000-L02/M002 ... NaN NaN NaN \n",
"\n",
" Meter Capacity Network Point ID Tenant Meter.Name \\\n",
"AP000-L01/M001 NaN NaN No \n",
"AP000-L02/M001 NaN NaN No \n",
"AP000-L02/M002 NaN NaN No \n",
"\n",
" Fiscal Meter.Name EIS Space.Space number Utility Type.Name \\\n",
"AP000-L01/M001 No NaN NaN \n",
"AP000-L02/M001 No NaN NaN \n",
"AP000-L02/M002 No NaN NaN \n",
"\n",
" Code \n",
"AP000-L01/M001 AP000-L01/M001 \n",
"AP000-L02/M001 AP000-L02/M001 \n",
"AP000-L02/M002 AP000-L02/M002 \n",
"\n",
"[3 rows x 30 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_meterssensors.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Validation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create list of all buildings present in Planon export. These are buildings to check the data against from Master."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'AP000',\n",
" 'AP001',\n",
" 'AP009',\n",
" 'AP010',\n",
" 'AP011',\n",
" 'AP057',\n",
" 'AP080',\n",
" 'AP081',\n",
" 'EX001',\n",
" 'MC000',\n",
" 'MC001',\n",
" 'MC003',\n",
" 'MC007',\n",
" 'MC008',\n",
" 'MC010',\n",
" 'MC011',\n",
" 'MC013',\n",
" 'MC014',\n",
" 'MC029',\n",
" 'MC030',\n",
" 'MC031',\n",
" 'MC032',\n",
" 'MC033',\n",
" 'MC043',\n",
" 'MC044',\n",
" 'MC045',\n",
" 'MC046',\n",
" 'MC047',\n",
" 'MC048',\n",
" 'MC050',\n",
" 'MC051',\n",
" 'MC053',\n",
" 'MC055',\n",
" 'MC061',\n",
" 'MC062',\n",
" 'MC063',\n",
" 'MC064',\n",
" 'MC065',\n",
" 'MC066',\n",
" 'MC067',\n",
" 'MC068',\n",
" 'MC069',\n",
" 'MC070',\n",
" 'MC071',\n",
" 'MC072',\n",
" 'MC075',\n",
" 'MC076',\n",
" 'MC077',\n",
" 'MC078',\n",
" 'MC083',\n",
" 'MC095',\n",
" 'MC099',\n",
" 'MC102',\n",
" 'MC103',\n",
" 'MC125',\n",
" 'MC126',\n",
" 'MC128',\n",
" 'MC129',\n",
" 'MC131',\n",
" 'MC134',\n",
" 'MC138',\n",
" 'MC139',\n",
" 'MC140',\n",
" 'MC141',\n",
" 'MC171',\n",
" 'MC181',\n",
" 'MC197',\n",
" 'MC198',\n",
" 'MC199',\n",
" 'MC200',\n",
" 'MC202',\n",
" 'MC203',\n",
" 'MC204',\n",
" 'MC207',\n",
" 'MC208',\n",
" 'MC210',\n",
" 'MC211',\n",
" 'OC004',\n",
" 'OC005',\n",
" 'OC006'}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"buildings=set(planon_meterssensors['BuildingNo.'])\n",
"buildings"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"80"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(buildings)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.1. Meters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create dataframe slice for validation from `master_meterssensors` where the only the buildings located in `buildings` are contained. Save this new slice into `master_meterssensors_for_validation`. This is done by creating sub-slices of the dataframe for each building, then concatenating them all together."
]
},
{
"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>Logger Asset Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Model</th>\n",
" <th>Meter Units</th>\n",
" <th>Meter Pulse Value</th>\n",
" <th>Classification Group</th>\n",
" <th>Logger Channel</th>\n",
" <th>Utility Type</th>\n",
" <th>??</th>\n",
" <th>...</th>\n",
" <th>Building Name</th>\n",
" <th>Space</th>\n",
" <th>Additional Location Info</th>\n",
" <th>Tenant meter</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Parent meter</th>\n",
" <th>Child meters</th>\n",
" <th>Communications type</th>\n",
" <th>Electrical panel ID</th>\n",
" <th>Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MC139-L01/M001</th>\n",
" <td>050157AC6600</td>\n",
" <td>Sub No.9 Transformer No.1</td>\n",
" <td>Carlo Gavazzi</td>\n",
" <td>EM24</td>\n",
" <td>kWh</td>\n",
" <td>1.0</td>\n",
" <td>Energy meter</td>\n",
" <td>1</td>\n",
" <td>Electricity</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>Sub Station 9</td>\n",
" <td>NaN</td>\n",
" <td>Graduate Field</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MC139-L01/M001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC139-L01/M002</th>\n",
" <td>050157AC6600</td>\n",
" <td>Sub No.9 Transformer No.2</td>\n",
" <td>Carlo Gavazzi</td>\n",
" <td>EM24</td>\n",
" <td>kWh</td>\n",
" <td>1.0</td>\n",
" <td>Energy meter</td>\n",
" <td>2</td>\n",
" <td>Electricity</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>Sub Station 9</td>\n",
" <td>NaN</td>\n",
" <td>Graduate Field</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MC139-L01/M002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" Logger Asset Code Description Make \\\n",
"MC139-L01/M001 050157AC6600 Sub No.9 Transformer No.1 Carlo Gavazzi \n",
"MC139-L01/M002 050157AC6600 Sub No.9 Transformer No.2 Carlo Gavazzi \n",
"\n",
" Model Meter Units Meter Pulse Value Classification Group \\\n",
"MC139-L01/M001 EM24 kWh 1.0 Energy meter \n",
"MC139-L01/M002 EM24 kWh 1.0 Energy meter \n",
"\n",
" Logger Channel Utility Type ?? ... Building Name \\\n",
"MC139-L01/M001 1 Electricity NaN ... Sub Station 9 \n",
"MC139-L01/M002 2 Electricity NaN ... Sub Station 9 \n",
"\n",
" Space Additional Location Info Tenant meter Fiscal meter \\\n",
"MC139-L01/M001 NaN Graduate Field 0.0 0.0 \n",
"MC139-L01/M002 NaN Graduate Field 0.0 0.0 \n",
"\n",
" Parent meter Child meters Communications type \\\n",
"MC139-L01/M001 NaN NaN NaN \n",
"MC139-L01/M002 NaN NaN NaN \n",
"\n",
" Electrical panel ID Code \n",
"MC139-L01/M001 NaN MC139-L01/M001 \n",
"MC139-L01/M002 NaN MC139-L01/M002 \n",
"\n",
"[2 rows x 22 columns]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation = \\\n",
" pd.concat([master_meterssensors.loc[master_meterssensors['Building Code'] == building] \\\n",
" for building in buildings])\n",
"master_meterssensors_for_validation.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Logger Asset Code {05937EE0-58E6-42F3-B6BD-A180D9634B6C}\n",
"Description Function 15\n",
"Make NaN\n",
"Model NaN\n",
"Meter Units NaN\n",
"Meter Pulse Value NaN\n",
"Classification Group Energy sensor\n",
"Logger Channel F15\n",
"Utility Type NaN\n",
"?? NaN\n",
"Meter Type NaN\n",
"Building Code MC202\n",
"Building Name Charles Carter Building\n",
"Space NaN\n",
"Additional Location Info NaN\n",
"Tenant meter 0\n",
"Fiscal meter 0\n",
"Parent meter NaN\n",
"Child meters NaN\n",
"Communications type NaN\n",
"Electrical panel ID NaN\n",
"Code MC202-B15/F15\n",
"Name: MC202-B15/F15, dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation.loc['MC202-B15/F15']"
]
},
{
"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>Logger Asset Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Model</th>\n",
" <th>Meter Units</th>\n",
" <th>Meter Pulse Value</th>\n",
" <th>Classification Group</th>\n",
" <th>Logger Channel</th>\n",
" <th>Utility Type</th>\n",
" <th>??</th>\n",
" <th>...</th>\n",
" <th>Building Name</th>\n",
" <th>Space</th>\n",
" <th>Additional Location Info</th>\n",
" <th>Tenant meter</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Parent meter</th>\n",
" <th>Child meters</th>\n",
" <th>Communications type</th>\n",
" <th>Electrical panel ID</th>\n",
" <th>Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MC055-B01/C1</th>\n",
" <td>{6BF82177-CB8B-4774-9B9C-0008202C0CE8}</td>\n",
" <td>Virtual CNC 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Energy sensor</td>\n",
" <td>C1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>Furness Residences</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MC055-B01/C1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC055-B01/D1</th>\n",
" <td>{6BF82177-CB8B-4774-9B9C-0008202C0CE8}</td>\n",
" <td>Heating Pumps 1 &amp; 2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Energy sensor</td>\n",
" <td>D1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>Furness Residences</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MC055-B01/D1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" Logger Asset Code Description \\\n",
"MC055-B01/C1 {6BF82177-CB8B-4774-9B9C-0008202C0CE8} Virtual CNC 1 \n",
"MC055-B01/D1 {6BF82177-CB8B-4774-9B9C-0008202C0CE8} Heating Pumps 1 & 2 \n",
"\n",
" Make Model Meter Units Meter Pulse Value Classification Group \\\n",
"MC055-B01/C1 NaN NaN NaN NaN Energy sensor \n",
"MC055-B01/D1 NaN NaN NaN NaN Energy sensor \n",
"\n",
" Logger Channel Utility Type ?? ... \\\n",
"MC055-B01/C1 C1 NaN NaN ... \n",
"MC055-B01/D1 D1 NaN NaN ... \n",
"\n",
" Building Name Space Additional Location Info Tenant meter \\\n",
"MC055-B01/C1 Furness Residences NaN NaN 0.0 \n",
"MC055-B01/D1 Furness Residences NaN NaN 0.0 \n",
"\n",
" Fiscal meter Parent meter Child meters Communications type \\\n",
"MC055-B01/C1 0.0 NaN NaN NaN \n",
"MC055-B01/D1 0.0 NaN NaN NaN \n",
"\n",
" Electrical panel ID Code \n",
"MC055-B01/C1 NaN MC055-B01/C1 \n",
"MC055-B01/D1 NaN MC055-B01/D1 \n",
"\n",
"[2 rows x 22 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#alternative method\n",
"master_meterssensors_for_validation2 = \\\n",
" master_meterssensors[master_meterssensors['Building Code'].isin(buildings)]\n",
"master_meterssensors_for_validation2.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Planon sensors are not unique because of the spaces served convention in the two data architectures. The Planon architecture devotes a new line for each space served - hence the not unique index. The Master architecture lists all the spaces only once, as a list, therefore it has a unique index. We will need to take this into account and create matching dataframe out of planon for comparison, with a unique index."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"30539"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(master_meterssensors_for_validation)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"29623"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(planon_meterssensors)-len(planon_meterssensors.index[planon_meterssensors.index.duplicated()])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sort datasets after index for easier comparison."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"master_meterssensors_for_validation.sort_index(inplace=True)\n",
"planon_meterssensors.sort_index(inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.1 Slicing of meters to only certain columns of comparison"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"scrolled": 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>AP000-L01/M001</th>\n",
" <th>AP000-L02/M001</th>\n",
" <th>AP000-L02/M002</th>\n",
" <th>AP000-L03/M001</th>\n",
" <th>AP000-L03/M002</th>\n",
" <th>AP001-L01/M001</th>\n",
" <th>AP001-L01/M002</th>\n",
" <th>AP001-L01/M003</th>\n",
" <th>AP001-L01/M004</th>\n",
" <th>AP001-L01/M005</th>\n",
" <th>...</th>\n",
" <th>OC006-B01/W4</th>\n",
" <th>OC006-B01/W5</th>\n",
" <th>OC006-B01/Y1</th>\n",
" <th>OC006-B01/Y2</th>\n",
" <th>OC006-B01/Y3</th>\n",
" <th>OC006-B01/Y4</th>\n",
" <th>OC006-B01/Y5</th>\n",
" <th>OC006-B01/Y6</th>\n",
" <th>OC006-B01/Z1</th>\n",
" <th>OC006-B01/Z2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BuildingNo.</th>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP001</td>\n",
" <td>AP001</td>\n",
" <td>AP001</td>\n",
" <td>AP001</td>\n",
" <td>AP001</td>\n",
" <td>...</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Building</th>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>House 01 - Bassenthwaite, Graduate College</td>\n",
" <td>House 01 - Bassenthwaite, Graduate College</td>\n",
" <td>House 01 - Bassenthwaite, Graduate College</td>\n",
" <td>House 01 - Bassenthwaite, Graduate College</td>\n",
" <td>House 01 - Bassenthwaite, Graduate College</td>\n",
" <td>...</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Locations.Space.Space number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Space Name</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Additional Location Info</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Description</th>\n",
" <td>NaN</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>Alex Park Main</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Bar Cellar</td>\n",
" <td>Bar Ground</td>\n",
" <td>Services Room</td>\n",
" <td>Bar Water</td>\n",
" <td>...</td>\n",
" <td>ShuntPumpRotate</td>\n",
" <td>Manual Shunt pump changeover</td>\n",
" <td>Outside air temperature</td>\n",
" <td>Immersion Sensor</td>\n",
" <td>Thermistor TBTC</td>\n",
" <td>Room temperature</td>\n",
" <td>Thermistor TBTI</td>\n",
" <td>4DIX V</td>\n",
" <td>Heating Boilers</td>\n",
" <td>DHWS Immersion</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Classification Group</th>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>EN.EN2 Energy Meter</td>\n",
" <td>...</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" <td>EN.EN3 Energy Sensor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Record</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HVAC Ref</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Element Description</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Servicable Area</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Model</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Make</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EIS ID</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>W4</td>\n",
" <td>W5</td>\n",
" <td>Y1</td>\n",
" <td>Y2</td>\n",
" <td>Y3</td>\n",
" <td>Y4</td>\n",
" <td>Y5</td>\n",
" <td>Y6</td>\n",
" <td>Z1</td>\n",
" <td>Z2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Channel</th>\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>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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Upstream Comms Target</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Modem Serial Number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger IP Address</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Serial Number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger MAC Address</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger SIM</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Pulse Value</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Units</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Capacity</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Network Point ID</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tenant Meter.Name</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fiscal Meter.Name</th>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>...</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EIS Space.Space number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utility Type.Name</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Code</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>AP000-L02/M001</td>\n",
" <td>AP000-L02/M002</td>\n",
" <td>AP000-L03/M001</td>\n",
" <td>AP000-L03/M002</td>\n",
" <td>AP001-L01/M001</td>\n",
" <td>AP001-L01/M002</td>\n",
" <td>AP001-L01/M003</td>\n",
" <td>AP001-L01/M004</td>\n",
" <td>AP001-L01/M005</td>\n",
" <td>...</td>\n",
" <td>OC006-B01/W4</td>\n",
" <td>OC006-B01/W5</td>\n",
" <td>OC006-B01/Y1</td>\n",
" <td>OC006-B01/Y2</td>\n",
" <td>OC006-B01/Y3</td>\n",
" <td>OC006-B01/Y4</td>\n",
" <td>OC006-B01/Y5</td>\n",
" <td>OC006-B01/Y6</td>\n",
" <td>OC006-B01/Z1</td>\n",
" <td>OC006-B01/Z2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>30 rows × 32712 columns</p>\n",
"</div>"
],
"text/plain": [
" AP000-L01/M001 AP000-L02/M001 \\\n",
"BuildingNo. AP000 AP000 \n",
"Building Alexandra Park Alexandra Park \n",
"Locations.Space.Space number NaN NaN \n",
"Space Name NaN NaN \n",
"Additional Location Info NaN NaN \n",
"Description NaN Graduate House 1-11 \n",
"Classification Group EN.EN2 Energy Meter EN.EN2 Energy Meter \n",
"Record NaN NaN \n",
"HVAC Ref NaN NaN \n",
"Element Description NaN NaN \n",
"Servicable Area NaN NaN \n",
"Model NaN NaN \n",
"Make NaN NaN \n",
"EIS ID 1 1 \n",
"Logger Channel 0 0 \n",
"Logger Upstream Comms Target NaN NaN \n",
"Logger Modem Serial Number NaN NaN \n",
"Logger IP Address NaN NaN \n",
"Logger Serial Number NaN NaN \n",
"Logger MAC Address NaN NaN \n",
"Logger SIM NaN NaN \n",
"Meter Pulse Value NaN NaN \n",
"Meter Units NaN NaN \n",
"Meter Capacity NaN NaN \n",
"Network Point ID NaN NaN \n",
"Tenant Meter.Name No No \n",
"Fiscal Meter.Name No No \n",
"EIS Space.Space number NaN NaN \n",
"Utility Type.Name NaN NaN \n",
"Code AP000-L01/M001 AP000-L02/M001 \n",
"\n",
" AP000-L02/M002 AP000-L03/M001 \\\n",
"BuildingNo. AP000 AP000 \n",
"Building Alexandra Park Alexandra Park \n",
"Locations.Space.Space number NaN NaN \n",
"Space Name NaN NaN \n",
"Additional Location Info NaN NaN \n",
"Description Alex Park Main NaN \n",
"Classification Group EN.EN2 Energy Meter EN.EN2 Energy Meter \n",
"Record NaN NaN \n",
"HVAC Ref NaN NaN \n",
"Element Description NaN NaN \n",
"Servicable Area NaN NaN \n",
"Model NaN NaN \n",
"Make NaN NaN \n",
"EIS ID 2 1 \n",
"Logger Channel 0 0 \n",
"Logger Upstream Comms Target NaN NaN \n",
"Logger Modem Serial Number NaN NaN \n",
"Logger IP Address NaN NaN \n",
"Logger Serial Number NaN NaN \n",
"Logger MAC Address NaN NaN \n",
"Logger SIM NaN NaN \n",
"Meter Pulse Value NaN NaN \n",
"Meter Units NaN NaN \n",
"Meter Capacity NaN NaN \n",
"Network Point ID NaN NaN \n",
"Tenant Meter.Name No No \n",
"Fiscal Meter.Name No No \n",
"EIS Space.Space number NaN NaN \n",
"Utility Type.Name NaN NaN \n",
"Code AP000-L02/M002 AP000-L03/M001 \n",
"\n",
" AP000-L03/M002 \\\n",
"BuildingNo. AP000 \n",
"Building Alexandra Park \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description NaN \n",
"Classification Group EN.EN2 Energy Meter \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID 2 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP000-L03/M002 \n",
"\n",
" AP001-L01/M001 \\\n",
"BuildingNo. AP001 \n",
"Building House 01 - Bassenthwaite, Graduate College \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description NaN \n",
"Classification Group EN.EN2 Energy Meter \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID 1 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP001-L01/M001 \n",
"\n",
" AP001-L01/M002 \\\n",
"BuildingNo. AP001 \n",
"Building House 01 - Bassenthwaite, Graduate College \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Bar Cellar \n",
"Classification Group EN.EN2 Energy Meter \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID 2 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP001-L01/M002 \n",
"\n",
" AP001-L01/M003 \\\n",
"BuildingNo. AP001 \n",
"Building House 01 - Bassenthwaite, Graduate College \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Bar Ground \n",
"Classification Group EN.EN2 Energy Meter \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID 3 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP001-L01/M003 \n",
"\n",
" AP001-L01/M004 \\\n",
"BuildingNo. AP001 \n",
"Building House 01 - Bassenthwaite, Graduate College \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Services Room \n",
"Classification Group EN.EN2 Energy Meter \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID 4 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP001-L01/M004 \n",
"\n",
" AP001-L01/M005 \\\n",
"BuildingNo. AP001 \n",
"Building House 01 - Bassenthwaite, Graduate College \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Bar Water \n",
"Classification Group EN.EN2 Energy Meter \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID 5 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP001-L01/M005 \n",
"\n",
" ... \\\n",
"BuildingNo. ... \n",
"Building ... \n",
"Locations.Space.Space number ... \n",
"Space Name ... \n",
"Additional Location Info ... \n",
"Description ... \n",
"Classification Group ... \n",
"Record ... \n",
"HVAC Ref ... \n",
"Element Description ... \n",
"Servicable Area ... \n",
"Model ... \n",
"Make ... \n",
"EIS ID ... \n",
"Logger Channel ... \n",
"Logger Upstream Comms Target ... \n",
"Logger Modem Serial Number ... \n",
"Logger IP Address ... \n",
"Logger Serial Number ... \n",
"Logger MAC Address ... \n",
"Logger SIM ... \n",
"Meter Pulse Value ... \n",
"Meter Units ... \n",
"Meter Capacity ... \n",
"Network Point ID ... \n",
"Tenant Meter.Name ... \n",
"Fiscal Meter.Name ... \n",
"EIS Space.Space number ... \n",
"Utility Type.Name ... \n",
"Code ... \n",
"\n",
" OC006-B01/W4 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description ShuntPumpRotate \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID W4 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/W4 \n",
"\n",
" OC006-B01/W5 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Manual Shunt pump changeover \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID W5 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/W5 \n",
"\n",
" OC006-B01/Y1 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Outside air temperature \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Y1 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Y1 \n",
"\n",
" OC006-B01/Y2 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Immersion Sensor \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Y2 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Y2 \n",
"\n",
" OC006-B01/Y3 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Thermistor TBTC \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Y3 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Y3 \n",
"\n",
" OC006-B01/Y4 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Room temperature \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Y4 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Y4 \n",
"\n",
" OC006-B01/Y5 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Thermistor TBTI \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Y5 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Y5 \n",
"\n",
" OC006-B01/Y6 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description 4DIX V \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Y6 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Y6 \n",
"\n",
" OC006-B01/Z1 \\\n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description Heating Boilers \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Z1 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Z1 \n",
"\n",
" OC006-B01/Z2 \n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description DHWS Immersion \n",
"Classification Group EN.EN3 Energy Sensor \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model NaN \n",
"Make NaN \n",
"EIS ID Z2 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name No \n",
"Fiscal Meter.Name No \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01/Z2 \n",
"\n",
"[30 rows x 32712 columns]"
]
},
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"metadata": {},
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}
],
"source": [
"planon_meterssensors.T"
]
},
{
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"metadata": {
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" <th></th>\n",
" <th>AP000-L01/M001</th>\n",
" <th>AP000-L02/M001</th>\n",
" <th>AP000-L02/M002</th>\n",
" <th>AP000-L03/M001</th>\n",
" <th>AP000-L03/M002</th>\n",
" <th>AP000-L99/M303</th>\n",
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" <th>OC006-B01/Y6</th>\n",
" <th>OC006-B01/Z1</th>\n",
" <th>OC006-B01/Z2</th>\n",
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" <th>OC006-L99/M503</th>\n",
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" <tr>\n",
" <th>Logger Asset Code</th>\n",
" <td>050157C7ED00</td>\n",
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" <td>37475126</td>\n",
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" <td>Graduate House 1-11</td>\n",
" <td>Alex Park Main</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Alex Park LU</td>\n",
" <td>Alex Park UPP</td>\n",
" <td>NaN</td>\n",
" <td>Bar Cellar</td>\n",
" <td>Bar Ground</td>\n",
" <td>...</td>\n",
" <td>Outside air temperature</td>\n",
" <td>Immersion Sensor</td>\n",
" <td>Thermistor TBTC</td>\n",
" <td>Room temperature</td>\n",
" <td>Thermistor TBTI</td>\n",
" <td>4DIX V</td>\n",
" <td>Heating Boilers</td>\n",
" <td>DHWS Immersion</td>\n",
" <td>Kent House</td>\n",
" <td>Kent House</td>\n",
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" <th>Make</th>\n",
" <td>Carlo Gavazzi</td>\n",
" <td>Actaris</td>\n",
" <td>Delta</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>Elster</td>\n",
" <td>Landis</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Model</th>\n",
" <td>EM24</td>\n",
" <td>Delta G</td>\n",
" <td>D35</td>\n",
" <td></td>\n",
" <td></td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>A1100</td>\n",
" <td>E1100</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Units</th>\n",
" <td>kWh</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>m3</td>\n",
" <td>kWh</td>\n",
" <td>kWh</td>\n",
" <td>...</td>\n",
" <td>Degrees Celsius</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Degrees Celsius</td>\n",
" <td>Degrees Celsius</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>Meter Pulse Value</th>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.001</td>\n",
" <td>0.001</td>\n",
" <td>0.001</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>Classification Group</th>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" <td>...</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy sensor</td>\n",
" <td>Energy meter</td>\n",
" <td>Energy meter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Channel</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
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" <td>Y1</td>\n",
" <td>Y2</td>\n",
" <td>Y3</td>\n",
" <td>Y4</td>\n",
" <td>Y5</td>\n",
" <td>Y6</td>\n",
" <td>Z1</td>\n",
" <td>Z2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utility Type</th>\n",
" <td>Electricity</td>\n",
" <td>Natural Gas</td>\n",
" <td>Natural Gas</td>\n",
" <td>Water</td>\n",
" <td>Water</td>\n",
" <td>Natural Gas</td>\n",
" <td>Natural Gas</td>\n",
" <td>Water</td>\n",
" <td>Electricity</td>\n",
" <td>Electricity</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Electricity</td>\n",
" <td>Water</td>\n",
" </tr>\n",
" <tr>\n",
" <th>??</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Type</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Building Code</th>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP001</td>\n",
" <td>AP001</td>\n",
" <td>AP001</td>\n",
" <td>...</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" <td>OC006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Building Name</th>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>House 01 - Bassenthwaite</td>\n",
" <td>House 01 - Bassenthwaite</td>\n",
" <td>House 01 - Bassenthwaite</td>\n",
" <td>...</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Space</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>AP001 A0, AP002 A0, AP003 A0, AP004 A0, Ap005 ...</td>\n",
" <td>AP012 A0, AP013 A0, AP014 A0, AP015 A0, AP016 ...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>OC006 A0</td>\n",
" <td>OC006 A0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Additional Location Info</th>\n",
" <td>Graduate 1-11 Plant Room</td>\n",
" <td>External Gas Meter Building</td>\n",
" <td>External Gas Meter Building</td>\n",
" <td>External Water Meter Chamber</td>\n",
" <td>External Water Meter Chamber</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Bassenthwaite Rm No 104</td>\n",
" <td>Bassenthwaite Rm No 104</td>\n",
" <td>Bassenthwaite Rm No 104</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tenant meter</th>\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>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>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fiscal meter</th>\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",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</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>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parent meter</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Child meters</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Communications type</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Electrical panel ID</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Code</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>AP000-L02/M001</td>\n",
" <td>AP000-L02/M002</td>\n",
" <td>AP000-L03/M001</td>\n",
" <td>AP000-L03/M002</td>\n",
" <td>AP000-L99/M303</td>\n",
" <td>AP000-L99/M308</td>\n",
" <td>AP001-L01/M001</td>\n",
" <td>AP001-L01/M002</td>\n",
" <td>AP001-L01/M003</td>\n",
" <td>...</td>\n",
" <td>OC006-B01/Y1</td>\n",
" <td>OC006-B01/Y2</td>\n",
" <td>OC006-B01/Y3</td>\n",
" <td>OC006-B01/Y4</td>\n",
" <td>OC006-B01/Y5</td>\n",
" <td>OC006-B01/Y6</td>\n",
" <td>OC006-B01/Z1</td>\n",
" <td>OC006-B01/Z2</td>\n",
" <td>OC006-L99/M200</td>\n",
" <td>OC006-L99/M503</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22 rows × 30539 columns</p>\n",
"</div>"
],
"text/plain": [
" AP000-L01/M001 \\\n",
"Logger Asset Code 050157C7ED00 \n",
"Description NaN \n",
"Make Carlo Gavazzi \n",
"Model EM24 \n",
"Meter Units kWh \n",
"Meter Pulse Value 1 \n",
"Classification Group Energy meter \n",
"Logger Channel 1 \n",
"Utility Type Electricity \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space NaN \n",
"Additional Location Info Graduate 1-11 Plant Room \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L01/M001 \n",
"\n",
" AP000-L02/M001 \\\n",
"Logger Asset Code 37475126 \n",
"Description Graduate House 1-11 \n",
"Make Actaris \n",
"Model Delta G \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel 1 \n",
"Utility Type Natural Gas \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space NaN \n",
"Additional Location Info External Gas Meter Building \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L02/M001 \n",
"\n",
" AP000-L02/M002 \\\n",
"Logger Asset Code 37475126 \n",
"Description Alex Park Main \n",
"Make Delta \n",
"Model D35 \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel 2 \n",
"Utility Type Natural Gas \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space NaN \n",
"Additional Location Info External Gas Meter Building \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L02/M002 \n",
"\n",
" AP000-L03/M001 \\\n",
"Logger Asset Code 48015355 \n",
"Description NaN \n",
"Make \n",
"Model \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel 1 \n",
"Utility Type Water \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space NaN \n",
"Additional Location Info External Water Meter Chamber \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L03/M001 \n",
"\n",
" AP000-L03/M002 \\\n",
"Logger Asset Code 48015355 \n",
"Description NaN \n",
"Make \n",
"Model \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel 2 \n",
"Utility Type Water \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space NaN \n",
"Additional Location Info External Water Meter Chamber \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L03/M002 \n",
"\n",
" AP000-L99/M303 \\\n",
"Logger Asset Code NaN \n",
"Description Alex Park LU \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel NaN \n",
"Utility Type Natural Gas \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space AP001 A0, AP002 A0, AP003 A0, AP004 A0, Ap005 ... \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 1 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L99/M303 \n",
"\n",
" AP000-L99/M308 \\\n",
"Logger Asset Code NaN \n",
"Description Alex Park UPP \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel NaN \n",
"Utility Type Natural Gas \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space AP012 A0, AP013 A0, AP014 A0, AP015 A0, AP016 ... \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 1 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP000-L99/M308 \n",
"\n",
" AP001-L01/M001 AP001-L01/M002 \\\n",
"Logger Asset Code 0501E3E97100 0501E3E97100 \n",
"Description NaN Bar Cellar \n",
"Make Elster \n",
"Model A1100 \n",
"Meter Units m3 kWh \n",
"Meter Pulse Value 0.001 0.001 \n",
"Classification Group Energy meter Energy meter \n",
"Logger Channel 1 2 \n",
"Utility Type Water Electricity \n",
"?? NaN NaN \n",
"Meter Type NaN NaN \n",
"Building Code AP001 AP001 \n",
"Building Name House 01 - Bassenthwaite House 01 - Bassenthwaite \n",
"Space NaN NaN \n",
"Additional Location Info Bassenthwaite Rm No 104 Bassenthwaite Rm No 104 \n",
"Tenant meter 0 0 \n",
"Fiscal meter 0 0 \n",
"Parent meter NaN NaN \n",
"Child meters NaN NaN \n",
"Communications type NaN NaN \n",
"Electrical panel ID NaN NaN \n",
"Code AP001-L01/M001 AP001-L01/M002 \n",
"\n",
" AP001-L01/M003 \\\n",
"Logger Asset Code 0501E3E97100 \n",
"Description Bar Ground \n",
"Make Landis \n",
"Model E1100 \n",
"Meter Units kWh \n",
"Meter Pulse Value 0.001 \n",
"Classification Group Energy meter \n",
"Logger Channel 3 \n",
"Utility Type Electricity \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code AP001 \n",
"Building Name House 01 - Bassenthwaite \n",
"Space NaN \n",
"Additional Location Info Bassenthwaite Rm No 104 \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code AP001-L01/M003 \n",
"\n",
" ... \\\n",
"Logger Asset Code ... \n",
"Description ... \n",
"Make ... \n",
"Model ... \n",
"Meter Units ... \n",
"Meter Pulse Value ... \n",
"Classification Group ... \n",
"Logger Channel ... \n",
"Utility Type ... \n",
"?? ... \n",
"Meter Type ... \n",
"Building Code ... \n",
"Building Name ... \n",
"Space ... \n",
"Additional Location Info ... \n",
"Tenant meter ... \n",
"Fiscal meter ... \n",
"Parent meter ... \n",
"Child meters ... \n",
"Communications type ... \n",
"Electrical panel ID ... \n",
"Code ... \n",
"\n",
" OC006-B01/Y1 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description Outside air temperature \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units Degrees Celsius \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Y1 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Y1 \n",
"\n",
" OC006-B01/Y2 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description Immersion Sensor \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Y2 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Y2 \n",
"\n",
" OC006-B01/Y3 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description Thermistor TBTC \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Y3 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Y3 \n",
"\n",
" OC006-B01/Y4 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description Room temperature \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units Degrees Celsius \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Y4 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Y4 \n",
"\n",
" OC006-B01/Y5 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description Thermistor TBTI \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units Degrees Celsius \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Y5 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Y5 \n",
"\n",
" OC006-B01/Y6 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description 4DIX V \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Y6 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Y6 \n",
"\n",
" OC006-B01/Z1 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description Heating Boilers \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Z1 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Z1 \n",
"\n",
" OC006-B01/Z2 \\\n",
"Logger Asset Code {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Description DHWS Immersion \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy sensor \n",
"Logger Channel Z2 \n",
"Utility Type NaN \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space NaN \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 0 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-B01/Z2 \n",
"\n",
" OC006-L99/M200 \\\n",
"Logger Asset Code NaN \n",
"Description Kent House \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel NaN \n",
"Utility Type Electricity \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space OC006 A0 \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 1 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-L99/M200 \n",
"\n",
" OC006-L99/M503 \n",
"Logger Asset Code NaN \n",
"Description Kent House \n",
"Make NaN \n",
"Model NaN \n",
"Meter Units NaN \n",
"Meter Pulse Value NaN \n",
"Classification Group Energy meter \n",
"Logger Channel NaN \n",
"Utility Type Water \n",
"?? NaN \n",
"Meter Type NaN \n",
"Building Code OC006 \n",
"Building Name Chancellor's Wharf, Kent House \n",
"Space OC006 A0 \n",
"Additional Location Info NaN \n",
"Tenant meter 0 \n",
"Fiscal meter 1 \n",
"Parent meter NaN \n",
"Child meters NaN \n",
"Communications type NaN \n",
"Electrical panel ID NaN \n",
"Code OC006-L99/M503 \n",
"\n",
"[22 rows x 30539 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create dictionary that maps Planon column names onto Master. \n",
" \n",
"From Nicola: \n",
"- Code (Asset Code) \n",
"- Description\n",
"- EIS ID (Channel)\n",
"- Utility Type\n",
"- Fiscal Meter\n",
"- Tenant Meter\n",
"\n",
"`Building code` and `Building name` are implicitly included. `Logger Serial Number`, `IP` or `MAC` would be essential to include, as well as `Make` and `Model`. `Additional Location Info` is not essnetial but would be useful to have. Locations (`Locations.Space.Space number` and `Space Name`) are included in the Planon export - but this is their only viable data source, therefore are not validated against."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Planon:Master\n",
"meters_match_dict={\n",
" \"BuildingNo.\":\"Building Code\",\n",
" \"Building\":\"Building Name\",\n",
" \"Description\":\"Description\",\n",
" \"EIS ID\":\"Logger Channel\",\n",
" \"Tenant Meter.Name\":\"Tenant meter\",\n",
" \"Fiscal Meter.Name\":\"Fiscal meter\",\n",
" \"Code\":\"Code\"\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filter both dataframes based on these new columns. Then remove duplicates. Currently, this leads to loss of information of spaces served, but also a unique index for the Planon dataframe, therefore bringing the dataframes closer to each other. When including spaces explicitly in the comparison (if we want to - or just trust the Planon space mapping), this needs to be modified."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"master_meterssensors_for_validation_filtered=master_meterssensors_for_validation[list(meters_match_dict.values())]\n",
"planon_meterssensors_filtered=planon_meterssensors[list(meters_match_dict.keys())]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>MC202-B15/F15</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Code</th>\n",
" <td>MC202-B15/F15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fiscal meter</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Description</th>\n",
" <td>Function 15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tenant meter</th>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Building Name</th>\n",
" <td>Charles Carter Building</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Channel</th>\n",
" <td>F15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Building Code</th>\n",
" <td>MC202</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MC202-B15/F15\n",
"Code MC202-B15/F15\n",
"Fiscal meter 0\n",
"Description Function 15\n",
"Tenant meter 0\n",
"Building Name Charles Carter Building\n",
"Logger Channel F15\n",
"Building Code MC202"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(master_meterssensors_for_validation_filtered.loc['MC202-B15/F15'])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>0.0</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>0.0</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description \\\n",
"AP000-L01/M001 AP000-L01/M001 0.0 NaN \n",
"AP000-L02/M001 AP000-L02/M001 0.0 Graduate House 1-11 \n",
"\n",
" Tenant meter Building Name Logger Channel Building Code \n",
"AP000-L01/M001 0.0 Alexandra Park 1 AP000 \n",
"AP000-L02/M001 0.0 Alexandra Park 1 AP000 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation_filtered.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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>Code</th>\n",
" <th>Fiscal Meter.Name</th>\n",
" <th>Description</th>\n",
" <th>Tenant Meter.Name</th>\n",
" <th>Building</th>\n",
" <th>EIS ID</th>\n",
" <th>BuildingNo.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>No</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>No</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal Meter.Name Description \\\n",
"AP000-L01/M001 AP000-L01/M001 No NaN \n",
"AP000-L02/M001 AP000-L02/M001 No Graduate House 1-11 \n",
"\n",
" Tenant Meter.Name Building EIS ID BuildingNo. \n",
"AP000-L01/M001 No Alexandra Park 1 AP000 \n",
"AP000-L02/M001 No Alexandra Park 1 AP000 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_meterssensors_filtered.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unify headers, drop duplicates (bear the mind the spaces argument, this where it needs to be brought back in in the future!)."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"planon_meterssensors_filtered.columns=[meters_match_dict[i] for i in planon_meterssensors_filtered]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\pandas\\util\\decorators.py:91: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" return func(*args, **kwargs)\n"
]
}
],
"source": [
"planon_meterssensors_filtered.drop_duplicates(inplace=True)\n",
"master_meterssensors_for_validation_filtered.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>No</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>No</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>No</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description Tenant meter \\\n",
"AP000-L01/M001 AP000-L01/M001 No NaN No \n",
"AP000-L02/M001 AP000-L02/M001 No Graduate House 1-11 No \n",
"\n",
" Building Name Logger Channel Building Code \n",
"AP000-L01/M001 Alexandra Park 1 AP000 \n",
"AP000-L02/M001 Alexandra Park 1 AP000 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_meterssensors_filtered.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fiscal/Tenant meter name needs fixing from Yes/No and 1/0."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\ipykernel\\__main__.py:1: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" if __name__ == '__main__':\n",
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n",
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\ipykernel\\__main__.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" app.launch_new_instance()\n",
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\ipykernel\\__main__.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"planon_meterssensors_filtered['Fiscal meter']=planon_meterssensors_filtered['Fiscal meter'].isin(['Yes'])\n",
"planon_meterssensors_filtered['Tenant meter']=planon_meterssensors_filtered['Tenant meter'].isin(['Yes'])\n",
"master_meterssensors_for_validation_filtered['Fiscal meter']=master_meterssensors_for_validation_filtered['Fiscal meter'].isin([1])\n",
"master_meterssensors_for_validation_filtered['Tenant meter']=master_meterssensors_for_validation_filtered['Tenant meter'].isin([1])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>False</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description Tenant meter \\\n",
"AP000-L01/M001 AP000-L01/M001 False NaN False \n",
"AP000-L02/M001 AP000-L02/M001 False Graduate House 1-11 False \n",
"\n",
" Building Name Logger Channel Building Code \n",
"AP000-L01/M001 Alexandra Park 1 AP000 \n",
"AP000-L02/M001 Alexandra Park 1 AP000 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation_filtered.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>False</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description Tenant meter \\\n",
"AP000-L01/M001 AP000-L01/M001 False NaN False \n",
"AP000-L02/M001 AP000-L02/M001 False Graduate House 1-11 False \n",
"\n",
" Building Name Logger Channel Building Code \n",
"AP000-L01/M001 Alexandra Park 1 AP000 \n",
"AP000-L02/M001 Alexandra Park 1 AP000 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_meterssensors_filtered.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cross-check missing meters"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AP000-L99/M303, AP000-L99/M308, MC000-L99/M201, MC000-L99/M202, MC000-L99/M203, MC000-L99/M506, MC001-L99/M100, MC001-L99/M222, MC001-L99/M224, MC001-L99/M306, MC001-L99/M508, MC003-L99/M207, MC003-L99/M304, MC003-L99/M509, MC007-L99/M211, MC007-L99/M511, MC008-L99/M302, MC047-L02/M00??, MC061-L99/M305, MC061-L99/M310, MC102-L99/M309, MC103-L99/M216, MC103-L99/M300, MC103-L99/M504, MC204-L99/M312, MC210-L01/M001, MC210-L01/M002, MC210-L01/M003, MC210-L01/M004, MC210-L01/M005, MC210-L01/M006, MC210-L01/M007, MC210-L01/M008, MC210-L01/M009, MC210-L01/M010, MC210-L01/M011, MC210-L01/M012, MC210-L01/M013, MC210-L01/M014, MC210-L01/M015, MC210-L01/M016, MC210-L01/M017, MC210-L01/M018, MC210-L01/M019, MC210-L01/M020, MC210-L01/M021, MC210-L01/M022, MC210-L01/M023, MC210-L01/M024, MC210-L01/M025, MC210-L01/M026, MC210-L01/M027, MC210-L01/M028, MC210-L01/M029, MC210-L01/M030, MC210-L02/M001, MC210-L02/M002, MC210-L02/M003, MC210-L02/M004, MC210-L02/M005, MC210-L02/M006, MC210-L02/M007, MC210-L02/M008, MC210-L02/M009, MC210-L02/M010, MC210-L02/M011, MC210-L02/M012, MC210-L02/M013, MC210-L02/M014, MC210-L02/M015, MC210-L02/M016, MC210-L02/M017, MC210-L02/M018, MC210-L02/M019, MC210-L02/M020, MC210-L02/M021, MC210-L02/M022, MC210-L02/M023, NAN, OC004-L99/M206, OC004-L99/M512, OC005-L99/M204, OC005-L99/M505, OC006-L99/M200, OC006-L99/M503, \n",
"\n",
"Meters in Master, but not in Planon: 85 / 29708 : 0.286 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_meterssensors_filtered.index)))\n",
"b=np.sort(list(set(master_meterssensors_for_validation_filtered.index)))\n",
"meterssensors_not_in_planon=[]\n",
"for i in b:\n",
" if i not in a:\n",
" print(i+',',end=\" \"),\n",
" meterssensors_not_in_planon.append(i)\n",
"print('\\n\\nMeters in Master, but not in Planon:',\n",
" len(meterssensors_not_in_planon),'/',len(b),':',\n",
" round(len(meterssensors_not_in_planon)/len(b)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"32"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#without MC210\n",
"len(set([i for i in meterssensors_not_in_planon if i[:5]!='MC210']))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'AP000-L99/M303',\n",
" 'AP000-L99/M308',\n",
" 'MC000-L99/M201',\n",
" 'MC000-L99/M202',\n",
" 'MC000-L99/M203',\n",
" 'MC000-L99/M506',\n",
" 'MC001-L99/M100',\n",
" 'MC001-L99/M222',\n",
" 'MC001-L99/M224',\n",
" 'MC001-L99/M306',\n",
" 'MC001-L99/M508',\n",
" 'MC003-L99/M207',\n",
" 'MC003-L99/M304',\n",
" 'MC003-L99/M509',\n",
" 'MC007-L99/M211',\n",
" 'MC007-L99/M511',\n",
" 'MC008-L99/M302',\n",
" 'MC047-L02/M00??',\n",
" 'MC061-L99/M305',\n",
" 'MC061-L99/M310',\n",
" 'MC102-L99/M309',\n",
" 'MC103-L99/M216',\n",
" 'MC103-L99/M300',\n",
" 'MC103-L99/M504',\n",
" 'MC204-L99/M312',\n",
" 'NAN',\n",
" 'OC004-L99/M206',\n",
" 'OC004-L99/M512',\n",
" 'OC005-L99/M204',\n",
" 'OC005-L99/M505',\n",
" 'OC006-L99/M200',\n",
" 'OC006-L99/M503'}"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set([i for i in meterssensors_not_in_planon if i[:5]!='MC210'])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'AP000',\n",
" 'MC000',\n",
" 'MC001',\n",
" 'MC003',\n",
" 'MC007',\n",
" 'MC008',\n",
" 'MC047',\n",
" 'MC061',\n",
" 'MC102',\n",
" 'MC103',\n",
" 'MC204',\n",
" 'MC210',\n",
" 'NAN',\n",
" 'OC004',\n",
" 'OC005',\n",
" 'OC006'}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(set([i[:5] for i in meterssensors_not_in_planon]))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Meters in Planon, not in Master: 0 / 29623 : 0.0 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_meterssensors_filtered.index)))\n",
"b=np.sort(list(set(master_meterssensors_for_validation_filtered.index)))\n",
"meterssensors_not_in_master=[]\n",
"for i in a:\n",
" if i not in b:\n",
" print(i+',',end=\" \"),\n",
" meterssensors_not_in_master.append(i)\n",
"print('\\n\\nMeters in Planon, not in Master:',\n",
" len(meterssensors_not_in_master),'/',len(a),':',\n",
" round(len(meterssensors_not_in_master)/len(a)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(set([i for i in meterssensors_not_in_master]))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"set()"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set([i[:9] for i in meterssensors_not_in_master])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"set()"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set([i[:5] for i in meterssensors_not_in_master])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Check for duplicates in index, but not duplicates over the entire row"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"29623\n",
"29623\n",
"30494\n",
"29708\n"
]
}
],
"source": [
"print(len(planon_meterssensors_filtered.index))\n",
"print(len(set(planon_meterssensors_filtered.index)))\n",
"print(len(master_meterssensors_for_validation_filtered.index))\n",
"print(len(set(master_meterssensors_for_validation_filtered.index)))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MC011-B01/I1</th>\n",
" <td>MC011-B01/I1</td>\n",
" <td>False</td>\n",
" <td>Fire alarm active</td>\n",
" <td>False</td>\n",
" <td>The Roundhouse</td>\n",
" <td>I1</td>\n",
" <td>MC011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC011-B01/I2</th>\n",
" <td>MC011-B01/I2</td>\n",
" <td>False</td>\n",
" <td>LTHW Pri Pmp Flow</td>\n",
" <td>False</td>\n",
" <td>The Roundhouse</td>\n",
" <td>I2</td>\n",
" <td>MC011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC011-B01/O1</th>\n",
" <td>MC011-B01/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>The Roundhouse</td>\n",
" <td>o1</td>\n",
" <td>MC011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B01/O1</th>\n",
" <td>MC014-B01/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B02/O1</th>\n",
" <td>MC014-B02/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B03/O1</th>\n",
" <td>MC014-B03/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>o1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B04/O1</th>\n",
" <td>MC014-B04/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>o1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B05/O1</th>\n",
" <td>MC014-B05/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>o1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B06/O1</th>\n",
" <td>MC014-B06/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>o1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B07/O1</th>\n",
" <td>MC014-B07/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B08/O1</th>\n",
" <td>MC014-B08/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B09/O1</th>\n",
" <td>MC014-B09/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>o1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B10/O1</th>\n",
" <td>MC014-B10/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B11/O1</th>\n",
" <td>MC014-B11/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B12/O1</th>\n",
" <td>MC014-B12/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B13/O1</th>\n",
" <td>MC014-B13/o1</td>\n",
" <td>False</td>\n",
" <td>Alarm Route 1</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>o1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B14/O1</th>\n",
" <td>MC014-B14/O1</td>\n",
" <td>False</td>\n",
" <td>Heating times</td>\n",
" <td>False</td>\n",
" <td>Bowland Hall</td>\n",
" <td>O1</td>\n",
" <td>MC014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC031-B01/I1</th>\n",
" <td>MC031-B01/I1</td>\n",
" <td>False</td>\n",
" <td>HWS Pump 1 or 2 Status</td>\n",
" <td>False</td>\n",
" <td>Great Hall</td>\n",
" <td>I1</td>\n",
" <td>MC031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC031-B01/I2</th>\n",
" <td>MC031-B01/i2</td>\n",
" <td>False</td>\n",
" <td>xcite/IO/2UI/2AO</td>\n",
" <td>False</td>\n",
" <td>Great Hall</td>\n",
" <td>i2</td>\n",
" <td>MC031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC031-B01/I3</th>\n",
" <td>MC031-B01/I3</td>\n",
" <td>False</td>\n",
" <td>Greathall ahu on-off</td>\n",
" <td>False</td>\n",
" <td>Great Hall</td>\n",
" <td>I3</td>\n",
" <td>MC031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC031-B01/I4</th>\n",
" <td>MC031-B01/I4</td>\n",
" <td>False</td>\n",
" <td>Greathall Ahu 4 hr EXT</td>\n",
" <td>False</td>\n",
" <td>Great Hall</td>\n",
" <td>I4</td>\n",
" <td>MC031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC031-B02/I1</th>\n",
" <td>MC031-B02/I1</td>\n",
" <td>False</td>\n",
" <td>Alarm Mute Pb</td>\n",
" <td>False</td>\n",
" <td>Great Hall</td>\n",
" <td>I1</td>\n",
" <td>MC031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC031-B02/I2</th>\n",
" <td>MC031-B02/i2</td>\n",
" <td>False</td>\n",
" <td>Module 2</td>\n",
" <td>False</td>\n",
" <td>Great Hall</td>\n",
" <td>i2</td>\n",
" <td>MC031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B01/I1</th>\n",
" <td>MC032-B01/i1</td>\n",
" <td>False</td>\n",
" <td>IQ4/IO/16DI</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>i1</td>\n",
" <td>MC032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B01/I2</th>\n",
" <td>MC032-B01/i2</td>\n",
" <td>False</td>\n",
" <td>IQ4/IO/8UI</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>i2</td>\n",
" <td>MC032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B01/I3</th>\n",
" <td>MC032-B01/i3</td>\n",
" <td>False</td>\n",
" <td>IQ4/IO/8DI</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>i3</td>\n",
" <td>MC032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B01/I4</th>\n",
" <td>MC032-B01/i4</td>\n",
" <td>False</td>\n",
" <td>IQ4/IO/4DO</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>i4</td>\n",
" <td>MC032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B01/I5</th>\n",
" <td>MC032-B01/I5</td>\n",
" <td>False</td>\n",
" <td>CT Pump Flow Prove</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>I5</td>\n",
" <td>MC032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B02/I1</th>\n",
" <td>MC032-B02/i1</td>\n",
" <td>False</td>\n",
" <td>Module 1</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>i1</td>\n",
" <td>MC032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC032-B02/I2</th>\n",
" <td>MC032-B02/I2</td>\n",
" <td>False</td>\n",
" <td>DHWS Gas Valve Shut</td>\n",
" <td>False</td>\n",
" <td>County South</td>\n",
" <td>I2</td>\n",
" <td>MC032</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>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB C2P</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>COMM 1 DB CP3</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>COMM 2 DB CP4</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>LIBRARY</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Library Phase L1, L2, L3</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Library ???</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Library Bindery</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB LG2LP</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Top Section Load</td>\n",
" <td>False</td>\n",
" <td>The Roundhouse</td>\n",
" <td>NaN</td>\n",
" <td>MC011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Total Load</td>\n",
" <td>False</td>\n",
" <td>The Roundhouse</td>\n",
" <td>NaN</td>\n",
" <td>MC011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/M1</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/L1</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/P1</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/L2</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/P2</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/L3</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB/N/P3</td>\n",
" <td>False</td>\n",
" <td>ISS Building</td>\n",
" <td>NaN</td>\n",
" <td>MC197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB C1P</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>MCP 03</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>2</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB C1L</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB B1L</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB C1 Power</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>4</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB D1 Lighting and Power</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>5</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>MCP 02</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>6</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB B1P</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Flat 3</td>\n",
" <td>False</td>\n",
" <td>Bowland Main</td>\n",
" <td>NaN</td>\n",
" <td>MC046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB A1P</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB A1L</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>NaN</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NAN</th>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>DB D2 Lighting and Power</td>\n",
" <td>False</td>\n",
" <td>Library</td>\n",
" <td>7</td>\n",
" <td>MC065</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>786 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description \\\n",
"MC011-B01/I1 MC011-B01/I1 False Fire alarm active \n",
"MC011-B01/I2 MC011-B01/I2 False LTHW Pri Pmp Flow \n",
"MC011-B01/O1 MC011-B01/o1 False Alarm Route 1 \n",
"MC014-B01/O1 MC014-B01/O1 False Heating times \n",
"MC014-B02/O1 MC014-B02/O1 False Heating times \n",
"MC014-B03/O1 MC014-B03/o1 False Alarm Route 1 \n",
"MC014-B04/O1 MC014-B04/o1 False Alarm Route 1 \n",
"MC014-B05/O1 MC014-B05/o1 False Alarm Route 1 \n",
"MC014-B06/O1 MC014-B06/o1 False Alarm Route 1 \n",
"MC014-B07/O1 MC014-B07/O1 False Heating times \n",
"MC014-B08/O1 MC014-B08/O1 False Heating times \n",
"MC014-B09/O1 MC014-B09/o1 False Alarm Route 1 \n",
"MC014-B10/O1 MC014-B10/O1 False Heating times \n",
"MC014-B11/O1 MC014-B11/O1 False Heating times \n",
"MC014-B12/O1 MC014-B12/O1 False Heating times \n",
"MC014-B13/O1 MC014-B13/o1 False Alarm Route 1 \n",
"MC014-B14/O1 MC014-B14/O1 False Heating times \n",
"MC031-B01/I1 MC031-B01/I1 False HWS Pump 1 or 2 Status \n",
"MC031-B01/I2 MC031-B01/i2 False xcite/IO/2UI/2AO \n",
"MC031-B01/I3 MC031-B01/I3 False Greathall ahu on-off \n",
"MC031-B01/I4 MC031-B01/I4 False Greathall Ahu 4 hr EXT \n",
"MC031-B02/I1 MC031-B02/I1 False Alarm Mute Pb \n",
"MC031-B02/I2 MC031-B02/i2 False Module 2 \n",
"MC032-B01/I1 MC032-B01/i1 False IQ4/IO/16DI \n",
"MC032-B01/I2 MC032-B01/i2 False IQ4/IO/8UI \n",
"MC032-B01/I3 MC032-B01/i3 False IQ4/IO/8DI \n",
"MC032-B01/I4 MC032-B01/i4 False IQ4/IO/4DO \n",
"MC032-B01/I5 MC032-B01/I5 False CT Pump Flow Prove \n",
"MC032-B02/I1 MC032-B02/i1 False Module 1 \n",
"MC032-B02/I2 MC032-B02/I2 False DHWS Gas Valve Shut \n",
"... ... ... ... \n",
"NAN NaN False DB C2P \n",
"NAN NaN False COMM 1 DB CP3 \n",
"NAN NaN False COMM 2 DB CP4 \n",
"NAN NaN False LIBRARY \n",
"NAN NaN False Library Phase L1, L2, L3 \n",
"NAN NaN False Library ??? \n",
"NAN NaN False Library Bindery \n",
"NAN NaN False DB LG2LP \n",
"NAN NaN False Top Section Load \n",
"NAN NaN False Total Load \n",
"NAN NaN False DB/N/M1 \n",
"NAN NaN False NaN \n",
"NAN NaN False DB/N/L1 \n",
"NAN NaN False DB/N/P1 \n",
"NAN NaN False DB/N/L2 \n",
"NAN NaN False DB/N/P2 \n",
"NAN NaN False DB/N/L3 \n",
"NAN NaN False DB/N/P3 \n",
"NAN NaN False DB C1P \n",
"NAN NaN False MCP 03 \n",
"NAN NaN False DB C1L \n",
"NAN NaN False DB B1L \n",
"NAN NaN False DB C1 Power \n",
"NAN NaN False DB D1 Lighting and Power \n",
"NAN NaN False MCP 02 \n",
"NAN NaN False DB B1P \n",
"NAN NaN False Flat 3 \n",
"NAN NaN False DB A1P \n",
"NAN NaN False DB A1L \n",
"NAN NaN False DB D2 Lighting and Power \n",
"\n",
" Tenant meter Building Name Logger Channel Building Code \n",
"MC011-B01/I1 False The Roundhouse I1 MC011 \n",
"MC011-B01/I2 False The Roundhouse I2 MC011 \n",
"MC011-B01/O1 False The Roundhouse o1 MC011 \n",
"MC014-B01/O1 False Bowland Hall O1 MC014 \n",
"MC014-B02/O1 False Bowland Hall O1 MC014 \n",
"MC014-B03/O1 False Bowland Hall o1 MC014 \n",
"MC014-B04/O1 False Bowland Hall o1 MC014 \n",
"MC014-B05/O1 False Bowland Hall o1 MC014 \n",
"MC014-B06/O1 False Bowland Hall o1 MC014 \n",
"MC014-B07/O1 False Bowland Hall O1 MC014 \n",
"MC014-B08/O1 False Bowland Hall O1 MC014 \n",
"MC014-B09/O1 False Bowland Hall o1 MC014 \n",
"MC014-B10/O1 False Bowland Hall O1 MC014 \n",
"MC014-B11/O1 False Bowland Hall O1 MC014 \n",
"MC014-B12/O1 False Bowland Hall O1 MC014 \n",
"MC014-B13/O1 False Bowland Hall o1 MC014 \n",
"MC014-B14/O1 False Bowland Hall O1 MC014 \n",
"MC031-B01/I1 False Great Hall I1 MC031 \n",
"MC031-B01/I2 False Great Hall i2 MC031 \n",
"MC031-B01/I3 False Great Hall I3 MC031 \n",
"MC031-B01/I4 False Great Hall I4 MC031 \n",
"MC031-B02/I1 False Great Hall I1 MC031 \n",
"MC031-B02/I2 False Great Hall i2 MC031 \n",
"MC032-B01/I1 False County South i1 MC032 \n",
"MC032-B01/I2 False County South i2 MC032 \n",
"MC032-B01/I3 False County South i3 MC032 \n",
"MC032-B01/I4 False County South i4 MC032 \n",
"MC032-B01/I5 False County South I5 MC032 \n",
"MC032-B02/I1 False County South i1 MC032 \n",
"MC032-B02/I2 False County South I2 MC032 \n",
"... ... ... ... ... \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False The Roundhouse NaN MC011 \n",
"NAN False The Roundhouse NaN MC011 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False ISS Building NaN MC197 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library 2 MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library 4 MC065 \n",
"NAN False Library 5 MC065 \n",
"NAN False Library 6 MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Bowland Main NaN MC046 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library NaN MC065 \n",
"NAN False Library 7 MC065 \n",
"\n",
"[786 rows x 7 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation_filtered[master_meterssensors_for_validation_filtered.index.duplicated()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The duplicates are the `nan`s. Remove these for now. Could revisit later to do an index-less comparison, only over row contents."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"good_index=[i for i in master_meterssensors_for_validation_filtered.index if str(i).lower().strip()!='nan']\n",
"master_meterssensors_for_validation_filtered=master_meterssensors_for_validation_filtered.loc[good_index]\n",
"master_meterssensors_for_validation_filtered.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"29623"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(planon_meterssensors_filtered)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"30139"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(master_meterssensors_for_validation_filtered)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do comparison only on common indices. Need to revisit and identify the cause missing meters, both ways (5 Planon->Meters and 30 Meters->Planon in this example)."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comon_index=list(set(master_meterssensors_for_validation_filtered.index).intersection(set(planon_meterssensors_filtered.index)))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"29623"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(comon_index)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"master_meterssensors_for_validation_intersected=master_meterssensors_for_validation_filtered.loc[comon_index].sort_index()\n",
"planon_meterssensors_intersected=planon_meterssensors_filtered.loc[comon_index].sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"30055"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(master_meterssensors_for_validation_intersected)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"29623"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(planon_meterssensors_intersected)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Still have duplicate indices. For now we just drop and keep the first."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"master_meterssensors_for_validation_intersected = master_meterssensors_for_validation_intersected[~master_meterssensors_for_validation_intersected.index.duplicated(keep='first')]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>False</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description Tenant meter \\\n",
"AP000-L01/M001 AP000-L01/M001 False NaN False \n",
"AP000-L02/M001 AP000-L02/M001 False Graduate House 1-11 False \n",
"\n",
" Building Name Logger Channel Building Code \n",
"AP000-L01/M001 Alexandra Park 1 AP000 \n",
"AP000-L02/M001 Alexandra Park 1 AP000 "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_meterssensors_for_validation_intersected.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>AP000-L01/M001</td>\n",
" <td>False</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>AP000-L02/M001</td>\n",
" <td>False</td>\n",
" <td>Graduate House 1-11</td>\n",
" <td>False</td>\n",
" <td>Alexandra Park</td>\n",
" <td>1</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description Tenant meter \\\n",
"AP000-L01/M001 AP000-L01/M001 False NaN False \n",
"AP000-L02/M001 AP000-L02/M001 False Graduate House 1-11 False \n",
"\n",
" Building Name Logger Channel Building Code \n",
"AP000-L01/M001 Alexandra Park 1 AP000 \n",
"AP000-L02/M001 Alexandra Park 1 AP000 "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_meterssensors_intersected.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.2. Primitive comparison"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>Code</th>\n",
" <th>Fiscal meter</th>\n",
" <th>Description</th>\n",
" <th>Tenant meter</th>\n",
" <th>Building Name</th>\n",
" <th>Logger Channel</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L03/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L03/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M003</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M004</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M005</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M006</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP009-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP009-L01/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP009-L01/M003</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M003</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M004</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M005</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M003</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M004</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP057-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP057-L01/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP057-L01/M003</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01/M002</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01/M003</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP081-L01/M001</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</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>OC006-B01/S21</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S22</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S23</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S24</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S25</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S26</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S27</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S28</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S29</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S3</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S5</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S6</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S8</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S9</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/V1</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W1</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W10</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W2</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W3</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W5</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y1</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y2</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y3</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y4</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y5</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y6</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Z1</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Z2</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>29623 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Code Fiscal meter Description Tenant meter Building Name \\\n",
"AP000-L01/M001 True True False True True \n",
"AP000-L02/M001 True True True True True \n",
"AP000-L02/M002 True True True True True \n",
"AP000-L03/M001 True True False True True \n",
"AP000-L03/M002 True True False True True \n",
"AP001-L01/M001 True True False True False \n",
"AP001-L01/M002 True True False True False \n",
"AP001-L01/M003 True True False True False \n",
"AP001-L01/M004 True True False True False \n",
"AP001-L01/M005 True True True True False \n",
"AP001-L01/M006 True True True True False \n",
"AP009-L01/M001 True True True True False \n",
"AP009-L01/M002 True True True True False \n",
"AP009-L01/M003 True True False True False \n",
"AP010-L01/M001 True True True True False \n",
"AP010-L01/M002 True True True True False \n",
"AP010-L01/M003 True True True True False \n",
"AP010-L01/M004 True True True True False \n",
"AP010-L01/M005 True True True True False \n",
"AP011-L01/M001 True True True True False \n",
"AP011-L01/M002 True True True True False \n",
"AP011-L01/M003 True True True True False \n",
"AP011-L01/M004 True True True True False \n",
"AP057-L01/M001 True True False True True \n",
"AP057-L01/M002 True True True True True \n",
"AP057-L01/M003 True True True True True \n",
"AP080-L01/M001 True True False True True \n",
"AP080-L01/M002 True True True True True \n",
"AP080-L01/M003 True True True True True \n",
"AP081-L01/M001 True True False True True \n",
"... ... ... ... ... ... \n",
"OC006-B01/S21 True True True True True \n",
"OC006-B01/S22 True True True True True \n",
"OC006-B01/S23 True True True True True \n",
"OC006-B01/S24 True True True True True \n",
"OC006-B01/S25 True True True True True \n",
"OC006-B01/S26 True True True True True \n",
"OC006-B01/S27 True True True True True \n",
"OC006-B01/S28 True True True True True \n",
"OC006-B01/S29 True True True True True \n",
"OC006-B01/S3 True True True True True \n",
"OC006-B01/S4 True True True True True \n",
"OC006-B01/S5 True True True True True \n",
"OC006-B01/S6 True True True True True \n",
"OC006-B01/S8 True True True True True \n",
"OC006-B01/S9 True True True True True \n",
"OC006-B01/V1 True True False True True \n",
"OC006-B01/W1 True True True True True \n",
"OC006-B01/W10 True True True True True \n",
"OC006-B01/W2 True True True True True \n",
"OC006-B01/W3 True True True True True \n",
"OC006-B01/W4 True True True True True \n",
"OC006-B01/W5 True True True True True \n",
"OC006-B01/Y1 True True True True True \n",
"OC006-B01/Y2 True True True True True \n",
"OC006-B01/Y3 True True True True True \n",
"OC006-B01/Y4 True True True True True \n",
"OC006-B01/Y5 True True True True True \n",
"OC006-B01/Y6 True True True True True \n",
"OC006-B01/Z1 True True True True True \n",
"OC006-B01/Z2 True True True True True \n",
"\n",
" Logger Channel Building Code \n",
"AP000-L01/M001 False True \n",
"AP000-L02/M001 False True \n",
"AP000-L02/M002 False True \n",
"AP000-L03/M001 False True \n",
"AP000-L03/M002 False True \n",
"AP001-L01/M001 False True \n",
"AP001-L01/M002 False True \n",
"AP001-L01/M003 False True \n",
"AP001-L01/M004 False True \n",
"AP001-L01/M005 False True \n",
"AP001-L01/M006 False True \n",
"AP009-L01/M001 False True \n",
"AP009-L01/M002 False True \n",
"AP009-L01/M003 False True \n",
"AP010-L01/M001 False True \n",
"AP010-L01/M002 False True \n",
"AP010-L01/M003 False True \n",
"AP010-L01/M004 False True \n",
"AP010-L01/M005 False True \n",
"AP011-L01/M001 False True \n",
"AP011-L01/M002 False True \n",
"AP011-L01/M003 False True \n",
"AP011-L01/M004 False True \n",
"AP057-L01/M001 False True \n",
"AP057-L01/M002 False True \n",
"AP057-L01/M003 False True \n",
"AP080-L01/M001 False True \n",
"AP080-L01/M002 False True \n",
"AP080-L01/M003 False True \n",
"AP081-L01/M001 False True \n",
"... ... ... \n",
"OC006-B01/S21 True True \n",
"OC006-B01/S22 True True \n",
"OC006-B01/S23 True True \n",
"OC006-B01/S24 True True \n",
"OC006-B01/S25 True True \n",
"OC006-B01/S26 True True \n",
"OC006-B01/S27 True True \n",
"OC006-B01/S28 True True \n",
"OC006-B01/S29 True True \n",
"OC006-B01/S3 True True \n",
"OC006-B01/S4 True True \n",
"OC006-B01/S5 True True \n",
"OC006-B01/S6 True True \n",
"OC006-B01/S8 True True \n",
"OC006-B01/S9 True True \n",
"OC006-B01/V1 True True \n",
"OC006-B01/W1 True True \n",
"OC006-B01/W10 True True \n",
"OC006-B01/W2 True True \n",
"OC006-B01/W3 True True \n",
"OC006-B01/W4 True True \n",
"OC006-B01/W5 True True \n",
"OC006-B01/Y1 True True \n",
"OC006-B01/Y2 True True \n",
"OC006-B01/Y3 True True \n",
"OC006-B01/Y4 True True \n",
"OC006-B01/Y5 True True \n",
"OC006-B01/Y6 True True \n",
"OC006-B01/Z1 True True \n",
"OC006-B01/Z2 True True \n",
"\n",
"[29623 rows x 7 columns]"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_meterssensors_intersected==master_meterssensors_for_validation_intersected"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.all(planon_meterssensors_intersected==master_meterssensors_for_validation_intersected)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.3. Horizontal comparison"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of cells matching"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Code 29382\n",
"Fiscal meter 29623\n",
"Description 27985\n",
"Tenant meter 29623\n",
"Building Name 29089\n",
"Logger Channel 28719\n",
"Building Code 29623\n",
"dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(planon_meterssensors_intersected==master_meterssensors_for_validation_intersected).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Percentage matching"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Code 99.186443\n",
"Fiscal meter 100.000000\n",
"Description 94.470513\n",
"Tenant meter 100.000000\n",
"Building Name 98.197347\n",
"Logger Channel 96.948317\n",
"Building Code 100.000000\n",
"dtype: float64"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(planon_meterssensors_intersected==master_meterssensors_for_validation_intersected).sum()/\\\n",
"len(planon_meterssensors_intersected)*100"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1edabf134e0>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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t3y1nymfY3rXl0u4TyrDlq2zfUG5vDvwjcBDNMFTXh/5ePtvxtru2ZuimW4Y3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\nVO7/A2jXHbRF2pTUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1eda8661d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"((planon_meterssensors_intersected==master_meterssensors_for_validation_intersected).sum()/\\\n",
"len(planon_meterssensors_intersected)*100).plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.4. Vertical comparison"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01/M001</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M001</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02/M002</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L03/M001</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L03/M002</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M001</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M002</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M003</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M004</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M005</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01/M006</th>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>AP009-L01/M001</th>\n",
" <td>5</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>AP009-L01/M003</th>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M001</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M002</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01/M003</th>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>AP010-L01/M004</th>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>AP010-L01/M005</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M001</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M002</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M003</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01/M004</th>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>AP057-L01/M001</th>\n",
" <td>5</td>\n",
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" <tr>\n",
" <th>AP057-L01/M002</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP057-L01/M003</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01/M001</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01/M002</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01/M003</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP081-L01/M001</th>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S21</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S22</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S23</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S24</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S25</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S26</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S27</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S28</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S29</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S3</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S4</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S5</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S6</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S8</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/S9</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/V1</th>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W1</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W10</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W2</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W3</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W4</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/W5</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y1</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y2</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y3</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y4</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y5</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Y6</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Z1</th>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/Z2</th>\n",
" <td>7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>29623 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" 0\n",
"AP000-L01/M001 5\n",
"AP000-L02/M001 6\n",
"AP000-L02/M002 6\n",
"AP000-L03/M001 5\n",
"AP000-L03/M002 5\n",
"AP001-L01/M001 4\n",
"AP001-L01/M002 4\n",
"AP001-L01/M003 4\n",
"AP001-L01/M004 4\n",
"AP001-L01/M005 5\n",
"AP001-L01/M006 5\n",
"AP009-L01/M001 5\n",
"AP009-L01/M002 5\n",
"AP009-L01/M003 4\n",
"AP010-L01/M001 5\n",
"AP010-L01/M002 5\n",
"AP010-L01/M003 5\n",
"AP010-L01/M004 5\n",
"AP010-L01/M005 5\n",
"AP011-L01/M001 5\n",
"AP011-L01/M002 5\n",
"AP011-L01/M003 5\n",
"AP011-L01/M004 5\n",
"AP057-L01/M001 5\n",
"AP057-L01/M002 6\n",
"AP057-L01/M003 6\n",
"AP080-L01/M001 5\n",
"AP080-L01/M002 6\n",
"AP080-L01/M003 6\n",
"AP081-L01/M001 5\n",
"... ..\n",
"OC006-B01/S21 7\n",
"OC006-B01/S22 7\n",
"OC006-B01/S23 7\n",
"OC006-B01/S24 7\n",
"OC006-B01/S25 7\n",
"OC006-B01/S26 7\n",
"OC006-B01/S27 7\n",
"OC006-B01/S28 7\n",
"OC006-B01/S29 7\n",
"OC006-B01/S3 7\n",
"OC006-B01/S4 7\n",
"OC006-B01/S5 7\n",
"OC006-B01/S6 7\n",
"OC006-B01/S8 7\n",
"OC006-B01/S9 7\n",
"OC006-B01/V1 6\n",
"OC006-B01/W1 7\n",
"OC006-B01/W10 7\n",
"OC006-B01/W2 7\n",
"OC006-B01/W3 7\n",
"OC006-B01/W4 7\n",
"OC006-B01/W5 7\n",
"OC006-B01/Y1 7\n",
"OC006-B01/Y2 7\n",
"OC006-B01/Y3 7\n",
"OC006-B01/Y4 7\n",
"OC006-B01/Y5 7\n",
"OC006-B01/Y6 7\n",
"OC006-B01/Z1 7\n",
"OC006-B01/Z2 7\n",
"\n",
"[29623 rows x 1 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame((planon_meterssensors_intersected.T==master_meterssensors_for_validation_intersected.T).sum())\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <tr>\n",
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" <tr>\n",
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" <td>85.714286</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>MC211-B01/S25</th>\n",
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" <th>OC004-B01/V1</th>\n",
" <td>85.714286</td>\n",
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" <td>85.714286</td>\n",
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" <td>85.714286</td>\n",
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" <td>85.714286</td>\n",
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" <th>OC006-B01/L1</th>\n",
" <td>85.714286</td>\n",
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" <td>85.714286</td>\n",
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" <th>OC006-B01/L3</th>\n",
" <td>85.714286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01/V1</th>\n",
" <td>85.714286</td>\n",
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],
"text/plain": [
" 0\n",
"AP000-L01/M001 71.428571\n",
"AP000-L02/M001 85.714286\n",
"AP000-L02/M002 85.714286\n",
"AP000-L03/M001 71.428571\n",
"AP000-L03/M002 71.428571\n",
"AP001-L01/M001 57.142857\n",
"AP001-L01/M002 57.142857\n",
"AP001-L01/M003 57.142857\n",
"AP001-L01/M004 57.142857\n",
"AP001-L01/M005 71.428571\n",
"AP001-L01/M006 71.428571\n",
"AP009-L01/M001 71.428571\n",
"AP009-L01/M002 71.428571\n",
"AP009-L01/M003 57.142857\n",
"AP010-L01/M001 71.428571\n",
"AP010-L01/M002 71.428571\n",
"AP010-L01/M003 71.428571\n",
"AP010-L01/M004 71.428571\n",
"AP010-L01/M005 71.428571\n",
"AP011-L01/M001 71.428571\n",
"AP011-L01/M002 71.428571\n",
"AP011-L01/M003 71.428571\n",
"AP011-L01/M004 71.428571\n",
"AP057-L01/M001 71.428571\n",
"AP057-L01/M002 85.714286\n",
"AP057-L01/M003 85.714286\n",
"AP080-L01/M001 71.428571\n",
"AP080-L01/M002 85.714286\n",
"AP080-L01/M003 85.714286\n",
"AP081-L01/M001 71.428571\n",
"... ...\n",
"MC210-B03/S8 85.714286\n",
"MC210-B03/U1 85.714286\n",
"MC210-B03/U2 85.714286\n",
"MC210-B03/V1 85.714286\n",
"MC211-B01/I1 85.714286\n",
"MC211-B01/I2 71.428571\n",
"MC211-B01/I3 71.428571\n",
"MC211-B01/I4 85.714286\n",
"MC211-B01/O1 71.428571\n",
"MC211-B01/P23 85.714286\n",
"MC211-B01/P24 85.714286\n",
"MC211-B01/S24 85.714286\n",
"MC211-B01/S25 85.714286\n",
"MC211-B01/V1 85.714286\n",
"OC004-B01/G50 85.714286\n",
"OC004-B01/L1 85.714286\n",
"OC004-B01/L2 85.714286\n",
"OC004-B01/L3 85.714286\n",
"OC004-B01/L4 85.714286\n",
"OC004-B01/V1 85.714286\n",
"OC005-B01/G50 85.714286\n",
"OC005-B01/L1 85.714286\n",
"OC005-B01/L2 85.714286\n",
"OC005-B01/L3 85.714286\n",
"OC005-B01/V1 85.714286\n",
"OC006-B01/G50 85.714286\n",
"OC006-B01/L1 85.714286\n",
"OC006-B01/L2 85.714286\n",
"OC006-B01/L3 85.714286\n",
"OC006-B01/V1 85.714286\n",
"\n",
"[2959 rows x 1 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.DataFrame((planon_meterssensors_intersected.T==master_meterssensors_for_validation_intersected.T).sum()/\\\n",
"len(planon_meterssensors_intersected.T)*100)\n",
"df[df[0]<100]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2.1.5. Smart(er) comparison"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Not all of the dataframe matches. Let us do some basic string formatting, maybe that helps."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1638"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_meterssensors_intersected['Description']!=master_meterssensors_for_validation_intersected['Description'])"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"planon_meterssensors_intersected['Description']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ') for s in planon_meterssensors_intersected['Description'].values]\n",
"master_meterssensors_for_validation_intersected['Description']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ') for s in master_meterssensors_for_validation_intersected['Description'].values]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"363"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_meterssensors_intersected['Description']!=master_meterssensors_for_validation_intersected['Description'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some errors fixed, some left. Let's see which ones. These are either: \n",
"- Wrong duplicate dropped\n",
"- Input human erros in the description.\n",
"- Actual erros somewhere in the indexing."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MC011-B01/I1 \t\tPlanon: fire alarm active \t\tMaster: xcite/io/16di\n",
"MC011-B01/I2 \t\tPlanon: xcite/io/8do \t\tMaster: lthw pri pmp flow\n",
"MC014-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B01/O1 \t\tPlanon: heating times \t\tMaster: alarm route 1\n",
"MC014-B02/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B03/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B03/O1 \t\tPlanon: alarm route 1 \t\tMaster: heating times\n",
"MC014-B04/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B05/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B05/O1 \t\tPlanon: alarm route 1 \t\tMaster: heating times\n",
"MC014-B06/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B06/O1 \t\tPlanon: alarm route 1 \t\tMaster: heating times\n",
"MC014-B07/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B07/O1 \t\tPlanon: heating times \t\tMaster: alarm route 1\n",
"MC014-B08/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B08/O1 \t\tPlanon: heating times \t\tMaster: alarm route 1\n",
"MC014-B09/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B09/O1 \t\tPlanon: alarm route 1 \t\tMaster: heating times\n",
"MC014-B10/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B11/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B12/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B13/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC014-B14/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC029-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC031-B01/I2 \t\tPlanon: heating pump status \t\tMaster: xcite/io/2ui/2ao\n",
"MC031-B01/I3 \t\tPlanon: xcite/io/4do \t\tMaster: greathall ahu on-off\n",
"MC031-B01/P16 \t\tPlanon: nan \t\tMaster: #name?\n",
"MC031-B01/S15 \t\tPlanon: nan \t\tMaster: #name?\n",
"MC031-B02/I2 \t\tPlanon: module 2 \t\tMaster: sump no1 pump 1 trip\n",
"MC032-B01/I3 \t\tPlanon: iq4/io/8di \t\tMaster: water meter pulse\n",
"MC032-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/I3 \t\tPlanon: module 3 \t\tMaster: ahu gas valve shut\n",
"MC032-B02/L1 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/L6 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/L8 \t\tPlanon: nan \t\tMaster: \n",
"MC032-B02/O1 \t\tPlanon: kitchen ahu \t\tMaster: alarm route 1\n",
"MC032-B03/I1 \t\tPlanon: fire alarm active \t\tMaster: xcite/io/8ao\n",
"MC032-B07/I1 \t\tPlanon: module 1 \t\tMaster: alarm mute pb\n",
"MC032-B07/I3 \t\tPlanon: sump no4 pump 2 trip \t\tMaster: module 3\n",
"MC033-B01/I1 \t\tPlanon: water booster alarm \t\tMaster: 8ui\n",
"MC043-B01/I3 \t\tPlanon: mod 3 \t\tMaster: heat ex pump 1 trip\n",
"MC043-B01/I4 \t\tPlanon: heat ex pump 2 trip \t\tMaster: mod 4\n",
"MC043-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/L6 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/L7 \t\tPlanon: nan \t\tMaster: \n",
"MC043-B01/O1 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/I1 \t\tPlanon: fire alarm active \t\tMaster: module 1\n",
"MC044-B01/I10 \t\tPlanon: ahu01 extr dmpr open \t\tMaster: module 10\n",
"MC044-B01/I11 \t\tPlanon: module 11 \t\tMaster: ahu01 extr fan flow\n",
"MC044-B01/I2 \t\tPlanon: ahu01 sply fan fault \t\tMaster: module 2\n",
"MC044-B01/I5 \t\tPlanon: module 5 \t\tMaster: ahu01 frost trip\n",
"MC044-B01/I7 \t\tPlanon: module 7 \t\tMaster: ahu01 bag fltr dirty\n",
"MC044-B01/I8 \t\tPlanon: ahu01 th wheel fault \t\tMaster: module 8\n",
"MC044-B01/L10 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L12 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L13 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L14 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L15 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L7 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L8 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B01/L9 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B02/I2 \t\tPlanon: module 2 \t\tMaster: fire alarm active\n",
"MC044-B02/I5 \t\tPlanon: plant reset pb \t\tMaster: module 5\n",
"MC044-B02/I6 \t\tPlanon: module 6 \t\tMaster: lthw pu fault\n",
"MC044-B02/I7 \t\tPlanon: lthw pu hi press \t\tMaster: module 7\n",
"MC044-B02/I8 \t\tPlanon: lthw pu lo press \t\tMaster: module 8\n",
"MC044-B02/N1 \t\tPlanon: byte 2 \t\tMaster: trend_0c_93_a0\n",
"MC044-B02/O2 \t\tPlanon: alarm route 2 \t\tMaster: vt system 102\n",
"MC044-B03/I1 \t\tPlanon: module 1 \t\tMaster: ups low battery\n",
"MC044-B03/I2 \t\tPlanon: module 2 \t\tMaster: ups lock\n",
"MC044-B04/I1 \t\tPlanon: module 1 \t\tMaster: phe1 high temp alarm\n",
"MC044-B04/I5 \t\tPlanon: 13% gas detect \t\tMaster: module 5\n",
"MC044-B04/I6 \t\tPlanon: 20% gas detect \t\tMaster: module 6\n",
"MC044-B04/O1 \t\tPlanon: alarm route 1 \t\tMaster: foyer vt heating\n",
"MC044-B04/O2 \t\tPlanon: physics vt heating \t\tMaster: alarm route 2\n",
"MC044-B06/I2 \t\tPlanon: fire alarm active \t\tMaster: module 2\n",
"MC044-B06/I3 \t\tPlanon: module 3 \t\tMaster: emergency stop\n",
"MC044-B06/I4 \t\tPlanon: module 4 \t\tMaster: mains failure\n",
"MC044-B06/I8 \t\tPlanon: ahu 2 fa dmpr open \t\tMaster: module 8\n",
"MC044-B06/L12 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B06/L13 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B06/N1 \t\tPlanon: ahu2 interface \t\tMaster: trend_0d_54_04\n",
"MC044-B07/N1 \t\tPlanon: trend_0c_92_22 \t\tMaster: fc interface lan87\n",
"MC044-B07/N3 \t\tPlanon: fc interface lan88 \t\tMaster: nan\n",
"MC044-B08/I1 \t\tPlanon: module 1 \t\tMaster: fire alarm active\n",
"MC044-B08/I2 \t\tPlanon: emergency stop \t\tMaster: module 2\n",
"MC044-B08/I5 \t\tPlanon: ups low battery \t\tMaster: module 5\n",
"MC044-B08/I8 \t\tPlanon: large room 3 ndrman status \t\tMaster: module 8\n",
"MC044-B08/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B08/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC044-B08/N1 \t\tPlanon: trend_0b_85_51 \t\tMaster: ic comms 1\n",
"MC044-B08/O1 \t\tPlanon: ahu 5 physics store vent \t\tMaster: alarm route 1\n",
"MC044-B10/N2 \t\tPlanon: network 2 \t\tMaster: variable air volume c e38\n",
"MC044-B10/N5 \t\tPlanon: bacnet mstp \t\tMaster: variable air volume c e38\n",
"MC044-B11/N1 \t\tPlanon: variable air volume b e08 \t\tMaster: trend_0b_9c_f8\n",
"MC044-B11/N5 \t\tPlanon: bacnet mstp \t\tMaster: variable air volume b e12\n",
"MC044-B12/N1 \t\tPlanon: mthw from central s \t\tMaster: trend_0d_11_6b\n",
"MC045-B01/I1 \t\tPlanon: process pump fail \t\tMaster: xcite/io/8ui\n",
"MC045-B01/I2 \t\tPlanon: xcite/io/8ui \t\tMaster: scc pump fail\n",
"MC045-B01/I7 \t\tPlanon: xcite/io/8ao \t\tMaster: chw pump2fail\n",
"MC046-B01/I1 \t\tPlanon: mod 1 - 8ao \t\tMaster: fire alarm\n",
"MC046-B01/I2 \t\tPlanon: mod 2 - 8ui \t\tMaster: pressurisation unit\n",
"MC046-B01/I3 \t\tPlanon: p1 a fault \t\tMaster: mod 3 - 8ao\n",
"MC046-B01/I4 \t\tPlanon: mod 4 - 8ui \t\tMaster: p1 b fault\n",
"MC046-B01/I7 \t\tPlanon: mod 7 - 4ui \t\tMaster: cal 2 45kw\n",
"MC051-B01/I4 \t\tPlanon: xcite/io/8do \t\tMaster: vt pumps flow proved\n",
"MC051-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC051-B01/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC055-B01/I1 \t\tPlanon: io module 1 \t\tMaster: heating p1 running\n",
"MC061-B01/I6 \t\tPlanon: boiler no.2 flow switch \t\tMaster: xcite/io/8ui\n",
"MC061-B01/I8 \t\tPlanon: boiler no.2 gas booster fault \t\tMaster: xcite/io/8ao\n",
"MC061-B01/N1 \t\tPlanon: trend_04_61_96 \t\tMaster: heating enabled\n",
"MC061-B02/I1 \t\tPlanon: io module 1 \t\tMaster: fire alarm\n",
"MC061-B02/N1 \t\tPlanon: trend_0e_2d_88 \t\tMaster: univreturntemp\n",
"MC061-B03/N1 \t\tPlanon: trend_0b_94_73 \t\tMaster: pressure ok os 11\n",
"MC063-B01/I3 \t\tPlanon: module 3 \t\tMaster: shower fans south fault\n",
"MC063-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC063-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC063-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B01/I1 \t\tPlanon: xcite/io/8ui \t\tMaster: phex no.1highlimitstatus\n",
"MC065-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B01/L6 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B01/L7 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B01/O2 \t\tPlanon: bookbinders \t\tMaster: alarm route 1\n",
"MC065-B02/I1 \t\tPlanon: module 1 \t\tMaster: fire alarm active\n",
"MC065-B02/I3 \t\tPlanon: module 3 \t\tMaster: for off\n",
"MC065-B03/I1 \t\tPlanon: fire alarm active \t\tMaster: module 1\n",
"MC065-B04/I4 \t\tPlanon: for extr only \t\tMaster: module 4\n",
"MC065-B06/I2 \t\tPlanon: xcite/io/8ui \t\tMaster: ahu-nw supply flow sts\n",
"MC065-B06/I3 \t\tPlanon: xcite/io/8ui \t\tMaster: ahu-nw extract vsd fault\n",
"MC065-B06/I4 \t\tPlanon: xcite/io/16di \t\tMaster: ahu-nw extract flow sts\n",
"MC065-B06/I6 \t\tPlanon: xcite/io/8ao \t\tMaster: ahu-nwsupplybagfiltersts\n",
"MC065-B06/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B06/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B06/N1 \t\tPlanon: trend_0b_b0_a0 \t\tMaster: oat frost setpoint\n",
"MC065-B06/O1 \t\tPlanon: nw zone 1 floor a oss \t\tMaster: alarm route 1\n",
"MC065-B07/I1 \t\tPlanon: xcite/io/8ui \t\tMaster: ahu-ne supply vsd fault\n",
"MC065-B07/I2 \t\tPlanon: xcite/io/8ui \t\tMaster: ahu-ne supply flow sts\n",
"MC065-B07/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B07/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B07/N1 \t\tPlanon: oat frost setpoint \t\tMaster: trend_0c_c1_8c\n",
"MC065-B07/O2 \t\tPlanon: ne zone 2 floor b oss \t\tMaster: alarm route 1\n",
"MC065-B08/I1 \t\tPlanon: c085 ac fault \t\tMaster: xcite/io/8ui\n",
"MC065-B08/I9 \t\tPlanon: xcite/io/8ao \t\tMaster: ahu-se frost stat\n",
"MC065-B08/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B08/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B08/N1 \t\tPlanon: trend_0c_c1_8b \t\tMaster: oat frost setpoint\n",
"MC065-B08/O1 \t\tPlanon: alarm route 1 \t\tMaster: se zone 4 floor a oss\n",
"MC065-B08/O2 \t\tPlanon: alarm route 1 \t\tMaster: se zone 4 floor b oss\n",
"MC065-B09/I1 \t\tPlanon: xcite/io/8ui \t\tMaster: ahu-sw supply vsd fault\n",
"MC065-B09/I2 \t\tPlanon: xcite/io/8ui \t\tMaster: ahu-sw supply flow sts\n",
"MC065-B09/I4 \t\tPlanon: ahu-sw extract flow sts \t\tMaster: xcite/io/8ao\n",
"MC065-B09/I5 \t\tPlanon: tef1 toilet extrct fault \t\tMaster: xcite/io/8ao\n",
"MC065-B09/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B09/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC065-B09/N1 \t\tPlanon: oat frost setpoint \t\tMaster: trend_0d_7f_af\n",
"MC065-B09/O2 \t\tPlanon: sw zone 3 floor b oss \t\tMaster: alarm route 1\n",
"MC065-B10/N1 \t\tPlanon: trend_04_5e_f7 \t\tMaster: ic comms 1\n",
"MC066-B01/I2 \t\tPlanon: ct pump flow proven \t\tMaster: xcite/io/8ui\n",
"MC066-B01/I5 \t\tPlanon: xcite/io/8ao \t\tMaster: n.gallery pumps flow proven\n",
"MC066-B01/I7 \t\tPlanon: pressure unit fault \t\tMaster: xcite/io/8ao\n",
"MC066-B01/L10 \t\tPlanon: nan \t\tMaster: \n",
"MC066-B01/L13 \t\tPlanon: nan \t\tMaster: \n",
"MC066-B01/L14 \t\tPlanon: nan \t\tMaster: \n",
"MC066-B01/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC066-B01/L6 \t\tPlanon: nan \t\tMaster: \n",
"MC066-B01/L9 \t\tPlanon: nan \t\tMaster: \n",
"MC070-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"MC070-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC070-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC070-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC070-B01/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC070-B03/I2 \t\tPlanon: io module 2 \t\tMaster: ahu supply fan run\n",
"MC070-B05/I1 \t\tPlanon: dhw cyl 1 high limit ok \t\tMaster: io module 1\n",
"MC070-B05/I3 \t\tPlanon: compressor fault \t\tMaster: 4ui\n",
"MC070-B06/I1 \t\tPlanon: xcite/io/4ui/4ao \t\tMaster: hws immersion 1\n",
"MC071-B01/I2 \t\tPlanon: 8ui_module 2 \t\tMaster: bar lossney hx04 fault\n",
"MC071-B01/I4 \t\tPlanon: toilet extract fan b013 fault \t\tMaster: 4ao_module 4\n",
"MC071-B01/I6 \t\tPlanon: 4ui4ao_module 6 \t\tMaster: toilet extract fan c047 fault\n",
"MC071-B01/I7 \t\tPlanon: toilet extract fan b070 fault \t\tMaster: 8ui_module 7\n",
"MC071-B01/I8 \t\tPlanon: 4ui4ao_module 8 \t\tMaster: eco hold off\n",
"MC071-B01/N1 \t\tPlanon: trend_0c_20_eb \t\tMaster: ic comms 1\n",
"MC072-B01/I2 \t\tPlanon: ct pump 2 fault \t\tMaster: 1 8do\n",
"MC072-B01/I3 \t\tPlanon: tower pump 1 fault \t\tMaster: io module 3\n",
"MC072-B01/I4 \t\tPlanon: tower pump 2 fault \t\tMaster: nan\n",
"MC072-B02/I2 \t\tPlanon: xcite/io/16di no2 \t\tMaster: presurisation unit hp fault\n",
"MC072-B02/I3 \t\tPlanon: presurisation unit lp fault \t\tMaster: xcite/io/8do no1\n",
"MC072-B02/I5 \t\tPlanon: xcite/io/8ui no1 \t\tMaster: mthw shunt pump p1b fault\n",
"MC072-B02/O1 \t\tPlanon: heating \t\tMaster: alarm route 1\n",
"MC072-B03/I1 \t\tPlanon: module 1 \t\tMaster: alarm mute pb\n",
"MC072-B05/I1 \t\tPlanon: alarm mute pb \t\tMaster: module 1\n",
"MC075-B01/N1 \t\tPlanon: trend_05_77_1d \t\tMaster: from man school 2 oat\n",
"MC076-B02/I1 \t\tPlanon: ac unit 01_1 fault =1 \t\tMaster: xcite/io/8ui\n",
"MC076-B02/I5 \t\tPlanon: ahu 1 filter dirty 1=dirty \t\tMaster: xcite/io/2ui/2ao\n",
"MC078-B01/I1 \t\tPlanon: f39 filter \t\tMaster: xcite/io/8ui\n",
"MC078-B01/I2 \t\tPlanon: f39 frost \t\tMaster: xcite/io/16di\n",
"MC078-B01/I3 \t\tPlanon: fan 1 frost \t\tMaster: xcite/io/16di\n",
"MC078-B01/I4 \t\tPlanon: xcite/io/8ui \t\tMaster: fan 1 filter\n",
"MC078-B01/I5 \t\tPlanon: f40 frost \t\tMaster: xcite/io/8ao\n",
"MC078-B01/I6 \t\tPlanon: ground floor toilet extract \t\tMaster: xcite/io/8ao\n",
"MC078-B01/I8 \t\tPlanon: f40 extract \t\tMaster: xcite/io/4ao\n",
"MC078-B01/I9 \t\tPlanon: f40 filter \t\tMaster: xcite/io/2ui/2ao\n",
"MC078-B02/I3 \t\tPlanon: xcite/io/8ao \t\tMaster: heat recovery pump\n",
"MC078-B02/I5 \t\tPlanon: xcite/io/4ao \t\tMaster: main supply fan air flow\n",
"MC078-B02/I7 \t\tPlanon: main extract air flow proved \t\tMaster: xcite/io/16di\n",
"MC078-B02/I9 \t\tPlanon: heat recovery pump flow proven \t\tMaster: io module 9\n",
"MC078-B02/N1 \t\tPlanon: ic comms 1 \t\tMaster: trend_05_50_40\n",
"MC078-B03/I1 \t\tPlanon: 2 flr hws cal 1 valve \t\tMaster: xcite/io/16di\n",
"MC078-B03/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC078-B03/N3 \t\tPlanon: ic comms 3 \t\tMaster: bacnet ip\n",
"MC078-B03/N6 \t\tPlanon: bacnet application \t\tMaster: ic comms 6\n",
"MC103-B01/I1 \t\tPlanon: iq4/io/16di \t\tMaster: heating failed\n",
"MC103-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"MC198-B01/I2 \t\tPlanon: gas safety cct trip \t\tMaster: xcite/io/8ui\n",
"MC198-B01/N1 \t\tPlanon: ic comms 1 \t\tMaster: trend_05_4d_dd\n",
"MC198-B01/O1 \t\tPlanon: heating \t\tMaster: alarm route 1\n",
"MC200-B01/N1 \t\tPlanon: ic comms 1 \t\tMaster: trend_0d_5d_de\n",
"MC202-B01/N1 \t\tPlanon: ic comms 1 \t\tMaster: waterside\n",
"MC202-B02/N1 \t\tPlanon: waterside \t\tMaster: ic comms 1\n",
"MC202-B03/N1 \t\tPlanon: ic comms 1 \t\tMaster: waterside\n",
"MC202-B04/N1 \t\tPlanon: ic comms 1 \t\tMaster: waterside\n",
"MC202-B13/N1 \t\tPlanon: ic comms 1 \t\tMaster: waterside\n",
"MC204-B01/I4 \t\tPlanon: gas valve shut \t\tMaster: module 4\n",
"MC204-B01/I5 \t\tPlanon: blr room water leak det \t\tMaster: module 5\n",
"MC204-B02/I1 \t\tPlanon: fire alarm active \t\tMaster: module 1\n",
"MC204-B02/I4 \t\tPlanon: ahu3 frost trip \t\tMaster: module 4\n",
"MC204-B02/L10 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L11 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L12 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L13 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L14 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L6 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L7 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L8 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/L9 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B02/N1 \t\tPlanon: trend_09_03_4a \t\tMaster: ic comms 1\n",
"MC204-B03/I1 \t\tPlanon: cws tank 1 hi level \t\tMaster: module 1\n",
"MC204-B04/L1 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B04/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B04/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B04/O2 \t\tPlanon: climbing wall \t\tMaster: alarm route 2\n",
"MC204-B05/I1 \t\tPlanon: module 1 \t\tMaster: vt pump 3a fault\n",
"MC204-B05/I2 \t\tPlanon: module 2 \t\tMaster: vt pump 3b fault\n",
"MC204-B05/I4 \t\tPlanon: ufhm1 heat dmd \t\tMaster: module 4\n",
"MC204-B05/I6 \t\tPlanon: ufhm1 htco \t\tMaster: module 6\n",
"MC204-B05/O3 \t\tPlanon: ufhm2-group change m \t\tMaster: alarm route 3\n",
"MC204-B06/I4 \t\tPlanon: ahu4 sply fan flow \t\tMaster: module 4\n",
"MC204-B06/L2 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/L3 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/L4 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/L5 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/L6 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/L7 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/L8 \t\tPlanon: nan \t\tMaster: \n",
"MC204-B06/O3 \t\tPlanon: alarm route 3 \t\tMaster: sports hall\n",
"MC207-B01/I2 \t\tPlanon: press unit fault \t\tMaster: io module 2\n",
"MC207-B01/I3 \t\tPlanon: sec hr pumps flow \t\tMaster: io module 3\n",
"MC207-B01/I4 \t\tPlanon: ct pumps flow \t\tMaster: io module 4\n",
"MC207-B01/I5 \t\tPlanon: io module 5 \t\tMaster: vt pumps flow\n",
"MC207-B01/I6 \t\tPlanon: lodge pumps flow \t\tMaster: io module 6\n",
"MC210-B01/I1 \t\tPlanon: fire alarm active \t\tMaster: xcite/io/8ui\n",
"MC210-B01/I10 \t\tPlanon: xcite/io/16di \t\tMaster: phe sec pump p1b flow prove\n",
"MC210-B01/I9 \t\tPlanon: phe sec pump p1a flow prove \t\tMaster: xcite/io/16di\n",
"MC210-B01/N1 \t\tPlanon: ic comms 1 \t\tMaster: mcp01a\n",
"MC210-B01/P17 \t\tPlanon: room co² a17 \t\tMaster: room co≤ a17\n",
"MC210-B01/S17 \t\tPlanon: room co² a17 \t\tMaster: room co≤ a17\n",
"MC210-B02/I2 \t\tPlanon: xcite/io/8ui \t\tMaster: summer switch active\n",
"MC210-B02/I3 \t\tPlanon: xcite/io/8ui \t\tMaster: holiday switch active\n",
"MC210-B02/N1 \t\tPlanon: mcp01b \t\tMaster: ic comms 1\n",
"MC210-B03/A32 \t\tPlanon: room co² a17 from os11 \t\tMaster: room co≤ a17 from os11\n",
"MC210-B03/F114 \t\tPlanon: c28 ave co² \t\tMaster: c28 ave co≤\n",
"MC210-B03/F126 \t\tPlanon: c30 ave co² \t\tMaster: c30 ave co≤\n",
"MC210-B03/F90 \t\tPlanon: b31 ave co² \t\tMaster: b31 ave co≤\n",
"MC210-B03/F99 \t\tPlanon: b04 ave co² \t\tMaster: b04 ave co≤\n",
"MC210-B03/I1 \t\tPlanon: xcite/io/8ui \t\tMaster: compressedairunit1 fault\n",
"MC210-B03/I10 \t\tPlanon: b30 override \t\tMaster: xcite/io/8do\n",
"MC210-B03/I11 \t\tPlanon: a18 override \t\tMaster: xcite/io/8do\n",
"MC210-B03/I2 \t\tPlanon: xcite/io/8ui \t\tMaster: compressedairunit2 fault\n",
"MC210-B03/I3 \t\tPlanon: nitrogen generation alarm \t\tMaster: xcite/io/8ui\n",
"MC210-B03/I6 \t\tPlanon: chemenglab condenser fault \t\tMaster: xcite/io/8ui\n",
"MC210-B03/I7 \t\tPlanon: nuclearlab condenser fault \t\tMaster: xcite/io/8ui\n",
"MC210-B03/K12 \t\tPlanon: a14 room co² setpt \t\tMaster: a14 room co≤ setpt\n",
"MC210-B03/K15 \t\tPlanon: a18 room co² setpt \t\tMaster: a18 room co≤ setpt\n",
"MC210-B03/K18 \t\tPlanon: a19 room co² setpt \t\tMaster: a19 room co≤ setpt\n",
"MC210-B03/K21 \t\tPlanon: a21 room co² setpt \t\tMaster: a21 room co≤ setpt\n",
"MC210-B03/K24 \t\tPlanon: a22 room co² setpt \t\tMaster: a22 room co≤ setpt\n",
"MC210-B03/K27 \t\tPlanon: a06 room co² setpt \t\tMaster: a06 room co≤ setpt\n",
"MC210-B03/K30 \t\tPlanon: b31 room co² setpt \t\tMaster: b31 room co≤ setpt\n",
"MC210-B03/K33 \t\tPlanon: b04 room co² setpt \t\tMaster: b04 room co≤ setpt\n",
"MC210-B03/K35 \t\tPlanon: c28 room co² setpt \t\tMaster: c28 room co≤ setpt\n",
"MC210-B03/K38 \t\tPlanon: c30 room co² setpt \t\tMaster: c30 room co≤ setpt\n",
"MC210-B03/K41 \t\tPlanon: c04 room co² setpt \t\tMaster: c04 room co≤ setpt\n",
"MC210-B03/K44 \t\tPlanon: d30 room co² setpt \t\tMaster: d30 room co≤ setpt\n",
"MC210-B03/K46 \t\tPlanon: d03 room co² setpt \t\tMaster: d03 room co≤ setpt\n",
"MC210-B03/K50 \t\tPlanon: atrium room co² setpt \t\tMaster: atrium room co≤ setpt\n",
"MC210-B03/K61 \t\tPlanon: b30 room co² setpt \t\tMaster: b30 room co≤ setpt\n",
"MC210-B03/K9 \t\tPlanon: a17 room co² setpt \t\tMaster: a17 room co≤ setpt\n",
"MC210-B03/P10 \t\tPlanon: room co² a22 \t\tMaster: room co≤ a22\n",
"MC210-B03/P12 \t\tPlanon: room co² a14 \t\tMaster: room co≤ a14\n",
"MC210-B03/P14 \t\tPlanon: room co² a06 \t\tMaster: room co≤ a06\n",
"MC210-B03/P16 \t\tPlanon: room co² b31 no1 \t\tMaster: room co≤ b31 no1\n",
"MC210-B03/P18 \t\tPlanon: room co² b31 no2 \t\tMaster: room co≤ b31 no2\n",
"MC210-B03/P2 \t\tPlanon: room co² b30 \t\tMaster: room co≤ b30\n",
"MC210-B03/P20 \t\tPlanon: room co² b04 no1 \t\tMaster: room co≤ b04 no1\n",
"MC210-B03/P22 \t\tPlanon: room co² b04 no2 \t\tMaster: room co≤ b04 no2\n",
"MC210-B03/P24 \t\tPlanon: room co² c28 no1 \t\tMaster: room co≤ c28 no1\n",
"MC210-B03/P26 \t\tPlanon: room co² c28 no2 \t\tMaster: room co≤ c28 no2\n",
"MC210-B03/P28 \t\tPlanon: room co² c30 no1 \t\tMaster: room co≤ c30 no1\n",
"MC210-B03/P30 \t\tPlanon: room co² c30 no2 \t\tMaster: room co≤ c30 no2\n",
"MC210-B03/P32 \t\tPlanon: room co² a02 \t\tMaster: room co≤ a02\n",
"MC210-B03/P34 \t\tPlanon: room co² c04 \t\tMaster: room co≤ c04\n",
"MC210-B03/P36 \t\tPlanon: room co² c25 \t\tMaster: room co≤ c25\n",
"MC210-B03/P38 \t\tPlanon: room co² d30 \t\tMaster: room co≤ d30\n",
"MC210-B03/P4 \t\tPlanon: room co² a18 \t\tMaster: room co≤ a18\n",
"MC210-B03/P40 \t\tPlanon: room co² d03 \t\tMaster: room co≤ d03\n",
"MC210-B03/P6 \t\tPlanon: room co² a19 \t\tMaster: room co≤ a19\n",
"MC210-B03/P8 \t\tPlanon: room co² a21 \t\tMaster: room co≤ a21\n",
"MC210-B03/S10 \t\tPlanon: room co² a22 \t\tMaster: room co≤ a22\n",
"MC210-B03/S12 \t\tPlanon: room co² a14 \t\tMaster: room co≤ a14\n",
"MC210-B03/S14 \t\tPlanon: room co² a06 \t\tMaster: room co≤ a06\n",
"MC210-B03/S16 \t\tPlanon: room co² b31 no1 \t\tMaster: room co≤ b31 no1\n",
"MC210-B03/S18 \t\tPlanon: room co² b31 no2 \t\tMaster: room co≤ b31 no2\n",
"MC210-B03/S2 \t\tPlanon: room co² b30 \t\tMaster: room co≤ b30\n",
"MC210-B03/S20 \t\tPlanon: room co² b04 no1 \t\tMaster: room co≤ b04 no1\n",
"MC210-B03/S22 \t\tPlanon: room co² b04 no2 \t\tMaster: room co≤ b04 no2\n",
"MC210-B03/S24 \t\tPlanon: room co² c28 no1 \t\tMaster: room co≤ c28 no1\n",
"MC210-B03/S26 \t\tPlanon: room co² c28 no2 \t\tMaster: room co≤ c28 no2\n",
"MC210-B03/S28 \t\tPlanon: room co² c30 no1 \t\tMaster: room co≤ c30 no1\n",
"MC210-B03/S30 \t\tPlanon: room co² c30 no2 \t\tMaster: room co≤ c30 no2\n",
"MC210-B03/S32 \t\tPlanon: room co² a02 \t\tMaster: room co≤ a02\n",
"MC210-B03/S34 \t\tPlanon: room co² c04 \t\tMaster: room co≤ c04\n",
"MC210-B03/S36 \t\tPlanon: room co² c25 \t\tMaster: room co≤ c25\n",
"MC210-B03/S38 \t\tPlanon: room co² d30 \t\tMaster: room co≤ d30\n",
"MC210-B03/S4 \t\tPlanon: room co² a18 \t\tMaster: room co≤ a18\n",
"MC210-B03/S40 \t\tPlanon: room co² d03 \t\tMaster: room co≤ d03\n",
"MC210-B03/S6 \t\tPlanon: room co² a19 \t\tMaster: room co≤ a19\n",
"MC210-B03/S8 \t\tPlanon: room co² a21 \t\tMaster: room co≤ a21\n",
"MC211-B01/I1 \t\tPlanon: heating press. unit lp \t\tMaster: xcite/io/8di/8ti\n",
"MC211-B01/I4 \t\tPlanon: xcite/io/4ao \t\tMaster: district heating heat meter\n",
"OC004-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"OC004-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"OC004-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"OC004-B01/L4 \t\tPlanon: nan \t\tMaster: \n",
"OC005-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"OC005-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"OC005-B01/L3 \t\tPlanon: nan \t\tMaster: \n",
"OC006-B01/L1 \t\tPlanon: nan \t\tMaster: \n",
"OC006-B01/L2 \t\tPlanon: nan \t\tMaster: \n",
"OC006-B01/L3 \t\tPlanon: nan \t\tMaster: \n"
]
}
],
"source": [
"for i in planon_meterssensors_intersected[planon_meterssensors_intersected['Description']!=master_meterssensors_for_validation_intersected['Description']].index:\n",
" print(i,'\\t\\tPlanon:',planon_meterssensors_intersected.loc[i]['Description'],'\\t\\tMaster:',master_meterssensors_for_validation_intersected.loc[i]['Description'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us repeat the exercise for `Logger Channel`. Cross-validate, flag as highly likely error where both mismatch."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"904"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_meterssensors_intersected['Logger Channel']!=master_meterssensors_for_validation_intersected['Logger Channel'])"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"planon_meterssensors_intersected['Logger Channel']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ') for s in planon_meterssensors_intersected['Logger Channel'].values]\n",
"master_meterssensors_for_validation_intersected['Logger Channel']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ') for s in master_meterssensors_for_validation_intersected['Logger Channel'].values]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_meterssensors_intersected['Logger Channel']!=master_meterssensors_for_validation_intersected['Logger Channel'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All errors fixed on logger channels."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in planon_meterssensors_intersected[planon_meterssensors_intersected['Logger Channel']!=master_meterssensors_for_validation_intersected['Logger Channel']].index:\n",
" print(i,'\\t\\tPlanon:',planon_meterssensors_intersected.loc[i]['Logger Channel'],'\\t\\tMaster:',master_meterssensors_for_validation_intersected.loc[i]['Logger Channel'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"New error percentage:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Code 0.813557\n",
"Fiscal meter 0.000000\n",
"Description 1.225399\n",
"Tenant meter 0.000000\n",
"Building Name 1.802653\n",
"Logger Channel 0.000000\n",
"Building Code 0.000000\n",
"dtype: float64"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(planon_meterssensors_intersected!=master_meterssensors_for_validation_intersected).sum()/\\\n",
"len(planon_meterssensors_intersected)*100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2.2. Loggers"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'AP000',\n",
" 'AP001',\n",
" 'AP009',\n",
" 'AP010',\n",
" 'AP011',\n",
" 'AP057',\n",
" 'AP080',\n",
" 'AP081',\n",
" 'EX001',\n",
" 'MC001',\n",
" 'MC003',\n",
" 'MC007',\n",
" 'MC008',\n",
" 'MC010',\n",
" 'MC011',\n",
" 'MC013',\n",
" 'MC014',\n",
" 'MC029',\n",
" 'MC030',\n",
" 'MC031',\n",
" 'MC032',\n",
" 'MC033',\n",
" 'MC042',\n",
" 'MC043',\n",
" 'MC044',\n",
" 'MC045',\n",
" 'MC046',\n",
" 'MC047',\n",
" 'MC048',\n",
" 'MC050',\n",
" 'MC051',\n",
" 'MC053',\n",
" 'MC055',\n",
" 'MC060',\n",
" 'MC061',\n",
" 'MC062',\n",
" 'MC063',\n",
" 'MC064',\n",
" 'MC065',\n",
" 'MC066',\n",
" 'MC067',\n",
" 'MC068',\n",
" 'MC069',\n",
" 'MC070',\n",
" 'MC071',\n",
" 'MC072',\n",
" 'MC075',\n",
" 'MC076',\n",
" 'MC077',\n",
" 'MC078',\n",
" 'MC083',\n",
" 'MC095',\n",
" 'MC099',\n",
" 'MC102',\n",
" 'MC103',\n",
" 'MC125',\n",
" 'MC126',\n",
" 'MC128',\n",
" 'MC129',\n",
" 'MC131',\n",
" 'MC134',\n",
" 'MC138',\n",
" 'MC139',\n",
" 'MC140',\n",
" 'MC141',\n",
" 'MC171',\n",
" 'MC181',\n",
" 'MC197',\n",
" 'MC198',\n",
" 'MC199',\n",
" 'MC200',\n",
" 'MC202',\n",
" 'MC203',\n",
" 'MC204',\n",
" 'MC207',\n",
" 'MC210',\n",
" 'MC211',\n",
" 'OC004',\n",
" 'OC005',\n",
" 'OC006'}"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"buildings=set(planon_loggerscontrollers['BuildingNo.'])\n",
"buildings"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false,
"scrolled": 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>Building Code</th>\n",
" <th>Building Name</th>\n",
" <th>Space</th>\n",
" <th>Description</th>\n",
" <th>Classification Group</th>\n",
" <th>Make</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Logger Mac Address</th>\n",
" <th>Logger Ip Address</th>\n",
" <th>Logger Modem Serial Number</th>\n",
" <th>Logger Sim</th>\n",
" <th>Network Point Id</th>\n",
" <th>Logger Upstream Comms Target</th>\n",
" <th>Additional Location Info</th>\n",
" <th>Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MC139-L01</th>\n",
" <td>MC139</td>\n",
" <td>Sub Station 9</td>\n",
" <td>A0</td>\n",
" <td>Data logger: Sub Station 9</td>\n",
" <td>Data logger</td>\n",
" <td>Enercom</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157AC6600</td>\n",
" <td>NaN</td>\n",
" <td>80.93.175.91:40455</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Graduate Field</td>\n",
" <td>MC139-L01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC008-L01</th>\n",
" <td>MC008</td>\n",
" <td>LEC Remote Workshops</td>\n",
" <td>A0</td>\n",
" <td>Data logger: LEC Workshops</td>\n",
" <td>Data logger</td>\n",
" <td>Enercom</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157B96E00</td>\n",
" <td>NaN</td>\n",
" <td>80.93.175.91:40453</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>BMS Panel</td>\n",
" <td>MC008-L01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Building Code Building Name Space \\\n",
"MC139-L01 MC139 Sub Station 9 A0 \n",
"MC008-L01 MC008 LEC Remote Workshops A0 \n",
"\n",
" Description Classification Group Make \\\n",
"MC139-L01 Data logger: Sub Station 9 Data logger Enercom \n",
"MC008-L01 Data logger: LEC Workshops Data logger Enercom \n",
"\n",
" Model Logger Serial Number Logger Mac Address \\\n",
"MC139-L01 Multilog G2 050157AC6600 NaN \n",
"MC008-L01 Multilog G2 050157B96E00 NaN \n",
"\n",
" Logger Ip Address Logger Modem Serial Number Logger Sim \\\n",
"MC139-L01 80.93.175.91:40455 NaN NaN \n",
"MC008-L01 80.93.175.91:40453 NaN NaN \n",
"\n",
" Network Point Id Logger Upstream Comms Target \\\n",
"MC139-L01 NaN NaN \n",
"MC008-L01 NaN NaN \n",
"\n",
" Additional Location Info Code \n",
"MC139-L01 Graduate Field MC139-L01 \n",
"MC008-L01 BMS Panel MC008-L01 "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation = \\\n",
" pd.concat([master_loggerscontrollers.loc[master_loggerscontrollers['Building Code'] == building] \\\n",
" for building in buildings])\n",
"master_loggerscontrollers_for_validation.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"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>Building Code</th>\n",
" <th>Building Name</th>\n",
" <th>Space</th>\n",
" <th>Description</th>\n",
" <th>Classification Group</th>\n",
" <th>Make</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Logger Mac Address</th>\n",
" <th>Logger Ip Address</th>\n",
" <th>Logger Modem Serial Number</th>\n",
" <th>Logger Sim</th>\n",
" <th>Network Point Id</th>\n",
" <th>Logger Upstream Comms Target</th>\n",
" <th>Additional Location Info</th>\n",
" <th>Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MC060-L01</th>\n",
" <td>MC060</td>\n",
" <td>Furness Residences, Bardsea</td>\n",
" <td>PL1</td>\n",
" <td>Data logger: Furness Residences</td>\n",
" <td>Data logger</td>\n",
" <td>Enercom</td>\n",
" <td>Multilog G2</td>\n",
" <td>0501988FCB00</td>\n",
" <td>00-50-C2-2C-3E-32</td>\n",
" <td>10.23.12.13</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MC060-L01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Building Code Building Name Space \\\n",
"MC060-L01 MC060 Furness Residences, Bardsea PL1 \n",
"\n",
" Description Classification Group Make \\\n",
"MC060-L01 Data logger: Furness Residences Data logger Enercom \n",
"\n",
" Model Logger Serial Number Logger Mac Address \\\n",
"MC060-L01 Multilog G2 0501988FCB00 00-50-C2-2C-3E-32 \n",
"\n",
" Logger Ip Address Logger Modem Serial Number Logger Sim \\\n",
"MC060-L01 10.23.12.13 NaN NaN \n",
"\n",
" Network Point Id Logger Upstream Comms Target \\\n",
"MC060-L01 NaN NaN \n",
"\n",
" Additional Location Info Code \n",
"MC060-L01 NaN MC060-L01 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers[master_loggerscontrollers['Building Code']=='MC060']"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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>BuildingNo.</th>\n",
" <th>Building</th>\n",
" <th>Locations.Space.Space number</th>\n",
" <th>Space Name</th>\n",
" <th>Additional Location Info</th>\n",
" <th>Description</th>\n",
" <th>Classification Group</th>\n",
" <th>Record</th>\n",
" <th>HVAC Ref</th>\n",
" <th>Element Description</th>\n",
" <th>...</th>\n",
" <th>Logger SIM</th>\n",
" <th>Meter Pulse Value</th>\n",
" <th>Meter Units</th>\n",
" <th>Meter Capacity</th>\n",
" <th>Network Point ID</th>\n",
" <th>Tenant Meter.Name</th>\n",
" <th>Fiscal Meter.Name</th>\n",
" <th>EIS Space.Space number</th>\n",
" <th>Utility Type.Name</th>\n",
" <th>Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>MC060-L01</th>\n",
" <td>MC060</td>\n",
" <td>Furness Residences, Bardsea</td>\n",
" <td>PL1</td>\n",
" <td>Plant Room</td>\n",
" <td>NaN</td>\n",
" <td>Data logger: Furness Residences</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>MC060-L01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" BuildingNo. Building \\\n",
"MC060-L01 MC060 Furness Residences, Bardsea \n",
"\n",
" Locations.Space.Space number Space Name Additional Location Info \\\n",
"MC060-L01 PL1 Plant Room NaN \n",
"\n",
" Description Classification Group Record \\\n",
"MC060-L01 Data logger: Furness Residences EN.EN1 Data Logger NaN \n",
"\n",
" HVAC Ref Element Description ... Logger SIM \\\n",
"MC060-L01 NaN NaN ... NaN \n",
"\n",
" Meter Pulse Value Meter Units Meter Capacity Network Point ID \\\n",
"MC060-L01 NaN NaN NaN NaN \n",
"\n",
" Tenant Meter.Name Fiscal Meter.Name EIS Space.Space number \\\n",
"MC060-L01 NaN NaN NaN \n",
"\n",
" Utility Type.Name Code \n",
"MC060-L01 NaN MC060-L01 \n",
"\n",
"[1 rows x 30 columns]"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_loggerscontrollers[planon_loggerscontrollers['BuildingNo.']=='MC060']"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Building Code MC060\n",
"Building Name Furness Residences, Bardsea\n",
"Space PL1\n",
"Description Data logger: Furness Residences\n",
"Classification Group Data logger\n",
"Make Enercom\n",
"Model Multilog G2\n",
"Logger Serial Number 0501988FCB00\n",
"Logger Mac Address 00-50-C2-2C-3E-32\n",
"Logger Ip Address 10.23.12.13\n",
"Logger Modem Serial Number NaN\n",
"Logger Sim NaN\n",
"Network Point Id NaN\n",
"Logger Upstream Comms Target NaN\n",
"Additional Location Info NaN\n",
"Code MC060-L01\n",
"Name: MC060-L01, dtype: object"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers.loc['MC060-L01']"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"295"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(master_loggerscontrollers_for_validation)"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"273"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(planon_loggerscontrollers)-len(planon_loggerscontrollers.index[planon_loggerscontrollers.index.duplicated()])"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"master_loggerscontrollers_for_validation.sort_index(inplace=True)\n",
"planon_loggerscontrollers.sort_index(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Building Code MC060\n",
"Building Name Furness Residences, Bardsea\n",
"Space PL1\n",
"Description Data logger: Furness Residences\n",
"Classification Group Data logger\n",
"Make Enercom\n",
"Model Multilog G2\n",
"Logger Serial Number 0501988FCB00\n",
"Logger Mac Address 00-50-C2-2C-3E-32\n",
"Logger Ip Address 10.23.12.13\n",
"Logger Modem Serial Number NaN\n",
"Logger Sim NaN\n",
"Network Point Id NaN\n",
"Logger Upstream Comms Target NaN\n",
"Additional Location Info NaN\n",
"Code MC060-L01\n",
"Name: MC060-L01, dtype: object"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation.loc['MC060-L01']"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AP000-L01</th>\n",
" <th>AP000-L02</th>\n",
" <th>AP000-L03</th>\n",
" <th>AP001-L01</th>\n",
" <th>AP009-L01</th>\n",
" <th>AP010-L01</th>\n",
" <th>AP011-L01</th>\n",
" <th>AP057-L01</th>\n",
" <th>AP080-L01</th>\n",
" <th>AP081-L01</th>\n",
" <th>...</th>\n",
" <th>MC204-L05</th>\n",
" <th>MC204-L06</th>\n",
" <th>MC207-B01</th>\n",
" <th>MC210-B01</th>\n",
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" <th>MC211-B01</th>\n",
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>BuildingNo.</th>\n",
" <td>AP000</td>\n",
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" <th>Building</th>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
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" <td>House 09 - Devoke, Graduate College</td>\n",
" <td>House 10 - Elterwater, Graduate College</td>\n",
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" <td>Alexandra Park Laundrette</td>\n",
" <td>Lonsdale House (Block 12)</td>\n",
" <td>Barker House Farm</td>\n",
" <td>...</td>\n",
" <td>Sports Centre</td>\n",
" <td>Sports Centre</td>\n",
" <td>HR Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Life Sciences & Environment Laboratories</td>\n",
" <td>Chancellor's Wharf, Wyre House</td>\n",
" <td>Chancellor's Wharf, Lune House</td>\n",
" <td>Chancellor's Wharf, Kent House</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Locations.Space.Space number</th>\n",
" <td>A0</td>\n",
" <td>A0</td>\n",
" <td>A0</td>\n",
" <td>A104</td>\n",
" <td>A124</td>\n",
" <td>A109</td>\n",
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" <td>A13</td>\n",
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" <th>Space Name</th>\n",
" <td>Whole Site</td>\n",
" <td>Whole Site</td>\n",
" <td>Whole Site</td>\n",
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" <td>Electrical Riser/Cupboard</td>\n",
" <td>Electrical Riser</td>\n",
" <td>Electrical Riser</td>\n",
" <td>Whole Building</td>\n",
" <td>Laundrette</td>\n",
" <td>Service Riser</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
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" <th>Additional Location Info</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Description</th>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Data logger: Graduate College - House 1</td>\n",
" <td>Data logger: Graduate College - House 9</td>\n",
" <td>Data logger: Graduate College - House 10</td>\n",
" <td>Data logger: Graduate College - House 11</td>\n",
" <td>Data logger: Alexandra Park Laundrette</td>\n",
" <td>Data logger: Lonsdale House - Laundrette</td>\n",
" <td>Data logger: Barker House Farm</td>\n",
" <td>...</td>\n",
" <td>Data logger: Sports Centre</td>\n",
" <td>Data logger: Sports Centre</td>\n",
" <td>BMS Controller: HR Building</td>\n",
" <td>BMS Controller: Engineering Building</td>\n",
" <td>BMS Controller: Engineering Building</td>\n",
" <td>BMS Controller: Engineering Building</td>\n",
" <td>BMS Controller: Life Sciences & Environment La...</td>\n",
" <td>BMS Controller: Chancellors Wharf</td>\n",
" <td>BMS Controller: Chancellors Wharf</td>\n",
" <td>BMS Controller: Chancellors Wharf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Classification Group</th>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>...</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN1 Data Logger</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" <td>EN.EN4 BMS Controller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Record</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <tr>\n",
" <th>HVAC Ref</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Element Description</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Servicable Area</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Model</th>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>...</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>IQ3xcite96</td>\n",
" <td>IQ3xcite128</td>\n",
" <td>IQ3xcite128</td>\n",
" <td>IQ3xcite128</td>\n",
" <td>IQ3xcite96</td>\n",
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" <td>IQ3xact12</td>\n",
" <td>IQ3xact12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Make</th>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>...</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EIS ID</th>\n",
" <td>050157C7ED00</td>\n",
" <td>37475126</td>\n",
" <td>48015355</td>\n",
" <td>0501E3E97100</td>\n",
" <td>0501E38A6300</td>\n",
" <td>0501E3D07500</td>\n",
" <td>0501E3839500</td>\n",
" <td>050157C16100</td>\n",
" <td>050157AB6700</td>\n",
" <td>050157C09600</td>\n",
" <td>...</td>\n",
" <td>0501F26DBC00</td>\n",
" <td>050200C3E800</td>\n",
" <td>3BF53C6F-96DB-4B9B-8B7C-ED6D6490F4E5</td>\n",
" <td>0F850990-984A-49FC-B351-82CCFD6A644B</td>\n",
" <td>B39C7903-72F6-4B03-A5B6-6CE8CF57D680</td>\n",
" <td>9DA6D329-30E9-46CC-A840-5B656A5FFDC2</td>\n",
" <td>C587E3F2-E604-4C9B-827D-80797E53FC58</td>\n",
" <td>48352BB4-4B1B-4013-AB9F-14E143E83948</td>\n",
" <td>281682DC-5479-4064-8290-E873933872B0</td>\n",
" <td>BBD3685B-B0DC-417F-A0E8-20139B1074E1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Channel</th>\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>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",
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" </tr>\n",
" <tr>\n",
" <th>Logger Upstream Comms Target</th>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Modem Serial Number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger IP Address</th>\n",
" <td>10.23.9.39</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10.23.12.36</td>\n",
" <td>NaN</td>\n",
" <td>10.23.12.37</td>\n",
" <td>10.23.12.38</td>\n",
" <td>80.93.175.91:40455</td>\n",
" <td>10.23.9.40</td>\n",
" <td>10.23.9.41</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Serial Number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger MAC Address</th>\n",
" <td>00-50-C2-2C-3E-43</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>00-50-C2-2C-3D-DE</td>\n",
" <td>00-50-C2-2C-3D-CB</td>\n",
" <td>00-50-C2-2C-3D-D1</td>\n",
" <td>00-50-C2-2C-3D-A7</td>\n",
" <td>NaN</td>\n",
" <td>00-50-C2-2C-3D-1B</td>\n",
" <td>00-50-C2-2C-3D-17</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger SIM</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Pulse Value</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Units</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Meter Capacity</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Network Point ID</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tenant Meter.Name</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fiscal Meter.Name</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EIS Space.Space number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utility Type.Name</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Code</th>\n",
" <td>AP000-L01</td>\n",
" <td>AP000-L02</td>\n",
" <td>AP000-L03</td>\n",
" <td>AP001-L01</td>\n",
" <td>AP009-L01</td>\n",
" <td>AP010-L01</td>\n",
" <td>AP011-L01</td>\n",
" <td>AP057-L01</td>\n",
" <td>AP080-L01</td>\n",
" <td>AP081-L01</td>\n",
" <td>...</td>\n",
" <td>MC204-L05</td>\n",
" <td>MC204-L06</td>\n",
" <td>MC207-B01</td>\n",
" <td>MC210-B01</td>\n",
" <td>MC210-B02</td>\n",
" <td>MC210-B03</td>\n",
" <td>MC211-B01</td>\n",
" <td>OC004-B01</td>\n",
" <td>OC005-B01</td>\n",
" <td>OC006-B01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>30 rows × 273 columns</p>\n",
"</div>"
],
"text/plain": [
" AP000-L01 \\\n",
"BuildingNo. AP000 \n",
"Building Alexandra Park \n",
"Locations.Space.Space number A0 \n",
"Space Name Whole Site \n",
"Additional Location Info NaN \n",
"Description Data logger: Alexandra Park \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 050157C7ED00 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 10.23.9.39 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3E-43 \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP000-L01 \n",
"\n",
" AP000-L02 \\\n",
"BuildingNo. AP000 \n",
"Building Alexandra Park \n",
"Locations.Space.Space number A0 \n",
"Space Name Whole Site \n",
"Additional Location Info NaN \n",
"Description Data logger: Alexandra Park \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 37475126 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP000-L02 \n",
"\n",
" AP000-L03 \\\n",
"BuildingNo. AP000 \n",
"Building Alexandra Park \n",
"Locations.Space.Space number A0 \n",
"Space Name Whole Site \n",
"Additional Location Info NaN \n",
"Description Data logger: Alexandra Park \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 48015355 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP000-L03 \n",
"\n",
" AP001-L01 \\\n",
"BuildingNo. AP001 \n",
"Building House 01 - Bassenthwaite, Graduate College \n",
"Locations.Space.Space number A104 \n",
"Space Name Electrical Riser \n",
"Additional Location Info NaN \n",
"Description Data logger: Graduate College - House 1 \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 0501E3E97100 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 10.23.12.36 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3D-DE \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP001-L01 \n",
"\n",
" AP009-L01 \\\n",
"BuildingNo. AP009 \n",
"Building House 09 - Devoke, Graduate College \n",
"Locations.Space.Space number A124 \n",
"Space Name Electrical Riser/Cupboard \n",
"Additional Location Info NaN \n",
"Description Data logger: Graduate College - House 9 \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 0501E38A6300 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3D-CB \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP009-L01 \n",
"\n",
" AP010-L01 \\\n",
"BuildingNo. AP010 \n",
"Building House 10 - Elterwater, Graduate College \n",
"Locations.Space.Space number A109 \n",
"Space Name Electrical Riser \n",
"Additional Location Info NaN \n",
"Description Data logger: Graduate College - House 10 \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 0501E3D07500 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 10.23.12.37 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3D-D1 \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP010-L01 \n",
"\n",
" AP011-L01 \\\n",
"BuildingNo. AP011 \n",
"Building House 11 - Ennerdale, Graduate College \n",
"Locations.Space.Space number A102 \n",
"Space Name Electrical Riser \n",
"Additional Location Info NaN \n",
"Description Data logger: Graduate College - House 11 \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 0501E3839500 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 10.23.12.38 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3D-A7 \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP011-L01 \n",
"\n",
" AP057-L01 \\\n",
"BuildingNo. AP057 \n",
"Building Alexandra Park Laundrette \n",
"Locations.Space.Space number A0 \n",
"Space Name Whole Building \n",
"Additional Location Info NaN \n",
"Description Data logger: Alexandra Park Laundrette \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 050157C16100 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 80.93.175.91:40455 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP057-L01 \n",
"\n",
" AP080-L01 \\\n",
"BuildingNo. AP080 \n",
"Building Lonsdale House (Block 12) \n",
"Locations.Space.Space number A13 \n",
"Space Name Laundrette \n",
"Additional Location Info NaN \n",
"Description Data logger: Lonsdale House - Laundrette \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 050157AB6700 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 10.23.9.40 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3D-1B \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP080-L01 \n",
"\n",
" AP081-L01 \\\n",
"BuildingNo. AP081 \n",
"Building Barker House Farm \n",
"Locations.Space.Space number A64 \n",
"Space Name Service Riser \n",
"Additional Location Info NaN \n",
"Description Data logger: Barker House Farm \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 050157C09600 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address 10.23.9.41 \n",
"Logger Serial Number NaN \n",
"Logger MAC Address 00-50-C2-2C-3D-17 \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code AP081-L01 \n",
"\n",
" ... \\\n",
"BuildingNo. ... \n",
"Building ... \n",
"Locations.Space.Space number ... \n",
"Space Name ... \n",
"Additional Location Info ... \n",
"Description ... \n",
"Classification Group ... \n",
"Record ... \n",
"HVAC Ref ... \n",
"Element Description ... \n",
"Servicable Area ... \n",
"Model ... \n",
"Make ... \n",
"EIS ID ... \n",
"Logger Channel ... \n",
"Logger Upstream Comms Target ... \n",
"Logger Modem Serial Number ... \n",
"Logger IP Address ... \n",
"Logger Serial Number ... \n",
"Logger MAC Address ... \n",
"Logger SIM ... \n",
"Meter Pulse Value ... \n",
"Meter Units ... \n",
"Meter Capacity ... \n",
"Network Point ID ... \n",
"Tenant Meter.Name ... \n",
"Fiscal Meter.Name ... \n",
"EIS Space.Space number ... \n",
"Utility Type.Name ... \n",
"Code ... \n",
"\n",
" MC204-L05 \\\n",
"BuildingNo. MC204 \n",
"Building Sports Centre \n",
"Locations.Space.Space number RF04 \n",
"Space Name Roof Top Plant Room \n",
"Additional Location Info NaN \n",
"Description Data logger: Sports Centre \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 0501F26DBC00 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC204-L05 \n",
"\n",
" MC204-L06 \\\n",
"BuildingNo. MC204 \n",
"Building Sports Centre \n",
"Locations.Space.Space number RF04 \n",
"Space Name Roof Top Plant Room \n",
"Additional Location Info NaN \n",
"Description Data logger: Sports Centre \n",
"Classification Group EN.EN1 Data Logger \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model Multilog G2 \n",
"Make Enercom \n",
"EIS ID 050200C3E800 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC204-L06 \n",
"\n",
" MC207-B01 \\\n",
"BuildingNo. MC207 \n",
"Building HR Building \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: HR Building \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xcite96 \n",
"Make Trend \n",
"EIS ID 3BF53C6F-96DB-4B9B-8B7C-ED6D6490F4E5 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC207-B01 \n",
"\n",
" MC210-B01 \\\n",
"BuildingNo. MC210 \n",
"Building Engineering Building \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Engineering Building \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xcite128 \n",
"Make Trend \n",
"EIS ID 0F850990-984A-49FC-B351-82CCFD6A644B \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC210-B01 \n",
"\n",
" MC210-B02 \\\n",
"BuildingNo. MC210 \n",
"Building Engineering Building \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Engineering Building \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xcite128 \n",
"Make Trend \n",
"EIS ID B39C7903-72F6-4B03-A5B6-6CE8CF57D680 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC210-B02 \n",
"\n",
" MC210-B03 \\\n",
"BuildingNo. MC210 \n",
"Building Engineering Building \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Engineering Building \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xcite128 \n",
"Make Trend \n",
"EIS ID 9DA6D329-30E9-46CC-A840-5B656A5FFDC2 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC210-B03 \n",
"\n",
" MC211-B01 \\\n",
"BuildingNo. MC211 \n",
"Building Life Sciences & Environment Laboratories \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Life Sciences & Environment La... \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xcite96 \n",
"Make Trend \n",
"EIS ID C587E3F2-E604-4C9B-827D-80797E53FC58 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code MC211-B01 \n",
"\n",
" OC004-B01 \\\n",
"BuildingNo. OC004 \n",
"Building Chancellor's Wharf, Wyre House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Chancellors Wharf \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xact12 \n",
"Make Trend \n",
"EIS ID 48352BB4-4B1B-4013-AB9F-14E143E83948 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC004-B01 \n",
"\n",
" OC005-B01 \\\n",
"BuildingNo. OC005 \n",
"Building Chancellor's Wharf, Lune House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Chancellors Wharf \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xact12 \n",
"Make Trend \n",
"EIS ID 281682DC-5479-4064-8290-E873933872B0 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC005-B01 \n",
"\n",
" OC006-B01 \n",
"BuildingNo. OC006 \n",
"Building Chancellor's Wharf, Kent House \n",
"Locations.Space.Space number NaN \n",
"Space Name NaN \n",
"Additional Location Info NaN \n",
"Description BMS Controller: Chancellors Wharf \n",
"Classification Group EN.EN4 BMS Controller \n",
"Record NaN \n",
"HVAC Ref NaN \n",
"Element Description NaN \n",
"Servicable Area NaN \n",
"Model IQ3xact12 \n",
"Make Trend \n",
"EIS ID BBD3685B-B0DC-417F-A0E8-20139B1074E1 \n",
"Logger Channel 0 \n",
"Logger Upstream Comms Target NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger IP Address NaN \n",
"Logger Serial Number NaN \n",
"Logger MAC Address NaN \n",
"Logger SIM NaN \n",
"Meter Pulse Value NaN \n",
"Meter Units NaN \n",
"Meter Capacity NaN \n",
"Network Point ID NaN \n",
"Tenant Meter.Name NaN \n",
"Fiscal Meter.Name NaN \n",
"EIS Space.Space number NaN \n",
"Utility Type.Name NaN \n",
"Code OC006-B01 \n",
"\n",
"[30 rows x 273 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_loggerscontrollers.T"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
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{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AP000-L01</th>\n",
" <th>AP000-L02</th>\n",
" <th>AP000-L03</th>\n",
" <th>AP001-L01</th>\n",
" <th>AP009-L01</th>\n",
" <th>AP010-L01</th>\n",
" <th>AP011-L01</th>\n",
" <th>AP057-L01</th>\n",
" <th>AP080-L01</th>\n",
" <th>AP081-L01</th>\n",
" <th>...</th>\n",
" <th>MC210-B01</th>\n",
" <th>MC210-B02</th>\n",
" <th>MC210-B03</th>\n",
" <th>MC210-L01</th>\n",
" <th>MC210-L02</th>\n",
" <th>MC211-B01</th>\n",
" <th>NAN</th>\n",
" <th>OC004-B01</th>\n",
" <th>OC005-B01</th>\n",
" <th>OC006-B01</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Building Code</th>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP000</td>\n",
" <td>AP001</td>\n",
" <td>AP009</td>\n",
" <td>AP010</td>\n",
" <td>AP011</td>\n",
" <td>AP057</td>\n",
" <td>AP080</td>\n",
" <td>AP081</td>\n",
" <td>...</td>\n",
" <td>MC210</td>\n",
" <td>MC210</td>\n",
" <td>MC210</td>\n",
" <td>MC210</td>\n",
" <td>MC210</td>\n",
" <td>MC211</td>\n",
" <td>MC202</td>\n",
" <td>OC004</td>\n",
" <td>OC005</td>\n",
" <td>OC006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Building Name</th>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Alexandra Park</td>\n",
" <td>House 01 - Bassenthwaite, Graduate College</td>\n",
" <td>House 09 - Devoke, Graduate College</td>\n",
" <td>House 10 - Elterwater, Graduate College</td>\n",
" <td>House 11 - Ennerdale, Graduate College</td>\n",
" <td>Alexandra Park Laundrette</td>\n",
" <td>Lonsdale House (Block 12)</td>\n",
" <td>Barker House Farm</td>\n",
" <td>...</td>\n",
" <td>Engineering Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Engineering Building</td>\n",
" <td>Life Sciences & Environment Laboratories</td>\n",
" <td>Charles Carter Building</td>\n",
" <td>Chancellors Wharf</td>\n",
" <td>Chancellors Wharf</td>\n",
" <td>Chancellors Wharf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Space</th>\n",
" <td>A0</td>\n",
" <td>A0</td>\n",
" <td>A0</td>\n",
" <td>A104</td>\n",
" <td>A124</td>\n",
" <td>A109</td>\n",
" <td>A102</td>\n",
" <td>A0</td>\n",
" <td>A13</td>\n",
" <td>A64</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Description</th>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Data logger: Graduate College - House 1</td>\n",
" <td>Data logger: Graduate College - House 9</td>\n",
" <td>Data logger: Graduate College - House 10</td>\n",
" <td>Data logger: Graduate College - House 11</td>\n",
" <td>Data logger: Alexandra Park Laundrette</td>\n",
" <td>Data logger: Lonsdale House - Laundrette</td>\n",
" <td>Data logger: Barker House Farm</td>\n",
" <td>...</td>\n",
" <td>BMS Controller: Engineering Building</td>\n",
" <td>BMS Controller: Engineering Building</td>\n",
" <td>BMS Controller: Engineering Building</td>\n",
" <td>Data logger: Engineering</td>\n",
" <td>Data logger: Engineering</td>\n",
" <td>BMS Controller: Life Sciences & Environment La...</td>\n",
" <td>Data logger: Charles Carter Building</td>\n",
" <td>BMS Controller: Chancellors Wharf</td>\n",
" <td>BMS Controller: Chancellors Wharf</td>\n",
" <td>BMS Controller: Chancellors Wharf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Classification Group</th>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>...</td>\n",
" <td>BMS controller</td>\n",
" <td>BMS controller</td>\n",
" <td>BMS controller</td>\n",
" <td>Data logger</td>\n",
" <td>Data logger</td>\n",
" <td>BMS controller</td>\n",
" <td>Data logger</td>\n",
" <td>BMS controller</td>\n",
" <td>BMS controller</td>\n",
" <td>BMS controller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Make</th>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>Enercom</td>\n",
" <td>...</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>SIP</td>\n",
" <td>SIP</td>\n",
" <td>Trend</td>\n",
" <td>Synetica</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" <td>Trend</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Model</th>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>Multilog G2</td>\n",
" <td>...</td>\n",
" <td>IQ3xcite128</td>\n",
" <td>IQ3xcite128</td>\n",
" <td>IQ3xcite128</td>\n",
" <td>SIP MLog</td>\n",
" <td>SIP MLog</td>\n",
" <td>IQ3xcite96</td>\n",
" <td>DS800-M-32</td>\n",
" <td>IQ3xact12</td>\n",
" <td>IQ3xact12</td>\n",
" <td>IQ3xact12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Serial Number</th>\n",
" <td>050157C7ED00</td>\n",
" <td>37475126</td>\n",
" <td>48015355</td>\n",
" <td>0501E3E97100</td>\n",
" <td>0501E38A6300</td>\n",
" <td>0501E3D07500</td>\n",
" <td>0501E3839500</td>\n",
" <td>050157C16100</td>\n",
" <td>050157AB6700</td>\n",
" <td>050157C09600</td>\n",
" <td>...</td>\n",
" <td>{0F850990-984A-49FC-B351-82CCFD6A644B}</td>\n",
" <td>{B39C7903-72F6-4B03-A5B6-6CE8CF57D680}</td>\n",
" <td>{9DA6D329-30E9-46CC-A840-5B656A5FFDC2}</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>{C587E3F2-E604-4C9B-827D-80797E53FC58}</td>\n",
" <td>47e22ca9-a8f8-4781-963d-31382df0d5c6</td>\n",
" <td>{48352BB4-4B1B-4013-AB9F-14E143E83948}</td>\n",
" <td>{281682DC-5479-4064-8290-E873933872B0}</td>\n",
" <td>{BBD3685B-B0DC-417F-A0E8-20139B1074E1}</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Mac Address</th>\n",
" <td>00-50-C2-2C-3E-43</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>00-50-C2-2C-3D-DE</td>\n",
" <td>00-50-C2-2C-3D-CB</td>\n",
" <td>00-50-C2-2C-3D-D1</td>\n",
" <td>00-50-C2-2C-3D-A7</td>\n",
" <td>NaN</td>\n",
" <td>00-50-C2-2C-3D-1B</td>\n",
" <td>00-50-C2-2C-3D-17</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Ip Address</th>\n",
" <td>10.23.9.39</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>10.23.12.36</td>\n",
" <td>NaN</td>\n",
" <td>10.23.12.37</td>\n",
" <td>10.23.12.38</td>\n",
" <td>80.93.175.91:40455</td>\n",
" <td>10.23.9.40</td>\n",
" <td>10.23.9.41</td>\n",
" <td>...</td>\n",
" <td>ng-bms01-e01.bms.local</td>\n",
" <td>ng-bms02-e01.bms.local</td>\n",
" <td>ng-bms03-e01.bms.local</td>\n",
" <td>10.23.16.51</td>\n",
" <td>10.23.16.52</td>\n",
" <td>bs-bms01-bslplant.bms.local</td>\n",
" <td>cc-meter01-plant.bms.local</td>\n",
" <td>cw-meter03-plant.bms.local</td>\n",
" <td>cw-meter02-plant.bms.local</td>\n",
" <td>cw-meter01-plant.bms.local</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Modem Serial Number</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Sim</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Network Point Id</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logger Upstream Comms Target</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Additional Location Info</th>\n",
" <td>Graduatae 1-11 Plant Room</td>\n",
" <td>External Gas Meter Building</td>\n",
" <td>External Water Meter Chamber</td>\n",
" <td>A017</td>\n",
" <td>Devoke House, Room A124</td>\n",
" <td>Elderwater House, Room A109</td>\n",
" <td>Ennerdale House - Room A102</td>\n",
" <td>Cartmel Laundrette</td>\n",
" <td>Plant Room MCC</td>\n",
" <td>Plant Room</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Code</th>\n",
" <td>AP000-L01</td>\n",
" <td>AP000-L02</td>\n",
" <td>AP000-L03</td>\n",
" <td>AP001-L01</td>\n",
" <td>AP009-L01</td>\n",
" <td>AP010-L01</td>\n",
" <td>AP011-L01</td>\n",
" <td>AP057-L01</td>\n",
" <td>AP080-L01</td>\n",
" <td>AP081-L01</td>\n",
" <td>...</td>\n",
" <td>MC210-B01</td>\n",
" <td>MC210-B02</td>\n",
" <td>MC210-B03</td>\n",
" <td>MC210-L01</td>\n",
" <td>MC210-L02</td>\n",
" <td>MC211-B01</td>\n",
" <td>NaN</td>\n",
" <td>OC004-B01</td>\n",
" <td>OC005-B01</td>\n",
" <td>OC006-B01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>16 rows × 295 columns</p>\n",
"</div>"
],
"text/plain": [
" AP000-L01 \\\n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space A0 \n",
"Description Data logger: Alexandra Park \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 050157C7ED00 \n",
"Logger Mac Address 00-50-C2-2C-3E-43 \n",
"Logger Ip Address 10.23.9.39 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Graduatae 1-11 Plant Room \n",
"Code AP000-L01 \n",
"\n",
" AP000-L02 \\\n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space A0 \n",
"Description Data logger: Alexandra Park \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 37475126 \n",
"Logger Mac Address NaN \n",
"Logger Ip Address NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info External Gas Meter Building \n",
"Code AP000-L02 \n",
"\n",
" AP000-L03 \\\n",
"Building Code AP000 \n",
"Building Name Alexandra Park \n",
"Space A0 \n",
"Description Data logger: Alexandra Park \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 48015355 \n",
"Logger Mac Address NaN \n",
"Logger Ip Address NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info External Water Meter Chamber \n",
"Code AP000-L03 \n",
"\n",
" AP001-L01 \\\n",
"Building Code AP001 \n",
"Building Name House 01 - Bassenthwaite, Graduate College \n",
"Space A104 \n",
"Description Data logger: Graduate College - House 1 \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 0501E3E97100 \n",
"Logger Mac Address 00-50-C2-2C-3D-DE \n",
"Logger Ip Address 10.23.12.36 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info A017 \n",
"Code AP001-L01 \n",
"\n",
" AP009-L01 \\\n",
"Building Code AP009 \n",
"Building Name House 09 - Devoke, Graduate College \n",
"Space A124 \n",
"Description Data logger: Graduate College - House 9 \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 0501E38A6300 \n",
"Logger Mac Address 00-50-C2-2C-3D-CB \n",
"Logger Ip Address NaN \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Devoke House, Room A124 \n",
"Code AP009-L01 \n",
"\n",
" AP010-L01 \\\n",
"Building Code AP010 \n",
"Building Name House 10 - Elterwater, Graduate College \n",
"Space A109 \n",
"Description Data logger: Graduate College - House 10 \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 0501E3D07500 \n",
"Logger Mac Address 00-50-C2-2C-3D-D1 \n",
"Logger Ip Address 10.23.12.37 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Elderwater House, Room A109 \n",
"Code AP010-L01 \n",
"\n",
" AP011-L01 \\\n",
"Building Code AP011 \n",
"Building Name House 11 - Ennerdale, Graduate College \n",
"Space A102 \n",
"Description Data logger: Graduate College - House 11 \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 0501E3839500 \n",
"Logger Mac Address 00-50-C2-2C-3D-A7 \n",
"Logger Ip Address 10.23.12.38 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Ennerdale House - Room A102 \n",
"Code AP011-L01 \n",
"\n",
" AP057-L01 \\\n",
"Building Code AP057 \n",
"Building Name Alexandra Park Laundrette \n",
"Space A0 \n",
"Description Data logger: Alexandra Park Laundrette \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 050157C16100 \n",
"Logger Mac Address NaN \n",
"Logger Ip Address 80.93.175.91:40455 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Cartmel Laundrette \n",
"Code AP057-L01 \n",
"\n",
" AP080-L01 \\\n",
"Building Code AP080 \n",
"Building Name Lonsdale House (Block 12) \n",
"Space A13 \n",
"Description Data logger: Lonsdale House - Laundrette \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 050157AB6700 \n",
"Logger Mac Address 00-50-C2-2C-3D-1B \n",
"Logger Ip Address 10.23.9.40 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Plant Room MCC \n",
"Code AP080-L01 \n",
"\n",
" AP081-L01 \\\n",
"Building Code AP081 \n",
"Building Name Barker House Farm \n",
"Space A64 \n",
"Description Data logger: Barker House Farm \n",
"Classification Group Data logger \n",
"Make Enercom \n",
"Model Multilog G2 \n",
"Logger Serial Number 050157C09600 \n",
"Logger Mac Address 00-50-C2-2C-3D-17 \n",
"Logger Ip Address 10.23.9.41 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info Plant Room \n",
"Code AP081-L01 \n",
"\n",
" ... \\\n",
"Building Code ... \n",
"Building Name ... \n",
"Space ... \n",
"Description ... \n",
"Classification Group ... \n",
"Make ... \n",
"Model ... \n",
"Logger Serial Number ... \n",
"Logger Mac Address ... \n",
"Logger Ip Address ... \n",
"Logger Modem Serial Number ... \n",
"Logger Sim ... \n",
"Network Point Id ... \n",
"Logger Upstream Comms Target ... \n",
"Additional Location Info ... \n",
"Code ... \n",
"\n",
" MC210-B01 \\\n",
"Building Code MC210 \n",
"Building Name Engineering Building \n",
"Space NaN \n",
"Description BMS Controller: Engineering Building \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xcite128 \n",
"Logger Serial Number {0F850990-984A-49FC-B351-82CCFD6A644B} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address ng-bms01-e01.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code MC210-B01 \n",
"\n",
" MC210-B02 \\\n",
"Building Code MC210 \n",
"Building Name Engineering Building \n",
"Space NaN \n",
"Description BMS Controller: Engineering Building \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xcite128 \n",
"Logger Serial Number {B39C7903-72F6-4B03-A5B6-6CE8CF57D680} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address ng-bms02-e01.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code MC210-B02 \n",
"\n",
" MC210-B03 \\\n",
"Building Code MC210 \n",
"Building Name Engineering Building \n",
"Space NaN \n",
"Description BMS Controller: Engineering Building \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xcite128 \n",
"Logger Serial Number {9DA6D329-30E9-46CC-A840-5B656A5FFDC2} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address ng-bms03-e01.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code MC210-B03 \n",
"\n",
" MC210-L01 \\\n",
"Building Code MC210 \n",
"Building Name Engineering Building \n",
"Space NaN \n",
"Description Data logger: Engineering \n",
"Classification Group Data logger \n",
"Make SIP \n",
"Model SIP MLog \n",
"Logger Serial Number NaN \n",
"Logger Mac Address NaN \n",
"Logger Ip Address 10.23.16.51 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code MC210-L01 \n",
"\n",
" MC210-L02 \\\n",
"Building Code MC210 \n",
"Building Name Engineering Building \n",
"Space NaN \n",
"Description Data logger: Engineering \n",
"Classification Group Data logger \n",
"Make SIP \n",
"Model SIP MLog \n",
"Logger Serial Number NaN \n",
"Logger Mac Address NaN \n",
"Logger Ip Address 10.23.16.52 \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code MC210-L02 \n",
"\n",
" MC211-B01 \\\n",
"Building Code MC211 \n",
"Building Name Life Sciences & Environment Laboratories \n",
"Space NaN \n",
"Description BMS Controller: Life Sciences & Environment La... \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xcite96 \n",
"Logger Serial Number {C587E3F2-E604-4C9B-827D-80797E53FC58} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address bs-bms01-bslplant.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code MC211-B01 \n",
"\n",
" NAN \\\n",
"Building Code MC202 \n",
"Building Name Charles Carter Building \n",
"Space NaN \n",
"Description Data logger: Charles Carter Building \n",
"Classification Group Data logger \n",
"Make Synetica \n",
"Model DS800-M-32 \n",
"Logger Serial Number 47e22ca9-a8f8-4781-963d-31382df0d5c6 \n",
"Logger Mac Address NaN \n",
"Logger Ip Address cc-meter01-plant.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code NaN \n",
"\n",
" OC004-B01 \\\n",
"Building Code OC004 \n",
"Building Name Chancellors Wharf \n",
"Space NaN \n",
"Description BMS Controller: Chancellors Wharf \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xact12 \n",
"Logger Serial Number {48352BB4-4B1B-4013-AB9F-14E143E83948} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address cw-meter03-plant.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code OC004-B01 \n",
"\n",
" OC005-B01 \\\n",
"Building Code OC005 \n",
"Building Name Chancellors Wharf \n",
"Space NaN \n",
"Description BMS Controller: Chancellors Wharf \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xact12 \n",
"Logger Serial Number {281682DC-5479-4064-8290-E873933872B0} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address cw-meter02-plant.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code OC005-B01 \n",
"\n",
" OC006-B01 \n",
"Building Code OC006 \n",
"Building Name Chancellors Wharf \n",
"Space NaN \n",
"Description BMS Controller: Chancellors Wharf \n",
"Classification Group BMS controller \n",
"Make Trend \n",
"Model IQ3xact12 \n",
"Logger Serial Number {BBD3685B-B0DC-417F-A0E8-20139B1074E1} \n",
"Logger Mac Address NaN \n",
"Logger Ip Address cw-meter01-plant.bms.local \n",
"Logger Modem Serial Number NaN \n",
"Logger Sim NaN \n",
"Network Point Id NaN \n",
"Logger Upstream Comms Target NaN \n",
"Additional Location Info NaN \n",
"Code OC006-B01 \n",
"\n",
"[16 rows x 295 columns]"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create dictionary that maps Planon column names onto Master. \n",
" \n",
"From Nicola: \n",
"- EIS ID (Serial Number)\n",
"- Make\n",
"- Model\n",
"- Description\n",
"- Code (Asset Code)\n",
"- Building Code\n",
"\n",
"`Building code` and `Building name` are implicitly included. `Logger IP` or `MAC` would be essential to include, as well as `Make` and `Model`. `Additional Location Info` is not essnetial but would be useful to have. Locations (`Locations.Space.Space number` and `Space Name`) are included in the Planon export - but this is their only viable data source, therefore are not validated against."
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Planon:Master\n",
"loggers_match_dict={\n",
" \"BuildingNo.\":\"Building Code\",\n",
" \"Building\":\"Building Name\",\n",
" \"Description\":\"Description\",\n",
" \"EIS ID\":\"Logger Serial Number\",\n",
" \"Make\":\"Make\",\n",
" \"Model\":\"Model\",\n",
" \"Code\":\"Code\"\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"master_loggerscontrollers_for_validation_filtered=master_loggerscontrollers_for_validation[list(loggers_match_dict.values())]\n",
"planon_loggerscontrollers_filtered=planon_loggerscontrollers[list(loggers_match_dict.keys())]"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>AP000-L01</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157C7ED00</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>AP000-L02</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>37475126</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building Name \\\n",
"AP000-L01 AP000-L01 Data logger: Alexandra Park Enercom Alexandra Park \n",
"AP000-L02 AP000-L02 Data logger: Alexandra Park Enercom Alexandra Park \n",
"\n",
" Model Logger Serial Number Building Code \n",
"AP000-L01 Multilog G2 050157C7ED00 AP000 \n",
"AP000-L02 Multilog G2 37475126 AP000 "
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation_filtered.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building</th>\n",
" <th>Model</th>\n",
" <th>EIS ID</th>\n",
" <th>BuildingNo.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>AP000-L01</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157C7ED00</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>AP000-L02</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>37475126</td>\n",
" <td>AP000</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building \\\n",
"AP000-L01 AP000-L01 Data logger: Alexandra Park Enercom Alexandra Park \n",
"AP000-L02 AP000-L02 Data logger: Alexandra Park Enercom Alexandra Park \n",
"\n",
" Model EIS ID BuildingNo. \n",
"AP000-L01 Multilog G2 050157C7ED00 AP000 \n",
"AP000-L02 Multilog G2 37475126 AP000 "
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_loggerscontrollers_filtered.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"planon_loggerscontrollers_filtered.columns=[loggers_match_dict[i] for i in planon_loggerscontrollers_filtered]"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Anaconda2\\envs\\python3\\lib\\site-packages\\pandas\\util\\decorators.py:91: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" return func(*args, **kwargs)\n"
]
}
],
"source": [
"planon_loggerscontrollers_filtered.drop_duplicates(inplace=True)\n",
"master_loggerscontrollers_for_validation_filtered.drop_duplicates(inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>AP000-L01</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157C7ED00</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>AP000-L02</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>37475126</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building Name \\\n",
"AP000-L01 AP000-L01 Data logger: Alexandra Park Enercom Alexandra Park \n",
"AP000-L02 AP000-L02 Data logger: Alexandra Park Enercom Alexandra Park \n",
"\n",
" Model Logger Serial Number Building Code \n",
"AP000-L01 Multilog G2 050157C7ED00 AP000 \n",
"AP000-L02 Multilog G2 37475126 AP000 "
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_loggerscontrollers_filtered.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>AP000-L01</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157C7ED00</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>AP000-L02</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>37475126</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building Name \\\n",
"AP000-L01 AP000-L01 Data logger: Alexandra Park Enercom Alexandra Park \n",
"AP000-L02 AP000-L02 Data logger: Alexandra Park Enercom Alexandra Park \n",
"\n",
" Model Logger Serial Number Building Code \n",
"AP000-L01 Multilog G2 050157C7ED00 AP000 \n",
"AP000-L02 Multilog G2 37475126 AP000 "
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation_filtered.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MC032-L01, MC032-L03, MC044-L02, MC044-L03, MC044-L04, MC044-L05, MC046-L01, MC046-L03, MC046-L04, MC046-L18, MC046-L19, MC061-L01, MC076-B03, MC204-L03, MC204-L04, MC204-L07, MC204-L08, MC204-L09, MC207-L01, MC210-L01, MC210-L02, NAN, \n",
"\n",
"Loggers in Master, but not in Planon: 22 / 295 : 7.458 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_loggerscontrollers_filtered.index)))\n",
"b=np.sort(list(set(master_loggerscontrollers_for_validation_filtered.index)))\n",
"loggerscontrollers_not_in_planon=[]\n",
"for i in b:\n",
" if i not in a:\n",
" print(i+',',end=\" \"),\n",
" loggerscontrollers_not_in_planon.append(i)\n",
"print('\\n\\nLoggers in Master, but not in Planon:',\n",
" len(loggerscontrollers_not_in_planon),'/',len(b),':',\n",
" round(len(loggerscontrollers_not_in_planon)/len(b)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Loggers in Planon, not in Master: 0 / 273 : 0.0 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_loggerscontrollers_filtered.index)))\n",
"b=np.sort(list(set(master_loggerscontrollers_for_validation_filtered.index)))\n",
"loggerscontrollers_not_in_master=[]\n",
"for i in a:\n",
" if i not in b:\n",
" print(i+',',end=\" \"),\n",
" loggerscontrollers_not_in_master.append(i)\n",
"print('\\n\\nLoggers in Planon, not in Master:',\n",
" len(loggerscontrollers_not_in_master),'/',len(a),':',\n",
" round(len(loggerscontrollers_not_in_master)/len(a)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"273\n",
"273\n",
"295\n",
"295\n"
]
}
],
"source": [
"print(len(planon_loggerscontrollers_filtered.index))\n",
"print(len(set(planon_loggerscontrollers_filtered.index)))\n",
"print(len(master_loggerscontrollers_for_validation_filtered.index))\n",
"print(len(set(master_loggerscontrollers_for_validation_filtered.index)))"
]
},
{
"cell_type": "code",
"execution_count": 89,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [Code, Description, Make, Building Name, Model, Logger Serial Number, Building Code]\n",
"Index: []"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation_filtered[master_loggerscontrollers_for_validation_filtered.index.duplicated()]"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comon_index=list(set(master_loggerscontrollers_for_validation_filtered.index).intersection(set(planon_loggerscontrollers_filtered.index)))"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"master_loggerscontrollers_for_validation_intersected=master_loggerscontrollers_for_validation_filtered.loc[comon_index].sort_index()\n",
"planon_loggerscontrollers_intersected=planon_loggerscontrollers_filtered.loc[comon_index].sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 92,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>AP000-L01</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157C7ED00</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>AP000-L02</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>37475126</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building Name \\\n",
"AP000-L01 AP000-L01 Data logger: Alexandra Park Enercom Alexandra Park \n",
"AP000-L02 AP000-L02 Data logger: Alexandra Park Enercom Alexandra Park \n",
"\n",
" Model Logger Serial Number Building Code \n",
"AP000-L01 Multilog G2 050157C7ED00 AP000 \n",
"AP000-L02 Multilog G2 37475126 AP000 "
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"master_loggerscontrollers_for_validation_intersected.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>AP000-L01</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>050157C7ED00</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>AP000-L02</td>\n",
" <td>Data logger: Alexandra Park</td>\n",
" <td>Enercom</td>\n",
" <td>Alexandra Park</td>\n",
" <td>Multilog G2</td>\n",
" <td>37475126</td>\n",
" <td>AP000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building Name \\\n",
"AP000-L01 AP000-L01 Data logger: Alexandra Park Enercom Alexandra Park \n",
"AP000-L02 AP000-L02 Data logger: Alexandra Park Enercom Alexandra Park \n",
"\n",
" Model Logger Serial Number Building Code \n",
"AP000-L01 Multilog G2 050157C7ED00 AP000 \n",
"AP000-L02 Multilog G2 37475126 AP000 "
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_loggerscontrollers_intersected.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"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>Code</th>\n",
" <th>Description</th>\n",
" <th>Make</th>\n",
" <th>Building Name</th>\n",
" <th>Model</th>\n",
" <th>Logger Serial Number</th>\n",
" <th>Building Code</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AP000-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP000-L03</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP001-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP009-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP010-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP011-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP057-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP080-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AP081-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EX001-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC000-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC001-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC001-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC001-L02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC003-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC003-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC003-L02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC007-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC008-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC010-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC011-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC011-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC013-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B03</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B04</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B05</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC014-B06</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</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>MC202-B10</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B11</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B12</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B13</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B14</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B15</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B16</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-B17</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-L02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC202-L03</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC203-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-B02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-B03</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-B04</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-B05</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-B06</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-L01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-L02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-L05</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC204-L06</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC207-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC210-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC210-B02</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC210-B03</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MC211-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC004-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC005-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OC006-B01</th>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>273 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Code Description Make Building Name Model Logger Serial Number \\\n",
"AP000-L01 True True True True True True \n",
"AP000-L02 True True True True True False \n",
"AP000-L03 True True True True True False \n",
"AP001-L01 True True True True True True \n",
"AP009-L01 True True True True True True \n",
"AP010-L01 True True True True True True \n",
"AP011-L01 True True True True True True \n",
"AP057-L01 True True True True True True \n",
"AP080-L01 True True True True True True \n",
"AP081-L01 True True True True True True \n",
"EX001-B01 True True True False True False \n",
"MC000-L01 True True True True True True \n",
"MC001-B01 True True True True True False \n",
"MC001-L01 True True True True True True \n",
"MC001-L02 True True True True True True \n",
"MC003-B01 True True True True True False \n",
"MC003-L01 True True True True True True \n",
"MC003-L02 True True True True True True \n",
"MC007-L01 True True True True True True \n",
"MC008-L01 True True True True True True \n",
"MC010-L01 True True True True True True \n",
"MC011-B01 True True True True True False \n",
"MC011-L01 True True True True True True \n",
"MC013-L01 True True True True True True \n",
"MC014-B01 True True True True True False \n",
"MC014-B02 True True True True True False \n",
"MC014-B03 True True True True True False \n",
"MC014-B04 True True True True True False \n",
"MC014-B05 True True True True True False \n",
"MC014-B06 True True True True True False \n",
"... ... ... ... ... ... ... \n",
"MC202-B10 True True True True True False \n",
"MC202-B11 True True True True True False \n",
"MC202-B12 True True True True True False \n",
"MC202-B13 True True True True True False \n",
"MC202-B14 True True True True True False \n",
"MC202-B15 True True True True True False \n",
"MC202-B16 True True True True True False \n",
"MC202-B17 True True True True True False \n",
"MC202-L01 True True True True True True \n",
"MC202-L02 True True True True True True \n",
"MC202-L03 True True True True True True \n",
"MC203-L01 True True True True True True \n",
"MC204-B01 True True True True True False \n",
"MC204-B02 True True True True True False \n",
"MC204-B03 True True True True True False \n",
"MC204-B04 True True True True True False \n",
"MC204-B05 True True True True True False \n",
"MC204-B06 True True True True True False \n",
"MC204-L01 True True True True True True \n",
"MC204-L02 True True True True True True \n",
"MC204-L05 True True True True True True \n",
"MC204-L06 True True True True True True \n",
"MC207-B01 True True True True True False \n",
"MC210-B01 True True True True True False \n",
"MC210-B02 True True True True True False \n",
"MC210-B03 True True True True True False \n",
"MC211-B01 True True True True True False \n",
"OC004-B01 True True True False True False \n",
"OC005-B01 True True True False True False \n",
"OC006-B01 True True True False True False \n",
"\n",
" Building Code \n",
"AP000-L01 True \n",
"AP000-L02 True \n",
"AP000-L03 True \n",
"AP001-L01 True \n",
"AP009-L01 True \n",
"AP010-L01 True \n",
"AP011-L01 True \n",
"AP057-L01 True \n",
"AP080-L01 True \n",
"AP081-L01 True \n",
"EX001-B01 True \n",
"MC000-L01 True \n",
"MC001-B01 True \n",
"MC001-L01 True \n",
"MC001-L02 True \n",
"MC003-B01 True \n",
"MC003-L01 True \n",
"MC003-L02 True \n",
"MC007-L01 True \n",
"MC008-L01 True \n",
"MC010-L01 True \n",
"MC011-B01 True \n",
"MC011-L01 True \n",
"MC013-L01 True \n",
"MC014-B01 True \n",
"MC014-B02 True \n",
"MC014-B03 True \n",
"MC014-B04 True \n",
"MC014-B05 True \n",
"MC014-B06 True \n",
"... ... \n",
"MC202-B10 True \n",
"MC202-B11 True \n",
"MC202-B12 True \n",
"MC202-B13 True \n",
"MC202-B14 True \n",
"MC202-B15 True \n",
"MC202-B16 True \n",
"MC202-B17 True \n",
"MC202-L01 True \n",
"MC202-L02 True \n",
"MC202-L03 True \n",
"MC203-L01 True \n",
"MC204-B01 True \n",
"MC204-B02 True \n",
"MC204-B03 True \n",
"MC204-B04 True \n",
"MC204-B05 True \n",
"MC204-B06 True \n",
"MC204-L01 True \n",
"MC204-L02 True \n",
"MC204-L05 True \n",
"MC204-L06 True \n",
"MC207-B01 True \n",
"MC210-B01 True \n",
"MC210-B02 True \n",
"MC210-B03 True \n",
"MC211-B01 True \n",
"OC004-B01 True \n",
"OC005-B01 True \n",
"OC006-B01 True \n",
"\n",
"[273 rows x 7 columns]"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"planon_loggerscontrollers_intersected==master_loggerscontrollers_for_validation_intersected"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loggers matching"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Code 273\n",
"Description 273\n",
"Make 273\n",
"Building Name 255\n",
"Model 273\n",
"Logger Serial Number 140\n",
"Building Code 273\n",
"dtype: int64"
]
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(planon_loggerscontrollers_intersected==master_loggerscontrollers_for_validation_intersected).sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Percentage matching"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Code 100.000000\n",
"Description 100.000000\n",
"Make 100.000000\n",
"Building Name 93.406593\n",
"Model 100.000000\n",
"Logger Serial Number 51.282051\n",
"Building Code 100.000000\n",
"dtype: float64"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(planon_loggerscontrollers_intersected==master_loggerscontrollers_for_validation_intersected).sum()/\\\n",
"len(planon_loggerscontrollers_intersected)*100"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1edac522748>"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1edabfd4898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"((planon_loggerscontrollers_intersected==master_loggerscontrollers_for_validation_intersected).sum()/\\\n",
"len(planon_loggerscontrollers_intersected)*100).plot(kind='bar')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loggers not matching on `Building Name`."
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"18"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_loggerscontrollers_intersected['Building Name']!=master_loggerscontrollers_for_validation_intersected['Building Name'])"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"planon_loggerscontrollers_intersected['Building Name']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ') for s in planon_loggerscontrollers_intersected['Building Name'].values]\n",
"master_loggerscontrollers_for_validation_intersected['Building Name']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ') for s in master_loggerscontrollers_for_validation_intersected['Building Name'].values]"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"16"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_loggerscontrollers_intersected['Building Name']!=master_loggerscontrollers_for_validation_intersected['Building Name'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That didnt help."
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EX001-B01 \t\tPlanon: roads - main campus \t\tMaster: underpass\n",
"MC029-B01 \t\tPlanon: cetad \t\tMaster: bowland hall cetad\n",
"MC033-L03 \t\tPlanon: county john creed \t\tMaster: john creed\n",
"MC047-B02 \t\tPlanon: welcome centre \t\tMaster: conference centre\n",
"MC047-L01 \t\tPlanon: welcome centre \t\tMaster: conference centre\n",
"MC047-L02 \t\tPlanon: welcome centre \t\tMaster: conference centre\n",
"MC055-B01 \t\tPlanon: furness residences \t\tMaster: furness blocks\n",
"MC071-B01 \t\tPlanon: furness college \t\tMaster: furness\n",
"MC072-B02 \t\tPlanon: psc \t\tMaster: psc building\n",
"MC072-B04 \t\tPlanon: psc \t\tMaster: psc building\n",
"MC103-B01 \t\tPlanon: lancaster house hotel \t\tMaster: hotel\n",
"MC198-B01 \t\tPlanon: grizedale college - offices, bar & social space \t\tMaster: grizedale\n",
"MC198-B02 \t\tPlanon: grizedale college - offices, bar & social space \t\tMaster: grizedale\n",
"OC004-B01 \t\tPlanon: chancellor's wharf, wyre house \t\tMaster: chancellors wharf\n",
"OC005-B01 \t\tPlanon: chancellor's wharf, lune house \t\tMaster: chancellors wharf\n",
"OC006-B01 \t\tPlanon: chancellor's wharf, kent house \t\tMaster: chancellors wharf\n"
]
}
],
"source": [
"for i in planon_loggerscontrollers_intersected[planon_loggerscontrollers_intersected['Building Name']!=master_loggerscontrollers_for_validation_intersected['Building Name']].index:\n",
" print(i,'\\t\\tPlanon:',planon_loggerscontrollers_intersected.loc[i]['Building Name'],'\\t\\tMaster:',master_loggerscontrollers_for_validation_intersected.loc[i]['Building Name'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Follow up with lexical distance comparison. That would flag this as a match."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loggers not matching on `Serial Number`."
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"133"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_loggerscontrollers_intersected['Logger Serial Number']!=master_loggerscontrollers_for_validation_intersected['Logger Serial Number'])"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"planon_loggerscontrollers_intersected['Logger Serial Number']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ').replace('{','').replace('}','') for s in planon_loggerscontrollers_intersected['Logger Serial Number'].values]\n",
"master_loggerscontrollers_for_validation_intersected['Logger Serial Number']=[str(s).lower().strip().replace(' ',' ').replace(' ',' ').replace('{','').replace('}','') for s in master_loggerscontrollers_for_validation_intersected['Logger Serial Number'].values]"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(planon_loggerscontrollers_intersected['Logger Serial Number']!=master_loggerscontrollers_for_validation_intersected['Logger Serial Number'])"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MC032-L04 \t\tPlanon: 50198367 \t\tMaster: 050198367e00\n",
"MC046-L05 \t\tPlanon: 50198895300 \t\tMaster: 050198895300\n",
"MC063-L01 \t\tPlanon: 50198829500 \t\tMaster: 050198829500\n",
"MC064-L03 \t\tPlanon: 50198872600 \t\tMaster: 050198872600\n",
"MC071-L02 \t\tPlanon: 50201286300 \t\tMaster: 050201286300\n",
"MC071-L05 \t\tPlanon: 50201221 \t\tMaster: 050201221e00\n",
"MC071-L16 \t\tPlanon: 50198904000 \t\tMaster: 050198904000\n",
"MC078-L03 \t\tPlanon: 50198864300 \t\tMaster: 050198864300\n",
"MC102-L01 \t\tPlanon: 50157909800 \t\tMaster: 050157909800\n"
]
}
],
"source": [
"for i in planon_loggerscontrollers_intersected[planon_loggerscontrollers_intersected['Logger Serial Number']!=master_loggerscontrollers_for_validation_intersected['Logger Serial Number']].index:\n",
" print(i,'\\t\\tPlanon:',planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number'],'\\t\\tMaster:',master_loggerscontrollers_for_validation_intersected.loc[i]['Logger Serial Number'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Technically the same, but there is a number format error. Compare based on float value, if they match, replace one of them. This needs to be amended, as it will throw `cannot onvert to float` exception if strings are left in from the previous step."
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"z1=[]\n",
"z2=[] \n",
"for i in planon_loggerscontrollers_intersected.index:\n",
" if planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number']!=master_loggerscontrollers_for_validation_intersected.loc[i]['Logger Serial Number']:\n",
" if float(planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number'])==\\\n",
" float(master_loggerscontrollers_for_validation_intersected.loc[i]['Logger Serial Number']):\n",
" z1.append(str(int(float(planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number']))))\n",
" z2.append(str(int(float(planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number']))))\n",
" else:\n",
" z1.append(planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number'])\n",
" z2.append(master_loggerscontrollers_for_validation_intersected.loc[i]['Logger Serial Number'])\n",
" else:\n",
" z1.append(planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number'])\n",
" z2.append(planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number'])\n",
"planon_loggerscontrollers_intersected['Logger Serial Number']=z1\n",
"master_loggerscontrollers_for_validation_intersected['Logger Serial Number']=z2"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for i in planon_loggerscontrollers_intersected[planon_loggerscontrollers_intersected['Logger Serial Number']!=master_loggerscontrollers_for_validation_intersected['Logger Serial Number']].index:\n",
" print(i,'\\t\\tPlanon:',planon_loggerscontrollers_intersected.loc[i]['Logger Serial Number'],'\\t\\tMaster:',master_loggerscontrollers_for_validation_intersected.loc[i]['Logger Serial Number'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"New error percentage:"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Code 0.000000\n",
"Description 0.000000\n",
"Make 0.000000\n",
"Building Name 5.860806\n",
"Model 0.000000\n",
"Logger Serial Number 0.000000\n",
"Building Code 0.000000\n",
"dtype: float64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(planon_loggerscontrollers_intersected!=master_loggerscontrollers_for_validation_intersected).sum()/\\\n",
"len(planon_loggerscontrollers_intersected)*100"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Bearing in my mind the above, this is technically 0)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"AP000-L99/M303, AP000-L99/M308, MC000-L99/M201, MC000-L99/M202, MC000-L99/M203, MC000-L99/M506, MC001-L99/M100, MC001-L99/M222, MC001-L99/M224, MC001-L99/M306, MC001-L99/M508, MC003-L99/M207, MC003-L99/M304, MC003-L99/M509, MC007-L99/M211, MC007-L99/M511, MC008-L99/M302, MC047-L02/M00??, MC061-L99/M305, MC061-L99/M310, MC102-L99/M309, MC103-L99/M216, MC103-L99/M300, MC103-L99/M504, MC204-L99/M312, MC210-L01/M001, MC210-L01/M002, MC210-L01/M003, MC210-L01/M004, MC210-L01/M005, MC210-L01/M006, MC210-L01/M007, MC210-L01/M008, MC210-L01/M009, MC210-L01/M010, MC210-L01/M011, MC210-L01/M012, MC210-L01/M013, MC210-L01/M014, MC210-L01/M015, MC210-L01/M016, MC210-L01/M017, MC210-L01/M018, MC210-L01/M019, MC210-L01/M020, MC210-L01/M021, MC210-L01/M022, MC210-L01/M023, MC210-L01/M024, MC210-L01/M025, MC210-L01/M026, MC210-L01/M027, MC210-L01/M028, MC210-L01/M029, MC210-L01/M030, MC210-L02/M001, MC210-L02/M002, MC210-L02/M003, MC210-L02/M004, MC210-L02/M005, MC210-L02/M006, MC210-L02/M007, MC210-L02/M008, MC210-L02/M009, MC210-L02/M010, MC210-L02/M011, MC210-L02/M012, MC210-L02/M013, MC210-L02/M014, MC210-L02/M015, MC210-L02/M016, MC210-L02/M017, MC210-L02/M018, MC210-L02/M019, MC210-L02/M020, MC210-L02/M021, MC210-L02/M022, MC210-L02/M023, OC004-L99/M206, OC004-L99/M512, OC005-L99/M204, OC005-L99/M505, OC006-L99/M200, OC006-L99/M503, \n",
"\n",
"Meters in Master, but not in Planon: 84 / 29707 : 0.283 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_meterssensors_filtered.index)))\n",
"b=np.sort(list(set(master_meterssensors_for_validation_filtered.index)))\n",
"meterssensors_not_in_planon=[]\n",
"for i in b:\n",
" if i not in a:\n",
" print(i+',',end=\" \"),\n",
" meterssensors_not_in_planon.append(i)\n",
"print('\\n\\nMeters in Master, but not in Planon:',\n",
" len(meterssensors_not_in_planon),'/',len(b),':',\n",
" round(len(meterssensors_not_in_planon)/len(b)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q1=pd.DataFrame(meterssensors_not_in_planon)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Meters in Planon, not in Master: 0 / 29623 : 0.0 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_meterssensors_filtered.index)))\n",
"b=np.sort(list(set(master_meterssensors_for_validation_filtered.index)))\n",
"meterssensors_not_in_master=[]\n",
"for i in a:\n",
" if i not in b:\n",
" print(i+',',end=\" \"),\n",
" meterssensors_not_in_master.append(i)\n",
"print('\\n\\nMeters in Planon, not in Master:',\n",
" len(meterssensors_not_in_master),'/',len(a),':',\n",
" round(len(meterssensors_not_in_master)/len(a)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"q2=pd.DataFrame(meterssensors_not_in_master)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MC032-L01, MC032-L03, MC044-L02, MC044-L03, MC044-L04, MC044-L05, MC046-L01, MC046-L03, MC046-L04, MC046-L18, MC046-L19, MC061-L01, MC076-B03, MC204-L03, MC204-L04, MC204-L07, MC204-L08, MC204-L09, MC207-L01, MC210-L01, MC210-L02, NAN, \n",
"\n",
"Loggers in Master, but not in Planon: 22 / 295 : 7.458 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_loggerscontrollers_filtered.index)))\n",
"b=np.sort(list(set(master_loggerscontrollers_for_validation_filtered.index)))\n",
"loggerscontrollers_not_in_planon=[]\n",
"for i in b:\n",
" if i not in a:\n",
" print(i+',',end=\" \"),\n",
" loggerscontrollers_not_in_planon.append(i)\n",
"print('\\n\\nLoggers in Master, but not in Planon:',\n",
" len(loggerscontrollers_not_in_planon),'/',len(b),':',\n",
" round(len(loggerscontrollers_not_in_planon)/len(b)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"q3=pd.DataFrame(loggerscontrollers_not_in_planon)"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
"Loggers in Planon, not in Master: 0 / 273 : 0.0 %\n"
]
}
],
"source": [
"a=np.sort(list(set(planon_loggerscontrollers_filtered.index)))\n",
"b=np.sort(list(set(master_loggerscontrollers_for_validation_filtered.index)))\n",
"loggerscontrollers_not_in_master=[]\n",
"for i in a:\n",
" if i not in b:\n",
" print(i+',',end=\" \"),\n",
" loggerscontrollers_not_in_master.append(i)\n",
"print('\\n\\nLoggers in Planon, not in Master:',\n",
" len(loggerscontrollers_not_in_master),'/',len(a),':',\n",
" round(len(loggerscontrollers_not_in_master)/len(a)*100,3),'%')"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"q4=pd.DataFrame(loggerscontrollers_not_in_master)"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q5=pd.DataFrame((planon_meterssensors_intersected!=master_meterssensors_for_validation_intersected).sum()/\\\n",
"len(planon_meterssensors_intersected)*100)"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q6=pd.DataFrame((planon_loggerscontrollers_intersected!=master_loggerscontrollers_for_validation_intersected).sum()/\\\n",
"len(planon_loggerscontrollers_intersected)*100)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w1=[]\n",
"for i in planon_meterssensors_intersected[planon_meterssensors_intersected['Description']!=master_meterssensors_for_validation_intersected['Description']].index:\n",
" w1.append({\"Meter\":i,'Planon':planon_meterssensors_intersected.loc[i]['Description'],\n",
" 'Master':master_meterssensors_for_validation_intersected.loc[i]['Description']})"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"q7=pd.DataFrame(w1)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"w2=[]\n",
"for i in planon_loggerscontrollers_intersected[planon_loggerscontrollers_intersected['Building Name']!=master_loggerscontrollers_for_validation_intersected['Building Name']].index:\n",
" w2.append({\"Logger\":i,'Planon':planon_loggerscontrollers_intersected.loc[i]['Building Name'],\n",
" 'Master':master_loggerscontrollers_for_validation_intersected.loc[i]['Building Name']})"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"q8=pd.DataFrame(w2)"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"writer = pd.ExcelWriter('final.xlsx')\n",
"q1.to_excel(writer,'Meters Master, not Planon')\n",
"q2.to_excel(writer,'Meters Planon, not Master')\n",
"q3.to_excel(writer,'Loggers Master, not Planon')\n",
"q4.to_excel(writer,'Loggers Planon, not Master')\n",
"q5.to_excel(writer,'Meters error perc')\n",
"q6.to_excel(writer,'Loggers error perc')\n",
"q7.to_excel(writer,'Meters naming conflcits')\n",
"q8.to_excel(writer,'Loggers naming conflicts')\n",
"writer.save()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda env:python3]",
"language": "python",
"name": "conda-env-python3-py"
},
"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": 2
}
| mit |
carlosb1/examples-python | notebooks/sent_analysis_2.ipynb | 1 | 8038 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from gensim.models.keyedvectors import KeyedVectors\n",
"# wordvectors_file_vec = './fasttext-sbwc.3.6.e20.vec'\n",
"# cantidad = 100000\n",
"# wordvectors = KeyedVectors.load_word2vec_format(wordvectors_file_vec, limit=cantidad)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"from torchtext import data\n",
"import torch\n",
"import random\n",
"import spacy\n",
"\n",
"SEED = 1234\n",
"torch.manual_seed(SEED)\n",
"torch.backends.cudnn.deterministic = True\n",
"\n",
"# tokenizer function using spacy\n",
"nlp = spacy.load('es')\n",
"def tokenizer(s): \n",
" return [w.text.lower() for w in nlp(tweet_clean(s))]\n",
"\n",
"def set_up_torch_text(path='cache/', path_test_csv='test_data.csv', path_valid_csv='valid_data.csv', path_train_csv='train_data.csv'):\n",
" random_state = random.seed(SEED)\n",
" txt_field = data.Field(sequential=False, use_vocab=True, tokenize=tokenizer)\n",
" label_field = data.Field(sequential=False, use_vocab=True, pad_token=None, unk_token=None)\n",
" val_fields = [('text', txt_field), ('tag', label_field)]\n",
"\n",
" train, valds, test = data.TabularDataset.splits(path=path, format='csv', train=path_train_csv, validation=path_valid_csv, test=path_test_csv, fields=val_fields)\n",
" # test = data.TabularDataset(path=path+path_test_csv, format='csv', skip_header=True, fields=val_fields)\n",
" txt_field.build_vocab(train, valds, test, max_size=25000)\n",
" label_field.build_vocab(train, valds, test, max_size=25000)\n",
" return train, valds, test, txt_field, label_field"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"train_data, valid_data, test_data, txt_field, label_field = set_up_torch_text()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4219"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(txt_field.vocab)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"BATCH_SIZE = 64\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n",
" (train_data, valid_data, test_data), \n",
" batch_size = BATCH_SIZE, \n",
" device = device)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"class CNN(nn.Module):\n",
" def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, \n",
" dropout, pad_idx):\n",
" \n",
" super().__init__()\n",
" \n",
" self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
" \n",
" self.convs = nn.ModuleList([\n",
" nn.Conv2d(in_channels = 1, \n",
" out_channels = n_filters, \n",
" kernel_size = (fs, embedding_dim)) \n",
" for fs in filter_sizes\n",
" ])\n",
" \n",
" self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)\n",
" \n",
" self.dropout = nn.Dropout(dropout)\n",
" \n",
" def forward(self, text):\n",
" \n",
" #text = [sent len, batch size]\n",
" \n",
" text = text.permute(1, 0)\n",
" \n",
" #text = [batch size, sent len]\n",
" \n",
" embedded = self.embedding(text)\n",
" \n",
" #embedded = [batch size, sent len, emb dim]\n",
" \n",
" embedded = embedded.unsqueeze(1)\n",
" \n",
" #embedded = [batch size, 1, sent len, emb dim]\n",
" \n",
" conved = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]\n",
" \n",
" #conv_n = [batch size, n_filters, sent len - filter_sizes[n]]\n",
" \n",
" pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]\n",
" \n",
" #pooled_n = [batch size, n_filters]\n",
" \n",
" cat = self.dropout(torch.cat(pooled, dim = 1))\n",
"\n",
" #cat = [batch size, n_filters * len(filter_sizes)]\n",
" \n",
" return self.fc(cat)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"INPUT_DIM = len(txt_field.vocab)\n",
"EMBEDDING_DIM = 100\n",
"N_FILTERS = 100\n",
"FILTER_SIZES = [2,3,4]\n",
"OUTPUT_DIM = len(label_field.vocab)\n",
"DROPOUT = 0.5\n",
"PAD_IDX = txt_field.vocab.stoi[txt_field.pad_token]\n",
"\n",
"model = CNN(INPUT_DIM, EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT, PAD_IDX)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The model has 554,340 trainable parameters\n"
]
}
],
"source": [
"def count_parameters(model):\n",
" return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
"\n",
"print(f'The model has {count_parameters(model):,} trainable parameters')"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "copy_(): argument 'other' (position 1) must be Tensor, not NoneType",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-56-d77a57109fd4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpretrained_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtxt_field\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvectors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membedding\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpretrained_embeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: copy_(): argument 'other' (position 1) must be Tensor, not NoneType"
]
}
],
"source": [
"pretrained_embeddings = txt_field.vocab.vectors\n",
"\n",
"model.embedding.weight.data.copy_(pretrained_embeddings)"
]
}
],
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
mne-tools/mne-tools.github.io | 0.19/_downloads/ee0919948773cee00620d9b2f882aba0/plot_xhemi.ipynb | 2 | 2460 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Cross-hemisphere comparison\n\n\nThis example illustrates how to visualize the difference between activity in\nthe left and the right hemisphere. The data from the right hemisphere is\nmapped to the left hemisphere, and then the difference is plotted. For more\ninformation see :func:`mne.compute_source_morph`.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Christian Brodbeck <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\n\ndata_dir = mne.datasets.sample.data_path()\nsubjects_dir = data_dir + '/subjects'\nstc_path = data_dir + '/MEG/sample/sample_audvis-meg-eeg'\n\nstc = mne.read_source_estimate(stc_path, 'sample')\n\n# First, morph the data to fsaverage_sym, for which we have left_right\n# registrations:\nstc = mne.compute_source_morph(stc, 'sample', 'fsaverage_sym', smooth=5,\n warn=False,\n subjects_dir=subjects_dir).apply(stc)\n\n# Compute a morph-matrix mapping the right to the left hemisphere,\n# and vice-versa.\nmorph = mne.compute_source_morph(stc, 'fsaverage_sym', 'fsaverage_sym',\n spacing=stc.vertices, warn=False,\n subjects_dir=subjects_dir, xhemi=True,\n verbose='error') # creating morph map\nstc_xhemi = morph.apply(stc)\n\n# Now we can subtract them and plot the result:\ndiff = stc - stc_xhemi\n\ndiff.plot(hemi='lh', subjects_dir=subjects_dir, initial_time=0.07,\n size=(800, 600))"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
mtakeo/Portfolio | Text Mining with Twitter API.ipynb | 1 | 43389 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Natural Language Processing - Text Mining with Twitter API"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Introduction "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Using Natural Language Processing we can extract relevant and insightful data from social media. In this demonstration I will use the Python Twitter API tweepy to stream data into my console and extract that data into a text file for Natural Language Procssing. \n",
"\n",
"In the past year we have seen the rise in fashion dominance of Adidas in the sneakers category. This was reflected in their stock price as Adidas have been climbing consistently in the past year. We will look to twitter to help acertain this trend by comparing the number of relevant tweets between Adidas and Nike. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Streaming Data from Twitter"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"To start off we will stream data from Twitter using the tweepy API. We will capture tweets that contains keywords Nike and Adidas."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Import the necessary methods from tweepy library\n",
"from tweepy.streaming import StreamListener\n",
"from tweepy import OAuthHandler\n",
"from tweepy import Stream\n",
" \n",
"access_token = \"Access Token\"\n",
"access_token_secret = \"Access Token Secret\"\n",
"consumer_key = \"Consumer Key\"\n",
"consumer_secret = \"Consumer Secret\"\n",
"\n",
"\n",
"class StdOutListener(StreamListener):\n",
"\n",
" def on_data(self, data):\n",
" print data\n",
" return True\n",
"\n",
" def on_error(self, status):\n",
" print status\n",
"\n",
"\n",
"if __name__ == '__main__':\n",
"\n",
" l = StdOutListener()\n",
" auth = OAuthHandler(consumer_key, consumer_secret)\n",
" auth.set_access_token(access_token, access_token_secret)\n",
" stream = Stream(auth, l)\n",
"\n",
" #key word capture\n",
" stream.filter(track=['Nike', 'Adidas'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code above was processed through Canopy and data was exported to a text file via the Canopy command steaming_data.py > data.text. Due to the lack of processing power of my laptop, I've only obtained roughly 3000 tweets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataframe Preparation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to create a dataframe to store the necessary information. For starters we will take in language and country for some quick visualizations. "
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"import re\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"\n",
"%matplotlib inline\n",
"\n",
"tweets_data_path = '/Users/data.txt'\n",
"\n",
"tweets_data = []\n",
"tweets_file = open(tweets_data_path, \"r\")\n",
"for line in tweets_file:\n",
" try:\n",
" tweet = json.loads(line)\n",
" tweets_data.append(tweet)\n",
" except:\n",
" continue\n",
"\n",
"tweets = pd.DataFrame()\n",
"\n",
"tweets['text'] = map(lambda tweet: tweet.get('text', None),tweets_data)\n",
"tweets['lang'] = map(lambda tweet: tweet.get('lang',None),tweets_data)\n",
"tweets['country'] = map(lambda tweet: tweet.get('place',{}).get('country',{}) if tweet.get('place',None) != None else None, tweets_data)\n",
"tweets_by_lang = tweets['lang'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Analyzing tweets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With dataframe created we can now use some of that information for some quick visualizations. "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11864e610>"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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F41eBprpEVH/+u2jXALwWEUOAnYA/F9tXIF2nASsivh8Ro4qnuwH3AAtIVbfr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\n7f8AJSOWcaErIZ4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1184e8410>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.tick_params(axis='x', labelsize=15)\n",
"ax.tick_params(axis='y', labelsize=10)\n",
"ax.set_xlabel('Languages', fontsize=15)\n",
"ax.set_ylabel('Number of tweets' , fontsize=15)\n",
"ax.set_title('Top 5 languages', fontsize=15, fontweight='bold')\n",
"tweets_by_lang[:5].plot(ax=ax, kind='bar', color='red')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the barchart above we can see that English was the primary language of the tweets collected. \n",
"\n",
"Next we will create new columns and extract information on tweets that are Nike or Adidas related. "
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#some quick data cleaning to remove rows will null values\n",
"tweets = tweets.drop(tweets.index[[878,881,886,925]])\n",
"\n",
"#function to return True if a certain word in found in the text\n",
"def word_in_text(word, text):\n",
" word = word.lower()\n",
" text = text.lower()\n",
" match = re.search(word, text)\n",
" if match:\n",
" return True\n",
" return False\n",
"\n",
"tweets['Nike'] = tweets['text'].apply(lambda tweet: word_in_text('nike', tweet))\n",
"tweets['Adidas'] = tweets['text'].apply(lambda tweet: word_in_text('adidas', tweet))"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x124fae090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"brands = ['nike', 'adidas']\n",
"tweets_by_brands = [tweets['Nike'].value_counts()[True], tweets['Adidas'].value_counts()[True]]\n",
"x_pos = list(range(len(brands)))\n",
"width = 0.8\n",
"fig, ax = plt.subplots()\n",
"plt.bar(x_pos, tweets_by_brands, width, alpha=1, color='g')\n",
"ax.set_ylabel('Number of tweets', fontsize=12)\n",
"ax.set_title('Nike vs Adidas', fontsize=15, fontweight='bold')\n",
"ax.set_xticks([p + 0.4 * width for p in x_pos])\n",
"ax.set_xticklabels(brands)\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the above barchart we can see that Nike is still ranked higher in terms of mentioned tweets. We will take this further and investigate whether this is still the case by placing some relevance filters. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Relevant data "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've seen Nike ranked higher in terms of mentioned tweets, but now lets add in some relevant keywords. As our focus is sneakers, we will add in shoes, sneakers and kicks as relevant keywords. The code in the following section will take those keywords and look for tweets that are relevant. "
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"623\n",
"59\n",
"46\n",
"701\n"
]
}
],
"source": [
"tweets['shoes'] = tweets['text'].apply(lambda tweet: word_in_text('shoes', tweet))\n",
"tweets['sneakers'] = tweets['text'].apply(lambda tweet: word_in_text('sneakers', tweet))\n",
"tweets['kicks'] = tweets['text'].apply(lambda tweet: word_in_text('kicks', tweet))\n",
"\n",
"tweets['relevant'] = tweets['text'].apply(lambda tweet: word_in_text('shoes', tweet) or \\\n",
" word_in_text('sneakers', tweet) or word_in_text('kicks', tweet))\n",
"\n",
"\n",
"print tweets['shoes'].value_counts()[True]\n",
"print tweets['sneakers'].value_counts()[True]\n",
"print tweets['kicks'].value_counts()[True]\n",
"print tweets['relevant'].value_counts()[True]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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uiojrKFdDXdn+jzQ8ONWGJKmWPQhJUi0DQpJUy4CQJNUyICRJtQwISVItA0KS\nVMuAkCTVMiAkSbX+P8o9EwQ9XamKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x125481190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tweets_by_brand_rel = [tweets[tweets['relevant'] == True]['Nike'].value_counts()[True], \n",
" tweets[tweets['relevant'] == True]['Adidas'].value_counts()[True]]\n",
"x_pos = list(range(len(brands)))\n",
"width = 0.8\n",
"fig, ax = plt.subplots()\n",
"plt.bar(x_pos, tweets_by_brand_rel, width,alpha=1,color='g')\n",
"ax.set_ylabel('Number of tweets', fontsize=15)\n",
"ax.set_title('Nike vs Adidas (Relevant data)', fontsize=10, fontweight='bold')\n",
"ax.set_xticks([p + 0.4 * width for p in x_pos])\n",
"ax.set_xticklabels(brands)\n",
"plt.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the relevant keywords added we can now see that Adidas actually ranked higher. This is no surprise but helps ascertain that Adidas is now big player in the sneakers market.\n",
"\n",
"Please note that the analysis and data above are for demonstration purposes only. For a more robust analysis, more data (tweets) is needed. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extract Links from tweets "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this last section we will pull links of relevant tweets into a new dataframe for further analysis."
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12 https://t.co/dTvEf6e1IM\n",
"17 https://t.co/qfSMYxTiN4\n",
"40 https://t.co/8Wc3LozPLu\n",
"43 https://t.co/6kWJNjI1fF\n",
"119 https://t.co/aAXTrXhWw1\n",
"Name: link, dtype: object"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def extract_link(text):\n",
" regex = r'https?://[^\\s<>\"]+|www\\.[^\\s<>\"]+'\n",
" match = re.search(regex, text)\n",
" if match:\n",
" return match.group()\n",
" return ''\n",
"\n",
"tweets['link'] = tweets['text'].apply(lambda tweet: extract_link(tweet))\n",
"tweets_relevant = tweets[tweets['relevant'] == True]\n",
"tweets_relevant_with_link = tweets_relevant[tweets_relevant['link'] != '']\n",
"\n",
"links_rel_nike = tweets_relevant_with_link[tweets_relevant_with_link['Nike'] == True]['link']\n",
"links_rel_nike.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Thank you for reading through this Natural Language Processing demonstration. I hope this was an enjoyable read. \n",
"\n",
"Mitsuaki Takeo"
]
}
],
"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 |
napsternxg/ipython-notebooks | LazyValues.ipynb | 1 | 8654 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "LazyValues.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyMlA+yFrEudIIn1Vg8z+ehW",
"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/napsternxg/ipython-notebooks/blob/master/LazyValues.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Lazy Loading Variables in Python\n",
"\n",
"How to get variables which are lazy loaded when first used"
],
"metadata": {
"id": "gWt4otPFwHwA"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "X0W2LDCFvWIr"
},
"outputs": [],
"source": [
"import time"
]
},
{
"cell_type": "code",
"source": [
"class LazyValues(object):\n",
" def __init__(self, **value_fn_dict):\n",
" self.value_fn_dict = value_fn_dict\n",
" \n",
" def __getattr__(self, name):\n",
" if name not in self.value_fn_dict:\n",
" raise AttributeError(f\"{name} not in {self.value_fn_dict.keys()}\")\n",
" try:\n",
" self.__getattribute__(name)\n",
" except AttributeError:\n",
" print(f\"Lazy loading value: {name}.\")\n",
" value = self.value_fn_dict[name]()\n",
" setattr(self, name, value)\n",
" return self.__getattribute__(name)"
],
"metadata": {
"id": "Q4-yVWVNvZ5r"
},
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def slow_function(v, sleep_time=2):\n",
" print(f\"In slow_function: Sleeping for: {sleep_time} secs before returning {v}.\")\n",
" time.sleep(sleep_time)\n",
" return v"
],
"metadata": {
"id": "kAdB_V40vbT0"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"lazy_values = LazyValues(\n",
" some_slow_value = lambda: slow_function(5, sleep_time=2),\n",
" extremely_slow_value = lambda: slow_function(10, sleep_time=10)\n",
")"
],
"metadata": {
"id": "cLgpt7nKvdWi"
},
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for i in range(10):\n",
" print(f\"lazy_values.some_slow_value={lazy_values.some_slow_value}\")\n",
" print(f\"lazy_values.extremely_slow_value={lazy_values.extremely_slow_value}\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q_n10qn7vffp",
"outputId": "3a372426-e006-4b4a-a7ee-5d9abfaebf23"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Lazy loading value: some_slow_value.\n",
"In slow_function: Sleeping for: 2 secs before returning 5.\n",
"lazy_values.some_slow_value=5\n",
"Lazy loading value: extremely_slow_value.\n",
"In slow_function: Sleeping for: 10 secs before returning 10.\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n",
"lazy_values.some_slow_value=5\n",
"lazy_values.extremely_slow_value=10\n"
]
}
]
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "0ygvGVp9vtNR"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "IFT4OU4Tv6Sl"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "rVf4LA9Xv605"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "gJMf9e81wBZn"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "2RGOI9IxwBXI"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "cirMdxbxwBUT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "zGS6H2jowBSE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "Tn7wl47ywBPO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "4rux0vdOwBMw"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "qXToL9bVwBKU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "Qz5uupfdwBHg"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "2i13liCTwA_9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "HeU2nJKFwA9e"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "oniBfjo5wA7C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "d2giXlADwA4N"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "kSZL5thLwA1u"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "0vRPWAG5wAzH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "yB9Q6aQMwAwH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "7NuELLmvwApz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "Q51AntvswAdH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "RScVCMtCwAG_"
},
"execution_count": null,
"outputs": []
}
]
} | apache-2.0 |
ecervera/ga-nb | Rastrigin_check.ipynb | 1 | 2703 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Rastrigin function: plotting results\n",
"\n",
"We are going to make the following plots for the results of the evolution stored in the database:\n",
"* Error bars graph (raw scores).\n",
"* Error bars graph (fitness scores).\n",
"* Max/min/avg/std. dev. graph (raw scores).\n",
"* Max/min/avg/std. dev. graph (fitness scores).\n",
"* Raw and Fitness min/max difference graph.\n",
"* Heat map of population raw score distribution."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from pyevolve_plot import plot_errorbars_raw, plot_errorbars_fitness, \\\n",
" plot_maxmin_raw, plot_maxmin_fitness, \\\n",
" plot_diff_raw, plot_pop_heatmap_raw"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_errorbars_raw('rastrigin.db','ex1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_errorbars_fitness('rastrigin.db','ex1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_maxmin_raw('rastrigin.db','ex1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_maxmin_raw('rastrigin.db','ex1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_diff_raw('rastrigin.db','ex1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_pop_heatmap_raw('rastrigin.db','ex1')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py27]",
"language": "python",
"name": "conda-env-py27-py"
},
"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
}
| mit |
yhat/ggplot | docs/examples/Boxplots.ipynb | 1 | 16264 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from ggplot import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<ggplot: (285113429)>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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tanmO1dbWxsDAQNYxmKBYLEa5XI5Dhw7F0NBQ1nE4ipnJH/OSX9W0MFUVZXvfvn2xc+fO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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10635bd10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ggplot(mtcars, aes(x='factor(cyl)', y='mpg')) + geom_boxplot()"
]
},
{
"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
}
| bsd-2-clause |
PYPIT/PYPIT | doc/nb/FluxDEIMOS.ipynb | 1 | 99539 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multi-detector Fluxing with DEIMOS [v1]"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# imports\n",
"from importlib import reload\n",
"import os\n",
"from matplotlib import pyplot as plt\n",
"import glob\n",
"import numpy as np\n",
"\n",
"from astropy.table import Table\n",
"from astropy import units as u\n",
"\n",
"from pypit import arload\n",
"from pypit import arutils\n",
"from pypit import fluxspec\n",
"from pypit.core import arflux"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/home/xavier/local/Python/PYPIT-development-suite/'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# PYPIT Development\n",
"os.getenv('PYPIT_DEV')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Script (user\n",
" pypit_flux_spec sensfunc --std_file=spec1d_G191B2B_DEIMOS_2017Sep14T152432.fits --instr=keck_deimos --sensfunc_file=sens.yaml --multi_det=3,7"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Development"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load from Cooked"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"std_spec1d_file = os.getenv('PYPIT_DEV')+'Cooked/Science/spec1d_G191B2B_DEIMOS_2017Sep14T152432.fits'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([<SpecObjExp: O700-S3681-D03-I0005 == Setup dum_config Object at 0.7 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O542-S3681-D03-I0005 == Setup dum_config Object at 0.542 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O462-S3681-D03-I0005 == Setup dum_config Object at 0.462 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O146-S3681-D03-I0005 == Setup dum_config Object at 0.146 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O032-S3681-D03-I0005 == Setup dum_config Object at 0.032 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O700-S3691-D07-I0005 == Setup dum_config Object at 0.7 in Slit at 0.3691 with det=07, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O462-S3691-D07-I0005 == Setup dum_config Object at 0.462 in Slit at 0.3691 with det=07, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O587-S3691-D07-I0005 == Setup dum_config Object at 0.587 in Slit at 0.3691 with det=07, scidx=5 and objtype=unknown>],\n",
" SIMPLE = T / conforms to FITS standard \n",
" BITPIX = 8 / array data type \n",
" NAXIS = 0 / number of array dimensions \n",
" EXTEND = T \n",
" RA = '05:05:30.63' \n",
" DEC = '+52:49:50.3' \n",
" EXPTIME = 60 \n",
" DATE = '2017-09-14T15:24:32' \n",
" TARGET = 'G191B2B ' \n",
" AIRMASS = 1.19898553 \n",
" MJD-OBS = 1392255.390264 \n",
" LON-OBS = 155.47833 \n",
" LAT-OBS = 19.82833 \n",
" ALT-OBS = 4160.0 \n",
" EXT0001 = 'O700-S3681-D03-I0005' \n",
" EXT0002 = 'O542-S3681-D03-I0005' \n",
" EXT0003 = 'O462-S3681-D03-I0005' \n",
" EXT0004 = 'O146-S3681-D03-I0005' \n",
" EXT0005 = 'O032-S3681-D03-I0005' \n",
" EXT0006 = 'O700-S3691-D07-I0005' \n",
" EXT0007 = 'O462-S3691-D07-I0005' \n",
" EXT0008 = 'O587-S3691-D07-I0005' \n",
" NSPEC = 8 \n",
" NPIX = 4096 )"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"std_specobjs, std_header = arload.load_specobj(std_spec1d_file)\n",
"std_specobjs, std_header"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.baseargflag\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 1393 run_ncpus()\u001b[0m - Setting 7 CPUs\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n"
]
}
],
"source": [
"reload(fluxspec)\n",
"FxSpec = fluxspec.FluxSpec(spectrograph='keck_deimos', std_specobjs=std_specobjs, std_header=std_header, multi_det=(3,7))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the standards and splice"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 429 find_standard()\u001b[0m - Putative standard star has a median boxcar count of 18286.36869975645\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34mfluxspec.py 173 find_standard()\u001b[0m - Using standard <SpecObjExp: O700-S3681-D03-I0005 == Setup dum_config Object at 0.7 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown> in det=3\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 429 find_standard()\u001b[0m - Putative standard star has a median boxcar count of 6693.603640276046\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34mfluxspec.py 173 find_standard()\u001b[0m - Using standard <SpecObjExp: O700-S3691-D07-I0005 == Setup dum_config Object at 0.7 in Slit at 0.3691 with det=07, scidx=5 and objtype=unknown> in det=7\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34mfluxspec.py 175 find_standard()\u001b[0m - Splicing the standards\n"
]
}
],
"source": [
"FxSpec.find_standard()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sensfunc"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 370 load_extinction_data()\u001b[0m - Using mthamextinct.dat for extinction corrections.\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 300 find_standard_file()\u001b[0m - Using standard star G191B2B\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 406 load_standard_file()\u001b[0m - Loading standard star file: /home/xavier/local/Python/PYPIT/pypit/data/standards/calspec/g191b2b_mod_005.fits.gz\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 407 load_standard_file()\u001b[0m - Fluxes are flambda, normalized to 1e-17\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 494 generate_sensfunc()\u001b[0m - Masking edges\n",
"\u001b[1;31m[WARNING] ::\u001b[0m \u001b[1;34marflux.py 500 generate_sensfunc()\u001b[0m - Should pull resolution from arc line analysis\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 504 generate_sensfunc()\u001b[0m - Masking Balmer\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 512 generate_sensfunc()\u001b[0m - Masking Telluric\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 519 generate_sensfunc()\u001b[0m - Masking Below the atmospheric cutoff\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 203 bspline_magfit()\u001b[0m - Difference between fits is 0.00020576\n",
"\u001b[1;30m[WORK IN ]::\u001b[0m\n",
"\u001b[1;33m[PROGRESS]::\u001b[0m \u001b[1;34marflux.py 206 bspline_magfit()\u001b[0m - Add QA for sensitivity function\n"
]
}
],
"source": [
"_ = FxSpec.generate_sensfunc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
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IFJTxdKmFPGr0dvfbCBqt3+nuP57EmERECsq21mDSi4bazCtnIljASFsl9T53/x7QYGYf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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3dc1f4b470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FxSpec.show_sensfunc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate two FluxSpec objects"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.baseargflag\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 1393 run_ncpus()\u001b[0m - Setting 7 CPUs\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n"
]
}
],
"source": [
"reload(fluxspec)\n",
"std3 = [sobj for sobj in std_specobjs if sobj.det==3]\n",
"FxSpec3 = fluxspec.FluxSpec(spectrograph='keck_deimos', std_specobjs=std3, std_header=std_header)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.baseargflag\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 1393 run_ncpus()\u001b[0m - Setting 7 CPUs\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n"
]
}
],
"source": [
"std7 = [sobj for sobj in std_specobjs if sobj.det==7]\n",
"FxSpec7 = fluxspec.FluxSpec(spectrograph='keck_deimos', std_specobjs=std7, std_header=std_header)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Find the standards in each"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 429 find_standard()\u001b[0m - Putative standard star has a median boxcar count of 18286.36869975645\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 429 find_standard()\u001b[0m - Putative standard star has a median boxcar count of 6693.603640276046\n"
]
},
{
"data": {
"text/plain": [
"(<SpecObjExp: O700-S3681-D03-I0005 == Setup dum_config Object at 0.7 in Slit at 0.3681 with det=03, scidx=5 and objtype=unknown>,\n",
" <SpecObjExp: O700-S3691-D07-I0005 == Setup dum_config Object at 0.7 in Slit at 0.3691 with det=07, scidx=5 and objtype=unknown>)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"FxSpec3.find_standard()\n",
"FxSpec7.find_standard()\n",
"FxSpec3.std, FxSpec7.std"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sensitivity functions"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 370 load_extinction_data()\u001b[0m - Using mthamextinct.dat for extinction corrections.\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 300 find_standard_file()\u001b[0m - Using standard star G191B2B\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 406 load_standard_file()\u001b[0m - Loading standard star file: /home/xavier/local/Python/PYPIT/pypit/data/standards/calspec/g191b2b_mod_005.fits.gz\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 407 load_standard_file()\u001b[0m - Fluxes are flambda, normalized to 1e-17\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 494 generate_sensfunc()\u001b[0m - Masking edges\n",
"\u001b[1;31m[WARNING] ::\u001b[0m \u001b[1;34marflux.py 500 generate_sensfunc()\u001b[0m - Should pull resolution from arc line analysis\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 504 generate_sensfunc()\u001b[0m - Masking Balmer\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 512 generate_sensfunc()\u001b[0m - Masking Telluric\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 519 generate_sensfunc()\u001b[0m - Masking Below the atmospheric cutoff\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 203 bspline_magfit()\u001b[0m - Difference between fits is 0.000120563\n",
"\u001b[1;30m[WORK IN ]::\u001b[0m\n",
"\u001b[1;33m[PROGRESS]::\u001b[0m \u001b[1;34marflux.py 206 bspline_magfit()\u001b[0m - Add QA for sensitivity function\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 370 load_extinction_data()\u001b[0m - Using mthamextinct.dat for extinction corrections.\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 300 find_standard_file()\u001b[0m - Using standard star G191B2B\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 406 load_standard_file()\u001b[0m - Loading standard star file: /home/xavier/local/Python/PYPIT/pypit/data/standards/calspec/g191b2b_mod_005.fits.gz\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 407 load_standard_file()\u001b[0m - Fluxes are flambda, normalized to 1e-17\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 494 generate_sensfunc()\u001b[0m - Masking edges\n",
"\u001b[1;31m[WARNING] ::\u001b[0m \u001b[1;34marflux.py 500 generate_sensfunc()\u001b[0m - Should pull resolution from arc line analysis\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 504 generate_sensfunc()\u001b[0m - Masking Balmer\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 512 generate_sensfunc()\u001b[0m - Masking Telluric\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 519 generate_sensfunc()\u001b[0m - Masking Below the atmospheric cutoff\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 203 bspline_magfit()\u001b[0m - Difference between fits is 0.000992083\n",
"\u001b[1;30m[WORK IN ]::\u001b[0m\n",
"\u001b[1;33m[PROGRESS]::\u001b[0m \u001b[1;34marflux.py 206 bspline_magfit()\u001b[0m - Add QA for sensitivity function\n"
]
}
],
"source": [
"_ = FxSpec3.generate_sensfunc()\n",
"_ = FxSpec7.generate_sensfunc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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04c9vzOOLt77H/LXb0x2WiBwkkUSRYzEz35hZLpBBY0qrShFlbVrkc/MFQ5lw8QjWbN3N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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3df8299208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FxSpec3.show_sensfunc()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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cYTTv7+C13ftZW91Ea0eCcSNK+NhbpnPmEeVMHzuc57c2sPClnWyo2cMFx47n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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3dc2108fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FxSpec7.show_sensfunc()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Together"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wave3 = np.linspace(FxSpec3.sensfunc['wave_min'], FxSpec3.sensfunc['wave_max'], 1000)\n",
"mag_func3 = arutils.func_val(FxSpec3.sensfunc['c'], wave3, FxSpec3.sensfunc['func'])\n",
"sens3 = 10.0**(0.4*mag_func3)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wave7 = np.linspace(FxSpec7.sensfunc['wave_min'], FxSpec7.sensfunc['wave_max'], 1000)\n",
"mag_func7 = arutils.func_val(FxSpec7.sensfunc['c'], wave7, FxSpec7.sensfunc['func'])\n",
"sens7 = 10.0**(0.4*mag_func7)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3dc1e79828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.clf()\n",
"ax = plt.gca()\n",
"ax.plot(np.concatenate([wave3,wave7]), np.concatenate([sens3,sens7]))\n",
"ax.set_xlim(8300., 8500.)\n",
"ax.set_xlabel('Wavelength')\n",
"ax.set_ylabel('Sensitivity Function')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Try a splice"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"std_splice = FxSpec3.std.copy()\n",
"# Append\n",
"std_splice.boxcar['wave'] = np.append(std_splice.boxcar['wave'].value, FxSpec7.std.boxcar['wave'].value)*u.AA\n",
"for key in ['counts', 'var']:\n",
" std_splice.boxcar[key] = np.append(std_splice.boxcar[key], FxSpec7.std.boxcar[key])"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 370 load_extinction_data()\u001b[0m - Using mthamextinct.dat for extinction corrections.\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 300 find_standard_file()\u001b[0m - Using standard star G191B2B\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 406 load_standard_file()\u001b[0m - Loading standard star file: /home/xavier/local/Python/PYPIT/pypit/data/standards/calspec/g191b2b_mod_005.fits.gz\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 407 load_standard_file()\u001b[0m - Fluxes are flambda, normalized to 1e-17\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 494 generate_sensfunc()\u001b[0m - Masking edges\n",
"\u001b[1;31m[WARNING] ::\u001b[0m \u001b[1;34marflux.py 500 generate_sensfunc()\u001b[0m - Should pull resolution from arc line analysis\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 504 generate_sensfunc()\u001b[0m - Masking Balmer\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 512 generate_sensfunc()\u001b[0m - Masking Telluric\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 519 generate_sensfunc()\u001b[0m - Masking Below the atmospheric cutoff\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marflux.py 203 bspline_magfit()\u001b[0m - Difference between fits is 0.00020576\n",
"\u001b[1;30m[WORK IN ]::\u001b[0m\n",
"\u001b[1;33m[PROGRESS]::\u001b[0m \u001b[1;34marflux.py 206 bspline_magfit()\u001b[0m - Add QA for sensitivity function\n"
]
}
],
"source": [
"sensfunc = arflux.generate_sensfunc(std_splice, FxSpec3.std_header['RA'],\n",
" FxSpec3.std_header['DEC'], FxSpec3.std_header['AIRMASS'],\n",
" FxSpec3.std_header['EXPTIME'], FxSpec3.settings)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<Quantity 6499.8059110514305 Angstrom>,\n",
" <Quantity 10329.396588147156 Angstrom>)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sensfunc['wave_min'], sensfunc['wave_max']"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.baseargflag\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 1393 run_ncpus()\u001b[0m - Setting 7 CPUs\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 87 load_file()\u001b[0m - Loading base settings from settings.basespect\n",
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34marparse.py 75 load_file()\u001b[0m - Loading default settings\n"
]
}
],
"source": [
"FxSpec = fluxspec.FluxSpec(spectrograph='keck_deimos')\n",
"FxSpec.sensfunc = sensfunc"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
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IFJTxdKmFPGr0dvfbCBqt3+nuP57EmERECsq21mDSi4bazCtnIljASFsl9T53/x7QYGYf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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3dc1c66d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FxSpec.show_sensfunc()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[1;32m[INFO] ::\u001b[0m \u001b[1;34mfluxspec.py 345 save_master()\u001b[0m - Wrote sensfunc to MasterFrame: tmp.yaml\n"
]
}
],
"source": [
"FxSpec.save_master('tmp.yaml')"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
ES-DOC/esdoc-jupyterhub | notebooks/niwa/cmip6/models/sandbox-3/ocean.ipynb | 1 | 164409 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocean \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NIWA \n",
"**Source ID**: SANDBOX-3 \n",
"**Topic**: Ocean \n",
"**Sub-Topics**: Timestepping Framework, Advection, Lateral Physics, Vertical Physics, Uplow Boundaries, Boundary Forcing. \n",
"**Properties**: 133 (101 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/ocean?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 16:54:30"
],
"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', 'niwa', 'sandbox-3', 'ocean')"
],
"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 --> Seawater Properties](#2.-Key-Properties--->-Seawater-Properties) \n",
"[3. Key Properties --> Bathymetry](#3.-Key-Properties--->-Bathymetry) \n",
"[4. Key Properties --> Nonoceanic Waters](#4.-Key-Properties--->-Nonoceanic-Waters) \n",
"[5. Key Properties --> Software Properties](#5.-Key-Properties--->-Software-Properties) \n",
"[6. Key Properties --> Resolution](#6.-Key-Properties--->-Resolution) \n",
"[7. Key Properties --> Tuning Applied](#7.-Key-Properties--->-Tuning-Applied) \n",
"[8. Key Properties --> Conservation](#8.-Key-Properties--->-Conservation) \n",
"[9. Grid](#9.-Grid) \n",
"[10. Grid --> Discretisation --> Vertical](#10.-Grid--->-Discretisation--->-Vertical) \n",
"[11. Grid --> Discretisation --> Horizontal](#11.-Grid--->-Discretisation--->-Horizontal) \n",
"[12. Timestepping Framework](#12.-Timestepping-Framework) \n",
"[13. Timestepping Framework --> Tracers](#13.-Timestepping-Framework--->-Tracers) \n",
"[14. Timestepping Framework --> Baroclinic Dynamics](#14.-Timestepping-Framework--->-Baroclinic-Dynamics) \n",
"[15. Timestepping Framework --> Barotropic](#15.-Timestepping-Framework--->-Barotropic) \n",
"[16. Timestepping Framework --> Vertical Physics](#16.-Timestepping-Framework--->-Vertical-Physics) \n",
"[17. Advection](#17.-Advection) \n",
"[18. Advection --> Momentum](#18.-Advection--->-Momentum) \n",
"[19. Advection --> Lateral Tracers](#19.-Advection--->-Lateral-Tracers) \n",
"[20. Advection --> Vertical Tracers](#20.-Advection--->-Vertical-Tracers) \n",
"[21. Lateral Physics](#21.-Lateral-Physics) \n",
"[22. Lateral Physics --> Momentum --> Operator](#22.-Lateral-Physics--->-Momentum--->-Operator) \n",
"[23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff](#23.-Lateral-Physics--->-Momentum--->-Eddy-Viscosity-Coeff) \n",
"[24. Lateral Physics --> Tracers](#24.-Lateral-Physics--->-Tracers) \n",
"[25. Lateral Physics --> Tracers --> Operator](#25.-Lateral-Physics--->-Tracers--->-Operator) \n",
"[26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff](#26.-Lateral-Physics--->-Tracers--->-Eddy-Diffusity-Coeff) \n",
"[27. Lateral Physics --> Tracers --> Eddy Induced Velocity](#27.-Lateral-Physics--->-Tracers--->-Eddy-Induced-Velocity) \n",
"[28. Vertical Physics](#28.-Vertical-Physics) \n",
"[29. Vertical Physics --> Boundary Layer Mixing --> Details](#29.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Details) \n",
"[30. Vertical Physics --> Boundary Layer Mixing --> Tracers](#30.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Tracers) \n",
"[31. Vertical Physics --> Boundary Layer Mixing --> Momentum](#31.-Vertical-Physics--->-Boundary-Layer-Mixing--->-Momentum) \n",
"[32. Vertical Physics --> Interior Mixing --> Details](#32.-Vertical-Physics--->-Interior-Mixing--->-Details) \n",
"[33. Vertical Physics --> Interior Mixing --> Tracers](#33.-Vertical-Physics--->-Interior-Mixing--->-Tracers) \n",
"[34. Vertical Physics --> Interior Mixing --> Momentum](#34.-Vertical-Physics--->-Interior-Mixing--->-Momentum) \n",
"[35. Uplow Boundaries --> Free Surface](#35.-Uplow-Boundaries--->-Free-Surface) \n",
"[36. Uplow Boundaries --> Bottom Boundary Layer](#36.-Uplow-Boundaries--->-Bottom-Boundary-Layer) \n",
"[37. Boundary Forcing](#37.-Boundary-Forcing) \n",
"[38. Boundary Forcing --> Momentum --> Bottom Friction](#38.-Boundary-Forcing--->-Momentum--->-Bottom-Friction) \n",
"[39. Boundary Forcing --> Momentum --> Lateral Friction](#39.-Boundary-Forcing--->-Momentum--->-Lateral-Friction) \n",
"[40. Boundary Forcing --> Tracers --> Sunlight Penetration](#40.-Boundary-Forcing--->-Tracers--->-Sunlight-Penetration) \n",
"[41. Boundary Forcing --> Tracers --> Fresh Water Forcing](#41.-Boundary-Forcing--->-Tracers--->-Fresh-Water-Forcing) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Ocean key properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of ocean model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.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 model code (NEMO 3.6, MOM 5.0,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.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 Family\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of ocean model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.model_family') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"OGCM\" \n",
"# \"slab ocean\" \n",
"# \"mixed layer ocean\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Basic approximations made in the ocean.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.basic_approximations') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Primitive equations\" \n",
"# \"Non-hydrostatic\" \n",
"# \"Boussinesq\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Prognostic Variables\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of prognostic variables in the ocean component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.prognostic_variables') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Potential temperature\" \n",
"# \"Conservative temperature\" \n",
"# \"Salinity\" \n",
"# \"U-velocity\" \n",
"# \"V-velocity\" \n",
"# \"W-velocity\" \n",
"# \"SSH\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 2. Key Properties --> Seawater Properties \n",
"*Physical properties of seawater in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Eos Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of EOS for sea water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Linear\" \n",
"# \"Wright, 1997\" \n",
"# \"Mc Dougall et al.\" \n",
"# \"Jackett et al. 2006\" \n",
"# \"TEOS 2010\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.2. Eos Functional Temp\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Temperature used in EOS for sea water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_temp') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Potential temperature\" \n",
"# \"Conservative temperature\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.3. Eos Functional Salt\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Salinity used in EOS for sea water*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_salt') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Practical salinity Sp\" \n",
"# \"Absolute salinity Sa\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.4. Eos Functional Depth\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Depth or pressure used in EOS for sea water ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.eos_functional_depth') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Pressure (dbars)\" \n",
"# \"Depth (meters)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.5. Ocean Freezing Point\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Equation used to compute the freezing point (in deg C) of seawater, as a function of salinity and pressure*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_freezing_point') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"TEOS 2010\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.6. Ocean Specific Heat\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Specific heat in ocean (cpocean) in J/(kg K)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_specific_heat') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 2.7. Ocean Reference Density\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Boussinesq reference density (rhozero) in kg / m3*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.seawater_properties.ocean_reference_density') \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 --> Bathymetry \n",
"*Properties of bathymetry in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Reference Dates\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Reference date of bathymetry*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.reference_dates') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Present day\" \n",
"# \"21000 years BP\" \n",
"# \"6000 years BP\" \n",
"# \"LGM\" \n",
"# \"Pliocene\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Type\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the bathymetry fixed in time in the ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.type') \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": [
"### 3.3. Ocean Smoothing\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe any smoothing or hand editing of bathymetry in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.ocean_smoothing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.4. Source\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe source of bathymetry in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.bathymetry.source') \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 --> Nonoceanic Waters \n",
"*Non oceanic waters treatement in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Isolated Seas\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how isolated seas is performed*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.isolated_seas') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. River Mouth\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe if/how river mouth mixing or estuaries specific treatment is performed*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.nonoceanic_waters.river_mouth') \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 --> Software Properties \n",
"*Software properties of ocean code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.software_properties.repository') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.2. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.software_properties.code_version') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.3. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.software_properties.code_languages') \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": [
"# 6. Key Properties --> Resolution \n",
"*Resolution in the ocean grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.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": [
"### 6.2. Canonical Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.canonical_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Range Horizontal Resolution\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Range of horizontal resolution with spatial details, eg. 50(Equator)-100km or 0.1-0.5 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.range_horizontal_resolution') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Number Of Horizontal Gridpoints\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_horizontal_gridpoints') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.5. Number Of Vertical Levels\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of vertical levels resolved on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.number_of_vertical_levels') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.6. Is Adaptive Grid\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Default is False. Set true if grid resolution changes during execution.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.is_adaptive_grid') \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": [
"### 6.7. Thickness Level 1\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Thickness of first surface ocean level (in meters)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.resolution.thickness_level_1') \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 --> Tuning Applied \n",
"*Tuning methodology for ocean component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.description') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.2. Global Mean Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List set of metrics of the global mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.global_mean_metrics_used') \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": [
"### 7.3. Regional Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List of regional metrics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.regional_metrics_used') \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": [
"### 7.4. Trend Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List observed trend metrics used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.tuning_applied.trend_metrics_used') \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": [
"# 8. Key Properties --> Conservation \n",
"*Conservation in the ocean component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Brief description of conservation methodology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.description') \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. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Properties conserved in the ocean by the numerical schemes*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.scheme') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Energy\" \n",
"# \"Enstrophy\" \n",
"# \"Salt\" \n",
"# \"Volume of ocean\" \n",
"# \"Momentum\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Consistency Properties\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Any additional consistency properties (energy conversion, pressure gradient discretisation, ...)?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.consistency_properties') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Corrected Conserved Prognostic Variables\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Set of variables which are conserved by *more* than the numerical scheme alone.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.corrected_conserved_prognostic_variables') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Was Flux Correction Used\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Does conservation involve flux correction ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.key_properties.conservation.was_flux_correction_used') \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": [
"# 9. Grid \n",
"*Ocean grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of grid in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.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": [
"# 10. Grid --> Discretisation --> Vertical \n",
"*Properties of vertical discretisation in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Coordinates\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of vertical coordinates in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.vertical.coordinates') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Z-coordinate\" \n",
"# \"Z*-coordinate\" \n",
"# \"S-coordinate\" \n",
"# \"Isopycnic - sigma 0\" \n",
"# \"Isopycnic - sigma 2\" \n",
"# \"Isopycnic - sigma 4\" \n",
"# \"Isopycnic - other\" \n",
"# \"Hybrid / Z+S\" \n",
"# \"Hybrid / Z+isopycnic\" \n",
"# \"Hybrid / other\" \n",
"# \"Pressure referenced (P)\" \n",
"# \"P*\" \n",
"# \"Z**\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 10.2. Partial Steps\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Using partial steps with Z or Z* vertical coordinate in ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.vertical.partial_steps') \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": [
"# 11. Grid --> Discretisation --> Horizontal \n",
"*Type of horizontal discretisation scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal grid type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Lat-lon\" \n",
"# \"Rotated north pole\" \n",
"# \"Two north poles (ORCA-style)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.2. Staggering\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Horizontal grid staggering type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.staggering') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Arakawa B-grid\" \n",
"# \"Arakawa C-grid\" \n",
"# \"Arakawa E-grid\" \n",
"# \"N/a\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Horizontal discretisation scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.grid.discretisation.horizontal.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Finite difference\" \n",
"# \"Finite volumes\" \n",
"# \"Finite elements\" \n",
"# \"Unstructured grid\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Timestepping Framework \n",
"*Ocean Timestepping Framework*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.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": [
"### 12.2. Diurnal Cycle\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Diurnal cycle type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.diurnal_cycle') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Via coupling\" \n",
"# \"Specific treatment\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Timestepping Framework --> Tracers \n",
"*Properties of tracers time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Tracers time stepping scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.tracers.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Leap-frog + Asselin filter\" \n",
"# \"Leap-frog + Periodic Euler\" \n",
"# \"Predictor-corrector\" \n",
"# \"Runge-Kutta 2\" \n",
"# \"AM3-LF\" \n",
"# \"Forward-backward\" \n",
"# \"Forward operator\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 13.2. Time Step\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Tracers time step (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.tracers.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 14. Timestepping Framework --> Baroclinic Dynamics \n",
"*Baroclinic dynamics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Baroclinic dynamics type*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Preconditioned conjugate gradient\" \n",
"# \"Sub cyling\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Baroclinic dynamics scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Leap-frog + Asselin filter\" \n",
"# \"Leap-frog + Periodic Euler\" \n",
"# \"Predictor-corrector\" \n",
"# \"Runge-Kutta 2\" \n",
"# \"AM3-LF\" \n",
"# \"Forward-backward\" \n",
"# \"Forward operator\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Time Step\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Baroclinic time step (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.baroclinic_dynamics.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 15. Timestepping Framework --> Barotropic \n",
"*Barotropic time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Splitting\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Time splitting method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.splitting') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"split explicit\" \n",
"# \"implicit\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.2. Time Step\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Barotropic time step (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.barotropic.time_step') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 16. Timestepping Framework --> Vertical Physics \n",
"*Vertical physics time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Method\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Details of vertical time stepping in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.timestepping_framework.vertical_physics.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 17. Advection \n",
"*Ocean advection*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 17.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of advection in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.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": [
"# 18. Advection --> Momentum \n",
"*Properties of lateral momemtum advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 18.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of lateral momemtum advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.momentum.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Flux form\" \n",
"# \"Vector form\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 18.2. Scheme Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of ocean momemtum advection scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.momentum.scheme_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": [
"### 18.3. ALE\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Using ALE for vertical advection ? (if vertical coordinates are sigma)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.momentum.ALE') \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": [
"# 19. Advection --> Lateral Tracers \n",
"*Properties of lateral tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 19.1. Order\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Order of lateral tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.2. Flux Limiter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Monotonic flux limiter for lateral tracer advection scheme in ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.flux_limiter') \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": [
"### 19.3. Effective Order\n",
"**Is Required:** TRUE **Type:** FLOAT **Cardinality:** 1.1\n",
"### *Effective order of limited lateral tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.effective_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.4. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Descriptive text for lateral tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.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": [
"### 19.5. Passive Tracers\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Passive tracers advected*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Ideal age\" \n",
"# \"CFC 11\" \n",
"# \"CFC 12\" \n",
"# \"SF6\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 19.6. Passive Tracers Advection\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Is advection of passive tracers different than active ? if so, describe.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.lateral_tracers.passive_tracers_advection') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 20. Advection --> Vertical Tracers \n",
"*Properties of vertical tracer advection scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 20.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Descriptive text for vertical tracer advection scheme in ocean (e.g. MUSCL, PPM-H5, PRATHER,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.vertical_tracers.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": [
"### 20.2. Flux Limiter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Monotonic flux limiter for vertical tracer advection scheme in ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.advection.vertical_tracers.flux_limiter') \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": [
"# 21. Lateral Physics \n",
"*Ocean lateral physics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 21.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of lateral physics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.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": [
"### 21.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of transient eddy representation in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Eddy active\" \n",
"# \"Eddy admitting\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 22. Lateral Physics --> Momentum --> Operator \n",
"*Properties of lateral physics operator for momentum in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 22.1. Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Direction of lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Horizontal\" \n",
"# \"Isopycnal\" \n",
"# \"Isoneutral\" \n",
"# \"Geopotential\" \n",
"# \"Iso-level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.2. Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Order of lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Harmonic\" \n",
"# \"Bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 22.3. Discretisation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Discretisation of lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.operator.discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Second order\" \n",
"# \"Higher order\" \n",
"# \"Flux limiter\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 23. Lateral Physics --> Momentum --> Eddy Viscosity Coeff \n",
"*Properties of eddy viscosity coeff in lateral physics momemtum scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 23.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Lateral physics momemtum eddy viscosity coeff type in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Space varying\" \n",
"# \"Time + space varying (Smagorinsky)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.2. Constant Coefficient\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant, value of eddy viscosity coeff in lateral physics momemtum scheme (in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.constant_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.3. Variable Coefficient\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If space-varying, describe variations of eddy viscosity coeff in lateral physics momemtum scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.variable_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.4. Coeff Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe background eddy viscosity coeff in lateral physics momemtum scheme (give values in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 23.5. Coeff Backscatter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there backscatter in eddy viscosity coeff in lateral physics momemtum scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.momentum.eddy_viscosity_coeff.coeff_backscatter') \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": [
"# 24. Lateral Physics --> Tracers \n",
"*Properties of lateral physics for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 24.1. Mesoscale Closure\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there a mesoscale closure in the lateral physics tracers scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.mesoscale_closure') \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": [
"### 24.2. Submesoscale Mixing\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there a submesoscale mixing parameterisation (i.e Fox-Kemper) in the lateral physics tracers scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.submesoscale_mixing') \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": [
"# 25. Lateral Physics --> Tracers --> Operator \n",
"*Properties of lateral physics operator for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 25.1. Direction\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Direction of lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.direction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Horizontal\" \n",
"# \"Isopycnal\" \n",
"# \"Isoneutral\" \n",
"# \"Geopotential\" \n",
"# \"Iso-level\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.2. Order\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Order of lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Harmonic\" \n",
"# \"Bi-harmonic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 25.3. Discretisation\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Discretisation of lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.operator.discretisation') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Second order\" \n",
"# \"Higher order\" \n",
"# \"Flux limiter\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 26. Lateral Physics --> Tracers --> Eddy Diffusity Coeff \n",
"*Properties of eddy diffusity coeff in lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 26.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Lateral physics tracers eddy diffusity coeff type in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Space varying\" \n",
"# \"Time + space varying (Smagorinsky)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.2. Constant Coefficient\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant, value of eddy diffusity coeff in lateral physics tracers scheme (in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.constant_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.3. Variable Coefficient\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If space-varying, describe variations of eddy diffusity coeff in lateral physics tracers scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.variable_coefficient') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.4. Coeff Background\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Describe background eddy diffusity coeff in lateral physics tracers scheme (give values in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 26.5. Coeff Backscatter\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there backscatter in eddy diffusity coeff in lateral physics tracers scheme ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_diffusity_coeff.coeff_backscatter') \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": [
"# 27. Lateral Physics --> Tracers --> Eddy Induced Velocity \n",
"*Properties of eddy induced velocity (EIV) in lateral physics tracers scheme in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 27.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of EIV in lateral physics tracers in the ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"GM\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.2. Constant Val\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If EIV scheme for tracers is constant, specify coefficient value (M2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.constant_val') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 27.3. Flux Type\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Type of EIV flux (advective or skew)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.flux_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": [
"### 27.4. Added Diffusivity\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Type of EIV added diffusivity (constant, flow dependent or none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.lateral_physics.tracers.eddy_induced_velocity.added_diffusivity') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 28. Vertical Physics \n",
"*Ocean Vertical Physics*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 28.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of vertical physics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.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": [
"# 29. Vertical Physics --> Boundary Layer Mixing --> Details \n",
"*Properties of vertical physics in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 29.1. Langmuir Cells Mixing\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there Langmuir cells mixing in upper ocean ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.details.langmuir_cells_mixing') \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": [
"# 30. Vertical Physics --> Boundary Layer Mixing --> Tracers \n",
"*Properties of boundary layer (BL) mixing on tracers in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 30.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of boundary layer mixing for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure - TKE\" \n",
"# \"Turbulent closure - KPP\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Turbulent closure - Bulk Mixed Layer\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.2. Closure Order\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If turbulent BL mixing of tracers, specific order of closure (0, 1, 2.5, 3)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.3. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant BL mixing of tracers, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 30.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background BL mixing of tracers coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.tracers.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 31. Vertical Physics --> Boundary Layer Mixing --> Momentum \n",
"*Properties of boundary layer (BL) mixing on momentum in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 31.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of boundary layer mixing for momentum in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure - TKE\" \n",
"# \"Turbulent closure - KPP\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Turbulent closure - Bulk Mixed Layer\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.2. Closure Order\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *If turbulent BL mixing of momentum, specific order of closure (0, 1, 2.5, 3)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.closure_order') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.3. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant BL mixing of momentum, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 31.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background BL mixing of momentum coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.boundary_layer_mixing.momentum.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 32. Vertical Physics --> Interior Mixing --> Details \n",
"*Properties of interior mixing in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 32.1. Convection Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of vertical convection in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.convection_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Non-penetrative convective adjustment\" \n",
"# \"Enhanced vertical diffusion\" \n",
"# \"Included in turbulence closure\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.2. Tide Induced Mixing\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe how tide induced mixing is modelled (barotropic, baroclinic, none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.tide_induced_mixing') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 32.3. Double Diffusion\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there double diffusion*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.double_diffusion') \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": [
"### 32.4. Shear Mixing\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there interior shear mixing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.details.shear_mixing') \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": [
"# 33. Vertical Physics --> Interior Mixing --> Tracers \n",
"*Properties of interior mixing on tracers in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 33.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of interior mixing for tracers in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure / TKE\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.2. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant interior mixing of tracers, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.3. Profile\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Is the background interior mixing using a vertical profile for tracers (i.e is NOT constant) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.profile') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 33.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background interior mixing of tracers coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.tracers.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 34. Vertical Physics --> Interior Mixing --> Momentum \n",
"*Properties of interior mixing on momentum in the ocean *"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 34.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of interior mixing for momentum in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant value\" \n",
"# \"Turbulent closure / TKE\" \n",
"# \"Turbulent closure - Mellor-Yamada\" \n",
"# \"Richardson number dependent - PP\" \n",
"# \"Richardson number dependent - KT\" \n",
"# \"Imbeded as isopycnic vertical coordinate\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.2. Constant\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If constant interior mixing of momentum, specific coefficient (m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.constant') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.3. Profile\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Is the background interior mixing using a vertical profile for momentum (i.e is NOT constant) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.profile') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 34.4. Background\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Background interior mixing of momentum coefficient, (schema and value in m2/s - may by none)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.vertical_physics.interior_mixing.momentum.background') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 35. Uplow Boundaries --> Free Surface \n",
"*Properties of free surface in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 35.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of free surface in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.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": [
"### 35.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Free surface scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Linear implicit\" \n",
"# \"Linear filtered\" \n",
"# \"Linear semi-explicit\" \n",
"# \"Non-linear implicit\" \n",
"# \"Non-linear filtered\" \n",
"# \"Non-linear semi-explicit\" \n",
"# \"Fully explicit\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 35.3. Embeded Seaice\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the sea-ice embeded in the ocean model (instead of levitating) ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.free_surface.embeded_seaice') \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": [
"# 36. Uplow Boundaries --> Bottom Boundary Layer \n",
"*Properties of bottom boundary layer in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 36.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of bottom boundary layer in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.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": [
"### 36.2. Type Of Bbl\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of bottom boundary layer in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.type_of_bbl') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Diffusive\" \n",
"# \"Acvective\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.3. Lateral Mixing Coef\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If bottom BL is diffusive, specify value of lateral mixing coefficient (in m2/s)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.lateral_mixing_coef') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 36.4. Sill Overflow\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe any specific treatment of sill overflows*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.uplow_boundaries.bottom_boundary_layer.sill_overflow') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 37. Boundary Forcing \n",
"*Ocean boundary forcing*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 37.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of boundary forcing in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.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": [
"### 37.2. Surface Pressure\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe how surface pressure is transmitted to ocean (via sea-ice, nothing specific,...)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.surface_pressure') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.3. Momentum Flux Correction\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any type of ocean surface momentum flux correction and, if applicable, how it is applied and where.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.momentum_flux_correction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.4. Tracers Flux Correction\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe any type of ocean surface tracers flux correction and, if applicable, how it is applied and where.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers_flux_correction') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.5. Wave Effects\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe if/how wave effects are modelled at ocean surface.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.wave_effects') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.6. River Runoff Budget\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe how river runoff from land surface is routed to ocean and any global adjustment done.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.river_runoff_budget') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 37.7. Geothermal Heating\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Describe if/how geothermal heating is present at ocean bottom.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.geothermal_heating') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 38. Boundary Forcing --> Momentum --> Bottom Friction \n",
"*Properties of momentum bottom friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 38.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of momentum bottom friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.momentum.bottom_friction.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Linear\" \n",
"# \"Non-linear\" \n",
"# \"Non-linear (drag function of speed of tides)\" \n",
"# \"Constant drag coefficient\" \n",
"# \"None\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 39. Boundary Forcing --> Momentum --> Lateral Friction \n",
"*Properties of momentum lateral friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 39.1. Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of momentum lateral friction in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.momentum.lateral_friction.type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Free-slip\" \n",
"# \"No-slip\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 40. Boundary Forcing --> Tracers --> Sunlight Penetration \n",
"*Properties of sunlight penetration scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 40.1. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of sunlight penetration scheme in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"1 extinction depth\" \n",
"# \"2 extinction depth\" \n",
"# \"3 extinction depth\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 40.2. Ocean Colour\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the ocean sunlight penetration scheme ocean colour dependent ?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.ocean_colour') \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": [
"### 40.3. Extinction Depth\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Describe and list extinctions depths for sunlight penetration scheme (if applicable).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.sunlight_penetration.extinction_depth') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 41. Boundary Forcing --> Tracers --> Fresh Water Forcing \n",
"*Properties of surface fresh water forcing in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 41.1. From Atmopshere\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of surface fresh water forcing from atmos in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_atmopshere') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Freshwater flux\" \n",
"# \"Virtual salt flux\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.2. From Sea Ice\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Type of surface fresh water forcing from sea-ice in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.from_sea_ice') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Freshwater flux\" \n",
"# \"Virtual salt flux\" \n",
"# \"Real salt flux\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 41.3. Forced Mode Restoring\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Type of surface salinity restoring in forced mode (OMIP)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.ocean.boundary_forcing.tracers.fresh_water_forcing.forced_mode_restoring') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \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 |
tpin3694/tpin3694.github.io | python/pandas_find_unique_values.ipynb | 1 | 5116 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Find Unique Values In Pandas Dataframes \n",
"Slug: pandas_find_unique_values \n",
"Summary: Find Unique Values In Pandas Dataframes \n",
"Date: 2016-05-01 12:00 \n",
"Category: Python \n",
"Tags: Data Wrangling \n",
"Authors: Chris Albon "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"raw_data = {'regiment': ['51st', '29th', '2nd', '19th', '12th', '101st', '90th', '30th', '193th', '1st', '94th', '91th'], \n",
" 'trucks': ['MAZ-7310', np.nan, 'MAZ-7310', 'MAZ-7310', 'Tatra 810', 'Tatra 810', 'Tatra 810', 'Tatra 810', 'ZIS-150', 'Tatra 810', 'ZIS-150', 'ZIS-150'],\n",
" 'tanks': ['Merkava Mark 4', 'Merkava Mark 4', 'Merkava Mark 4', 'Leopard 2A6M', 'Leopard 2A6M', 'Leopard 2A6M', 'Arjun MBT', 'Leopard 2A6M', 'Arjun MBT', 'Arjun MBT', 'Arjun MBT', 'Arjun MBT'],\n",
" 'aircraft': ['none', 'none', 'none', 'Harbin Z-9', 'Harbin Z-9', 'none', 'Harbin Z-9', 'SH-60B Seahawk', 'SH-60B Seahawk', 'SH-60B Seahawk', 'SH-60B Seahawk', 'SH-60B Seahawk']}\n",
"\n",
"df = pd.DataFrame(raw_data, columns = ['regiment', 'trucks', 'tanks', 'aircraft'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>regiment</th>\n",
" <th>trucks</th>\n",
" <th>tanks</th>\n",
" <th>aircraft</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>51st</td>\n",
" <td>MAZ-7310</td>\n",
" <td>Merkava Mark 4</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>29th</td>\n",
" <td>NaN</td>\n",
" <td>Merkava Mark 4</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2nd</td>\n",
" <td>MAZ-7310</td>\n",
" <td>Merkava Mark 4</td>\n",
" <td>none</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>19th</td>\n",
" <td>MAZ-7310</td>\n",
" <td>Leopard 2A6M</td>\n",
" <td>Harbin Z-9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>12th</td>\n",
" <td>Tatra 810</td>\n",
" <td>Leopard 2A6M</td>\n",
" <td>Harbin Z-9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" regiment trucks tanks aircraft\n",
"0 51st MAZ-7310 Merkava Mark 4 none\n",
"1 29th NaN Merkava Mark 4 none\n",
"2 2nd MAZ-7310 Merkava Mark 4 none\n",
"3 19th MAZ-7310 Leopard 2A6M Harbin Z-9\n",
"4 12th Tatra 810 Leopard 2A6M Harbin Z-9"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# View the top few rows\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[nan, 'Tatra 810', 'MAZ-7310', 'ZIS-150']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a list of unique values by turning the\n",
"# pandas column into a set\n",
"list(set(df.trucks))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['MAZ-7310', nan, 'Tatra 810', 'ZIS-150']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a list of unique values in df.trucks\n",
"list(df['trucks'].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 |
KwatME/match | code/2.ipynb | 1 | 2979 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:30:18.520186Z",
"iopub.status.busy": "2021-06-05T22:30:18.519425Z",
"iopub.status.idle": "2021-06-05T22:30:18.532898Z",
"shell.execute_reply": "2021-06-05T22:30:18.533367Z"
},
"tags": []
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:30:18.536833Z",
"iopub.status.busy": "2021-06-05T22:30:18.536240Z",
"iopub.status.idle": "2021-06-05T22:30:19.080764Z",
"shell.execute_reply": "2021-06-05T22:30:19.081300Z"
},
"tags": []
},
"outputs": [],
"source": [
"from __init__ import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:30:19.085295Z",
"iopub.status.busy": "2021-06-05T22:30:19.084718Z",
"iopub.status.idle": "2021-06-05T22:30:19.125883Z",
"shell.execute_reply": "2021-06-05T22:30:19.126293Z"
},
"tags": []
},
"outputs": [],
"source": [
"nu_fe_sa = pd.read_csv(\"../output/number_feature_sample.tsv\", \"\\t\", index_col=0)\n",
"\n",
"bi_fe_sa = pd.read_csv(\"../input/01_feature_sample.tsv\", \"\\t\", index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2021-06-05T22:30:19.131101Z",
"iopub.status.busy": "2021-06-05T22:30:19.130487Z",
"iopub.status.idle": "2021-06-05T22:30:19.164549Z",
"shell.execute_reply": "2021-06-05T22:30:19.164968Z"
},
"tags": []
},
"outputs": [],
"source": [
"bi_co_sa = pd.DataFrame(columns=nu_fe_sa.columns)\n",
"\n",
"for co in SETTING[\"co_\"]:\n",
"\n",
" if isinstance(co, str):\n",
"\n",
" bi_co_sa.loc[co, :] = bi_fe_sa.loc[co, :]\n",
"\n",
" else:\n",
"\n",
" fe0_, fe1_ = co\n",
"\n",
" co = \"{} vs {}\".format(\",\".join(fe0_), \",\".join(fe1_))\n",
"\n",
" for fe_, it in [[fe0_, 0], [fe1_, 1]]:\n",
"\n",
" bi_co_sa.loc[co, bi_fe_sa.loc[fe_].all()] = it\n",
"\n",
"bi_co_sa.index.name = \"Comparison\"\n",
"\n",
"bi_co_sa.to_csv(\"../output/01_comparison_sample.tsv\", \"\\t\")\n",
"\n",
"bi_co_sa"
]
}
],
"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": "3.8.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
steinam/teacher | jup_notebooks/data-science-ipython-notebooks-master/matplotlib/04.15-Further-Resources.ipynb | 1 | 5234 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--BOOK_INFORMATION-->\n",
"<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
"*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
"\n",
"*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*\n",
"\n",
"*No changes were made to the contents of this notebook from the original.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--NAVIGATION-->\n",
"< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.ipynb) >"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Further Resources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Matplotlib Resources\n",
"\n",
"A single chapter in a book can never hope to cover all the available features and plot types available in Matplotlib.\n",
"As with other packages we've seen, liberal use of IPython's tab-completion and help functions (see [Help and Documentation in IPython](01.01-Help-And-Documentation.ipynb)) can be very helpful when exploring Matplotlib's API.\n",
"In addition, Matplotlib’s [online documentation](http://matplotlib.org/) can be a helpful reference.\n",
"See in particular the [Matplotlib gallery](http://matplotlib.org/gallery.html) linked on that page: it shows thumbnails of hundreds of different plot types, each one linked to a page with the Python code snippet used to generate it.\n",
"In this way, you can visually inspect and learn about a wide range of different plotting styles and visualization techniques.\n",
"\n",
"For a book-length treatment of Matplotlib, I would recommend [*Interactive Applications Using Matplotlib*](https://www.packtpub.com/application-development/interactive-applications-using-matplotlib), written by Matplotlib core developer Ben Root."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Other Python Graphics Libraries\n",
"\n",
"Although Matplotlib is the most prominent Python visualization library, there are other more modern tools that are worth exploring as well.\n",
"I'll mention a few of them briefly here:\n",
"\n",
"- [Bokeh](http://bokeh.pydata.org) is a JavaScript visualization library with a Python frontend that creates highly interactive visualizations capable of handling very large and/or streaming datasets. The Python front-end outputs a JSON data structure that can be interpreted by the Bokeh JS engine.\n",
"- [Plotly](http://plot.ly) is the eponymous open source product of the Plotly company, and is similar in spirit to Bokeh. Because Plotly is the main product of a startup, it is receiving a high level of development effort. Use of the library is entirely free.\n",
"- [Vispy](http://vispy.org/) is an actively developed project focused on dynamic visualizations of very large datasets. Because it is built to target OpenGL and make use of efficient graphics processors in your computer, it is able to render some quite large and stunning visualizations.\n",
"- [Vega](https://vega.github.io/) and [Vega-Lite](https://vega.github.io/vega-lite) are declarative graphics representations, and are the product of years of research into the fundamental language of data visualization. The reference rendering implementation is JavaScript, but the API is language agnostic. There is a Python API under development in the [Altair](https://altair-viz.github.io/) package. Though as of summer 2016 it's not yet fully mature, I'm quite excited for the possibilities of this project to provide a common reference point for visualization in Python and other languages.\n",
"\n",
"The visualization space in the Python community is very dynamic, and I fully expect this list to be out of date as soon as it is published.\n",
"Keep an eye out for what's coming in the future!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!--NAVIGATION-->\n",
"< [Visualization with Seaborn](04.14-Visualization-With-Seaborn.ipynb) | [Contents](Index.ipynb) | [Machine Learning](05.00-Machine-Learning.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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
unmrds/cc-python | Loops and Functions.ipynb | 2 | 33105 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python 2: Loops & Functions\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First: A Review of Lists and Dictionaries"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[] <class 'list'>\n",
"['avengers', 'frozen', 'cats']\n",
"['j', 'u', 'm', 'a', 'n', 'j', 'i']\n",
"[2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
]
}
],
"source": [
"# A list is a mutable, ordered collection\n",
"# Collections can be heterogenous: integers, strings, other data structures, etc.\n",
"# can all be added to a single list.\n",
"\n",
"# Creating lists:\n",
"\n",
"# Initialize an empty list:\n",
"my_movies = []\n",
"print(my_movies, type(my_movies))\n",
"\n",
"# Initialize a list with one or more objects in it\n",
"unwatched = ['avengers', 'frozen', 'cats']\n",
"print(unwatched)\n",
"\n",
"# Use the list() type constructor\n",
"# Note that the constructed \"list\" may not be what we expect \n",
"watched = list('jumanji')\n",
"print(watched)\n",
"\n",
"# Use list comprehension\n",
"even_numbers = [i for i in range(2, 20, 2)]\n",
"print(even_numbers)"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7\n"
]
}
],
"source": [
"# We can use index numbers to select and slice from lists\n",
"print(len(watched))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'j'"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# The first object is a list has index/position 0\n",
"watched[0]"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"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-68-96938fcea399>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# So even though the list is 7 objects long, if we try to select the object in position 7\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# we will get an index out of range error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mwatched\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"source": [
"# So even though the list is 7 objects long, if we try to select the object in position 7\n",
"# we will get an index out of range error\n",
"watched[7]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'i'"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watched[6]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['j', 'u', 'm']"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# We can slice out subsections using start and finish index positions separated by a colon\n",
"# Note the \"finish\" position should be read as \"up to but not including\" the object at that position\n",
"watched[0:3]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['j', 'u', 'm']"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same output as above\n",
"watched[:3]"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['n', 'j', 'i']"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"watched[4:7]"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['n', 'j', 'i']"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Same output as above\n",
"watched[4:]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['j', 'u', 'm', 'a', 'n', 'j', 'i']\n",
"['jumanji', 'u', 'm', 'a', 'n', 'j', 'i']\n"
]
}
],
"source": [
"# Lists are mutable - we can edit, add, remove objects\n",
"print(watched)\n",
"watched[0] = 'jumanji'\n",
"print(watched)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['jumanji', 'm', 'a', 'n', 'j', 'i']\n"
]
}
],
"source": [
"watched.remove('u')\n",
"print(watched)"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['jumanji']\n"
]
}
],
"source": [
"# There are other ways we can fix the \"watched\" list, but \n",
"# in this case it is most efficient to reassign the \"watched\" variable as a new list\n",
"watched = [watched[0]]\n",
"print(watched)"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"[['avengers', 'frozen', 'cats']]\n",
"[['avengers', 'frozen', 'cats'], ['jumanji']]\n"
]
}
],
"source": [
"# We can make a list of lists using append()\n",
"print(my_movies)\n",
"my_movies.append(unwatched)\n",
"print(my_movies)\n",
"my_movies.append(watched)\n",
"print(my_movies)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is okay - we have a master list of all of our movies. The nested lists break our collection down further by watched and unwatched, but we don't know which list is which.\n",
"\n",
"One way to improve this design is to use a dictionary."
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'unwatched': ['avengers', 'frozen', 'cats']}\n"
]
}
],
"source": [
"# A dictionary is a mutable, unordered list of key-value pairs\n",
"# Like lists, the values in a dictionary can be composed of different data types or structures\n",
"\n",
"# Re-assign my_movies to an empty dictionary\n",
"my_movies = {}\n",
"\n",
"# Now add our lists of unwatched and watched movies\n",
"# Note we are using a string as the key and the list as the value\n",
"my_movies[\"unwatched\"] = unwatched\n",
"print(my_movies)"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'unwatched': ['avengers', 'frozen', 'cats'], 'watched': ['jumanji']}\n"
]
}
],
"source": [
"my_movies[\"watched\"] = watched\n",
"print(my_movies)"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['unwatched', 'watched'])\n"
]
}
],
"source": [
"# We retrieve items from a dictionary using the keys.\n",
"# If we don't know the keys, we can ask for them first using the 'keys()' attribute:\n",
"print(my_movies.keys())"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['jumanji']\n"
]
}
],
"source": [
"print(my_movies['watched'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Building a Workflow with US Census Surname Data\n",
"\n",
"Using the 2010 surname data from the US Census, we will develop a workflow to accomplish the following:\n",
"\n",
"* Write functions to:\n",
" * Request information about surnames\n",
" * Tabulate data\n",
" * Visualize data\n",
"\n",
"## Sample dataset\n",
"\n",
"_Decennial Census Surname Files (2010)_\n",
"\n",
"[https://www.census.gov/data/developers/data-sets/surnames.html](https://www.census.gov/data/developers/data-sets/surnames.html)\n",
"\n",
"[https://api.census.gov/data/2010/surname.html](https://api.census.gov/data/2010/surname.html)\n",
"\n",
"#### Citation \n",
"\n",
"US Census Bureau (2016) _Decennial Census Surname Files (2010)_ Retrieved from [https://api.census.gov/data/2010/surname.json](https://api.census.gov/data/2010/surname.json)structures\n",
"\n",
"----------------------------------------------------------------------------------------------------\n",
"\n",
"## 1. Import modules\n",
"\n",
"The modules used in this exercise are popular and under active development. Follow the links for more information about methods, syntax, etc.\n",
"\n",
"**Requests:** [http://docs.python-requests.org/en/master/](http://docs.python-requests.org/en/master/)\n",
"\n",
"**JSON:** [https://docs.python.org/3/library/json.html](https://docs.python.org/3/library/json.html)\n",
"\n",
"**Pandas:** [http://pandas.pydata.org/](http://pandas.pydata.org/)\n",
"\n",
"**Matplotlib:** [https://matplotlib.org/](https://matplotlib.org/)\n",
"\n",
"Look for information about or links to the API, developer's documentation, etc. Helpful examples are often included.\n",
"\n",
"Note that we are providing an alias for Pandas and matplotlib. Whenever we need to call a method from those module, we can use the alias. "
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [],
"source": [
"# http://api.census.gov/data/2010/surname\n",
"import requests\n",
"import json\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get API and Variable information"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"@context\": \"https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld\",\n",
" \"@id\": \"https://api.census.gov/data/2010/surname.json\",\n",
" \"@type\": \"dcat:Catalog\",\n",
" \"conformsTo\": \"https://project-open-data.cio.gov/v1.1/schema\",\n",
" \"describedBy\": \"https://project-open-data.cio.gov/v1.1/schema/catalog.json\",\n",
" \"dataset\": [\n",
" {\n",
" \"c_vintage\": 2010,\n",
" \"c_dataset\": [\n",
" \"surname\"\n",
" ],\n",
" \"c_geographyLink\": \"https://api.census.gov/data/2010/surname/geography.json\",\n",
" \"c_variablesLink\": \"https://api.census.gov/data/2010/surname/variables.json\",\n",
" \"c_examplesLink\": \"https://api.census.gov/data/2010/surname/examples.json\",\n",
" \"c_groupsLink\": \"https://api.census.gov/data/2010/surname/groups.json\",\n",
" \"c_valuesLink\": \"https://api.census.gov/data/2010/surname/values.json\",\n",
" \"c_documentationLink\": \"http://www.census.gov/developer/\",\n",
" \"c_isAggregate\": true,\n",
" \"c_isAvailable\": true,\n",
" \"@type\": \"dcat:Dataset\",\n",
" \"title\": \"2010 Decennial Census of Population and Housing: Surnames\",\n",
" \"accessLevel\": \"public\",\n",
" \"bureauCode\": [\n",
" \"006:07\"\n",
" ],\n",
" \"description\": \"The Census Bureau's Census surnames product is a data release based on names recorded in the decennial census. The product contains rank and frequency data on surnames reported 100 or more times in the decennial census, along with Hispanic origin and race category percentages. The latter are suppressed where necessary for confidentiality. The data focus on summarized aggregates of counts and characteristics associated with surnames, and the data do not in any way identify any specific individuals.\",\n",
" \"distribution\": [\n",
" {\n",
" \"@type\": \"dcat:Distribution\",\n",
" \"accessURL\": \"https://api.census.gov/data/2010/surname\",\n",
" \"description\": \"API endpoint\",\n",
" \"format\": \"API\",\n",
" \"mediaType\": \"application/json\",\n",
" \"title\": \"API endpoint\"\n",
" }\n",
" ],\n",
" \"contactPoint\": {\n",
" \"fn\": \"Joshua Comenetz\",\n",
" \"hasEmail\": \"[email protected]\"\n",
" },\n",
" \"identifier\": \"http://api.census.gov/data/id/DecennialSurname2010\",\n",
" \"keyword\": [],\n",
" \"license\": \"http://creativecommons.org/publicdomain/zero/1.0/Public Domain\",\n",
" \"modified\": \"2016-12-15\",\n",
" \"programCode\": [\n",
" \"006:004\"\n",
" ],\n",
" \"references\": [\n",
" \"http://www.census.gov/developers/\"\n",
" ],\n",
" \"spatial\": \"United States\",\n",
" \"temporal\": \"2010/2010\",\n",
" \"publisher\": {\n",
" \"@type\": \"org:Organization\",\n",
" \"name\": \"U.S. Census Bureau\",\n",
" \"subOrganizationOf\": {\n",
" \"@type\": \"org:Organization\",\n",
" \"name\": \"U.S. Department Of Commerce\",\n",
" \"subOrganizationOf\": {\n",
" \"@type\": \"org:Organization\",\n",
" \"name\": \"U.S. Government\"\n",
" }\n",
" }\n",
" }\n",
" }\n",
" ]\n",
"}\n"
]
}
],
"source": [
"# First, get the basic info about the dataset.\n",
"# References: Dataset API (https://api.census.gov/data/2010/surname.html) \n",
"# Requests API (http://docs.python-requests.org/en/master/)\n",
"# Python 3 JSON API (https://docs.python.org/3/library/json.html)\n",
"\n",
"api_base_url = \"http://api.census.gov/data/2010/surname\"\n",
"api_info = requests.get(api_base_url) \n",
"api_json = api_info.json() \n",
"\n",
"# Uncomment the next line(s) to see the response content.\n",
"# NOTE: JSON and TEXT don't look much different to us. They can look very different to a machine!\n",
"#print(api_info.text)\n",
"print(json.dumps(api_json, indent=4))\n",
"\n",
"# The output is a dictionary - data are stored as key:value pairs and can be nested."
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://api.census.gov/data/2010/surname/variables.json\n"
]
}
],
"source": [
"# Request and store a local copy of the dataset variables. \n",
"# Note that the URL could be hard coded just from referencing the API, but\n",
"# we are navigating the JSON data.\n",
"var_link = api_json['dataset'][0]['c_variablesLink']\n",
"print(var_link)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"PCTAPI\": {\n",
" \"label\": \"Percent Non-Hispanic Asian and Native Hawaiian and Other Pacific Islander Alone\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"PCTBLACK\": {\n",
" \"label\": \"Percent Non-Hispanic Black or African American Alone\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"PCTAIAN\": {\n",
" \"label\": \"Percent Non-Hispanic American Indian and Alaska Native Alone\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"CUM_PROP100K\": {\n",
" \"label\": \"Cumulative proportion per 100,000 population\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"PROP100K\": {\n",
" \"label\": \"Proportion per 100,000 population for name\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"PCTWHITE\": {\n",
" \"label\": \"Percent Non-Hispanic White Alone\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"RANK\": {\n",
" \"label\": \"National Rank\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"COUNT\": {\n",
" \"label\": \"Frequency: number of occurrences nationally\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"PCT2PRACE\": {\n",
" \"label\": \"Percent Non-Hispanic Two or More Races\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"PCTHISPANIC\": {\n",
" \"label\": \"Percent Hispanic or Latino origin\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"int\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" },\n",
" \"NAME\": {\n",
" \"label\": \"Surname\",\n",
" \"concept\": \"Surnames Variables\",\n",
" \"predicateType\": \"string\",\n",
" \"group\": \"N/A\",\n",
" \"limit\": 0\n",
" }\n",
"}\n"
]
}
],
"source": [
"# Use the variable info link to make a new request\n",
"\n",
"variables = requests.get(var_link)\n",
"jsonData = variables.json()\n",
"variable_data = jsonData['variables']\n",
"\n",
"# Note that this is a dictionary of dictionaries.\n",
"# We are going to use it throughout this exercise.\n",
"print(json.dumps(variable_data, indent=4))"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['PCTAPI', 'PCTBLACK', 'PCTAIAN', 'CUM_PROP100K', 'PROP100K', 'PCTWHITE', 'RANK', 'COUNT', 'PCT2PRACE', 'PCTHISPANIC', 'NAME'])\n"
]
}
],
"source": [
"print(variable_data.keys())"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"# Now request info about a single surname\n",
"# Update 2020-02-26: Surnames should be all caps!\n",
"name = 'WHEELER'\n",
"name_query = '&NAME=' + name"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"# Default vars: 'RANK,COUNT,PCTWHITE,PCTAPI,PCT2PRACE,PCTAIAN,PCTBLACK,PCTHISPANIC'\n",
"desired_vars = 'RANK,COUNT,PCTWHITE,PCTAPI,PCT2PRACE,PCTAIAN,PCTBLACK,PCTHISPANIC'"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Raw response data:\n",
"\n",
"[['RANK', 'COUNT', 'PCTWHITE', 'PCTAPI', 'PCT2PRACE', 'PCTAIAN', 'PCTBLACK', 'PCTHISPANIC', 'NAME'], ['243', '125058', '80.96', '0.5', '1.97', '0.93', '13.29', '2.35', 'WHEELER']]\n",
"\n",
"\n",
"Pandas dataframe:\n"
]
},
{
"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>National Rank</th>\n",
" <th>Frequency: number of occurrences nationally</th>\n",
" <th>Percent Non-Hispanic White Alone</th>\n",
" <th>Percent Non-Hispanic Asian and Native Hawaiian and Other Pacific Islander Alone</th>\n",
" <th>Percent Non-Hispanic Two or More Races</th>\n",
" <th>Percent Non-Hispanic American Indian and Alaska Native Alone</th>\n",
" <th>Percent Non-Hispanic Black or African American Alone</th>\n",
" <th>Percent Hispanic or Latino origin</th>\n",
" <th>Surname</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>243</td>\n",
" <td>125058</td>\n",
" <td>80.96</td>\n",
" <td>0.5</td>\n",
" <td>1.97</td>\n",
" <td>0.93</td>\n",
" <td>13.29</td>\n",
" <td>2.35</td>\n",
" <td>WHEELER</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" National Rank Frequency: number of occurrences nationally \\\n",
"0 243 125058 \n",
"\n",
" Percent Non-Hispanic White Alone \\\n",
"0 80.96 \n",
"\n",
" Percent Non-Hispanic Asian and Native Hawaiian and Other Pacific Islander Alone \\\n",
"0 0.5 \n",
"\n",
" Percent Non-Hispanic Two or More Races \\\n",
"0 1.97 \n",
"\n",
" Percent Non-Hispanic American Indian and Alaska Native Alone \\\n",
"0 0.93 \n",
"\n",
" Percent Non-Hispanic Black or African American Alone \\\n",
"0 13.29 \n",
"\n",
" Percent Hispanic or Latino origin Surname \n",
"0 2.35 WHEELER "
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# References: Pandas (http://pandas.pydata.org/)\n",
"\n",
"base_url = 'http://api.census.gov/data/2010/surname?get='\n",
"query_url = base_url + desired_vars + name_query\n",
"name_stats = requests.get(query_url)\n",
"name_data = name_stats.json()\n",
"\n",
"# The response data are not very human readable.\n",
"print('Raw response data:\\n')\n",
"print(name_data)\n",
"\n",
"# The simplest dataframe would use the variable names returned with the data. Example: PCTWHITE\n",
"# It's easier to read the descriptive labels provide via the variables API.\n",
"# The code block below replaces variable names with labels as it builds the dataframe.\n",
"column_list = []\n",
"\n",
"for each in name_data[0]:\n",
" label = variable_data[each]['label']\n",
" column_list.append(label)\n",
"\n",
"df = pd.DataFrame([name_data[1]], columns=column_list) \n",
"\n",
"print('\\n\\nPandas dataframe:')\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we want to query data for a different surname, we can go back up a few cells, change the value of the \"name\" variable, then re-run all of the other cells. That works in a notebook context but it's not very portable. Instead, we will define a function that can be called later without having to re-execute a bunch of code or code blocks. \n",
"\n",
"In Python, the syntax for defining a function is:\n",
"\n",
"```\n",
"def function_name(arg1, arg2, arg3=Default_value):\n",
" do stuff\n",
" return something\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"def get_surname_data(desired_variables, surname):\n",
" name = str.upper(surname) # API requires surnames in all uppercase\n",
" base_url = 'http://api.census.gov/data/2010/surname?get='\n",
" query_url = base_url + desired_vars + '&NAME=' + name\n",
" name_request = requests.get(query_url)\n",
" name_data = name_request.json()\n",
" return name_data"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[['RANK', 'COUNT', 'PCTWHITE', 'PCTAPI', 'PCT2PRACE', 'PCTAIAN', 'PCTBLACK', 'PCTHISPANIC', 'NAME'], ['5', '1425470', '55.19', '0.44', '2.61', '1', '38.48', '2.29', 'JONES']]\n"
]
}
],
"source": [
"# Now we can get data for any name just by changing the value of the surname variable.\n",
"# We also have the option to update the variables of interest.\n",
"desired_vars = 'RANK,COUNT,PCTWHITE,PCTAPI,PCT2PRACE,PCTAIAN,PCTBLACK,PCTHISPANIC'\n",
"surname = \"Jones\"\n",
"\n",
"print(get_surname_data(desired_vars, surname))"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"# That is not nicely formatted, so we can write another function to create a more human-readable data frame\n",
"\n",
"def format_surname_data(surname_data, variable_data):\n",
" column_list = []\n",
" for each in surname_data[0]:\n",
" label = variable_data[each]['label']\n",
" column_list.append(label)\n",
" surname_dataframe = pd.DataFrame([surname_data[1]], columns=column_list)\n",
" return surname_dataframe"
]
},
{
"cell_type": "code",
"execution_count": 106,
"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>National Rank</th>\n",
" <th>Frequency: number of occurrences nationally</th>\n",
" <th>Percent Non-Hispanic White Alone</th>\n",
" <th>Percent Non-Hispanic Asian and Native Hawaiian and Other Pacific Islander Alone</th>\n",
" <th>Percent Non-Hispanic Two or More Races</th>\n",
" <th>Percent Non-Hispanic American Indian and Alaska Native Alone</th>\n",
" <th>Percent Non-Hispanic Black or African American Alone</th>\n",
" <th>Percent Hispanic or Latino origin</th>\n",
" <th>Surname</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2442977</td>\n",
" <td>70.9</td>\n",
" <td>0.5</td>\n",
" <td>2.19</td>\n",
" <td>0.89</td>\n",
" <td>23.11</td>\n",
" <td>2.4</td>\n",
" <td>SMITH</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" National Rank Frequency: number of occurrences nationally \\\n",
"0 1 2442977 \n",
"\n",
" Percent Non-Hispanic White Alone \\\n",
"0 70.9 \n",
"\n",
" Percent Non-Hispanic Asian and Native Hawaiian and Other Pacific Islander Alone \\\n",
"0 0.5 \n",
"\n",
" Percent Non-Hispanic Two or More Races \\\n",
"0 2.19 \n",
"\n",
" Percent Non-Hispanic American Indian and Alaska Native Alone \\\n",
"0 0.89 \n",
"\n",
" Percent Non-Hispanic Black or African American Alone \\\n",
"0 23.11 \n",
"\n",
" Percent Hispanic or Latino origin Surname \n",
"0 2.4 SMITH "
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now we can get a nice table for any name by calling both functions\n",
"desired_vars = 'RANK,COUNT,PCTWHITE,PCTAPI,PCT2PRACE,PCTAIAN,PCTBLACK,PCTHISPANIC'\n",
"surname = \"Smith\"\n",
"\n",
"surname_data = get_surname_data(desired_vars, surname)\n",
"surname_dataframe = format_surname_data(surname_data, variable_data)\n",
"\n",
"surname_dataframe"
]
},
{
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
tcstewar/testing_notebooks | Random Binding.ipynb | 1 | 216483 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import nengo\n",
"import numpy as np\n",
"import pylab\n",
"import nengo_spa as spa"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's make a random binding network. We use the same structure as the circular convolution network: a hidden layer optimized to do pairwise products, and linear transforms into and out of it. But, instead of using the DFT matrix, we randomly generate the matrices."
]
},
{
"cell_type": "code",
"execution_count": 282,
"metadata": {},
"outputs": [],
"source": [
"D = 16\n",
"D_bind = 32\n",
"scaling_fudge_factor = 2.0\n",
"\n",
"model = spa.Network()\n",
"model.config[nengo.Ensemble].neuron_type=nengo.LIFRate()\n",
"with model:\n",
" in1 = spa.State(D)\n",
" in2 = spa.State(D)\n",
" out = spa.State(D)\n",
" bind = nengo.networks.Product(n_neurons=50, dimensions=D_bind)\n",
" \n",
" T1 = np.random.normal(size=(D_bind, D))\n",
" T2 = np.random.normal(size=(D_bind, D))\n",
" T3 = np.random.normal(size=(D, D_bind))\n",
" T1 = T1 / np.linalg.norm(T1, axis=1)[:, None]*np.sqrt(D)\n",
" T2 = T2 / np.linalg.norm(T2, axis=1)[:, None]*np.sqrt(D)\n",
" T3 = T3 / np.linalg.norm(T3, axis=1)[:, None]*scaling_fudge_factor/np.sqrt(D)\n",
" \n",
" nengo.Connection(in1.output, bind.input_a, transform=T1)\n",
" nengo.Connection(in2.output, bind.input_b, transform=T2)\n",
" nengo.Connection(bind.output, out.input, transform=T3)\n",
" \n",
" p_in1 = nengo.Probe(in1.output, synapse=0.01)\n",
" p_in2 = nengo.Probe(in2.output, synapse=0.01)\n",
" p_bind = nengo.Probe(bind.output, synapse=0.01)\n",
" p_bind_in1 = nengo.Probe(bind.input_a, synapse=0.01)\n",
" p_out = nengo.Probe(out.output, synapse=0.01)\n",
" \n",
" stim1 = nengo.Node(nengo.processes.WhiteSignal(high=0.5, period=10.0, rms=1.0/np.sqrt(D)), size_out=D)\n",
" nengo.Connection(stim1, in1.input)\n",
" stim2 = nengo.Node(nengo.processes.WhiteSignal(high=0.5, period=10.0, rms=1.0/np.sqrt(D)), size_out=D)\n",
" nengo.Connection(stim2, in2.input)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building finished in 0:00:01. \n",
"Simulating finished in 0:00:12. \n"
]
}
],
"source": [
"sim = nengo.Simulator(model)\n",
"sim.run(10)"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {},
"outputs": [
{
"data": {
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+nQ0V/Rp3URR3Atbc0auAZaLEfsBTEISBlUucIwwegTWdca1OT1qhA+GOs/gm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\nBNLgrpRSI9D/B10lbLKr8MfBAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb1427ad68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.plot(sim.trange(), np.linalg.norm(sim.data[p_in1], axis=1), label='in1')\n",
"pylab.plot(sim.trange(), np.linalg.norm(sim.data[p_in2], axis=1), label='in2')\n",
"pylab.plot(sim.trange(), np.linalg.norm(sim.data[p_out], axis=1), label='out')\n",
"pylab.legend(loc='best')\n",
"pylab.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This seems to give us something like binding. But, can we now unbind?\n",
"\n",
"To do this, we build anothe binding network, feed in the bound result and one of the two inputs, and then do PES to learn the function that decodes out the other input."
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {},
"outputs": [],
"source": [
"D = 2\n",
"D_bind = 16\n",
"scaling_fudge_factor = 2.0\n",
"T = 500.0\n",
"\n",
"learning_rate = 1e-5 / D_bind\n",
"\n",
"model = spa.Network()\n",
"model.config[nengo.Ensemble].neuron_type=nengo.LIFRate()\n",
"with model:\n",
" in1 = spa.State(D, subdimensions=D)\n",
" in2 = spa.State(D, subdimensions=D)\n",
" out = spa.State(D, subdimensions=D)\n",
" bind = nengo.networks.Product(n_neurons=50, dimensions=D_bind)\n",
" unbind = nengo.networks.Product(n_neurons=50, dimensions=D_bind)\n",
" unbind_out = nengo.Node(None, size_in=D)\n",
" error = nengo.Node(None, size_in=D)\n",
" \n",
" T1 = np.random.normal(size=(D_bind, D))\n",
" T2 = np.random.normal(size=(D_bind, D))\n",
" T3 = np.random.normal(size=(D, D_bind))\n",
" T1 = T1 / np.linalg.norm(T1, axis=1)[:, None]*np.sqrt(D)\n",
" T2 = T2 / np.linalg.norm(T2, axis=1)[:, None]*np.sqrt(D)\n",
" T3 = T3 / np.linalg.norm(T3, axis=1)[:, None]*scaling_fudge_factor/np.sqrt(D)\n",
" \n",
" nengo.Connection(in1.output, bind.input_a, transform=T1)\n",
" nengo.Connection(in2.output, bind.input_b, transform=T2)\n",
" nengo.Connection(bind.output, out.input, transform=T3)\n",
" \n",
" \n",
" T4 = np.random.normal(size=(D_bind, D))\n",
" T5 = np.random.normal(size=(D_bind, D))\n",
" T4 = T4 / np.linalg.norm(T4, axis=1)[:, None]*np.sqrt(D)\n",
" T5 = T5 / np.linalg.norm(T5, axis=1)[:, None]*np.sqrt(D)\n",
" nengo.Connection(out.output, unbind.input_a, transform=T4)\n",
" nengo.Connection(in2.output, unbind.input_b, transform=T5)\n",
" for ens in unbind.all_ensembles:\n",
" c = nengo.Connection(ens, unbind_out, learning_rule_type=nengo.PES(learning_rate=learning_rate),\n",
" function=lambda x: np.zeros(D))\n",
" nengo.Connection(error, c.learning_rule)\n",
" nengo.Connection(unbind_out, error)\n",
" nengo.Connection(in1.output, error, transform=-1)\n",
" \n",
" p_in1 = nengo.Probe(in1.output, synapse=0.01)\n",
" p_error = nengo.Probe(error, synapse=0.01)\n",
" p_unbind_out = nengo.Probe(unbind_out, synapse=0.01)\n",
" \n",
" stim1 = nengo.Node(nengo.processes.WhiteSignal(high=0.5, period=T, rms=1.0/np.sqrt(D)), size_out=D)\n",
" nengo.Connection(stim1, in1.input)\n",
" stim2 = nengo.Node(nengo.processes.WhiteSignal(high=0.5, period=T, rms=1.0/np.sqrt(D)), size_out=D)\n",
" nengo.Connection(stim2, in2.input)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Building finished in 0:00:01. \n",
"Simulating finished in 0:20:46. \n"
]
}
],
"source": [
"sim = nengo.Simulator(model)\n",
"sim.run(T)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the norms of the vectors, just to make sure we're in the right ranges"
]
},
{
"cell_type": "code",
"execution_count": 333,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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myjZSqhY8GXP+oPIPnjzU9Xbnw4Ltr6WUb1jg+XcA72jz+C7gqHq1hpZROlQI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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb17bde550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.plot(sim.trange(), np.linalg.norm(sim.data[p_in1], axis=1), label='in1')\n",
"pylab.plot(sim.trange(), np.linalg.norm(sim.data[p_error], axis=1), label='error')\n",
"pylab.plot(sim.trange(), np.linalg.norm(sim.data[p_unbind_out], axis=1), label='unbind_out')\n",
"pylab.legend(loc='best')\n",
"pylab.xlim(450,500)\n",
"pylab.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's one of the dimensions plotted at various points in the learning process"
]
},
{
"cell_type": "code",
"execution_count": 327,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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SK/lo6HChKAz7eta7/XhCr4mH/o5avJy7atZQkpfF9y6dD14Xza1t/Ofz27lx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\nAKFcPgBK/MubpzSYof7Hpw3810sHWD0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pcM0GuOsg3H0c/uV5EVDPWMuU+WfRYJ3MS/ZZ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qYJdS5rwS2CaXHCWwTB23Jb4kdDCql1xkb0tXvAmNI6wfYxZtPKj+T1+0K0SEoZ1DTYiTTXj1td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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb1dce3cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"i = 0\n",
"T_window = 10\n",
"\n",
"pylab.figure(figsize=(12,4))\n",
"pylab.subplot(1, 3, 1)\n",
"pylab.plot(sim.trange(), sim.data[p_in1][:,i], label='in1[%d]'%i)\n",
"pylab.plot(sim.trange(), sim.data[p_unbind_out][:,i], label='unbind_out[%d]'%i)\n",
"pylab.legend(loc='best')\n",
"pylab.xlim(0,T_window)\n",
"pylab.subplot(1, 3, 2)\n",
"pylab.plot(sim.trange(), sim.data[p_in1][:,i], label='in1[%d]'%i)\n",
"pylab.plot(sim.trange(), sim.data[p_unbind_out][:,i], label='unbind_out[%d]'%i)\n",
"pylab.legend(loc='best')\n",
"pylab.xlim((T-T_window)/2,(T+T_window)/2)\n",
"pylab.subplot(1, 3, 3)\n",
"pylab.plot(sim.trange(), sim.data[p_in1][:,i], label='in1[%d]'%i)\n",
"pylab.plot(sim.trange(), sim.data[p_unbind_out][:,i], label='unbind_out[%d]'%i)\n",
"pylab.legend(loc='best')\n",
"pylab.xlim(T-T_window,T)\n",
"pylab.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And here's the cosine of the angle between the output and the ideal output"
]
},
{
"cell_type": "code",
"execution_count": 332,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\users\\terry\\py3\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in true_divide\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n",
"c:\\users\\terry\\py3\\lib\\site-packages\\ipykernel_launcher.py:4: RuntimeWarning: invalid value encountered in true_divide\n",
" after removing the cwd from sys.path.\n"
]
},
{
"data": {
"image/png": 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iXImIELOCRD1drSC71pK5ZcRDeEyJkswj9xUFoGsckSoeZ6xbxjgztCLujCnh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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1fb2b26b1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ideal = sim.data[p_in1]\n",
"actual = sim.data[p_unbind_out]\n",
"ideal = ideal / np.linalg.norm(ideal, axis=1)[:,None]\n",
"actual = actual / np.linalg.norm(actual, axis=1)[:,None]\n",
"prod = ideal*actual\n",
"cos_a = np.sum(prod, axis=1)\n",
"cos_a[np.isnan(cos_a)] = 0\n",
"pylab.plot(sim.trange(), nengo.synapses.Lowpass(10.0).filt(cos_a, dt=0.001))\n",
"pylab.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It learns! \n",
"\n",
"Open questions: does this generalize to higher dimensions? How much longer does the learning take? Do we need backprop (or feedback alignment)?"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
queirozfcom/nn | sandbox/.ipynb_checkpoints/Untitled-checkpoint.ipynb | 1 | 2315 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"def sigmoid(x):\n",
" return 1.0 / ( 1.0 + np.exp(-x) )"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.81757447619364365"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sigmoid(1.5)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"h1=sigmoid( (0.5 * 9) - 1)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"h2=sigmoid( ((0.5 * 4) - 1) - h1 )"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"-0.35512927312785325"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h2 * (-0.7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# q 4"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.54983399731247795"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(0.2) / (1+ np.exp(0.2))"
]
}
],
"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 |
xaibeing/cn-deep-learning | tutorials/sentiment-rnn/Sentiment_RNN_x.ipynb | 1 | 75749 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# # Sentiment Analysis with an RNN\n",
" \n",
"In this notebook, you'll implement a recurrent neural network that performs sentiment analysis. Using an RNN rather than a feedfoward network is more accurate since we can include information about the *sequence* of words. Here we'll use a dataset of movie reviews, accompanied by labels.\n",
" \n",
"The architecture for this network is shown below.\n",
"\n",
"<img src=\"assets/network_diagram.png\" width=400px>\n",
"\n",
"Here, we'll pass in words to an embedding layer. We need an embedding layer because we have tens of thousands of words, so we'll need a more efficient representation for our input data than one-hot encoded vectors. You should have seen this before from the word2vec lesson. You can actually train up an embedding with word2vec and use it here. But it's good enough to just have an embedding layer and let the network learn the embedding table on it's own.\n",
" \n",
"From the embedding layer, the new representations will be passed to LSTM cells. These will add recurrent connections to the network so we can include information about the sequence of words in the data. Finally, the LSTM cells will go to a sigmoid output layer here. We're using the sigmoid because we're trying to predict if this text has positive or negative sentiment. The output layer will just be a single unit then, with a sigmoid activation function.\n",
" \n",
"We don't care about the sigmoid outputs except for the very last one, we can ignore the rest. We'll calculate the cost from the output of the last step and the training label."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the ins'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('../sentiment-network/reviews.txt', 'r') as f:\n",
" reviews = f.read()\n",
"with open('../sentiment-network/labels.txt', 'r') as f:\n",
" labels = f.read()\n",
"\n",
"reviews[:300]\n",
"\n",
"\n",
"# ## Data preprocessing\n",
"# \n",
"# The first step when building a neural network model is getting your data into the proper form to feed into the network. Since we're using embedding layers, we'll need to encode each word with an integer. We'll also want to clean it up a bit.\n",
"# \n",
"# You can see an example of the reviews data above. We'll want to get rid of those periods. Also, you might notice that the reviews are delimited with newlines `\\n`. To deal with those, I'm going to split the text into each review using `\\n` as the delimiter. Then I can combined all the reviews back together into one big string.\n",
"# \n",
"# First, let's remove all punctuation. Then get all the text without the newlines and split it into individual words."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bromwell high is a cartoon comedy it ran at the same time as some other programs about school life such as teachers my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled at high a classic line inspector i m here to sack one of your teachers student welcome to bromwell high i expect that many adults of my age think that bromwell high is far fetched what a pity that it isn t ', 'story of a man who has unnatural feelings for a pig starts out with a opening scene that is a terrific example of absurd comedy a formal orchestra audience is turned into an insane violent mob by the crazy chantings of it s singers unfortunately it stays absurd the whole time with no general narrative eventually making it just too off putting even those from the era should be turned off the cryptic dialogue would make shakespeare seem easy to a third grader on a technical level it s better than you might think with some good cinematography by future great vilmos zsigmond future stars sally kirkland and frederic forrest can be seen briefly ', 'homelessness or houselessness as george carlin stated has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school work or vote for the matter most people think of the homeless as just a lost cause while worrying about things such as racism the war on iraq pressuring kids to succeed technology the elections inflation or worrying if they ll be next to end up on the streets br br but what if you were given a bet to live on the streets for a month without the luxuries you once had from a home the entertainment sets a bathroom pictures on the wall a computer and everything you once treasure to see what it s like to be homeless that is goddard bolt s lesson br br mel brooks who directs who stars as bolt plays a rich man who has everything in the world until deciding to make a bet with a sissy rival jeffery tambor to see if he can live in the streets for thirty days without the luxuries if bolt succeeds he can do what he wants with a future project of making more buildings the bet s on where bolt is thrown on the street with a bracelet on his leg to monitor his every move where he can t step off the sidewalk he s given the nickname pepto by a vagrant after it s written on his forehead where bolt meets other characters including a woman by the name of molly lesley ann warren an ex dancer who got divorce before losing her home and her pals sailor howard morris and fumes teddy wilson who are already used to the streets they re survivors bolt isn t he s not used to reaching mutual agreements like he once did when being rich where it s fight or flight kill or be killed br br while the love connection between molly and bolt wasn t necessary to plot i found life stinks to be one of mel brooks observant films where prior to being a comedy it shows a tender side compared to his slapstick work such as blazing saddles young frankenstein or spaceballs for the matter to show what it s like having something valuable before losing it the next day or on the other hand making a stupid bet like all rich people do when they don t know what to do with their money maybe they should give it to the homeless instead of using it like monopoly money br br or maybe this film will inspire you to help others ']\n"
]
}
],
"source": [
"from string import punctuation\n",
"all_text = ''.join([c for c in reviews if c not in punctuation])\n",
"reviews = all_text.split('\\n')\n",
"\n",
"# list of string, one item one review\n",
"print(reviews[:3])\n",
"\n",
"# one big string of all words\n",
"all_text = ' '.join(reviews)\n",
"words = all_text.split()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"bromwell high is a cartoon comedy it ran at the same time as some other programs about school life such as teachers my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled at high a classic line inspector i m here to sack one of your teachers student welcome to bromwell high i expect that many adults of my age think that bromwell high is far fetched what a pity that it isn t story of a man who has unnatural feelings for a pig starts out with a opening scene that is a terrific example of absurd comedy a formal orchestra audience is turned into an insane violent mob by the crazy chantings of it s singers unfortunately it stays absurd the whole time with no general narrative eventually making it just too off putting even those from the era should be turned off the cryptic dialogue would make shakespeare seem easy to a third grader on a technical level it s better than you might think with some good cinematography by future great vilmos zsigmond future stars sally kirkland and frederic forrest can be seen briefly homelessness or houselessness as george carlin stated has been an issue for years but never a plan to help those on the street that were once considered human who did everything from going to school work or vote for the matter most people think of the homeless as just a lost cause while worrying about things such as racism the war on iraq pressuring kids to succeed technology the elections inflation or worrying if they ll be next to end up on the streets br br but what if you were given a bet to live on the st\n",
"['bromwell', 'high', 'is', 'a', 'cartoon', 'comedy', 'it', 'ran', 'at', 'the', 'same', 'time', 'as', 'some', 'other', 'programs', 'about', 'school', 'life', 'such', 'as', 'teachers', 'my', 'years', 'in', 'the', 'teaching', 'profession', 'lead', 'me', 'to', 'believe', 'that', 'bromwell', 'high', 's', 'satire', 'is', 'much', 'closer', 'to', 'reality', 'than', 'is', 'teachers', 'the', 'scramble', 'to', 'survive', 'financially', 'the', 'insightful', 'students', 'who', 'can', 'see', 'right', 'through', 'their', 'pathetic', 'teachers', 'pomp', 'the', 'pettiness', 'of', 'the', 'whole', 'situation', 'all', 'remind', 'me', 'of', 'the', 'schools', 'i', 'knew', 'and', 'their', 'students', 'when', 'i', 'saw', 'the', 'episode', 'in', 'which', 'a', 'student', 'repeatedly', 'tried', 'to', 'burn', 'down', 'the', 'school', 'i', 'immediately', 'recalled', 'at', 'high']\n",
"6020196\n"
]
}
],
"source": [
"print(all_text[:2000])\n",
"\n",
"# list of all words, one item one word. len=6020196\n",
"print(words[:100])\n",
"print(len(words))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"74072\n",
"[[68529, 5510, 18022, 21997, 28466, 58796, 60701, 32113, 30822, 475, 42856, 10834, 26453, 22145, 13933, 35650, 57687, 4209, 4292, 60115, 26453, 18611, 64775, 43320, 11159, 475, 44869, 23619, 38146, 7031, 3484, 17667, 23693, 68529, 5510, 59484, 29246, 18022, 12899, 14878, 3484, 43136, 37605, 18022, 18611, 475, 7172, 3484, 73693, 403, 475, 42157, 57230, 34202, 57663, 25044, 59721, 40620, 66946, 28205, 18611, 63534, 475, 58852, 41537, 475, 39908, 60798, 42064, 33508, 7031, 41537, 475, 7524, 69997, 71159, 50273, 66946, 57230, 20193, 69997, 45854, 475, 24869, 11159, 12627, 21997, 17453, 44287, 50518, 3484, 45430, 41709, 475, 4209, 69997, 38771, 72309, 30822, 5510, 21997, 5381, 58747, 55177, 69997, 49841, 13042, 3484, 11812, 57244, 41537, 9354, 18611, 17453, 51146, 3484, 68529, 5510, 69997, 37448, 23693, 26501, 54364, 41537, 64775, 40038, 66356, 23693, 68529, 5510, 18022, 11478, 17950, 62882, 21997, 5927, 23693, 60701, 36178, 46316], [63145, 41537, 21997, 35484, 34202, 9367, 4600, 69172, 23843, 21997, 64290, 13009, 47171, 52935, 21997, 29503, 46465, 23693, 18022, 21997, 3731, 30155, 41537, 11277, 58796, 21997, 49485, 17196, 56933, 18022, 68619, 49368, 53603, 26618, 19865, 64215, 74051, 475, 2467, 3443, 41537, 60701, 59484, 56840, 62422, 60701, 59132, 11277, 475, 39908, 10834, 52935, 53989, 2533, 70413, 51460, 56878, 60701, 8972, 65279, 51366, 66111, 13575, 62880, 37707, 475, 68498, 49165, 1238, 68619, 51366, 475, 49607, 32043, 40861, 53694, 10042, 4201, 49869, 3484, 21997, 73629, 6835, 17454, 21997, 30347, 49120, 60701, 59484, 18461, 37605, 18137, 34432, 66356, 52935, 22145, 60719, 50661, 74051, 40528, 53615, 20633, 15500, 40528, 54237, 29925, 27990, 50273, 422, 11076, 57663, 1238, 42466, 37422]]\n",
"25001\n"
]
}
],
"source": [
"# Create your dictionary that maps vocab words to integers here\n",
"# word set, len=74072\n",
"vocab_set = set(words)\n",
"print(len(vocab_set))\n",
"\n",
"# dict, vocab to int, int value starts at 1, leave 0 for padding\n",
"vocab_to_int = {word : i+1 for i, word in enumerate(vocab_set)}\n",
"\n",
"# Convert the reviews to integers, same shape as reviews list, but with integers\n",
"# review list, one item one review, but int represent word\n",
"reviews_ints = []\n",
"for review in reviews:\n",
" words_in_one_review = review.split()\n",
" one_review_int = []\n",
" for word in words_in_one_review:\n",
" word_int = vocab_to_int[word]\n",
" one_review_int.append(word_int)\n",
" reviews_ints.append(one_review_int)\n",
" \n",
"print(reviews_ints[0:2])\n",
"print(len(reviews_ints))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25001"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# split labels\n",
"labels = labels.split('\\n')\n",
"len(labels)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Zero-length reviews: 1\n",
"Maximum review length: 2514\n"
]
}
],
"source": [
"from collections import Counter\n",
"review_lens = Counter([len(x) for x in reviews_ints])\n",
"print(\"Zero-length reviews: {}\".format(review_lens[0]))\n",
"print(\"Maximum review length: {}\".format(max(review_lens)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"25000\n",
"25000\n",
"25000\n"
]
}
],
"source": [
"# Filter out that review with 0 length\n",
"for i, e in enumerate(reviews_ints):\n",
" if(len(e) == 0):\n",
" print(i)\n",
" reviews_ints.remove(e)\n",
" del labels[i]\n",
" break\n",
"\n",
"# make sure reviews and labels still the same size\n",
"print(len(reviews_ints))\n",
"print(len(labels))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
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" 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0. 0. 0. 0. 68529. 5510. 18022.\n",
" 21997. 28466. 58796. 60701. 32113. 30822. 475. 42856. 10834.\n",
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" 18611. 64775. 43320. 11159. 475. 44869. 23619. 38146. 7031.\n",
" 3484. 17667. 23693. 68529. 5510. 59484. 29246. 18022. 12899.\n",
" 14878.]\n",
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" 52935.]\n",
" [ 29156. 30378. 44599. 26453. 19600. 72077. 58306. 9367. 2696.\n",
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" 17454. 475. 63099. 24052. 24052. 41422. 62882. 33803. 18137.\n",
" 43706. 45715. 21997. 69552. 3484. 36225. 17454. 475. 63099.\n",
" 23843.]\n",
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" 59395. 44516. 47905. 50428. 25747. 34202. 18022. 47625. 61373.\n",
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" 21997. 71992. 45271. 17454. 17797. 18022. 47905. 55211. 44473.\n",
" 72348. 51702. 36880. 49010. 475. 26017. 47035. 25713. 51366.\n",
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" 63830.]\n",
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" 62816. 41537. 70754. 38030. 11159. 475. 58616. 23646. 41537.\n",
" 34242.]]\n",
"(25000, 200)\n"
]
}
],
"source": [
"# truncate review to the limitation \"seq_len\"\n",
"# left pad \"0\" to reviews whose words less than \"seq_len\"\n",
"\n",
"seq_len = 200\n",
"# np array 2d, shape=(num_reviews, seq_len), the reviews are truncated to seq_len\n",
"features = np.zeros(shape=(len(reviews_ints), seq_len))\n",
"for i, e in enumerate(reviews_ints):\n",
"# e = np.asarray(e)\n",
"# print(i, e[0:3])\n",
" if(len(e) >= seq_len):\n",
" features[i,:] = e[0:seq_len]\n",
" else:\n",
" features[i,seq_len-len(e):] = e\n",
" \n",
"print(features[:10,:100])\n",
"print(features.shape)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 0, 1, 0, 1, 0, 1, 0])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# construct labels array with 0,1\n",
"dict_label = {'positive':1, 'negative':0}\n",
"labels_int = np.asarray([dict_label[word] for word in labels])\n",
"labels_int[:10]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\t\t\tFeature Shapes:\n",
"Train set: \t\t(20000, 200) \n",
"Validation set: \t(2500, 200) \n",
"Test set: \t\t(2500, 200)\n",
"\t\t\tlabels\n",
"Train set: \t\t(20000,) \n",
"Validation set: \t(2500,) \n",
"Test set: \t\t(2500,)\n"
]
}
],
"source": [
"# split data set into train, valid, test set\n",
"\n",
"split_frac = 0.8\n",
"\n",
"train_end_index = int(len(features) * 0.8)\n",
"train_x, val_x = features[: train_end_index, :], features[train_end_index :, :]\n",
"train_y, val_y = labels_int[: train_end_index], labels_int[train_end_index :]\n",
"\n",
"valid_end_index = int(len(features) * 0.8) + int(len(val_x) * 0.5)\n",
"val_x, test_x = features[train_end_index : valid_end_index, :], features[valid_end_index :, :]\n",
"val_y, test_y = labels_int[train_end_index : valid_end_index], labels_int[valid_end_index :]\n",
"\n",
"print(\"\\t\\t\\tFeature Shapes:\")\n",
"print(\"Train set: \\t\\t{}\".format(train_x.shape), \n",
" \"\\nValidation set: \\t{}\".format(val_x.shape),\n",
" \"\\nTest set: \\t\\t{}\".format(test_x.shape))\n",
"print(\"\\t\\t\\tlabels\")\n",
"print(\"Train set: \\t\\t{}\".format(train_y.shape), \n",
" \"\\nValidation set: \\t{}\".format(val_y.shape),\n",
" \"\\nTest set: \\t\\t{}\".format(test_y.shape))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# parameters\n",
"lstm_size = 256\n",
"lstm_layers = 1\n",
"dropout_rate = 0.5\n",
"batch_size = 500\n",
"learning_rate = 0.0005\n",
"epochs = 3\n",
"\n",
"# vocab size\n",
"n_words = len(vocab_to_int)\n",
"# Size of the embedding vectors (number of units in the embedding layer)\n",
"embed_size = 300 "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"from keras.layers.embeddings import Embedding\n",
"from keras.models import Sequential\n",
"from keras.layers.recurrent import LSTM\n",
"from keras.layers import Dense, Dropout\n",
"\n",
"# model\n",
"model = Sequential()\n",
"\n",
"# Embedding layer\n",
"# input_dim: vocab_size + 1\n",
"# output_dim: the embed_size of output\n",
"# input_length: seq_len\n",
"model.add(Embedding(input_dim=n_words+1, output_dim=embed_size, input_length=seq_len))\n",
"\n",
"# the model will take as input an integer matrix of size (batch, input_length).\n",
"# the largest integer (i.e. word index) in the input should be no larger than input_dim.\n",
"# now model.output_shape == (None, input_length, output_dim), where None is the batch dimension."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## unit test, test embedding layer --------------------\n",
"## (batch, input_length)\n",
"#batch_size = 32\n",
"#input_array = np.random.randint(n_words, size=(batch_size, seq_len))\n",
"#\n",
"#model.compile('rmsprop', 'mse')\n",
"#output_array = model.predict(input_array)\n",
"#print(output_array.shape)\n",
"#assert output_array.shape == (batch_size, seq_len, embed_size)\n",
"## unit test end --------------------"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Develop\\Anaconda3\\envs\\py35\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: The `input_dim` and `input_length` arguments in recurrent layers are deprecated. Use `input_shape` instead.\n",
"C:\\Develop\\Anaconda3\\envs\\py35\\lib\\site-packages\\ipykernel_launcher.py:21: UserWarning: Update your `LSTM` call to the Keras 2 API: `LSTM(input_shape=(None, 300..., return_sequences=False, units=256)`\n",
"C:\\Develop\\Anaconda3\\envs\\py35\\lib\\site-packages\\ipykernel_launcher.py:29: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1)`\n"
]
}
],
"source": [
"\n",
"#model.add(LSTM(\n",
"# input_dim=embed_size,\n",
"# output_dim=lstm_size,\n",
"# return_sequences=True))\n",
"model.add(LSTM(\n",
" input_dim=embed_size,\n",
" output_dim=lstm_size,\n",
" return_sequences=False))\n",
" \n",
"#model.add(LSTM(\n",
"# lstm_size,\n",
"# return_sequences=False))\n",
"model.add(Dropout(dropout_rate))\n",
"\n",
"#model.add(Dense(20))\n",
"model.add(Dense(output_dim=1, activation='sigmoid'))\n",
"\n",
"#model.compile(loss='mse', optimizer='rmsprop', metrics=['accuracy'])\n",
"#model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
"#model.compile(loss='mse', optimizer='adam', metrics=['accuracy'])\n",
"model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Develop\\Anaconda3\\envs\\py35\\lib\\site-packages\\keras\\models.py:837: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
" warnings.warn('The `nb_epoch` argument in `fit` '\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 20000 samples, validate on 2500 samples\n",
"Epoch 1/3\n",
"20000/20000 [==============================] - 43s - loss: 0.7218 - acc: 0.5787 - val_loss: 0.6731 - val_acc: 0.5888\n",
"Epoch 2/3\n",
"20000/20000 [==============================] - 39s - loss: 0.5662 - acc: 0.7220 - val_loss: 0.4716 - val_acc: 0.7852\n",
"Epoch 3/3\n",
"20000/20000 [==============================] - 39s - loss: 0.2557 - acc: 0.9000 - val_loss: 0.4888 - val_acc: 0.8048\n"
]
}
],
"source": [
"# training\n",
"history = model.fit(\n",
" train_x,\n",
" train_y,\n",
" batch_size=batch_size,\n",
" nb_epoch=epochs,\n",
" validation_data=(val_x, val_y))"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dict_keys(['val_loss', 'loss', 'acc', 'val_acc'])\n"
]
},
{
"data": {
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B/Pium6k46eQprnwoqDIVF6JUNAxVPWPGIZgE0+yJxvjM3nXO5aeUFdDoSrh2HNRq7uuq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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c485929e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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6PIYlk5MJ8ncw8o00Ptt6xO6SVCPn7pHCeGPMaeBmIAoYA7x8gWXaApnVnme5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\nUkpV0VBQSilVRUNBKaVUFQ0FpZRSVf4/KrKyuemUtVIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1c488c37e10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1c488edc160>"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# summarize loss and accuracy\n",
"\n",
"# data in history\n",
"print(history.history.keys())\n",
"\n",
"import matplotlib.pyplot as plt\n",
"# summarize history for loss\n",
"plt.figure()\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('model loss')\n",
"plt.ylabel('loss')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='upper left')\n",
"plt.show()\n",
"\n",
"# summarize history for accuracy\n",
"plt.figure()\n",
"plt.plot(history.history['acc'])\n",
"plt.plot(history.history['val_acc'])\n",
"plt.title('model accuracy')\n",
"plt.ylabel('accuracy')\n",
"plt.xlabel('epoch')\n",
"plt.legend(['train', 'valid'], loc='upper left')"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 81.32%\n"
]
}
],
"source": [
"# evaluate on test data\n",
"#pred_test = model.predict(test_x)\n",
"scores = model.evaluate(test_x, test_y, verbose=0)\n",
"print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
]
},
{
"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": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
flohorovicic/pynoddy | docs/notebooks/Training_Set_2.ipynb | 1 | 131166 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate Training sets\n",
"=====================\n",
"\n",
"Based on \"Reproducible Experiments\" notebook\n"
]
},
{
"cell_type": "raw",
"metadata": {
"collapsed": false
},
"source": [
"from IPython.core.display import HTML\n",
"css_file = 'pynoddy.css'\n",
"HTML(open(css_file, \"r\").read())"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# here the usual imports. If any of the imports fails, \n",
"# make sure that pynoddy is installed\n",
"# properly, ideally with 'python setup.py develop' \n",
"# or 'python setup.py install'\n",
"import sys, os\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"# adjust some settings for matplotlib\n",
"from matplotlib import rcParams\n",
"# print rcParams\n",
"rcParams['font.size'] = 15\n",
"# determine path of repository to set paths corretly below\n",
"repo_path = os.path.realpath('../..')\n",
"import pynoddy.history\n",
"import pynoddy.experiment\n",
"reload(pynoddy.experiment)\n",
"rcParams.update({'font.size': 15})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# From notebook 4/ Traning Set example 1:\n",
"reload(pynoddy.history)\n",
"reload(pynoddy.events)\n",
"nm = pynoddy.history.NoddyHistory()\n",
"# add stratigraphy\n",
"strati_options = {'num_layers' : 3,\n",
" 'layer_names' : ['layer 1', 'layer 2', 'layer 3'],\n",
" 'layer_thickness' : [1500, 500, 1500]}\n",
"nm.add_event('stratigraphy', strati_options )\n",
"\n",
"# The following options define the fault geometry:\n",
"fault_options = {'name' : 'Fault_E',\n",
" 'pos' : (4000, 0, 5000),\n",
" 'dip_dir' : 90.,\n",
" 'dip' : 60,\n",
" 'slip' : 1000}\n",
"\n",
"nm.add_event('fault', fault_options)\n",
"history = 'normal_fault.his'\n",
"output_name = 'normal_fault_out'\n",
"nm.write_history(history)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Initiate experiment with this input file:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(pynoddy.history)\n",
"reload(pynoddy.experiment)\n",
"\n",
"from pynoddy.experiment import monte_carlo\n",
"ue = pynoddy.experiment.Experiment(history)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8W+Bw4LiqOrWqzgOeChTwkrE3VJIkaUwmFs6S3A14M3AscO3Q5j2BHYAzpgqqah3wGeCA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\nUkP+P2/VCwfqMXe+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1108f8490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ue.change_cube_size(100)\n",
"ue.plot_section('y')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before we start to draw random realisations of the model, we should first store the base state of the model for later reference. This is simply possibel with the freeze() method which stores the current state of the model as the \"base-state\":"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ue.freeze()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now intialise the random generator. We can directly assign a random seed to simplify reproducibility (note that this is not *essential*, as it would be for the definition in a script function: the random state is preserved within the model and could be retrieved at a later stage, as well!):"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ue.set_random_seed(12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The next step is to define probability distributions to the relevant event parameters. Let's first look at the different events:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This model consists of 2 events:\n",
"\t(1) - STRATIGRAPHY\n",
"\t(2) - FAULT\n",
"\n"
]
}
],
"source": [
"ue.info(events_only = True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ev2 = ue.events[2]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Amplitude': 2000.0,\n",
" 'Blue': 254.0,\n",
" 'Color Name': 'Custom Colour 8',\n",
" 'Cyl Index': 0.0,\n",
" 'Dip': 60.0,\n",
" 'Dip Direction': 90.0,\n",
" 'Geometry': 'Translation',\n",
" 'Green': 0.0,\n",
" 'Movement': 'Hanging Wall',\n",
" 'Pitch': 90.0,\n",
" 'Profile Pitch': 90.0,\n",
" 'Radius': 1000.0,\n",
" 'Red': 0.0,\n",
" 'Rotation': 30.0,\n",
" 'Slip': 1000.0,\n",
" 'X': 4000.0,\n",
" 'XAxis': 2000.0,\n",
" 'Y': 0.0,\n",
" 'YAxis': 2000.0,\n",
" 'Z': 5000.0,\n",
" 'ZAxis': 2000.0}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ev2.properties"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we define the probability distributions for the uncertain input parameters:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"param_stats = [{'event' : 2, \n",
" 'parameter': 'Slip',\n",
" 'stdev': 300.0,\n",
" 'type': 'normal'},\n",
" {'event' : 2, \n",
" 'parameter': 'Dip',\n",
" 'stdev': 10.0,\n",
" 'type': 'normal'},]\n",
"\n",
"ue.set_parameter_statistics(param_stats)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"resolution = 100\n",
"ue.change_cube_size(resolution)\n",
"tmp = ue.get_section('y')\n",
"prob_2 = np.zeros_like(tmp.block[:,:,:])\n",
"n_draws = 100\n",
"\n",
"\n",
"for i in range(n_draws):\n",
" ue.random_draw()\n",
" tmp = ue.get_section('y', resolution = resolution)\n",
" prob_2 += (tmp.block[:,:,:] == 2)\n",
"\n",
"# Normalise\n",
"prob_2 = prob_2 / float(n_draws)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x111fcebd0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JklSJxbUkSZJUicW1JEmSVInFtSRJklSJxbUkSZJUicW1JEmSVInFtSRJklSJxbUkSZJU\nicW1JEmSVInFtSRJklSJxbUkSZJUicW1JEmSVInFtSRJklTJ0IvriFgSEWsjYlNEzO+LnRAR10fE\nuoi4KCIOGVaekiRJ0tYMvbgG3g/c0T8YEW8HTgROBZ4DrAXOi4hF05ueJEmSNDFDLa4j4kjgP9EU\n2L3jc4C3Aqdk5hmZeQHwIiCB1097opIkSdIEDK24jogHAB8CTgZu7QsfDuwKfH58IDPXAV8BnjVd\nOUqSJEmTMcyV69cAOwEfGRBbCmwCru0bv7qNSZIkSSNnx2FcNCL2BP4G+K+ZuSki+g9ZCKzNzOwb\nXwXMj4gdM/PuaUhVkiRJmrBhrVy/G7g0M78+pOtLkiRJ1U37ynVEHAS8EnhKROzeDu/c/rkgIu6h\nWaHeJSKib/V6IbDOVWtJkiSNomFsCzmwve7lA2I3AJ8A/rk95gA233e9FFhWPvWFPR+PtQ9JkiTp\n/ljePmDFil06jxxGcX0x8LS+sWcBb2n//CVwPU3v6xcBpwC0bzBzLPDR8qmPqp2rJEmSZr0xxhdt\nlyx5CCtXfrV45LQX15l5G/Ct3rGI2K/98Nttyz0i4jTgHRGxmma1+ngggNOnMV1JkiRpwobSLWQi\nMvO0aNqIvA3YE/gecHRm/ma4mUmSJEmDjcLbn5OZZ2bmDuOr1j3jp2bmPpm5c2YelZk/HlaOkiRJ\n0taMRHEtSZIkzQQW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15Ik\nSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJ\nUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElS\nJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiU7DjuBmt7F\nycNOQdIMNa8j9uiO2AEdsQOfOHg8X1qe84vX7VWMncuzB45/Lf+gOOebtz+1GNv4D3sMDpxdnALf\n6YhxbWH8jo45P+mIre+IdZ1zKjZUPp+kmcqVa0mSJKkSi2tJkiSpEotrSZIkqRKLa0mSJKkSi2tJ\nkiSpEotrSZIkqZIZ1Yovhp2ApBmr6/tLV0O4mztiD7+zcK3bynP2/9HKYuzxh35/4PhNsag4Z/WC\nBcXY5cc8fXDgluIUKKcH1x1YCJRa9AE8qiO2vCNWsmYKcwDmFsZt0Sdpc65cS5IkSZVYXEuSJEmV\nWFxLkiRJlVhcS5IkSZVYXEuSJEmVWFxLkiRJlcyoVnyStK10tdvrau62uOukpZZ7XZ3pri+HFh16\nUyGHckPAsVhejK075PKB4z/mSeUkutr0/WuhoeEtpRZ9wKY7Ok740I5Y6ZM4+HO0bUy17Z+k0bZn\nZ9SVa0mSJKkSi2tJkiSpEotrSZIkqRKLa0mSJKkSi2tJkiSpEotrSZIkqRJb8UnSNtTVjO2mFYPH\nF3e04ouO2MOWrRw4fvjSS4tzVrOgHIvBsZsO+UVxzk3Pf1gxxtzC+NmFFn0At+xWjm3atRxjXmG8\nq8/hVBsulsyfwpx1U5gjaXp1/9t25VqSJEmqZNqL64j4zxFxSUTcEhHrI2JZRJwYEQ/sO+6EiLg+\nItZFxEURcch05ypJkiRNxjBWrvcEzgdeBRwDfAI4EfjA+AER8fZ27FTgOcBa4LyIWDTt2UqSJEkT\nNO17rjPzY31DF0XE7sBrgTdGxBzgrcApmXkGQERcDiwHXg+8axrTlSRJkiZsVPZc3wbs1H58BLAr\n8PnxYGauA74CPGv6U5MkSZImZmjdQiLiAcAc4HHAG4Az2tAjgE1s+ZLuq4EXT1uCkjRBXT0muno/\nlPpWLL5tCpMArhk8vGRpoS0JMMbyYmw5YwPHN967FrKl9ceUX0V/x4a9irGib3d0ElnbMa/UZWTD\n48pzNmXHCW/uiE2XO4adgCSgKV/LhtmK707uy+6zwFvajxcCazOz/7vcKmB+ROyYmXdPU46SJEnS\nhA1zW8hhwJOBNwPP5r6Va0mSJGm7NLSV68z8YfvhpRFxK/CpiHgvzQr1LhERfavXC4F1rlpLkiRp\nVI3KOzReAQQwBiwDdgAOYPPdhUvbWNGFPR+PtQ9JkiTp/vkO8F0AVqzYofPIUSmunwwk8Avg1zTv\nM/si4BSAiJgPHAt8tOskR23TFCVJkjQ7PbF9wJIlc1i58oPFI6e9uI6Ic4HzgH+n6Qoyvu/6c5m5\nvD3mNOAdEbGaZrX6eJqV7dOnO19JkiRpooaxcv1d4DiaXRt306xWvxX4h/EDMvO0iAjgbTTv6Pg9\n4OjM/M20ZytJW9HVIG1eR6zYtO7Ojkkdm+NiyeDxvR5+e3HO4UsvKcbuKrTcWxCri3MWzinHvv9H\njx84fv1DlxbndO7v+35Hm74NhfFbOs7XZeXiuuebkkIOUH6+kurr3hUylHdoPAk4aQLHnUrz9ueS\nJEnSdmFU3qFRkiRJ2u5ZXEuSJEmVWFxLkiRJlVhcS5IkSZVYXEuSJEmVjMqbyEiSWnfcWo7ttnPH\nxNsK49eUpyxYWm6dN8bygePryk0Em3ckmKT5R6wrxpaNHVSeeMzccqzUsnBtRyIbppB8Vyu+rmtJ\n2n79Dk2T6AJXriVJkqRKLK4lSZKkSiyuJUmSpEosriVJkqRKLK4lSZKkSiIzh51DFRGRJw07CUmz\n0ryO2G5TmLe4Y84RczqudWAhcGh5Tj6j42KF8912RLlLx80d2a9gyaTGAW7ORcXYVZQ7iZTOuZoF\nxTldXVDuYqdirGRVx7XWb+zouCJppB0SO3Lx3AeTmQNbDLlyLUmSJFVicS1JkiRVYnEtSZIkVWJx\nLUmSJFVicS1JkiRVYnEtSZIkVbLjsBOQJG3upq7YxnJs/U8Hjy/euTwn9uy42K2Dh/fYuKE4ZY85\n1xVjS3cuxDpyWLn37sVYVwu/Usu9rjlTabfX1b6vy11zJn8tSaNhEXtzcUfclWtJkiSpEotrSZIk\nqRKLa0mSJKkSi2tJkiSpEotrSZIkqRKLa0mSJKkSW/FJ0v20viO2W+VrdbXpK1l8wxQvtmLwcPyw\nY86BHbGOloAle829vRzbuxzrau9XtE85lFPIfeOcKeQgaeTFAx7LyzvirlxLkiRJlVhcS5IkSZVY\nXEuSJEmVTLi4jogjI2K/QmzXiDiyXlqSJEnS9mcyK9cXAj+NiJcOiB0EfLNKRpIkSdJ2arLdQr4K\nnBkRTwD+MjM3bYOcJGnG6OruMW8K5+vqTLKmMH5HoesHwEM7YsUuI12dM67tiJXMLYei61p7dMRK\n8zqu1al0vo6OIFO9lKQR95Du8GT3XL8feB7wMuCbEbFoallJkiRJM8+kX9CYmecATwQeBFwZEYdV\nz0qSJEnaDk2pW0hmXgM8AfgOzV7rP62ZlCRJkrQ9mnIrvsxcm5kvAN4N/Em9lCRJkqTt02Re0Lgf\n8Ov+wcz824i4ADigWlaSJEnSdmjCxXVmXtcRuwS4pEpGkiRJ0nZqsq34JEmVlNrqdbXou6Mjtq4w\nvnxC2Qy4VkebvqnYre7pmNfRBm/XrhZ+BdHVErAUmMJ1JG3nDu4O+/bnkiRJUiUW15IkSVIlFteS\nJElSJRbXkiRJUiUW15IkSVIlFteSJElSJbbik6QZ4ubCeFdrv+UdscVTT2Wgmyqfj41TjE1B1+ew\npHbrQUmjYc6u3XFXriVJkqRKLK4lSZKkSiyuJUmSpEosriVJkqRKLK4lSZKkSuwWIkkjZn3l890x\nxWtNpfuIJmYrzQYkjbAFW4m7ci1JkiRVYnEtSZIkVTLtxXVEvDgizomIFRGxJiK+HxEvGXDcCRFx\nfUSsi4iLIuKQ6c5VkiRJmoxhrFy/CVgNvBE4FrgA+GxEvG78gIh4O3AicCrwHGAtcF5ELJr+dCVJ\nkqSJGcYLGp+Tmbf1/P3CiPgd4M3AhyNiDvBW4JTMPAMgIi6neZfe1wPvmuZ8JUmSpAmZ9pXrvsJ6\n3JXAkvbjI2heSP35njnrgK8Az9rmCUqSJElTNCqt+A4Hrmk/fgSwCbi275irgRdPZ1KSNNN1tenT\ntlNqgWibQ2n07bSV+NCL64h4BvA84BXt0EJgbWZm36GrgPkRsWNm3j2NKUqSJEkTMtRWfBExBnwG\nOCszPz3MXCRJkqT7a2jFdUQsBM4Ffgm8tCe0CtglIqJvykJgnavWkiRJGlVD2RYSEfOArwI70HQP\n2dATXtaOH8Dm+66XtrGiC3s+HmsfkiRJ0v2xjPuK0HkrVnQeO+3FdUTsAHwB2B84LDNv7TvkUmAN\n8CLglHbOfJqe2B/tOvdRtZOVJEnSrLe0fQDsuWQJn1u5snjsMFauz6BpqfdG4MER8eCe2BWZuTEi\nTgPeERGraX5QOB4I4PRpz1aSJEmaoGEU188EEvjggNh+wPWZeVq75/ptwJ7A94CjM/M3XSc+2feX\nkTRyupqrLS6M79oxZ35HrDTvyeUpu3Sc7nGF8cd3zHlyf6On+8w9atDbHMDi3W8uzjmcy4qx/fmP\nYuwR93Z3nficRXlTMfawZYXYT4tT4JKOWOm3yj/smHNnOXRH/++Ae9x0V8c5S+friJW+WuWvYvOf\nfkmpLeGajjmjopS7ZraNW4lPe3GdmftN8LhTad7+XJIkSdouDLUVnyRJkjSTWFxLkiRJlVhcS5Ik\nSZVYXEuSJEmVWFxLkiRJlQzlHRq3nf53TJekYdswhVjX97KdO2KleV1NzXYrh0rt3TpawnFrOfcN\n/77nwPGNR9xenLOaBcXY+o62hHex08DxX7OkOGdOlHvWrXrk6oHje0RHU66u3nRT0dGmr+OrWPw6\n3tzRoq/zfFOwrvL5RqVN39zCeNe/es18rlxLkiRJlVhcS5IkSZVYXEuSJEmVWFxLkiRJlVhcS5Ik\nSZXMsG4hkqQt3dQR6+gLccskxwFu7Ig9aPDw+o3lrh9XzTmoGFvA4A4eXQ7iqmJsBQ8pxkrdRw7d\n+8fFOXMPnXhe9+pqBtMhujqJFM65a6kbDLCmoyPM4sL4Qzu6j3TdFutLOXTMqd19ZDqNSqcTTd3g\nvkf3ceVakiRJqsTiWpIkSarE4lqSJEmqxOJakiRJqsTiWpIkSarE4lqSJEmqxFZ8kjTj3VEOrZ3C\n6bpa8a3siG2IgcO3L9urOGXeIeWma6tZUIwtYcXA8XWU2/7dxZxibF0MnnfVzkuLcx7zyGXF2ODP\nBFNuxUdH67y4vhBYUp6z223lWG4YPL5rR+7zOvK7qdDCb155SrF9H4x+q7vyHVi2PbcenIm29jV0\n5VqSJEmqxOJakiRJqsTiWpIkSarE4lqSJEmqxOJakiRJqsTiWpIkSarEVnySNDQdLfKKbuqIlRp2\ndTWO6shhw26Dx8sd5uBBHbHvd8QKVo+V2+1dufuhxdj8wueiqxVfl+K8Yk89+Nke+xRjY4cO7o83\nd6qt+MpdBIvt/aKjPR7l1OHWwnjH+Xbb2BErjGfpOsCajmutL7T2m6mm8l1E90/XPzdw5VqSJEmq\nxuJakiRJqsTiWpIkSarE4lqSJEmqxOJakiRJqsRuIZI0q13bEXvc4OG1HVNu7IiVOomsLbfc2PDT\nPYqx9Y8vt4X4jzkHDBzfifKcSzm8GDuIqwaOr6bczWRFLCnG1u08uPvIkkNXFOfstWdHX4iuL2Op\n80fXnI5uHLH35OfQ0S1kKna7uSO2oRDoym87trgrWPnzrtbewBXlsCvXkiRJUiUW15IkSVIlFteS\nJElSJRbXkiRJUiUW15IkSVIlFteSJElSJbbik6TtyvqO2K51z3d3Dh7fUG6d19mm75bCeFf7vg63\nj5Xb4P1sl4cPHJ+/+7rinAWsLsZWMLit3kZ2Ks65qysWg2MreEhxzpK9f12Mje39y2Js4W2D+7FF\nV2u66ztipfZuXV/7LoXzddxl3Upt+kot+qTJWgx8qRx25VqSJEmqxOJakiRJqsTiWpIkSarE4lqS\nJEmqxOJakiRJqsTiWpIkSaokMgutlrYzEZFw0rDTkKRJmDeFObtN4XxTadEHMFYYP7BjTse1frfQ\nXG2XjtPlY5hlAAAPwUlEQVQd2hEb6/j/a2lh/EHlKXN/97aOiw22ePdS37fu1n7zGdwScF5hHGBx\nscdc97WW5IpJzynlB7CImwpzym0du85XshN3TXpOlwWsmtK8ruel2WlHDuHBD7iYzBz4Tc2Va0mS\nJKkSi2tJkiSpEotrSZIkqRKLa0mSJKkSi2tJkiSpkh2HnYAkzV6lLgRT6SLSZc0U55Xy6+qe0BH7\n6eLB43t1nG5uR+yWQvcRgFs65hVsuHGPSedx3S7lOdd1Xaz0vDo6p8wdK3czmTO33Flj3pzBnTq6\nuo90dfcodRnpmlO788dUuo90qZ2fZra9WQRcXIy7ci1JkiRVYnEtSZIkVTKU4joi9o+If4iIH0XE\n3RFxQeG4EyLi+ohYFxEXRcQh052rJEmSNFHDWrk+GDgGWAb8bNABEfF24ETgVOA5wFrgvIhYNF1J\nSpIkSZMxlOI6M7+cmftm5h8BV/XHI2IO8FbglMw8IzMvAF4EJPD66c1WkiRJmphR3XN9OLAr8Pnx\ngcxcB3wFeNawkpIkSZK6jGorvqXAJuDavvGrgRdPfzqSNBvdVBgvtNSbqpUd5+tqqffQjtiNhfGu\n1n7LOlr7dV2rpKOt3lTmbKDc9m/Dg8rzbi8855W7PKw8qevzVNKRA3vlFE44RXM3Tt+1NCs99oEB\nvLwYH9WV64XA2szs/9e4CpgfEaP6Q4EkSZJmsVEtriVJkqTtzqiuAK8CdomI6Fu9Xgisy8y7B0+7\nsOfjsfYhSZIk3Q+Xfgsu+xYAK3bo2ELG6BbXy4AdgAPYfN/10jZWcNS2zEmSJEmz0eFHNg9gyQOD\nle9/d/HQUd0Wcimwhqb9HgARMR84FvjasJKSJEmSugxl5Toi5gF/AATwO8CuEfGf2/BXM3NDRJwG\nvCMiVtOsVh/fHn/6MHKWJEmStmZY20IW0fSw7t1P/X/bP/cDrs/M0yIigLcBewLfA47OzN9Ma6aS\nNO3WTzE2r3IepVZ8azrmTKVN3wHl0N27lWPLO96wd3lhT+RU/9frajNXMpV2dl126d7nWTQ2hTnV\nc5/Ga9U/obS5vbvDQymuM/M6JrAlJTNPpXn7c0mSJGnkjeqea0mSJGm7Y3EtSZIkVWJxLUmSJFVi\ncS1JkiRVYnEtSZIkVTKq79AoSZq0Upu+2i36utoBrpvC+Uot/wDumGKs4O7JTwFgZSnQ0Sqwuil+\nHX+6a900mEpLwCnMsULRqHpMd9iVa0mSJKkSi2tJkiSpEotrSZIkqRKLa0mSJKkSi2tJkiSpEl+L\nK0mqaE3lOYs7Yjd3xGp3yCjp6nQy21TuStPZ2WU6u7RIfTbN6Qy7ci1JkiRVYnEtSZIkVWJxLUmS\nJFVicS1JkiRVYnEtSZIkVWJxLUmSJFViKz5JmvHWT+P5Krdjm3LutfPQ9mO62jBq9lrQGXXlWpIk\nSarE4lqSJEmqxOJakiRJqsTiWpIkSarE4lqSJEmqxOJakiRJqsRWfJKkEXbHsBPQdqfUvtH2jKpl\np86oK9eSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiV2C5EkVVTq1DDdRiUPSTPPxs6o\nK9eSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW\n15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJRbX\nkiRJUiUW15IkSVIlFteSJElSJRbXkiRJUiUW15IkSVIlFteSJElSJSNdXEfEIyPi/Ii4MyJ+FREn\nR0QMOy9JkiRpkB2HnUBJRCwAzgN+CjwX2B/4OyCAdw0xNUmSJGmgkS2ugdcAc4EXZOadwPkRsTtw\nUkS8NzPXDjc9SZIkaXOjvC3kGODrbWE97nPAfOCpw0lJkiRJKhvl4nopsKx3IDNvANa1MUmSJGmk\njHJxvRBYPWB8VRuTJEmSRsooF9eSJEnSdmWUX9C4Cth9wPjCNjbAhT0fzwWeVDsnbdeWA2NDzkGj\nZzneF9rScrwvtKXleF/MVsvbB6xYsUvnkaO8cr2Mvr3VEfFQmhc0Lhs4g6N6Hhu2YWraPi0fdgIa\nScuHnYBG0vJhJ6CRtHzYCWhoxhivMZcsObbzyFEurs8Ffj8idu4ZewnNCxovGk5KkiRJUtkobwv5\nKPAG4KyIeA/Nm8icBHyg1OP6sY99yL0fr1ixC0uWPGTQYZqlvCc0iPeFBvG+0CDeFwJYuvRBXHFF\nOR6ZOX3ZTFJELAVOBw6j6Rzyj8DJOSDpiBjdJyJJkqQZJTNj0PhIF9eSJEnS9mSU91xLkiRJ2xWL\na0mSJKmSGVVcR8QjI+L8iLgzIn4VESdHxMD9MJp5IuLFEXFORKyIiDUR8f2IeMmA406IiOsjYl1E\nXBQRhwwjXw1HRCyJiLURsSki5vfFvDdmkYjYISLeFhHXRMSGiLghIj4w4Djvi1kkIv44Iq5o/x+5\nMSLOjIgtXsXofaGSGVNcR8QC4DzgbuC5wMnA8e2fmh3eRPPC1zcCxwIXAJ+NiNeNHxARbwdOBE4F\nngOsBc6LiEXTn66G5P3AHf2D3huz0pnA64H3As8E3gqs7z3A+2J2iYgXAJ8GLqapJd4CHAmc03ec\n94WKZswLGtsb/a+AfTLzznbsv9O079ur1L5PM0dE7JGZt/WNfQZ4UmbuHxFzgJuA92Xmu9v4fJp3\nBfhoZr5runPW9IqII4EvAqcA7wN2zcx13huzT0QcA3wJeHRm/qxwjPfFLBMR/wI8LDN/r2fsWOBs\n4KDM/Jn3hbZmxqxcA8cAXx8vrFufo3lHx6cOJyVNp/7CunUlsKT9+AhgV+DzPXPWAV8BnrXNE9RQ\nRcQDgA/R/Dbr1r7w4XhvzDavBC4oFdYt74vZJ4Db+8Zu74mB/5doK2ZScb2UvrdFz8wbaN7RcenA\nGZoNDgeuaT9+BLAJuLbvmKvxHpkNXgPsBHxkQGwp3huzzROBayLi7yPi9va1Ov+vb2+t98Xs8zHg\niIh4WUTsGhEPB/4WOD8zx2sM/y9Rp5lUXC+k2W/bb1Ub0ywTEc8Ankezxxaa+2DtgDchWgXMj4hR\nfsdS3Q8RsSfwN8BfZuamAYd4b8w+e9GsXh8CvBh4BfA4mm1D47wvZpnMPA/4U+DjNCvWy2hqpRf2\nHOZ9oU7eAJqRImIM+AxwVmZ+erjZaAS8G7g0M78+7EQ0MsZ/xf/czFwNEBErgYsi4qjMvHBomWlo\nIuLZNIX1B4B/BRYDfw2cHRHPGPQO0VK/mVRcrwJ2HzC+sI1ploiIhcC5wC+Bl/aEVgG7RET0fYNc\nCKzLzLunMU1Nk4g4iGaF8ikRMf49Yuf2zwURcQ/eG7PRKuDn44V169vAXcDBwIV4X8xGpwKfz8wT\nxgci4kc0K9jPo3lho/eFOs2kbSHL6NvrFBEPpXlB47KBMzTjRMQ84KvADsBzMnNDT3hZO35A37Qt\n9utrRjmQZiHhcpr/FFcBp9OsXN5A8yLHZe0x3huzx9Xct3rdK4DxgsnvGbPP/sCPewcy8xqaFo37\nt0PeF+o0k4rrc4Hfj4ide8ZeQvOCxouGk5KmU0TsAHyB5hvgMZnZ3xHiUmAN8KKeOfNpemJ/bbry\n1LS7GHgacFTP4z00BdSzaFryXUrT+9p7Y/Y4B3hUROzRM/ZUmh+yftj+3e8Zs89y4DG9AxHxSGBe\nGwPvC23FTNoW8lHgDcBZEfEemgLrJOAD9rieNc6gKZbeCDw4Ih7cE7siMzdGxGnAOyJiNc0Kw/E0\nK1WnT3u2mhZti8Zv9Y5FxH7th99uW2jhvTHrfIzm/4xzIuIUYDfgNOAbmXkpgN8zZqUPAx+KiF/T\nLNrtBbwT+AVt4ex9oa2ZMcV1Zq5uu0OcDnyZpnPIB/AdGmeTZ9KsRn5wQGw/4PrMPC0iAngbsCfw\nPeDozPzN9KWpUeS9Mbtk5pqIeDrNtqB/ptlrfTbw5r7jvC9mkcz8SET8Fngt8GqajiEXAydk5vqe\n47wvVDRj3qFRkiRJGraZtOdakiRJGiqLa0mSJKkSi2tJkiSpEotrSZIkqRKLa0mSJKkSi2tJkiSp\nEotrSZIkqRKLa0maQSLiCRFxS0Ts0v5934i4p/D4t47zzImIjRFxQt/40tLciLgxIj7afvyHEXFN\nRPj/jKRZZca8Q6MkCYBTgQ9m5tq+8TcDl/aN3V46SfsWz1cCh/eFDgfWAU+IiMj2ncgiYh9gCfDt\ndv5ZEXEy8CrgH6f6ZCRpe+OKgiTNEBFxCPBU4H8PCF+Tmd/te/xsK6e8BHhS39hhNG8XPgd4VM/4\nEUACl/WMnQn8xWSegyRt7yyuJWmERMTuEXFDRJzZN/7liFgWEXM7ph8HfDczb6yUzqXAwohY2jN2\nOHAhcAWbr2ofBtycmT/vGfsCcFBE/F6lfCRp5FlcS9IIyczbabZSvCwijgWIiFcCzwJenpkbOqY/\nnc1Xjns9ICJ26HvEVtK5FAjaIjoidgce0V7jcjYvrg+nb9tJZl4HrGzzkqRZweJakkZMZv4b8DHg\nYxFxKPB3wPsy87ulORGxA/C7wFWFQ74E/LbncRfwzq3k8WtgOfcV0YcDt2TmL2gK7PGiez7waLbc\n0w3wU+CxXdeRpJnEFzRK0mg6HngmTRF7DXDSVo7fg2bB5LZC/E00e6h7rRj/oC3Ox90z/kJFmoJ5\nvLg+jGbFmvbP/SJiEXAwsMOA8wPcCuy1ldwlacZw5VqSRlBm3gmcA+wEfDIzfzvBqaWtHj/PzCv6\nHisBIuI4Nl/R/kTPvEuBh0fEQpri+tI2vxuBX9EU3ocBG4EfTCIfSZqRXLmWpBHUvgjwNcCVwDsi\n4p8z8+aOKbcCm4A9p3C5LwOP7/n7LT0fX0JTID8FeALwNz2xy2m6hDwS+EHhB4A9gK68JWlGceVa\nkkZMRMyhaWN3LvBkYBXNHuyizLwH+AnNFo1JycxVfSva1/eEfwKsBV4NzAW+1xO7jKa4fhKDt4RA\nsw/8ysnmJEnbK1euJWn0vBtYBByVmRsi4hXAtyLiuMw8s2Pe+TR9rgdZGhG39o1tyMwfdSWSmfdE\nxHdpupVc0det5DLg/e3HWxTXEbEvzX7rC7quIUkzicW1JI2QiDic5o1XXjq+DSQzL42IvwP+Z0R8\nIzNXFKafCbw5IvbpW31O4H0Djv858PAJpHUJTTu9/m4gV9Ds095xQAzgRcDVXV1OJGmmifteEC5J\n2t5FxDeAyzLzXSOQy0+Av8/Mzi0tkjSTWFxL0gzSvhDyXGAsM9cOMY/nA+8BDsrMTcPKQ5Kmm8W1\nJEmSVIndQiRJkqRKLK4lSZKkSiyuJUmSpEosriVJkqRKLK4lSZKkSiyuJUmSpEosriVJkqRK/j9E\n3UGVUY2ZrwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111e265d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (12,8))\n",
"ax = fig.add_subplot(111)\n",
"ax.imshow(prob_2.transpose()[:,0,:], \n",
" origin = 'lower left',\n",
" interpolation = 'none')\n",
"plt.title(\"Estimated probability of unit 4\")\n",
"plt.xlabel(\"x (E-W)\")\n",
"plt.ylabel(\"z\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example shows how the base module for reproducible experiments with kinematics can be used. For further specification, child classes of `Experiment` can be defined, and we show examples of this type of extension in the next sections."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Adjustments to generate training set\n",
"\n",
"First step: generate more layers and randomly select layers to visualise:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MqroEOBIo4EVjr6gkSdKYTCycJbkX8BbgRODGodX7ATsB500VVNUa4BPAIeOqoyRJ0rhN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\nkiQ1xHAmSZLUkP8Pkzqf3cbPPCYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ee45ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ue.random_draw()\n",
"s1 = ue.get_section('y')\n",
"s1.block.shape\n",
"s1.block[np.where(s1.block == 3)] = 1\n",
"s1.plot_section('y', cmap='Greys')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Idea**: generate many layers, then randomly extract a couple of these and also assign different density/ color values:"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"nm = pynoddy.history.NoddyHistory()\n",
"# add stratigraphy\n",
"\n",
"n_layers = 8\n",
"\n",
"strati_options['num_layers'] = n_layers\n",
"strati_options['layer_names'] = []\n",
"strati_options['layer_thickness'] = []\n",
"\n",
"for n in range(n_layers):\n",
"\n",
" strati_options['layer_names'].append(\"layer %d\" % n)\n",
" strati_options['layer_thickness'].append(5000./n_layers)\n",
"\n",
"nm.add_event('stratigraphy', strati_options )\n",
"\n",
"# The following options define the fault geometry:\n",
"fault_options = {'name' : 'Fault_E',\n",
" 'pos' : (1000, 0, 5000),\n",
" 'dip_dir' : 90.,\n",
" 'dip' : 60,\n",
" 'slip' : 500}\n",
"\n",
"nm.add_event('fault', fault_options)\n",
"history = 'normal_fault.his'\n",
"output_name = 'normal_fault_out'\n",
"nm.write_history(history)"
]
},
{
"cell_type": "code",
"execution_count": 512,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reload(pynoddy.history)\n",
"reload(pynoddy.experiment)\n",
"\n",
"from pynoddy.experiment import monte_carlo\n",
"ue = pynoddy.experiment.Experiment(history)\n",
"ue.freeze()\n",
"ue.set_random_seed(12345)\n",
"ue.set_extent(2800, 100, 2800)"
]
},
{
"cell_type": "code",
"execution_count": 513,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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udr+FqleSlqTTTz+X00//f3NaN7c9krP4kjwV+ARwv/52CrCyqi4cWOc9wC5Vtess26ib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\n/X99GgkvFhYCpQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11e9f80d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ue.change_cube_size(50)\n",
"ue.plot_section('y')"
]
},
{
"cell_type": "code",
"execution_count": 514,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"param_stats = [{'event' : 2, \n",
" 'parameter': 'Slip',\n",
" 'stdev': 100.0,\n",
" 'type': 'lognormal'},\n",
" {'event' : 2, \n",
" 'parameter': 'Dip',\n",
" 'stdev': 10.0,\n",
" 'type': 'normal'},\n",
"# {'event' : 2, \n",
"# 'parameter': 'Y',\n",
"# 'stdev': 150.0,\n",
"# 'type': 'normal'},\n",
" {'event' : 2, \n",
" 'parameter': 'X',\n",
" 'stdev': 150.0,\n",
" 'type': 'normal'},]\n",
"\n",
"ue.set_parameter_statistics(param_stats)"
]
},
{
"cell_type": "code",
"execution_count": 515,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# randomly select layers:\n",
"ue.random_draw()"
]
},
{
"cell_type": "code",
"execution_count": 516,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s1 = ue.get_section('y')"
]
},
{
"cell_type": "code",
"execution_count": 517,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create \"feature\" model:\n",
"f1 = s1.block.copy()"
]
},
{
"cell_type": "code",
"execution_count": 518,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# randomly select layers:\n",
"f1 = np.squeeze(f1)\n",
"# n_featuers: number of \"features\" -> gray values in image\n",
"n_features = 5\n",
"vals = np.random.randint(0,255,size=n_features)\n",
"for n in range(n_layers):\n",
" f1[f1 == n] = np.random.choice(vals)"
]
},
{
"cell_type": "code",
"execution_count": 519,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(56, 56)"
]
},
"execution_count": 519,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f1.shape"
]
},
{
"cell_type": "code",
"execution_count": 520,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11ebc7710>"
]
},
"execution_count": 520,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11e104550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(f1.T, origin='lower_left', cmap='Greys', interpolation='nearest')"
]
},
{
"cell_type": "code",
"execution_count": 521,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# blur image\n",
"from scipy import ndimage\n",
"f2 = ndimage.filters.gaussian_filter(f1, 1, mode='nearest')\n"
]
},
{
"cell_type": "code",
"execution_count": 522,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11ed2f7d0>"
]
},
"execution_count": 522,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11e3223d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(f2.T, origin='lower_left', cmap='Greys', interpolation='nearest', vmin=0, vmax=255)"
]
},
{
"cell_type": "code",
"execution_count": 523,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# randomly swap image\n",
"if np.random.randint(2) == 1:\n",
" f2 = f2[::-1,:]"
]
},
{
"cell_type": "code",
"execution_count": 524,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11ee1add0>"
]
},
"execution_count": 524,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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fdjSzpK4WwmFgkSRlrYR+YHC81sFdd92Vnq9YsYIVK1bUFI6Zjdi5cye7du1qa11N4RZR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\n4oRgZokTgpklTghmlvw/0wZtUsTOwRIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11eb0c890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(f2.T, origin='lower_left', cmap='Greys', interpolation='nearest', vmin=0, vmax=255)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## All in one function\n",
"\n",
"### Generate images for normal faults"
]
},
{
"cell_type": "code",
"execution_count": 591,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# back to before: re-initialise model:\n",
"nm = pynoddy.history.NoddyHistory()\n",
"# add stratigraphy\n",
"\n",
"n_layers = 18\n",
"\n",
"strati_options['num_layers'] = n_layers\n",
"strati_options['layer_names'] = []\n",
"strati_options['layer_thickness'] = []\n",
"\n",
"for n in range(n_layers):\n",
"\n",
" strati_options['layer_names'].append(\"layer %d\" % n)\n",
" strati_options['layer_thickness'].append(5000./n_layers)\n",
"\n",
"nm.add_event('stratigraphy', strati_options )\n",
"\n",
"# The following options define the fault geometry:\n",
"fault_options = {'name' : 'Fault_E',\n",
" 'pos' : (1000, 0, 5000),\n",
" 'dip_dir' : 90.,\n",
" 'dip' : 60,\n",
" 'slip' : 500}\n",
"\n",
"nm.add_event('fault', fault_options)\n",
"history = 'normal_fault.his'\n",
"output_name = 'normal_fault_out'\n",
"nm.write_history(history)\n",
"\n",
"reload(pynoddy.history)\n",
"reload(pynoddy.experiment)\n",
"\n",
"from pynoddy.experiment import monte_carlo\n",
"ue = pynoddy.experiment.Experiment(history)\n",
"ue.freeze()\n",
"ue.set_random_seed(12345)\n",
"ue.set_extent(2800, 100, 2800)\n",
"ue.change_cube_size(50)\n",
"\n",
"param_stats = [{'event' : 2, \n",
" 'parameter': 'Slip',\n",
" 'stdev': 100.0,\n",
" 'type': 'lognormal'},\n",
" {'event' : 2, \n",
" 'parameter': 'Dip',\n",
" 'stdev': 10.0,\n",
" 'type': 'normal'},\n",
"# {'event' : 2, \n",
"# 'parameter': 'Y',\n",
"# 'stdev': 150.0,\n",
"# 'type': 'normal'},\n",
" {'event' : 2, \n",
" 'parameter': 'X',\n",
" 'stdev': 150.0,\n",
" 'type': 'normal'},]\n",
"\n",
"ue.set_parameter_statistics(param_stats)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate training set for normal faults:"
]
},
{
"cell_type": "code",
"execution_count": 592,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_train = 10000\n",
"F_train = np.empty((n_train, 28*28))\n",
"\n",
"ue.change_cube_size(100)\n",
"\n",
"for i in range(n_train):\n",
" # randomly select layers:\n",
" ue.random_draw()\n",
" s1 = ue.get_section('y')\n",
" # create \"feature\" model:\n",
" f1 = s1.block.copy()\n",
" # randomly select layers:\n",
" f1 = np.squeeze(f1)\n",
" # n_featuers: number of \"features\" -> gray values in image\n",
" n_features = 4\n",
" vals = np.random.randint(0,255,size=n_features)\n",
" for n in range(n_layers):\n",
" f1[f1 == n+1] = np.random.choice(vals)\n",
" f1 = f1.T\n",
" f2 = ndimage.filters.gaussian_filter(f1, 0, mode='nearest')\n",
" # scale image\n",
" f2 = f2 - np.min(f2)\n",
" if np.max(f2) != 0:\n",
" f2 = f2/np.max(f2)*255\n",
" # randomly swap image\n",
" if np.random.randint(2) == 1:\n",
" f2 = f2[::-1,:]\n",
" F_train[i] = f2.flatten().T\n"
]
},
{
"cell_type": "code",
"execution_count": 583,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x122de2a90>"
]
},
"execution_count": 583,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x1235facd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(f2, origin='lower_left', cmap='Greys', interpolation='nearest', vmin=0, vmax=255)"
]
},
{
"cell_type": "code",
"execution_count": 593,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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eLpCkuRYWFkpHmFrf+973uq5/8MEHI04Cn7t8+XLpCI03M9NfdXKDBQAAEETBAgAACKJg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\niIIFAAAQRMECAAAIomABAAAE+T+GDAyuUFBz8gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120e3ec50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True, figsize=(12,6))\n",
"ax = ax.flatten()\n",
"for i in range(10):\n",
" img = F_train[i].reshape(28, 28)\n",
" ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
"\n",
"ax[0].set_xticks([])\n",
"ax[0].set_yticks([])\n",
"plt.tight_layout()\n",
"# plt.savefig('./figures/mnist_all.png', dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 594,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle\n"
]
},
{
"cell_type": "code",
"execution_count": 596,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pickle.dump(F_train, open(\"f_train_normal.pkl\", 'w'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate reverse faults\n",
"\n",
"And now: the same for reverse faults:"
]
},
{
"cell_type": "code",
"execution_count": 597,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# back to before: re-initialise model:\n",
"nm = pynoddy.history.NoddyHistory()\n",
"# add stratigraphy\n",
"\n",
"n_layers = 18\n",
"\n",
"strati_options['num_layers'] = n_layers\n",
"strati_options['layer_names'] = []\n",
"strati_options['layer_thickness'] = []\n",
"\n",
"for n in range(n_layers):\n",
"\n",
" strati_options['layer_names'].append(\"layer %d\" % n)\n",
" strati_options['layer_thickness'].append(5000./n_layers)\n",
"\n",
"nm.add_event('stratigraphy', strati_options )\n",
"\n",
"# The following options define the fault geometry:\n",
"fault_options = {'name' : 'Fault_E',\n",
" 'pos' : (1000, 0, 5000),\n",
" 'dip_dir' : 90.,\n",
" 'dip' : 60,\n",
" 'slip' : -500}\n",
"\n",
"nm.add_event('fault', fault_options)\n",
"history = 'normal_fault.his'\n",
"output_name = 'normal_fault_out'\n",
"nm.write_history(history)\n",
"\n",
"reload(pynoddy.history)\n",
"reload(pynoddy.experiment)\n",
"\n",
"from pynoddy.experiment import monte_carlo\n",
"ue = pynoddy.experiment.Experiment(history)\n",
"ue.freeze()\n",
"ue.set_random_seed(12345)\n",
"ue.set_extent(2800, 100, 2800)\n",
"ue.change_cube_size(50)\n",
"\n",
"param_stats = [{'event' : 2, \n",
" 'parameter': 'Slip',\n",
" 'stdev': -100.0,\n",
" 'type': 'lognormal'},\n",
" {'event' : 2, \n",
" 'parameter': 'Dip',\n",
" 'stdev': 10.0,\n",
" 'type': 'normal'},\n",
"# {'event' : 2, \n",
"# 'parameter': 'Y',\n",
"# 'stdev': 150.0,\n",
"# 'type': 'normal'},\n",
" {'event' : 2, \n",
" 'parameter': 'X',\n",
" 'stdev': 150.0,\n",
" 'type': 'normal'},]\n",
"\n",
"ue.set_parameter_statistics(param_stats)"
]
},
{
"cell_type": "code",
"execution_count": 598,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_train = 10000\n",
"F_train_rev = np.empty((n_train, 28*28))\n",
"\n",
"ue.change_cube_size(100)\n",
"\n",
"for i in range(n_train):\n",
" # randomly select layers:\n",
" ue.random_draw()\n",
" s1 = ue.get_section('y')\n",
" # create \"feature\" model:\n",
" f1 = s1.block.copy()\n",
" # randomly select layers:\n",
" f1 = np.squeeze(f1)\n",
" # n_featuers: number of \"features\" -> gray values in image\n",
" n_features = 4\n",
" vals = np.random.randint(0,255,size=n_features)\n",
" for n in range(n_layers):\n",
" f1[f1 == n+1] = np.random.choice(vals)\n",
" f1 = f1.T\n",
" f2 = ndimage.filters.gaussian_filter(f1, 0, mode='nearest')\n",
" # scale image\n",
" f2 = f2 - np.min(f2)\n",
" if np.max(f2) != 0:\n",
" f2 = f2/np.max(f2)*255\n",
" # randomly swap image\n",
" if np.random.randint(2) == 1:\n",
" f2 = f2[::-1,:]\n",
" F_train_rev[i] = f2.flatten().T\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 599,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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evXvT2bNn77we5Dv1ySefTNevX48POaFee+213BFqpSzL1Gw277we5KwuLy+n27dvx4ec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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1212bd350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True, figsize=(12,6))\n",
"ax = ax.flatten()\n",
"for i in range(10):\n",
" img = F_train_rev[i].reshape(28, 28)\n",
" ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
"\n",
"ax[0].set_xticks([])\n",
"ax[0].set_yticks([])\n",
"plt.tight_layout()\n",
"# plt.savefig('./figures/mnist_all.png', dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 600,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pickle.dump(F_train_rev, open(\"f_train_reverse.pkl\", 'w'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate simple layer structure\n",
"\n",
"No need for noddy, in this simple case - just adapt a numpy array:"
]
},
{
"cell_type": "code",
"execution_count": 604,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"l1 = np.empty_like(s1.block[:,0,:])"
]
},
{
"cell_type": "code",
"execution_count": 650,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_layers = 18\n",
"for i in range(l1.shape[0]):\n",
" l1[:,i] = i\n",
"l1_ori = np.floor(l1*n_layers/l1.shape[0])"
]
},
{
"cell_type": "code",
"execution_count": 655,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"F_train_line = np.empty((n_train, 28*28))\n",
"\n",
"\n",
"\n",
"for i in range(n_train):\n",
" n_features = 4\n",
" vals = np.random.randint(0,255,size=n_features)\n",
" l1 = l1_ori.copy()\n",
" for n in range(n_layers):\n",
" l1[l1 == n+1] = np.random.choice(vals)\n",
" f1 = l1.T\n",
" f2 = ndimage.filters.gaussian_filter(f1, 0, mode='nearest')\n",
" # scale image\n",
" f2 = f2 - np.min(f2)\n",
" if np.max(f2) != 0:\n",
" f2 = f2/np.max(f2)*255\n",
" F_train_line[i] = f2.flatten().T"
]
},
{
"cell_type": "code",
"execution_count": 656,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AiMACAAAIEVgAAAAhAgsAACBEYAEAAIQILAAAgBCBBQAAECKwAAAAQlpNLwDjcOvWrdLt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\nBQAAECKwAAAAQgQWAABAyP8Ak9J46IcDZ/0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1138f2810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(nrows=2, ncols=5, sharex=True, sharey=True, figsize=(12,6))\n",
"ax = ax.flatten()\n",
"for i in range(10):\n",
" img = F_train_line[i].reshape(28, 28)\n",
" ax[i].imshow(img, cmap='Greys', interpolation='nearest')\n",
"\n",
"ax[0].set_xticks([])\n",
"ax[0].set_yticks([])\n",
"plt.tight_layout()\n",
"# plt.savefig('./figures/mnist_all.png', dpi=300)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 657,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pickle.dump(F_train_line, open(\"f_train_line.pkl\", 'w'))"
]
},
{
"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
}
| gpl-2.0 |
bougui505/SOM | test/testset.ipynb | 1 | 135160 | {
"metadata": {
"name": "testset"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"SOM tutorial with a 2D test set"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Imports"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"import sys\n",
"sys.path.append('.')\n",
"sys.path.append('../')\n",
"import generatedatasets\n",
"import SOM2\n",
"import SOMclust\n",
"from pylab import *\n",
"pylab.rcParams['figure.figsize'] = 10, 10 # that's default image size for this interactive session"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Generate dataset"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xy, c = generatedatasets.dataset_fixed_cov()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scatter(*xy.T, c=c)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"<matplotlib.collections.PathCollection at 0x1015ce90>"
]
},
{
"output_type": "display_data",
"png": 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XMxJxoQYh7lQO8B/gWcQBSkUETNiBCvs6NjNuf8Rt8wFLkVywjebcACTx/GrE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FNeKzm5PPA1kAE4gIXs3VtMnTqNWLZsMXPmzOS6625l//45NG/eggkT3j/h/Li4OKDwuFeOAluA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\n"
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xy.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"(4000, 2)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Learn"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"som = SOM2.SOM(xy)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"smap = som.learn(verbose=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1 7999 8000 99.99% 1.00013637015 1.13641787895e-05 (25, 34)\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"and U-matrix"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"umat = som.umatrix()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"matshow(umat)\n",
"colorbar()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<matplotlib.colorbar.Colorbar instance at 0x2ba7f9260830>"
]
},
{
"output_type": "display_data",
"png": 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WVqaysjJJ0lNPPaUHHnhAvXv3liRlZGTo9ddfb9MxW91pOeusszR8+JG3MLp2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\nIBJYtAAAgEj4/zmOLuqceY8YAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Compute BMUs -- the position of each input data in the SOM"
]
},
{
"cell_type": "code",
"collapsed": true,
"input": [
"bmus = som.get_allbmus()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Flood the periodic map"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clust = SOMclust.clusters(umat, bmus, som.smap)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"100.00/100: flooding: 2499/2499, 1.28, (145, 160)\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"imshow(ma.masked_array(clust.umat_cont, clust.mask), interpolation='nearest')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 11,
"text": [
"<matplotlib.image.AxesImage at 0x2ba7f9527210>"
]
},
{
"output_type": "display_data",
"png": 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rlUpJkmpra1VZWammpiZNmzZNtbW1iS4SAAD43Ka+efNmPfLII5ozZ46i6LX/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\n1AEACMT/B9l/Ga3XaC1sAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
} | gpl-2.0 |
SamAinsworth/reproduce-cgo2017-paper | jnotebook/example-cgo2017/example-cgo2017-ck.ipynb | 1 | 1887 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"# Import Collective Knowledge Engine\n",
"import ck.kernel as ck\n",
"print ('CK version: %s' % ck.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"# Compile NAS CG\n",
"r=ck.access({'action':'compile',\n",
" 'module_uoa':'program',\n",
" 'data_uoa':'nas-cg',\n",
" 'speed':'yes',\n",
" 'env.CK_COMPILE_TYPE':'auto'})\n",
"if r['return']>0: \n",
" ck.err(r)\n",
" exit(1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Run NAS CG\n",
"r=ck.access({'action':'run',\n",
" 'module_uoa':'program',\n",
" 'data_uoa':'nas-cg'})\n",
"if r['return']>0: \n",
" ck.err(r)\n",
" exit(1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
],
"source": [
"# Print CK output\n",
"import json\n",
"print (json.dumps(r, indent=2))"
]
},
{
"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"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
amigao0502/AI | mxnet-week2/cifar10/step_by_step_debug.ipynb | 2 | 2544 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mxnet as mx \n",
"import matplotlib.pyplot as plt \n",
"import numpy as np\n",
"%pylab inline\n",
"pylab.rcParams['figure.figsize'] = (2, 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Step 1 data\n",
"# input data debug\n",
"data_iter = mx.io.ImageRecordIter(\n",
" path_imgrec = 'data/cifar10_train.rec',\n",
" data_shape = (3,28,28),\n",
" label_width = 1,\n",
" batch_size = 128\n",
")\n",
"print (data_iter)\n",
"i = 0\n",
"for each in data_iter:\n",
" i+=1\n",
" if i>5:\n",
" break\n",
" print each\n",
"batch_numpy = each.data[0].asnumpy()\n",
"label_numpy = each.label[0].asnumpy()\n",
"print (type(batch_numpy))\n",
"print (type(label_numpy))\n",
"\n",
"#show img\n",
"randidx = np.random.randint(0,128)\n",
"img = batch_numpy[randidx]\n",
"img = np.squeeze(img).sum(axis=0)\n",
"plt.imshow(img, cmap='gray')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# debug model\n",
"from importlib import import_module\n",
"net = import_module('symbols.'+'resnet')\n",
"sym = net.get_symbol(10,20,\"3,28,28\")\n",
"model_prefix = 'cifar10_resnet'\n",
"#check_point = mx.callback.do_checkpoint(model_prefix)\n",
"arg_name = sym.list_arguments()\n",
"out_name = sym.list_outputs()\n",
"print (arg_name)\n",
"print (out_name)\n",
"mx.viz.plot_network(sym,hide_weights=True,save_format='pdf',title='resnet8')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": 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": 2
}
| mit |
wcmckee/wcmckee.com | posts/education-counts-url.ipynb | 2 | 4998 | {
"cells": [
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import bs4\n",
"import requests\n",
"import dominate\n",
"from dominate.tags import *\n",
"import os\n",
"import shutil\n",
"import json\n",
"from urlparse import urlparse\n",
"from bs4 import BeautifulSoup"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#eceall = requests.get('')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"opind = open('/media/removable/WILLIAMS30G/www.educationcounts.govt.nz/index.html', 'r')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"optfz = opind.read()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"edcountz = bs4.BeautifulSoup(optfz)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"afind = edcountz.findAll('a')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"gidict = dict()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for afi in afind:\n",
" gied = afi.text\n",
" gifz = gied.replace(' ', '-')\n",
" giflow = gifz.lower()\n",
" gidict.update({giflow[:12] : afi.attrs['href']})\n",
" \n",
" gihrd = urlparse(afi.attrs['href'])\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"doc = dominate.document(title='educount')\n",
"\n",
"with doc.head:\n",
" link(rel='stylesheet', href='style.css')\n",
" script(type='text/javascript', src='script.js')\n",
"\n",
"with doc:\n",
" #with div(id='header').add(ol()):\n",
" #for i in ['home', 'about', 'contact']:\n",
" #li(a(i.title(), href='/%s.html' % i))\n",
"\n",
" with div(cls='row'):\n",
" h1('education-counts-url')\n",
" for afi in afind:\n",
" if 'https://' in afi.attrs['href']:\n",
" gied = afi.text\n",
" gifz = gied.replace(' ', '-')\n",
" giflow = gifz.lower()\n",
" a(dominate.tags.li(giflow[:12]), href = (afi.attrs['href']))\n",
" #print afi.attrs['href']\n",
" #p(edu.txt)\n",
" #dominate.tags.p(edu.attrs['href'])\n",
" #a(dominate.tags.p(edu.text), href=dominate.tags.a(edu.attrs['href']))\n",
" \n",
" #print edu.text\n",
"\n",
"#print doc"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"docre = doc.render()\n",
"#s = docre.decode('ascii', 'ignore')\n",
"yourstring = docre.encode('ascii', 'ignore').decode('ascii')\n",
"indfil = ('/media/removable/WILLIAMS30G/education-counts-url/index.json')\n",
"mkind = open(indfil, 'w')\n",
"mkind.write(yourstring)\n",
"mkind.close()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"dicon = json.dumps(gidict)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"opeducon = open('/media/removable/WILLIAMS30G/education-counts-url/index.json', 'w')\n",
"\n",
"opeducon.write(dicon)\n",
"\n",
"opeducon.close()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rdeducon = open('/media/removable/WILLIAMS30G/education-counts-url/index.json', 'r')\n",
"\n",
"rdqa = rdeducon.read()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"loadic = json.loads(rdqa)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"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 |
agile-geoscience/gio | docs/userguide/Random_grids.ipynb | 1 | 173820 | {"cells": [{"cell_type": "markdown", "id": "1abd588d", "metadata": {}, "source": ["# Random grids\n", "\n", "Because sometimes you just want some random data.\n", "\n", "This module implements 2D fractal noise, combining one or more octaves of [Perlin noise](https://en.wikipedia.org/wiki/Perlin_noise)."]}, {"cell_type": "code", "execution_count": 1, "id": "b476a72b", "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.collections.QuadMesh at 0x7fafa390dd00>"]}, "execution_count": 1, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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", "text/plain": ["<Figure size 432x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import gio\n", "\n", "g = gio.generate_random_surface(200, res=3, octaves=3)\n", "\n", "g.plot()"]}, {"cell_type": "markdown", "id": "61796fce", "metadata": {}, "source": ["The noise does not have to be isotropic:"]}, {"cell_type": "code", "execution_count": 6, "id": "ecd30c5b", "metadata": {}, "outputs": [{"data": {"text/plain": ["<matplotlib.collections.QuadMesh at 0x7fafa137b580>"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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", "text/plain": ["<Figure size 432x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["g = gio.generate_random_surface(200, res=(2, 5), octaves=3)\n", "\n", "g.plot()"]}, {"cell_type": "markdown", "id": "e4b86c81", "metadata": {}, "source": ["Note that the `size` and `res` can both be tuples.\n", "\n", "The size will be 'at least' the specified size in that dimension: it might have to be larger for the algorithm to work."]}], "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} | apache-2.0 |
anugrah-saxena/pycroscopy | docs/auto_examples/example_example.ipynb | 1 | 1342 | {
"metadata": {
"language_info": {
"codemirror_mode": {
"version": 3,
"name": "ipython"
},
"name": "python",
"file_extension": ".py",
"version": "3.5.2",
"nbconvert_exporter": "python",
"mimetype": "text/x-python",
"pygments_lexer": "ipython3"
},
"kernelspec": {
"language": "python",
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0,
"cells": [
{
"execution_count": null,
"outputs": [],
"source": [
"%matplotlib inline"
],
"cell_type": "code",
"metadata": {
"collapsed": false
}
},
{
"source": [
"\nAn Example on writing examples\n==============================\n\nThe example docstring will be printed as text along with the code of the example.\nIt should contain the desciption of the example code.\nOnly examples that begine with plot_* will be run when generating the docs.\n\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"outputs": [],
"source": [
"# Code source: Chris Smith\n# Liscense: MIT\nprint('I am an example')"
],
"cell_type": "code",
"metadata": {
"collapsed": false
}
}
]
} | mit |
mlperf/training_results_v0.5 | v0.5.0/google/cloud_v2.512/resnet-tpuv2-512/code/resnet/model/tpu/tools/colab/Regression_Sine_data_with_Keras.ipynb | 1 | 47051 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Simple Regression Model Using Keras on Colab TPU - [Full Sine as input]",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python2",
"display_name": "Python 2"
},
"accelerator": "TPU"
},
"cells": [
{
"metadata": {
"id": "afRHQRnkeSeJ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"# A simple regression model using Keras with Cloud TPUs\n",
"\n",
"This notebook demonstrates using Cloud TPUs in colab to build a simple regression model using **y = sin(x)** to predict y for given x.\n",
"\n",
"**Advantages:**\n",
"* GCP account is not compulsory which is a must pre-requisite for the models using TPUEstimator()\n",
"* Generated huge amount of data to compare the training performance on TPU vs CPU."
]
},
{
"metadata": {
"id": "6IhP5cGzewbQ",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Imports"
]
},
{
"metadata": {
"id": "JeMli385le2A",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n",
"#\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,0\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",
"\"\"\"An Example of a Regression model using Keras for the y = sin(x) dataset.\"\"\"\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import tensorflow.keras as keras\n",
"import math\n",
"import os\n",
"import pprint\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn import metrics"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "U-fdEJFNfICv",
"colab_type": "code",
"outputId": "3724d7bc-6dc1-4a6c-fe14-3e9c7fcd04ad",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"cell_type": "code",
"source": [
"print(tf.__version__)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"1.12.0-rc2\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "kK51TQYnfiXO",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Resolve TPU Address"
]
},
{
"metadata": {
"id": "YWmHCZrufCO4",
"colab_type": "code",
"outputId": "e7945232-1450-46f0-ec7e-3bce1786796c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 251
}
},
"cell_type": "code",
"source": [
"use_tpu = True #@param {type:\"boolean\"}\n",
"\n",
"if use_tpu:\n",
" assert 'COLAB_TPU_ADDR' in os.environ, 'Missing TPU; did you request a TPU in Notebook Settings?'\n",
"\n",
"if 'COLAB_TPU_ADDR' in os.environ:\n",
" TF_MASTER = 'grpc://{}'.format(os.environ['COLAB_TPU_ADDR'])\n",
"else:\n",
" TF_MASTER=''\n",
"\n",
"with tf.Session(TF_MASTER) as session:\n",
" print ('List of devices:')\n",
" pprint.pprint(session.list_devices())"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"List of devices:\n",
"[_DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:CPU:0, CPU, -1, 3204130715220914658),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 1757131222346187914),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 14189073471542166552),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 18089629823860601296),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 3346547477748453737),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 6512962655626301676),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 1886142505182871901),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 49656553673112525),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 1173330136390527977),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 8029696445054773714),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 867116612521745007),\n",
" _DeviceAttributes(/job:tpu_worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 18125553718687585414)]\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Vy1qljh-vWxW",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"### Creating data for y = sin(x).\n",
"Sine wave data is created using numpy. And to make it more difficult, random noice is added to the sine wave."
]
},
{
"metadata": {
"id": "89lavEaRsMiu",
"colab_type": "code",
"outputId": "6f8bd410-37e0-4049-b30c-d50627b315b3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 365
}
},
"cell_type": "code",
"source": [
"data_size = 2**18\n",
"\n",
"x = np.linspace(0, 6, data_size)\n",
"np.random.shuffle(x)\n",
"\n",
"y = -20 * np.sin(x) + 3 + np.random.normal(0, 1, (data_size,))\n",
"\n",
"x = x.reshape(-1, 1)\n",
"y = y.reshape(-1, 1)\n",
"\n",
"train_x, test_x = x[:data_size/2], x[data_size/2:]\n",
"train_y, test_y = y[:data_size/2], y[data_size/2:]\n",
"\n",
"plt.plot(x, y, 'bo')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb150f2f250>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
},
{
"output_type": "display_data",
"data": {
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8+LC2b9+u733ve/roRz+qEydOFKWRAOzX02N2tMJ+4/CxsYDIQhT8DaqoqNCTTz6pxsbG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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb150fae390>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "XPNNUZ7ygjGk",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Define model:\n",
"Model will have an input layer where it takes in the x coordinate, two densely connected layers with 200 and 80 nodes, and an output layer where it returns the predicted y value."
]
},
{
"metadata": {
"id": "mzINcHJdln91",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"def get_model():\n",
" return keras.Sequential([\n",
" keras.layers.Dense(1, input_shape=(1,)),\n",
" keras.layers.Dense(200, activation=tf.nn.sigmoid),\n",
" keras.layers.Dense(80, activation=tf.nn.sigmoid),\n",
" keras.layers.Dense(1)\n",
" ])"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "aKk5vUvalpfF",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"model = get_model()\n",
"model.compile(optimizer=tf.train.GradientDescentOptimizer(.01),\n",
" loss='mean_squared_error',\n",
" metrics=['mean_squared_error'])"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "ggY7UortwcgK",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Creating a TPU model from a Keras Model\n",
"To make the model usable by a TPU, converting it using keras_to_tpu_model."
]
},
{
"metadata": {
"id": "j-2PBN7smmCy",
"colab_type": "code",
"outputId": "cbb899b1-d088-4c49-df45-1d0023a0eaab",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 341
}
},
"cell_type": "code",
"source": [
"tpu_model = tf.contrib.tpu.keras_to_tpu_model(\n",
" model,\n",
" strategy=tf.contrib.tpu.TPUDistributionStrategy(\n",
" tf.contrib.cluster_resolver.TPUClusterResolver(TF_MASTER)))"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:tensorflow:Querying Tensorflow master (grpc://10.63.218.106:8470) for TPU system metadata.\n",
"INFO:tensorflow:Found TPU system:\n",
"INFO:tensorflow:*** Num TPU Cores: 8\n",
"INFO:tensorflow:*** Num TPU Workers: 1\n",
"INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 3204130715220914658)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 1757131222346187914)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 14189073471542166552)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 18089629823860601296)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 3346547477748453737)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 6512962655626301676)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 1886142505182871901)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 49656553673112525)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 1173330136390527977)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 8029696445054773714)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 867116612521745007)\n",
"INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 18125553718687585414)\n",
"WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "ze8cEk8cw3oL",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Training of the model on TPU"
]
},
{
"metadata": {
"id": "nedmSZk4lsE1",
"colab_type": "code",
"outputId": "d20be76b-b9fb-4c54-d7a2-2ee9ebcec7f8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 523
}
},
"cell_type": "code",
"source": [
"tpu_model.fit(train_x, train_y, epochs=10, steps_per_epoch=512)\n",
"tpu_model.save_weights('/tmp/sine.h5', overwrite=True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"INFO:tensorflow:New input shapes; (re-)compiling: mode=train (# of cores 8), [TensorSpec(shape=(16384,), dtype=tf.int32, name=u'core_id0'), TensorSpec(shape=(16384, 1), dtype=tf.float32, name=u'dense_4_input_10'), TensorSpec(shape=(16384, 1), dtype=tf.float32, name=u'dense_7_target_30')]\n",
"INFO:tensorflow:Overriding default placeholder.\n",
"INFO:tensorflow:Remapping placeholder for dense_4_input\n",
"INFO:tensorflow:Started compiling\n",
"INFO:tensorflow:Finished compiling. Time elapsed: 18.0318541527 secs\n",
"INFO:tensorflow:Setting weights on TPU model.\n",
"512/512 [==============================] - 33s 65ms/step - loss: 38.1645 - mean_squared_error: 38.1645\n",
"Epoch 2/10\n",
"512/512 [==============================] - 14s 26ms/step - loss: 8.4381 - mean_squared_error: 8.4381\n",
"Epoch 3/10\n",
"512/512 [==============================] - 14s 26ms/step - loss: 7.3682 - mean_squared_error: 7.3682\n",
"Epoch 4/10\n",
"512/512 [==============================] - 13s 26ms/step - loss: 7.3235 - mean_squared_error: 7.3235\n",
"Epoch 5/10\n",
"512/512 [==============================] - 13s 26ms/step - loss: 6.6394 - mean_squared_error: 6.6394\n",
"Epoch 6/10\n",
"512/512 [==============================] - 14s 26ms/step - loss: 5.6076 - mean_squared_error: 5.6076\n",
"Epoch 7/10\n",
"512/512 [==============================] - 14s 28ms/step - loss: 5.0474 - mean_squared_error: 5.0474\n",
"Epoch 8/10\n",
"512/512 [==============================] - 13s 26ms/step - loss: 4.6826 - mean_squared_error: 4.6826\n",
"Epoch 9/10\n",
"512/512 [==============================] - 13s 26ms/step - loss: 4.3695 - mean_squared_error: 4.3695\n",
"Epoch 10/10\n",
"512/512 [==============================] - 13s 26ms/step - loss: 4.0963 - mean_squared_error: 4.0963\n",
"INFO:tensorflow:Copying TPU weights to the CPU\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "ZFphWL2ExXjC",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Prediction\n",
"For predictions, same model architecture is being used which is loaded with the earlier learned weights."
]
},
{
"metadata": {
"colab_type": "code",
"id": "vpz43puBo3vz",
"colab": {}
},
"cell_type": "code",
"source": [
"prediction_model = get_model()\n",
"prediction_model.load_weights('/tmp/sine.h5')"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "1Vc44SqfluY1",
"colab_type": "code",
"outputId": "dc1e33eb-0ce3-49ea-d324-09687ab14cfc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 369
}
},
"cell_type": "code",
"source": [
"predictions = prediction_model.predict(test_x)\n",
"plt.plot(test_x, predictions, 'ro')"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fb14c970ed0>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
},
{
"output_type": "display_data",
"data": {
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Yj3FezEUyWTnp7UZb1q+++qp27NghSUqn0+rr61NdXZ3JpwBgCbY5AWYZrZCXLFmiNWvW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\nAAALEMgAAFiAQAYAwAIEMgAAFvh/G8ht0ew/M7IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb14c9af7d0>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "q8Sdh-1n4pqt",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"###Training on CPU for comparison with TPU\n",
"How much time did we save by using a TPU? Let's try training the exact same model on the same data, but without the TPU speedup.\n",
"\n",
"**(WARNING: This will take a long time to execute!)**"
]
},
{
"metadata": {
"id": "YCkqpEXu4Xx4",
"colab_type": "code",
"outputId": "18a9e694-4baf-455d-a346-c341552f1e8a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 395
}
},
"cell_type": "code",
"source": [
"slow_model = get_model()\n",
"slow_model.compile(optimizer=tf.train.GradientDescentOptimizer(.01),\n",
" loss='mean_squared_error',\n",
" metrics=['mean_squared_error'])\n",
"slow_model.fit(train_x, train_y, epochs=10, steps_per_epoch=512)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"512/512 [==============================] - 45s 88ms/step - loss: 40.1559 - mean_squared_error: 40.1559\n",
"Epoch 2/10\n",
"512/512 [==============================] - 44s 87ms/step - loss: 8.6203 - mean_squared_error: 8.6203\n",
"Epoch 3/10\n",
"512/512 [==============================] - 44s 86ms/step - loss: 7.5388 - mean_squared_error: 7.5388\n",
"Epoch 4/10\n",
"512/512 [==============================] - 44s 85ms/step - loss: 7.3395 - mean_squared_error: 7.3395\n",
"Epoch 5/10\n",
"512/512 [==============================] - 44s 86ms/step - loss: 6.3310 - mean_squared_error: 6.3310\n",
"Epoch 6/10\n",
"512/512 [==============================] - 44s 87ms/step - loss: 5.3646 - mean_squared_error: 5.3646\n",
"Epoch 7/10\n",
"512/512 [==============================] - 45s 88ms/step - loss: 4.9005 - mean_squared_error: 4.9005\n",
"Epoch 8/10\n",
"512/512 [==============================] - 44s 86ms/step - loss: 4.5581 - mean_squared_error: 4.5581\n",
"Epoch 9/10\n",
"512/512 [==============================] - 44s 87ms/step - loss: 4.2622 - mean_squared_error: 4.2622\n",
"Epoch 10/10\n",
"512/512 [==============================] - 44s 86ms/step - loss: 4.0092 - mean_squared_error: 4.0092\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7fb14c9af3d0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
}
]
} | apache-2.0 |
mramire8/active | other/score_analysis.ipynb | 2 | 37152 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib as mpl\n",
"\n",
"mpl.style.use('bmh')\n",
"\n",
"path = \"C:/Users/mramire8/Google Drive/AAL-Experiments/aal_python/sr-oracle-test/results-calibrated/zscores/score-analysis\"\n",
"trial = \"trial0-student5k.txt\"\n",
"\n",
"def load_trial(filename):\n",
" data = np.loadtxt(path+\"/\"+filename, skiprows=1)\n",
" return data\n",
"\n",
"def read_data(filename):\n",
" f = open(path+\"/\"+filename)\n",
" with f:\n",
" lines = f.readlines() \n",
" return np.array([l.strip().split(\"\\t\") for l in lines[1:]]), lines[0]\n",
"\n",
"t,headers = read_data(trial)\n",
"t0 = load_trial(trial) \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Number of sentences\", len(t)\n",
"print \"Number of Documents\", len(np.unique(t[:,1]))\n",
"print \"Headers\", headers"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Number of sentences 3259\n",
"Number of Documents 250\n",
"Headers SENTID\tDOCID\tSCORE\tRANK\tCALSCORE\tCALRANK\tORAPRED\t1LABEL\tPy0\tSTUDENTLABEL\tCorrectlyLabeled?\tStudentCorrect?\tRankDiff\n",
"\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_doc(data, docid):\n",
" return np.array([d for d in data if d[1] == docid])\n",
"# return data[data[1]==docid,:]\n",
"RANK=3\n",
"CRANK=5\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.stats import pearsonr\n",
"def get_pearson_doc(data):\n",
" result = dict()\n",
" for did in np.unique(data[:,1]):\n",
" doc =get_doc(data,did)\n",
" result[did]=pearsonr(doc[:,RANK], doc[:,CRANK])[0]\n",
" return result"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"corr = sorted(get_pearson_doc(t0).items(),key=lambda x: x[1], reverse=True)\n",
"\n",
"print \"\\n\".join([\"docid:{}\\tcorrel:{}\".format(d,p) for d,p in corr[:10]])\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"docid:8198.0\tcorrel:1.0\n",
"docid:8203.0\tcorrel:1.0\n",
"docid:4108.0\tcorrel:1.0\n",
"docid:6744.0\tcorrel:1.0\n",
"docid:35.0\tcorrel:1.0\n",
"docid:11300.0\tcorrel:1.0\n",
"docid:8355.0\tcorrel:1.0\n",
"docid:3629.0\tcorrel:1.0\n",
"docid:2610.0\tcorrel:1.0\n",
"docid:7469.0\tcorrel:1.0\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"C:\\Python27\\lib\\site-packages\\scipy\\stats\\stats.py:2436: RuntimeWarning: invalid value encountered in double_scalars\n",
" r = r_num / r_den\n",
"C:\\Python27\\lib\\site-packages\\scipy\\stats\\stats.py:4184: RuntimeWarning: invalid value encountered in less\n",
" x = np.where(x < 1.0, x, 1.0) # if x > 1 then return 1.0\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import Counter\n",
"corr_dist = Counter([c[1] for c in corr])\n",
"plt.hist(corr_dist.keys(),weights=corr_dist.values(), bins=np.arange(.8,1.001,.01))\n",
"plt.title(\"Correlation Distribution Ranks - (mean: {0:.3f})\".format(np.mean([c[1] for c in corr])))\n",
"plt.xlabel(\"Pearson Correlation\")\n",
"plt.ylabel(\"Frequency\")\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.text.Text at 0xb4381d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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AwPLly2FlZYWWLVti06ZNSEhIQGJiItavX4/WrVsjMTER1tbWmDBhAt555x1s3boVJ0+e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\n2dlIT0/HihUrsGbNGjRo0ACPP/44pk6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"text": [
"<matplotlib.figure.Figure at 0x511a4e0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#SENTID\tDOCID\tSCORE\tRANK\tCALSCORE\tCALRANK\tORAPRED\t1LABEL\tPy0\tSTUDENTLABEL\tCorrectlyLabeled?\tStudentCorrect?\tRankDiff\n",
"# Chek if the sentences picked by the students are correct?\n",
"SCORRECT = 11 ## correctly labeled by the student\n",
"OCORRECT = 10 # correctly labeled by the oracle\n",
"snippets = t0[t0[:,CRANK] == 0]\n",
"print \"Correctly labeled snippets by student: %s (%s)\" % (len(snippets[snippets[:,SCORRECT]==1]),snippets[:,SCORRECT].sum()/len(snippets))\n",
"print \"Snippets by student: Correct 0:%s, correct-1:%s\" % (len([s for s in snippets if s[9]==0 and s[SCORRECT]]),len([s for s in snippets if s[9]==1 and s[SCORRECT]]))\n",
"print \"Correctly labeled snippets by oracle: %s\" % (snippets[:,OCORRECT].sum()/len(snippets))\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Correctly labeled snippets by student: 189 (0.756)\n",
"Snippets by student: Correct 0:99, correct-1:90\n",
"Correctly labeled snippets by oracle: 0.78\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print \"Snippets\" \n",
"from itertools import product\n",
"\n",
"def get_selected(snippets, label, student, oracle):\n",
" return [s for s in snippets if s[7]==label and s[SCORRECT] == student and s[OCORRECT] == oracle]\n",
"\n",
"def get_options_selected(snippets,labels, student, oracle):\n",
" options = product(labels, student, oracle)\n",
" tot = 0\n",
" ans= []\n",
" for opt in options:\n",
" s = get_selected(snippets, *opt)\n",
" print \"label=%s\\tstudent=%s\\toracle=%s\" % opt,\n",
" print \"\\t%s\\t%s\" % (len(s), len(s)/250.)\n",
" tot += len(s)\n",
" ans.append(s)\n",
" print \"Total\", tot\n",
" return ans\n",
"\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Snippets\n"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels=[0,1]\n",
"student=[True, False]\n",
"oracle=[True]\n",
"\n",
"print \"Snippets where the oracle is correct\"\n",
"get_options_selected(snippets, labels,student, oracle)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Snippets where the oracle is correct\n",
"label=0\tstudent=True\toracle=True \t95\t0.38\n",
"label=0\tstudent=False\toracle=True \t6\t0.024\n",
"label=1\tstudent=True\toracle=True \t83\t0.332\n",
"label=1\tstudent=False\toracle=True \t11\t0.044\n",
"Total 195\n"
]
}
],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"labels=[0,1]\n",
"student=[True]\n",
"oracle=[True, False]\n",
"\n",
"print \"Snippets where the student is correct\"\n",
"get_options_selected(snippets, labels,student, oracle)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Snippets where the student is correct\n",
"label=0\tstudent=True\toracle=True \t95\t0.38\n",
"label=0\tstudent=True\toracle=False \t4\t0.016\n",
"label=1\tstudent=True\toracle=True \t83\t0.332\n",
"label=1\tstudent=True\toracle=False \t7\t0.028\n",
"Total 189\n"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# wrong = get_selected(snippets, labels, slabels=[0,1]\n",
"student=[False]\n",
"oracle=[False]\n",
"\n",
"print \"Snippets where the oracle is correct\"\n",
"wrong = get_options_selected(snippets, labels,student, oracle)\n",
"print len(wrong)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Snippets where the oracle is correct\n",
"label=0\tstudent=False\toracle=False \t18\t0.072\n",
"label=1\tstudent=False\toracle=False \t26\t0.104\n",
"Total 44\n",
"2\n"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wrong_docs= [s[1] for s in wrong[0]]\n",
"wrong_docs.extend([s[1] for s in wrong[1]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"w0=get_doc(t0, wrong_docs[0])\n",
"\n",
"print \"Document wrong\", wrong_docs[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Document wrong 11235.0\n"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scc_rank = w0[w0[:,CRANK]==1]\n",
"print \"Sencond ranked in w0\", scc_rank[0]\n",
"print \"correct?\", scc_rank[0][OCORRECT]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Sencond ranked in w0 [ 1.00000000e+00 1.12350000e+04 7.67367050e-01 0.00000000e+00\n",
" 1.67110300e+00 1.00000000e+00 0.00000000e+00 0.00000000e+00\n",
" 7.67367050e-01 0.00000000e+00 1.00000000e+00 1.00000000e+00\n",
" 1.00000000e+00]\n",
"correct? 1.0\n"
]
}
],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"seg_option = []\n",
"for d in wrong_docs:\n",
" sents = get_doc(t0,d)\n",
" seg_option.append([d, sents[sents[:,CRANK] ==1][0][OCORRECT]])\n",
"\n",
"print \"Numb. of correct sencond choise\", np.array(seg_option)[:,1].sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Numb. of correct sencond choise 25.0\n"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(snippets)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 82,
"text": [
"250"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bad = snippets[snippets[snippets[:,OCORRECT]==False][:,SCORRECT]==False][:,1]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bad==wrong_docs"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 86,
"text": [
"array([ True, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False, False,\n",
" False, False, False, False, False, False, False, False], dtype=bool)"
]
}
],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(bad)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 88,
"text": [
"44"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(wrong_docs)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 89,
"text": [
"44"
]
}
],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print zip(sorted(bad), sorted(wrong_docs))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[(77.0, 30.0), (790.0, 77.0), (828.0, 284.0), (1263.0, 447.0), (1388.0, 452.0), (1768.0, 604.0), (1844.0, 605.0), (1857.0, 732.0), (1868.0, 790.0), (2193.0, 828.0), (2464.0, 1376.0), (2617.0, 1768.0), (2633.0, 1844.0), (3191.0, 1913.0), (3508.0, 3304.0), (3652.0, 3629.0), (3812.0, 3681.0), (3907.0, 3698.0), (4251.0, 4015.0), (4910.0, 4408.0), (5304.0, 4426.0), (5421.0, 4698.0), (6753.0, 4734.0), (7285.0, 5147.0), (7857.0, 5159.0), (7879.0, 5232.0), (8009.0, 5256.0), (8190.0, 5320.0), (8965.0, 5483.0), (8971.0, 6232.0), (9826.0, 6377.0), (9865.0, 6744.0), (9977.0, 6797.0), (10319.0, 6805.0), (10339.0, 9697.0), (10545.0, 10442.0), (11235.0, 10545.0), (11345.0, 11235.0), (11670.0, 11321.0), (11770.0, 11770.0), (12063.0, 12002.0), (12113.0, 12099.0), (12164.0, 12137.0), (12389.0, 12389.0)]\n"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print sorted(bad)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[77.0, 790.0, 828.0, 1263.0, 1388.0, 1768.0, 1844.0, 1857.0, 1868.0, 2193.0, 2464.0, 2617.0, 2633.0, 3191.0, 3508.0, 3652.0, 3812.0, 3907.0, 4251.0, 4910.0, 5304.0, 5421.0, 6753.0, 7285.0, 7857.0, 7879.0, 8009.0, 8190.0, 8965.0, 8971.0, 9826.0, 9865.0, 9977.0, 10319.0, 10339.0, 10545.0, 11235.0, 11345.0, 11670.0, 11770.0, 12063.0, 12113.0, 12164.0, 12389.0]\n"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print sorted(wrong_docs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[30.0, 77.0, 284.0, 447.0, 452.0, 604.0, 605.0, 732.0, 790.0, 828.0, 1376.0, 1768.0, 1844.0, 1913.0, 3304.0, 3629.0, 3681.0, 3698.0, 4015.0, 4408.0, 4426.0, 4698.0, 4734.0, 5147.0, 5159.0, 5232.0, 5256.0, 5320.0, 5483.0, 6232.0, 6377.0, 6744.0, 6797.0, 6805.0, 9697.0, 10442.0, 10545.0, 11235.0, 11321.0, 11770.0, 12002.0, 12099.0, 12137.0, 12389.0]\n"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
ComputationalModeling/spring-2017-danielak | past-semesters/spring_2016/day-by-day/day08-modeling-viral-load-2/viral_load_model_INSTRUCTOR.ipynb | 1 | 9819 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Viral load modeling - the predictive version"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Names of group members"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"put your names here!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Goal of this assignment\n",
"\n",
"The main goal of this assignment is to model a biological process (namely, the competition between viruses and the human body's immune system) in a mathematical way, and in doing so reproduce the data points that your group was empirically fitting in the last assignment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Some background knowledge we need for this model\n",
"\n",
"Viruses multiply in more than one way. One of the most common is called the [Lytic Cycle](https://en.wikipedia.org/wiki/Lytic_cycle), and the other is the [Lysogenic Cycle](https://en.wikipedia.org/wiki/Lysogenic_cycle). Both cycles are similar in that the virus takes cells hostage and use the cell's resources to multiply, making many copies of itself. Once enough new viruses are produced inside the cell it bursts, and the newly-created viruses then are released into the bloodstream to carry on the process and search for new host cells to invade.\n",
"\n",
"[Antiviral drugs](https://en.wikipedia.org/wiki/Antiviral_drug) behave differently than [antibiotics](https://en.wikipedia.org/wiki/Antibiotics) - rather than directly destroying the virus population in a patient, they instead generally inhibit the creation of new viruses by preventing viruses from entering target cells, by preventing the viruses from synthesizing new viruses once they have invaded new cells, or by preventing the release of newly-created viruses from the host cell.\n",
"\n",
"In general, we can think of what happenes to an infected patient that has been administered an antiviral drug using a simple model. The key points are:\n",
"\n",
"* Viruses multiply rapidly if uninhibited by the body's immune system and infect cells at a rate that is proportional to the number of virions (virus particles), $N_v$, that are in the bloodstream. In other words, $\\frac{dN_I}{dt}$, the rate at which the number of cells that are infected ($N_I$) changes, depends on $N_v$ and the time scale for multiplication $t_{mul}$. $N_v$ in turn depends on the number of infected cells and the number of virions produced per infected cell, $\\gamma$.\n",
"* As antiviral drugs are administered at a constant rate, it takes some amount of time $T_{crit}$ for the amount of drug in the bloodstream to reach a high enough level that it suppresses the formation of new viruses. (This time varies from patient to patient, but is typically one day to a few days.)\n",
"* After the drug takes effect, we can assume that cell infection immediately stops. After infection stops, the number of infected cells $N_I$ decreases at a rate $\\frac{dN_I}{dt} = -N_{I}/t_{rel}$, where $t_{rel}$ is the time scale on which infected cells release virions into the bloodstream and die.\n",
"* Once cells can no longer be infected, virions are released into the bloodstream through the death of previously infected cells. The rate at which these virions are released behaves as $\\frac{dN_v}{dt} = \\gamma N_{I}/t_{rel}$.\n",
"* The body clears virions out of the body at a rate that depends on the amount of virions that are in the bloodstream, $\\frac{dN_v}{dt} = -N_v/t_{clr}$ ($N_v$ is the number of virions in the bloodstream and $t_{clr}$ is the time scale on which virions are cleared from the body).\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Your mission\n",
"\n",
"You have a mission that will take place in three parts:\n",
"\n",
"1. Using the information above and your whiteboards, create a mathematical model for how the viral load in the bloodstream, $N_v$, varies as a function of time. Don't use numbers - just symbols!\n",
"2. After you are happy with your model (and after you've talked to one of the instructors about it), figure out how to implement it as a computer program. Do so below! \n",
"3. Compare the shape of the plot created by your model to the data from the HIV viral load project you just completed. How do they compare? (Suggestion: assume that all of the time scales in your model are roughly equal, and roughly a day, and vary them from there.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**INSTRUCTOR NOTES**\n",
"\n",
"It's instructive to look at section 1.2 of Nelson's book.\n",
"\n",
"There is an initial phase where the number of viruses grows exponentially, since $\\frac{dN_v}{dt} \\propto \\frac{dN_I}{dt} \\propto \\gamma N_v / t_{mul}$. This leads to $N_v(t) \\simeq N_v(0) e^{t/t_{mul}}$, where $N_v(0)$ is the number of virions in the bloodstream at t=0.\n",
"\n",
"Some time $T_{crit}$ after the drug is administered, cell infection stops and the number of infected cells changes as $\\frac{dN_I}{dt} = -N_{I}/t_{rel}$. This leads to the exponential solution $N_I(t) = N_I(0) e^{-t/t_{rel}}$. (Where $t=0$ is assumed to be at the time that cell infection stops, which is really $T_{crit}$\n",
"\n",
"The total viral load depends on the rate that virions are dumped into the bloodstream by dying cells and cleared from the body by its immune system. In other words:\n",
"\n",
"$\\frac{dN_v}{dt} = -N_v/t_{clr} + \\gamma N_{I}/t_{rel}$\n",
"\n",
"Or, taking into account the fact that we know $N_I(t)$ already, with t=0 redefined to be the time where cell infection stops:\n",
"\n",
"$\\frac{dN_v}{dt} = -N_v/t_{clr} + \\frac{\\gamma N_I(0)}{t_{rel}} e^{-t/t_{rel}}$\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# put your computer program here!\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import numpy as np\n",
"\n",
"NV0 = 1.0e+4 # number of initial viral cells\n",
"NI0 = 1.0e+4 # number of initial infected cells\n",
"T_crit = 1.0 # time scale (in days) on which the drug becomes effective\n",
"t_clr = 1.0 # time scale (in days) on which \n",
"t_rel = 1.0 # time scale (in days) where inected cells die and release virions\n",
"t_mul = 1.0 # growth time of viruses (in days) prior to drug administration\n",
"dt = 0.01 # timestep length (in days)\n",
"end_time = 10.0 # simulation end time (in days)\n",
"gamma = 10.0 # number of virions/infected cell\n",
"\n",
"NV = NV0\n",
"NI = NI0\n",
"\n",
"time = []\n",
"viral_load = []\n",
"infected_cells = []\n",
"\n",
"this_time = 0.0\n",
"\n",
"while(this_time <= end_time):\n",
"\n",
" if this_time <= T_crit:\n",
"\n",
" # change in virions w/time \n",
" dNVdt = gamma * NI0 / t_mul * math.exp(this_time/t_mul) \n",
" \n",
" NI_inf = NI0 * math.exp(this_time/t_mul)\n",
" \n",
" NV += dNVdt*dt\n",
" \n",
" else: # this_time > T_crit\n",
" \n",
" dNVdt = gamma*NI_inf/t_rel*math.exp(-(this_time-T_crit)/t_rel) - NV/t_clr\n",
" \n",
" NV += dNVdt*dt\n",
" #NI_inf += \n",
" \n",
" this_time += dt\n",
" time.append(this_time)\n",
" #infected_cells.append(NI_inf)\n",
" \n",
" viral_load.append(NV)\n",
" \n",
"plt.plot(time,viral_load)\n",
"plt.yscale('log')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Computer program notes:** The students should create either a for loop or a while loop that evolves the system forward in time, and which basically solves $N_v(t)$ with two distinct phases:\n",
"\n",
"1. Prior to the drug kicking in, where the virus load grows exponentially\n",
"2. After the drug starts working, where the virus load starts falling, first relatively slowly and then more quickly. (Double exponential model.)\n",
"\n",
"There should be if statements involved, and possibly break/continue statements if they're feeling clever."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## wrapup\n",
"\n",
"Do you have any lingering questions that remain after this project?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"put your answers here!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Turn it in!\n",
"\n",
"Whether you've completed it or not, turn this assignment in to the Day 8 dropbox in the \"in-class activities\" folder."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Note: this assignment (as with the previous assignment) was inspired by Nelson's __Physical models of Living Systems__, Chapter 1, and Kinder and Nelson's __A Student's Guide to Python for Physical Modeling__, Chapter 4."
]
}
],
"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
}
| agpl-3.0 |
richardotis/pycalphad-sandbox | ContourMap.ipynb | 1 | 226676 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Only needed in a Jupyter Notebook\n",
"%matplotlib inline\n",
"# Optional plot styling\n",
"import matplotlib\n",
"matplotlib.style.use('bmh')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from pycalphad import equilibrium\n",
"from pycalphad import Database, Model\n",
"import pycalphad.variables as v\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"db = Database('alfe_sei.TDB')\n",
"my_phases = list(set(db.phases.keys()) - {'BCC_A2'})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": []
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<xarray.Dataset>\n",
"Dimensions: (P: 1, T: 170, X_AL: 100, component: 2, internal_dof: 5, vertex: 2)\n",
"Coordinates:\n",
" * P (P) float64 1.013e+05\n",
" * T (T) float64 300.0 310.0 320.0 330.0 340.0 350.0 ...\n",
" * X_AL (X_AL) float64 1e-09 0.01 0.02 0.03 0.04 0.05 0.06 ...\n",
" * vertex (vertex) int64 0 1\n",
" * component (component) object 'AL' 'FE'\n",
" * internal_dof (internal_dof) int64 0 1 2 3 4\n",
"Data variables:\n",
" GM (P, T, X_AL) float64 -8.184e+03 -9.283e+03 ...\n",
" NP (P, T, X_AL, vertex) float64 1.0 nan 1.0 1.08e-05 ...\n",
" MU (P, T, X_AL, component) float64 -1.569e+05 ...\n",
" X (P, T, X_AL, vertex, component) float64 1e-09 1.0 ...\n",
" Y (P, T, X_AL, vertex, internal_dof) float64 1e-09 1.0 ...\n",
" Phase (P, T, X_AL, vertex) object 'B2_BCC' '' 'B2_BCC' ...\n",
" curie_temperature (P, T, X_AL) float64 1.043e+03 1.028e+03 1.013e+03 ...\n",
" degree_of_ordering (P, T, X_AL, vertex) float64 3.257e-15 nan 4.538e-15 ...\n",
" heat_capacity (P, T, X_AL) float64 24.89 24.91 24.92 24.94 24.95 ...\n",
"Attributes:\n",
" hull_iterations: 1\n",
" solve_iterations: 0\n",
" engine: pycalphad 0.4.1+3.gfbd8ab2.dirty\n",
" created: 2016-08-09 19:54:51.600873\n"
]
}
],
"source": [
"eq = equilibrium(db, ['AL', 'FE', 'VA'], my_phases, {v.X('AL'): (0, 1, 0.01), v.T: (300, 2000, 10), v.P: 101325},\n",
" output=['heat_capacity', 'degree_of_ordering', 'curie_temperature'])\n",
"print(eq)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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efE4YwHpMtXS10PmHwtWmqwh4MwA/P/dzOn9bdxtKO0oBMK4P5PJQ2Fm8EwFv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YRcXFwglUoZ1XB1dcXRo0eZRsjV1RWnTp1iK3c3NzecO3cOI0aMACASmYsXL7aJMz08PHDz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\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EICYmBm9vbxISEkTDWLUiIyOZM2cO5ubmxMTEaHsL1Dj9+eeftGjRgvr163P27NkqPfeF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\nRzpEEolEIpFIhj3SIZJIJBKJRDLs+f9nQj0tSHPG3AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fda289f4438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pycalphad import eqplot\n",
"fig = plt.figure(figsize=(9,6))\n",
"eqplot(eq, ax=fig.gca())\n",
"tc_indices = np.logical_and(np.abs(eq['curie_temperature'].values - eq['T'].values[..., None]) < 10,\n",
" np.any(eq.Phase.values == 'B2_BCC', axis=-1))\n",
"tc_indices = np.nonzero(np.logical_and(tc_indices, np.sum(eq.Phase.values != '', axis=-1, dtype=np.int) == 1))\n",
"bcc_indices = np.logical_and(np.any(eq.Phase.values == 'B2_BCC', axis=-1),\n",
" np.sum(eq.Phase.values != '', axis=-1, dtype=np.int) == 1)\n",
"tc_arr = np.array([eq['X'].sel(component='AL', vertex=0).values[tc_indices], np.take(eq['T'].values, tc_indices[1])])\n",
"tc_arr = tc_arr[:, tc_arr[0].argsort()]\n",
"fig.gca().plot(tc_arr[0], tc_arr[1], '--', color='grey', linewidth=2)\n",
"X, Y = np.meshgrid(eq['X_AL'].values, eq['T'].values)\n",
"CS = fig.gca().contour(X, Y, np.squeeze(np.ma.array(eq['degree_of_ordering'].sel(vertex=0).values, mask=~bcc_indices)), colors='k')\n",
"fig.gca().clabel(CS, inline=1, fontsize=10)\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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
flowersteam/fishing-baxter | notebooks/random-dmp.ipynb | 1 | 38730 | {
"metadata": {
"name": "",
"signature": "sha256:d9a43ff02a4f890ac85b590d8144a366dcb32d7eae9e4ca19fddcee99d552bd6"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from pydmps.dmp_discrete import DMPs_discrete"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dmps = 7\n",
"bfs = 10\n",
"W = 200\n",
"\n",
"init = load('../data/init_traj.npy')[0, 1:]\n",
"goal = load('../data/end_traj.npy')[0, 1:]\n",
"duration = 5.\n",
"f = 100.\n",
"dt = 1. / (f * duration)\n",
"\n",
"dmp = DMPs_discrete(dmps=dmps, bfs=bfs, \n",
" w=W*rand(dmps, bfs), \n",
" y0=init, goal=goal,\n",
" ay=ones(dmps) * 25,\n",
" dt=dt)\n",
"y, _, _ = dmp.rollout()\n",
"\n",
"plot(y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"[<matplotlib.lines.Line2D at 0x7a407d0>,\n",
" <matplotlib.lines.Line2D at 0x7a40950>,\n",
" <matplotlib.lines.Line2D at 0x7a40ad0>,\n",
" <matplotlib.lines.Line2D at 0x7a40c50>,\n",
" <matplotlib.lines.Line2D at 0x7a40dd0>,\n",
" <matplotlib.lines.Line2D at 0x7a40f50>,\n",
" <matplotlib.lines.Line2D at 0x7a44110>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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brdv08Dx0HR3Y1daGnW1t6OJ5LNJosECjwUKNBjNDQgZ9CTchRHy2k1EQxMLz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\np0BDxRVE0QAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x68e8c90>"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t = linspace(0, duration, len(y))\n",
"traj = hstack((t.reshape(-1, 1), y))\n",
"\n",
"save('../data/test.npy', traj)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"traj = load('/tmp/baxter-playtraj.npy')\n",
"traj = load('../data/test.npy')\n",
"\n",
"for i in range(1, 8):\n",
" plot(traj[:, 0], traj[:, i])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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//MM//J8333xz8b59+5Zcf/31j919993fH8+BERHRyY06wF0uVzTX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\n",
"text": [
"<matplotlib.figure.Figure at 0x373a9d0>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-3.0 |
erlendd/erlendd.github.io | assets/images/optimization/graphics2.ipynb | 1 | 177848 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib\n",
"import matplotlib.gridspec as gridspec\n",
"%matplotlib inline\n",
"import numpy as np\n",
"import seaborn as sns; sns.set()\n",
"import matplotlib.animation as animation\n",
"from IPython.display import HTML"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f1e17c0aed0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/dQT3Lfhjxoxh+/btvPvuu6xfv57Kyp7VtOHh4QwfPpzt27c/0pP5l/nBD37Q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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e17ccc190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Euler integration of harmonic oscillator V(x) = x^2 with mass = 1\n",
"n_iter = 10000 # number of iterations\n",
"dt = 0.001 # step size\n",
"x = 4.3 # our starting position x(0)\n",
"\n",
"# first we need to calculate the v(0+1/2t) term\n",
"v_old = -x*dt\n",
"\n",
"# save these for plotting\n",
"trajectory = []\n",
"velocities = []\n",
"\n",
"# do the iterations\n",
"for i in range(n_iter):\n",
" \n",
" v_next = v_old - 2*x*dt\n",
" x_next = x + v_next*dt\n",
"\n",
" trajectory.append(x_next)\n",
" velocities.append(0.5*(v_next + v_old))\n",
" \n",
" x = x_next # new becomes old\n",
" v_old = v_next\n",
" \n",
"fig, ax = sns.plt.subplots(2, sharex=True)\n",
"ax[0].plot(trajectory)\n",
"ax[0].set_ylabel('x(t)')\n",
"ax[1].plot(velocities)\n",
"ax[1].set_xlabel('Timestep (s)')\n",
"ax[1].set_ylabel('v(t)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f1e17b79a50>]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e34732610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.plt.plot(trajectory, velocities)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Animating in browser...\n"
]
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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wzz//PPr164c33ngDavW9l0az2YyNGzfi+eefx7p164p8jvnz5+PEiRMwm80Y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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1e17b38050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"trajectory = np.array(trajectory)[::10]\n",
"velocities = np.array(velocities)[::10]\n",
"\n",
"def parabola(x):\n",
" return x**2\n",
"x = np.linspace(-5, 5, 100)\n",
"y = parabola(x)\n",
"\n",
"\n",
"fig = sns.plt.figure()\n",
"\n",
"gs2 = gridspec.GridSpec(1, 5)\n",
"gs2.update(left=0.1, right=0.95, hspace=0.25, wspace=0.55)\n",
"ax_ps = sns.plt.subplot(gs2[:, :-2])\n",
"ax_fn = sns.plt.subplot(gs2[:, -2:])\n",
"\n",
"line_fn, = ax_fn.plot(x, y, lw=1, alpha=1.0, color='b')\n",
"line_ps, = ax_ps.plot(trajectory, velocities, lw=1, alpha=1.0, color='b')\n",
"spot_fn, = ax_fn.plot([], [], color='r', marker='o', markersize=5, linestyle='')\n",
"spot_ps, = ax_ps.plot([], [], color='r', marker='o', markersize=5, linestyle='')\n",
"\n",
"ax_fn.set_xlim(-5, 5)\n",
"ax_fn.set_ylim(-0.05, 40)\n",
"ax_ps.set_xlim(-5, 5)\n",
"ax_ps.set_ylim(-8, 8)\n",
"\n",
"ax_fn.set_xlabel('x')\n",
"ax_fn.set_ylabel('V(x)')\n",
"ax_ps.set_xlabel('x(t)')\n",
"ax_ps.set_ylabel('v(t)')\n",
"\n",
"\n",
"# initialization function: plot the background of each frame\n",
"def init():\n",
" spot_fn.set_data([], [])\n",
" spot_ps.set_data([], [])\n",
" return [line_fn, line_ps, line_fn, spot_ps]\n",
"\n",
"# animation function. This is called sequentially\n",
"def animate(t):\n",
" spot_ps.set_data( [trajectory[t]], [velocities[t]] )\n",
" spot_fn.set_data( [trajectory[t], parabola(trajectory[t])] )\n",
" return [spot_ps, spot_fn]\n",
"\n",
"# call the animator. \n",
"anim = animation.FuncAnimation(fig, animate, init_func=init, \n",
" frames=435, \n",
" interval=10, blit=True)\n",
"\n",
"mywriter = animation.FFMpegWriter(fps=100, extra_args=['-vcodec', 'libx264', '-pix_fmt', 'yuv420p'])\n",
"anim.save('./harmonic_oscillator_ps.mp4', writer=mywriter)\n",
"anim.save('./harmonic_oscillator_ps.gif', dpi=72, writer='imagemagick')\n",
"print 'Animating in browser...'\n",
"HTML(anim.to_html5_video())"
]
},
{
"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 |
openp2pdesign/Labs-Survey---Analysis | Q020.ipynb | 1 | 66610 | {
"metadata": {
"name": "",
"signature": "sha256:018ed7a7c955774f399f03b580b3e22f1ee2be225aa2ca36ff769bd1bbfc30e6"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Q020 - In che percentuale la gestione del laboratorio e\u0300 dipendente da organizzazioni terze?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# -*- coding: UTF-8 -*-\n",
"\n",
"# Render our plots inline\n",
"%matplotlib inline \n",
"\n",
"import pandas as pd\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn\n",
"import shutil\n",
"\n",
"pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier, overridden by seaborn\n",
"pd.set_option('display.max_columns', None) # Display all the columns\n",
"plt.rcParams['font.family'] = 'sans-serif' # Sans Serif fonts for all the graphs\n",
"\n",
"# Reference for color palettes: http://web.stanford.edu/~mwaskom/software/seaborn/tutorial/color_palettes.html\n",
"\n",
"# Change the font\n",
"matplotlib.rcParams.update({'font.family': 'Source Sans Pro'})"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Load csv file first\n",
"data = pd.read_csv(\"data/lab-survey.csv\", encoding=\"utf-8\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Check data\n",
"#data[0:4] # Equals to data.head()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%capture output\n",
"\n",
"# Save the output as a variable that can be saved to a file\n",
"# Get the distribution of way of living\n",
"grado = data[\"D20[SQ001]\"].value_counts(dropna=False)\n",
"print \"Data:\"\n",
"print grado\n",
"print \"\"\n",
"print \"Data %:\"\n",
"print data[\"D20[SQ001]\"].value_counts(normalize=True,dropna=False) * 100"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Save+show the output to a text file\n",
"%save Q020-GradoDipendenza01.py str(output)\n",
"shutil.move(\"Q020-GradoDipendenza01.py\", \"text/Q020-GradoDipendenza01.txt\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The following commands were written to file `Q020-GradoDipendenza01.py`:\n",
"Data:\n",
"0% 39\n",
"NaN 12\n",
"10% 5\n",
"50% 4\n",
"30% 2\n",
"20% 2\n",
"60% 2\n",
"80% 1\n",
"100% 1\n",
"70% 1\n",
"40% 1\n",
"dtype: int64\n",
"\n",
"Data %:\n",
"0% 55.714286\n",
"NaN 17.142857\n",
"10% 7.142857\n",
"50% 5.714286\n",
"30% 2.857143\n",
"20% 2.857143\n",
"60% 2.857143\n",
"80% 1.428571\n",
"100% 1.428571\n",
"70% 1.428571\n",
"40% 1.428571\n",
"dtype: float64\n",
"\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Swap nan for a more understandable word\n",
"old_dict = grado.to_dict()\n",
"new_dict = {}\n",
"for i in old_dict:\n",
" if type(i) is float and np.isnan(i):\n",
" new_dict[\"Nessuna risposta\"] = old_dict[i]\n",
" else:\n",
" new_dict[i.capitalize()] = old_dict[i]\n",
"\n",
"gradou = pd.Series(new_dict)\n",
"grado = gradou.order()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot the data\n",
"plt.figure(figsize=(8,6))\n",
"plt.xlabel(u'Dipendenza da altri', fontsize=16)\n",
"plt.ylabel('Lab', fontsize=16)\n",
"plt.title(u\"n che percentuale la gestione del laboratorio e\u0300 dipendente da organizzazioni terze?\", fontsize=18, y=1.02)\n",
"my_colors = seaborn.color_palette(\"husl\", len(grado)) # Set color palette\n",
"grado.plot(kind=\"bar\",color=my_colors)\n",
"plt.savefig(\"svg/Q020-GradoDipendenza01.svg\")\n",
"plt.savefig(\"png/Q020-GradoDipendenza01.png\")\n",
"plt.savefig(\"pdf/Q020-GradoDipendenza01.pdf\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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4cK5812Y5evSoERgYaHz00Uf2YTnr7dXvmWEYxn333Wc8+uijDsN++uknIzAw0FiwYIF9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\n",
"text": [
"<matplotlib.figure.Figure at 0x10b252f50>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%capture output\n",
"\n",
"# Save the output as a variable that can be saved to a file\n",
"# Order of the choices\n",
"grado_order = [NaN,\"0%\",\"10%\",\"20%\",\"30%\",\"40%\",\"50%\",\"60%\",\"70%\",\"80%\",\"90%\",\"100%\"]\n",
"\n",
"grado2 = grado.reindex(grado_order)\n",
"\n",
"# Get the distribution of way of living, reindexed\n",
"print \"Data:\"\n",
"print grado2\n",
"print \"\"\n",
"print \"Data %:\"\n",
"grado2_normalized = data[\"D3\"].value_counts(normalize=True, dropna=False) * 100\n",
"print grado2_normalized.reindex(grado_order)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Save+show the output to a text file\n",
"%save Q020-GradoDipendenza02.py str(output)\n",
"shutil.move(\"Q020-GradoDipendenza02.py\", \"text/Q020-GradoDipendenza02.txt\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The following commands were written to file `Q020-GradoDipendenza02.py`:\n",
"Data:\n",
"NaN NaN\n",
"0% 39\n",
"10% 5\n",
"20% 2\n",
"30% 2\n",
"40% 1\n",
"50% 4\n",
"60% 2\n",
"70% 1\n",
"80% 1\n",
"90% NaN\n",
"100% 1\n",
"dtype: float64\n",
"\n",
"Data %:\n",
"NaN 0\n",
"0% NaN\n",
"10% NaN\n",
"20% NaN\n",
"30% NaN\n",
"40% NaN\n",
"50% NaN\n",
"60% NaN\n",
"70% NaN\n",
"80% NaN\n",
"90% NaN\n",
"100% NaN\n",
"dtype: float64\n",
"\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Swap nan for a more understandable word\n",
"old_dict = grado.to_dict()\n",
"new_dict = {}\n",
"for i in old_dict:\n",
" if type(i) is float and np.isnan(i):\n",
" new_dict[\"Nessuna risposta\"] = old_dict[i]\n",
" else:\n",
" new_dict[i.capitalize()] = old_dict[i]\n",
"\n",
"gradou = pd.Series(new_dict)\n",
"grado2 = gradou.order()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot the data\n",
"plt.figure(figsize=(8,6))\n",
"plt.xlabel(u'Dipendenza da altri', fontsize=16)\n",
"plt.ylabel('Lab', fontsize=16)\n",
"plt.title(u\"In che percentuale la gestione del laboratorio e\u0300 dipendente da organizzazioni terze?\", fontsize=18, y=1.02)\n",
"my_colors = seaborn.color_palette(\"husl\", len(grado2)) # Set color palette\n",
"grado2.plot(kind=\"bar\",color=my_colors)\n",
"plt.savefig(\"svg/Q020-GradoDipendenza02.svg\")\n",
"plt.savefig(\"png/Q020-GradoDipendenza02.png\")\n",
"plt.savefig(\"pdf/Q020-GradoDipendenza02.pdf\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fyRIREaHPP/9cS5cuVWhoqGJjY1W3bl3de++9mjdvnvLz89W2bVt9++23mjt3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\nSERERO4AZWVlhIeH07hxY6OjiMloZlBERMQgK1e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"text": [
"<matplotlib.figure.Figure at 0x10b0b6190>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
}
],
"metadata": {}
}
]
} | gpl-3.0 |
dvirsamuel/MachineLearningCourses | EllipsesProject/MyEllipsesNotebook.ipynb | 1 | 349763 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "l_cZbWA_3JAu"
},
"source": [
"# Ellipses Project (Dvir Samuel)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "fdHvS5VF3JAx"
},
"source": [
"## Data Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"collapsed": true,
"id": "NXh7HgW-3JA0"
},
"outputs": [],
"source": [
"import numpy as np\n",
"from numpy import genfromtxt\n",
"from PIL import Image\n",
"import pandas as pd\n",
"from collections import Counter\n",
"import keras\n",
"from keras.layers.normalization import BatchNormalization\n",
"from keras.models import Model\n",
"from keras.layers import Input, Dense, Dropout, Activation, Flatten, Concatenate, Add\n",
"from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
"from keras import backend as K\n",
"from keras.utils import plot_model\n",
"from keras.optimizers import Adam,Nadam,SGD\n",
"from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.utils import class_weight\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"collapsed": true,
"id": "uOQnnr_G3JBA"
},
"outputs": [],
"source": [
"# Load train data\n",
"train_data = pd.read_csv(\"images/train_data.txt\",delimiter=\", \",header=0, engine='python')\n",
"# organazie the data and seperate to different dataframes\n",
"seperated_col = train_data[train_data.columns[0]].str.partition(\" \")[[0,2]]\n",
"X_train_images = seperated_col[[0]].rename(columns = {0:'paths'})\n",
"Y_train_class = seperated_col[[2]].rename(columns = {2:'class'})\n",
"del train_data[train_data.columns[0]]\n",
"Y_train_features = train_data"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zP2TOCn73JBJ"
},
"source": [
"##### Training images paths"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
},
"colab_type": "code",
"id": "vzqpIVR-3JBK",
"outputId": "4f1f16d3-d05d-4d8e-b9d4-9262f3029d0c"
},
"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>paths</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>images/train/0000.jpg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>images/train/0001.jpg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>images/train/0002.jpg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>images/train/0003.jpg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>images/train/0004.jpg</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" paths\n",
"0 images/train/0000.jpg\n",
"1 images/train/0001.jpg\n",
"2 images/train/0002.jpg\n",
"3 images/train/0003.jpg\n",
"4 images/train/0004.jpg"
]
},
"execution_count": 315,
"metadata": {
"tags": []
},
"output_type": "execute_result"
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"source": [
"X_train_images.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nsCUfC1A3JBO"
},
"source": [
"##### Training true labels - ellipse or not"
]
},
{
"cell_type": "code",
"execution_count": 316,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
},
"colab_type": "code",
"id": "UBAFmw-63JBP",
"outputId": "a4e6b2e2-e715-4121-92cc-5a49246c1383"
},
"outputs": [
{
"data": {
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},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "iFgHcfGN3JBU"
},
"source": [
"##### Training true features - ellipse parameters"
]
},
{
"cell_type": "code",
"execution_count": 317,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
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"colab_type": "code",
"id": "YNxD3PDs3JBW",
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"outputs": [
{
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" center_x center_y angle axis_1 axis_2\n",
"0 0 0 0 0 0\n",
"1 34 32 25 16 17\n",
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"execution_count": 317,
"metadata": {
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],
"source": [
"Y_train_features.head()"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"collapsed": true,
"id": "ILW6TgLg3JBd"
},
"outputs": [],
"source": [
"# Load test data\n",
"test_data = pd.read_csv(\"images/test_data.txt\",delimiter=\", \",header=0, engine='python')\n",
"# organazie the data and seperate to different dataframes\n",
"seperated_col = test_data[test_data.columns[0]].str.partition(\" \")[[0,2]]\n",
"X_test_images = seperated_col[[0]].rename(columns = {0:'paths'})\n",
"Y_test_class = seperated_col[[2]].rename(columns = {2:'class'})\n",
"del test_data[test_data.columns[0]]\n",
"Y_test_features = test_data"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"colab_type": "code",
"id": "1K6e_3oV3JBk",
"outputId": "92f2bd33-fc8c-44b0-e318-76c9206d385d"
},
"outputs": [
{
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D5/YG7xvRli3Dc3b55T6vs+V88QtfbtJv+L7X2fyXfr29dg4vNnhixdh79vGd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\njRMn045jn42XGp5L5Rx9Jel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Pabktbyq74Se93LqW5RwrN6/Nsgy2Rp/YFnSqPcYkQc45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\n",
"text/plain": [
"<Figure size 1440x1440 with 5 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# show some data samples\n",
"fig = plt.figure(figsize=(20,20))\n",
"for idx,path in enumerate(X_train_images['paths'].head()):\n",
" img = Image.open(path)\n",
" fig.add_subplot(1,5,idx+1)\n",
" plt.imshow(img)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ElXWEnJi3JBt"
},
"source": [
"##### More preprocessing (data reordering and image normalization)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nrywB8iJ3JBv"
},
"source": [
"* Normalize the angles - If angles are greater than 180 degrees we can normalize them"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"collapsed": true,
"id": "0NzhXycp3JBw"
},
"outputs": [],
"source": [
"Y_train_features['angle'] = Y_train_features['angle']%180\n",
"Y_test_features['angle'] = Y_test_features['angle']%180"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3cCrPnIx3JB2"
},
"source": [
"* We would like to constrain the features so that the angle will allways be the ratio between the long axis and the x_axis now, it is just the ratio between the first axis and the x axis (as we can see in the images), so we make sure that the long axis is the first axis (axis_1) and the angle is adjusted accordingly."
]
},
{
"cell_type": "code",
"execution_count": 321,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 421
},
"colab_type": "code",
"id": "DgcqanUs3JB4",
"outputId": "03ac0d7d-917d-4280-e30b-a795fb2befd1",
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before:\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>center_x</th>\n",
" <th>center_y</th>\n",
" <th>angle</th>\n",
" <th>axis_1</th>\n",
" <th>axis_2</th>\n",
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"16 24 25 160 4 13"
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"metadata": {
"tags": []
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"After:\n"
]
},
{
"data": {
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"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>center_x</th>\n",
" <th>center_y</th>\n",
" <th>angle</th>\n",
" <th>axis_1</th>\n",
" <th>axis_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>25</td>\n",
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"text/plain": [
" center_x center_y angle axis_1 axis_2\n",
"1 34 32 115 17 16\n",
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"16 24 25 70 13 4"
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"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# For TRAIN\n",
"# check where axis_1 is smaller than axis_2\n",
"indices = np.where(Y_train_features['axis_1'] < Y_train_features['axis_2'])[0]\n",
"print(\"Before:\")\n",
"display(Y_train_features.loc[indices].head())\n",
"# swap axis_1 with axis_2\n",
"tmp = Y_train_features.loc[indices,'axis_1']\n",
"Y_train_features.loc[indices,'axis_1'] = Y_train_features.loc[indices,'axis_2']\n",
"Y_train_features.loc[indices,'axis_2'] = tmp\n",
"# rotate angle by 90\n",
"Y_train_features.loc[indices,'angle'] = (Y_train_features.loc[indices,'angle']+90)%180\n",
"print(\"After:\")\n",
"display(Y_train_features.loc[indices].head())\n",
"\n",
"# For TEST\n",
"# check where axis_1 is smaller than axis_2\n",
"indices = np.where(Y_test_features['axis_1'] < Y_test_features['axis_2'])[0]\n",
"# swap axis_1 with axis_2\n",
"tmp = Y_test_features.loc[indices,'axis_1']\n",
"Y_test_features.loc[indices,'axis_1'] = Y_test_features.loc[indices,'axis_2']\n",
"Y_test_features.loc[indices,'axis_2'] = tmp\n",
"# rotate angle by 90\n",
"Y_test_features.loc[indices,'angle'] = (Y_test_features.loc[indices,'angle']+90)%180"
]
},
{
"cell_type": "code",
"execution_count": 322,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"colab_type": "code",
"id": "LWCDcaiG3JCA",
"outputId": "dc477f16-dae0-433f-e65c-a4a9dee6ec47"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train shape: (10000, 50, 50, 3)\n",
"Test shape: (1000, 50, 50, 3)\n"
]
}
],
"source": [
"# load train images to memory\n",
"X_train_o = np.array([np.array(Image.open(path)) for path in X_train_images['paths']]).astype('float32')\n",
"X_test_o = np.array([np.array(Image.open(path)) for path in X_test_images['paths']]).astype('float32')\n",
"print(\"Train shape:\", X_train_o.shape)\n",
"print(\"Test shape:\", X_test_o.shape)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"collapsed": true,
"id": "T2rEsMTg3JCE"
},
"outputs": [],
"source": [
"# normalize rgb images\n",
"X_train = X_train_o / 255\n",
"X_test = X_test_o / 255\n",
"# y to one-hote encoding\n",
"Y_train_class['class'] = (Y_train_class['class'].values == 'True').astype(float)\n",
"Y_test_class['class'] = (Y_test_class['class'].values == 'True').astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Axeix56e3JCK"
},
"source": [
"## Data Distribuation\n",
"### Check if data is balanced"
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 87
},
"colab_type": "code",
"id": "NEfLxO2c3JCL",
"outputId": "8a7d77e4-9cd3-436d-d626-af7582fb9f79"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Data:\n",
"Counter({1.0: 6997, 0.0: 3003})\n",
"Test Data\n",
"Counter({1.0: 688, 0.0: 312})\n"
]
}
],
"source": [
"print(\"Train Data:\")\n",
"print(Counter(Y_train_class['class']))\n",
"print(\"Test Data\")\n",
"print(Counter(Y_test_class['class']))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "j3tY-Sn23JCW"
},
"source": [
"As we can see, there are more ellipses than non-ellipses (greater by almost 2.4) - in training we will give all non ellipses data more weight"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"colab_type": "code",
"id": "uuRGfOJw3JCc",
"outputId": "a8960649-0b85-422e-a26b-b7ca75ee0a16"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: 1.665001665001665, 1: 0.7145919679862799}\n"
]
}
],
"source": [
"class_weights = class_weight.compute_class_weight('balanced', np.unique(Y_train_class), Y_train_class[\"class\"])\n",
"class_weights = {cid : weight for cid, weight in enumerate(class_weights)}\n",
"print(class_weights)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zss3tiVY3JCl"
},
"source": [
"## Approach 1: Image Processing method \n",
" #### I will try first to see if we could get reasonable results using opencv with traditional image processing methods"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ibSf5b7J3JCm"
},
"source": [
"I tried playing with contour and fitEllipse, but it did not give good results.\n",
"Most of the time it couldn't find the ellipses if they where drawn in the image.."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "pqTHB1dH3JCo"
},
"source": [
"## Approach 2: Deep Learning method \n",
"#### I will try and implement a cnn network which outputs True/False if , and the features"
]
},
{
"cell_type": "code",
"execution_count": 326,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1213
},
"colab_type": "code",
"id": "dBpb3ch93JCq",
"outputId": "f9965d91-f3b2-40df-c870-3ab5f2cb2768"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"input_28 (InputLayer) (None, 50, 50, 3) 0 \n",
"__________________________________________________________________________________________________\n",
"conv1 (Conv2D) (None, 50, 50, 32) 896 input_28[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn1 (BatchNormalization) (None, 50, 50, 32) 128 conv1[0][0] \n",
"__________________________________________________________________________________________________\n",
"act1 (Activation) (None, 50, 50, 32) 0 bn1[0][0] \n",
"__________________________________________________________________________________________________\n",
"mp1 (MaxPooling2D) (None, 25, 25, 32) 0 act1[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv2 (Conv2D) (None, 25, 25, 64) 18496 mp1[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn2 (BatchNormalization) (None, 25, 25, 64) 256 conv2[0][0] \n",
"__________________________________________________________________________________________________\n",
"act2 (Activation) (None, 25, 25, 64) 0 bn2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv3 (Conv2D) (None, 25, 25, 64) 36928 act2[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv4 (Conv2D) (None, 25, 25, 64) 2112 mp1[0][0] \n",
"__________________________________________________________________________________________________\n",
"add1 (Add) (None, 25, 25, 64) 0 conv3[0][0] \n",
" conv4[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn3 (BatchNormalization) (None, 25, 25, 64) 256 add1[0][0] \n",
"__________________________________________________________________________________________________\n",
"act3 (Activation) (None, 25, 25, 64) 0 bn3[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv5 (Conv2D) (None, 13, 13, 128) 73856 act3[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn4 (BatchNormalization) (None, 13, 13, 128) 512 conv5[0][0] \n",
"__________________________________________________________________________________________________\n",
"act4 (Activation) (None, 13, 13, 128) 0 bn4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv6 (Conv2D) (None, 13, 13, 128) 147584 act4[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv7 (Conv2D) (None, 13, 13, 128) 8320 add1[0][0] \n",
"__________________________________________________________________________________________________\n",
"add2 (Add) (None, 13, 13, 128) 0 conv6[0][0] \n",
" conv7[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn5 (BatchNormalization) (None, 13, 13, 128) 512 add2[0][0] \n",
"__________________________________________________________________________________________________\n",
"act5 (Activation) (None, 13, 13, 128) 0 bn5[0][0] \n",
"__________________________________________________________________________________________________\n",
"flat1 (Flatten) (None, 21632) 0 act5[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense1 (Dense) (None, 512) 11076096 flat1[0][0] \n",
"__________________________________________________________________________________________________\n",
"bn6 (BatchNormalization) (None, 512) 2048 dense1[0][0] \n",
"__________________________________________________________________________________________________\n",
"act6 (Activation) (None, 512) 0 bn6[0][0] \n",
"__________________________________________________________________________________________________\n",
"do1 (Dropout) (None, 512) 0 act6[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense2 (Dense) (None, 1) 513 do1[0][0] \n",
"__________________________________________________________________________________________________\n",
"dense3 (Dense) (None, 5) 2565 do1[0][0] \n",
"__________________________________________________________________________________________________\n",
"class (Activation) (None, 1) 0 dense2[0][0] \n",
"__________________________________________________________________________________________________\n",
"features (Activation) (None, 5) 0 dense3[0][0] \n",
"==================================================================================================\n",
"Total params: 11,371,078\n",
"Trainable params: 11,369,222\n",
"Non-trainable params: 1,856\n",
"__________________________________________________________________________________________________\n"
]
}
],
"source": [
"# build the model\n",
"inputs = Input(shape=X_train.shape[1:])\n",
"conv1 = Conv2D(32, (3, 3), padding='same', name='conv1')(inputs)\n",
"bn1 = BatchNormalization(axis=3,name='bn1')(conv1)\n",
"act1 = Activation('relu',name='act1')(bn1)\n",
"mp1 = MaxPooling2D(padding='same',name='mp1')(act1)\n",
"conv2 = Conv2D(64, (3, 3), padding='same',name='conv2')(mp1)\n",
"bn2 = BatchNormalization(axis=3,name='bn2')(conv2)\n",
"act2 = Activation('relu',name='act2')(bn2)\n",
"conv3 = Conv2D(64, (3, 3), padding='same',name='conv3')(act2)\n",
"conv4 = Conv2D(64, (1,1),name='conv4')(mp1)\n",
"add1 = Add(name='add1')([conv3, conv4])\n",
"bn3 = BatchNormalization(axis=3,name='bn3')(add1)\n",
"act3 = Activation('relu',name='act3')(bn3)\n",
"conv5 = Conv2D(128, (3, 3), padding='same', strides=(2,2),name='conv5')(act3)\n",
"bn4 = BatchNormalization(axis=3,name='bn4')(conv5)\n",
"act4 = Activation('relu',name='act4')(bn4)\n",
"conv6 = Conv2D(128, (3, 3), padding='same',name='conv6')(act4)\n",
"conv7 = Conv2D(128, (1,1), strides=(2,2),name='conv7')(add1)\n",
"add2 = Add(name='add2')([conv6, conv7])\n",
"bn5 = BatchNormalization(axis=3,name='bn5')(add2)\n",
"act5 = Activation('relu',name='act5')(bn5)\n",
"flat1 = Flatten(name='flat1')(act5)\n",
"dense1 = Dense(512,name='dense1')(flat1)\n",
"bn6 = BatchNormalization(name='bn6')(dense1)\n",
"act6 = Activation('relu',name='act6')(bn6)\n",
"do1 = Dropout(0.2,name='do1')(act6)\n",
"dense2 = Dense(1,name='dense2')(do1)\n",
"classification = Activation('sigmoid', name = 'class')(dense2)\n",
"dense3 = Dense(5,name='dense3')(do1)\n",
"features = Activation('relu',name='features')(dense3)\n",
"\n",
"model = Model(inputs=inputs, outputs=[classification,features])\n",
"\n",
"model.summary()\n",
"plot_model(model, to_file=\"model.png\")"
]
},
{
"cell_type": "code",
"execution_count": 327,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1717
},
"colab_type": "code",
"id": "EeihsbWn3JCx",
"outputId": "0917fbf3-3a2d-44ce-9b00-c37d3495955b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model structure:\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f22a2c67ef0>"
]
},
"execution_count": 327,
"metadata": {
"tags": []
},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eX0U6Ox4DdVLx8fEQEZe5bdq0CZs3b9Y9R2Xe4uPj9f4zc3ncAyWn0K1bN70j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\nKyvAswAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 1800x2160 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"print(\"Model structure:\")\n",
"fig = plt.figure(figsize=(25,30))\n",
"model_img = Image.open(\"model.png\")\n",
"plt.imshow(model_img)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7LBRDnEr3JC5"
},
"source": [
"#### Model training"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab": {},
"colab_type": "code",
"collapsed": true,
"id": "Pf_1q-Jt3JC6"
},
"outputs": [],
"source": [
"# hyperparams\n",
"epochs = 50\n",
"batch_size = 64\n",
"optAlgo = Adam()\n",
"\n",
"# compile model\n",
"model.compile(optAlgo,loss=['binary_crossentropy','mse'], metrics=['accuracy'],loss_weights=[1.,.01])\n",
"\n",
"# values to track:\n",
"save_to = 'best_model.h5'\n",
"checkpoint = ModelCheckpoint(filepath=save_to,verbose=0, save_best_only=True)\n",
"callbacks = [checkpoint]"
]
},
{
"cell_type": "code",
"execution_count": 329,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1788
},
"colab_type": "code",
"id": "LHRAVK1g3JC-",
"outputId": "eb4fc309-2985-429e-b4bc-290cf0645389"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 7000 samples, validate on 3000 samples\n",
"Epoch 1/50\n",
"7000/7000 [==============================] - 17s 2ms/step - loss: 10.8294 - class_loss: 0.1448 - features_loss: 1068.4537 - class_acc: 0.9486 - features_acc: 0.5153 - val_loss: 10.5512 - val_class_loss: 0.9715 - val_features_loss: 957.9696 - val_class_acc: 0.7003 - val_features_acc: 0.5167\n",
"Epoch 2/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 3.8368 - class_loss: 0.0517 - features_loss: 378.5116 - class_acc: 0.9846 - features_acc: 0.7049 - val_loss: 29.1661 - val_class_loss: 11.3740 - val_features_loss: 1779.2139 - val_class_acc: 0.2943 - val_features_acc: 0.3447\n",
"Epoch 3/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 2.0242 - class_loss: 0.0290 - features_loss: 199.5207 - class_acc: 0.9917 - features_acc: 0.7614 - val_loss: 21.5872 - val_class_loss: 6.2837 - val_features_loss: 1530.3551 - val_class_acc: 0.3513 - val_features_acc: 0.5870\n",
"Epoch 4/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 1.7765 - class_loss: 0.0263 - features_loss: 175.0226 - class_acc: 0.9914 - features_acc: 0.7843 - val_loss: 28.9654 - val_class_loss: 11.1783 - val_features_loss: 1778.7097 - val_class_acc: 0.2943 - val_features_acc: 0.3483\n",
"Epoch 5/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 1.4826 - class_loss: 0.0180 - features_loss: 146.4652 - class_acc: 0.9953 - features_acc: 0.8054 - val_loss: 12.9822 - val_class_loss: 2.6349 - val_features_loss: 1034.7276 - val_class_acc: 0.7063 - val_features_acc: 0.5920\n",
"Epoch 6/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 1.2916 - class_loss: 0.0112 - features_loss: 128.0408 - class_acc: 0.9973 - features_acc: 0.8249 - val_loss: 5.7238 - val_class_loss: 1.1425 - val_features_loss: 458.1265 - val_class_acc: 0.7413 - val_features_acc: 0.7087\n",
"Epoch 7/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 1.1563 - class_loss: 0.0113 - features_loss: 114.5037 - class_acc: 0.9964 - features_acc: 0.8476 - val_loss: 9.3236 - val_class_loss: 2.0869 - val_features_loss: 723.6677 - val_class_acc: 0.7070 - val_features_acc: 0.5990\n",
"Epoch 8/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 1.0565 - class_loss: 0.0112 - features_loss: 104.5290 - class_acc: 0.9969 - features_acc: 0.8521 - val_loss: 19.1562 - val_class_loss: 3.1398 - val_features_loss: 1601.6349 - val_class_acc: 0.7067 - val_features_acc: 0.5920\n",
"Epoch 9/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.8177 - class_loss: 0.0047 - features_loss: 81.2919 - class_acc: 0.9990 - features_acc: 0.8659 - val_loss: 11.7534 - val_class_loss: 2.5790 - val_features_loss: 917.4462 - val_class_acc: 0.7200 - val_features_acc: 0.5923\n",
"Epoch 10/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.7562 - class_loss: 0.0108 - features_loss: 74.5440 - class_acc: 0.9966 - features_acc: 0.8641 - val_loss: 13.1303 - val_class_loss: 1.2666 - val_features_loss: 1186.3668 - val_class_acc: 0.7420 - val_features_acc: 0.5923\n",
"Epoch 11/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.5876 - class_loss: 0.0031 - features_loss: 58.4437 - class_acc: 0.9989 - features_acc: 0.8866 - val_loss: 11.7086 - val_class_loss: 2.7054 - val_features_loss: 900.3190 - val_class_acc: 0.7077 - val_features_acc: 0.5920\n",
"Epoch 12/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.4532 - class_loss: 0.0017 - features_loss: 45.1452 - class_acc: 0.9999 - features_acc: 0.8970 - val_loss: 4.6767 - val_class_loss: 0.2938 - val_features_loss: 438.2862 - val_class_acc: 0.9007 - val_features_acc: 0.6397\n",
"Epoch 13/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.3918 - class_loss: 0.0013 - features_loss: 39.0481 - class_acc: 0.9997 - features_acc: 0.9070 - val_loss: 13.9242 - val_class_loss: 3.5958 - val_features_loss: 1032.8414 - val_class_acc: 0.7060 - val_features_acc: 0.5920\n",
"Epoch 14/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.3657 - class_loss: 0.0017 - features_loss: 36.4013 - class_acc: 0.9996 - features_acc: 0.9167 - val_loss: 2.5552 - val_class_loss: 0.2512 - val_features_loss: 230.4003 - val_class_acc: 0.9213 - val_features_acc: 0.6383\n",
"Epoch 15/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2922 - class_loss: 8.4930e-04 - features_loss: 29.1357 - class_acc: 0.9999 - features_acc: 0.9214 - val_loss: 1.8963 - val_class_loss: 0.0221 - val_features_loss: 187.4237 - val_class_acc: 0.9910 - val_features_acc: 0.6773\n",
"Epoch 16/50\n",
"7000/7000 [==============================] - 9s 1ms/step - loss: 0.2785 - class_loss: 3.9760e-04 - features_loss: 27.8080 - class_acc: 1.0000 - features_acc: 0.9233 - val_loss: 3.4773 - val_class_loss: 0.4469 - val_features_loss: 303.0363 - val_class_acc: 0.8783 - val_features_acc: 0.6283\n",
"Epoch 17/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2452 - class_loss: 6.8853e-04 - features_loss: 24.4510 - class_acc: 0.9999 - features_acc: 0.9347 - val_loss: 4.1712 - val_class_loss: 0.3624 - val_features_loss: 380.8826 - val_class_acc: 0.9443 - val_features_acc: 0.7600\n",
"Epoch 18/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2723 - class_loss: 0.0032 - features_loss: 26.9099 - class_acc: 0.9990 - features_acc: 0.9323 - val_loss: 14.5993 - val_class_loss: 3.5775 - val_features_loss: 1102.1792 - val_class_acc: 0.7057 - val_features_acc: 0.5920\n",
"Epoch 19/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2597 - class_loss: 0.0027 - features_loss: 25.7056 - class_acc: 0.9991 - features_acc: 0.9370 - val_loss: 9.0545 - val_class_loss: 2.3833 - val_features_loss: 667.1223 - val_class_acc: 0.7150 - val_features_acc: 0.6120\n",
"Epoch 20/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2412 - class_loss: 0.0011 - features_loss: 24.0122 - class_acc: 0.9997 - features_acc: 0.9429 - val_loss: 2.2649 - val_class_loss: 0.0678 - val_features_loss: 219.7012 - val_class_acc: 0.9887 - val_features_acc: 0.9157\n",
"Epoch 21/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2162 - class_loss: 2.0164e-04 - features_loss: 21.6009 - class_acc: 1.0000 - features_acc: 0.9486 - val_loss: 11.5993 - val_class_loss: 1.2268 - val_features_loss: 1037.2513 - val_class_acc: 0.7363 - val_features_acc: 0.5953\n",
"Epoch 22/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2118 - class_loss: 3.0694e-04 - features_loss: 21.1533 - class_acc: 1.0000 - features_acc: 0.9474 - val_loss: 14.6615 - val_class_loss: 3.5237 - val_features_loss: 1113.7791 - val_class_acc: 0.7087 - val_features_acc: 0.5927\n",
"Epoch 23/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2237 - class_loss: 0.0014 - features_loss: 22.2290 - class_acc: 0.9996 - features_acc: 0.9403 - val_loss: 3.9270 - val_class_loss: 0.0459 - val_features_loss: 388.1073 - val_class_acc: 0.9823 - val_features_acc: 0.6297\n",
"Epoch 24/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1830 - class_loss: 2.1850e-04 - features_loss: 18.2802 - class_acc: 1.0000 - features_acc: 0.9549 - val_loss: 2.6152 - val_class_loss: 0.1551 - val_features_loss: 246.0081 - val_class_acc: 0.9707 - val_features_acc: 0.8820\n",
"Epoch 25/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1831 - class_loss: 2.1331e-04 - features_loss: 18.2865 - class_acc: 1.0000 - features_acc: 0.9544 - val_loss: 2.3945 - val_class_loss: 0.0671 - val_features_loss: 232.7457 - val_class_acc: 0.9880 - val_features_acc: 0.9150\n",
"Epoch 26/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.3130 - class_loss: 0.0145 - features_loss: 29.8464 - class_acc: 0.9949 - features_acc: 0.9374 - val_loss: 29.1661 - val_class_loss: 11.3740 - val_features_loss: 1779.2139 - val_class_acc: 0.2943 - val_features_acc: 0.3447\n",
"Epoch 27/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.5472 - class_loss: 0.0282 - features_loss: 51.9006 - class_acc: 0.9923 - features_acc: 0.9051 - val_loss: 17.6174 - val_class_loss: 2.6951 - val_features_loss: 1492.2268 - val_class_acc: 0.6193 - val_features_acc: 0.5383\n",
"Epoch 28/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2909 - class_loss: 0.0019 - features_loss: 28.9050 - class_acc: 0.9994 - features_acc: 0.9397 - val_loss: 15.9246 - val_class_loss: 3.9881 - val_features_loss: 1193.6467 - val_class_acc: 0.7067 - val_features_acc: 0.5920\n",
"Epoch 29/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1936 - class_loss: 5.2164e-04 - features_loss: 19.3108 - class_acc: 1.0000 - features_acc: 0.9554 - val_loss: 13.2837 - val_class_loss: 2.3017 - val_features_loss: 1098.1985 - val_class_acc: 0.7330 - val_features_acc: 0.6003\n",
"Epoch 30/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1622 - class_loss: 3.0444e-04 - features_loss: 16.1876 - class_acc: 1.0000 - features_acc: 0.9576 - val_loss: 11.8859 - val_class_loss: 4.0488 - val_features_loss: 783.7060 - val_class_acc: 0.7067 - val_features_acc: 0.5920\n",
"Epoch 31/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1678 - class_loss: 4.9130e-04 - features_loss: 16.7317 - class_acc: 1.0000 - features_acc: 0.9543 - val_loss: 15.5311 - val_class_loss: 3.7586 - val_features_loss: 1177.2509 - val_class_acc: 0.7067 - val_features_acc: 0.5940\n",
"Epoch 32/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1463 - class_loss: 2.8496e-04 - features_loss: 14.5966 - class_acc: 1.0000 - features_acc: 0.9587 - val_loss: 3.7972 - val_class_loss: 0.7580 - val_features_loss: 303.9147 - val_class_acc: 0.8200 - val_features_acc: 0.6497\n",
"Epoch 33/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1444 - class_loss: 1.8008e-04 - features_loss: 14.4200 - class_acc: 1.0000 - features_acc: 0.9614 - val_loss: 5.9506 - val_class_loss: 0.5128 - val_features_loss: 543.7827 - val_class_acc: 0.9063 - val_features_acc: 0.8470\n",
"Epoch 34/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1434 - class_loss: 3.0296e-04 - features_loss: 14.3054 - class_acc: 1.0000 - features_acc: 0.9580 - val_loss: 1.7689 - val_class_loss: 0.0350 - val_features_loss: 173.3881 - val_class_acc: 0.9887 - val_features_acc: 0.8107\n",
"Epoch 35/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1450 - class_loss: 4.0905e-04 - features_loss: 14.4622 - class_acc: 0.9999 - features_acc: 0.9587 - val_loss: 2.2573 - val_class_loss: 0.0988 - val_features_loss: 215.8562 - val_class_acc: 0.9527 - val_features_acc: 0.6603\n",
"Epoch 36/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1547 - class_loss: 1.4593e-04 - features_loss: 15.4565 - class_acc: 1.0000 - features_acc: 0.9609 - val_loss: 11.8610 - val_class_loss: 2.7836 - val_features_loss: 907.7497 - val_class_acc: 0.7340 - val_features_acc: 0.5947\n",
"Epoch 37/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1491 - class_loss: 1.0867e-04 - features_loss: 14.8973 - class_acc: 1.0000 - features_acc: 0.9594 - val_loss: 1.7409 - val_class_loss: 0.0284 - val_features_loss: 171.2533 - val_class_acc: 0.9950 - val_features_acc: 0.9253\n",
"Epoch 38/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1432 - class_loss: 9.9859e-05 - features_loss: 14.3100 - class_acc: 1.0000 - features_acc: 0.9664 - val_loss: 12.3351 - val_class_loss: 2.4550 - val_features_loss: 988.0021 - val_class_acc: 0.7413 - val_features_acc: 0.5947\n",
"Epoch 39/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1332 - class_loss: 1.2234e-04 - features_loss: 13.3038 - class_acc: 1.0000 - features_acc: 0.9643 - val_loss: 2.9682 - val_class_loss: 0.0783 - val_features_loss: 288.9950 - val_class_acc: 0.9747 - val_features_acc: 0.7110\n",
"Epoch 40/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1439 - class_loss: 1.0725e-04 - features_loss: 14.3829 - class_acc: 1.0000 - features_acc: 0.9629 - val_loss: 2.9160 - val_class_loss: 0.1196 - val_features_loss: 279.6394 - val_class_acc: 0.9823 - val_features_acc: 0.8610\n",
"Epoch 41/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1358 - class_loss: 2.3824e-04 - features_loss: 13.5561 - class_acc: 1.0000 - features_acc: 0.9589 - val_loss: 9.3048 - val_class_loss: 3.1435 - val_features_loss: 616.1346 - val_class_acc: 0.7087 - val_features_acc: 0.5923\n",
"Epoch 42/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1448 - class_loss: 1.9449e-04 - features_loss: 14.4578 - class_acc: 1.0000 - features_acc: 0.9633 - val_loss: 2.5233 - val_class_loss: 0.2807 - val_features_loss: 224.2669 - val_class_acc: 0.9120 - val_features_acc: 0.6727\n",
"Epoch 43/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.2086 - class_loss: 0.0068 - features_loss: 20.1849 - class_acc: 0.9983 - features_acc: 0.9496 - val_loss: 19.3632 - val_class_loss: 4.6908 - val_features_loss: 1467.2480 - val_class_acc: 0.7057 - val_features_acc: 0.5920\n",
"Epoch 44/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1734 - class_loss: 6.2381e-04 - features_loss: 17.2814 - class_acc: 0.9997 - features_acc: 0.9569 - val_loss: 9.1110 - val_class_loss: 1.9863 - val_features_loss: 712.4671 - val_class_acc: 0.7647 - val_features_acc: 0.6177\n",
"Epoch 45/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1612 - class_loss: 5.6875e-04 - features_loss: 16.0647 - class_acc: 0.9999 - features_acc: 0.9591 - val_loss: 21.1116 - val_class_loss: 5.8439 - val_features_loss: 1526.7732 - val_class_acc: 0.4293 - val_features_acc: 0.4563\n",
"Epoch 46/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1503 - class_loss: 8.8300e-04 - features_loss: 14.9406 - class_acc: 0.9997 - features_acc: 0.9629 - val_loss: 2.0129 - val_class_loss: 0.0483 - val_features_loss: 196.4606 - val_class_acc: 0.9910 - val_features_acc: 0.9180\n",
"Epoch 47/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1959 - class_loss: 0.0076 - features_loss: 18.8292 - class_acc: 0.9980 - features_acc: 0.9523 - val_loss: 22.6670 - val_class_loss: 6.3410 - val_features_loss: 1632.6016 - val_class_acc: 0.4423 - val_features_acc: 0.4300\n",
"Epoch 48/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1475 - class_loss: 5.3088e-04 - features_loss: 14.6976 - class_acc: 1.0000 - features_acc: 0.9613 - val_loss: 10.1780 - val_class_loss: 2.7504 - val_features_loss: 742.7626 - val_class_acc: 0.7283 - val_features_acc: 0.5950\n",
"Epoch 49/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1364 - class_loss: 1.0950e-04 - features_loss: 13.6242 - class_acc: 1.0000 - features_acc: 0.9659 - val_loss: 6.4206 - val_class_loss: 0.5300 - val_features_loss: 589.0608 - val_class_acc: 0.9050 - val_features_acc: 0.8000\n",
"Epoch 50/50\n",
"7000/7000 [==============================] - 8s 1ms/step - loss: 0.1275 - class_loss: 1.5437e-04 - features_loss: 12.7307 - class_acc: 1.0000 - features_acc: 0.9666 - val_loss: 8.1595 - val_class_loss: 1.3029 - val_features_loss: 685.6563 - val_class_acc: 0.7920 - val_features_acc: 0.7730\n"
]
}
],
"source": [
"# train model\n",
"history = model.fit(X_train, [Y_train_class,Y_train_features], shuffle=True, batch_size=batch_size, \n",
" epochs=epochs, verbose=1, validation_split=0.3, callbacks = callbacks)"
]
},
{
"cell_type": "code",
"execution_count": 330,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"colab_type": "code",
"id": "1qkgSaw-3JDE",
"outputId": "c7c5797b-54c9-474c-bf44-0ebefc80019c"
},
"outputs": [
{
"data": {
"image/png": 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d8PcblBN4zXlw7n/Dhx6Gz/XBp7bDhx+m+4SPMihzDKz/lEroMpyn9+2+j05f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\ncdLQ96TyMsEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# Plot training & validation accuracy values\n",
"plt.plot(history.history['class_acc'])\n",
"plt.plot(history.history['features_acc'])\n",
"plt.plot(history.history['val_class_acc'])\n",
"plt.plot(history.history['val_features_acc'])\n",
"plt.title('Model accuracy')\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['class_acc','features_acc','val_class_acc', 'val_features_acc'], loc='lower left')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 331,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"colab_type": "code",
"id": "iT2bia7z3JDO",
"outputId": "1746dbb8-1fa4-4a62-b041-94ff4237b138"
},
"outputs": [
{
"data": {
"image/png": 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AGN5XnvFY/DH/1+5VuU1Dk0f1JN7sihrK9X+fzggfbameekO1IQjCMg1NjZQm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\ngg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# Plot training & validation loss values (general loss)\n",
"plt.plot(history.history['loss'])\n",
"plt.plot(history.history['val_loss'])\n",
"plt.title('Model loss (General Loss)')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['loss','val_loss'], loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 332,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"colab_type": "code",
"id": "gLRmXHpW3JDT",
"outputId": "b26a42d9-08b0-4661-b731-02994b8ac9df"
},
"outputs": [
{
"data": {
"image/png": 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WUqcki9hzxFJWBEcIsrEIfGUN2Ys88KSyCECfI1YILGVCcIQg3UVrsK4hSzak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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# Plot training & validation loss values (classification loss)\n",
"plt.plot(history.history['class_loss'])\n",
"plt.plot(history.history['val_class_loss'])\n",
"plt.title('Model loss (Classification Loss)')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['class_loss','val_class_loss'], loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 333,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"colab_type": "code",
"id": "WbQ1leQB3JDX",
"outputId": "d7d8ba87-4266-431b-ecec-460cec5900f5"
},
"outputs": [
{
"data": {
"image/png": 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u9tj80zT73w7c7rH9GaA481KHnA9Crm6rhvZEG5N8MdSjUzpbD9R/59stZgmX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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
},
"output_type": "display_data"
}
],
"source": [
"# Plot training & validation loss values (features loss)\n",
"plt.plot(history.history['features_loss'])\n",
"plt.plot(history.history['val_features_loss'])\n",
"plt.title('Model loss (Features Loss)')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['features_loss','val_features_loss'], loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "_GXz8vxY3JDd"
},
"source": [
"#### Model evaluation"
]
},
{
"cell_type": "code",
"execution_count": 334,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 69
},
"colab_type": "code",
"id": "GyJTX2PU3JDe",
"outputId": "605b18d7-403a-405c-c009-7d33202739ee"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000/1000 [==============================] - 0s 475us/step\n",
"Test Features Acc: 0.92\n",
"Test Classification Acc: 0.993\n"
]
}
],
"source": [
"# load best model wights\n",
"model.load_weights(save_to)\n",
"# loss,features_l,ellipses_l,featres_a,ellipses_a\n",
"#Y_test_features =Y_test_features.drop(columns=['angle'])\n",
"_,_,_,acc_classes,acc_features = model.evaluate(X_test, [Y_test_class,Y_test_features])\n",
"print('Test Features Acc:', acc_features)\n",
"print('Test Classification Acc:', acc_classes)"
]
},
{
"cell_type": "code",
"execution_count": 335,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 104
},
"colab_type": "code",
"id": "8FLoWJMS3JDk",
"outputId": "e34ba910-cc5f-40d9-9579-2e392bc85c91"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"center_x avg error: 1.93428353536129 pixels\n",
"center_y avg error: 1.891286836028099 pixels\n",
"axis_1 avg error: 1.1033067197799682 pixels\n",
"axis_2 avg error: 1.0726100867986679 pixels\n",
"Avg angle error: 0.09177505779148392\n"
]
}
],
"source": [
"# Check errors\n",
"features_predicted = model.predict(X_test)[1]\n",
"for feat,error in zip([\"center_x\",\"center_y\",\"angle\",\"axis_1\",\"axis_2\"],\n",
" (pd.DataFrame(features_predicted) - Y_test_features.values).abs().mean()):\n",
" if feat == \"angle\":\n",
" continue #we will evaluate error later...\n",
" print(feat,\"avg error:\",error,\"pixels\")\n",
"\n",
"# check error percentages for angle\n",
"predictions = pd.DataFrame(features_predicted)\n",
"ellipses_idx = np.where(Y_test_class['class'] == 1)[0]\n",
"angle_idx = Y_test_features.columns.get_loc(\"angle\")\n",
"true_angles = Y_test_features.loc[ellipses_idx,\"angle\"]\n",
"predicted_angles = predictions.loc[ellipses_idx,predictions.columns[angle_idx]]\n",
"err_percentages = (((true_angles - predicted_angles).abs() +180)%360 - 180)/true_angles\n",
"print(\"Avg angle error:\", np.mean(err_percentages[err_percentages != np.inf]))"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"collapsed_sections": [],
"name": "Copy of MyEllipsesNotebook.ipynb",
"provenance": [],
"toc_visible": true,
"version": "0.3.2"
},
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
rdipietro/tensorflow | tensorflow/tools/docker/notebooks/3_mnist_from_scratch.ipynb | 8 | 209782 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "9yupXUk1DKOe"
},
"source": [
"# MNIST from scratch\n",
"\n",
"This notebook walks through an example of training a TensorFlow model to do digit classification using the [MNIST data set](http://yann.lecun.com/exdb/mnist/). MNIST is a labeled set of images of handwritten digits.\n",
"\n",
"An example follows."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:20.863031",
"start_time": "2016-09-16T14:49:20.818734"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"id": "sbUKaF8_uDI_",
"outputId": "67a51332-3aea-4c29-8c3d-4752db08ccb3"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: decodestring() is a deprecated alias, use decodebytes()\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from __future__ import print_function\n",
"\n",
"from IPython.display import Image\n",
"import base64\n",
"Image(data=base64.decodestring(\"iVBORw0KGgoAAAANSUhEUgAAAMYAAABFCAYAAAARv5krAAAYl0lEQVR4Ae3dV4wc1bYG4D3YYJucc8455yCSSIYrBAi4EjriAZHECyAk3rAID1gCIXGRgIvASIQr8UTmgDA5imByPpicTcYGY+yrbx+tOUWpu2e6u7qnZ7qXVFPVVbv2Xutfce+q7hlasmTJktSAXrnn8vR/3/xXmnnadg1aTfxL3/7rwfSPmT+kf/7vf098YRtK+FnaZaf/SS++OjNNathufF9caiT2v/xxqbTGki/SXyM1nODXv/r8+7Tb+r+lnxZNcEFHEG/e3LnpoINXSh/PWzxCy/F9eWjOnDlLrr/++jR16tQakgylqdOWTZOGFqX5C/5IjXNLjdt7/NTvv/+eTjnllLT//vunr776Kl100UVpueWWq8n10lOmpSmTU5o/f0Fa3DDH1ry9p0/++eefaZ999slYYPS0005LK664Yk2eJ02ekqZNnZx+XzA/LfprYgGxePHitOqqq6YZM2akyfPmzUvXXXddHceoic2EOckxDj300CzPggUL0g033NC3OKy00krDer3pppv6FgcBIjvGUkv9u5paZZVVhoHpl4Mvv/wyhfxDQ0NZ7H7EQbacPHny39Tejzj88ccfacqUKRmHEecYf0Nr8GGAQJ8gMHCMPlH0QMzmEBg4RnN4DVr3CQIDx+gTRQ/EbA6BgWM0h9egdZ8g8PeliD4RutfF/Ouvfz9OtZy8aNGiNH/+/GGWl1122XzseYuVNKtqsaI23Ghw0DYCA8doG8JqO+AUG2+8cVq4cGHaY4890vLLL5/WXXfdfI6jvPDCC3lJ8amnnkoezP3000/pl19+GThHtWpIPekYomTxFS7HnkqKjMsss0yGgFE4r62tSBFVJ02aNPyconi9V4/JwzHwT9ZNNtkkeZ6w5ZZbph133DH99ttv6ccff8zXX3nllcRRnHNfv2cNGMQWGRaOrWbUrjsGBRLAA6U4Lhoqw9h2223ztRBq6aWXzsbgvueffz4Lu9NOO2UnYTgrr7xy7tO9nOH111/Pbb744ov0ww8/jAvngAdFMvQDDjggG/0GG2yQX1GZNm1aziCCwzrrrJPl3muvvXKwePnll9M333wzHDCKWPbLMbuAkfISjnvvvXcW/emnn85lqCBqa4a65hiYR/Gk2RNGRlwm3n7ggQfmdrKD9sqJtdZaKxvCnDlz8n3Tp09PXmPYeuutc0SVNQjvnmuvvTa3efzxx9N33303PGZ5rF75DBvvqq233nrp22+/TWeddVbyikpgxCE4vQDhlQUBRfDw2esbs2fPTquvvnqviNN1PuIdJ4GErVx44YUZowsuuCB9+umn6eeff84BspmsWqljhPFDxjGGYx/lDkN33udajCoVlAjRzl4U8LjefRwnPjsXG8OJqKBd8NB1LTU5IHyCd7LJGOYXNoGjFqaGIKtrERDIDKtukfGMH/zRZa1A101+YBF44KfMYzO8VOYYjDWiukiGqc022yyXOUqdzTffPJ/z1ialeqNVxA9gi0wzlOJ5juJlR8JeddVV+ZrIKTq4ZvJp/8EHH+SU+txzz+W2SqmxVFZRplrH5DTRXmGFFdKuu+6azjjjjOzosl5g6D54CQCI4mGjhNQO5occckh2LvLTA6fqJOEnyhU6kNlkZmUuvrtNcFx77bUzhsZWXgoSsm6t4Dsa/tp2DErCmA04HAI4FLjaaqtlBhmnSKiNY4rDtHZFB6jFMMH0RVDH+nCPYxtDCFJnKkniRbDitWjTK3sykQUuMLPn3DZGX8SFnCG/fVyz5zCCBtIHTLshdzif8fERn8cKXxjCNOwCTu3Qf6yqhV4AQokiP489//zzM0DxnQYKwqAtIkko1kQzFFxvaNcJ6u3Pe+65J/cRRvDee+9lA2BInIyRff/997nNO++8k7t0vl2A6vHWynmyiPJ43WKLLbIijz/++LTddtvlTCdzwIWSg9yjxBJ0GN/DDz+c7zv77LOzbEceeWSekwVGgsOsWbNyNo0+qt7DfPvtt8/dmtvIGnPnzk3PPPPMsJ6rHrNef/BBeJA90RprrJEDcNhctMkXR/mnbccwuCjNGTbaaKMc8TBZprITxOdgOvbuKxqGz6LSJ598kseJ9Gi1CYmSv/76a3YyJZWMZJ6Ceskp8EMusihFEAyUmVaa8G2rxTNHIrd733///eH7YeaLNe5xrEzlWNF/HqQDf0Tm+GIbvYdD43MsKAIo/JDgE0G5aFfN8NaWYxiUshikqGYTTUSt0TCkjXsYNqJQQso+rgGa0vX58ccf56hQTtk+48F92rmvlnE1A0on2uKP0Yrw+Nxzzz0zn+ZhjKwRXq6vueaa2TmUiRQfS7SyNeMks9IV9vrvJOl/q622yo4Mfw5Pvm6TMclLdit6shh+YAMnq1E29tEsteUYBgMSgxa5MOAzJZcVXQs4bUR8XxhCHIwzMALCBuCcx5q0tF3u133l8XrRMchFiRYNyMxBKM/5IjZlWVzjULKwACISytIWFsi56aab5mvOKyEikmdAO/iHY+BDCRUZuoPD1e1akECyLseA7d13352DhdKak8Cmlt3U7TSl9p58FwejYK8ncAwKpDTnGDcARbWiAUjHiNEHsITSPlagpEZChcfrZzwSOfBOiQwXLuR3PjAhtwAD08iAMCO/a+5xPTIm3ALjwERf0V+c69QeT7ZujVdLDhgKBrANXAMreMESRkU7rdVPrXNtZ4xIpSLH1VdfnR3j4IMPzkbw2Wefpa+//jovo5188slZsZjArAcvFP3YY4+lSy+9NEdTdTTy0I5xHHfccfm1CH2LtuORKEqmkwVlVU+sBY+IdJRmE0zeeOONnEXuu+++7AhnnnlmWn/99XMJ5brtzTffzHMJx/o555xzkgdb0U8rRtAKrnTYqtG1Ml6teyxInHDCCdlGYByBmG2Z97ChVvFo2zEwbHCRTbqP7EDxPjN2pUBEe86AXAcsg+f10TYMSTvnRM1ulQe1wG/nHEXZZEJZUIYQ5cgWMsEgMgqclFdkdh+MbFFyuddnWMLNfTYkcuuXHlBkpFYNI3dS+mMMfCHHsZWadfUjmQVn8iLywscG21apMscQwR555JEM3KuvvpoZ5LHOmzgjAvBwzFt2/Oijj3Lm4Ayin/MU/eGHH+b2N998c/5MGSaZ44nw7OEd5Rx77LE5+1EehYXxkpes5li2K6+8Mhv8Lrvsko381ltvzcEBfvHQKh5auk9GPvHEE3NJAx+/eKL/HXbYIQcbK3nwN067xAk4s5VHdbvsx0nxrYQeKxJMZAfBA7GlRx99NC9EtCN7JY4RoPBeAHIAyrB3jpHYwqu1d02d7HpZcfqINo5dL7eJMXtxTzk2sgWFM/gcsnCakI2cFOk+523O+Qw7WaeYHYpYRp9xn4BkbPdWSfgJXYYM+ne+2xRj2sdx8EDu8rm4Ntp9pY4RSmb0CIPOAVNGoLA47yU4S2xen37ppZdy9CkLE/3lm8bJHzJbbiavt2Q9p7AkK7oyXAZOLk7gs9c4PJC0AOE8DDyrgJkaWgYQkSPYuAdpWySfteU8HhqKouYq+io6ZfGeZo7xpbT1+jt+jGULfprpq922ePHMBibwjWVq523KVrzBsIzTaMeu1DFi0HI0YyyYtAekY5MltbRyihFJiROBKIYTwMCTWJNubwdQFCXFapK9z96mtbjgs3thFKWnUgjBzNZIya5FOyUcPG36q4LwRgZ6Ix8HtBk3tirGGU0feAkslHfk5PzBh2cXSkvtWqWOOEaRGcoSHdXDMoYn1tK8yaON0ahbCWgFS/vxSnjn5F4ItLeiFAGAzCKc7MDA1OlIjc4pLFKE7FEyxb5ZPNTbtuiv2fvrtddfOFsYXcwj8d8qv/XGq3femLvvvnvOvrIYPPEjG+PDseDbDnXcMXiyiGiyyACOPvrovN95552zV3/++ef5zVveznlEo6CICvG5l/d4JSvHP+qoo7JjKDs4PkVSGPm9HSz9W5rlPEoCQYHjVFXyRGnBOcKA28VOP/qTBWX6YnS2IKB8qYL/enyGHPbKziOOOCLj6sGeslGW8L6Y4ANr2MY99fpsdL7jjmFwkSTSr6gDVCk+tmDQedcJ5LgdwaLPbu7xjJRRNlErSsiQhVHJlOEQoh182o1wRTnharwYs3itnWP9Rd/RD5mLW5yveh/YRhYMjItyBh/wjPat8tEVx6B00RKo5513XpIl7rzzzuwEourMmTOz95uIcyBfTSXYiy++mCOrSFS1klsFrNZ9eGPoJtmeyRx00EE5cpGbIi21XnbZZbkMee2117KMHIKMIVcotVb/vXoOz6I0+URoMlVFcBFE7L1+IjNYIo6v/fo+D3tC+FCR+FHuwNUCgfOtUlccI5hnJMoIBhN1sBICqMoNNaLP3pkiFGciIIBC4HaEbRWk0dyHb3Mp/EY0I6+NsytvyKxsKhpQr8ozGpm1IZ8IbV+PyllGuyh1YBXXOQEcy6R8M5eAHzuxxX3GRvbaCKJ4aRfXrjkG5jEbk00Prxi8SZTJKmc5/PDDc5v99tsvC+hBjWtqStmD0F4Ma1foMvDtfqZMUc3/lYjMSFFW3NS7JtyyoKzSiTocHoFJHMc+MlK7Mta7n9NbATJerbEYvQWIWCVitIyaXrV3nsG7H2Y2GVcbxyj6NX+waKEPmOvbfShwtjhQDDz5Ygt/uuoY+OPtnICDEMBTWsAQUu0NBBsDEgFEWOADAiDaVRERWsCq5i34IRN+TbTJgn8KwzOFuR4KDUXW7Kyik53Ep8w/+RkxWeO5S1EM5wVABguXMGp69dk1x87D0ObdL32GHI5tsDQGHtwbm/Hw4TpnKvNY5Ge0x113DEwT3tIsIdSnDIfxcxJAevCHfE9cXcmotHXfAw88kIFUdgFjLMn4HuZRuh9FExmjRCCnZxRqcPxz8ioUVk9eRhJkPAYHV8ZVFRkjjFSfAtw222yTy2OZ0iv15fHcQ4dKaMcwsBdEEL26RzaIh5+yK7LSBGPno8yOZX+vzRhfXzZ8cRrtyzzkzpr803XHwB8wTJYIRol+VY8zqMMBbP0f+cExE1qTdbU7x3jwwQdzVBYdesExKNiEWx2MfwoOAyCbJ9uRHZvUTcPmsENhGNE4HBKOHKNqZzQu3KNfX9H1nRABQZlbNkpt4SNo4DWIIesDj9qYnwki2giWqol3330348kZLPm7xvi1Pffcc7MzhA3gy/0oeIuxWtmPiWNgNCIFYwcCAa2FA1ikJZz1aeUVsBmge9TyoqGoIqKUFdEKCFXcU0/pHJizVMUnXBiBh6IicdTTzsEOnuZkDE/2rcJI4KMf/TF+0TucwDhkZ+DGL4/nGkPGV/AIC+2RvfP6ZPTI4gu5XNM/Um7RPzuIFyn1zW7wpQ9UHj+fbOHPmDlGCOGBGIeQQfwuq0jnISBQfOHft7JEHN94Q5xF6XLFFVfkyKIEGyuiGAo3r6BIx0imcM6k+6GHHspOEQbcDq+UTl4BwRu7PstUiPEJFsa9/PLL83nXg6d2xnUvoxS5L7744uGyh/wyRpRF9YwSHsHjE088kWWADQeRFThZkTgBstensZG5h4m56oEdcAp9CwTOVUlj6hgECcGBpA6XDazeiLKhVABQAhKB3cNxbEAL4KoEppm+gjf3OMafDf+UW7zeTL/ltqIiAxBMOIIxnLOHgbFsMGQ4InhE0nJfrXw2hnIRD3SFBKmYWDfqE49woFvOzZno3NxM0HDciMjBDsjEBgLTsJHYN+qjmWtj7hjBLKFFQgL7qRz14jHHHJPBcC2M3wRPVDT5ohzZRv0Z16O/sdozAKmdopUH5kftTrzJpl+lk29CcgpLw3BgpMbwwqF/S80pGJ6xO0WM+8Ybbxw2TuOEoTYakwyovB/JKdzDMVQOHvCRzXju890fL11aGhcMqqIxdwwCRkYQDZAaE7lWBhyosQEmQM439MgffDHm0Si8EcuBC0ezcQSZVKYktzFEW+3sfQ4natRvu9eMTS9F7IvHo+m/2fb6LNuCc0WsW+mzHq9j6hgE9YCHp5tkez2EAVjlMOmyUlU2Lis8ygVR0rykyoltPZCaOY9fr32Qp50X6xi7pWCGbsHBvwLgGIcddljGxvcsjOU1GseyiKjJQWydpiqNsBlei85BfhNxeJunVCl31x0jBOMAjJ9jRC3OEERDS7QMI0qQohIYgLSq7FJuMZbi9WZA7kRbvFAWx5Dyy449mjEDG/dyDPW4VSiy2iNvBcCSUdxyyy35OYHrqJUx843j8I/qQpA074BVVdR1x+AIHCIiIGewsqIuds41tSSlOxeOFHuOQ/E+2zPEuFYVKM32U3RMvGy44YbZMTg2B2+GOIXXJcjpR9lkUy/QyZ7GUU8zAD9RCiuR0oQYVv1IMAk7qFL+rjkGg7GZQPLufffdN69QKJtkCAKKjNGu1p7gMgWDYEDRpkpAmu0rnMLehie/RavcI49Sr1ZW0w6V91ac/IsxmdHPB0U5pQ+4+TExDudNUhPufnaKIn7N6m2k9h11jKLRqP+UQJb2eHh4uYjK0LW1D0MpCq0NR4g24RTR/0hCdvM6/m14FtljeTL4D/liedFeO7LYcyh7eMGDY8X16IM8Vp9kWjj2GwWG5IZb2FKVOHTMMTCvDKBgD2Z22223bNynnnpqVrZXBFxjQDZUFJiwIqKHN8qHO+64IxvN/fffn9vG/VWC0UpfeC5uZMEbg/ctM/8SzYOxZ599Nhs4ebSx0ECpcDFvMCdRggkesoQ+zaHU0N4EgAEnue2227JTON+LgaEVDFu5h+w2Wdl33GFkEUIQqYIqdYwwbJGO8q2xOydqUiTFWpJVPzsuUwhlzzFETxlGdFSCqaMB4XwvUzgKWU3AyW4uwFns4QMbilUyxbq8p/4cw3UEB8FDGQUDx/acqB8zRS2dw5qthe3VatPKucocg6JiYu3lP2nfawvekKVITzgJQLH24QTBtPZeE2D89957b27jwZ1IwIm8R2OMWHmJ+3pxTzaK8l+HyMrgTzrppMxqOIEsGoZvz0nsyWiliRMUl2G9aOk6POyLZVUvYtBpniL4wA1m9lVSW46BOQqKpTLK9FnUsxftvW4swssa4dkhCGFCMNfcp08lhM9KKc4h0obgsa8ShHb6Cv5DJnu8IwHB9TB852DkOlzIRV6kXbSVMfQj48BWdhE0TLr1Fe3zQR/+gRMK5yjuq4KjZccQ2SlYjexHmCnSkiLjtsesmlnpQ5naFo1A5GMAHoJxBI709ttv54ygntZWmWEcQMS9VQleRT9kNmfAG0P3HRPGbHnVudg4gEyJOAYiE0wikHAAcxHyxndO4KI/WHEK/Qzo7wjAXfaFNdurikaNtIERRTqmYIYdE2tGEs8hfJ8iFB/3xV67MCjG8NZbb6Unn3wyC+XfDxfnDxFp496qhK6qn5CDA5twK/fIRH5Gb0MMOhxCFgkKjOBoHqKEkmWvueaanG04iTHcP3CKQO0/e3ZhgceP2smqcKyKRuUYlEKhPDL+d5z1c4qVFTDnmBIZMwZ9DiKAzTmvCetPNFR7W7fXXt/KLddqTcyjr17bRybkEF5XiQhPHnMuDlF07MCB3I49l4EDxTrnfsFBJBxQbQSKeGoROqjdurWzIzoGJqRxS2KUf/rpp2flcRDRjRKVCdpFhCwz7rOVKE5z++235/7uuuuuXDq5P5yKEY0np8B3TKb9K1/vLTF0/7MiJtyRPYrq4fx+7R2e7vFDDzDyfx1goPwcUGMEYG/rFI3oGAYW0UUyimQIcRwGzbgpVsZAUTYE065xCtc5GUeSHTyg4kzKs/FKoSBljyhvTz6y2gseZAwlwgI+cNBGtpV9ZRj4BobjFY9O8g0bQcXWaRpxBE5hHuFnJ0XB6dOn56ge2QGDlK2dFSSG4b8kxVzEdSWGVxgYQLzrxJkIGgbTaUE73b9MZ/KNfIMOJpdcckndYZWmFAwv+wgydW/o8wsCK3xnz56dFzx8oxPGtk7QiI5h0FBaeGzRKYIpjDN2ig6lB9OiprmI60qNieIMIXvsQy7yotjH9eI+2hbPDY4bI8D+2JdnWTYY+iwDs78qaUTHEM0sI1pClAVMnqX9ImGQszB6DHoNOLzZNZlGRlEq9JNB9JOsRXvoxDGnsDTudwFUHTNmzMjDqEaU9xYvGgWiZnka0TEo16CeNyCM1SLtwmt5cNEoCOUa5xjQAIFWEGBP5rbKdTRr1qwcfGUMthXVTCt917pnRMdwE6ZiQm0JckADBMYCgWLwtXjTSeq/d5Y7ieag7wmDwMAxJowqB4JUicDAMapEc9DXhEFgcjxcM7vvR4on7bHS1q84WNkpUr/iEL+aOLRw4cIlQCmuIhUBmsjHlpQ9c7EmzjEsN1vd6DeCg8UVT+qRd7b6EQey8wMT+6El8RSu36xhIO8AgQYI9F94bADG4NIAgUDg/wHX+3lgThDIegAAAABJRU5ErkJggg==\".encode('utf-8')), embed=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "J0QZYD_HuDJF"
},
"source": [
"We're going to be building a model that recognizes these digits as 5, 0, and 4.\n",
"\n",
"# Imports and input data\n",
"\n",
"We'll proceed in steps, beginning with importing and inspecting the MNIST data. This doesn't have anything to do with TensorFlow in particular -- we're just downloading the data archive."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:20.958307",
"start_time": "2016-09-16T14:49:20.864840"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 110,
"status": "ok",
"timestamp": 1446749124399,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "w5vKZqr6CDz9",
"outputId": "794eac6d-a918-4888-e8cf-a8628474d7f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Already downloaded train-images-idx3-ubyte.gz\n",
"Already downloaded train-labels-idx1-ubyte.gz\n",
"Already downloaded t10k-images-idx3-ubyte.gz\n",
"Already downloaded t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"import os\n",
"from six.moves.urllib.request import urlretrieve\n",
"\n",
"SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\n",
"WORK_DIRECTORY = \"/tmp/mnist-data\"\n",
"\n",
"def maybe_download(filename):\n",
" \"\"\"A helper to download the data files if not present.\"\"\"\n",
" if not os.path.exists(WORK_DIRECTORY):\n",
" os.mkdir(WORK_DIRECTORY)\n",
" filepath = os.path.join(WORK_DIRECTORY, filename)\n",
" if not os.path.exists(filepath):\n",
" filepath, _ = urlretrieve(SOURCE_URL + filename, filepath)\n",
" statinfo = os.stat(filepath)\n",
" print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')\n",
" else:\n",
" print('Already downloaded', filename)\n",
" return filepath\n",
"\n",
"train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\n",
"train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\n",
"test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\n",
"test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gCtMhpIoC84F"
},
"source": [
"## Working with the images\n",
"\n",
"Now we have the files, but the format requires a bit of pre-processing before we can work with it. The data is gzipped, requiring us to decompress it. And, each of the images are grayscale-encoded with values from [0, 255]; we'll normalize these to [-0.5, 0.5].\n",
"\n",
"Let's try to unpack the data using the documented format:\n",
"\n",
" [offset] [type] [value] [description] \n",
" 0000 32 bit integer 0x00000803(2051) magic number \n",
" 0004 32 bit integer 60000 number of images \n",
" 0008 32 bit integer 28 number of rows \n",
" 0012 32 bit integer 28 number of columns \n",
" 0016 unsigned byte ?? pixel \n",
" 0017 unsigned byte ?? pixel \n",
" ........ \n",
" xxxx unsigned byte ?? pixel\n",
" \n",
"Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).\n",
"\n",
"We'll start by reading the first image from the test data as a sanity check."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.112407",
"start_time": "2016-09-16T14:49:20.960204"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 57,
"status": "ok",
"timestamp": 1446749125010,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "P_3Fm5BpFMDF",
"outputId": "c8e777e0-d891-4eb1-a178-9809f293cc28"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"magic number 2051\n",
"image count 10000\n",
"rows 28\n",
"columns 28\n",
"First 10 pixels: [0 0 0 0 0 0 0 0 0 0]\n"
]
}
],
"source": [
"import gzip, binascii, struct, numpy\n",
"import matplotlib.pyplot as plt\n",
"\n",
"with gzip.open(test_data_filename) as f:\n",
" # Print the header fields.\n",
" for field in ['magic number', 'image count', 'rows', 'columns']:\n",
" # struct.unpack reads the binary data provided by f.read.\n",
" # The format string '>i' decodes a big-endian integer, which\n",
" # is the encoding of the data.\n",
" print(field, struct.unpack('>i', f.read(4))[0])\n",
" \n",
" # Read the first 28x28 set of pixel values. \n",
" # Each pixel is one byte, [0, 255], a uint8.\n",
" buf = f.read(28 * 28)\n",
" image = numpy.frombuffer(buf, dtype=numpy.uint8)\n",
" \n",
" # Print the first few values of image.\n",
" print('First 10 pixels:', image[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7NXKCQENNRQT"
},
"source": [
"The first 10 pixels are all 0 values. Not very interesting, but also unsurprising. We'd expect most of the pixel values to be the background color, 0.\n",
"\n",
"We could print all 28 * 28 values, but what we really need to do to make sure we're reading our data properly is look at an image."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.525418",
"start_time": "2016-09-16T14:49:22.114324"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 887,
"status": "ok",
"timestamp": 1446749126640,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "F_5w-cOoNLaG",
"outputId": "77dabc81-e3ee-4fcf-ac72-88038494fb6c"
},
"outputs": [
{
"data": {
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sbIyRkRFOnTrFiRMnmJycZGZmhtnZWWZmZpLbfkxCue4GP2jRz27o6OgoGbio\ncCAi8jyFBKm75SoJ6e6GEydOMD09vWTfBr9DpF9AyU99zOpuCNdLCHd8rBQMFBpEZLtRSJC68WEg\nfSwuLpZ0J/j1EKanp5dUDdIrLIabT/k1EvzPtra2JBCE+zKkV1RUGBARiSgkSN34FRX9Usvhz/Hx\n8SQo+HAQ7vwYhoNw8SRgyZLKfh+G8Ehvja1gICKylEKC1E24o6MfW+CXWx4fHy+pIvjHwoCQriAA\nJbs+VgoL4f3beU0EEZFKFBKkbnx3w9zcHDMzM8myyzMzMxUrCb6bwb+G5y/yvkKQDgfhbpCqJIiI\nLE8hQerGdzeEIcFXD3xICMcipLsbym1IlVVJyOpySG/gpKAgIlJKIUHqJt3dMDU1xeTkZLI/g68k\n+KCQ7m7IWvjIL8m80u4GBQQRkfKa6t0A2b6yuht8SBgbG1vxwEV4PiCkxyRkVRDKDVxUUBARKaVK\ngtRMue2g00svhwFhfHy8ZMllX0nw20H7tRDCGQ1hIGhubl6y/kFbWxutra3JdEgFBBGRlVFIkJpK\nb+Dkf19cXFwSDsbGxigWi4yMjDA8PEyxWGRsbCypJvi1EZxzNDc3JyEgl8uV3O7o6GDv3r3s2rUr\n2RK6o6ODXC6XGRIUFEREsikkSE2ll1oOf4bjEHwFYWRkhKGhIUZGRpKKgt/l0c9sgOc3cAqXV/ZH\nZ2cne/bsYffu3fT29tLV1UWhUCCXy9Ha2qqAICKyQgoJUjNhOMg60pUEX0XwISE9DTK9FbQPCZ2d\nnXR1dSU//R4Nu3btore3t2S3R19JCMOB1kkQEcmmkCA1lQ4K4fLLvpLgZzP4SsLw8DDDw8PJQkrh\nmISwu6GtrY329nY6OzuTjZt27tyZVA+6u7vp7u6mq6sr6W7wlQQ/yFEBQUSkPIUEqamsgOCXYs4a\nsOgrCcPDw0tWYUxvBe3HH3R1ddHT05N0MezatSvZCjq9JbSvJGRVERQWRERKKSRIzaTHI4QBYX5+\nPtnlcXJykrGxsSUhwU91DJ+TriT47obe3l52797N/v372bNnD+3t7cmRz+dpb28vqSRA6QJMYZsV\nFEREIgoJUlPlKgl+6mO4gJKf3eC7G7IGOy4uLgLPj0nw3Q09PT3s2rWLffv2sW/fvmTqo5/+mN75\nUURElqeQIDWTriDMzc0lqyv6KkJ4+G2gfRdD+I3e7/DY0hL9JxtWCtrb2ykUCiVHuEW0/5neElpE\nRCpTSJC+UCxwAAAgAElEQVSaCUNCuNOjP/yAxHChJN+dAJSsoJg+Ojo6SroT/DoJ4aJJ4f4MGm8g\nIrJ6q16W2cwuNbNPm9mPzWzRzK5MPf6R+P7wuL96TZbNwoeE9CZOfkZDuCdDuJpi2KXQ3NycdBf4\nsQW+WuCDQnp1Re30KCJSHWupJBSAbwN/B3yyzDmfBd4G+H+VZ9bwPrIFZFUS/DiErJCQriT4kJA+\n/MJJ5SoJ4XLNqiSIiKzNqkOCc+4B4AEAK/+v7oxz7uR6GiabX7qS4ENCuCV0ursha1+GlpaWpErg\nl2AuFApJQMjn8yWDFNPbQGs9BBGRtanVLpCvNrPjZvZ9M7vdzHbW6H2kgWWNSfDdDX4VRV9JSG8D\nDVElwQ86THc3VKoklNvpUUREVqcWAxc/S9QN8RTwE8CfAveb2SWu3LaAsiX5raDTlYSwuyFrC+h0\nJSEMCT4cZI1J8OsgtLa2llQNVEUQEVmbqocE59zdwa/fNbMngB8Crwb+odrvJ/WzXOZLVxLCgYvh\nmITluhtaW1vJ5XJJSPArKKZDQjizwctaMElERFam5lMgnXNPmdkg8EIqhIRDhw7R3d1dcl9/fz/9\n/f01bqHUiq8khOsj+PEI6Y2b/L4M4cwGPxYhn89TKBSSDZz8Msx+46as1RRh6weDgYEBBgYGSu4r\nFot1ao2IbEU1DwlmdgawC/jXSucdPnyYgwcP1ro5sgF8JcBPZ8yaApnubsjqamhpaSGXyyXjEDo7\nO+nu7qanp4eenp5kC+gwJPixB2FA2KphIStEHzlyhL6+vjq1SES2mlWHBDMrEFUF/L+855nZS4Ch\n+LieaEzCsfi8Pwf+GXiwGg2WxpXufgjHJIQzG8qtk1CpkuD3aOju7mbnzp3J7o47duxIQoKf1bBV\nQ4GIyEZbSyXhZUTdBi4+/iK+/2PA7wI/C7wV6AGeIwoH/8U5N7fu1krDCgNCWElI79UQdjeEIaFc\nJcHvz+ArCT09PclW0H5sQlYlAbZuBUFEZKOsZZ2EL1N56uQvrr05stn5i7zf/TGsJIRTIH1ICGc3\nrGRMgq8k7Nixo2QapF9pUZUEEZHq0eRxWbcwGIQ/syoJfjxCesXFrNUW/ayG9JiE3t7esmMStMPj\n6pjZO8zscTMrxsfDZvaLweM5M7vNzAbNbMzM7jGzvanXONPM7jOzCTM7ZmY3mZn+bRHZArTBk1RF\nVlBYbkyCn/64lkqCrx74aY9hJaESVRmWeAZ4F/CD+Pe3AZ8ys59zzh0FbgFeD7wZGAVuIxpzdClA\nHAbuJ+pavBg4HbgTmAXes2F/ChGpCYUEWRe/FoJf3yD86UNAekvoqakppqamSsKBc65krwa/OJJf\ndtmvtui7GPL5fLKyol8bobm5WYsmrZJz7r7UXe8xs98BLjazHwPXAG+Juxkxs7cDR83sIufco8Dl\nwIuA1zjnBoEnzOy9wJ+Z2Q3OufmN+9OISLWpJCjrEnYnhFWC8fHxksOvixB2M4QBoampqaR7IdwK\nermll7UddHWYWZOZvQXoAB4B+oi+SHzRn+OcexJ4Grgkvuti4Ik4IHgPAt3Aizei3SJSO6okyJql\nN3BKH2NjYyUBIZzRMD09XTIjwl/kW1pacM6tem8GBYS1M7OfJgoFeWAM+BXn3PfN7KXArHNuNPWU\n48D++Pb++Pf04/6xx2vTahHZCAoJsi7pxZL8MTs7WxISsioJ/uKetWNjub0ZwqAQVhEUFNbl+8BL\niKYtvxm4w8xeVeF8I5r+vBzt1SKyySkkyJqFezP46Y3huIOxsbEl1QQ/HmF6ejqpCPiLfDi+oFIl\nodJ20LJ68biB/x3/esTMLgKuA+4G2sysK1VN2Mvz1YJjwIWpl9wX/0xXGDIcIuqZCPXHh4iE6rEU\nu0KCrEu6khBOcUxXEcKAMD09TS6Xo6mpacmYhLa2tmRqox+06O/3sxjCNRHSh6xbE5ADHgPmgcuA\newHM7HzgLODh+NxHgD8ys93BuITXAUXge8u/1WFAy7GLrEQ9lmJXSJB18SEh3AbaD1xMdzeEYWF6\nejoZgwDZezWElYSsnR7T20HL6pnZHxNt7/4M0AlcBfw88Drn3KiZfRi42cyGicYrfAB4yDn3jfgl\nPkcUBu40s3cBpwE3ArdqlVWRzU8hQdYsHLgYhgRfRcia3ZDubpifny87uyGru8F3OWjRpKrZB9xB\ndHEvAv9EFBD+Pn78ELAA3ENUXXgAuNY/2Tm3aGZXAH9FVF2YAD5KtIeLiGxyCglSkV8UyR/hffPz\n80komJiYYHR0lGKxSLFYZGRkhJGREYrFIuPj48m6CGEoaG5uLtnAaceOHXR1dSV7NKQ3cCq3y6Os\nnXPuN5d5fAZ4Z3yUO+cZ4IoqN01EGoBCglSUXiApXDzJr43gKwc+JAwNDTE8PEyxWGR0dLQkJCws\nLACUVA7SIcEvvexDQkdHh5ZdFhGpA4UEqShcXtmHA3873b0wNjaWVBGGhoYYHR1Nxif4zZzCSoKf\nzRCGBF9F8FtBZ+3NoCqCiMjGUEiQZYXhwK+wGE57DCsJIyMjDA8Pc+rUKcbGxpLpkH4jp3CFxXCg\nog8J3d3dyXbQnZ2dqiSIiNSRQoJU5CsJPiTMzc0xPz/P/Px85mwG391w6tQpJiYmki2i5+bmkjEJ\nAM3NzSUbOIXdDT09PfT09CyZ4eDHJKiSICKyMRQSpKIwIPhw4Jdd9ps3laskTE5OJtUHf/hxDeld\nHrNCQtb0R1USREQ2jkKCLCsdFMKQEI5J8AMXfUiYmpoq+5rLhYTe3t5kuqOmPoqI1IdCwjYXbrKU\nJVxR0Y9B8PszpNdCSB8zMzPJksnhPgt+Y6ZwK2g/LqFQKLBjxw4KhYK2ghYRqTOFBFkiDA5+oaRw\nuWV/jI6OcurUKUZGRhgbG2NycrJkBgM8XzFIH62trZmbOKV3ekzv0aCAICKycRQSJBEuluT5WQxT\nU1PJ4ES/3LIfpFguJJhZyQDFdPdBpZDguxa0HbSISP0oJAiwNCD4n2Elwa+q6FdSHB4eThZNCkPC\n3Nwci4uLyTbQvnLgt3v23QzLVRLS3RQKCSIiG0shQZYstxzeXlhYSAYo+urB8PAwQ0NDyYJJflXF\nyclJpqenM7sb2trakjUR/OFDgg8KWSFBXQ0iIvWjkCBAaRdDuFdDWEnwMxiGhoY4efJkshZCuMOj\n725YXFxMugrSIcEPUFxJJSHcAlpBQURkYykkSCK9mZOvJITdDeEUx8HBwWQ1Rb+7Y3pMQrq7IZzu\n6EOCDwjpkBAGBEDdDSIiG0whQUpUCgm+kjA8PMzg4CAnTpwoWU3R356bm6vY3RBOc/SVhHR3Q2tr\na50/CRERUUjY4pZbB8F3KfgjXFlxfn6eU6dOMTw8zMjICKOjo4yNjTExMZFUEMLnhcHAbwOdrh50\ndnbS3d2dbODkw0I+n6etra2km0FEROpLIWGb81s++wWSZmZmmJ2dTW4PDg4yODiYzGLwOzpOT08n\nsxgWFxcBkimP/na4mmKhUKCzs7NkK+ju7u5kA6cwJCggiIg0hqbVnGxm7zazR81s1MyOm9m9ZnZ+\n6pycmd1mZoNmNmZm95jZ3uo2W6rFh4Tp6elkiqMfmHjs2DFOnDiRVBPCWQx+7IEfpBiOQWhubk7W\nQ/CrKfouhnDZ5e7ubjo7O0tCgp/yKCIi9bfaf40vBT4IvBx4LdAKfM7M2oNzbgHeCLwZeBVwOvDJ\n9TdV1mK5b+W+u2FmZiZZRXFkZITBwUGOHz+ezGLw3Q2+q8GPPwg3bSo3UDHcCjqsJPjuBlUSREQa\n06q6G5xzbwh/N7O3ASeAPuCrZtYFXAO8xTn35fictwNHzewi59yjVWm1rJsfP5BVSRgZGUmOYrGY\n/PTdDX49hLm5uWT8gP/2H/5errvBVxJ8gAi3gvb7M4iISP2td0xCD+CAofj3vvg1v+hPcM49aWZP\nA5cACgkNJgwJ4ewFP8XRL8McLsfspzrOzc0layH4YBBu4JTubkiPSfDLM/tZDeHARRERqb81hwSL\nvu7dAnzVOfe9+O79wKxzbjR1+vH4MWkA6YWT0pWE4eFhTp48yYkTJxgfH08WSgoPHxKAkoWOwg2d\nwpkNWWMSsjZ+UiVBRKRxrKeScDvwU8ArV3CuEVUcpI7S4QCe3wo6a+nlwcFBJiYmmJ6eLjl8QFhY\nWEhmM4TrIfjDL7nsA4I/Ojs76ezszNxCWtMfRUQax5pCgpndCrwBuNQ591zw0DGgzcy6UtWEvUTV\nhLIOHTpEd3d3yX39/f309/evpYkSy9q4KbwdrosQLoo0MzOTBAI/SNGvhxAOVPTBINyTwR+9vb2Z\nAxTDikF6VUVZuYGBAQYGBkruKxaLdWqNiGxFqw4JcUD4ZeDnnXNPpx5+DJgHLgPujc8/HzgLeKTS\n6x4+fJiDBw+utjmyAumAkF5R0QcFHxJ8QPBjD8qFBIDm5mZyuVxmxaCnp4edO3cm6yH4VRVbW1tL\nNm1K7/KowLAyWSH6yJEj9PX11alFIrLVrCokmNntQD9wJTBhZvvih4rOuWnn3KiZfRi42cyGgTHg\nA8BDmtlQXz4U+At8+LsPCGFI8EHBz2JYSSUhHJhYaT2EdEhQOBARaUyrrSS8g2hswZdS978duCO+\nfQhYAO4BcsADwLVrb6JUQzoY+J9hd0O6q8Ef4TlhSAiXX25vb2fHjh3JzAVfQejp6SnpbvCVhKzu\nBgUFEZHGstp1Epadm+acmwHeGR/SALK6G/xyylndDT4o+EWTfDDwP8PuBj+LIQwJO3fuZM+ePUk3\ngx+sGIaEcICiAoKISGPS3g3bRDochCEhq7vBVxFmZ2eXVB98QPB7NYSVhK6uLnbu3Mnu3bvp7u5O\ndnn0Mx3C7oYwJHgKCiIijUMhYZtID1gsFxTSYxL8WghZ0yfTYxLC7obdu3fT1dVFPp8nl8slaybk\ncrlkdoOIiDQ2hYQtIGs76PC+sFIQ3p6bm0uWXQ73ZfAzGvz2z+n1DPyRy+Xo6upKjnBmg98COlw3\nobW1VQsmiYhsIgoJW0h6TQR/26+oGE5r9D9HRkY4efIkQ0NDFItFJiYmkh0efUBobW1dcrS0tNDR\n0cGuXbuSGQw+KPipjulgoIWSREQ2F4WELaLcYknOOWZnZ5mammJiYiJZZtkfIyMjnDhxgqGhoaSa\nMD09zfz8PEAy5sB3F/gug3w+T6FQYPfu3clMhs7OTgqFQjJA0e/F4EOCgoKIyOaikLCFZC2W5Ddw\nCpdcHh0dTX6OjIwwNDRUUknwISGsJISDD30Q6OzsTCoJPT09SUgIKwk+HPiNmxQSREQ2D223t0WU\nCwiLi4vMzs4muzwWi0WGhoY4efIkx44d49ixY5w8eZLh4eGkkuC7GyCqJLS2tpLL5SgUCskiSbt2\n7WLPnj1JSOjq6ioJCX4Wgz/C3SIVEhqHmb3bzB41s1EzO25m98arpIbn5MzsNjMbNLMxM7vHzPam\nzjnTzO4zswkzO2ZmN5mZ/n0R2eRUSdhishZMSlcSfEg4efIkIyMjjI+PMzk5mXRFVKok+JAQHn7R\npKxKQjjQMdwtUhrGpcAHgW8S/Xvwp8DnzOyAc24qPucW4PXAm4FR4Dbgk/FzicPA/cBzwMXA6cCd\nwCzwng37k4hI1SkkbAHpMQhhQAgrCRMTE0lIOHHiBMePH2d4eDjZyCnc5dFXEpqammhra0vGIHR1\nddHb27ukghDOcAj3aNCqio3NOfeG8HczextwAugDvmpmXcA1wFucc1+Oz3k7cNTMLoqXW78ceBHw\nGufcIPCEmb0X+DMzu8E5N79xfyIRqSaFhC2i3BoIfrllX0kYHR1NtoH2IcFPiwx/+umPfjyB727o\n7OxMVlXctWtXMtXRT3v0iyf5mQ2y6fQQLb0+FP/eR/TvxBf9Cc65J83saeAS4FGi6sETcUDwHgT+\nCngx8PgGtFtEakAhYQtIb/nsL/L+ou9nMkxOTjI1NZWspOgDgT8/3JchnEbp10loaWlJxieEiyP5\nQKBxB5ubRX9ptwBfdc59L757PzCb2vodoq3f9wfnpLeCPx48ppAgskkpJGwBYUjwF3//c2ZmJhlz\n4ANCevvn9MZNYUjw4wjCkOCnRKbXQghnMMimdDvwU8ArV3CuEVUclrOSc0SkQSkkbAHhAMVwWWW/\naJKvJGSFhLm5uZKNnnw3hVcpJKiSsHWY2a3AG4BLnXPPBQ8dA9rMrCtVTdjL89WCY8CFqZf028in\nKwwph4Du1H398SEioYGBAQYGBkruKxaLNX1PhYQtIF1J8Csr+sNXEsp1N6Q3fVppJSGfzydLLquS\nsHnFAeGXgZ93zj2devgxYB64DLg3Pv984Czg4ficR4A/MrPdwbiE1wFF4HtUdBg4uP4/hMg20N/f\nT39/aYA+cuQIfX19NXtPhYQtwIcEX0mYnp5OQkG4ymK57oYwGKwlJGgthM3LzG4n+tp+JTBhZr4C\nUHTOTTvnRs3sw8DNZjYMjAEfAB5yzn0jPvdzRGHgTjN7F3AacCNwq3NubiP/PCJSXQoJW0C4k2NY\nSZiYmGBsbKykkuBDgg8Kfj2ErANWPibBj0tQJWHTeQfRuIEvpe5/O3BHfPsQsADcA+SAB4Br/YnO\nuUUzu4JoNsPDwATwUeD6GrZbRDaAQsIWEFYS/HoHfnGksbGxkjEJ6e4GvxV0+FqhciHB7+MQDloM\nd3hMVxLMLHO3Sqkv59yyic45NwO8Mz7KnfMMcEUVmyYiDUAhYRNI7+qYvp0VEHwVoVgsJkHBVxJ8\nBcGvhbCccKVEHxjCDZvCFRXDgJAVFEREZPNQSNgkynUJ+F0ewy4Gv2hSsVhkZGQkCQrpkKBv9iIi\nUolCwiZRbsllHxL8dEffzeB3eBweHi4ZlzAzM8Pc3NyKqwgiIrJ9KSRsIumpin5dg6zBir6SMDw8\nXLLioioJIiKyUgoJm0RYSfALH/kjrCSE3Q2+kuAHK4aDFn3AEBERKUchYZMIuxh8OPCDD8NKQtjd\n4Mck+OmO6fURVEkQEZFKFBI2gawdHsPNnMpVEnx3gz8vvVeDQoKIiFSikLBJZHU3+At/uMpiOCbB\ndzekxzKEgx5FRETKUUjYJML9GcJdHsNuhLArIb1hU9YW0OGqiv5n+rZfLMkvpuTXRgjXTkivjyAi\nIluDQsImEVYRfFDwyyv7pZb9Coq+SyErIKSDAlBykU8HgHBFxfQCSlkBQWFBRGTrWNUi+2b2bjN7\n1MxGzey4md0b7wgXnvMlM1sMjoV4ExlZo6yA4CsI6b0YfFAIKwmV9mYAlqyk6EOB35MhrCasZoVF\nERHZ3Fa7E8+lwAeBlwOvBVqBz5lZe3COA/6GaD/5/UQ7wv3h+pu6vS0XFNLdDb7LIT0OIS1dRUjv\n0VCukpCuIigoiIhsPavqbnDOvSH83czeBpwA+oCvBg9NOudOrrt1kvAhYSWVhHRA8M/31YOVVBLC\nsLBcUPCvoYAgIrK1rHdP3x6iysFQ6v6rzOykmT1hZn+SqjTIGmTNavAhwQeFSgMXlxuT4LsQ0uEg\n7GpYbjyCiIhsLWseuGjRleEW4KvOue8FD30c+BfgOeBngZuA84F/v452bmvh9MewkuCnPmaNSfAD\nF7NCQSirqyErJKwkKCg0iIhsLeuZ3XA78FPAK8I7nXN/G/z6XTM7BnzBzM51zj1V7sUOHTpEd3d3\nyX39/f309/evo4lbR7kVF8NFkspNe/TCi7e/3dzcnIw/aGtrI5fLldzesWMHhUKB9vZ28vk8bW1t\nSwYxZoUDBYXaGxgYYGBgoOS+YrFYp9aIyFa0ppBgZrcCbwAudc796zKnfx0w4IVA2ZBw+PBhDh48\nuJbmyDLKTW/0Uxzb29vLHjt37mTPnj3s3LmT7u5uduzYQXt7O62trWVnO8jGyArRR44coa+vr04t\nEpGtZtUhIQ4Ivwz8vHPu6RU85aVE4xaWCxNSI2F3QvizqamppFpQKBSS2/5nb28vO3fupLe3l+7u\nbgqFQlJRWG6mg4iIbG6rCgnxegf9wJXAhJntix8qOuemzew84NeB+4FTwEuAm4EvO+e+U71my2qY\nWTKeID17IZ/PUygU6OrqKnt0d3cnt8NKQlhBUCVBRGTrWW0l4R1EVYEvpe5/O3AHMEu0fsJ1QAF4\nBvifwB+vq5WyLmElIRyI6LsafEjo6emht7c3+dnb21tSZfAVhnw+n3Q3ZK3WqKAgIrI1rHadhIpT\nJp1zzwKvXk+DpPoqLZSUz+fZsWMHXV1d9Pb2smvXrpKjo6ODfD6/5AhDgn8PBQQRka1FezdsA1kh\nobW1lVwul1QSOjs76enpYdeuXezZs4e9e/eyZ8+eJBCkj5aWlqRy4N8jfD8REdn8FBK2gayQkMvl\nSkKC727wIWH//v3s378/c+xB+LuIiGxdCgmbRFNTU0lXQS6XS9ZG8Asr+QWUIFr/oK2tjfb29mTt\nAx8Mwt+7urrYvXt3Mnuhs7OzZAZDS0tL5pgDdS2IiGx9CgmbQFgJaGtrI5/PlyyU5FdVbGpqSsYZ\ndHR0JF0IfnEkf4SLJxUKBXbt2pWsg+ADgq8gKBCIiGxfCgmbRFhJyOVyOOeSqY2+/O8fCwPC+Ph4\nyTiC9LiCjo4Ouru7k8OvrpgVEhQURES2F4WETcBXElpaWmhra0sCgr/PjzXwAWHHjh1MTk4yOTnJ\n1NRUyTnhPgx+nYQdO3aUHL6S4N/DtyH8KSIiW59CwibhL+q+i8H/7rsNfBdDuH20/xkupBSuuujX\nTUgvxZzubvAUFEREtpf1bhVdE+lNaxpFvdqVriTk8/lkVsLnP/95uru76e3tZffu3ezbt4/TTz+d\nM844g7PPPpvzzjuPc889l3POOYezzjqLM888kzPPPJMzzjiD008/ndNOO429e/eW7M1QaUzCSgOC\n/g5Xp1HbJSLbm0LCKtSzXekxCT4kfOYzn6G7uzvZiMmHhDPPPJNzzjmH8847j3POOYezzz6bs88+\nm7POOoszzjiDF7zgBbzgBS9g//79JRs4hQMX17Mng/4OV6dR2yUi25u6GzaB8ALd3Nxc8lhzczOF\nQqEezRIRkS2uISsJIiIiUn8KCSIiIpKpEbob8gBHjx5N7igWixw5cqRuDSqnEdvViG0CtWu1qtWu\n4P+j/LpfTES2vUYICecAXH311SV39vX11aMty2rEdjVim0DtWq0qt+sc4OHlTjKzS4E/APqA04A3\nOec+nTrnfcBvAj3AQ8DvOOd+EDzeC9wKXAEsAp8ErnPOTVTlTyIiddMIIeFB4CrgR8B0fZsisunl\niQLCgys8vwB8G/g7oot7CTN7F/B7wG8ATwH/DXjQzA4452bj0z4B7AMuA9qAjwIfAq5Ov56IbC51\nDwnOuVNE/8iISHUsW0HwnHMPAA8AWPYc1+uAG51zn4nPeStwHHgTcLeZHQAuB/qcc9+Kz3kncJ+Z\n/b5z7ti6/iQiUlcauCgimczsXGA/8EV/n3NuFPg6cEl818XAsA8IsS8ADnj5BjVVRGpEIUFEytlP\ndLE/nrr/ePyYP+dE+KBzbgEYCs4RkU2q7t0NIrLpGFF4WO85wCGgO3Vff3yISGhgYGDJ6qzFYrGm\n76mQICLlHCO62O+jtJqwF/hWcM7e8Elm1gz0srQCkeEwcHD9LRXZBvr7++nvLw3QR44cqemMrYbq\nbjCza83sKTObMrOvmdmFdW7P9Wa2mDq+V4d2XGpmnzazH8dtuDLjnPeZ2XNmNmlmnzezF9a7XWb2\nkYzP7/4at+ndZvaomY2a2XEzu9fMzk+dkzOz28xs0MzGzOweM9tb7jU3sF1fSn1WC2Z2ey3bVYlz\n7imiEHBZ0MYuorEGfnDkI0CPmb00eOplROHi6xvUVBGpkYYJCWb2a8BfANcDLwUeJ5pqtbuuDYPv\nEH2T2h8fr6xDG/w0tWvJKOEG09R+G7gImCD67Nrq2a7YZyn9/GpdR74U+CDRhey1QCvwOTNrD865\nBXgj8GbgVcDpZEz/q0O7HPA3PP95nQb8YS0bZWYFM3uJmf1cfNd58e9nxr/fArzHzH7JzH4GuAN4\nFvgUgHPu+0TTLf+7mV1oZq+I/5wDmtkgsvk1UnfDIeBDzrk7AMzsHUT/kF8D3FTHds07507W8f3X\nPU2tju0CmNnIz88594bwdzN7G9HAuj7gq/E34WuAtzjnvhyf83bgqJld5Jx7tB7tCh6a3OD/3l4G\n/ANRQHFEQR3gY8A1zrmbzKyDaN2DHuArwOuDNRIAfp1oMaUvEC2mdA/Rf5Missk1RCXBzFqJ/rEM\np1o5on90Lin3vA3yk3E5/YdmdlfwDashrHCaWj29Oi6vf9/MbjeznRv8/j1EF7+h+Pc+onAcfl5P\nAk+zsZ9Xul3eVWZ20syeMLM/SVUaqs4592XnXJNzrjl1XBOcc4Nz7nTnXIdz7vJwtcX48RHn3NXO\nuW7nXK9z7recc5O1bLeIbIxGqSTsBprJnmp1wcY3J/E14G3Ak0Sl3xuAfzSzn26gJWdXMk2tXj5L\nVMZ/CvgJ4E+B+83skjgE1lRc3bgF+Kpzzo8l2Q/MxkEqtGGfV5l2AXwc+BfgOeBniSpo5wP/fiPa\nJSKS1ighoZwVTqOqDedcuLTtd8zsUaJ/xH8V+Eh9WrVidf3sAJxzYVfHd83sCeCHwKuJSty1djvw\nU6xsHMlGfl6+Xa8I73TO/W3w63fN7BjwBTM7Nx5EKCKyoRqiuwEYBBaIBmyF9rKiaVQbwzlXBP4Z\nqPnMgVUIp6mFGuqzg2S0/CAb8PmZ2a3AG4BXO+eeCx46BrTFYxNCG/J5pdr1r8uc/nWiv9tG+u9N\nRLaRhggJzrk54DFKp1pZ/PuK16GvNTPbQVQ2X+4f9w2zwmlqDcHMzgB2UePPL74Q/zLwGufc06mH\nHwPmKf28zgfOIprOV692ZXkpUXWjYf57E5HtpZG6G24GPmZmjwGPEs126CDaUa4uzOz9wGeIuhhe\nAKUyqPEAAAr8SURBVPxXogvMQKXn1aAdBaJvk34GwXlm9hJgyDn3DM9PU/sB0W6aNxJMU6tHu+Lj\neqIxCcfi8/6cqBKz0h0K19Km24mmWV4JTJiZr7AUnXPTzrlRM/swcLOZDQNjwAeAh2o1s2El7TKz\n84hmCdwPnAJeQvT/xJedc9+pVbtERCppmJDgnLs7XhPhfUSl828Dl9d5+uEZRDtU7gJOEk1Vuzje\nuXIjVWOa2ka363eJBt+9NW7Tc0Th4L/ElaNaeUfcli+l7n870Rx/iALoAtFUvRzRNM5ra9imlbRr\nlmj9hOuI1p94BvifwB/XuF0iImU1TEgAcM7dTjSoqyE45xpiAfl4Pn/FriHn3A1Esy82zAra9Ysb\n1RbPObdsF5pzbgZ4Z3xsiOXa5Zx7lmhAp4hIw2iIMQkiIiLSeBQSREREJJNCgoiIiGRSSBAREZFM\nCgkiIiKSSSFBREREMikkiIiISCaFBBEREcmkkCAiIiKZFBJEREQkk0KCiIiIZFJIEBERkUwKCSIi\nIpJJIUFEREQyKSSIiIhIJoUEERERydRS7waIiIg0qqeffprBwcGK5+zevZuzzjprg1q0sRQSRERE\nMjz99NNccMEBpqcnK56Xz3fw5JNHt2RQUEgQERHJMDg4GAeEu4ADZc46yvT01QwODiokiIiIbD8H\ngIP1bkRdaOCiiIiIZFJIEBERkUwKCSIiIpJJIUFEREQyKSSIiIhIJoUEEdn2BgYG6t2EhNpSXmO1\np5HaUjuaAikiVWFm1wK/D+wHHgfe6Zz7Rn1btTIDAwO84hWvaIiV9QYGBujv76/pe6xUvdpSbpXD\nv/7rv+aCCy4AGmGVwwGgMf6eakkhQUTWzcx+DfgL4D8AjwKHgAfN7HznXOUrbwUjIyMcOXKk4jnV\nuFhMTU2taGW9XC7PJz95D6eddlpN27OdLbfKYV9fH7CyVQ63+5LK1aCQICLVcAj4kHPuDgAzewfw\nRuAa4Ka1vKBz89x66+3ccsstFc+rxpK4s7OzK1hZ7yvMzPxHrrjiipq3ZzurvMrhIeAwK1nlcKVL\nKlcKfkePHl3LH2FLUUgQkXUxs1agD/gTf59zzpnZF4BL1v7Ki8zPz7KSJXG/8pWvcOBAuXNW822x\n0sp6R4HFDWvPSr4Fz8zMkMvl1n3ORn6bXu7P9fyFOevvorvkvkoX8aNHj1Yt+G1nCgkisl67gWbg\neOr+48AFZZ6Tj378L+CbmScsLEzHt56q8NbfAoyrr766YgNbW3O8//1/zu7duzMfP37cN/1+ojCQ\n5aENac+zzz7LX/7lX/IHf/CfmJubznh2qIkouKzvnHLtefbZZ/n4xz8evUpTE4uLlV9nuXMGBwdX\n+OeC7L+LZ4GPs9LPOVLp7+tJos/m/wSyupCeAD61TFui169X1SF433wtXt+cc7V4XRHZJszsNODH\nwCXOua8H998EvNI5928ynvPrRP/Cikh1XOWc+0S1X1SVBBFZr0FgAdiXun8vS6sL3oPAVcCPgJV8\nrRSRbHngHKL/p6pOlQQRWTcz+xrwdefcdfHvBjwNfMA59/66Nk5E1kyVBBGphpuBj5nZYzw/BbID\n+Gg9GyUi66OQICLr5py728x2A+8j6nb4NnC5c+5kfVsmIuuh7gYRERHJpL0bREREJJNCgoiIiGRS\nSBCRDWVm15rZU2Y2ZWZfM7MLN+A9rzezxdTxveDxnJndZmaDZjZmZveY2d4qvv+lZvZpM/tx/N5X\nZpzzPjN7zswmzezzZvbC1OO9ZvZxMyua2bCZ/a2ZFardFjP7SMZndX+N2vJuM3vUzEbN7LiZ3Wtm\n56fOWfbvxszONLP7zGzCzI6Z2U1mtqrr2wrb8qXU57JgZrfXoC3vMLPH48+3aGYPm9kvbvRnAgoJ\nIrKBgo2grgdeSrRb5IPxoMda+w7RoMr98fHK4LFbiPaaeDPwKuB04JNVfO8C0WDOa4ElA8HM7F3A\n7wG/DVwETBB9Lm3BaZ8gWl/4sritrwI+VO22xD5L6WeV3u6wWm25FPgg8HLgtUAr8Dkzaw/Oqfh3\nE1/47icaiH8x8BvA24gG0Va7LQ74G57/bE4D/rAGbXkGeBfRcud9wN8DnzIzv770Rn0m4JzToUOH\njg05gK8Bfxn8bkTr2/5hjd/3euBImce6gBngV4L7LiBar/eiGrRlEbgydd9zwKFUm6aAX41/PxA/\n76XBOZcD88D+KrflI8D/qvCcF9WiLfHr7I5f+5Ur/bsBXg/MAbuDc34bGAZaqtWW+L5/AG6u8Jya\ntCV+nVPA2zf6M1ElQUQ2RLAR1Bf9fS7612udG0Gt2E/GJfYfmtldZnZmfH8f0TeusF1PEi0GVfN2\nmdm5RN9Kw/cfBb4evP/FwLBz7lvBU79A9M325TVo1qvjkvv3zex2M9sZPHZJDdvSE7/OUPz7Sv5u\nLgaecKVbkj9ItBvUi6vYFu8qMztpZk+Y2Z+kKg1Vb4uZNZnZW4jWHXmEDf5MFBJEZKNU2ghqf43f\n+2tE5dbLgXcA5wL/GPej7wdm4wvzRreL+D0clT+X/cCJ8EHn3ALRBazabfws8FbgF4hK6T8P3G9m\nVsu2xK9/C/BV55wfL7KSv5v9ZH92sMb2lGkLRPuNXA28mmjX0/8DuDN4vGptMbOfNrMxoqrB7USV\ng++zwZ+JFlMSkXozyveNV4VzLlzX/jtm9ijwL8CvUn7viJq3axkref+qt9E5d3fw63fN7Angh0QX\nxn+oYVtuB36K0rEi632vtbbHt+UVJS/m3N8Gv37XzI4BXzSzc51zlbabXEtbvg+8hKii8WbgDjN7\nVYXza/KZqJIgIhtlLRtB1YRzrgj8M/BC4BjQZmZddWrXMaJ/4Ct9Lsfi3xNm1gz0UuM2xhe/QaLP\nqiZtMbNbgTcAr3bOPRc8tJK/m2Ms/ez876tuT6ot/7rM6X7X0/CzqUpbnHPzzrn/7Zw74pz7z0SD\nfK9jgz8ThQQR2RDOuTngMaIR8UBS1r0MeHgj22JmO4CfIBow+BjRoLuwXecDZxH1AddUfBE+lnr/\nLqL+ff+5PAL0mNlLg6deRhQuvk4NmdkZwC7AXzCr2pb4ovzLwGucc0+nHq70dxN+Nj+TmiHzOqAI\nhF0F621LlpcSfTMPP5uqtCVDE5Bjgz+Tqo7a1aFDh45KB1F5f4qoz/tFRNPmTgF7avy+7yeaKnY2\n8G+AzxN9o9oVP3478BRRSb0PeAj4ShXfv0BUOv45olHo/1f8+5nx438Yfw6/BPwM8P8A/x/QFrzG\n/cA3gQuJyuBPAndWsy3xYzcRBZSz4wvRN4GjQGsN2nI70Yj7S4m+6fojnzqn7N8N0cXzcaKxFD9L\nNO7kOHBjNdsCnAe8BzgYfzZXAj8A/r4Gbfljom6Xs4GfBv6UKBj8wkZ+Js45hQQdOnRs7AH8LvAj\norDwCPCyDXjP/7+9O0TJIIjDOPzDoojBZLII3sObeBarJzAoJtFgNYjJa1i8gIJBUYt8ht0gH1OE\nT9PzwKZddv/MhHl3mGGumrZafjStAr+s9n7cX2/aI/9cvVbX1c4Kv38wD8hfS9f5j2eOmmY23ptW\nou8vvWO7umj6G3ypTqvNVdZSbVS3TTMbn9VjddJSiFthLaM6vqrD3/RNU8C5qd7mwfC4WltlLdVu\ndV89zX30MA/eW39Qy9nc9h9zX9w1B4T/bJPFYuGAJwBgzJoEAGBISAAAhoQEAGBISAAAhoQEAGBI\nSAAAhoQEAGBISAAAhoQEAGBISAAAhoQEAGDoG5E93Be1HK29AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f84680e3fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"# We'll show the image and its pixel value histogram side-by-side.\n",
"_, (ax1, ax2) = plt.subplots(1, 2)\n",
"\n",
"# To interpret the values as a 28x28 image, we need to reshape\n",
"# the numpy array, which is one dimensional.\n",
"ax1.imshow(image.reshape(28, 28), cmap=plt.cm.Greys);\n",
"\n",
"ax2.hist(image, bins=20, range=[0,255]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "weVoVR-nN0cN"
},
"source": [
"The large number of 0 values correspond to the background of the image, another large mass of value 255 is black, and a mix of grayscale transition values in between.\n",
"\n",
"Both the image and histogram look sensible. But, it's good practice when training image models to normalize values to be centered around 0.\n",
"\n",
"We'll do that next. The normalization code is fairly short, and it may be tempting to assume we haven't made mistakes, but we'll double-check by looking at the rendered input and histogram again. Malformed inputs are a surprisingly common source of errors when developing new models."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.895369",
"start_time": "2016-09-16T14:49:22.527595"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 531,
"status": "ok",
"timestamp": 1446749126656,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "jc1xCZXHNKVp",
"outputId": "bd45b3dd-438b-41db-ea8f-d202d4a09e63"
},
"outputs": [
{
"data": {
"image/png": 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55ufnmZubS+77MQeluhX8YEQ/W6GzszM1IFGhQERE4UAawEqVg2y3wvHjx5md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\nmpHQ0tKSO6ZAXQgiIluXwsEmEH7zb2t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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8444471358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Let's convert the uint8 image to 32 bit floats and rescale \n",
"# the values to be centered around 0, between [-0.5, 0.5]. \n",
"# \n",
"# We again plot the image and histogram to check that we \n",
"# haven't mangled the data.\n",
"scaled = image.astype(numpy.float32)\n",
"scaled = (scaled - (255 / 2.0)) / 255\n",
"_, (ax1, ax2) = plt.subplots(1, 2)\n",
"ax1.imshow(scaled.reshape(28, 28), cmap=plt.cm.Greys);\n",
"ax2.hist(scaled, bins=20, range=[-0.5, 0.5]);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PlqlwkX-O0Hd"
},
"source": [
"Great -- we've retained the correct image data while properly rescaling to the range [-0.5, 0.5].\n",
"\n",
"## Reading the labels\n",
"\n",
"Let's next unpack the test label data. The format here is similar: a magic number followed by a count followed by the labels as `uint8` values. In more detail:\n",
"\n",
" [offset] [type] [value] [description] \n",
" 0000 32 bit integer 0x00000801(2049) magic number (MSB first) \n",
" 0004 32 bit integer 10000 number of items \n",
" 0008 unsigned byte ?? label \n",
" 0009 unsigned byte ?? label \n",
" ........ \n",
" xxxx unsigned byte ?? label\n",
"\n",
"As with the image data, let's read the first test set value to sanity check our input path. We'll expect a 7."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:22.925176",
"start_time": "2016-09-16T14:49:22.897739"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 90,
"status": "ok",
"timestamp": 1446749126903,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "d8zv9yZzQOnV",
"outputId": "ad203b2c-f095-4035-e0cd-7869c078da3d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"magic number 2049\n",
"label count 10000\n",
"First label: 7\n"
]
}
],
"source": [
"with gzip.open(test_labels_filename) as f:\n",
" # Print the header fields.\n",
" for field in ['magic number', 'label count']:\n",
" print(field, struct.unpack('>i', f.read(4))[0])\n",
"\n",
" print('First label:', struct.unpack('B', f.read(1))[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "zAGrQSXCQtIm"
},
"source": [
"Indeed, the first label of the test set is 7.\n",
"\n",
"## Forming the training, testing, and validation data sets\n",
"\n",
"Now that we understand how to read a single element, we can read a much larger set that we'll use for training, testing, and validation.\n",
"\n",
"### Image data\n",
"\n",
"The code below is a generalization of our prototyping above that reads the entire test and training data set."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.525119",
"start_time": "2016-09-16T14:49:22.928289"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 734,
"status": "ok",
"timestamp": 1446749128718,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "ofFZ5oJeRMDA",
"outputId": "ff2de90b-aed9-4ce5-db8c-9123496186b1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/mnist-data/train-images-idx3-ubyte.gz\n",
"Extracting /tmp/mnist-data/t10k-images-idx3-ubyte.gz\n"
]
}
],
"source": [
"IMAGE_SIZE = 28\n",
"PIXEL_DEPTH = 255\n",
"\n",
"def extract_data(filename, num_images):\n",
" \"\"\"Extract the images into a 4D tensor [image index, y, x, channels].\n",
" \n",
" For MNIST data, the number of channels is always 1.\n",
"\n",
" Values are rescaled from [0, 255] down to [-0.5, 0.5].\n",
" \"\"\"\n",
" print('Extracting', filename)\n",
" with gzip.open(filename) as bytestream:\n",
" # Skip the magic number and dimensions; we know these values.\n",
" bytestream.read(16)\n",
"\n",
" buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)\n",
" data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)\n",
" data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\n",
" data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)\n",
" return data\n",
"\n",
"train_data = extract_data(train_data_filename, 60000)\n",
"test_data = extract_data(test_data_filename, 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "0x4rwXxUR96O"
},
"source": [
"A crucial difference here is how we `reshape` the array of pixel values. Instead of one image that's 28x28, we now have a set of 60,000 images, each one being 28x28. We also include a number of channels, which for grayscale images as we have here is 1.\n",
"\n",
"Let's make sure we've got the reshaping parameters right by inspecting the dimensions and the first two images. (Again, mangled input is a very common source of errors.)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.829853",
"start_time": "2016-09-16T14:49:23.527283"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{},
{}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 400,
"status": "ok",
"timestamp": 1446749129657,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "0AwSo8mlSja_",
"outputId": "11490c39-7c67-4fe5-982c-ca8278294d96"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data shape (60000, 28, 28, 1)\n"
]
},
{
"data": {
"image/png": 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4Yn2gl5aWsLq6ivPz86Q5f3V1FZ1OJ8/tVwGj4MfIlg+1+upUyCngGxsbOfEy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8444461630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print('Training data shape', train_data.shape)\n",
"_, (ax1, ax2) = plt.subplots(1, 2)\n",
"ax1.imshow(train_data[0].reshape(28, 28), cmap=plt.cm.Greys);\n",
"ax2.imshow(train_data[1].reshape(28, 28), cmap=plt.cm.Greys);"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "cwBhQ3ouTQcW"
},
"source": [
"Looks good. Now we know how to index our full set of training and test images."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PBCB9aYxRvBi"
},
"source": [
"### Label data\n",
"\n",
"Let's move on to loading the full set of labels. As is typical in classification problems, we'll convert our input labels into a [1-hot](https://en.wikipedia.org/wiki/One-hot) encoding over a length 10 vector corresponding to 10 digits. The vector [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], for example, would correspond to the digit 1."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.854577",
"start_time": "2016-09-16T14:49:23.831545"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 191,
"status": "ok",
"timestamp": 1446749131421,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "9pK1j2WlRwY9",
"outputId": "1ca31655-e14f-405a-b266-6a6c78827af5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting /tmp/mnist-data/train-labels-idx1-ubyte.gz\n",
"Extracting /tmp/mnist-data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"NUM_LABELS = 10\n",
"\n",
"def extract_labels(filename, num_images):\n",
" \"\"\"Extract the labels into a 1-hot matrix [image index, label index].\"\"\"\n",
" print('Extracting', filename)\n",
" with gzip.open(filename) as bytestream:\n",
" # Skip the magic number and count; we know these values.\n",
" bytestream.read(8)\n",
" buf = bytestream.read(1 * num_images)\n",
" labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n",
" # Convert to dense 1-hot representation.\n",
" return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)\n",
"\n",
"train_labels = extract_labels(train_labels_filename, 60000)\n",
"test_labels = extract_labels(test_labels_filename, 10000)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hb3Vaq72UUxW"
},
"source": [
"As with our image data, we'll double-check that our 1-hot encoding of the first few values matches our expectations."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:23.864350",
"start_time": "2016-09-16T14:49:23.857177"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 127,
"status": "ok",
"timestamp": 1446749132853,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "uEBID71nUVj1",
"outputId": "3f318310-18dd-49ed-9943-47b4aae7ee69"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training labels shape (60000, 10)\n",
"First label vector [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
"Second label vector [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
]
}
],
"source": [
"print('Training labels shape', train_labels.shape)\n",
"print('First label vector', train_labels[0])\n",
"print('Second label vector', train_labels[1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5EwtEhxRUneF"
},
"source": [
"The 1-hot encoding looks reasonable.\n",
"\n",
"### Segmenting data into training, test, and validation\n",
"\n",
"The final step in preparing our data is to split it into three sets: training, test, and validation. This isn't the format of the original data set, so we'll take a small slice of the training data and treat that as our validation set."
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Validation shape (5000, 28, 28, 1)\n",
"Train size 55000\n"
]
}
],
"source": [
"VALIDATION_SIZE = 5000\n",
"\n",
"validation_data = train_data[:VALIDATION_SIZE, :, :, :]\n",
"validation_labels = train_labels[:VALIDATION_SIZE]\n",
"train_data = train_data[VALIDATION_SIZE:, :, :, :]\n",
"train_labels = train_labels[VALIDATION_SIZE:]\n",
"\n",
"train_size = train_labels.shape[0]\n",
"\n",
"print('Validation shape', validation_data.shape)\n",
"print('Train size', train_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "1JFhEH8EVj4O"
},
"source": [
"# Defining the model\n",
"\n",
"Now that we've prepared our data, we're ready to define our model.\n",
"\n",
"The comments describe the architecture, which fairly typical of models that process image data. The raw input passes through several [convolution](https://en.wikipedia.org/wiki/Convolutional_neural_network#Convolutional_layer) and [max pooling](https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer) layers with [rectified linear](https://en.wikipedia.org/wiki/Convolutional_neural_network#ReLU_layer) activations before several fully connected layers and a [softmax](https://en.wikipedia.org/wiki/Convolutional_neural_network#Loss_layer) loss for predicting the output class. During training, we use [dropout](https://en.wikipedia.org/wiki/Convolutional_neural_network#Dropout_method).\n",
"\n",
"We'll separate our model definition into three steps:\n",
"\n",
"1. Defining the variables that will hold the trainable weights.\n",
"1. Defining the basic model graph structure described above. And,\n",
"1. Stamping out several copies of the model graph for training, testing, and validation.\n",
"\n",
"We'll start with the variables."
]
},
{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"# We'll bundle groups of examples during training for efficiency.\n",
"# This defines the size of the batch.\n",
"BATCH_SIZE = 60\n",
"# We have only one channel in our grayscale images.\n",
"NUM_CHANNELS = 1\n",
"# The random seed that defines initialization.\n",
"SEED = 42\n",
"\n",
"# This is where training samples and labels are fed to the graph.\n",
"# These placeholder nodes will be fed a batch of training data at each\n",
"# training step, which we'll write once we define the graph structure.\n",
"train_data_node = tf.placeholder(\n",
" tf.float32,\n",
" shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\n",
"train_labels_node = tf.placeholder(tf.float32,\n",
" shape=(BATCH_SIZE, NUM_LABELS))\n",
"\n",
"# For the validation and test data, we'll just hold the entire dataset in\n",
"# one constant node.\n",
"validation_data_node = tf.constant(validation_data)\n",
"test_data_node = tf.constant(test_data)\n",
"\n",
"# The variables below hold all the trainable weights. For each, the\n",
"# parameter defines how the variables will be initialized.\n",
"conv1_weights = tf.Variable(\n",
" tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32.\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"conv1_biases = tf.Variable(tf.zeros([32]))\n",
"conv2_weights = tf.Variable(\n",
" tf.truncated_normal([5, 5, 32, 64],\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))\n",
"fc1_weights = tf.Variable( # fully connected, depth 512.\n",
" tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))\n",
"fc2_weights = tf.Variable(\n",
" tf.truncated_normal([512, NUM_LABELS],\n",
" stddev=0.1,\n",
" seed=SEED))\n",
"fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "QHB_u04Z4HO6"
},
"source": [
"Now that we've defined the variables to be trained, we're ready to wire them together into a TensorFlow graph.\n",
"\n",
"We'll define a helper to do this, `model`, which will return copies of the graph suitable for training and testing. Note the `train` argument, which controls whether or not dropout is used in the hidden layer. (We want to use dropout only during training.)"
]
},
{
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"def model(data, train=False):\n",
" \"\"\"The Model definition.\"\"\"\n",
" # 2D convolution, with 'SAME' padding (i.e. the output feature map has\n",
" # the same size as the input). Note that {strides} is a 4D array whose\n",
" # shape matches the data layout: [image index, y, x, depth].\n",
" conv = tf.nn.conv2d(data,\n",
" conv1_weights,\n",
" strides=[1, 1, 1, 1],\n",
" padding='SAME')\n",
"\n",
" # Bias and rectified linear non-linearity.\n",
" relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))\n",
"\n",
" # Max pooling. The kernel size spec ksize also follows the layout of\n",
" # the data. Here we have a pooling window of 2, and a stride of 2.\n",
" pool = tf.nn.max_pool(relu,\n",
" ksize=[1, 2, 2, 1],\n",
" strides=[1, 2, 2, 1],\n",
" padding='SAME')\n",
" conv = tf.nn.conv2d(pool,\n",
" conv2_weights,\n",
" strides=[1, 1, 1, 1],\n",
" padding='SAME')\n",
" relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))\n",
" pool = tf.nn.max_pool(relu,\n",
" ksize=[1, 2, 2, 1],\n",
" strides=[1, 2, 2, 1],\n",
" padding='SAME')\n",
"\n",
" # Reshape the feature map cuboid into a 2D matrix to feed it to the\n",
" # fully connected layers.\n",
" pool_shape = pool.get_shape().as_list()\n",
" reshape = tf.reshape(\n",
" pool,\n",
" [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\n",
" \n",
" # Fully connected layer. Note that the '+' operation automatically\n",
" # broadcasts the biases.\n",
" hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)\n",
"\n",
" # Add a 50% dropout during training only. Dropout also scales\n",
" # activations such that no rescaling is needed at evaluation time.\n",
" if train:\n",
" hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)\n",
" return tf.matmul(hidden, fc2_weights) + fc2_biases\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7bvEtt8C4fLC"
},
"source": [
"Having defined the basic structure of the graph, we're ready to stamp out multiple copies for training, testing, and validation.\n",
"\n",
"Here, we'll do some customizations depending on which graph we're constructing. `train_prediction` holds the training graph, for which we use cross-entropy loss and weight regularization. We'll adjust the learning rate during training -- that's handled by the `exponential_decay` operation, which is itself an argument to the `MomentumOptimizer` that performs the actual training.\n",
"\n",
"The vaildation and prediction graphs are much simpler the generate -- we need only create copies of the model with the validation and test inputs and a softmax classifier as the output."
]
},
{
"cell_type": "code",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"# Training computation: logits + cross-entropy loss.\n",
"logits = model(train_data_node, True)\n",
"loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n",
" logits, train_labels_node))\n",
"\n",
"# L2 regularization for the fully connected parameters.\n",
"regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +\n",
" tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))\n",
"# Add the regularization term to the loss.\n",
"loss += 5e-4 * regularizers\n",
"\n",
"# Optimizer: set up a variable that's incremented once per batch and\n",
"# controls the learning rate decay.\n",
"batch = tf.Variable(0)\n",
"# Decay once per epoch, using an exponential schedule starting at 0.01.\n",
"learning_rate = tf.train.exponential_decay(\n",
" 0.01, # Base learning rate.\n",
" batch * BATCH_SIZE, # Current index into the dataset.\n",
" train_size, # Decay step.\n",
" 0.95, # Decay rate.\n",
" staircase=True)\n",
"# Use simple momentum for the optimization.\n",
"optimizer = tf.train.MomentumOptimizer(learning_rate,\n",
" 0.9).minimize(loss,\n",
" global_step=batch)\n",
"\n",
"# Predictions for the minibatch, validation set and test set.\n",
"train_prediction = tf.nn.softmax(logits)\n",
"# We'll compute them only once in a while by calling their {eval()} method.\n",
"validation_prediction = tf.nn.softmax(model(validation_data_node))\n",
"test_prediction = tf.nn.softmax(model(test_data_node))\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4T21uZJq5UfH"
},
"source": [
"# Training and visualizing results\n",
"\n",
"Now that we have the training, test, and validation graphs, we're ready to actually go through the training loop and periodically evaluate loss and error.\n",
"\n",
"All of these operations take place in the context of a session. In Python, we'd write something like:\n",
"\n",
" with tf.Session() as s:\n",
" ...training / test / evaluation loop...\n",
" \n",
"But, here, we'll want to keep the session open so we can poke at values as we work out the details of training. The TensorFlow API includes a function for this, `InteractiveSession`.\n",
"\n",
"We'll start by creating a session and initializing the varibles we defined above."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:29.357483",
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"source": [
"# Create a new interactive session that we'll use in\n",
"# subsequent code cells.\n",
"s = tf.InteractiveSession()\n",
"\n",
"# Use our newly created session as the default for \n",
"# subsequent operations.\n",
"s.as_default()\n",
"\n",
"# Initialize all the variables we defined above.\n",
"tf.global_variables_initializer().run()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hcG8H-Ka6_mw"
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"source": [
"Now we're ready to perform operations on the graph. Let's start with one round of training. We're going to organize our training steps into batches for efficiency; i.e., training using a small set of examples at each step rather than a single example."
]
},
{
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"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"BATCH_SIZE = 60\n",
"\n",
"# Grab the first BATCH_SIZE examples and labels.\n",
"batch_data = train_data[:BATCH_SIZE, :, :, :]\n",
"batch_labels = train_labels[:BATCH_SIZE]\n",
"\n",
"# This dictionary maps the batch data (as a numpy array) to the\n",
"# node in the graph it should be fed to.\n",
"feed_dict = {train_data_node: batch_data,\n",
" train_labels_node: batch_labels}\n",
"\n",
"# Run the graph and fetch some of the nodes.\n",
"_, l, lr, predictions = s.run(\n",
" [optimizer, loss, learning_rate, train_prediction],\n",
" feed_dict=feed_dict)\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7bL4-RNm_K-B"
},
"source": [
"Let's take a look at the predictions. How did we do? Recall that the output will be probabilities over the possible classes, so let's look at those probabilities."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"[ 2.25393116e-04 4.76219611e-05 1.66867452e-03 5.67827519e-05\n",
" 6.03432178e-01 4.34969068e-02 2.19316553e-05 1.41286102e-04\n",
" 1.54903100e-05 3.50893795e-01]\n"
]
}
],
"source": [
"print(predictions[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "X5MgraJb_eQZ"
},
"source": [
"As expected without training, the predictions are all noise. Let's write a scoring function that picks the class with the maximum probability and compares with the example's label. We'll start by converting the probability vectors returned by the softmax into predictions we can match against the labels."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
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"collapsed": false,
"executionInfo": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First prediction 4\n",
"(60, 10)\n",
"All predictions [4 4 2 7 7 7 7 7 7 7 7 7 0 8 9 0 7 7 0 7 4 0 5 0 9 9 7 0 7 4 7 7 7 0 7 7 9\n",
" 7 9 9 0 7 7 7 2 7 0 7 2 9 9 9 9 9 0 7 9 4 8 7]\n"
]
}
],
"source": [
"# The highest probability in the first entry.\n",
"print('First prediction', numpy.argmax(predictions[0]))\n",
"\n",
"# But, predictions is actually a list of BATCH_SIZE probability vectors.\n",
"print(predictions.shape)\n",
"\n",
"# So, we'll take the highest probability for each vector.\n",
"print('All predictions', numpy.argmax(predictions, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8pMCIZ3_C2ni"
},
"source": [
"Next, we can do the same thing for our labels -- using `argmax` to convert our 1-hot encoding into a digit class."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
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},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 232,
"status": "ok",
"timestamp": 1446750498351,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "kZWp4T0JDDUe",
"outputId": "47b588cd-bc82-45c3-a5d0-8d84dc27a3be"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Batch labels [7 3 4 6 1 8 1 0 9 8 0 3 1 2 7 0 2 9 6 0 1 6 7 1 9 7 6 5 5 8 8 3 4 4 8 7 3\n",
" 6 4 6 6 3 8 8 9 9 4 4 0 7 8 1 0 0 1 8 5 7 1 7]\n"
]
}
],
"source": [
"print('Batch labels', numpy.argmax(batch_labels, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "bi5Z6whtDiht"
},
"source": [
"Now we can compare the predicted and label classes to compute the error rate and confusion matrix for this batch."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:29.841313",
"start_time": "2016-09-16T14:49:29.618274"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
},
{
"item_id": 2
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 330,
"status": "ok",
"timestamp": 1446751307304,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "U4hrLW4CDtQB",
"outputId": "720494a3-cbf9-4687-9d94-e64a33fdd78f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.06666666666666667\n"
]
},
{
"data": {
"image/png": 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0RauALaWsA/5L7yZJ2gHnTiWpEgNWkioxYCWpEgNWkioxYCWpEgNWkioxYCWpEgNWkiox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\n9OY6UgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841ece8128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"correct = numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(batch_labels, 1))\n",
"total = predictions.shape[0]\n",
"\n",
"print(float(correct) / float(total))\n",
"\n",
"confusions = numpy.zeros([10, 10], numpy.float32)\n",
"bundled = zip(numpy.argmax(predictions, 1), numpy.argmax(batch_labels, 1))\n",
"for predicted, actual in bundled:\n",
" confusions[predicted, actual] += 1\n",
"\n",
"plt.grid(False)\n",
"plt.xticks(numpy.arange(NUM_LABELS))\n",
"plt.yticks(numpy.arange(NUM_LABELS))\n",
"plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "iZmx_9DiDXQ3"
},
"source": [
"Now let's wrap this up into our scoring function."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:49:29.857607",
"start_time": "2016-09-16T14:49:29.843904"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 178,
"status": "ok",
"timestamp": 1446751995007,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "DPJie7bPDaLa",
"outputId": "a06c64ed-f95f-416f-a621-44cccdaba0f8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Done\n"
]
}
],
"source": [
"def error_rate(predictions, labels):\n",
" \"\"\"Return the error rate and confusions.\"\"\"\n",
" correct = numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(labels, 1))\n",
" total = predictions.shape[0]\n",
"\n",
" error = 100.0 - (100 * float(correct) / float(total))\n",
"\n",
" confusions = numpy.zeros([10, 10], numpy.float32)\n",
" bundled = zip(numpy.argmax(predictions, 1), numpy.argmax(labels, 1))\n",
" for predicted, actual in bundled:\n",
" confusions[predicted, actual] += 1\n",
" \n",
" return error, confusions\n",
"\n",
"print('Done')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "sLv22cjeB5Rd"
},
"source": [
"We'll need to train for some time to actually see useful predicted values. Let's define a loop that will go through our data. We'll print the loss and error periodically.\n",
"\n",
"Here, we want to iterate over the entire data set rather than just the first batch, so we'll need to slice the data to that end.\n",
"\n",
"(One pass through our training set will take some time on a CPU, so be patient if you are executing this notebook.)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:53:26.998313",
"start_time": "2016-09-16T14:49:29.860079"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
}
},
"colab_type": "code",
"collapsed": false,
"id": "4cgKJrS1_vej"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 0 of 916\n",
"Mini-batch loss: 7.71249 Error: 91.66667 Learning rate: 0.01000\n",
"Validation error: 88.9%\n",
"Step 100 of 916\n",
"Mini-batch loss: 3.28715 Error: 8.33333 Learning rate: 0.01000\n",
"Validation error: 5.8%\n",
"Step 200 of 916\n",
"Mini-batch loss: 3.30949 Error: 8.33333 Learning rate: 0.01000\n",
"Validation error: 3.6%\n",
"Step 300 of 916\n",
"Mini-batch loss: 3.15385 Error: 3.33333 Learning rate: 0.01000\n",
"Validation error: 3.1%\n",
"Step 400 of 916\n",
"Mini-batch loss: 3.08212 Error: 1.66667 Learning rate: 0.01000\n",
"Validation error: 2.7%\n",
"Step 500 of 916\n",
"Mini-batch loss: 3.02827 Error: 1.66667 Learning rate: 0.01000\n",
"Validation error: 2.2%\n",
"Step 600 of 916\n",
"Mini-batch loss: 3.03260 Error: 5.00000 Learning rate: 0.01000\n",
"Validation error: 1.9%\n",
"Step 700 of 916\n",
"Mini-batch loss: 3.16032 Error: 6.66667 Learning rate: 0.01000\n",
"Validation error: 2.2%\n",
"Step 800 of 916\n",
"Mini-batch loss: 3.06246 Error: 3.33333 Learning rate: 0.01000\n",
"Validation error: 2.0%\n",
"Step 900 of 916\n",
"Mini-batch loss: 2.85098 Error: 0.00000 Learning rate: 0.01000\n",
"Validation error: 1.9%\n"
]
}
],
"source": [
"# Train over the first 1/4th of our training set.\n",
"steps = train_size // BATCH_SIZE\n",
"for step in range(steps):\n",
" # Compute the offset of the current minibatch in the data.\n",
" # Note that we could use better randomization across epochs.\n",
" offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)\n",
" batch_data = train_data[offset:(offset + BATCH_SIZE), :, :, :]\n",
" batch_labels = train_labels[offset:(offset + BATCH_SIZE)]\n",
" # This dictionary maps the batch data (as a numpy array) to the\n",
" # node in the graph it should be fed to.\n",
" feed_dict = {train_data_node: batch_data,\n",
" train_labels_node: batch_labels}\n",
" # Run the graph and fetch some of the nodes.\n",
" _, l, lr, predictions = s.run(\n",
" [optimizer, loss, learning_rate, train_prediction],\n",
" feed_dict=feed_dict)\n",
" \n",
" # Print out the loss periodically.\n",
" if step % 100 == 0:\n",
" error, _ = error_rate(predictions, batch_labels)\n",
" print('Step %d of %d' % (step, steps))\n",
" print('Mini-batch loss: %.5f Error: %.5f Learning rate: %.5f' % (l, error, lr))\n",
" print('Validation error: %.1f%%' % error_rate(\n",
" validation_prediction.eval(), validation_labels)[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "J4LskgGXIDAm"
},
"source": [
"The error seems to have gone down. Let's evaluate the results using the test set.\n",
"\n",
"To help identify rare mispredictions, we'll include the raw count of each (prediction, label) pair in the confusion matrix."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:55:10.942063",
"start_time": "2016-09-16T14:53:26.999971"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
},
{
"item_id": 2
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 436,
"status": "ok",
"timestamp": 1446752934104,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "6Yh1jGFuIKc_",
"outputId": "4e411de4-0fe2-451b-e4ca-8a4854f0db89"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test error: 2.0%\n"
]
},
{
"data": {
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AvwLTrl2whQCO7IPjh2HgZJgXBTXqwuN9oXz+Htf74+DQHsjOhBYR8PoyOJdm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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841eb30f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test_error, confusions = error_rate(test_prediction.eval(), test_labels)\n",
"print('Test error: %.1f%%' % test_error)\n",
"\n",
"plt.xlabel('Actual')\n",
"plt.ylabel('Predicted')\n",
"plt.grid(False)\n",
"plt.xticks(numpy.arange(NUM_LABELS))\n",
"plt.yticks(numpy.arange(NUM_LABELS))\n",
"plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');\n",
"\n",
"for i, cas in enumerate(confusions):\n",
" for j, count in enumerate(cas):\n",
" if count > 0:\n",
" xoff = .07 * len(str(count))\n",
" plt.text(j-xoff, i+.2, int(count), fontsize=9, color='white')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "yLnS4dGiMwI1"
},
"source": [
"We can see here that we're mostly accurate, with some errors you might expect, e.g., '9' is often confused as '4'.\n",
"\n",
"Let's do another sanity check to make sure this matches roughly the distribution of our test set, e.g., it seems like we have fewer '5' values."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2016-09-16T14:55:18.083458",
"start_time": "2016-09-16T14:55:17.830485"
},
"cellView": "both",
"colab": {
"autoexec": {
"startup": false,
"wait_interval": 0
},
"output_extras": [
{
"item_id": 1
}
]
},
"colab_type": "code",
"collapsed": false,
"executionInfo": {
"elapsed": 352,
"status": "ok",
"timestamp": 1446753006584,
"user": {
"color": "#1FA15D",
"displayName": "Michael Piatek",
"isAnonymous": false,
"isMe": true,
"permissionId": "00327059602783983041",
"photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg",
"sessionId": "716a6ad5e180d821",
"userId": "106975671469698476657"
},
"user_tz": 480
},
"id": "x5KOv1AJMgzV",
"outputId": "2acdf737-bab6-408f-8b3c-05fa66d04fe6"
},
"outputs": [
{
"data": {
"image/png": 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PNo4g8QNg7UjbWuC3+9+vpwsE+/PQWYn9gBuH+uw3fIAkuwL78PCZjBFnAcu23kWSpJ3W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\nG1beKa9dAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f841c174f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.xticks(numpy.arange(NUM_LABELS))\n",
"plt.hist(numpy.argmax(test_labels, 1));"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "E6DzLSK5M1ju"
},
"source": [
"Indeed, we appear to have fewer 5 labels in the test set. So, on the whole, it seems like our model is learning and our early results are sensible.\n",
"\n",
"But, we've only done one round of training. We can greatly improve accuracy by training for longer. To try this out, just re-execute the training cell above."
]
}
],
"metadata": {
"anaconda-cloud": {},
"colab": {
"default_view": {},
"name": "Untitled",
"provenance": [],
"version": "0.3.2",
"views": {}
},
"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": 0
}
| apache-2.0 |
mne-tools/mne-tools.github.io | 0.17/_downloads/f842efaa37b452d681d7a7c25b5a5e28/plot_compute_mne_inverse_raw_in_label.ipynb | 1 | 2829 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Compute sLORETA inverse solution on raw data\n\n\nCompute sLORETA inverse solution on raw dataset restricted\nto a brain label and stores the solution in stc files for\nvisualisation.\n\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Author: Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.minimum_norm import apply_inverse_raw, read_inverse_operator\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'\nlabel_name = 'Aud-lh'\nfname_label = data_path + '/MEG/sample/labels/%s.label' % label_name\n\nsnr = 1.0 # use smaller SNR for raw data\nlambda2 = 1.0 / snr ** 2\nmethod = \"sLORETA\" # use sLORETA method (could also be MNE or dSPM)\n\n# Load data\nraw = mne.io.read_raw_fif(fname_raw)\ninverse_operator = read_inverse_operator(fname_inv)\nlabel = mne.read_label(fname_label)\n\nraw.set_eeg_reference('average', projection=True) # set average reference.\nstart, stop = raw.time_as_index([0, 15]) # read the first 15s of data\n\n# Compute inverse solution\nstc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label,\n start, stop, pick_ori=None)\n\n# Save result in stc files\nstc.save('mne_%s_raw_inverse_%s' % (method, label_name))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View activation time-series\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(1e3 * stc.times, stc.data[::100, :].T)\nplt.xlabel('time (ms)')\nplt.ylabel('%s value' % method)\nplt.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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
daniestevez/jupyter_notebooks | Voyager1/Data sideband.ipynb | 1 | 596728 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "b9c586bd",
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams['figure.figsize'] = (10, 5)\n",
"plt.rcParams['figure.facecolor'] = 'w'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "d761c37f",
"metadata": {},
"outputs": [],
"source": [
"def process(sideband, symbols):\n",
" fs = 2.9296875e6/35\n",
" x = np.fromfile(sideband, dtype='float32')\n",
" NFFT = 2**16\n",
" f = np.average(np.abs(np.fft.rfft(\n",
" x[:x.size//NFFT*NFFT].reshape((-1, NFFT))))**2,\n",
" axis=0)*10**(-4.6)\n",
" subcarrier = 22.5e3\n",
" a = int(np.round(subcarrier/fs*NFFT))\n",
" span = 1550\n",
" width = int(np.round(span/fs*NFFT))\n",
" signal = f[a-width:a+width+1]\n",
" noise = np.concatenate((f[a-4*width:a-width], f[a+width+1:a+4*width+1]))\n",
" freqs = np.fft.rfftfreq(NFFT, 1/fs)\n",
" plt.figure()\n",
" plt.plot(freqs[a-width:a+width+1], 10*np.log10(signal))\n",
" plt.plot(freqs[a-4*width:a-width], 10*np.log10(noise[:3*width]), color='C0', alpha=0.5)\n",
" plt.plot(freqs[a+width+1:a+4*width+1], 10*np.log10(noise[3*width:]), color='C0', alpha=0.5)\n",
" plt.title('Voyager 1 demodulated data sideband spectrum')\n",
" plt.ylabel('PSD (dB)')\n",
" plt.xlabel('Frequency (Hz)')\n",
" plt.legend(['Signal', 'Noise'])\n",
" noise_pwr = np.average(noise)\n",
" signal_pwr = np.sum(signal) - noise_pwr * signal.size\n",
" signal_cn0 = signal_pwr / noise_pwr * fs / NFFT\n",
" bitrate = 160\n",
" ebn0 = signal_cn0 / bitrate\n",
" print('Sideband:')\n",
" print(f'Data sideband CN0 {10*np.log10(signal_cn0):.2f} dB')\n",
" print(f'EbN0 {10*np.log10(ebn0):.2f} dB')\n",
" cfreq = np.average(freqs[10*np.log10(f) >= 12])\n",
" print(f'Subcarrier frequency {cfreq:.3f} Hz')\n",
" print(f'Subcarrier frequency error {(cfreq/22.5e3-1)*1e6:.1f} ppm')\n",
" symbols = np.fromfile(symbols, dtype='complex64')[5000:]\n",
" M2 = np.average(np.abs(symbols)**2)\n",
" M4 = np.average(np.abs(symbols)**4)\n",
" snr = np.sqrt(2*M2**2 - M4)/(M2 - np.sqrt(2*M2**2 - M4))\n",
" ebn0 = 2*snr\n",
" print('Symbols fourth-order moment SNR estimate:')\n",
" print(f'SNR {10*np.log10(snr):.2f} dB')\n",
" print(f'EbN0 {10*np.log10(ebn0):.2f} dB')\n",
" plt.figure()\n",
" plt.plot(symbols.real, '.', alpha=0.01)\n",
" plt.plot(symbols.imag, '.', alpha=0.01)\n",
" plt.title('Voyager 1 symbols')\n",
" leg = plt.legend(['I', 'Q'])\n",
" for lh in leg.legendHandles: \n",
" lh._legmarker.set_alpha(1)\n",
" plt.xlabel('Symbol number')\n",
" plt.ylabel('Amplitude')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0d35baf1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sideband:\n",
"Data sideband CN0 28.02 dB\n",
"EbN0 5.97 dB\n",
"Subcarrier frequency 22497.347 Hz\n",
"Subcarrier frequency error -117.9 ppm\n",
"Symbols fourth-order moment SNR estimate:\n",
"SNR 2.39 dB\n",
"EbN0 5.40 dB\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"process('2020/sideband_0011.f32', '2020/symbols_0011.c64')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5d1654e1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sideband:\n",
"Data sideband CN0 27.36 dB\n",
"EbN0 5.32 dB\n",
"Subcarrier frequency 22497.347 Hz\n",
"Subcarrier frequency error -117.9 ppm\n",
"Symbols fourth-order moment SNR estimate:\n",
"SNR 1.76 dB\n",
"EbN0 4.77 dB\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"process('2020/sideband_0015.f32', '2020/symbols_0015.c64')"
]
}
],
"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-3.0 |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n05_day56_model_choosing_close_feat_all_syms_equal.ipynb | 2 | 183499 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# On this notebook the best models and input parameters will be searched for. The problem at hand is predicting the price of any stock symbol 56 days ahead, assuming one model for all the symbols. The best training period length, base period length, and base period step will be determined, using the MRE metrics (and/or the R^2 metrics). The step for the rolling validation will be determined taking into consideration a compromise between having enough points, and the time needed to compute the validation."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"# Basic imports\n",
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime as dt\n",
"import scipy.optimize as spo\n",
"import sys\n",
"from time import time\n",
"from sklearn.metrics import r2_score, median_absolute_error\n",
"\n",
"%matplotlib inline\n",
"\n",
"%pylab inline\n",
"pylab.rcParams['figure.figsize'] = (20.0, 10.0)\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"sys.path.append('../../')\n",
"import predictor.feature_extraction as fe\n",
"import utils.preprocessing as pp\n",
"import utils.misc as misc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"AHEAD_DAYS = 56"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Let's get the data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"datasets_params_list_df = pd.read_pickle('../../data/datasets_params_list_df.pkl')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(25, 8)\n"
]
},
{
"data": {
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"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
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" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>base_days</th>\n",
" <th>ahead_days</th>\n",
" <th>train_val_time</th>\n",
" <th>step_days</th>\n",
" <th>GOOD_DATA_RATIO</th>\n",
" <th>SAMPLES_GOOD_DATA_RATIO</th>\n",
" <th>x_filename</th>\n",
" <th>y_filename</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base7_ahead1.pkl</td>\n",
" <td>y_base7_ahead1.pkl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base7_ahead7.pkl</td>\n",
" <td>y_base7_ahead7.pkl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7</td>\n",
" <td>14</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base7_ahead14.pkl</td>\n",
" <td>y_base7_ahead14.pkl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7</td>\n",
" <td>28</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base7_ahead28.pkl</td>\n",
" <td>y_base7_ahead28.pkl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7</td>\n",
" <td>56</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base7_ahead56.pkl</td>\n",
" <td>y_base7_ahead56.pkl</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base_days ahead_days train_val_time step_days GOOD_DATA_RATIO \\\n",
"0 7 1 -1 7 0.99 \n",
"1 7 7 -1 7 0.99 \n",
"2 7 14 -1 7 0.99 \n",
"3 7 28 -1 7 0.99 \n",
"4 7 56 -1 7 0.99 \n",
"\n",
" SAMPLES_GOOD_DATA_RATIO x_filename y_filename \n",
"0 0.9 x_base7_ahead1.pkl y_base7_ahead1.pkl \n",
"1 0.9 x_base7_ahead7.pkl y_base7_ahead7.pkl \n",
"2 0.9 x_base7_ahead14.pkl y_base7_ahead14.pkl \n",
"3 0.9 x_base7_ahead28.pkl y_base7_ahead28.pkl \n",
"4 0.9 x_base7_ahead56.pkl y_base7_ahead56.pkl "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(datasets_params_list_df.shape)\n",
"datasets_params_list_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"train_days_arr = 252 * np.array([1, 2, 3])\n",
"params_list_df = pd.DataFrame()\n",
"\n",
"for train_days in train_days_arr:\n",
" temp_df = datasets_params_list_df[datasets_params_list_df['ahead_days'] == AHEAD_DAYS].copy()\n",
" temp_df['train_days'] = train_days\n",
" params_list_df = params_list_df.append(temp_df, ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(15, 9)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>base_days</th>\n",
" <th>ahead_days</th>\n",
" <th>train_val_time</th>\n",
" <th>step_days</th>\n",
" <th>GOOD_DATA_RATIO</th>\n",
" <th>SAMPLES_GOOD_DATA_RATIO</th>\n",
" <th>x_filename</th>\n",
" <th>y_filename</th>\n",
" <th>train_days</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>7</td>\n",
" <td>56</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base7_ahead56.pkl</td>\n",
" <td>y_base7_ahead56.pkl</td>\n",
" <td>252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>14</td>\n",
" <td>56</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base14_ahead56.pkl</td>\n",
" <td>y_base14_ahead56.pkl</td>\n",
" <td>252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>28</td>\n",
" <td>56</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base28_ahead56.pkl</td>\n",
" <td>y_base28_ahead56.pkl</td>\n",
" <td>252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>56</td>\n",
" <td>56</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base56_ahead56.pkl</td>\n",
" <td>y_base56_ahead56.pkl</td>\n",
" <td>252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>112</td>\n",
" <td>56</td>\n",
" <td>-1</td>\n",
" <td>7</td>\n",
" <td>0.99</td>\n",
" <td>0.9</td>\n",
" <td>x_base112_ahead56.pkl</td>\n",
" <td>y_base112_ahead56.pkl</td>\n",
" <td>252</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base_days ahead_days train_val_time step_days GOOD_DATA_RATIO \\\n",
"0 7 56 -1 7 0.99 \n",
"1 14 56 -1 7 0.99 \n",
"2 28 56 -1 7 0.99 \n",
"3 56 56 -1 7 0.99 \n",
"4 112 56 -1 7 0.99 \n",
"\n",
" SAMPLES_GOOD_DATA_RATIO x_filename y_filename \\\n",
"0 0.9 x_base7_ahead56.pkl y_base7_ahead56.pkl \n",
"1 0.9 x_base14_ahead56.pkl y_base14_ahead56.pkl \n",
"2 0.9 x_base28_ahead56.pkl y_base28_ahead56.pkl \n",
"3 0.9 x_base56_ahead56.pkl y_base56_ahead56.pkl \n",
"4 0.9 x_base112_ahead56.pkl y_base112_ahead56.pkl \n",
"\n",
" train_days \n",
"0 252 \n",
"1 252 \n",
"2 252 \n",
"3 252 \n",
"4 252 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(params_list_df.shape)\n",
"params_list_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's find the best params set for some different models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### - Dummy Predictor (mean)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating: base28_ahead56_train756\n",
"Generating: base7_ahead56_train252\n",
"Generating: base112_ahead56_train252\n",
"Generating: base56_ahead56_train504\n",
"Evaluating approximately 86 training/evaluation pairs\n",
"Evaluating approximately 78 training/evaluation pairs\n",
"Evaluating approximately 81 training/evaluation pairs\n",
"Evaluating approximately 85 training/evaluation pairs\n",
"Approximately 96.3 percent complete. (0.36465896953931198, 0.15780714555021338)\n",
"Generating: base7_ahead56_train504\n",
"Approximately 101.2 percent complete. Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 92.3 percent complete. (0.023225128508772255, 0.11495476261730708)\n",
"Generating: base14_ahead56_train252\n",
"Approximately 1.2 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 3.5 percent complete. (0.27509762291129858, 0.13358270498689478)\n",
"Generating: base112_ahead56_train504\n",
"Approximately 6.1 percent complete. Evaluating approximately 80 training/evaluation pairs\n",
"Approximately 8.1 percent complete. (0.18042650755195533, 0.1293564669106877)\n",
"Generating: base56_ahead56_train756\n",
"Approximately 3.8 percent complete. Evaluating approximately 77 training/evaluation pairs\n",
"Approximately 93.8 percent complete. (0.064888278154167034, 0.11769686410551169)\n",
"Generating: base28_ahead56_train252\n",
"Approximately 98.8 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 97.5 percent complete. (-0.0088938308863940468, 0.12137397143029877)\n",
"Generating: base14_ahead56_train504\n",
"Approximately 87.0 percent complete. Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 90.9 percent complete. (0.36952433355989561, 0.15397185639357974)\n",
"Generating: base7_ahead56_train756\n",
"Approximately 8.1 percent complete. Evaluating approximately 78 training/evaluation pairs\n",
"Approximately 10.3 percent complete. (0.30468050774283234, 0.14001460348642863)\n",
"Generating: base112_ahead56_train756\n",
"Approximately 15.9 percent complete. Evaluating approximately 76 training/evaluation pairs\n",
"Approximately 84.6 percent complete. (0.13931049458823525, 0.12771822959668033)\n",
"Generating: base56_ahead56_train252\n",
"Approximately 92.7 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 9.3 percent complete. (0.045988289321718258, 0.12392736929445884)\n",
"Generating: base28_ahead56_train504\n",
"Approximately 81.6 percent complete. Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 7.3 percent complete. (-0.0061867643236446501, 0.1217351082386469)\n",
"Generating: base14_ahead56_train756\n",
"Approximately 89.5 percent complete. Evaluating approximately 78 training/evaluation pairs\n",
"Approximately 32.6 percent complete. (0.3964232916316846, 0.15580546503497)\n",
"Approximately 71.8 percent complete. (0.25352102061395887, 0.1346596266824889)\n",
"Approximately 101.2 percent complete. (0.12361693853730206, 0.12808920854289804)\n",
"Approximately 101.3 percent complete. (0.062571486804533297, 0.12409811871446941)\n",
"Minimum MRE param set: \n",
" base_days 7\n",
"ahead_days 56\n",
"train_val_time -1\n",
"step_days 7\n",
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"x_filename x_base7_ahead56.pkl\n",
"y_filename y_base7_ahead56.pkl\n",
"train_days 252\n",
"scores (0.0232251285088, 0.114954762617)\n",
"r2 0.0232251\n",
"mre 0.114955\n",
"Name: 0, dtype: object\n",
"Maximum R^2 param set: \n",
" base_days 112\n",
"ahead_days 56\n",
"train_val_time -1\n",
"step_days 7\n",
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"x_filename x_base112_ahead56.pkl\n",
"y_filename y_base112_ahead56.pkl\n",
"train_days 756\n",
"scores (0.396423291632, 0.155805465035)\n",
"r2 0.396423\n",
"mre 0.155805\n",
"Name: 14, dtype: object\n",
"Elapsed time: 438.63243675231934 seconds.\n"
]
},
{
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3PqKcKBRHAHLa631h2baMLyxNtFafW5FYXDfGF0xHAYCcde76hGaX19JiTG3d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"text/plain": [
"<matplotlib.figure.Figure at 0x7f073ec8ffd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tic = time()\n",
"\n",
"from predictor.dummy_mean_predictor import DummyPredictor\n",
"PREDICTOR_NAME = 'dummy'\n",
"\n",
"# Global variables\n",
"eval_predictor = DummyPredictor()\n",
"step_eval_days = 60 # The step to move between training/validation pairs\n",
"params = {'eval_predictor': eval_predictor, 'step_eval_days': step_eval_days}\n",
"\n",
"results_df = misc.parallelize_dataframe(params_list_df, misc.apply_mean_score_eval, params)\n",
"\n",
"results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n",
"results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n",
"# Pickle that!\n",
"results_df.to_pickle('../../data/results_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n",
"results_df['mre'].plot()\n",
"\n",
"print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n",
"print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n",
"\n",
"toc = time()\n",
"print('Elapsed time: {} seconds.'.format((toc-tic)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### - Linear Predictor"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating: base56_ahead56_train504\n",
"Generating: base112_ahead56_train252\n",
"Generating: base28_ahead56_train756\n",
"Generating: base7_ahead56_train252\n",
"Evaluating approximately 86 training/evaluation pairs\n",
"Evaluating approximately 78 training/evaluation pairs\n",
"Evaluating approximately 81 training/evaluation pairs\n",
"Evaluating approximately 85 training/evaluation pairs\n",
"Approximately 96.5 percent complete. (-0.14681918662261664, 0.12582692470388054)\n",
"Generating: base14_ahead56_train252\n",
"Approximately 91.4 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 95.1 percent complete. (0.50256742402460519, 0.13553335951119391)\n",
"Generating: base7_ahead56_train504\n",
"Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 97.4 percent complete. (0.41564260359726168, 0.12150985324601241)\n",
"Generating: base112_ahead56_train504\n",
"Approximately 98.7 percent complete. Evaluating approximately 80 training/evaluation pairs\n",
"Approximately 1.2 percent complete. (0.27894730829038861, 0.12449911518796193)\n",
"Generating: base56_ahead56_train756\n",
"Approximately 11.0 percent complete. Evaluating approximately 77 training/evaluation pairs\n",
"Approximately 90.2 percent complete. (-0.079954092608562552, 0.12630154339343688)\n",
"Generating: base28_ahead56_train252\n",
"Approximately 74.0 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 84.4 percent complete. (-0.0036068071743561012, 0.12335521323465509)\n",
"Generating: base14_ahead56_train504\n",
"Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 15.9 percent complete. (0.57563540561516158, 0.12857889684920581)\n",
"Generating: base7_ahead56_train756\n",
"Approximately 100.0 percent complete. Evaluating approximately 78 training/evaluation pairs\n",
"Approximately 32.6 percent complete. (0.43161536281393553, 0.12760930243382548)\n",
"Generating: base112_ahead56_train756\n",
"Approximately 33.7 percent complete. Evaluating approximately 76 training/evaluation pairs\n",
"Approximately 64.1 percent complete. (0.08961253236280825, 0.12908001035836947)\n",
"Generating: base56_ahead56_train252\n",
"Approximately 52.6 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 82.1 percent complete. (0.10336092151527008, 0.12234878589176611)\n",
"Generating: base28_ahead56_train504\n",
"Approximately 19.8 percent complete. Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 40.7 percent complete. (-0.019774335241657445, 0.12536381968723814)\n",
"Generating: base14_ahead56_train756\n",
"Approximately 20.7 percent complete. Evaluating approximately 78 training/evaluation pairs\n",
"Approximately 65.1 percent complete. (0.5904255687669967, 0.12791323475523578)\n",
"Approximately 75.6 percent complete. (0.30868460583104856, 0.12817284018907107)\n",
"Approximately 76.9 percent complete. (0.18107531577089553, 0.12467719164386398)\n",
"Approximately 101.3 percent complete. (0.099817877988638434, 0.12426108146674826)\n",
"Minimum MRE param set: \n",
" base_days 56\n",
"ahead_days 56\n",
"train_val_time -1\n",
"step_days 7\n",
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"x_filename x_base56_ahead56.pkl\n",
"y_filename y_base56_ahead56.pkl\n",
"train_days 504\n",
"scores (0.415642603597, 0.121509853246)\n",
"r2 0.415643\n",
"mre 0.12151\n",
"Name: 8, dtype: object\n",
"Maximum R^2 param set: \n",
" base_days 112\n",
"ahead_days 56\n",
"train_val_time -1\n",
"step_days 7\n",
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"x_filename x_base112_ahead56.pkl\n",
"y_filename y_base112_ahead56.pkl\n",
"train_days 756\n",
"scores (0.590425568767, 0.127913234755)\n",
"r2 0.590426\n",
"mre 0.127913\n",
"Name: 14, dtype: object\n",
"Elapsed time: 462.7516870498657 seconds.\n"
]
},
{
"data": {
"image/png": 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2CiUAAAAAr0WhBCCvRGJxrVs4W/VVZV5HyXnhYIAVSgAAAABOi0IJQN7oODGk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"text/plain": [
"<matplotlib.figure.Figure at 0x7f073d13b278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tic = time()\n",
"\n",
"from predictor.linear_predictor import LinearPredictor\n",
"PREDICTOR_NAME = 'linear'\n",
"\n",
"# Global variables\n",
"eval_predictor = LinearPredictor()\n",
"step_eval_days = 60 # The step to move between training/validation pairs\n",
"params = {'eval_predictor': eval_predictor, 'step_eval_days': step_eval_days}\n",
"\n",
"results_df = misc.parallelize_dataframe(params_list_df, misc.apply_mean_score_eval, params)\n",
"\n",
"results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n",
"results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n",
"# Pickle that!\n",
"results_df.to_pickle('../../data/results_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n",
"results_df['mre'].plot()\n",
"\n",
"print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n",
"print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n",
"\n",
"toc = time()\n",
"print('Elapsed time: {} seconds.'.format((toc-tic)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### - Random Forest model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating: base7_ahead56_train252\n",
"Generating: base112_ahead56_train252\n",
"Generating: base56_ahead56_train504\n",
"Generating: base28_ahead56_train756\n",
"Evaluating approximately 86 training/evaluation pairs\n",
"Evaluating approximately 78 training/evaluation pairs\n",
"Evaluating approximately 81 training/evaluation pairs\n",
"Evaluating approximately 85 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (-0.90869295385223947, 0.13948508549601199)\n",
"Generating: base14_ahead56_train252\n",
"Approximately 9.0 percent complete. Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (-0.55035695757776726, 0.13744468223625314)\n",
"Generating: base28_ahead56_train252\n",
"Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 44.4 percent complete. (-0.40420138483344326, 0.14167447669880928)\n",
"Generating: base56_ahead56_train252\n",
"Evaluating approximately 86 training/evaluation pairs\n",
"Approximately 100.0 percent complete. (0.36524128059059779, 0.13926840080547542)\n",
"Generating: base7_ahead56_train504\n",
"Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (-0.38255406873461262, 0.13735104539255971)\n",
"Generating: base14_ahead56_train504\n",
"Approximately 100.0 percent complete. Evaluating approximately 82 training/evaluation pairs\n",
"(-0.024958551740092202, 0.13896313978206987)\n",
"Approximately 100.0 percent complete. (-0.19336986200746786, 0.13490374540996627)\n",
"Generating: base56_ahead56_train756\n",
"Approximately 9.8 percent complete. Evaluating approximately 77 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (0.26040426203912598, 0.12953267826377354)\n",
"Generating: base112_ahead56_train504\n",
"Evaluating approximately 80 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (-0.21195858665327988, 0.13505690199220877)\n",
"Generating: base28_ahead56_train504\n",
"Evaluating approximately 82 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (-0.073904003673092383, 0.13435399683504232)\n",
"Approximately 101.3 percent complete. (0.29710869866366985, 0.13776935390045578)\n",
"Generating: base112_ahead56_train756\n",
"Evaluating approximately 76 training/evaluation pairs\n",
"Approximately 101.2 percent complete. (0.46601109130569995, 0.13442851693070257)\n",
"Generating: base7_ahead56_train756\n",
"Evaluating approximately 78 training/evaluation pairs\n",
"Approximately 101.3 percent complete. (-0.33145355101243112, 0.13723807927976267)\n",
"Generating: base14_ahead56_train756\n",
"Evaluating approximately 78 training/evaluation pairs\n",
"Approximately 101.3 percent complete. (-0.1253444785890577, 0.13415754753232975)\n",
"Approximately 101.3 percent complete. (0.5128609445867266, 0.13609470847086683)\n",
"Minimum MRE param set: \n",
" base_days 56\n",
"ahead_days 56\n",
"train_val_time -1\n",
"step_days 7\n",
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"x_filename x_base56_ahead56.pkl\n",
"y_filename y_base56_ahead56.pkl\n",
"train_days 504\n",
"scores (0.260404262039, 0.129532678264)\n",
"r2 0.260404\n",
"mre 0.129533\n",
"Name: 8, dtype: object\n",
"Maximum R^2 param set: \n",
" base_days 112\n",
"ahead_days 56\n",
"train_val_time -1\n",
"step_days 7\n",
"GOOD_DATA_RATIO 0.99\n",
"SAMPLES_GOOD_DATA_RATIO 0.9\n",
"x_filename x_base112_ahead56.pkl\n",
"y_filename y_base112_ahead56.pkl\n",
"train_days 756\n",
"scores (0.512860944587, 0.136094708471)\n",
"r2 0.512861\n",
"mre 0.136095\n",
"Name: 14, dtype: object\n",
"Elapsed time: 7860.228952169418 seconds.\n"
]
},
{
"data": {
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Yw560nAJd8uJ36tm6sd4Y3++HGULHSiuUfrDgR8VRoXYdLFBxWeUP14cH+ant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\n0Jf/A+A2MBcl+j8fAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f073c18fa90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tic = time()\n",
"\n",
"from predictor.random_forest_predictor import RandomForestPredictor\n",
"PREDICTOR_NAME = 'random_forest'\n",
"\n",
"# Global variables\n",
"eval_predictor = RandomForestPredictor()\n",
"step_eval_days = 60 # The step to move between training/validation pairs\n",
"params = {'eval_predictor': eval_predictor, 'step_eval_days': step_eval_days}\n",
"\n",
"results_df = misc.parallelize_dataframe(params_list_df, misc.apply_mean_score_eval, params)\n",
"\n",
"results_df['r2'] = results_df.apply(lambda x: x['scores'][0], axis=1)\n",
"results_df['mre'] = results_df.apply(lambda x: x['scores'][1], axis=1)\n",
"# Pickle that!\n",
"results_df.to_pickle('../../data/results_ahead{}_{}_df.pkl'.format(AHEAD_DAYS, PREDICTOR_NAME))\n",
"results_df['mre'].plot()\n",
"\n",
"print('Minimum MRE param set: \\n {}'.format(results_df.iloc[np.argmin(results_df['mre'])]))\n",
"print('Maximum R^2 param set: \\n {}'.format(results_df.iloc[np.argmax(results_df['r2'])]))\n",
"\n",
"toc = time()\n",
"print('Elapsed time: {} seconds.'.format((toc-tic)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "cap_env",
"language": "python",
"name": "cap_env"
},
"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 |
bollwyvl/K3D-jupyter | examples/marching_cubes.ipynb | 1 | 894 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from k3d import K3D\n",
"import numpy\n",
"\n",
"X, Y, Z = numpy.mgrid[:30, :30, :30]\n",
"scalars_field = ((X-15.0)/15.0)**2 + ((Y-15.0)/15.0)**2 + ((Z-15.0)/15.0)**2\n",
"\n",
"plot = K3D()\n",
"plot += K3D.marching_cubes(scalars_field, level=0.8)\n",
"plot.display()"
]
}
],
"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
}
| gpl-2.0 |
rdeits/iris | python/irispy_exploration.ipynb | 1 | 16783 | {
"metadata": {
"name": "",
"signature": "sha256:c34a721349d1e847d93b1ccfbf7014796b1ad2bab422a19573229618b56402b3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext autoreload\n",
"%autoreload 2\n",
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n",
"Populating the interactive namespace from numpy and matplotlib"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from irispy.utils import lcon_to_vert\n",
"from irispy.iris import inflate_region"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.spatial import ConvexHull\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"obs_1 = np.array([[2,2,3,3],[0,2,2,0]])\n",
"obs_2 = np.array([[-1,-1,0,0.2],[0,2,2,0]])\n",
"obs_3 = np.array([[0.5,0.5,0.5,0.5],[2,2,2,2]])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"obstacle_pts = np.dstack((obs_1, obs_2, obs_3))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"A_bounds = np.array([[-1,0],\n",
" [0,-1],\n",
" [1,0],\n",
" [0,1]])\n",
"lb = np.array([-1,-1])\n",
"ub = np.array([3,3])\n",
"b_bounds = np.hstack((-lb, ub))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"start = [1,2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"A, b, C, d, results = inflate_region(obstacle_pts, A_bounds, b_bounds, start)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = figure()\n",
"ax = f.add_subplot(111)\n",
"hold(True)\n",
"for j in range(obstacle_pts.shape[2]):\n",
" obs = obstacle_pts[:,:,j]\n",
" k = range(obs.shape[1]) + [0]\n",
" ax.plot(obs[0,k], obs[1,k], 'k.-')\n",
"ax.set_xlim(lb[0]-0.05, ub[0]+0.05)\n",
"ax.set_ylim(lb[1]-0.05, ub[1]+0.05)\n",
"poly = lcon_to_vert(A, b)\n",
"hull = ConvexHull(poly.T)\n",
"k = np.hstack((hull.vertices, hull.vertices[0]))\n",
"ax.plot(poly[0,k], poly[1,k], 'r.-')\n",
"th = np.linspace(0, 2*np.pi, 100)\n",
"y = np.vstack((np.cos(th), np.sin(th)))\n",
"x = C.dot(y) + d.reshape((-1,1))\n",
"ax.plot(x[0,:], x[1,:], 'b-')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"[<matplotlib.lines.Line2D at 0x3aae690>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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S/vlPC4EtVii0JVASEkxwjxgBgwfDpk22K3KH48CECWZ5+nvvaYl6mKhPWwIn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\n8RGFtoiIjyi0RUR8RKEtIuIj/w/J0bNjHUwE/gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x3958e90>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-2-clause |
machinelearningdeveloper/lc101-kc | October 23, 2016/Studio Solution.ipynb | 2 | 4754 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Studio: Holiday\n",
"\n",
"It is possible to name the days 0 through 6, where day 0 is Sunday and day 6 is Saturday. If you go on a wonderful holiday leaving on day number 3 (a Wednesday) and you return home after 10 nights, you arrive home on day number 6 (a Saturday).\n",
"\n",
"Write a general version of the program which asks for the starting day number, and the length of your stay, and it will tell you the number of day of the week you will return on.\n",
"\n",
"\n",
"## What to think about\n",
"\n",
"* How many days in week?\n",
"* What happens when we go past the last day in the week?\n",
"* Why start at 0 instead of 1?\n",
"* What mathematic operations could be useful?\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution with day"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What day are you leaving? 2\n",
"How long will you be gone? 10\n",
"Friday 5\n"
]
}
],
"source": [
"days_of_week = [\n",
" # 0 1 2\n",
" 'Sunday', 'Monday', 'Tuesday',\n",
" # 3 4 5\n",
" 'Wednesday', 'Thursday', 'Friday',\n",
" # 6\n",
" 'Saturday',\n",
"]\n",
"\n",
"# Gather user input\n",
"# Need to use the `int` call so that it'll correctly be \n",
"# an integer for mathmatical operations\n",
"leaving_day = int(input('What day are you leaving? '))\n",
"length_of_vacation = int(input('How long will you be gone? '))\n",
"\n",
"# Get the day we will hit, it may be larger than 7\n",
"final_day = length_of_vacation + leaving_day\n",
"\n",
"# Get the index for the day of the week\n",
"# By using the modulo operator we can take advantage of\n",
"# knowing the remainder of any numbers.\n",
"# For example if the day is 2 and our stay is 5\n",
"# Then we will have 7, which means we leave on a Tuesday\n",
"# and get back on a Sunday.\n",
"final_day_index = final_day % len(days_of_week)\n",
"\n",
"print(days_of_week[final_day_index], final_day_index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution with only Number"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Which day are you leaving on? (0=Sun, 1=Mon, etc)2\n",
"How many days will you be done?10\n",
"You will return on day 5\n"
]
}
],
"source": [
"\"\"\"\n",
"Studio: Holiday\n",
"\"\"\"\n",
"\n",
"# get input: departure day (0-6) and duration\n",
"departure_day = input(\"Which day are you leaving on? (0=Sun, 1=Mon, etc)\")\n",
"departure_day = int(departure_day)\n",
"\n",
"duration = input(\"How many days will you be done?\")\n",
"duration = int(duration)\n",
"\n",
"# calculate return day and respond\n",
"return_day = (departure_day + duration) % 7\n",
"print(\"You will return on day\", return_day)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Solution provided in class"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What day will you leave?2\n",
"How many days will you be gone?10\n",
"You will return on 5\n"
]
}
],
"source": [
"day = int(input(\"What day will you leave?\"))\n",
"\n",
"travelDays = int(input(\"How many days will you be gone?\"))\n",
"\n",
"newDay = day + travelDays\n",
"\n",
"newDay = newDay % 7\n",
"print(\"You will return on\", newDay)"
]
},
{
"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": 1
}
| unlicense |
Py101/py101-assignments-marcosco | 03/Factorial.ipynb | 1 | 1194 | {
"cells": [
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from functools import reduce\n",
"\n",
"def fact(n):\n",
" \"\"\"\n",
" Returns the factorial of the given positive number.\n",
" If the given number is negative returns 0\n",
" Inputs:\n",
" @n: The number to calculate\n",
" Outputs:\n",
" The Factorial\n",
" \"\"\"\n",
" if n > 0:\n",
" fact_lamba = lambda x, y: x * y\n",
" return reduce(fact_lamba, range(1,n+1))\n",
" return 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
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
ShorensteinCenter/Shorenstein-Center-Notebooks | Shorenstein_Center_Notebook_1.ipynb | 1 | 422796 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://shorensteincenter.org/)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents:\n",
"### 0 [Import Libraries ](#0-bullet)\n",
"### 1 [Pull Data from the API](#1-bullet)\n",
"### 2 [Turn the Data into a pandas DataFrame](#2-bullet)\n",
"### 3 [Explore the Data](#3-bullet)\n",
"##### - 3.1 [Basic List Composition](#3.1-bullet)\n",
"##### - 3.2 [List Composition Over Time](#3.2-bullet)\n",
"##### - 3.3 [Subscriber (In)Activity ](#3.3-bullet)\n",
"##### - 3.4 [Subscriber Engagement Distributions](#3.4-bullet)\n",
"##### - 3.5 [ Investigating Churn](#3.5-bullet)\n",
"### 4 [Export Results to a Folder](#4-bullet)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. Import libraries and set global variables <a class=\"anchor\" id=\"0-bullet\"></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this is a comment\n",
"# comments are used throughout this notebook to explain what is happening in each cell\n",
"# this is a cell\n",
"# the In [] to the left stands for input \n",
"# when you click inside the cell it will turn green and you can edit the code \n",
"# run the cells in this notebook in order by pressing shift + enter \n",
"# you will see an input number appear on the left after the cell has run\n",
"\n",
"# set colors \n",
"c1='#18a45f' # subscribers\n",
"c2='#ec3038' # unsubscribes\n",
"c3='#3286ec' # cleaned\n",
"c4='#fecf5f' # pending\n",
"c_ev= '#cccccc' # ever opened\n",
"c_nev='#000000' # never opened"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# import libraries\n",
"%matplotlib inline\n",
"import os\n",
"from mailchimp3 import MailChimp # import your wrapper of choice for your email service provider - in this case mailchimp3. \n",
"import pandas as pd # standard code for importing the pandas library and aliasing it as pd - if you want to learn all about pandas read 'Python for Data Analysis' version 2nd Edition by Wes McKinney, the creator of pandas\n",
"import time # allows you to time things \n",
"import matplotlib.pyplot as plt # allows you to plot data \n",
"import seaborn as sns # makes the plots look nicer\n",
"import numpy as np\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Pull Data from the API <a class=\"anchor\" id=\"1-bullet\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" [ We will pull data from the MailChimp API as an example in this notebook](https://developer.mailchimp.com/)\n",
" ### You pull from an API, or read in your own data from a csv or other file format"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# run this cell to define global variables to pull data from the API of your email service provider, in this case MailChimp\n",
"# and specify LIST_NAME so the files in the exported executive summary are labled correctly\n",
"# replace the variable values in quotes in red caps with the unique values for your MailChimp account\n",
"LIST_NAME='YOURLISTNAME'\n",
"NAME='YOURUSERNAME'# your MailChimp user name (used at login)\n",
"SECRET_KEY='YOURAPIKEY'# your MailChimp API Key\n",
"LIST_ID='YOURLISTID' # the ID for the individual list you want to look at\n",
"#OUT_FILE='OUTFILENAME.csv'# if you want to export your data, you can specify the outfile name and type, in this example CSV \n",
"\n",
"# Make an output directly to explort the results and images from this notebook\n",
"oupt_dir='Shorenstein_Notebook_1_'+str(LIST_NAME)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# included in case the directory is already there so it doesn't error out \n",
"try:\n",
" os.mkdir(oupt_dir)\n",
"except:\n",
" print 'e'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# initalizes client - create a connection with the API; calling that connection client \n",
"client=MailChimp(NAME,SECRET_KEY)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%%time \n",
"# %%time, this magic command above tells jupyter to time how long this cell takes to run\n",
"# magic commands have to be first which is why this comment is not first\n",
"# makes a MailChimp API call and stores the results in member_data; kernal will be busy - filled in - while data is being pulled from the API\n",
"# from the client object - created in the input directly above - we are calling the method to get all member information for members on the list you are interested in \n",
"# names what is returned from the API member_data and specifies the fields for the API to return\n",
"# TIP: limit the fields to the ones you want speeds up the time it takes the API calls to complete\n",
"# if this times out or if you have issues, consider doing a batch request which does not require you to keep the connection open while the request processes \n",
"# batch requests are the recommended method by MailChimp, although they are significantly slower in pulling the data, they are more robust\n",
"# for simplicity we do a normal API request in this notebook\n",
"\n",
"member_data=client.lists.members.all(LIST_ID,get_all=True,\n",
" fields='members.status,members.email_address,members.timestamp_opt,members.timestamp_signup,members.stats,members.id')\n",
"print \"api calls done\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2. Turn the Data into a pandas Data Frame <a class=\"anchor\" id=\"2-bullet\"></a>"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>email_address</th>\n",
" <th>id</th>\n",
" <th>stats</th>\n",
" <th>status</th>\n",
" <th>timestamp_opt</th>\n",
" <th>timestamp_signup</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [email_address, id, stats, status, timestamp_opt, timestamp_signup]\n",
"Index: []"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# turns the member_data returned by the API call into a pandas data frame\n",
"# pandas allows you to do a number of complex calculations \n",
"member_data_frame=pd.DataFrame(member_data['members'])\n",
"\n",
"# makes a copy of the data frame so you don't have to repull it if you make changes and want to go back to the original data\n",
"member_data_frame.to_pickle(LIST_NAME+'_members.pkl')\n",
"\n",
"# look to see what the data frame looks like\n",
"# TIP: you may need to scroll to the right to see all the columns\n",
"member_data_frame.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Explore the Data <a class=\"anchor\" id=\"3-bullet\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.1 Basic List Composition<a class=\"anchor\" id=\"3.1-bullet\"></a>\n",
"### We go from asking 'What is my list size?' "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"45972\n"
]
}
],
"source": [
"# List size - number of current subscribers, as measured by unique email addresses with status of subscribed on your list\n",
"list_size=sum(member_data_frame.status=='subscribed')\n",
"print list_size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### To asking 'What is my list composition?' "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"128361\n"
]
}
],
"source": [
"# total unique email records in your list dataset\n",
"total_un_eml=member_data_frame.email_address.nunique()\n",
"print total_un_eml"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"pending 50497\n",
"subscribed 45972\n",
"unsubscribed 17560\n",
"cleaned 14332\n",
"Name: status, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# explore member status by seeing the values and counts for each value returned\n",
"# MailChimp has four possible status values: subscribed, unsubscribed, pending, and cleaned \n",
"# subscribed = current subscribers\n",
"# unsubscribed = anyone who unsubscribed themselves or you unsubscribed, e.g. due to inactivity \n",
"# pending = if you have or have ever had double opt in enabled, pending emails are email addresses that were entered into your subscribe form, but the email confirmation link was never clicked\n",
"# cleaned = emailed that were cleaned due to bouncing. One hard bounce or about 15 soft bounces. \n",
"member_data_frame.status.value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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lnnWysrKIj4/nxo0balPx491TNjY2DB48mDVr1rBt2zb27t1LlSpVSE1N5cqV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\nRCQJhgoiIiKSxP8Df3VwsuAqzWEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1107ac2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# store_values for later\n",
"statuses=member_data_frame.status.unique()\n",
"if 'subscribed' in statuses:\n",
" subscribed=member_data_frame.status.value_counts()['subscribed']\n",
"else:\n",
" subscribe=0\n",
" print \"looks like you have no subscribers something might have gone wrong.\"\n",
"if 'unsubscribed' in statuses:\n",
" unsubscribed=member_data_frame.status.value_counts()['unsubscribed']\n",
"else:\n",
" unsubscribed=0\n",
"if 'cleaned' in statuses:\n",
" cln=member_data_frame.status.value_counts()['cleaned']\n",
"else:\n",
" cln=0\n",
"if 'pending' in statuses:\n",
" pndg=member_data_frame.status.value_counts()['pending']\n",
"else:\n",
" pndg=0\n",
"\n",
"\n",
"bar=pd.DataFrame([['subscribed',subscribed],['unsubscribed',unsubscribed],\n",
" ['cleaned',cln],['pending',pndg]],columns=['status','sizes'])\n",
"bar.sort_values('status',inplace=True)\n",
"objects=bar.status.tolist()\n",
"y_pos=np.arange(len(bar.status.tolist()))\n",
"performance =bar.sizes.tolist()\n",
"\n",
"def autolabel(rects):\n",
" \"\"\"\n",
" Attach a text label above each bar displaying its height\n",
" \"\"\"\n",
" for rect in rects:\n",
" height = rect.get_height()\n",
" ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n",
" \"{0:.0f}%\".format(int(height)/float(bar.sizes.sum())*100),\n",
" ha='center', va='bottom',fontsize=15)\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax_b=ax.bar(y_pos, performance, align='center',color=[c3,c4,c1,c2])\n",
"plt.yticks(fontsize=15)\n",
"plt.xticks(y_pos, objects, fontsize=15)\n",
"plt.ylabel('Counts',fontdict={'fontsize':20})\n",
"plt.xlabel('Status',fontdict={'fontsize':20})\n",
"plt.title('Section 3.1 Basic List Composition',fontdict={'fontsize':25})\n",
"autolabel(ax_b)\n",
"plt.savefig(oupt_dir+'/Section_3.1_ListComposition.png')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.2 List Composition Over Time<a class=\"anchor\" id=\"3.2-bullet\"></a>\n",
"#### Looking at the types of emails you acquired over time, users are Subscribed, Unsubscribed, Pending or Cleaned. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"45035"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# prepare the data by calculating the month joined, and for each of those months, what % of those people are subscribed, unsubscribed, cleaned or pending \n",
"# NOTE: There is no output from this cell but you need to run it to see the graphs below. \n",
"member_data_frame['timestamp_opt']=member_data_frame.timestamp_opt.apply(pd.to_datetime)\n",
"\n",
"member_data_frame['timestamp_signup']=member_data_frame.timestamp_signup.apply(pd.to_datetime)\n",
"\n",
"# records missing signup_time\n",
"sum(member_data_frame.timestamp_signup.isnull())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# make sure index is unique because we are about to do some manipulations based on it\n",
"member_data_frame.reset_index(drop=True,inplace=True)\n",
"\n",
"# index of members where we don't know when they signed up but we have opt in time\n",
"guess_time_ix=member_data_frame[(member_data_frame.timestamp_signup.isnull())&\n",
" (member_data_frame.timestamp_opt.isnull()!=True)].index\n",
"\n",
"# when we don't have signup time use opt in time\n",
"member_data_frame.loc[guess_time_ix,'timestamp_signup']=member_data_frame.loc[guess_time_ix,'timestamp_opt']\n",
"\n",
"# using integer division to break down people into groups by the month they joined\n",
"member_data_frame['join_month']=member_data_frame.timestamp_signup.apply(lambda x:\n",
" pd.to_datetime(2592000*int((x.value/1e9)/2592000),unit='s'))\n",
"\n",
"# represent the joined month as an integer of ms - milliseconds - since epoch time 0\n",
"# this format is not nice for people but very nice for computers!\n",
"member_data_frame['jv']=member_data_frame.join_month.apply(lambda x: x.value)\n",
"member_data_frame['jv']=member_data_frame.join_month.apply(lambda x: x.value)\n",
"\n",
"# any times we get before this we assume are data errors\n",
"mim_time=pd.to_datetime('01-01-1990')\n",
"\n",
"# ignore records before mintime\n",
"member_data_frame=member_data_frame[member_data_frame.join_month>mim_time].copy()\n",
"\n",
"tot=member_data_frame.groupby('join_month').size().reset_index()\n",
"sub=member_data_frame[member_data_frame.status=='subscribed'].groupby('join_month').size().reset_index()\n",
"unsub=member_data_frame[member_data_frame.status=='unsubscribed'].groupby('join_month').size().reset_index()\n",
"cleaned=member_data_frame[member_data_frame.status=='cleaned'].groupby('join_month').size().reset_index()\n",
"pending=member_data_frame[member_data_frame.status=='pending'].groupby('join_month').size().reset_index()\n",
"\n",
"unsub.columns=['join_month','un']\n",
"tot.columns=['join_month','tot']\n",
"sub.columns=['join_month','sub']\n",
"cleaned.columns=['join_month','clean']\n",
"pending.columns=['join_month','pen']\n",
"\n",
"comps=pd.merge(tot,unsub, how='left', on='join_month')\n",
"comps=pd.merge(comps,sub, how='left', on='join_month')\n",
"comps=pd.merge(comps,cleaned, how='left', on='join_month')\n",
"comps=pd.merge(comps,pending, how='left', on='join_month')\n",
"\n",
"comps.fillna(0,inplace=True)\n",
"\n",
"comps['un_per']=comps.apply(lambda x: x['un']/float(x['tot']),axis=1)\n",
"comps['sub_per']=comps.apply(lambda x: x['sub']/float(x['tot']),axis=1)\n",
"comps['cleaned_per']=comps.apply(lambda x: x['clean']/float(x['tot']),axis=1)\n",
"comps['pending_per']=comps.apply(lambda x: x['pen']/float(x['tot']),axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Breakdown of subscribed, unsubscribed and pending by time joined"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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0aZNcrxFlQZqCggKVv0SrQ09PD3l5edi5c2e1en/VtOnTpyMyMhLz589XOgQJ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\nhKHihxJ0rcJUHFGnlGlmsLq5hT1gtdnlf74TjHA8TdRqJ8KTIOQlub8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ngfFynlJptYsJ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fkB7mr8fW89uqf3HYV4NFKFw3ZAr3jfocFbmxz/MxeWWMySvjG2ddS2XTYUOEOvEJm93tJ/26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\nAAAAABQhPAEAAABQhPAEAAAAQBHftJc3yKVJXfYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11118ca10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# green line with triangles represents currently subscribed users\n",
"# red line with circles represents unsubscribed users\n",
"# solid yellow line represents pending users\n",
"plt.figure(figsize=(20,5))\n",
"plt.plot(comps.join_month,comps.un_per,'-o',color=c2)\n",
"plt.plot(comps.join_month,comps.sub_per,'->',color=c1)\n",
"plt.plot(comps.join_month,comps.pending_per,'-',color=c4)\n",
"# plt.plot(comps.join_month,comps.cleaned_per,'^-',color=c3) \n",
"# Usually there will be much smaller proportion of cleaned; which we plot separately to see variations \n",
"# Can also be plotted on this graph if desired \n",
"plt.title('Section 3.2 Proportion of Current Status by Time Joined',fontdict={'fontsize':25})\n",
"plt.yticks(fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.ylabel('Fraction of List Cohort',fontdict={'fontsize':20})\n",
"plt.xlabel('Time Joined',fontdict={'fontsize':20})\n",
"plt.legend(loc='best')\n",
"plt.savefig(oupt_dir+'/Section_3.2_Proportion_of_Current_Status_by_Time_Joined.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Breakdown of cleaned by time joined"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x111c95ed0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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58Pjjj+Obb77BxRdfrOkCiYZj95ZZBVtqByjldg2dgDsBM4pe3eGBuqy/bveg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\n4wzNi5RSnE5WhNXSS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IjLedM0+eCDD1iwYAGpqalcc801vP7669SrVy/f5ffu3cs777xD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\n/j+uU9IuJe2xQwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1111b9110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Blue line represents cleaned users. NOTE: Y-Axis should be smaller than the graph above. \n",
"# If it's close to the same magnitude as above, you likely have an issue with bounces. \n",
"plt.figure(figsize=(20,5))\n",
"plt.plot(comps.join_month,comps.cleaned_per,'^-',color=c3)\n",
"# plt.title('Section 3.2 Proportion of Cleaned by Time Joined',fontdict={'fontsize':25})\n",
"plt.ylabel('Fraction of List Cohort',fontdict={'fontsize':20})\n",
"plt.xlabel('Time Joined',fontdict={'fontsize':20})\n",
"plt.yticks(fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.savefig(oupt_dir+'/Section_3.2_Proportion_of_Cleaned_by_Time_Joined.png')\n",
"plt.legend(loc='best')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Section 3.3 Subscriber (In)Activity<a class=\"anchor\" id=\"3.3-bullet\"></a>"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# this cell does not have an output but you need to run it \n",
"# unpack open rate and click rate from stats for each record, add the value to a column named open and click respectively.\n",
"# create a column for those who never opened or clicked\n",
"# false = number of subscribers who have ever opened (gray)\n",
"# true = number of subscribers who have never opened (black)\n",
"member_data_frame['open']=member_data_frame.stats.apply(lambda x: x['avg_open_rate'])\n",
"member_data_frame['click']=member_data_frame.stats.apply(lambda x: x['avg_click_rate'])\n",
"\n",
"member_data_frame['never_opened']=member_data_frame.open.apply(lambda x:x==0)\n",
"member_data_frame['never_clicked']=member_data_frame.click.apply(lambda x:x==0)\n",
"\n",
"\n",
"# continued from above, now as three separate arrays\n",
"ever_opened=member_data_frame[(member_data_frame.status=='subscribed')&\n",
" (member_data_frame.never_opened!=True)]\n",
"never_opened=member_data_frame[(member_data_frame.status=='subscribed')&\n",
" (member_data_frame.never_opened)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Opens: Ever Opened vs. Never Opened"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 38932\n",
"True 7040\n",
"Name: never_opened, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# unpack individual user open rate and click rate from stats for each user record, add the value to a column named open and click respectively.\n",
"# create a column for those who never opened or clicked\n",
"# false = number of subscribers who have ever opened \n",
"# true = number of subscribers who have never opened\n",
"member_data_frame['open']=member_data_frame.stats.apply(lambda x: x['avg_open_rate'])\n",
"member_data_frame['click']=member_data_frame.stats.apply(lambda x: x['avg_click_rate'])\n",
"\n",
"member_data_frame['never_opened']=member_data_frame.open.apply(lambda x:x==0)\n",
"member_data_frame['never_clicked']=member_data_frame.click.apply(lambda x:x==0)\n",
"member_data_frame[member_data_frame.status=='subscribed'].never_opened.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save ever_open and never open for later\n",
"ev_opn=member_data_frame[member_data_frame.status=='subscribed'].never_opened.value_counts()[False]\n",
"nv_opn=member_data_frame[member_data_frame.status=='subscribed'].never_opened.value_counts()[True]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 0.846863\n",
"True 0.153137\n",
"Name: never_opened, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# false = percent subscribers ever opened\n",
"# true = percent subscribers never opened \n",
"member_data_frame[member_data_frame.status=='subscribed'].never_opened.value_counts()/float(list_size)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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2QFqlShUtWLBABw4cUPfu3fXZZ59pxowZOT7HAgAAAAAo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\nEEIIIYQQQohKIR1OIYQQQgghhBCV4v8BHCk4uhBY7WYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111bb91d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# stack the data - never opened vs ever opened\n",
"plt.figure(figsize=(15,10))\n",
"ax=plt.hist([ever_opened.jv,never_opened.jv], stacked=True,normed=False,label=['EVER OPEND',\n",
" 'NEVER OPEND'],color=[c_ev,c_nev])\n",
"\n",
"plt.xticks(ax[1],map(lambda x: pd.to_datetime(x).date(),ax[1]), rotation=35)\n",
"#plt.title('Section 3.3 Never Opened vs EverOpened by Time Joined, Stacked Histogram',fontdict={'fontsize':25})\n",
"plt.ylabel('Subscriber Count',fontdict={'fontsize':20})\n",
"plt.xlabel('Time Joined',fontdict={'fontsize':20})\n",
"plt.legend(loc='best', prop={'size': 20})\n",
"plt.yticks(fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.savefig(oupt_dir+'/Section_3.3_NeverOpened_vs_EverOpened_by_Time_Joined.png')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Clicks: Ever Clicked vs. Never Clicked"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save ever open and never open rates for later\n",
"ev_opn_r=ev_opn/float(list_size)\n",
"\n",
"nv_opn_r=nv_opn/float(list_size)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 23721\n",
"True 22251\n",
"Name: never_clicked, dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# false = number of subscribers who have ever clicked \n",
"# true = number of subscribers who have never clicked \n",
"member_data_frame[member_data_frame.status=='subscribed'].never_clicked.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save ever clicked and never clicked for later\n",
"ev_clk=member_data_frame[member_data_frame.status=='subscribed'].never_clicked.value_counts()[False]\n",
"nv_clk=member_data_frame[member_data_frame.status=='subscribed'].never_clicked.value_counts()[True]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False 0.515988\n",
"True 0.484012\n",
"Name: never_clicked, dtype: float64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# false = percent subscribers ever clicked\n",
"# true = percent subscribers never clicked \n",
"member_data_frame[member_data_frame.status=='subscribed'].never_clicked.value_counts()/float(list_size)\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# save ever clicked and never clicked rates for later\n",
"ev_clk_r=ev_clk/float(list_size)\n",
"nv_clk_r=nv_clk/float(list_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3.4 Subscriber Engagement Distributions <a class=\"anchor\" id=\"3.4-bullet\"></a>\n",
"#### Distribution of average user open rate for subscribers"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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ut9xyS77xjW/k2WefzfHHH5/99tvvi5wfAACA/+U+9T4w559/fpo1a5b/+I//\nyJgxYzJmzJiN9imVSmnWrFnOOuusXHjhhfU2KAAAAI3Tp4ZpWVlZzj333Bx55JGZPHly/vCHP2TJ\nkiVZsWJF2rdvn5122ikHHnhgjjrqqOy0005f1MwAAAA0Ip8app/Yddddc+GFF3pFFAAAgDq32c+Y\nAgAAwBdBmAIAAFAoYQoAAEChhCkAAACFEqYAAAAUqtZhev311+fRRx+tz1kAAABogmodpvfdd18e\nf/zxehwFAACApqjWYdq6des0a9asPmcBAACgCap1mF588cX51a9+lQkTJmTZsmX1ORMAAABNSHlt\nd5w8eXJatmyZkSNHZuTIkWnWrFlatmy50X5lZWV56qmn6nRIAAAAGq9ah+lbb72VVq1apVWrVvU5\nDwAAAE1MrcP0scceq885AAAAaKI+131MP/zww7qaAwAAgCbqM4VpqVTK//t//y8nnnhivvrVr6Zv\n375JkgkTJuTyyy/PO++8Uy9DAgAA0HjV+q2869evz7nnnps//OEPKS8vT5s2bfL+++8nSf76179m\n8uTJmTNnTu67775su+229TYwAAAAjUutXzG96667MmPGjJxxxhl5+umnc8opp1SvXXTRRRkxYkTe\neOON3H777fUyKAAAAI1TrcN0ypQp2XvvvXPppZemVatWKSsrq17bcsstc84552TffffN448//rkG\nuuqqq/KjH/2oxraBAweme/fu1f++8pWv1NinsrIyI0aMSL9+/bLffvtl1KhR2bBhQ41j3H333Rkw\nYEB69eqVM888M4sWLaqxPm/evAwaNCi9evXK4YcfnilTpnyu8wAAAKB2ah2mb775ZvVnSjenR48e\nefvtt//pYW666abcf//9G21fuHBhbrzxxvzxj3/MH//4xzzxxBO57LLLqteHDx+eysrKTJgwIddd\nd10mTZqUMWPGVK9PnDgxN998cy6//PJMnDgxLVq0yJAhQ7Ju3bokH4ftkCFD0qNHj0yePDmnnXZa\nrrzyyjz55JP/9LkAAABQO7X+jOlWW22Vt95661P3eeONN9KuXbvPPMSbb76ZK664Iq+99loqKio2\nWlu9enV69uyZ7bbbbqPHzp07N3Pnzs20adNSUVGRbt265ZJLLsnIkSMzbNiwNGvWLOPGjcvgwYNz\n2GGHJUlGjx6dAw44II8++mj+7d/+LRMnTsxWW22VK664IknSpUuXvPjiixk3blz222+/z3w+AAAA\n1F6tXzH/SxznAAAgAElEQVT913/91/z3f/935s+fv8n1Z599No899lj23XffzzzE3LlzU1FRkV//\n+tfZcccda6y9+uqradmy5UbbPzFnzpxUVFTUCNr+/funqqoq8+fPT2VlZV5//fX079+/er1169bp\n0aNHZs+eXX2Mv381eJ999skzzzzzmc8FAACAz6bWr5ief/75efzxxzNo0KAMHDiw+jOakydPzrx5\n8/LAAw+kefPmOeeccz7zEMccc0yOOeaYTa4tWLAgbdu2zcUXX5ynn3467du3zwknnJAzzjgjSfL2\n22+nY8eONR6zww47VK+Vl5enrKxsk/ssXry4er8999xzo/XVq1dn+fLlad++/Wc+JwAAAGqn1mG6\n88475xe/+EUuu+yy3HvvvdXbf/jDH6ZUKuVLX/pSrr/++nz5y1+u0wEXLFiQ1atX58ADD8zQoUPz\nzDPP5Prrr09VVVWGDx+e1atXp0WLFjUe80mMrlmzJqtWrUqSjfZp3rx51q5dmySbPEbz5s2TJGvW\nrKnT8wEAAKCmWodpkuy111759a9/nWeffTYvvvhiVq5cmdatW2ePPfZIv379ssUWtX5ncK2NGjUq\nH374Ydq0aZMk2X333bNixYrcfvvtGT58eFq2bFkdmJ9Yv359SqVSWrVqlZYtWybJRvusXbs2rVq1\nSvJxtG5qPfn4bb8AAADUn88Upp/o1atXevXqVdezbFJZWVl1lH6iW7du+eCDD1JVVZVOnTplxowZ\nNdaXLl2aJOnUqVM6d+6cUqmUpUuXZqeddqqxz2677ZYk6dy5c5YtW7bRMVq3bv1PfZkT/5zyLbdI\nhw6e77rk+aQhc33SULk2achcnzRWnzlM//SnP+VXv/pVXnnllXz44Ydp3759evTokW9961vZa6+9\n6nzAE088Mb169ar+xtzk43uO7rDDDmnbtm369OmT0aNHZ8mSJdWfI505c2batm2b7t27p7y8PLvs\nsktmzZqVPn36JEk++OCDvPDCCxk0aFCSpE+fPpk0aVKN3ztz5szsvffedX4+bN76jzZk2bKVRY/R\naHTo0M7zSYPl+qShcm3SkLk+aajq4g8mtX7v7fr163PxxRfnzDPPzOTJk/Pyyy9nyZIlefbZZ3Pv\nvffmxBNPrHHv0LryzW9+M/fff3+mTJmSN998MxMnTsy4ceNy/vnnJ0l69+6dnj175oILLshLL72U\n6dOnZ9SoURk8eHDKyz/u7sGDB+eOO+7I1KlT8+qrr+biiy9Ox44dq28fM3DgwLz33nu5+uqrs3Dh\nwtxzzz156KGHctZZZ9X5+QAAAFBTrV8xveuuu/LQQw9l3333zYUXXpi99tor5eXlqaqqypw5c/Kz\nn/0st956a3beeeccd9xx//RAZWVlNX4+88wzU15enttuuy2LFy9ORUVFfvjDH+aEE06o3ueWW27J\nNddck1NOOSVt2rTJSSedlGHDhlWvn3zyyVmxYkWuu+66VFVVpW/fvrnzzjurw3W77bbL2LFjM3Lk\nyBx//PGpqKjIDTfcUOMWMwAAANSPslKpVKrNjocffni23HLLTJkypfoba/9WVVVVjj322LRp0ya/\n+tWv6nzQhmrXkcdnTZetih6jUej8yto8ct7tRY/RaHi7Dw2Z65OGyrVJQ+b6pKH6Qt/Ku3jx4hx0\n0EGbjNIkadu2bQYMGJDXX3/9cw8FAABA01HrMN15553z17/+9VP3Wb58eTp37vy5hwIAAKDpqHWY\nnnvuufnd736XiRMnbnJ9+vTpefjhhzNkyJA6Gw4AAIDGb7NffnTJJZdstG3bbbfNVVddlfHjx6dX\nr17ZbrvtsnLlyrz44ot57rnnsvPOO2fx4sX1OjAAAACNy2bD9NO+wGjBggVZsGDBRtsXLVqUW2+9\ntfpWLgAAAPCPbDZMp02b9kXOAQAAQBO12TDdcccdv8g5AAAAaKI+9RXTrl27pkuXLtU/19ahhx76\n+ScDAACgSdhsmA4bNizDhw/P8OHDq38uKyv71IOVSqWUlZVl/vz5dTslAAAAjdZmw3T48OHp379/\njZ8BAACgrn1qmP6t/fffP3vttVeaN29e70MBAADQdGxR2x3PO+88t4EBAACgztU6TFeuXJnddtut\nPmcBAACgCap1mB566KH57//+71RWVtbnPAAAADQxm/2M6d/r169fnn766Rx66KHZe++986UvfSkt\nW7bcaL+ysrJcdtlldTokAAAAjVetw/Taa6+t/u8//vGPm91PmAIAAPBZ1DpMx48fX59zAAAA0ETV\nOkz/9p6mH330Ubbccsvqn996663suOOOdTsZAAAATUKtv/woSf70pz/l2GOPzb333lu9rVQq5Zvf\n/GaOPvrovPDCC3U+IAAAAI1brcN09uzZOeuss/LGG2+kVatW1dvXrl2bY445Jn/961/z7W9/O88/\n/3y9DAoAAEDjVOswveWWW9KmTZv813/9V0466aTq7S1atMjIkSMzefLktGjRImPGjKmXQQEAAGic\nah2m8+fPz9FHH52dd955k+s777xzjjzyyDzzzDN1NhwAAACNX63D9KOPPsqaNWs+dZ+ysrKUSqXP\nPRQAAABNR63DtHv37vn973+fysrKTa4vX748v//977PHHnvU2XAAAAA0frUO0+985zt55513cvrp\np2fq1Kl566238v777+evf/1rHn744ZxxxhlZunRpzjjjjHocFwAAgMam1vcx/frXv54LL7wwP//5\nz3PxxRdvtF5WVpbzzjsvRxxxRJ0OCAAAQONW6zBNku9973s5/PDD89vf/javvPJKVqxYkdatW6db\nt2456qij0rVr1/qaEwAAgEbqM4Vpkuy6664555xz6mMWAAAAmqDPHKYLFy7Ml7/85eqf77vvvsye\nPTs77rhjTj311HTo0KFOBwQAAKBxq3WYVlVVZfjw4Xnqqafypz/9Ke3bt89Pf/rT3HHHHdW3iJky\nZUruv//+dOzYsd4GBgAAoHGp9bfy3n777Zk5c2YOPvjgJMmqVasyfvz4bLvttrn33ntz3XXX5d13\n380tt9xSb8MCAADQ+NT6FdNHH300/fr1y2233ZYk+d3vfpdVq1bl1FNPTd++fdO3b9/84Q9/yPTp\n0+ttWAAAABqfWr9iunjx4vTu3bv65xkzZqSsrCwHHXRQ9bYdd9wx7733Xt1OCAAAQKNW6zDdeuut\n8/7771f/PGPGjLRq1apGrL7++uvZfvvt63ZCAAAAGrVah+mee+6Zhx9+OLNmzcq4cePy9ttv55BD\nDkl5+cfvBn744Yczbdq09OrVq96GBQAAoPGp9WdMzzvvvAwePDinn356SqVSWrRoke9973tJkh//\n+MeZMGFC2rVrl3PPPbfehgUAAKDxqXWY9ujRIxMnTswvf/nLlEqlHHfccdljjz2SJHvssUeOO+64\nfO9730uXLl3qbVgAAAAan1qHaZLsuuuuufTSSzfaftJJJ+Wkk06qs6EAAABoOj5TmCZJVVVVfve7\n3+Xll1/Ohx9+mPbt26dHjx455JBD0rx58/qYEQAAgEbsM4XpL3/5y1x//fVZtWpVSqVS9faysrJs\nt912ue6663LAAQfU+ZAAAAA0XrUO00ceeSRXX311tt9++wwdOjT/8i//kjZt2mTp0qWZM2dO7rvv\nvpxzzjm5995707Nnz/qcGQAAgEak1mE6bty4bLPNNrn//vtTUVFRY+3QQw/Nt771rZx88sn52c9+\nlv/8z/+s80EBAABonGp9H9NXXnkl3/jGNzaK0k/svvvu+cY3vpFnn322zoYDAACg8at1mG699dY1\nPle6KW3atEnr1q0/91AAAAA0HbUO0xNOOCG/+c1vsmDBgk2uL168OL/5zW9yzDHH1NlwAAAANH6b\n/YzpAw88UOPnTp06pXXr1hk4cGBOOOGE9O7dO9tvv31WrFiRF198MQ888EC23nrr7LvvvvU+NAAA\nAI1HWWkz78/t3r17ysrKqt+++7f//cnPn/j77fPnz6+veRucXUcenzVdtip6jEah8ytr88h5txc9\nRqPRoUO7LFu2sugxYJNcnzRUrk0aMtcnDVWHDu0+9zE2+4rpT37yk899cAAAAPhHNhum3/rWt77I\nOQAAAGiiav3lRwAAAFAfNvuK6d/r379/rfYrKyvLU0899U8PBAAAQNNS6zBt27btJrevXr06y5cv\nz4YNG9KtW7fstNNOdTYcAAAAjV+tw/Sxxx7b7NrKlStz66235sEHH8xPf/rTOhkMAACApqFOPmPa\nrl27XHLJJdltt90yatSoujgkAAAATUSdfvlR7969M2vWrLo8JAAAAI1cnYbp/PnzU1ZWVpeHBAAA\noJGr9WdMp02btsntpVIpH374YR5//PE8+eSTOeyww+psOAAAABq/WofpsGHDPvXV0FKplB122CHf\n//7362QwAAAAmoY6CdPmzZuna9euOfjgg9OsWbM6Gw4AAIDGr9Zhet5559XnHAAAADRRn+vLj9as\nWZNFixblgw8+qKt5AAAAaGL+YZg+9thjufzyy/Pyyy9XbyuVShk9enT23XffHHHEEenfv38uuOCC\nvPfee/U6LAAAAI3Pp76V96qrrsrEiROTJIcccki6d++eJPnpT3+aO++8M2VlZdlvv/1SVlaWRx99\nNK+99lomTZqU5s2b1//kAAAANAqbfcX0sccey/3335+vfOUrGTt2bA455JAkyZIlS3LXXXelrKws\nP/7xjzNu3LiMHTs2Y8aMyWuvvZbx48d/UbMDAADQCGw2TB944IG0b98+48ePz/77758WLVokSR5+\n+OGsX78+O++8cwYOHFi9/9e//vX07t07Dz/8cP1PDQAAQKOx2TB9/vnnc8ghh6Rt27Y1tj/55JMp\nKyvLgAEDNnpMz549s2jRorqfEgAAgEZrs2H6/vvvp2PHjjW2bdiwIXPmzEmS/Ou//utGj2nWrFnW\nrVtXxyMCAADQmG02TNu1a7fRt+w+//zzqaqqSnl5efr167fRYxYtWpRtttmm7qcEAACg0dpsmH71\nq1/Nk08+mQ0bNlRv+81vfpPk41dLW7VqVWP/ysrKPPHEE/nqV79aT6MCAADQGG02TE866aT85S9/\nyUUXXZRZs2ZlwoQJ+eUvf5mysrKccsopNfatqqrK97///axatSrHHHNMvQ8NAABA47HZ+5geeuih\nOeWUUzJhwoQ88sgjSZJSqZRvf/vbOfjgg6v3u+iiizJ9+vR88MEHOeKII/L1r3+9/qcGAACg0dhs\nmCbJj370oxx++OH5/e9/n/Xr12f//fevvp/pJ1544YW0aNEiZ555ZoYOHVqfswIAANAIfWqYJkn/\n/v3Tv3//za5PmjRpo1vKAAAAQG1t9jOmtSVKAQAA+Dw+d5gCAADA5yFMAQAAKJQwBQAAoFDCFAAA\ngEIJUwAAAAolTAEAACiUMAUAAKBQDS5Mr7rqqvzoRz+qse2JJ57Icccdl549e+bYY4/NjBkzaqxX\nVlZmxIgR6devX/bbb7+MGjUqGzZsqLHP3XffnQEDBqRXr14588wzs2jRohrr8+bNy6BBg9KrV68c\nfvjhmTJlSv2cIAAAADU0qDC96aabcv/999fY9tprr+Xcc8/NkUcemSlTpmTAgAEZNmxYFi5cWL3P\n8OHDU1lZmQkTJuS6667LpEmTMmbMmOr1iRMn5uabb87ll1+eiRMnpkWLFhkyZEjWrVuX5OOwHTJk\nSHr06JHJkyfntNNOy5VXXpknn3zyizlxAACAJqxBhOmbb76Z008/Pb/85S9TUVFRY238+PHp1atX\nzj777HTp0iUjRoxI796984tf/CJJMnfu3MydOzfXX399unXrloMOOiiXXHJJ7r333urwHDduXAYP\nHpzDDjssu+++e0aPHp133303jz76aJKPw3WrrbbKFVdckS5duuTUU0/N0UcfnXHjxn2xTwQAAEAT\n1CDCdO7cuamoqMivf/3r7LjjjjXW5syZk/79+9fY1r9//8yZM6d6vaKiokbQ9u/fP1VVVZk/f34q\nKyvz+uuv1zhG69at06NHj8yePbv6GH379q3xO/bZZ58888wzdXqeAAAAbKy86AGS5Jhjjskxxxyz\nybW33347HTt2rLGtY8eOWbx48WbXd9hhh+q18vLylJWVbXKfvz3GnnvuudH66tWrs3z58rRv3/6f\nPzkAAAA+VYN4xfTTrF69Oi1atKixrVmzZlm7du1m1z+J0TVr1mTVqlVJstE+zZs3/9RjNG/ePEmy\nZs2aujsZAAAANtLgw7RFixbVAfmJdevWpVWrVkmSli1bbrS+fv36lEqltGrVKi1btkySjfZZu3Zt\n9TE29Ts++bl169Z1dzIAAABspEG8lffTdO7cOcuWLauxbcmSJdVvze3UqdNGt49ZunRp9Vrnzp1T\nKpWydOnS7LTTTjX22W233Tb7O5YuXZrWrVunXbt2dX5ObFr5llukQwfPd13yfNKQuT5pqFybNGSu\nTxqrBh+mffr0yaxZs3LOOedUb3vqqaeqv6yoT58+GT16dI1YnTlzZtq2bZvu3bunvLw8u+yyS2bN\nmpU+ffokST744IO88MILGTRoUPUxJk2aVOP3zpw5M3vvvfcXcYr8/63/aEOWLVtZ9BiNRocO7Tyf\nNFiuTxoq1yYNmeuThqou/mDS4N/Ke+qpp2bWrFn5+c9/nj//+c+56aabMm/evJx++ulJkt69e6dn\nz5654IIL8tJLL2X69OkZNWpUBg8enPLyj7t78ODBueOOOzJ16tS8+uqrufjii9OxY8ccdthhSZKB\nAwfmvffey9VXX52FCxfmnnvuyUMPPZSzzjqrsPMGAABoKhrcK6ZlZWU1fu7WrVtuvvnmjBo1KmPH\njk3Xrl1z2223pWvXrtX73HLLLbnmmmtyyimnpE2bNjnppJMybNiw6vWTTz45K1asyHXXXZeqqqr0\n7ds3d955Z3W4brfddhk7dmxGjhyZ448/PhUVFbnhhhs2uk0NAAAAda+sVCqVih7if7NdRx6fNV22\nKnqMRqHzK2vzyHm3Fz1Go+HtPjRkrk8aKtcmDZnrk4aqSbyVFwAAgMZNmAIAAFAoYQoAAEChhCkA\nAACFEqYAAAAUSpgCAABQKGEKAABAoYQpAAAAhRKmAAAAFEqYAgAAUChhCgAAQKGEKQAAAIUSpgAA\nABRKmAIAAFAoYQoAAEChhCkAAACFEqYAAAAUSpgCAABQKGEKAABAoYQpAAAAhRKmAAAAFEqYAgAA\nUChhCgAAQKGEKQAAAIUSpgAAABRKmAIAAFAoYQoAAEChhCkAAACFEqYAAAAUSpgCAABQKGEKAABA\noYQpAAAAhRKmAAAAFEqYAgAAUChhCgAAQKGEKQAAAIUSpgAAABRKmAIAAFAoYQoAAEChhCkAAACF\nEqYAAAAUSpgCAABQKGEKAABAoYQpAAAAhRKmAAAAFEqYAgAAUChhCgAAQKGEKQAAAIUSpgAAABRK\nmAIAAFCo8qIHgE+UNmzIwoULih6j0dh2255FjwAAALUiTGkw1rxXlSMnX5otO7QtepT/9T5aVpWZ\n296SbbbpXPQoAADwDwlTGpQtO7RNeeetix4DAAD4AvmMKQAAAIUSpgAAABRKmAIAAFAoYQoAAECh\nhCkAAACFEqYAAAAUSpgCAABQKGEKAABAoYQpAAAAhRKmAAAAFEqYAgAAUChhCgAAQKGEKQAAAIUS\npgAAABRKmAIAAFAoYQoAAEChhCkAAACFEqYAAAAUSpgCAABQKGEKAABAoYQpAADw/2vv3sNruvI/\njn+Ok0SkCNGISKsI4xhKQoT4tWZaTanOGGlLmUoGVYpWXOrSacco5kFFi4pbdWpKtMgMUQ2Gjkvd\nRVOktO6tqEsqWg1y378/TM44TaIOSXbkvF/P43natdfZ+e69l8vnrL32BkxFMAUAAAAAmIpgCgAA\nAAAwFcEUAAAAAGAqgikAAAAAwFQEUwAAAACAqQimAAAAAABTEUwBAAAAAKa6K4Lp8ePHZbPZ1LRp\nU9lsNvt/f/7555Kkbdu2qVu3bmrZsqX+8Ic/aOvWrQ6fT09PV3R0tNq0aaP27dsrJiZG+fn5Dn0W\nLVqkRx99VEFBQerXr5+++eabMjs+AAAAAHBlbmYXcCu+/vpr+fj4aM2aNTIMw95eo0YNHTt2TIMH\nD9ZLL72k8PBwrV69WkOGDNGqVasUGBgoSXrppZdktVoVFxenc+fOaezYsXJzc9OwYcMkSStWrNDs\n2bM1efJk1a9fX2+99Zb69++vxMREubu7m3LMAAAAAOAq7ooZ06NHjyowMFA+Pj6qVauW/ZfVatUH\nH3ygoKAgDRgwQA0aNFB0dLSCg4P1j3/8Q5KUnJys5ORkTZ06Vb/61a/UoUMHjR49WkuWLFFOTo4k\n6b333lPfvn0VHh6uxo0ba/r06bp48aL+/e9/m3nYAAAAAOAS7qpgWpR9+/YpNDTUoS00NFT79u2z\nb69bt67q1q3rsD0jI0OHDx9Wenq6Tp0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6e/Zs6RUK/II7yUQE02LYbDZ5eXlpz5499rbU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\nAAAAAGCq/wdQO9HMTQst5gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111bc3d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,10))\n",
"member_data_frame[member_data_frame.status=='subscribed'].open.hist(color=c1)\n",
"#plt.title('Section 3.4 Distribution of User Unique Open Rates',fontdict={'fontsize':25})\n",
"plt.xlabel(\"User Unique Open Rate\",fontdict={'fontsize':20})\n",
"plt.ylabel(\"Subscriber Count\",fontdict={'fontsize':20})\n",
"plt.yticks(fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.savefig(oupt_dir+'/Section_3.4_Distribution_of_User_Average_Open_Rates.png') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Distribution of average user click rate for subscribers"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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Z8vDwkLu7e61+JpNJ+/btc2iRAAAAAADXZXcw/fe//y1JCgwMbLBiAAAAAACN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\nhQYPHtzgRQMAAAAAXEed7zHt16+fRo8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dTiZymzZt2rRiqusvzc3NTXa7XYsW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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11638d250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15,10))\n",
"member_data_frame[member_data_frame.status=='subscribed'].click.hist(color=c1)\n",
"#plt.title('Section 3.4 Distribution of User Unique Click Rates',fontdict={'fontsize':25})\n",
"plt.xlabel(\"User Unique Click Rate\",fontdict={'fontsize':20})\n",
"plt.ylabel(\"Subscriber Count\",fontdict={'fontsize':20})\n",
"plt.yticks(fontsize=15)\n",
"plt.xticks(fontsize=15)\n",
"plt.savefig(oupt_dir+'/Section_3.4_Distribution_of_User_Average_Click_Rates.png') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Section 3.5 Investigating Churn <a class=\"anchor\" id=\"3.5-bullet\"></a>\n",
"#### Lifetime Unsubscribe Rate"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22.5521421967\n"
]
}
],
"source": [
"# unsubscribe rate: over the lifetime of your list for everyone who successfully subscribed x% unsubscribed \n",
"# or, another way to think about this is on average over the lifetime of your list for every 100 people who successfully subscribed, x number of people unsubscribed \n",
"life_unsub=unsubscribed/float(unsubscribed+subscribed+cln)*100\n",
"print life_unsub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Lifetime Subscribe Rate"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"59.0414055276\n"
]
}
],
"source": [
"# subscribe rate: over the lifetime of your list on average for everyone who successfully subscribed x% are still subscribed. \n",
"life_sub=subscribed/float(subscribed+unsubscribed+cln)*100\n",
"print life_sub"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Lifetime Cleaned Rate "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"18.4064522758\n"
]
}
],
"source": [
"# cleaned rate: of everyone who has ever had a status of subscribed, what been removed due to bounces? \n",
"life_clean=cln/float(subscribed+unsubscribed+cln)*100\n",
"print life_clean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Section 4 Export Results of Notebook 1 to Folder <a class=\"anchor\" id=\"4-bullet\"></a>\n",
":bowtie:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# export data \n",
"\n",
"exec_dict={'Shorenstein Notebook 1':{\n",
" 1: u'3.1 List Composition Analysis',\n",
" 2: u'Tr12d86b1d6aaditionally measured list size',\n",
" 3: u'Total unique email records ',\n",
" 4: u'Composition of total unique email records by status ',\n",
" 10: u'3.2 List Lifetime Ratios',\n",
" 11: u'Lifetime Unsubscribe Rate',\n",
" 12: u'Lifetime Subscribe Rate',\n",
" 13: u'Lifetime Cleaned Rate',\n",
" 15: u'Section 3.3 Reader Activity',\n",
" 16: u'Ever Opened',\n",
" 17: u'Never Opened',\n",
" 19: u'Ever Clicked',\n",
" 20: u'Never Clicked'},\n",
" '1':{2:list_size,3:total_un_eml,5: u'subscribed',\n",
" 6: u'unsubscribed',7: u'cleaned',8: u'pending',\n",
" 11:life_unsub,\n",
" 12:life_sub,\n",
" 13:life_clean,\n",
" 16:ev_opn,\n",
" 17:nv_opn,\n",
" 19:ev_clk,\n",
" 20:nv_clk},\n",
" \"2\":{5:subscribed,\n",
" 6:unsubscribed,\n",
" 7:cln,\n",
" 8:pndg,\n",
" 16:ev_opn_r,\n",
" 17:nv_opn_r,\n",
" 19:ev_clk_r,\n",
" 20:nv_clk_r}}\n",
"\n",
"ex_rpt=pd.DataFrame(exec_dict)\n",
"\n",
"ex_rpt['']=ex_rpt['1']\n",
"\n",
"ex_rpt[' ']=ex_rpt['2']\n",
"\n",
"del ex_rpt[\"1\"]\n",
"del ex_rpt[\"2\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ex_rpt.to_csv(oupt_dir+'/Shorenstein Notebook 1 executive report.csv',index=False)"
]
}
],
"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": 1
}
| mit |
hasadna/knesset-data-pipelines | jupyter-notebooks/Render site pages for development and debugging.ipynb | 1 | 24521 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Render site pages\n",
"\n",
"[dpp](https://github.com/frictionlessdata/datapackage-pipelines) runs the knesset data pipelines periodically on our server.\n",
"\n",
"This notebook shows how to run pipelines that render pages for the static website at https://oknesset.org"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the source data\n",
"\n",
"Download the source data, can take a few minutes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!{'cd /pipelines; KNESSET_LOAD_FROM_URL=1 dpp run --concurrency 4 '\\\n",
" './committees/kns_committee,'\\\n",
" './people/committee-meeting-attendees,'\\\n",
" './members/mk_individual'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run the build pipeline\n",
"\n",
"This pipeline aggregates the relevant data and allows to filter for quicker development cycles.\n",
"\n",
"You can uncomment and modify the filter step in committees/dist/knesset.source-spec.yaml under the `build` pipeline to change the filter.\n",
"\n",
"The build pipeline can take a few minutes to process for the first time."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[./committees/dist/build:T_0] >>> INFO :168911d3 RUNNING ./committees/dist/build\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 Collecting dependencies\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 Running async task\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 Waiting for completion\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 Async task starting\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 Searching for existing caches\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 Building process chain:\n",
"[./committees/dist/build:T_0] >>> INFO :- load_resource\n",
"[./committees/dist/build:T_0] >>> INFO :- knesset.load_large_csv_resource\n",
"[./committees/dist/build:T_0] >>> INFO :- knesset.rename_resource\n",
"[./committees/dist/build:T_0] >>> INFO :- load_resource\n",
"[./committees/dist/build:T_0] >>> INFO :- filter\n",
"[./committees/dist/build:T_0] >>> INFO :- build_meetings\n",
"[./committees/dist/build:T_0] >>> INFO :- dump.to_path\n",
"[./committees/dist/build:T_0] >>> INFO :- (sink)\n",
"[./committees/dist/build:T_0] >>> INFO :load_resource: INFO :Processed 756 rows\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /usr/local/lib/python3.6/site-packages/datapackage_pipelines/specs/../lib/load_resource.py\n",
"[./committees/dist/build:T_0] >>> INFO :knesset.load_large_csv_resource: INFO :Processed 1771 rows\n",
"[./committees/dist/build:T_0] >>> INFO :knesset.rename_resource: INFO :Processed 1771 rows\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /pipelines/datapackage_pipelines_knesset/processors/rename_resource.py\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /pipelines/datapackage_pipelines_knesset/processors/load_large_csv_resource.py\n",
"[./committees/dist/build:T_0] >>> INFO :load_resource: INFO :Processed 76185 rows\n",
"[./committees/dist/build:T_0] >>> INFO :filter: INFO :Processed 1865 rows\n",
"[./committees/dist/build:T_0] >>> INFO :build_meetings: INFO :Processed 1865 rows\n",
"[./committees/dist/build:T_0] >>> INFO :dump.to_path: INFO :Processed 1865 rows\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /usr/local/lib/python3.6/site-packages/datapackage_pipelines/specs/../lib/filter.py\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /usr/local/lib/python3.6/site-packages/datapackage_pipelines/specs/../lib/load_resource.py\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /pipelines/committees/dist/build_meetings.py\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /usr/local/lib/python3.6/site-packages/datapackage_pipelines/specs/../lib/dump/to_path.py\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE /usr/local/lib/python3.6/site-packages/datapackage_pipelines/manager/../lib/internal/sink.py\n",
"[./committees/dist/build:T_0] >>> INFO :168911d3 DONE V ./committees/dist/build {'.dpp': {'out-datapackage-url': '../../data/committees/dist/build_meetings/datapackage.json'}, 'bytes': 7557637, 'committees': 756, 'count_of_rows': 1865, 'dataset_name': '_', 'hash': 'c68616bddaacb22cb62c85cb3b4015e8', 'meetings': 94, 'mks': 1015, 'skipped committees': 0, 'skipped meetings': 0, 'skipped mks': 0}\n",
"INFO :RESULTS:\n",
"INFO :SUCCESS: ./committees/dist/build {'bytes': 7557637, 'committees': 756, 'count_of_rows': 1865, 'dataset_name': '_', 'hash': 'c68616bddaacb22cb62c85cb3b4015e8', 'meetings': 94, 'mks': 1015, 'skipped committees': 0, 'skipped meetings': 0, 'skipped mks': 0}\n"
]
}
],
"source": [
"!{'cd /pipelines; dpp run --verbose ./committees/dist/build'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download some protocol files for rendering\n",
"\n",
"upgrade to latest dataflows library"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!{'pip install --upgrade dataflows'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Restart the kernel if an upgrade was done"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Choose some session IDs to download protocol files for:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"session_ids = [2063122, 2063126]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>kns_committeesession</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead>\n",
"<tr><th style=\"text-align: right;\"> #</th><th style=\"text-align: right;\"> CommitteeSessionID\n",
"(integer)</th><th style=\"text-align: right;\"> Number\n",
"(integer)</th><th style=\"text-align: right;\"> KnessetNum\n",
"(integer)</th><th style=\"text-align: right;\"> TypeID\n",
"(integer)</th><th>TypeDesc\n",
"(string) </th><th style=\"text-align: right;\"> CommitteeID\n",
"(integer)</th><th>Location\n",
"(string) </th><th>SessionUrl\n",
"(string) </th><th>BroadcastUrl\n",
"(string) </th><th>StartDate\n",
"(datetime) </th><th>FinishDate\n",
"(datetime) </th><th>Note\n",
"(string) </th><th>LastUpdatedDate\n",
"(datetime) </th><th>download_crc32c\n",
"(string) </th><th>download_filename\n",
"(string) </th><th style=\"text-align: right;\"> download_filesize\n",
"(integer)</th><th>parts_crc32c\n",
"(string) </th><th style=\"text-align: right;\"> parts_filesize\n",
"(integer)</th><th>parts_parsed_filename\n",
"(string) </th><th>text_crc32c\n",
"(string) </th><th style=\"text-align: right;\"> text_filesize\n",
"(integer)</th><th>text_parsed_filename\n",
"(string) </th><th>topics\n",
"(array) </th><th>committee_name\n",
"(string) </th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\">2063122</td><td style=\"text-align: right;\">29</td><td style=\"text-align: right;\">15</td><td style=\"text-align: right;\">161</td><td>פתוחה</td><td style=\"text-align: right;\">2045</td><td>חדר הוועדה, באגף הוועדות (קדמה), קומה 3, חדר 3710</td><td>http://main.knesset.gov.il/Activity/committees/Pages/AllCommitteesAgenda.aspx?Tab=3&ItemID=2063122</td><td>None</td><td>2000-07-05 00:00:00</td><td>2000-07-05 00:00:00</td><td>פניות ציבור בנושא איכות והתאמה לתקנים של שירותי הסעדה בבתי-הספר, פעוטונים, קייטנות ומוסדות ציבור </td><td>2018-10-10 11:03:06</td><td>UCgupg==</td><td>files/23/4/3/434231.DOC</td><td style=\"text-align: right;\">47154</td><td>/4kpmQ==</td><td style=\"text-align: right;\">85239</td><td>files/2/0/2063122.csv</td><td>pybkkw==</td><td style=\"text-align: right;\">85134</td><td>files/2/0/2063122.txt</td><td>None</td><td>המיוחדת לפניות הציבור</td></tr>\n",
"<tr><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\">2063126</td><td style=\"text-align: right;\">33</td><td style=\"text-align: right;\">15</td><td style=\"text-align: right;\">161</td><td>פתוחה</td><td style=\"text-align: right;\">2045</td><td>חדר הוועדה, באגף הוועדות (קדמה), קומה 3, חדר 3710</td><td>http://main.knesset.gov.il/Activity/committees/Pages/AllCommitteesAgenda.aspx?Tab=3&ItemID=2063126</td><td>None</td><td>2000-10-30 00:00:00</td><td>2000-10-30 00:00:00</td><td>פניות של דיירי רחוב מאור הגולה בשכונת שפירא בתל-אביב שביתם נהרס והם ממשיכים לשלם משכנתא ולא מקבלים כ ...</td><td>2018-10-10 11:03:06</td><td>ryN9+g==</td><td>files/23/4/3/434233.DOC</td><td style=\"text-align: right;\">36724</td><td>qiGAHw==</td><td style=\"text-align: right;\">56525</td><td>files/2/0/2063126.csv</td><td>+Gw5Mw==</td><td style=\"text-align: right;\">56419</td><td>files/2/0/2063126.txt</td><td>None</td><td>המיוחדת לפניות הציבור</td></tr>\n",
"</tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from dataflows import Flow, load, printer, filter_rows\n",
"\n",
"sessions_data = Flow(\n",
" load('/pipelines/data/committees/kns_committeesession/datapackage.json'),\n",
" filter_rows(lambda row: row['CommitteeSessionID'] in session_ids),\n",
" printer(tablefmt='html')\n",
").results()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"curl -s -o /pipelines/data/committees/meeting_protocols_text/files/2/0/2063122.txt https://production.oknesset.org/pipelines/data/committees/meeting_protocols_text/files/2/0/2063122.txt\n",
"curl -s -o /pipelines/data/committees/meeting_protocols_parts/files/2/0/2063122.csv https://production.oknesset.org/pipelines/data/committees/meeting_protocols_parts/files/2/0/2063122.csv\n",
"curl -s -o /pipelines/data/committees/meeting_protocols_text/files/2/0/2063126.txt https://production.oknesset.org/pipelines/data/committees/meeting_protocols_text/files/2/0/2063126.txt\n",
"curl -s -o /pipelines/data/committees/meeting_protocols_parts/files/2/0/2063126.csv https://production.oknesset.org/pipelines/data/committees/meeting_protocols_parts/files/2/0/2063126.csv\n"
]
}
],
"source": [
"import os\n",
"import subprocess\n",
"import sys\n",
"\n",
"for session in sessions_data[0][0]:\n",
" for attr in ['text_parsed_filename', 'parts_parsed_filename']:\n",
" pathpart = 'meeting_protocols_text' if attr == 'text_parsed_filename' else 'meeting_protocols_parts'\n",
" url = 'https://production.oknesset.org/pipelines/data/committees/{}/{}'.format(pathpart, session[attr])\n",
" filename = '/pipelines/data/committees/{}/{}'.format(pathpart, session[attr])\n",
" os.makedirs(os.path.dirname(filename), exist_ok=True)\n",
" cmd = 'curl -s -o {} {}'.format(filename, url)\n",
" print(cmd, file=sys.stderr)\n",
" subprocess.check_call(cmd, shell=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Delete dist hash files"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"find /pipelines/data/committees/dist -type f -name '*.hash' -delete"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Render pages\n",
"\n",
"Should run the render pipelines in the following order:\n",
"\n",
"## Meetings:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1A\n",
"\u001b[2K./committees/dist/render_meetings: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/render_meetings: \u001b[33mRUNNING, processed 94 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/render_meetings: \u001b[32mSUCCESS, processed 94 rows\u001b[0m\n",
"INFO :RESULTS:\n",
"INFO :SUCCESS: ./committees/dist/render_meetings {'bytes': 1742, 'count_of_rows': 94, 'dataset_name': '_', 'failed meetings': 0, 'hash': 'fb41c59fff6c4eced438aa6e29556b24', 'kns_committees': 756, 'meetings': 94, 'mk_individuals': 1015}\n"
]
}
],
"source": [
"!{'cd /pipelines; dpp run ./committees/dist/render_meetings'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Rendered meetings stats"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<h3>meetings_stats</h3>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<table>\n",
"<thead>\n",
"<tr><th style=\"text-align: right;\"> #</th><th style=\"text-align: right;\"> CommitteeSessionID\n",
"(integer)</th><th style=\"text-align: right;\"> num_speech_parts\n",
"(integer)</th><th>hash\n",
"(string) </th><th>rendered_html\n",
"(string) </th><th>rendered_json\n",
"(string) </th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td style=\"text-align: right;\"> 1</td><td style=\"text-align: right;\">2063122</td><td style=\"text-align: right;\">186</td><td>None</td><td>/pipelines/data/committees/dist/dist/meetings/2/0/2063122.html</td><td>/pipelines/data/committees/dist/dist/meetings/2/0/2063122.json</td></tr>\n",
"<tr><td style=\"text-align: right;\"> 2</td><td style=\"text-align: right;\">2063126</td><td style=\"text-align: right;\">209</td><td>None</td><td>/pipelines/data/committees/dist/dist/meetings/2/0/2063126.html</td><td>/pipelines/data/committees/dist/dist/meetings/2/0/2063126.json</td></tr>\n",
"</tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from dataflows import Flow, load, printer, filter_rows, add_field\n",
"\n",
"def add_filenames():\n",
" \n",
" def _add_filenames(row):\n",
" for ext in ['html', 'json']:\n",
" row['rendered_'+ext] = '/pipelines/data/committees/dist/dist/meetings/{}/{}/{}.{}'.format(\n",
" str(row['CommitteeSessionID'])[0], str(row['CommitteeSessionID'])[1], str(row['CommitteeSessionID']), ext)\n",
" \n",
" return Flow(\n",
" add_field('rendered_html', 'string'),\n",
" add_field('rendered_json', 'string'),\n",
" _add_filenames\n",
" )\n",
"\n",
"rendered_meetings = Flow(\n",
" load('/pipelines/data/committees/dist/rendered_meetings_stats/datapackage.json'), \n",
" add_filenames(),\n",
" filter_rows(lambda row: row['CommitteeSessionID'] in session_ids),\n",
" printer(tablefmt='html')\n",
").results()[0][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Committees and homepage"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1A\n",
"\u001b[2K./committees/dist/render_committees: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/render_committees: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/render_committees: \u001b[32mSUCCESS, processed 0 rows\u001b[0m\n",
"INFO :RESULTS:\n",
"INFO :SUCCESS: ./committees/dist/render_committees {'all chairpersons': 756, 'all committees': 756, 'all meeting stats': 94, 'all meetings': 94, 'all members': 7446, 'all mks': 1015, 'all others': 2, 'all replacements': 244, 'all watchers': 2, 'built index': 1, 'built_committees': 756, 'built_knesset_nums': 21, 'failed_committees': 0, 'failed_knesset_nums': 0}\n"
]
}
],
"source": [
"!{'cd /pipelines; dpp run ./committees/dist/render_committees'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Members / Factions"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 100 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 200 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 300 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 400 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 500 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 600 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 700 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 800 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 900 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1000 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1100 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1200 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1300 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1400 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1500 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1600 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1700 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1800 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 1900 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 2000 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 2100 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[33mRUNNING, processed 2144 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_members: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[3A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_members: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[3A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_members: \u001b[32mSUCCESS, processed 0 rows\u001b[0m\n",
"\u001b[3A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_members: \u001b[32mSUCCESS, processed 0 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_factions: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[4A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_members: \u001b[32mSUCCESS, processed 0 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_factions: \u001b[31mWAITING FOR OUTPUT\u001b[0m\n",
"\u001b[4A\n",
"\u001b[2K./committees/dist/build_positions: \u001b[32mSUCCESS, processed 2144 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_members: \u001b[32mSUCCESS, processed 0 rows\u001b[0m\n",
"\u001b[2K./committees/dist/create_factions: \u001b[32mSUCCESS, processed 0 rows\u001b[0m\n",
"INFO :RESULTS:\n",
"INFO :SUCCESS: ./committees/dist/build_positions {'bytes': 282211, 'count_of_rows': 2144, 'dataset_name': 'positions_aggr', 'hash': '0c318cd33a56a9fbb49f96172a462df0'}\n",
"INFO :SUCCESS: ./committees/dist/create_members {}\n",
"INFO :SUCCESS: ./committees/dist/create_factions {}\n"
]
}
],
"source": [
"!{'cd /pipelines; dpp run ./committees/dist/create_members,./committees/dist/build_positions,./committees/dist/create_factions'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Showing the rendered pages\n",
"\n",
"To serve the site, locate the correspondoing local directory for /pipelines/data/committees/dist/dist and run:\n",
" \n",
"`python -m http.server 8000`\n",
"\n",
"Pages should be available at http://localhost:8000/"
]
}
],
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
sophie63/FlyLFM | Notebooks/Utils/.ipynb_checkpoints/Sort_ICs_from_matlab_QUICK_temp-100411MB-lobes-checkpoint.ipynb | 1 | 19788028 | null | bsd-2-clause |
marc-moreaux/Deep-Learning-classes | notebooks/activations_initializers.ipynb | 1 | 204318 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaluate impact of kernel activation and initialization\n",
"\n",
"### 1. Generate random training data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"0.521\n",
"(1000, 100)\n",
"(1000,)\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7efd08337a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%pylab inline\n",
"%matplotlib inline\n",
"pylab.rcParams['figure.figsize'] = (5, 3)\n",
"\n",
"# Create random train data\n",
"X_train = np.random.normal(size=(1000, 100))\n",
"Y_train = (X_train.sum(axis=1) > 0) * 1\n",
"\n",
"print Y_train.mean()\n",
"print X_train.shape\n",
"print Y_train.shape\n",
"\n",
"# Normalize it\n",
"X_train -= X_train.mean()\n",
"X_train /= X_train.std()\n",
"\n",
"plt.hist(X_train.reshape(-1), 50)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Build a simple fully connected model "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import keras\n",
"import keras.backend as K\n",
"from keras.layers import Input, Dense, multiply, Lambda\n",
"from keras.models import Model\n",
"from keras.activations import tanh_perso, sig_perso\n",
"from keras.initializers import VarianceScaling\n",
"import shutil\n",
"import time\n",
"import os\n",
"\n",
"\n",
"def _func_to_str(func):\n",
" \"\"\"if func is a function, returns its string name\"\"\"\n",
" return func.func_name if callable(func) else str(func)\n",
"\n",
"\n",
"def simple_FC_model(activation, initializer):\n",
" # Define input tensor\n",
" input_tensor = Input(shape=(100,))\n",
" if callable(initializer) is True:\n",
" initializer = initializer()\n",
" \n",
" # Propagate it through 10 fully connected layers\n",
" x = Dense(256,\n",
" activation=activation,\n",
" kernel_initializer=initializer)(input_tensor)\n",
" for _ in range(9):\n",
" x = Dense(256,\n",
" activation=activation,\n",
" kernel_initializer=initializer)(x)\n",
" \n",
" x = Dense(1,\n",
" activation='sigmoid',\n",
" kernel_initializer='lecun_normal')(x)\n",
" \n",
" # Build the keras model \n",
" model = Model(input_tensor, x, name='')\n",
" sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
" model.compile(optimizer=sgd, loss='binary_crossentropy')\n",
"\n",
" return model\n",
"\n",
"\n",
"def show_model(activations, initializers, func_model=None):\n",
" \"\"\"Shows prediction distribution for each pair of activation/initializer\n",
" \n",
" Params:\n",
" activations: a list of activations\n",
" initializers: a list of initializers (same lenght as activations)\n",
" \"\"\"\n",
" start = time.time()\n",
" n_fig = len(activations)\n",
" is_gated = False if func_model is None else True\n",
"\n",
" fig, axs = plt.subplots(2, n_fig)\n",
" for i in range(n_fig):\n",
" act, init = zip(activations, initializers)[i]\n",
" \n",
" # Parameters to Strings\n",
" act_str = _func_to_str(act)\n",
" if is_gated is True:\n",
" act_str = 'gated_' + act_str\n",
" init_str = _func_to_str(init)\n",
" \n",
" # Build the model and evaluate it\n",
" K.clear_session()\n",
" func_model = func_model or simple_FC_model\n",
" model = func_model(act, init)\n",
" get_activations = K.function([model.layers[0].input, K.learning_phase()],\n",
" [model.layers[-2].output] )\n",
" act_hist = get_activations([X_train, False])[0]\n",
" \n",
" # Show the 1st results\n",
" axs[0, i].hist(act_hist.reshape(-1), 50)\n",
" axs[0, i].set_title(act_str + \" - \" + init_str)\n",
" \n",
" # Show the 2nd results\n",
" log_dir = './logs/' + act_str + '-' + init_str\n",
" if os.path.isdir(log_dir):\n",
" shutil.rmtree(log_dir)\n",
" tensorboard = keras.callbacks.TensorBoard(histogram_freq=1,\n",
" log_dir=log_dir,\n",
" write_grads=True)\n",
" model.fit(X_train,\n",
" Y_train,\n",
" validation_data=(X_train, Y_train),\n",
" epochs=10,\n",
" batch_size=128,\n",
" verbose=False,\n",
" callbacks=[tensorboard, ])\n",
" pred2 = model.predict(X_train)\n",
" act_hist2 = get_activations([X_train, False])[0]\n",
" axs[1, i].hist(act_hist2.reshape(-1), 50)\n",
" \n",
" # Write some debug\n",
" print \"{} {} std: {:.4f}, mean: {:.3f}, acc: {}\".format(\n",
" act_str,\n",
" init_str,\n",
" act_hist.std(),\n",
" act_hist.mean(),\n",
" (pred2.round().T == Y_train).mean())\n",
" \n",
" K.clear_session()\n",
"\n",
" end = time.time()\n",
" forward_pass_time = (end - start) / n_fig\n",
" print \"\\nTook and average of {:.3} sec. to perfom training\".format(forward_pass_time)\n",
" plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3. Weights initialization\n",
"\n",
"http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf \n",
"http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf\n",
"\n",
"- Normal: draws weights from a normal distribution with $\\mu=0$ and $\\sigma = 1$\n",
"\n",
"- Glorot normal initializer: draws weights from truncated normal with $\\mu=0$ and\n",
"$\\sigma = \\sqrt\\frac{2}{\\text{fan_in} + \\text{fan_out}}$\n",
"\n",
"- Lecun normal initializer: draws weights from truncated normal with $\\mu=0$ and\n",
"$\\sigma = \\sqrt\\frac{1}{\\text{fan_in}}$\n",
"\n",
"--- \n",
"- Uniform: draws weights from a uniform distribution with $f : \\mathbb{R} \\to [-x_{max}; x_{max}]$ and $x_{max} = 0.05$\n",
"\n",
"- Glorot uniform initializer: draws weights Uniform distribution with\n",
"$x_{max} = \\sqrt\\frac{6}{\\text{fan_in} + \\text{fan_out}}$\n",
"\n",
"- Lecun Uniform initializer: draws weights Uniform distribution with\n",
"$x_{max} = \\sqrt\\frac{3}{\\text{fan_in}}$\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 4. Show activation distributions\n",
"##### a) Relu"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"relu uniform std: 0.0000, mean: 0.000, acc: 0.521\n",
"relu glorot_uniform std: 0.0204, mean: 0.015, acc: 0.521\n",
"relu normal std: 0.0015, mean: 0.001, acc: 0.521\n",
"relu glorot_normal std: 0.0048, mean: 0.003, acc: 0.521\n",
"\n",
"Took and average of 8.29 sec. to perfom training\n"
]
},
{
"data": {
"image/png": 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Al0wmbqntxk0MI+IK4Bhg34hYR3UpzFLg6og4E3gQd1alno3VkEbE3Zn5ps5yG1KpNgPv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\nJkkaZhs3buSoo46C5zvFsPd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"text/plain": [
"<matplotlib.figure.Figure at 0x7efd0832ee50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = ['relu']*4\n",
"initializers = ['uniform', 'glorot_uniform', 'normal', 'glorot_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### b) Sigmoid"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sigmoid uniform std: 0.0570, mean: 0.503, acc: 0.521\n",
"sigmoid glorot_uniform std: 0.1237, mean: 0.490, acc: 0.521\n",
"sigmoid lecun_uniform std: 0.1246, mean: 0.505, acc: 0.521\n",
"sigmoid normal std: 0.1007, mean: 0.500, acc: 0.521\n",
"sigmoid glorot_normal std: 0.1045, mean: 0.507, acc: 0.479\n",
"sigmoid lecun_normal std: 0.1070, mean: 0.513, acc: 0.479\n",
"\n",
"Took and average of 8.13 sec. to perfom training\n"
]
},
{
"data": {
"image/png": 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m9rSZbTWzb5uZhfQhZnZHSH/SzFobtW5SX4odEUmCyh4RyRtVCKWuFi9ezP33H/p8zMsv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\nlCqEIiIiIiIiOfX/Ay5L1zXbOJxLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc178dfdd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = ['sigmoid']*6\n",
"initializers = ['uniform', 'glorot_uniform', 'lecun_uniform', 'normal', 'glorot_normal', 'lecun_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### c) tanh"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tanh uniform std: 0.0002, mean: -0.000, acc: 0.521\n",
"tanh glorot_uniform std: 0.2178, mean: 0.000, acc: 1.0\n",
"tanh normal std: 0.0435, mean: 0.000, acc: 0.995\n",
"tanh glorot_normal std: 0.1007, mean: 0.000, acc: 0.997\n",
"\n",
"Took and average of 8.38 sec. to perfom training\n"
]
},
{
"data": {
"image/png": 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+LMnXk3xo1nsdnuSW9r1+bxj7b187q13/jiQ/0pN+bZL7knwzyeNJ3jPH/pLk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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc17e94bd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = ['tanh']*4\n",
"initializers = ['uniform', 'glorot_uniform', 'normal', 'glorot_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### c) Lecun tanh"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lecun_tanh uniform std: 0.0010, mean: 0.000, acc: 0.521\n",
"lecun_tanh glorot_uniform std: 0.6285, mean: -0.001, acc: 1.0\n",
"lecun_tanh lecun_uniform std: 0.6386, mean: -0.002, acc: 0.999\n",
"lecun_tanh normal std: 0.1725, mean: -0.000, acc: 1.0\n",
"lecun_tanh glorot_normal std: 0.3700, mean: -0.001, acc: 0.999\n",
"lecun_tanh lecun_normal std: 0.3824, mean: 0.001, acc: 0.998\n",
"\n",
"Took and average of 10.7 sec. to perfom training\n"
]
},
{
"data": {
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SjwwSd6+LPX8P+G54dLXsfxEK4bT0rUStcXFzY+9b2vIXZ7Bdu4lGpoqn3U90/1xXy7eR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\nK9E4AgAAAABW+g3D8khbQKbgswAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc48624e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"\n",
"def lecun_tanh(x):\n",
" return 1.7159 * K.tanh(2 * x / 3)\n",
"\n",
"activations = [lecun_tanh]*6\n",
"initializers = ['uniform', 'glorot_uniform', 'lecun_uniform', 'normal', 'glorot_normal', 'lecun_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"##### d) SNN - SeLU"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"selu uniform std: 0.0078, mean: -0.002, acc: 0.885\n",
"selu glorot_uniform std: 0.9727, mean: -0.010, acc: 0.993\n",
"selu lecun_uniform std: 0.9902, mean: -0.000, acc: 0.999\n",
"selu normal std: 0.3998, mean: -0.057, acc: 1.0\n",
"selu glorot_normal std: 0.6134, mean: -0.053, acc: 1.0\n",
"selu lecun_normal std: 0.6363, mean: -0.049, acc: 0.999\n",
"\n",
"Took and average of 10.4 sec. to perfom training\n"
]
},
{
"data": {
"image/png": 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A0hOS9kq6NTeAR3rf+zfT6Y+mj1Cqa7USk0uXLuWiiy5i9+7dTJ06la6uLtrb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc6c4cf250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = ['selu']*6\n",
"initializers = ['uniform', 'glorot_uniform', 'lecun_uniform', 'normal', 'glorot_normal', 'lecun_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"### Travail perso\n",
"##### Self normalizing Exponential Unit (SExU)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tanh_perso glorot_uniform std: 0.8919, mean: 0.001, acc: 1.0\n",
"tanh_perso lecun_uniform std: 0.8918, mean: 0.001, acc: 1.0\n",
"tanh_perso glorot_normal std: 0.7398, mean: 0.001, acc: 1.0\n",
"tanh_perso lecun_normal std: 0.7389, mean: -0.001, acc: 0.999\n",
"\n",
"Took and average of 11.7 sec. to perfom training\n"
]
},
{
"data": {
"image/png": 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VfQe4K8ku4NQkdwOHVtWN7XovB17LHCd3szL0R77PKqh7cGK/kI0omo5ZXY6yqJfBaHp6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\nO0MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efc6c4e89d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = [keras.activations.tanh_perso]*4\n",
"initializers = ['glorot_uniform', 'lecun_uniform', 'glorot_normal', 'lecun_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Self normalizing Shifted Exponential Unit (SSExU)\n",
"The weights should be shifted by $\\alpha$ to converge"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sig_perso glorot_uniform std: 0.1147, mean: 0.322, acc: 0.521\n",
"sig_perso lecun_uniform std: 0.1061, mean: 0.318, acc: 0.521\n",
"sig_perso glorot_normal std: 0.0887, mean: 0.312, acc: 0.521\n",
"sig_perso lecun_normal std: 0.0934, mean: 0.322, acc: 0.521\n",
"\n",
"Took and average of 12.7 sec. to perfom training\n"
]
},
{
"data": {
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ulclHrH3afcLBJzDMttfJ/1/tOKlZ9Um0XOmzMSmaNO1ez8zMzMzMWuMxfWZm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efbdf13ae50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = [keras.activations.sig_perso]*4\n",
"initializers = ['glorot_uniform', 'lecun_uniform', 'glorot_normal', 'lecun_normal']\n",
"show_model(activations, initializers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Self normalizing Gated Exponential Neural Network (SGENN)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gated_tanh_perso uniform std: 0.0003, mean: 0.000, acc: 0.521\n",
"gated_tanh_perso glorot_uniform std: 0.8173, mean: 0.008, acc: 0.998\n",
"gated_tanh_perso lecun_uniform std: 0.8197, mean: 0.002, acc: 0.976\n",
"gated_tanh_perso normal std: 0.3986, mean: 0.000, acc: 0.988\n",
"gated_tanh_perso glorot_normal std: 0.5848, mean: -0.002, acc: 1.0\n",
"gated_tanh_perso lecun_normal std: 0.5826, mean: 0.002, acc: 1.0\n",
"\n",
"Took and average of 1.13e+02 sec. to perfom training\n"
]
},
{
"data": {
"image/png": 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+/xDw49gKLTmk2KlOvXvW1XMvrUCjMtIrDXmQetZFhkaVUamruXPn8t73vpfH\nH3+ccePGsWzZMhYuXMg999zDpEmTWL16NQsXLgRgypQpzJ49m8mTJwOcDHzM3Q/Er7oE+CYho34K\nWBWnLwNGmdkm4NPAwgZuntSRYkeqoaFyUksalSHVUM+6SP9UGZW6uvnmm+nt7WXfvn1s3bqV+fPn\nM2rUKNasWcOTTz7J6tWrD2lBXLRoEU899RTABncvVBpw9y53n+ruv+nulxZ6sNz9FXf/sLtPdPfT\n3P3phm9kjqShVblAsSPV0FA5qSWNypBaUc96uqTpvKdVqTIqIiK5o6FyUimNypBaU8+6SP+GNzsB\nIiIijTDQULlp06YlZy0MidtHmUPlzKwwVG5n8XrNbAGwAGDChAm126AcKfRMbF7y/ianJIzKKGXN\nmjUlpy9atIhFixZhZoeNygCmFs/v7q8AH65NaiULCj3rCxcuPKxn/bzzzuPTn/40HNqzfsDMXjSz\nacA6Qs/69fHrCj3rP0E965IDqoyKiEjLafRQOWApQGdnp04apWW04vDHuXPnsnbtWnbu3Mm4ceO4\n4oorWLhwIbNnz2bZsmWceOKJ3HrrrUDJnvVzinrWbwSOIvSqJ3vWvx171ncBcxq3dSK1p8qoiIi0\nhMJQuba2tloOlduax6FyrViJEKkF9ayLDI2uGRXJAF1gL9VQ/AS6CY2IiEi6qGdURERyR0PlRFpX\nmq5BlqFRw2nrUWU0BfTDk7RJxqQKcxmqNJwIaqiciIhI+mmYroiIiIiIiDScKqMiIiIiIiLScBqm\nKyIiIiIiLUuXJzWPKqM5lIbrtaQ2dD2xiIiIiOSVKqMiIjmkhgwRERFJO1VGRUSkLjRKQ4ZKQ+Wy\nTw1hIjIUqoyKiIiIiEjTqBGjdeluuiIyoI6FP1AhISIiIiI1p55RERERAdQ7ISIijTVoz6iZ3WBm\n281sQ2LasWZ2j5k9Gf8ek/jscjPbZGaPm9n7EtNPNbNH4mfXmZnF6SPM7JY4fZ2ZddR2E0VERESk\n1Whkj0j6lTNM90ZgZtG0hcAad58ErIn/Y2aTgTnAlLjMV81sWFzma8BFwKT4KnznfGC3u08ErgWu\nqXRjRPJCBahUSrEjIiJSOZWjjTVoZdTd7wN2FU2eBSyP75cDH0xMX+Hue939GWATcJqZtQFvcvcH\n3N2Bm4qWKXzX94DphV5TERHJPhXsIiIiUkql14yOdffe+P4XwNj4vh14IDHf1jhtX3xfPL2wzBYA\nd99vZi9tONTeAAAgAElEQVQAo4CdxSs1swXAAoAJEyZUmPT00MmZiIiIiIi0qqpvYOTubmZei8SU\nsa6lwFKAzs7OhqxTRAI9M1JERPqjBnYRqUSlldFtZtbm7r1xCO72OL0HGJ+Yb1yc1hPfF09PLrPV\nzIYDRwPPV5gukUxTYS4iIiKtQuc9UulzRu8A5sX384CVielz4h1y30K4UdH6OKT3RTObFq8HPb9o\nmcJ3fQj4cbyuVKqU9uu0Ojo6eMc73sEpp5xCZ2cnALt27WLGjBkAU6u9U7Pkl2LncGn/vYuIiIgU\nK+fRLjcDPwHeamZbzWw+sASYYWZPAmfG/3H3jcCtwKPA3cDH3P1A/KpLgG8Sbmr0FLAqTl8GjDKz\nTcCniXfmldZw77330t3dTVdXFwBLlixh+vTpABuo/k7NkmOKnewpVJhVaZZypDFW1BAmIlJb5dxN\nd667t7n769x9nLsvc/fn3X26u09y9zPdfVdi/ivd/Tfd/a3uvioxvcvdp8bPLi30frr7K+7+YXef\n6O6nufvT9dlUyYKVK1cyb16ho7zqOzVLHaTxBBEUOyLSGGoIy560NISpMUPkcJUO0xWpmplx5pln\ncuqpp7J06VIAtm3bRltbW2GW4js1b0ksXrgjczv936lZckqxIyJpoYYwGQo1Zogcquq76YpU6v77\n76e9vZ3t27czY8YM3va2tx3yea3v1Jy3RwO1MsWOVKOjo4ORI0cybNgwhg8fTldXF7t27eLcc8+F\n2DsBzHb33RB6J4D5wAHg4+7+wzj9VOBG4CjgLuATWb3nQbN7jLKi0BA2bNgwPvrRj7JgwYLBGsKG\n+ri74vUp78m5lStXsnbtWi6//HIIjRlrgc+QaMwAnomXs51mZpuJjRkAZlZozFhV4uulCnqKQGOo\nMipN094eyt4xY8ZwzjnnsH79esaOHUtvb3iEbQ3u1HyItD4aSCeBQ6fY6ZPV+Gl2IX/vvfdy3HHH\nHfy/0DuxevXqZO/EZ4p6J04AVpvZyfF+CIXeiXWEyuhMdEKYa41uCEtz3lOQ1TyoGdSYIXI4DdOV\npnj55ZfZs2fPwfc/+tGPmDp1KmeffTbLly8vzFbtnZqljpp1/Y1iR+pBQy2lHI1uCJN8uf/+++nu\n7mbVqlV85Stf4b777jvk85iX1LQxw9073b1z9OjRtfpakZpSz6g0xbZt2zjnnHMA2L9/P+eddx4z\nZ87k3e9+N7NnzwaYCvwSmA3hTs1mVrhT834Ov1PzjYShcqvISM+EWpMro9iRaql3Qirx8ssv89pr\nrzFy5MiDDWGf//znB2sI+66ZfYnQq15oCDtgZi+a2TRCr/r5wPUN3hxpAjVmBDr/kSRVRptAP0I4\n6aSTePjhhw+bPmrUKNasWYOZbXD3M5OfufuVwJXFy7h7F6ECIi1AsRMoH6mchlpKJdQQlg/NukRA\njRkipakyKiJVafa1f5JtzYgf9U5kS1ryGDWESTXUmJFdacmD8kqV0RagH5GISKDeCRFpBjVmiJSm\nyqiIiLQM9U6I1IYuFRCRWlBlVKTBVIBLNfIaP40awaHeiT55jSUREckOVUZFpCaSJ7YaEi4iImmm\nS5gaTw1gUoqeMyoiIiIiIiINp55RkQZRi6CIiIhINqk3vT5UGW0gVUZEpBKtlHeosJf+KDZERPJH\nlVERqTmdNIqI5FMrNY6JSP2pMtpCVEEQERFVJkSkkZTnyEBUGRWpM2XCUqlWjh01nolIIyivEWku\nVUZFpG5UyIuI5EMrN46JJOncprb0aBcRERERERFpOPWMitSJWpGlUoqdPsl9oVZoAfVKiGSFyjIp\nhyqjdaYfoohOHkXSQOWRSP9UTslQqbG0NlQZbUH68YiIiMhg1IAhIvWmyqhIDanglmoofgamngsR\nkfRTWSZDoRsYiUjDdCz8gQopEakJ5SdSD4orkcZSz2idKCNrLTreUg3Fj9ST4kuGSjEjMjQauVM5\nVUZbnH480gyKO6mG4kdEJH3UiCGVUGVUREREMks35ZN6UFxJJdRYOnSqjNaYWoVai453dVq9sFf8\nVEeF/sAUXzJUihkRaTRVRgXQSZ1Io+hkT6R+VJZJPSiu+qcyrTTFTPlSUxk1s5nAl4FhwDfdfUmT\nkzQkefkxZvXH0+j4ycvxTpNmxV7W8x5pbr6VxvhR/pQNaYodxUx50nSO1Oz4UcxIraSiMmpmw4Cv\nADOArcCDZnaHuz/a3JQNTj/G5mtk/Oh454tiJ18afaKYprJL8XWoNFUaSklL7ChuKtPs+Gpm/Chm\nhqbZsZIFqaiMAqcBm9z9aQAzWwHMAlJbGc37j7F4+1L+I6p7/OT9eKdJg2NPsZNDDbwWuelll+Jr\nYCkuyxoeO4qV2mtiRUPxkzGl9l+K8qOmSktltB3Ykvh/K/Ce4pnMbAGwIP77kpk9Ht8fB+ysawrT\noynbatc0eo2cOIR5q42f/uQlrjK9HTH2hrINaYidcmXh2KQ9jQOmr4K8K0vxM5hmHbtmxkyt42Eo\n3jqEeRsRO2n/7Q4mM+kfIK6yWHZlZr/3IzPpLxE3laZ9KLGTOmmpjJbF3ZcCS4unm1mXu3c2IUkN\n10rbWmv9xU9/8rKv87Adzd6GocZOuZq9XeVIexrTnj6oX/wMpln7ppnHpNnrrvV3VhM7WfhtDCTr\n6Yfmb0Ml8dPsNFcry+nPctqrcUSzExD1AOMT/4+L00TKofiRSil2pBqKH6mUYkeqofiR3EhLZfRB\nYJKZvcXMjgTmAHc0OU2SHYofqZRiR6qh+JFKKXakGoofyY1UDNN19/1mdinwQ8Itqm9w941D+IqG\nD39qolba1rLUIH76k5d9nYftqMs21DF2ypWFY5P2NDYtfSmIn8E0a980M2Yyse4GxU7af7uDyXr6\nIZtlV9b3e5bTn+W0V8zcvdlpEBERERERkRaTlmG6IiIiIiIi0kJUGRUREREREZGGS3Vl1MyONbN7\nzOzJ+PeYfuabaWaPm9kmM1s42PJmNsPMHjKzR+Lf/9mobSon3YnPzcyui5//l5m9a7Bly91ncjgz\n+wcz+3nc17eZ2Zv7mW9zjJ3uetzKvxLVxFJamNl4M7vXzB41s41m9okS85xhZi/Efd9tZp9vRlpr\nqdy4a7TBYqrZyomXVmdmH4775jUza8jjApoVN2Z2g5ltN7MNjVpnYt2pjcW05i+DSXv+M5A0x0O5\nshg3WY2ZPMRL1dw9tS/g74GF8f1C4JoS8wwDngJOAo4EHgYmD7Q88NvACfH9VKCnCdvWb7oT85wF\nrAIMmAasq3Sb9SrrmPwhMDy+v6a/fQdsBo5rdnprEUtpegFtwLvi+5HAEyW24wzgzmantcbbXVbc\npS2mmv0qJ15a/QW8HXgrsBbozHPcAKcD7wI2NGE/pzYW05i/pDmO8h4PeY2bLMdMHuKl2leqe0aB\nWcDy+H458MES85wGbHL3p939VWBFXK7f5d39Z+7+XJy+ETjKzEbUIf0DGSjdBbOAmzx4AHizmbUN\nsmw5+0xKcPcfufv++O8DhOd2ZUE1sZQa7t7r7j+N7/cAjwHtzU1V/aU07sqJqaZq1XgZCnd/zN0f\nb+AqmxY37n4fsKsR6yqx7tTGYkrzl8GkPv8ZSJrjoVwZjJvMxkwe4qVaaa+MjnX33vj+F8DYEvO0\nA1sS/2+l7yCWs/yfAD919701SO9QDJTuweapdptlcBcSehJLcWB1HOK9oIFp6k81sZRKZtZBGMGw\nrsTHvxOHDq0ysykNTVj9DRR3jZSneJHGyVTc1EPKYzEt+ctgchNHKY+HcmUhbnIRMzmJlyFr+nNG\nzWw1cHyJjxYl/3F3N7OKn0NTavl4InsNYThC7lS7z/JooHhz95VxnkXAfuA7/XzN77l7j5mNAe4x\ns5/HVnmpATN7I/B94JPu/mLRxz8FJrj7S2Z2FnA7MKnRaRyqGsWdlDBIvOReObEljdGsWFT+kk5p\nz5sUN+mS9nipp6ZXRt39zP4+M7NtZtbm7r1xSOH2ErP1AOMT/4+L0wD6Xd7MxgG3Aee7+1NVb8jQ\nDZTuweZ53QDLlrPPWtZA8QZgZhcAHwCmu3vJiry798S/283sNsLwkGZWRquJpVQxs9cRMuPvuPu/\nFX+ezKDd/S4z+6qZHefuOxuZzqGqRdw1WC7ipRUMFlsNlom4qYdmxmIG85fBZD6OspA35SxuMh0z\nWYiXekr7MN07gHnx/TygVCvvg8AkM3uLmR0JzInL9bt8vCvYDwg3+vmPOqV9MAOlu+AO4HwLpgEv\nxCG4Q95mGZyZzQT+Gjjb3X/VzzxvMLORhfeEXvWG372xSDWxlBpmZsAy4DF3/1I/8xwf58PMTiPk\nYc83LpW1V07cNUE5MdVU5cSLNFzq46Ye0hyLKc1fBpPpOEpzPJQrg3GT2ZjJQ7xUbSh3O2r0CxgF\nrAGeBFYDx8bpJwB3JeY7i3D3qacIwwsGW/5vgJeB7sRrTBO277B0A38B/EV8b8BX4uePkLgb4lC3\nWa+yjscmwjUHhZj45+J4I9yp7eH42pjc901Oe8WxlJYX8HuE63H/K3EMzirajkvjfn+YcFOF32l2\nuusVd81+9ZfHpOXVX7w0O11pegHnEK6d2gtsA36Y17gBbgZ6gX1xm+c3cN2pjcW05i9pjaO8x0Oe\n4yarMZOHeKn2ZXFHiIiIiIiIiDRM2ofpioiIiIiISA6pMip1ZWY3mNl2M9uQmLbYzHrMrDu+zkp8\ndrmZbTKzx83sfYnpp5rZI/Gz6xLXDY4ws1vi9HXxttiSA4odERERkXxTZVTq7UZgZonp17r7KfF1\nF4CZTSZcdD4lLvNVMxsW5/8acBHhMR6TEt85H9jt7hOBawmP6pF8uBHFjoiIiEhuZfaa0eOOO847\nOjqanQwpw969e9m0aRNTpkwB4LnnnuOII47g+OMPfbxVb2+4uWtbWxsPPfTQTsIzJRcDm4F73f1t\nAGY2FzjD3T9qZj8EFrv7T8xsOPALYLQPEtiKn2xQ7EijPfTQQzvdfXS9vl/xk1+KHamG4kcqVe/Y\nqbemP2e0Uh0dHXR1dTU7GVKGzZs384EPfODg8Vq8eDHf+ta3ePXVV+ns7OSLX/wixxxzDJdeeinT\npk3jIx/5CGb2LOGOiO303R2xoDCd+HcLgLvvN7MXCHcUPuy5k2a2AFgAMGHCBMVPBih2pNFi/NSN\nyq78UuxINRQ/Uql6x069aZiuNNzFF1/M008/TXd3N21tbVx22WUNWa+7L3X3TnfvHD06sw1ILU2x\nIyIiIpIfqoxKw40dO5Zhw4ZxxBFHcNFFF7F+/XoA2tvb2bJlS3LWcUBPfI0rMZ34dzxAHGp5NPB8\nfbdAmkWxIyIiIpIfqoxKwxWu7wO47bbbmDp1KgBnn302K1asYO/evQBHEm42s97de4EXzWxavBPq\n+cDK+BV3APPi+w8BPx7smj/JLsWOiIiISH5k9prRVtGx8AcAbF7y/ianpDJz585l7dq17Ny5k3Hj\nxnHFFVewdu1auru7MTM6Ojr4+te/DsCUKVOYPXs2kydPBjgZOMfdD8SvuoRwd9WjgFXxBbAM+LaZ\nbQJ2Ee6oKgmFGIJsxZFip/GyGivSHOWWT4or6U8yNgoUI1KurJ8jS6DKqNTVzTfffNi0+fPn9zv/\nokWLWLRoEWa2wd0LlQbcvQuYWjy/u78CfLg2qZU0UeyIZMNQTgh18igitdZfvqKGsGzQMF2RFtKx\n8AclW6JFRETSTOWXVEPxk16qjIrkjDJcqSXFk4iIiNSLhumKiMigNLxSBlMcI2rEkFpQHInkmyqj\nIiItTid7IiKSdWo0zSZVRkVyShUMEam1WuUrOmkUERFQZVREREREUkINFTKYwRrFBvpc8ZU+qoyK\niMhh1LMulVLsyGAUIyJSMOjddM3sBjPbbmYbEtMWm1mPmXXH11mJzy43s01m9riZvS8x/VQzeyR+\ndp2ZWZw+wsxuidPXmVlHbTdRpDXorqciIpJ3KutE8qWcR7vcCMwsMf1adz8lvu4CMLPJwBxgSlzm\nq2Y2LM7/NeAiYFJ8Fb5zPrDb3ScC1wLXVLgtIiIiIiIikhGDDtN19/uG0Fs5C1jh7nuBZ8xsE3Ca\nmW0G3uTuDwCY2U3AB4FVcZnFcfnvAf9kZubuPoTtEBEREZGcUO+nSGuo5prRvzSz84Eu4DJ33w20\nAw8k5tkap+2L74unE/9uAXD3/Wb2AjAK2FlF2jJFF1NLoynmpFKKndakioGIiNRDOcN0S/kacBJw\nCtALfLFmKRqAmS0wsy4z69qxY0cjVikiklu69kpERESaqaLKqLtvc/cD7v4a8A3gtPhRDzA+Meu4\nOK0nvi+efsgyZjYcOBp4vp/1LnX3TnfvHD16dCVJFxERERERkRSoaJiumbW5e2/89xygcKfdO4Dv\nmtmXgBMINypa7+4HzOxFM5sGrAPOB65PLDMP+AnwIeDHul5UREQk/zTsWyqVHNWh+Mk/jeLJr0Er\no2Z2M3AGcJyZbQW+AJxhZqcADmwGPgrg7hvN7FbgUWA/8DF3PxC/6hLCnXmPIty4aFWcvgz4drzZ\n0S7C3XhFRERERERqTg1h6VHO3XTnlpi8bID5rwSuLDG9C5haYvorwIcHS4eIiIiIZJd6t0SkWKU3\nMBIRERERERGpmCqjIiIiIiIi0nCqjIqIiIiIiEjDqTIqknF6VqSIiEg6XHjhhYwZM4apU/tuk7Jr\n1y5mzJjBpEmTmDFjBrt37z742dVXX83EiRMBpprZ+wrTzexUM3vEzDaZ2XVmZnH6CDO7JU5fZ2Yd\njdo2kXpQZbSJVImQZlMMtqZaHPfCd6Q1fnRCWBtpPsYiaXTBBRdw9913HzJtyZIlTJ8+nSeffJLp\n06ezZMkSAB599FFWrFjBxo0bAZ4Avmpmw+JiXwMuIjwmcRIwM06fD+x294nAtcA19d4mkXpSZVRE\nRHJHJ4Qi0gynn346xx577CHTVq5cybx58wCYN28et99++8Hpc+bMYcSIEQCvApuA08ysDXiTuz/g\n7g7cBHwwft0sYHl8/z1geqGRLI/q3SCmBrfmU2U0I/RjEREpn04IpVLqVZda27ZtG21tbQAcf/zx\nbNu2DYCenh7Gjx+fnHUr0B5fW0tMJ/7dAuDu+4EXgFGl1mtmC8ysy8y6duzYUbPtEaklVUalrlSo\ni0ha6IQwvdLU4Kpe9dpT71YfM6NR7VbuvtTdO929c/To0Q1Zp8hQqTIqdaVCXSqlhgypJ50QSn/U\nqy61NnbsWHp7ewHo7e1lzJgxALS3t7Nly5bkrOOAnvgaV2I68e94ADMbDhwNPF/H5IvUlSqjTZCl\nFrxqqVCXSqkhQ2pNJ4RSKfWqSzXOPvtsli8PpyrLly9n1qxZB6evWLGCvXv3AhxJKKPWu3sv8KKZ\nTYvnNOcDK+PX3QHMi+8/BPw4nhuJZJIqoxmTh4psswp1yRY1ZEit6YRQakG96jKQuXPn8t73vpfH\nH3+ccePGsWzZMhYuXMg999zDpEmTWL16NQsXLgRgypQpzJ49m8mTJwOcDHzM3Q/Er7oE+CahPHsK\nWBWnLwNGmdkm4NPAwgZunkjNDW92AqR2CpXUzUve3+SUlK+RhbqZLQAWAEyYMKEh66yXrDdIVGqg\nhoxp06YlZy00WOyjzIYMMys0ZOwsXm+eYqdVzJ07l7Vr17Jz507GjRvHFVdcwcKFC5k9ezbLli3j\nxBNP5NZbbwVKnhCeU3RCeCNwFOFkMHlC+O14QrgLmNO4rZNGK/Sqt7W11bJXfat61fPn5ptvLjl9\nzZo1JacvWrSIRYsWYWYb3L2Qv+DuXcDU4vnd/RXgw7VJrUjzqTKaUnmubDSrUHf3pcBSgM7OTvVg\nJKghY2CKnYGlMX50Qii1VOhVX7hw4WG96ueddx6f/vSn4dBe9QNm9qKZTQPWEXrVr49fV+hV/wnq\nVReRFqdhuimS9ofI14qGykmldM1fdVohf5HaaOVY0TBLEZHGUc9oDqWpl0JD5aSW1DshIvWmXnWR\n5mvVxrBWpMqo1JUKdamUGjJEWk/yBDQNDaoi0hqU9zSPKqMikkpqyBARkaFI08gwESmPKqMiIiIi\nUnMaaikig9ENjERERERERKThBq2MmtkNZrbdzDYkph1rZveY2ZPx7zGJzy43s01m9riZvS8x/VQz\neyR+dl3h4fJmNsLMbonT15lZR203USQ/WvkOlyIiIiKSL+X0jN4IzCyathBY4+6TgDXxf8xsMuEm\nIFPiMl81s2Fxma8BFxHucjkp8Z3zgd3uPhG4Frim0o0RERERERGRbBj0mlF3v69Eb+Us4Iz4fjmw\nFvhMnL7C3fcCz8S7VJ5mZpuBN7n7AwBmdhPwQcJdLWcBi+N3fQ/4JzMzPWJBRKQ2GtWbrpuHiIhI\nNdIw+ktlWWNVegOjse7eG9//Ahgb37cDDyTm2xqn7Yvvi6cXltkC4O77zewFYBSws3ilZrYAWAAw\nYcKECpPeGApkySLFrUhrS8OJoIiItI6qb2AUezAb0ovp7kvdvdPdO0ePHt2IVYqIiIiIiEgdVNoz\nus3M2ty918zagO1xeg8wPjHfuDitJ74vnp5cZquZDQeOBp6vMF0iuaTeChFpNRqpISKSf5VWRu8A\n5gFL4t+VienfNbMvAScQblS03t0PmNmLZjYNWAecD1xf9F0/AT4E/DiL14s2q9BUJUVERETSIg3n\nJWrIEMmOQSujZnYz4WZFx5nZVuALhErorWY2H3gWmA3g7hvN7FbgUWA/8DF3PxC/6hLCnXmPIty4\naFWcvgz4drzZ0S7C3XilBpQZi4iIiIhIWpVzN925/Xw0vZ/5rwSuLDG9C5haYvorwIcHS4eIiIiI\niIjkR6XDdEUkx5LDrNSzLiIiIiL1oMqoiEgONfO6LV0iICIiQ5GGa42LqSxrDFVG66xePUxp/NGK\niIiIiIiUS5VRkRRTo4OIiEhl1LMlkn6qjIqIiLS4NDd8qUKRDWmOIRFJL1VGRURyJE0nhKpEiIiI\nyECOaHYCpP46Fv4gVSeoIiIiIiIi6hmtMVX6JG/UuyUiIiL1kIXzZp0H1Zcqow2UhR+cpINiRURE\nRETyTpVREREREcmtej1mT0Sqp8qoSIqkuUdUw1TSTbFTvo6ODkaOHMmwYcMYPnw4XV1d7Nq1i3PP\nPRdgqpndA8x2990AZnY5MB84AHzc3X8Yp58K3AgcBdwFfMLdvQmbJNI0ac570kZ5j8jhdAMjERFp\nOffeey/d3d10dXUBsGTJEqZPnw6wAVgDLAQws8nAHGAKMBP4qpkNi1/zNeAiYFJ8zWzkNrSaNNyM\nr6Ojg3e84x2ccsopdHZ2ArBr1y5mzJgBsTJhZscU5jezy81sk5k9bmbvS0w/1cweiZ9dZ2bW8I2R\nplDeI3IoVUalaVSoS6UUO1JrK1euZN68eYV/lwMfjO9nASvcfa+7PwNsAk4zszbgTe7+QOyRuCmx\nTCYUKnfNruBljSoTUkutmPdklfLL+lBlVJpKhXr2pCUzVuwEaTkeWWJmnHnmmZx66qksXboUgG3b\nttHW1laY5RfA2Pi+HdiSWHxrnNYe3xdPL7W+BWbWZWZdO3bsqN2GSCqoMiHlUt4TqCFMknTNaAtJ\n23VbpaxcuZK1a9dy+eWXQyjU1wKfIVGoA8+YWaFQ30ws1AHMrFCor2pC8iuizLg2WjF2siYtNxG5\n//77aW9vZ/v27cyYMYO3ve1th3zu7m5mNbv+yt2XAksBOjs7dV1XhhUqE8OGDeOjH/0oCxYsGKwy\n8UBi8UKlYR9DqEwACwAmTJhQuw2RplDeI3I4VUalaVSoS6UUO1KN9vZwmMeMGcM555zD+vXrGTt2\nLL29vQDEnqvtcfYeYHxi8XFxWk98XzxdckyViT5ZbUhtZsO88h6Rw2mYrjTN/fffT3d3N6tWreIr\nX/kK99133yGfx+FLNS3U3b3T3TtHjx5dq6+VJlDsSKVefvll9uzZc/D9j370I6ZOncrZZ5/N8uXL\nC7PNA1bG93cAc8xshJm9hTCce7279wIvmtm0eK3x+YllJKdUmZBKKe8RKU09oy1OLYTNk9VW5QLF\nTnNlPX6aZdu2bZxzzjkA7N+/n/POO4+ZM2fy7ne/m9mzZwNMBX4JzAZw941mdivwKLAf+Ji7H4hf\ndwl9j1dYRUaGeCt2KvPyyy/z2muvMXLkyIOVic9//vODVSa+a2ZfAk6grzJxwMxeNLNpwDpCZeL6\nBm+ONJjynvzkPVm47C1LqqqMxmuu9hCef7Tf3TvN7FjgFqAD2IyelyQlqFCXSil28qFZhflJJ53E\nww8/fNj0UaNGsWbNGsxsg7ufmfzM3a8Erixext27CCeQ0kDNih1VJqQayntESqtFz+j/cPedif8X\nAmvcfYmZLYz/f6bojpYnAKvN7OSYMRfuaLmOUBmdiTLmXFOhLpVS7IhIM6gykS/q3RJJh3oM050F\nnBHf646WUlIrF+p5GaZS0OgCvZVjBxQ/ItJcecuDRCqVljvEZ121NzByQg/nQ/FukwBj48XV0CLP\nSxIREREREZGhqbZn9PfcvcfMxgD3mNnPkx+20i3ORQaj1mQRkdpRr7pINuj8RwZSVWXU3Xvi3+1m\ndhtwGrDNzNrcvbcV7mgpIoGGq9RPKxTkqliISDMo7xFprooro2b2BuAId98T3/8h8LeEO1fOA5ag\nO1qKiIikQis0akj9KH5EpB6q6RkdC9wWnrfLcOC77n63mT0I3Gpm84FnyfkdLZU5y2AUIyIiIiIi\nh6u4MuruTwO/VWL688D0fpbRHS1FWoCGPdWG8hkREcmqVirDdN5TuXo82kUySD8iEUkD5UUyVLpe\nXUQku1QZbUGt1FLVTNrPqliIiGRZK5VjKq9EmqPa54yKiIiIiIiIDJl6RkVEUqaVeiP6o16K2mml\neFLciEgzKQ8aOlVGRWqolU76hkKZs4iIZIHKq+rpXEiGQpXRMuhHJSIiIiIiUluqjIpIw6jFeWBq\n+OIxsQEAAAfASURBVDqc7pQq0njKi0Sqo/Od8qkyKlIDKrhFJG1aOV/SiaCISDaoMjqAVizIVYCL\niIi0plY87+mPRmUMneJHKqHKqEgVlPFWRo0eh1IclUdxIyIiki+qjEpJOukTEckmNW70UVkmIs2k\nPGhwqoyKiEimqHAXkUZSntM/NX5JtVQZFZGm0TU5IiLNpwqFSH2pQaN/qoyKVEAFt9SC4kikMXQi\nKLWgOBKpvdxXRpVxiGSDfqsyVIqZPmrYkEoobqRSih2pldxXRiuhH5j0R7Eh1VIMiTSPGjBEpJl0\nedLhVBlN0EmiSPPpZFGGqpVjRuWWDJVipnrKc0RqR5VRqdpQMuWsZuDKfKVaiqH6y2r+UgnFU3XU\nOyG10Ep5jtSe4idITWXUzGYCXwaGAd909yW1/H4V3JXJyg+l3vEjjdeo2FPeI9VQ3iOVanTsKC/K\nF8WP5EUqKqNmNgz4CjAD2Ao8aGZ3uPujlX6nfjT1V80+rmVFox7xk0yjNFc9K6X1ih1prkb1ejUj\nfpQv1V7xPm1E42sjY0cxU3+NbrhX/ORLVjp+6iUVlVHgNGCTuz8NYGYrgFmATgilHDWNH2W86VSn\nCkbN8x7FT7rUuZCva9mlWGqOBlVO6xY7ipvmKbXvsxQ/ip3makbDWBqkpTLaDmxJ/L8VeE/xTGa2\nAFgQ/33JzB5vQNqa6ThgZ7MTAWDX1GaeQZZJbu+JQ/iaRsRPao5FBXKX9kFiLU2xk+V9DzlK/xDy\npzTET9b3O+RoGzIWO+XKwvHJRRpzGD9ZOC6QjXQOmMY6xU7qpKUyWhZ3XwosbXY6GsXMuty9s9np\naJR6b2818ZPlY6G0V6/S2ElL+iul9NfGUOMnLemuhrahNup13pOGbRuM0li9esRP2re5IAvpzEIa\nG+GIZicg6gHGJ/4fF6eJlEPxI5VS7Eg1FD9SKcWOVEPxI7mRlsrog8AkM3uLmR0JzAHuaHKaJDsU\nP1IpxY5UQ/EjlVLsSDUUP5IbqRim6+77zexS4IeEW1Tf4O4bm5ysNGiZIclRRdvboPjJ8rFQ2vvR\ngNjJ8r4HpX9AdYyfrO930DYMKAXnPVk4PkpjP5ocP1k4LpCNdGYhjXVn7t7sNIiIiIiIiEiLScsw\nXREREREREWkhqoyKiIiIiIhIw6ky2gRmNtPMHjezTWa2sMTnZmbXxc//y8zeVe6yaVTp9prZeDO7\n18weNbONZvaJBqb5H8zs5zE9t5nZm/uZb7OZPWJm3WbW1aj0lUhHxTHVbOUcZzM7w8xeiPu528w+\n34y0DiZrcZNIj+InZcqNpbTJYhmV1Mxyp9HSHGNpj6NWipNSFDvVafX4OYy769XAF+FC86eAk4Aj\ngYeByUXznAWsAgyYBqwrd9m0varc3jbgXfH9SOCJRm0v8IfA8Pj+GuCafubbDByX1X2chlc5xxk4\nA7iz2WnNU9woftL9KjeW0vTKYhlVYhuaVu40YVtTGWNZiKNWihPFjuKn3i/1jDbeacAmd3/a3V8F\nVgCziuaZBdzkwQPAm82srcxl06bi7XX3Xnf/KYC77wEeA9obkWh3/5G774//PkB4hldaVRNTTdfM\n41xrGYubAsVPCuU4llItr/FUSopjLPVx1EpxUopipzqtHj/FVBltvHZgS+L/rRwegP3NU86yaVPN\n9h5kZh3AbwPrap7CwV1I6BUqxYHVZvaQmS1oYJqSarKP02CQ4/w7cUjQKjOb0tCEVSbtcVOg+Em/\ngWIpTTIRJ+VqcrnTaGmKsUzFUYvFSSmKnSooflLynFGRgZjZG4HvA5909xdr+L2rgeNLfLTI3VfG\neRYB+4Hv9PM1v+fuPWY2BrjHzH7u7vfVKo2tZJDj/FNggru/ZGZnAbcDkxqdRlDcpFVW4iepRrEk\ndVCvcqfRFGP1lZc4KUWxU395jp+hUGW08XqA8Yn/x8Vp5czzujKWTZtqthczex3hh/odd/+3WibM\n3c8c6HMzuwD4ADDd3Us+kNfde+Lf7WZ2G2GISKMrFVXt4zQY7DgnM2l3v8vMvmpmx7n7zkamM64/\nL3FToPhpklrEUsqkOk7KVc9yp9EyGmOZiKM8xUkpip36ynv8DIWG6Tbeg8AkM3uLmR0JzAHuKJrn\nDuD8eAfLacAL7t5b5rJpU/H2mpkBy4DH3P1LjUy0mc0E/ho4291/1c88bzCzkYX3hAv6NzQulQdV\nE1NNV85xNrPj43yY2WmEvOv5xqWyPBmLmwLFTwqVE0v/fzt3jBIxEMVh/HsgeATBwot4A2sLGwu7\nFTyB57CwEEtbu0VvYCmInefQZuFZbBRXwiIEXibm+5UZAjPJHx4vGaZBU6xRG8asO9UazljzOZpT\nTvqYnWHmnp/f/DNaLDNXEXEBPLI+9es2M18jYtGNXwNL1qdXvgHvwNm2e0dYxp8NWS9wCJwCLxHx\n3F27zMxlwdSvgF3WWygBnjJzERH7wE1mHgF7wH03vgPcZeZDwdw2DHzGLeh9z8ABfM//GDiPiBXw\nAZw09CX2p8nk5ov5aVZvlsad0nZTrFE9xqw71ZrM2ERyNKec9DE7w8w9Pxui/XosSZIkSfpv3KYr\nSZIkSSpnMypJkiRJKmczKkmSJEkqZzMqSZIkSSpnMypJkiRJKmczKkmSJEkqZzMqSZIkSSr3CZul\niVFrJ0X7AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efbddfcf1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def gated_activation(n_units, activation=None, initializer=None):\n",
"\n",
" def func(x):\n",
" alpha = 1.7580993408473768599402175208123\n",
" normalizer = np.sqrt(1 + alpha ** 2)\n",
" gate = Dense(n_units,\n",
" activation='linear',\n",
" kernel_initializer=initializer)(x)\n",
" gate = Lambda(lambda x: x + alpha)(gate)\n",
" gate = keras.layers.Activation(sig_perso)(gate)\n",
"\n",
" act = Dense(n_units,\n",
" activation=activation,\n",
" kernel_initializer=initializer)(x)\n",
" gated_act = multiply([gate, act])\n",
" gated_act = Lambda(lambda x: x / normalizer)(gated_act)\n",
" return gated_act\n",
" return func\n",
"\n",
"\n",
"def simple_gated_model(activation, initializer):\n",
" # Define input tensor\n",
" input_tensor = Input(shape=(100,))\n",
" \n",
" # Propagate it through 20 fully connected layers\n",
" x = gated_activation(256, activation, initializer)(input_tensor)\n",
" for _ in range(19):\n",
" x = gated_activation(256, activation, initializer)(x)\n",
" \n",
" x = Dense(1,\n",
" activation='sigmoid',\n",
" kernel_initializer='lecun_normal')(x)\n",
" \n",
" # Build the keras model \n",
" model = Model(input_tensor, x, name='')\n",
" sgd = keras.optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
" model.compile(optimizer=sgd, loss='binary_crossentropy')\n",
"\n",
" return model\n",
"\n",
"pylab.rcParams['figure.figsize'] = (15, 4)\n",
"activations = [tanh_perso]*6\n",
"initializers = ['uniform', 'glorot_uniform', 'lecun_uniform', 'normal', 'glorot_normal', 'lecun_normal']\n",
"\n",
"show_model(activations, initializers, func_model=simple_gated_model)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.508\n",
"(1000, 100)\n",
"(1000,)\n",
"Train on 1000 samples, validate on 1000 samples\n",
"Epoch 1/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7437 - acc: 0.5120 - val_loss: 0.7202 - val_acc: 0.5440\n",
"Epoch 2/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7224 - acc: 0.5260 - val_loss: 0.7348 - val_acc: 0.5250\n",
"Epoch 3/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7282 - acc: 0.5170 - val_loss: 0.7308 - val_acc: 0.5080\n",
"Epoch 4/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7472 - acc: 0.4730 - val_loss: 0.7023 - val_acc: 0.5530\n",
"Epoch 5/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7285 - acc: 0.5050 - val_loss: 0.7027 - val_acc: 0.5370\n",
"Epoch 6/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7106 - acc: 0.5190 - val_loss: 0.6977 - val_acc: 0.5330\n",
"Epoch 7/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7038 - acc: 0.5380 - val_loss: 0.6960 - val_acc: 0.5450\n",
"Epoch 8/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7061 - acc: 0.5220 - val_loss: 0.6766 - val_acc: 0.5910\n",
"Epoch 9/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7005 - acc: 0.5390 - val_loss: 0.6821 - val_acc: 0.5440\n",
"Epoch 10/10\n",
"1000/1000 [==============================] - 0s - loss: 0.7032 - acc: 0.5390 - val_loss: 0.6858 - val_acc: 0.5570\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x7efbdf0255d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import keras\n",
"import keras.backend as K\n",
"from keras.layers import Input, Dense, multiply, Lambda, Dense_gated\n",
"from keras.models import Model\n",
"from keras.activations import tanh_perso, sig_perso\n",
"from keras.initializers import VarianceScaling\n",
"import shutil\n",
"import time\n",
"import os\n",
"\n",
"# Create random train data\n",
"X_train = np.random.normal(size=(1000, 100))\n",
"Y_train = (X_train.sum(axis=1) > 0) * 1\n",
"\n",
"print Y_train.mean()\n",
"print X_train.shape\n",
"print Y_train.shape\n",
"\n",
"# Normalize it\n",
"X_train -= X_train.mean()\n",
"X_train /= X_train.std()\n",
"\n",
"# Define input tensor\n",
"input_tensor = Input(shape=(100,))\n",
"\n",
"my_dense_layer = lambda : Dense_gated(256,\n",
" activation1=tanh_perso,\n",
" kernel_initializer1='lecun_uniform',\n",
" activation2=tanh_perso,\n",
" kernel_initializer2='lecun_uniform',\n",
" shift=1.75809934084737685994,\n",
" normalizer=np.sqrt(1 + 1.75809934084737685994 ** 2) )\n",
"\n",
"# Propagate it through 20 fully connected layers\n",
"x = my_dense_layer()(input_tensor)\n",
"for _ in range(30):\n",
" x = my_dense_layer()(x)\n",
"\n",
"x = Dense(1,\n",
" activation='sigmoid',\n",
" kernel_initializer='lecun_normal')(x)\n",
"\n",
"# Build the keras model \n",
"model = Model(input_tensor, x, name='')\n",
"sgd = keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9, nesterov=True)\n",
"model.compile(optimizer=sgd, loss='binary_crossentropy', metrics=['acc'])\n",
"\n",
"model.fit(X_train,\n",
" Y_train,\n",
" validation_data=(X_train, Y_train),\n",
" epochs=10,\n",
" batch_size=128,\n",
" verbose=True )\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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
hktxt/MachineLearning | PyTorch Tutorials/autoencoder.ipynb | 1 | 71852 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### autoencoder https://github.com/MorvanZhou/PyTorch-Tutorial/blob/master/tutorial-contents/404_autoencoder.py"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#%matplotlib inline\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.utils.data as Data\n",
"import torchvision\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"from matplotlib import cm\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Hyper Parameters\n",
"EPOCH = 10\n",
"BATCH_SIZE = 64\n",
"LR = 0.005 # learning rate\n",
"DOWNLOAD_MNIST = False\n",
"N_TEST_IMG = 5"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Mnist digits dataset\n",
"train_data = torchvision.datasets.MNIST(\n",
" root='./mnist/',\n",
" train=True, # this is training data\n",
" transform=torchvision.transforms.ToTensor(), # Converts a PIL.Image or numpy.ndarray to\n",
" # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]\n",
" download=DOWNLOAD_MNIST, # download it if you don't have it\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([60000, 28, 28])\n",
"torch.Size([60000])\n"
]
}
],
"source": [
"# plot one example\n",
"print(train_data.train_data.size()) # (60000, 28, 28)\n",
"print(train_data.train_labels.size()) # (60000)\n",
"plt.imshow(train_data.train_data[2].numpy(), cmap='gray')\n",
"plt.title('%i' % train_data.train_labels[2])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Data Loader for easy mini-batch return in training, the image batch shape will be (50, 1, 28, 28)\n",
"train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class AutoEncoder(nn.Module):\n",
" def __init__(self):\n",
" super(AutoEncoder, self).__init__()\n",
"\n",
" self.encoder = nn.Sequential(\n",
" nn.Linear(28*28, 128),\n",
" nn.Tanh(),\n",
" nn.Linear(128, 64),\n",
" nn.Tanh(),\n",
" nn.Linear(64, 12),\n",
" nn.Tanh(),\n",
" nn.Linear(12, 3), # compress to 3 features which can be visualized in plt\n",
" )\n",
" self.decoder = nn.Sequential(\n",
" nn.Linear(3, 12),\n",
" nn.Tanh(),\n",
" nn.Linear(12, 64),\n",
" nn.Tanh(),\n",
" nn.Linear(64, 128),\n",
" nn.Tanh(),\n",
" nn.Linear(128, 28*28),\n",
" nn.Sigmoid(), # compress to a range (0, 1)\n",
" )\n",
"\n",
" def forward(self, x):\n",
" encoded = self.encoder(x)\n",
" decoded = self.decoder(encoded)\n",
" return encoded, decoded"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"autoencoder = AutoEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"optimizer = torch.optim.Adam(autoencoder.parameters(), lr=LR)\n",
"loss_func = nn.MSELoss()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0 | train loss: 0.0453\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x144 with 10 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0 | train loss: 0.0394\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 | train loss: 0.0402\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 | train loss: 0.0399\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 2 | train loss: 0.0364\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 2 | train loss: 0.0408\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 3 | train loss: 0.0381\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 3 | train loss: 0.0367\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 4 | train loss: 0.0353\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 4 | train loss: 0.0389\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 5 | train loss: 0.0382\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 5 | train loss: 0.0361\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 6 | train loss: 0.0359\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 6 | train loss: 0.0368\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 7 | train loss: 0.0392\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 7 | train loss: 0.0389\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 8 | train loss: 0.0387\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 8 | train loss: 0.0365\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 9 | train loss: 0.0349\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 9 | train loss: 0.0374\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 432x288 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# initialize figure\n",
"f, a = plt.subplots(2, N_TEST_IMG, figsize=(5, 2))\n",
"plt.ion() # continuously plot\n",
"\n",
"# original data (first row) for viewing\n",
"view_data = train_data.train_data[:N_TEST_IMG].view(-1, 28*28).type(torch.FloatTensor)/255.\n",
"\n",
"\n",
"for i in range(N_TEST_IMG):\n",
" a[0][i].imshow(np.reshape(view_data.data.numpy()[i], (28, 28)), cmap='gray'); a[0][i].set_xticks(()); a[0][i].set_yticks(())\n",
"\n",
"for epoch in range(EPOCH):\n",
" for step, (x, b_label) in enumerate(train_loader):\n",
" b_x = x.view(-1, 28*28) # batch x, shape (batch, 28*28)\n",
" b_y = x.view(-1, 28*28) # batch y, shape (batch, 28*28)\n",
"\n",
" encoded, decoded = autoencoder(b_x)\n",
"\n",
" loss = loss_func(decoded, b_y) # mean square error\n",
" optimizer.zero_grad() # clear gradients for this training step\n",
" loss.backward() # backpropagation, compute gradients\n",
" optimizer.step() # apply gradients\n",
"\n",
" if step % 500 == 0:\n",
" print('Epoch: ', epoch, '| train loss: %.4f' % loss.data.numpy())\n",
"\n",
" # plotting decoded image (second row)\n",
" _, decoded_data = autoencoder(view_data)\n",
" for i in range(N_TEST_IMG):\n",
" a[1][i].clear()\n",
" a[1][i].imshow(np.reshape(decoded_data.data.numpy()[i], (28, 28)), cmap='gray')\n",
" a[1][i].set_xticks(()); a[1][i].set_yticks(())\n",
" plt.draw(); plt.pause(0.05)\n",
"\n",
"plt.ioff()\n",
"#plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# visualize in 3D plot\n",
"view_data = train_data.train_data[:200].view(-1, 28*28).type(torch.FloatTensor)/255.\n",
"encoded_data, _ = autoencoder(view_data)\n",
"fig = plt.figure(2); ax = Axes3D(fig)\n",
"X, Y, Z = encoded_data.data[:, 0].numpy(), encoded_data.data[:, 1].numpy(), encoded_data.data[:, 2].numpy()\n",
"values = train_data.train_labels[:200].numpy()\n",
"for x, y, z, s in zip(X, Y, Z, values):\n",
" c = cm.rainbow(int(255*s/9)); ax.text(x, y, z, s, backgroundcolor=c)\n",
"ax.set_xlim(X.min(), X.max()); ax.set_ylim(Y.min(), Y.max()); ax.set_zlim(Z.min(), Z.max())\n",
"plt.show()"
]
},
{
"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": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
udibr/seizure-prediction | notebooks/141105-train-test-feature-diff.ipynb | 1 | 54815 | {
"metadata": {
"name": "",
"signature": "sha256:0d3340e11a86cc6bcdc30383b98cd0bbe025e57774c6bb5ef455adc8340a8300"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from matplotlib import pylab as pl\n",
"import cPickle as pickle\n",
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"import random"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"sys.path.append('..')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Read precomputed features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"uncommoent the relevant pipeline in `../seizure_detection.py` and run\n",
"```bash\n",
"cd ..\n",
"./doall data\n",
"```\n",
"or\n",
"```bash\n",
"./doall td\n",
"./doall tt\n",
"```"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"FEATURES = 'gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nbands = 0\n",
"nwindows = 0\n",
"for p in FEATURES.split('-'):\n",
" if p[0] == 'b':\n",
" nbands += 1\n",
" elif p[0] == 'w':\n",
" nwindows = int(p[1:])\n",
"\n",
"nbands -= 1\n",
"nbands, nwindows"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"(5, 60)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"NUNITS = 1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from common.data import CachedDataLoader\n",
"cached_data_loader = CachedDataLoader('../data-cache')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def read_data(target, data_type):\n",
" fname = 'data_%s_%s_%s'%(data_type,target,FEATURES)\n",
" print fname\n",
" return cached_data_loader.load(fname,None)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def process(X, percentile=None,nunits=NUNITS,band=4):\n",
" N, Nf = X.shape\n",
" print '# samples',N,'# power points', Nf\n",
" nchannels = Nf / (nbands*nwindows)\n",
" print '# channels', nchannels\n",
" \n",
" newX = []\n",
" for i in range(N):\n",
" nw = nwindows//nunits\n",
" windows = X[i,:].reshape((nunits,nw,-1))\n",
" if band is not None:\n",
" windows = windows[:,:,range(band,windows.shape[2],nbands)]\n",
" if percentile is not None:\n",
" windows = np.sort(windows, axis=1)\n",
" windows = np.concatenate([windows[:,int(p*nw),:] for p in percentile], axis=-1)\n",
" newX.append(windows.ravel())\n",
" newX = np.array(newX)\n",
"\n",
" return newX"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import preprocessing\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"scale = StandardScaler()\n",
"\n",
"min_max_scaler = preprocessing.MinMaxScaler() # scale features to be [0..1] which is DBN requirement\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for itarget, target in enumerate(['Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2']):\n",
" pdata = read_data(target, 'preictal') # positive examples\n",
" ndata = read_data(target, 'interictal') # negative examples\n",
" tdata = read_data(target, 'test') # test examples\n",
" Xp = pdata.X\n",
" Np, NF = Xp.shape\n",
" Xp = process(Xp)\n",
" print 'P',Xp.mean(),Xp.std()\n",
" \n",
" Xn = ndata.X\n",
" Nn = Xn.shape[0]\n",
" Xn = process(Xn)\n",
" print 'N',Xn.mean(),Xn.std()\n",
" \n",
" Xt = tdata.X\n",
" Nt = Xt.shape[0]\n",
" Xt = process(Xt)\n",
" print 'T',Xt.mean(),Xt.std()\n",
" \n",
" # normalize train and test together (allowed)\n",
" X = scale.fit_transform(np.concatenate((Xp, Xn, Xt)))\n",
" X = np.clip(X,-3,3)\n",
" X = min_max_scaler.fit_transform(X)\n",
" Xp = X[:Np,:]\n",
" Xn = X[Np:(Np+Nn),:]\n",
" Xt = X[(Np+Nn):,:]\n",
" \n",
" pl.subplot(4,2,itarget+1)\n",
" pl.hist(Xp.ravel(),bins=50,normed=True)\n",
" pl.hist(Xn.ravel(),bins=50,alpha=0.5,normed=True)\n",
" pl.hist(Xt.ravel(),bins=50,alpha=0.3,normed=True);"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"data_preictal_Dog_1_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_interictal_Dog_1_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Dog_1_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"# samples 184 # power points 4800\n",
"# channels 16\n",
"P 6.02509709696 0.331752199925\n",
"# samples 480 # power points 4800\n",
"# channels 16\n",
"N"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5.98886455133 0.314228578872\n",
"# samples 502 # power points 4800\n",
"# channels 16\n",
"T 6.03814863311 0.336207912619\n",
"data_preictal_Dog_2_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data_interictal_Dog_2_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Dog_2_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"# samples 322 # power points 4800\n",
"# channels 16\n",
"P"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 6.04860279883 0.301324429127\n",
"# samples 500 # power points 4800\n",
"# channels 16\n",
"N 5.87524987884 0.336356837952\n",
"# samples 1000 # power points 4800\n",
"# channels 16\n",
"T"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5.88568475309 0.343104860578\n",
"data_preictal_Dog_3_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data_interictal_Dog_3_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Dog_3_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"# samples"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 552 # power points 4800\n",
"# channels 16\n",
"P 6.02050505522 0.259973583548\n",
"# samples 1440 # power points 4800\n",
"# channels 16\n",
"N"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5.96504608162 0.411540362205\n",
"# samples 907 # power points 4800\n",
"# channels 16\n",
"T"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5.95505777921 0.446728152071\n",
"data_preictal_Dog_4_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data_interictal_Dog_4_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Dog_4_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"# samples"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 737 # power points 4800\n",
"# channels 16\n",
"P 5.57427877297 0.251072149032\n",
"# samples 804 # power points 4800\n",
"# channels 16\n",
"N"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5.56952219729 0.274401018881\n",
"# samples 990 # power points 4800\n",
"# channels 16\n",
"T"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 5.6000850071 0.250126109339\n",
"data_preictal_Dog_5_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data_interictal_Dog_5_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Dog_5_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"# samples"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 230 # power points 4500\n",
"# channels 15\n",
"P 5.92826831464 0.278521132295\n",
"# samples 450 # power points 4500\n",
"# channels 15\n",
"N 5.9480261183 0.32075653254\n",
"# samples 191 # power points 4500\n",
"# channels 15\n",
"T 5.94585753847 0.435330438824\n",
"data_preictal_Patient_1_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data_interictal_Patient_1_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Patient_1_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"# samples 138 # power points 4500\n",
"# channels 15\n",
"P 3.61719368674 0.496425998609\n",
"# samples 50 # power points 4500\n",
"# channels 15\n",
"N 3.43675866827 0.600261020395\n",
"# samples 195 # power points 4500\n",
"# channels 15\n",
"T 3.88938284754 0.49122969672\n",
"data_preictal_Patient_2_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"data_interictal_Patient_2_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"data_test_Patient_2_gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70\n",
"# samples 138 # power points 7200\n",
"# channels 24\n",
"P 3.82128485251 0.499804941626\n",
"# samples 42 # power points 7200\n",
"# channels 24\n",
"N 3.68356459524 0.543461590136\n",
"# samples 150 # power points 7200\n",
"# channels 24\n",
"T 4.06574033709 0.525487586722\n"
]
},
{
"metadata": {},
"output_type": "display_data",
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Y/ZBSyiNCiPcDVuAnUsr103TNZHdMAjTml8Puxl2i+gHzbqpso3FFzBN7kFzsQXfnpZMf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\n8ni9vgd8bzzH9xrHUtvcH/2+BI7/aQ/7+HHfx4yT6H335K2qlAoAe0T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"text": [
"<matplotlib.figure.Figure at 0x113e80990>"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
jphall663/GWU_data_mining | 10_model_interpretability/src/lime.ipynb | 1 | 251172 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# License \n",
"***\n",
"Copyright 2017 J. Patrick Hall, [email protected]\n",
"\n",
"Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
"\n",
"The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
"\n",
"THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Local Interpretable Model Agnostic Explanations (LIME)\n",
"***\n",
"\n",
"Based on: Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. \"Why should i trust you?: Explaining the predictions of any classifier.\" In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135-1144. ACM, 2016.\n",
"\n",
"http://www.kdd.org/kdd2016/papers/files/rfp0573-ribeiroA.pdf\n",
"\n",
"**Instead of perturbing a sample of interest to create a local region in which to fit a linear model, some of these examples use a practical sample, say all one story homes, from the data to create an approximately local region in which to fit a linear model. That model can be validated and the region examined to explain local prediction behavior.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries: imports, start h2o, load and clean data "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# imports\n",
"import h2o \n",
"import operator\n",
"import numpy as np\n",
"import pandas as pd\n",
"from h2o.estimators.glm import H2OGeneralizedLinearEstimator\n",
"from h2o.estimators.gbm import H2OGradientBoostingEstimator"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n",
"Attempting to start a local H2O server...\n",
" Java Version: java version \"1.8.0_112\"; Java(TM) SE Runtime Environment (build 1.8.0_112-b16); Java HotSpot(TM) 64-Bit Server VM (build 25.112-b16, mixed mode)\n",
" Starting server from /Users/phall/anaconda/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n",
" Ice root: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpk5t7btqn\n",
" JVM stdout: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpk5t7btqn/h2o_phall_started_from_python.out\n",
" JVM stderr: /var/folders/tc/0ss1l73113j3wdyjsxmy1j2r0000gn/T/tmpk5t7btqn/h2o_phall_started_from_python.err\n",
" Server is running at http://127.0.0.1:54321\n",
"Connecting to H2O server at http://127.0.0.1:54321... successful.\n"
]
},
{
"data": {
"text/html": [
"<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
"<td>01 secs</td></tr>\n",
"<tr><td>H2O cluster version:</td>\n",
"<td>3.12.0.1</td></tr>\n",
"<tr><td>H2O cluster version age:</td>\n",
"<td>2 months and 9 days </td></tr>\n",
"<tr><td>H2O cluster name:</td>\n",
"<td>H2O_from_python_phall_og2isp</td></tr>\n",
"<tr><td>H2O cluster total nodes:</td>\n",
"<td>1</td></tr>\n",
"<tr><td>H2O cluster free memory:</td>\n",
"<td>3.556 Gb</td></tr>\n",
"<tr><td>H2O cluster total cores:</td>\n",
"<td>8</td></tr>\n",
"<tr><td>H2O cluster allowed cores:</td>\n",
"<td>8</td></tr>\n",
"<tr><td>H2O cluster status:</td>\n",
"<td>accepting new members, healthy</td></tr>\n",
"<tr><td>H2O connection url:</td>\n",
"<td>http://127.0.0.1:54321</td></tr>\n",
"<tr><td>H2O connection proxy:</td>\n",
"<td>None</td></tr>\n",
"<tr><td>H2O internal security:</td>\n",
"<td>False</td></tr>\n",
"<tr><td>Python version:</td>\n",
"<td>3.5.2 final</td></tr></table></div>"
],
"text/plain": [
"-------------------------- ------------------------------\n",
"H2O cluster uptime: 01 secs\n",
"H2O cluster version: 3.12.0.1\n",
"H2O cluster version age: 2 months and 9 days\n",
"H2O cluster name: H2O_from_python_phall_og2isp\n",
"H2O cluster total nodes: 1\n",
"H2O cluster free memory: 3.556 Gb\n",
"H2O cluster total cores: 8\n",
"H2O cluster allowed cores: 8\n",
"H2O cluster status: accepting new members, healthy\n",
"H2O connection url: http://127.0.0.1:54321\n",
"H2O connection proxy:\n",
"H2O internal security: False\n",
"Python version: 3.5.2 final\n",
"-------------------------- ------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# start h2o\n",
"h2o.init()\n",
"h2o.remove_all()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Load and prepare data for modeling"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
]
}
],
"source": [
"# load data\n",
"path = '../../03_regression/data/train.csv'\n",
"frame = h2o.import_file(path=path)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# assign target and inputs\n",
"y = 'SalePrice'\n",
"X = [name for name in frame.columns if name not in [y, 'Id']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### LIME is simpler to use with data containing no missing values"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# determine column types\n",
"# impute\n",
"reals, enums = [], []\n",
"for key, val in frame.types.items():\n",
" if key in X:\n",
" if val == 'enum':\n",
" enums.append(key)\n",
" else: \n",
" reals.append(key)\n",
" \n",
"_ = frame[reals].impute(method='median')\n",
"_ = frame[enums].impute(method='mode')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# split into training and validation\n",
"train, valid = frame.split_frame([0.7])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### LIME can be unstable with data in which strong correlations exist between input variables"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"YearBuilt GarageYrBlt\n",
"GrLivArea TotRmsAbvGrd\n",
"1stFlrSF TotalBsmtSF\n",
"TotalBsmtSF 1stFlrSF\n",
"TotRmsAbvGrd GrLivArea\n",
"GarageCars GarageArea\n",
"GarageArea GarageCars\n",
"GarageYrBlt YearBuilt\n"
]
}
],
"source": [
"# print out correlated pairs\n",
"corr = train[reals].cor().as_data_frame()\n",
"for i in range(0, corr.shape[0]):\n",
" for j in range(0, corr.shape[1]):\n",
" if i != j:\n",
" if np.abs(corr.iat[i, j]) > 0.7:\n",
" print(corr.columns[i], corr.columns[j])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### Remove one var from each correlated pair"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_reals_decorr = [i for i in reals if i not in ['GarageYrBlt', 'TotRmsAbvGrd', 'TotalBsmtSF', 'GarageCars']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train a predictive model"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gbm Model Build progress: |███████████████████████████████████████████████| 100%\n",
"gbm prediction progress: |████████████████████████████████████████████████| 100%\n"
]
}
],
"source": [
"# train GBM model\n",
"model = H2OGradientBoostingEstimator(ntrees=100,\n",
" max_depth=10,\n",
" distribution='huber',\n",
" learn_rate=0.1,\n",
" stopping_rounds=5,\n",
" seed=12345)\n",
"\n",
"model.train(y=y, x=X_reals_decorr, training_frame=train, validation_frame=valid)\n",
"\n",
"preds = valid['Id'].cbind(model.predict(valid))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build local linear surrogate models to help interpret the model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a local region based on HouseStyle"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Rows:209\n",
"Cols:82\n",
"\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead>\n",
"<tr><th> </th><th>Id </th><th>predict </th><th>MSSubClass </th><th>MSZoning </th><th>LotFrontage </th><th>LotArea </th><th>Street </th><th>Alley </th><th>LotShape </th><th>LandContour </th><th>Utilities </th><th>LotConfig </th><th>LandSlope </th><th>Neighborhood </th><th>Condition1 </th><th>Condition2 </th><th>BldgType </th><th>HouseStyle </th><th>OverallQual </th><th>OverallCond </th><th>YearBuilt </th><th>YearRemodAdd </th><th>RoofStyle </th><th>RoofMatl </th><th>Exterior1st </th><th>Exterior2nd </th><th>MasVnrType </th><th>MasVnrArea </th><th>ExterQual </th><th>ExterCond </th><th>Foundation </th><th>BsmtQual </th><th>BsmtCond </th><th>BsmtExposure </th><th>BsmtFinType1 </th><th>BsmtFinSF1 </th><th>BsmtFinType2 </th><th>BsmtFinSF2 </th><th>BsmtUnfSF </th><th>TotalBsmtSF </th><th>Heating </th><th>HeatingQC </th><th>CentralAir </th><th>Electrical </th><th>1stFlrSF </th><th>2ndFlrSF </th><th>LowQualFinSF </th><th>GrLivArea </th><th>BsmtFullBath </th><th>BsmtHalfBath </th><th>FullBath </th><th>HalfBath </th><th>BedroomAbvGr </th><th>KitchenAbvGr </th><th>KitchenQual </th><th>TotRmsAbvGrd </th><th>Functional </th><th>Fireplaces </th><th>FireplaceQu </th><th>GarageType </th><th>GarageYrBlt </th><th>GarageFinish </th><th>GarageCars </th><th>GarageArea </th><th>GarageQual </th><th>GarageCond </th><th>PavedDrive </th><th>WoodDeckSF </th><th>OpenPorchSF </th><th>EnclosedPorch </th><th>3SsnPorch </th><th>ScreenPorch </th><th>PoolArea </th><th>PoolQC </th><th>Fence </th><th>MiscFeature </th><th>MiscVal </th><th>MoSold </th><th>YrSold </th><th>SaleType </th><th>SaleCondition </th><th>SalePrice </th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>type </td><td>int </td><td>real </td><td>int </td><td>enum </td><td>real </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>int </td><td>enum </td><td>int </td><td>enum </td><td>enum </td><td>real </td><td>enum </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>int </td></tr>\n",
"<tr><td>mins </td><td>14.0 </td><td>11.036545441593269</td><td>20.0 </td><td> </td><td>32.0 </td><td>3010.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>2.0 </td><td>3.0 </td><td>1913.0 </td><td>1950.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0.0 </td><td> </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td> </td><td>480.0 </td><td>0.0 </td><td>0.0 </td><td>480.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td> </td><td>3.0 </td><td> </td><td>0.0 </td><td> </td><td> </td><td>1920.0 </td><td> </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>0.0 </td><td>1.0 </td><td>2006.0 </td><td> </td><td> </td><td>35311.0 </td></tr>\n",
"<tr><td>mean </td><td>687.1291866028708 </td><td>12.005547541208653</td><td>42.057416267942585</td><td> </td><td>70.77917923261715</td><td>9969.435406698565</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>5.947368421052632 </td><td>5.483253588516747</td><td>1974.6076555023924</td><td>1984.909090909091</td><td> </td><td> </td><td> </td><td> </td><td> </td><td>89.69856459330144 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>574.1052631578947</td><td> </td><td>53.61244019138756</td><td>602.0430622009569</td><td>1229.7607655502393</td><td> </td><td> </td><td> </td><td> </td><td>1337.6602870813397</td><td>2.23444976076555 </td><td>0.0 </td><td>1339.8947368421052</td><td>0.5789473684210527</td><td>0.0430622009569378 </td><td>1.4784688995215312</td><td>0.11961722488038277</td><td>2.650717703349282 </td><td>1.0526315789473684 </td><td> </td><td>5.980861244019139 </td><td> </td><td>0.5358851674641149</td><td> </td><td> </td><td>1979.9238578680201</td><td> </td><td>1.7272727272727273</td><td>479.52153110047846</td><td> </td><td> </td><td> </td><td>111.377990430622 </td><td>38.79904306220096 </td><td>17.267942583732058</td><td>2.569377990430622 </td><td>9.229665071770334</td><td>3.1004784688995217</td><td> </td><td> </td><td> </td><td>19.138755980861244</td><td>6.555023923444976 </td><td>2007.8086124401914</td><td> </td><td> </td><td>178322.78468899522</td></tr>\n",
"<tr><td>maxs </td><td>1452.0 </td><td>12.985784787643702</td><td>190.0 </td><td> </td><td>313.0 </td><td>50271.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>10.0 </td><td>9.0 </td><td>2009.0 </td><td>2010.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>922.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>1810.0 </td><td> </td><td>791.0 </td><td>2336.0 </td><td>3094.0 </td><td> </td><td> </td><td> </td><td> </td><td>2898.0 </td><td>467.0 </td><td>0.0 </td><td>2898.0 </td><td>3.0 </td><td>1.0 </td><td>3.0 </td><td>1.0 </td><td>6.0 </td><td>2.0 </td><td> </td><td>10.0 </td><td> </td><td>3.0 </td><td> </td><td> </td><td>2009.0 </td><td> </td><td>4.0 </td><td>1356.0 </td><td> </td><td> </td><td> </td><td>857.0 </td><td>304.0 </td><td>286.0 </td><td>216.0 </td><td>224.0 </td><td>648.0 </td><td> </td><td> </td><td> </td><td>1200.0 </td><td>12.0 </td><td>2010.0 </td><td> </td><td> </td><td>555000.0 </td></tr>\n",
"<tr><td>sigma </td><td>422.43200130977914</td><td>0.3916492463134954</td><td>41.068382877079394</td><td> </td><td>24.43557111295915</td><td>5321.718702537052</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>1.5070683663088231</td><td>0.985938121107469</td><td>24.489973198543225</td><td>20.1376599844756 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>150.47734958562475</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>488.1206585752494</td><td> </td><td>155.9077267731607</td><td>489.8865086996808</td><td>457.11586457436033</td><td> </td><td> </td><td> </td><td> </td><td>398.88055422682845</td><td>32.30306546254569</td><td>0.0 </td><td>399.1265805830717 </td><td>0.5501196042201809</td><td>0.20348455090889342</td><td>0.5287553875332314</td><td>0.32529254125200097</td><td>0.7767733815416329</td><td>0.22383300599978273</td><td> </td><td>1.2746105079593406</td><td> </td><td>0.612297302582734 </td><td> </td><td> </td><td>21.14726636557282 </td><td> </td><td>0.7827913117569955</td><td>229.0783598834146 </td><td> </td><td> </td><td> </td><td>135.56536066875606</td><td>58.122035069484006</td><td>53.86418976031431 </td><td>21.584944452443935</td><td>39.59731051937812</td><td>44.823097258521635</td><td> </td><td> </td><td> </td><td>114.00584027036803</td><td>2.8416690187150433</td><td>1.2978245436848066</td><td> </td><td> </td><td>77114.87392119177 </td></tr>\n",
"<tr><td>zeros </td><td>0 </td><td>0 </td><td>0 </td><td> </td><td>0 </td><td>0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>114 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>39 </td><td> </td><td>182 </td><td>22 </td><td>7 </td><td> </td><td> </td><td> </td><td> </td><td>0 </td><td>208 </td><td>209 </td><td>0 </td><td>93 </td><td>200 </td><td>2 </td><td>184 </td><td>2 </td><td>0 </td><td> </td><td>0 </td><td> </td><td>109 </td><td> </td><td> </td><td>0 </td><td> </td><td>14 </td><td>14 </td><td> </td><td> </td><td> </td><td>95 </td><td>104 </td><td>184 </td><td>206 </td><td>198 </td><td>208 </td><td> </td><td> </td><td> </td><td>202 </td><td>0 </td><td>0 </td><td> </td><td> </td><td>0 </td></tr>\n",
"<tr><td>missing</td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td></tr>\n",
"<tr><td>0 </td><td>14.0 </td><td>12.268609123183118</td><td>20.0 </td><td>RL </td><td>91.0 </td><td>10652.0 </td><td>Pave </td><td>NA </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>CollgCr </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>7.0 </td><td>5.0 </td><td>2006.0 </td><td>2007.0 </td><td>Gable </td><td>CompShg </td><td>VinylSd </td><td>VinylSd </td><td>Stone </td><td>306.0 </td><td>Gd </td><td>TA </td><td>PConc </td><td>Gd </td><td>TA </td><td>Av </td><td>Unf </td><td>0.0 </td><td>Unf </td><td>0.0 </td><td>1494.0 </td><td>1494.0 </td><td>GasA </td><td>Ex </td><td>Y </td><td>SBrkr </td><td>1494.0 </td><td>0.0 </td><td>0.0 </td><td>1494.0 </td><td>0.0 </td><td>0.0 </td><td>2.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>Gd </td><td>7.0 </td><td>Typ </td><td>1.0 </td><td>Gd </td><td>Attchd </td><td>2006.0 </td><td>RFn </td><td>3.0 </td><td>840.0 </td><td>TA </td><td>TA </td><td>Y </td><td>160.0 </td><td>33.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>8.0 </td><td>2007.0 </td><td>New </td><td>Partial </td><td>279500.0 </td></tr>\n",
"<tr><td>1 </td><td>15.0 </td><td>11.89861004166866 </td><td>20.0 </td><td>RL </td><td>70.04995836802665</td><td>10920.0 </td><td>Pave </td><td>NA </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>Corner </td><td>Gtl </td><td>NAmes </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>6.0 </td><td>5.0 </td><td>1960.0 </td><td>1960.0 </td><td>Hip </td><td>CompShg </td><td>MetalSd </td><td>MetalSd </td><td>BrkFace </td><td>212.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>No </td><td>BLQ </td><td>733.0 </td><td>Unf </td><td>0.0 </td><td>520.0 </td><td>1253.0 </td><td>GasA </td><td>TA </td><td>Y </td><td>SBrkr </td><td>1253.0 </td><td>0.0 </td><td>0.0 </td><td>1253.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>2.0 </td><td>1.0 </td><td>TA </td><td>5.0 </td><td>Typ </td><td>1.0 </td><td>Fa </td><td>Attchd </td><td>1960.0 </td><td>RFn </td><td>1.0 </td><td>352.0 </td><td>TA </td><td>TA </td><td>Y </td><td>0.0 </td><td>213.0 </td><td>176.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>GdWo </td><td>NA </td><td>0.0 </td><td>5.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>157000.0 </td></tr>\n",
"<tr><td>2 </td><td>27.0 </td><td>11.771056091638249</td><td>20.0 </td><td>RL </td><td>60.0 </td><td>7200.0 </td><td>Pave </td><td>NA </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Corner </td><td>Gtl </td><td>NAmes </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>5.0 </td><td>7.0 </td><td>1951.0 </td><td>2000.0 </td><td>Gable </td><td>CompShg </td><td>Wd Sdng </td><td>Wd Sdng </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>Mn </td><td>BLQ </td><td>234.0 </td><td>Rec </td><td>486.0 </td><td>180.0 </td><td>900.0 </td><td>GasA </td><td>TA </td><td>Y </td><td>SBrkr </td><td>900.0 </td><td>0.0 </td><td>0.0 </td><td>900.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>Gd </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>NA </td><td>Detchd </td><td>2005.0 </td><td>Unf </td><td>2.0 </td><td>576.0 </td><td>TA </td><td>TA </td><td>Y </td><td>222.0 </td><td>32.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>5.0 </td><td>2010.0 </td><td>WD </td><td>Normal </td><td>134800.0 </td></tr>\n",
"<tr><td>3 </td><td>28.0 </td><td>12.654713854906523</td><td>20.0 </td><td>RL </td><td>98.0 </td><td>11478.0 </td><td>Pave </td><td>NA </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>NridgHt </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>8.0 </td><td>5.0 </td><td>2007.0 </td><td>2008.0 </td><td>Gable </td><td>CompShg </td><td>VinylSd </td><td>VinylSd </td><td>Stone </td><td>200.0 </td><td>Gd </td><td>TA </td><td>PConc </td><td>Ex </td><td>TA </td><td>No </td><td>GLQ </td><td>1218.0 </td><td>Unf </td><td>0.0 </td><td>486.0 </td><td>1704.0 </td><td>GasA </td><td>Ex </td><td>Y </td><td>SBrkr </td><td>1704.0 </td><td>0.0 </td><td>0.0 </td><td>1704.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>Gd </td><td>7.0 </td><td>Typ </td><td>1.0 </td><td>Gd </td><td>Attchd </td><td>2008.0 </td><td>RFn </td><td>3.0 </td><td>772.0 </td><td>TA </td><td>TA </td><td>Y </td><td>0.0 </td><td>50.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>5.0 </td><td>2010.0 </td><td>WD </td><td>Normal </td><td>306000.0 </td></tr>\n",
"<tr><td>4 </td><td>29.0 </td><td>12.059393610692608</td><td>20.0 </td><td>RL </td><td>47.0 </td><td>16321.0 </td><td>Pave </td><td>NA </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>CulDSac </td><td>Gtl </td><td>NAmes </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>5.0 </td><td>6.0 </td><td>1957.0 </td><td>1997.0 </td><td>Gable </td><td>CompShg </td><td>MetalSd </td><td>MetalSd </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>Gd </td><td>BLQ </td><td>1277.0 </td><td>Unf </td><td>0.0 </td><td>207.0 </td><td>1484.0 </td><td>GasA </td><td>TA </td><td>Y </td><td>SBrkr </td><td>1600.0 </td><td>0.0 </td><td>0.0 </td><td>1600.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>TA </td><td>6.0 </td><td>Typ </td><td>2.0 </td><td>Gd </td><td>Attchd </td><td>1957.0 </td><td>RFn </td><td>1.0 </td><td>319.0 </td><td>TA </td><td>TA </td><td>Y </td><td>288.0 </td><td>258.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>12.0 </td><td>2006.0 </td><td>WD </td><td>Normal </td><td>207500.0 </td></tr>\n",
"<tr><td>5 </td><td>30.0 </td><td>11.17125449658085 </td><td>30.0 </td><td>RM </td><td>60.0 </td><td>6324.0 </td><td>Pave </td><td>NA </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>BrkSide </td><td>Feedr </td><td>RRNn </td><td>1Fam </td><td>1Story </td><td>4.0 </td><td>6.0 </td><td>1927.0 </td><td>1950.0 </td><td>Gable </td><td>CompShg </td><td>MetalSd </td><td>MetalSd </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>BrkTil </td><td>TA </td><td>TA </td><td>No </td><td>Unf </td><td>0.0 </td><td>Unf </td><td>0.0 </td><td>520.0 </td><td>520.0 </td><td>GasA </td><td>Fa </td><td>N </td><td>SBrkr </td><td>520.0 </td><td>0.0 </td><td>0.0 </td><td>520.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>Fa </td><td>4.0 </td><td>Typ </td><td>0.0 </td><td>NA </td><td>Detchd </td><td>1920.0 </td><td>Unf </td><td>1.0 </td><td>240.0 </td><td>Fa </td><td>TA </td><td>Y </td><td>49.0 </td><td>0.0 </td><td>87.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>5.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>68500.0 </td></tr>\n",
"<tr><td>6 </td><td>32.0 </td><td>11.810399452875627</td><td>20.0 </td><td>RL </td><td>70.04995836802665</td><td>8544.0 </td><td>Pave </td><td>NA </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>CulDSac </td><td>Gtl </td><td>Sawyer </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>5.0 </td><td>6.0 </td><td>1966.0 </td><td>2006.0 </td><td>Gable </td><td>CompShg </td><td>HdBoard </td><td>HdBoard </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>No </td><td>Unf </td><td>0.0 </td><td>Unf </td><td>0.0 </td><td>1228.0 </td><td>1228.0 </td><td>GasA </td><td>Gd </td><td>Y </td><td>SBrkr </td><td>1228.0 </td><td>0.0 </td><td>0.0 </td><td>1228.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>3.0 </td><td>1.0 </td><td>Gd </td><td>6.0 </td><td>Typ </td><td>0.0 </td><td>NA </td><td>Attchd </td><td>1966.0 </td><td>Unf </td><td>1.0 </td><td>271.0 </td><td>TA </td><td>TA </td><td>Y </td><td>0.0 </td><td>65.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>MnPrv </td><td>NA </td><td>0.0 </td><td>6.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>149350.0 </td></tr>\n",
"<tr><td>7 </td><td>38.0 </td><td>11.867148537642631</td><td>20.0 </td><td>RL </td><td>74.0 </td><td>8532.0 </td><td>Pave </td><td>NA </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>NAmes </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>5.0 </td><td>6.0 </td><td>1954.0 </td><td>1990.0 </td><td>Hip </td><td>CompShg </td><td>Wd Sdng </td><td>Wd Sdng </td><td>BrkFace </td><td>650.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>No </td><td>Rec </td><td>1213.0 </td><td>Unf </td><td>0.0 </td><td>84.0 </td><td>1297.0 </td><td>GasA </td><td>Gd </td><td>Y </td><td>SBrkr </td><td>1297.0 </td><td>0.0 </td><td>0.0 </td><td>1297.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>TA </td><td>5.0 </td><td>Typ </td><td>1.0 </td><td>TA </td><td>Attchd </td><td>1954.0 </td><td>Fin </td><td>2.0 </td><td>498.0 </td><td>TA </td><td>TA </td><td>Y </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>10.0 </td><td>2009.0 </td><td>WD </td><td>Normal </td><td>153000.0 </td></tr>\n",
"<tr><td>8 </td><td>39.0 </td><td>11.8126303757069 </td><td>20.0 </td><td>RL </td><td>68.0 </td><td>7922.0 </td><td>Pave </td><td>NA </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>NAmes </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>5.0 </td><td>7.0 </td><td>1953.0 </td><td>2007.0 </td><td>Gable </td><td>CompShg </td><td>VinylSd </td><td>VinylSd </td><td>None </td><td>0.0 </td><td>TA </td><td>Gd </td><td>CBlock </td><td>TA </td><td>TA </td><td>No </td><td>GLQ </td><td>731.0 </td><td>Unf </td><td>0.0 </td><td>326.0 </td><td>1057.0 </td><td>GasA </td><td>TA </td><td>Y </td><td>SBrkr </td><td>1057.0 </td><td>0.0 </td><td>0.0 </td><td>1057.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>Gd </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>NA </td><td>Detchd </td><td>1953.0 </td><td>Unf </td><td>1.0 </td><td>246.0 </td><td>TA </td><td>TA </td><td>Y </td><td>0.0 </td><td>52.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>NA </td><td>NA </td><td>0.0 </td><td>1.0 </td><td>2010.0 </td><td>WD </td><td>Abnorml </td><td>109000.0 </td></tr>\n",
"<tr><td>9 </td><td>44.0 </td><td>11.801547414216035</td><td>20.0 </td><td>RL </td><td>70.04995836802665</td><td>9200.0 </td><td>Pave </td><td>NA </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>CulDSac </td><td>Gtl </td><td>CollgCr </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>5.0 </td><td>6.0 </td><td>1975.0 </td><td>1980.0 </td><td>Hip </td><td>CompShg </td><td>VinylSd </td><td>VinylSd </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>Gd </td><td>TA </td><td>Av </td><td>LwQ </td><td>280.0 </td><td>BLQ </td><td>491.0 </td><td>167.0 </td><td>938.0 </td><td>GasA </td><td>TA </td><td>Y </td><td>SBrkr </td><td>938.0 </td><td>0.0 </td><td>0.0 </td><td>938.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>TA </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>NA </td><td>Detchd </td><td>1977.0 </td><td>Unf </td><td>1.0 </td><td>308.0 </td><td>TA </td><td>TA </td><td>Y </td><td>145.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>NA </td><td>MnPrv </td><td>NA </td><td>0.0 </td><td>7.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>130250.0 </td></tr>\n",
"</tbody>\n",
"</table>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"local_frame = preds.cbind(valid.drop(['Id']))\n",
"local_frame = local_frame[local_frame['HouseStyle'] == '1Story']\n",
"local_frame['predict'] = local_frame['predict'].log()\n",
"local_frame.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train penalized linear model in local region \n",
"* Check R<sup>2</sup> to ensure surrogate model is a good fit for predictions\n",
"* Use ranked predictions plot to ensure surrogate model is a good fit for predictions\n",
"* Use trained GLM and coefficients to understand local region of response function"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"glm Model Build progress: |███████████████████████████████████████████████| 100%\n",
"\n",
"Local GLM Coefficients:\n",
"KitchenAbvGr: -0.1508258670073023\n",
"YrSold: -0.0038514394943816705\n",
"MSSubClass: -0.00011210487426281287\n",
"EnclosedPorch: -9.130093138694107e-05\n",
"2ndFlrSF: -3.9194722085630606e-05\n",
"MoSold: -1.5547455415244997e-05\n",
"LotArea: 3.329666938941041e-06\n",
"MiscVal: 6.695561375680934e-06\n",
"OpenPorchSF: 7.334408876566789e-05\n",
"GrLivArea: 7.801707438135216e-05\n",
"WoodDeckSF: 8.338161186914433e-05\n",
"ScreenPorch: 9.387310676953871e-05\n",
"BsmtFinSF1: 0.00010125805853647395\n",
"GarageArea: 0.00016468298708688275\n",
"1stFlrSF: 0.000240835733035765\n",
"HalfBath: 0.0004316804511827515\n",
"LotFrontage: 0.0005389665055779621\n",
"YearRemodAdd: 0.0010353227962118725\n",
"BsmtFullBath: 0.0019456452443123145\n",
"YearBuilt: 0.003081698342094866\n",
"BedroomAbvGr: 0.010692862700010637\n",
"Fireplaces: 0.014672329995422593\n",
"OverallCond: 0.016722109721306774\n",
"OverallQual: 0.09232478225180206\n",
"Intercept: 10.437151063047025\n",
"\n",
"Local GLM R-square:\n",
"0.97\n"
]
}
],
"source": [
"# initialize\n",
"local_glm = H2OGeneralizedLinearEstimator(lambda_search=True)\n",
"\n",
"# train \n",
"local_glm.train(x=X_reals_decorr, y='predict', training_frame=local_frame)\n",
"\n",
"# coefs\n",
"print('\\nLocal GLM Coefficients:')\n",
"for c_name, c_val in sorted(local_glm.coef().items(), key=operator.itemgetter(1)):\n",
" if c_val != 0.0:\n",
" print('%s %s' % (str(c_name + ':').ljust(25), c_val))\n",
" \n",
"# r2\n",
"print('\\nLocal GLM R-square:\\n%.2f' % local_glm.r2())"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"glm prediction progress: |████████████████████████████████████████████████| 100%\n"
]
},
{
"data": {
"image/png": 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LsBISEkLr1q2pWbMmb7/9Nvb29syfP5/evXuzaNEievXqBcD58+cJDAwkNTWVd955Bzs7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\nVo7h7JWzGfb57jv4M2wtZVIdoeVkhn0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mcPWnVzPebzzb7thGgF1/PvsMdu+2wdLDkyzr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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1190dafd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# ranked predictions plot\n",
"pred_frame = local_frame.cbind(local_glm.predict(local_frame))\\\n",
" .as_data_frame()[['predict', 'predict0']]\n",
"pred_frame.columns = ['ML Preds.', 'Surrogate Preds.']\n",
"pred_frame.sort_values(by='ML Preds.', inplace=True)\n",
"pred_frame.reset_index(inplace=True, drop=True)\n",
"_ = pred_frame.plot(title='Ranked Predictions Plot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A ranked predictions plot is a way to visually check whether the surrogate model is a good fit for the complex model. The y-axis is the numeric prediction of both models for a given point. The x-axis is the rank of a point when the predictions are sorted by their GBM prediction, from lowest on the left to highest on the right. When both sets of predictions are aligned, as they are above, this a good indication that the linear model fits the complex, nonlinear GBM well in the approximately local region."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Both the R<sup>2</sup> and ranked predictions plot show the linear model is a good fit in the practical, approximately local sample. This means the regression coefficients are likely a very accurate representation of the behavior of the nonlinear model in this region."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create explanations (or 'reason codes') for a row in the local set\n",
"The local glm coefficient multiplied by the value in a specific row are estimates of how much each variable contributed to each prediction decision. These values can tell you how a variable and it's values were weighted in any given decision by the model. These values are crucially important for machine learning interpretability and are often to referred to \"local feature importance\", \"reason codes\", or \"turn-down codes.\" The latter phrases are borrowed from credit scoring. Credit lenders must provide reasons for turning down a credit application, even for automated decisions. Reason codes can be easily extracted from LIME local feature importance values, by simply ranking the variables that played the largest role in any given decision."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8RUREKkbBX0REpGIU/EVERCpGwV9ERKRiFPxFREQqRsFfRESkYhT8RUREKkbB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\nTkEdFOLMC/wIIsSZ56Ka/kyS9XPowpdNOosB2w3271IdmNljwNdcwkv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"text/plain": [
"<matplotlib.figure.Figure at 0x1190c4630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"row = 20 # select a row to describe\n",
"local_contrib_frame = pd.DataFrame(columns=['Name', 'Local Contribution', 'Sign'])\n",
"\n",
"# multiply values in row by local glm coefficients\n",
"for name in local_frame[row, :].columns:\n",
" contrib = 0.0\n",
" try:\n",
" contrib = local_frame[row, name]*local_glm.coef()[name]\n",
" except:\n",
" pass\n",
" if contrib != 0.0:\n",
" local_contrib_frame = local_contrib_frame.append({'Name':name,\n",
" 'Local Contribution': contrib,\n",
" 'Sign': contrib > 0}, \n",
" ignore_index=True)\n",
"\n",
"# plot\n",
"_ = local_contrib_frame.plot(x = 'Name',\n",
" y = 'Local Contribution',\n",
" kind='bar', \n",
" title='Local Contributions for Row ' + str(row) + '\\n', \n",
" color=local_contrib_frame.Sign.map({True: 'r', False: 'b'}), \n",
" legend=False) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a local region based on predicted SalePrice quantiles"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
"Rows:44\n",
"Cols:82\n",
"\n",
"\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
"<thead>\n",
"<tr><th> </th><th>Id </th><th>predict </th><th>MSSubClass </th><th>MSZoning </th><th>LotFrontage </th><th>LotArea </th><th>Street </th><th>Alley </th><th>LotShape </th><th>LandContour </th><th>Utilities </th><th>LotConfig </th><th>LandSlope </th><th>Neighborhood </th><th>Condition1 </th><th>Condition2 </th><th>BldgType </th><th>HouseStyle </th><th>OverallQual </th><th>OverallCond </th><th>YearBuilt </th><th>YearRemodAdd </th><th>RoofStyle </th><th>RoofMatl </th><th>Exterior1st </th><th>Exterior2nd </th><th>MasVnrType </th><th>MasVnrArea </th><th>ExterQual </th><th>ExterCond </th><th>Foundation </th><th>BsmtQual </th><th>BsmtCond </th><th>BsmtExposure </th><th>BsmtFinType1 </th><th>BsmtFinSF1 </th><th>BsmtFinType2 </th><th>BsmtFinSF2 </th><th>BsmtUnfSF </th><th>TotalBsmtSF </th><th>Heating </th><th>HeatingQC </th><th>CentralAir </th><th>Electrical </th><th>1stFlrSF </th><th>2ndFlrSF </th><th>LowQualFinSF </th><th>GrLivArea </th><th>BsmtFullBath </th><th>BsmtHalfBath </th><th>FullBath </th><th>HalfBath </th><th>BedroomAbvGr </th><th>KitchenAbvGr </th><th>KitchenQual </th><th>TotRmsAbvGrd </th><th>Functional </th><th>Fireplaces </th><th>FireplaceQu </th><th>GarageType </th><th>GarageYrBlt </th><th>GarageFinish </th><th>GarageCars </th><th>GarageArea </th><th>GarageQual </th><th>GarageCond </th><th>PavedDrive </th><th>WoodDeckSF </th><th>OpenPorchSF </th><th>EnclosedPorch </th><th>3SsnPorch </th><th>ScreenPorch </th><th>PoolArea </th><th>PoolQC </th><th>Fence </th><th>MiscFeature </th><th>MiscVal </th><th>MoSold </th><th>YrSold </th><th>SaleType </th><th>SaleCondition </th><th>SalePrice </th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>type </td><td>int </td><td>real </td><td>int </td><td>enum </td><td>real </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>int </td><td>enum </td><td>int </td><td>enum </td><td>enum </td><td>real </td><td>enum </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>enum </td><td>int </td><td>int </td><td>int </td><td>enum </td><td>enum </td><td>int </td></tr>\n",
"<tr><td>mins </td><td>30.0 </td><td>10.968339120935102 </td><td>20.0 </td><td> </td><td>21.0 </td><td>1596.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>2.0 </td><td>3.0 </td><td>1892.0 </td><td>1950.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0.0 </td><td> </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td> </td><td>372.0 </td><td>0.0 </td><td>0.0 </td><td>480.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td> </td><td>3.0 </td><td> </td><td>0.0 </td><td> </td><td> </td><td>1920.0 </td><td> </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>0.0 </td><td>1.0 </td><td>2006.0 </td><td> </td><td> </td><td>35311.0 </td></tr>\n",
"<tr><td>mean </td><td>656.5454545454544 </td><td>11.412471417415036 </td><td>69.88636363636363</td><td> </td><td>54.774998107637586</td><td>6930.522727272727 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>4.340909090909091 </td><td>5.454545454545455 </td><td>1938.9090909090908</td><td>1964.4545454545455</td><td> </td><td> </td><td> </td><td> </td><td> </td><td>14.068181818181818</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>183.11363636363637</td><td> </td><td>25.886363636363637</td><td>427.568181818182 </td><td>636.5681818181822 </td><td> </td><td> </td><td> </td><td> </td><td>784.2954545454546 </td><td>209.50000000000003</td><td>5.318181818181818</td><td>999.1136363636363</td><td>0.25000000000000006</td><td>0.022727272727272728</td><td>1.0909090909090913 </td><td>0.11363636363636363</td><td>2.340909090909091 </td><td>1.1136363636363633 </td><td> </td><td>5.295454545454544 </td><td> </td><td>0.11363636363636363</td><td> </td><td> </td><td>1966.070423231591 </td><td> </td><td>0.8181818181818182</td><td>223.54545454545453</td><td> </td><td> </td><td> </td><td>42.88636363636364</td><td>17.15909090909091</td><td>37.65909090909091</td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>90.9090909090909 </td><td>6.25 </td><td>2007.8863636363633</td><td> </td><td> </td><td>95765.61363636363 </td></tr>\n",
"<tr><td>maxs </td><td>1405.0 </td><td>11.592682530778966 </td><td>190.0 </td><td> </td><td>140.0 </td><td>21750.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>6.0 </td><td>9.0 </td><td>1977.0 </td><td>2006.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>381.0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>1440.0 </td><td> </td><td>499.0 </td><td>994.0 </td><td>1440.0 </td><td> </td><td> </td><td> </td><td> </td><td>1440.0 </td><td>994.0 </td><td>234.0 </td><td>2372.0 </td><td>2.0 </td><td>1.0 </td><td>2.0 </td><td>1.0 </td><td>4.0 </td><td>2.0 </td><td> </td><td>11.0 </td><td> </td><td>2.0 </td><td> </td><td> </td><td>1998.0 </td><td> </td><td>3.0 </td><td>936.0 </td><td> </td><td> </td><td> </td><td>321.0 </td><td>287.0 </td><td>286.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>3500.0 </td><td>12.0 </td><td>2010.0 </td><td> </td><td> </td><td>150000.0 </td></tr>\n",
"<tr><td>sigma </td><td>427.57740789978624</td><td>0.16918497542025435</td><td>60.95641443595537</td><td> </td><td>21.1543469370321 </td><td>3959.5263370035746</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0.9386576714124697</td><td>1.5166447891614192</td><td>23.893969446254026</td><td>18.047037437033808</td><td> </td><td> </td><td> </td><td> </td><td> </td><td>62.471722990177895</td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>294.3891757291177 </td><td> </td><td>91.37482628980807 </td><td>332.9897266190454</td><td>306.01674963485647</td><td> </td><td> </td><td> </td><td> </td><td>214.22815261820085</td><td>283.3505101952474 </td><td>35.27682731559835</td><td>315.365805752038 </td><td>0.4882336459352794 </td><td>0.15075567228888181 </td><td>0.36204726777242763</td><td>0.32103822064055043</td><td>0.7134319875402745</td><td>0.32103822064055043</td><td> </td><td>1.4560147177659126</td><td> </td><td>0.3867520743564635 </td><td> </td><td> </td><td>19.044164062233055</td><td> </td><td>0.755529300763972 </td><td>213.9337492043408 </td><td> </td><td> </td><td> </td><td>78.70794289550093</td><td>54.01978241512631</td><td>66.17698018292371</td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td> </td><td> </td><td> </td><td>531.2636019897791</td><td>3.4108240837417223</td><td>1.3845645624108944</td><td> </td><td> </td><td>23115.542274867905</td></tr>\n",
"<tr><td>zeros </td><td>0 </td><td>0 </td><td>0 </td><td> </td><td>0 </td><td>0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>41 </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td> </td><td>22 </td><td> </td><td>40 </td><td>10 </td><td>5 </td><td> </td><td> </td><td> </td><td> </td><td>0 </td><td>27 </td><td>43 </td><td>0 </td><td>34 </td><td>43 </td><td>1 </td><td>39 </td><td>0 </td><td>0 </td><td> </td><td>0 </td><td> </td><td>40 </td><td> </td><td> </td><td>0 </td><td> </td><td>16 </td><td>16 </td><td> </td><td> </td><td> </td><td>30 </td><td>38 </td><td>30 </td><td>44 </td><td>44 </td><td>44 </td><td> </td><td> </td><td> </td><td>42 </td><td>0 </td><td>0 </td><td> </td><td> </td><td>0 </td></tr>\n",
"<tr><td>missing</td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td><td>0 </td></tr>\n",
"<tr><td>0 </td><td>706.0 </td><td>10.968339120935102 </td><td>190.0 </td><td>RM </td><td>70.0 </td><td>5600.0 </td><td>Pave </td><td>nan </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>IDOTRR </td><td>Norm </td><td>Norm </td><td>2fmCon </td><td>2Story </td><td>4.0 </td><td>5.0 </td><td>1930.0 </td><td>1950.0 </td><td>Hip </td><td>CompShg </td><td>VinylSd </td><td>Wd Shng </td><td>None </td><td>0.0 </td><td>Fa </td><td>Fa </td><td>Slab </td><td>nan </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>nan </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>GasA </td><td>Fa </td><td>N </td><td>SBrkr </td><td>372.0 </td><td>720.0 </td><td>0.0 </td><td>1092.0 </td><td>0.0 </td><td>0.0 </td><td>2.0 </td><td>0.0 </td><td>3.0 </td><td>2.0 </td><td>Fa </td><td>7.0 </td><td>Mod </td><td>0.0 </td><td>nan </td><td>nan </td><td>1978.5061638868744</td><td>nan </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>N </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>Othr </td><td>3500.0 </td><td>7.0 </td><td>2010.0 </td><td>WD </td><td>Normal </td><td>55000.0 </td></tr>\n",
"<tr><td>1 </td><td>621.0 </td><td>11.036545441593269 </td><td>30.0 </td><td>RL </td><td>45.0 </td><td>8248.0 </td><td>Pave </td><td>Grvl </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>Edwards </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>3.0 </td><td>3.0 </td><td>1914.0 </td><td>1950.0 </td><td>Gable </td><td>CompShg </td><td>Stucco </td><td>Stucco </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>BrkTil </td><td>TA </td><td>TA </td><td>No </td><td>BLQ </td><td>41.0 </td><td>Unf </td><td>0.0 </td><td>823.0 </td><td>864.0 </td><td>GasA </td><td>TA </td><td>N </td><td>FuseF </td><td>864.0 </td><td>0.0 </td><td>0.0 </td><td>864.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>TA </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>nan </td><td>1978.5061638868744</td><td>nan </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>N </td><td>0.0 </td><td>0.0 </td><td>100.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>9.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>67000.0 </td></tr>\n",
"<tr><td>2 </td><td>1326.0 </td><td>11.081865084108477 </td><td>30.0 </td><td>RM </td><td>40.0 </td><td>3636.0 </td><td>Pave </td><td>nan </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>IDOTRR </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>4.0 </td><td>4.0 </td><td>1922.0 </td><td>1950.0 </td><td>Gable </td><td>CompShg </td><td>AsbShng </td><td>AsbShng </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>BrkTil </td><td>TA </td><td>Fa </td><td>No </td><td>Unf </td><td>0.0 </td><td>Unf </td><td>0.0 </td><td>796.0 </td><td>796.0 </td><td>GasA </td><td>Fa </td><td>N </td><td>SBrkr </td><td>796.0 </td><td>0.0 </td><td>0.0 </td><td>796.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>TA </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>nan </td><td>1978.5061638868744</td><td>nan </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>N </td><td>0.0 </td><td>0.0 </td><td>100.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>MnPrv </td><td>nan </td><td>0.0 </td><td>1.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>55000.0 </td></tr>\n",
"<tr><td>3 </td><td>1381.0 </td><td>11.12330045348205 </td><td>30.0 </td><td>RL </td><td>45.0 </td><td>8212.0 </td><td>Pave </td><td>Grvl </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>Edwards </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>3.0 </td><td>3.0 </td><td>1914.0 </td><td>1950.0 </td><td>Gable </td><td>CompShg </td><td>Stucco </td><td>Stucco </td><td>None </td><td>0.0 </td><td>TA </td><td>Fa </td><td>BrkTil </td><td>TA </td><td>Fa </td><td>No </td><td>Rec </td><td>203.0 </td><td>Unf </td><td>0.0 </td><td>661.0 </td><td>864.0 </td><td>GasA </td><td>TA </td><td>N </td><td>FuseF </td><td>864.0 </td><td>0.0 </td><td>0.0 </td><td>864.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>TA </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>Detchd </td><td>1938.0 </td><td>Unf </td><td>1.0 </td><td>200.0 </td><td>TA </td><td>Fa </td><td>Y </td><td>0.0 </td><td>0.0 </td><td>96.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>6.0 </td><td>2010.0 </td><td>WD </td><td>Normal </td><td>58500.0 </td></tr>\n",
"<tr><td>4 </td><td>30.0 </td><td>11.17125449658085 </td><td>30.0 </td><td>RM </td><td>60.0 </td><td>6324.0 </td><td>Pave </td><td>nan </td><td>IR1 </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>BrkSide </td><td>Feedr </td><td>RRNn </td><td>1Fam </td><td>1Story </td><td>4.0 </td><td>6.0 </td><td>1927.0 </td><td>1950.0 </td><td>Gable </td><td>CompShg </td><td>MetalSd </td><td>MetalSd </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>BrkTil </td><td>TA </td><td>TA </td><td>No </td><td>Unf </td><td>0.0 </td><td>Unf </td><td>0.0 </td><td>520.0 </td><td>520.0 </td><td>GasA </td><td>Fa </td><td>N </td><td>SBrkr </td><td>520.0 </td><td>0.0 </td><td>0.0 </td><td>520.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>Fa </td><td>4.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>Detchd </td><td>1920.0 </td><td>Unf </td><td>1.0 </td><td>240.0 </td><td>Fa </td><td>TA </td><td>Y </td><td>49.0 </td><td>0.0 </td><td>87.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>5.0 </td><td>2008.0 </td><td>WD </td><td>Normal </td><td>68500.0 </td></tr>\n",
"<tr><td>5 </td><td>917.0 </td><td>11.177260209023242 </td><td>20.0 </td><td>C (all) </td><td>50.0 </td><td>9000.0 </td><td>Pave </td><td>nan </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>IDOTRR </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>2.0 </td><td>3.0 </td><td>1949.0 </td><td>1950.0 </td><td>Gable </td><td>CompShg </td><td>AsbShng </td><td>AsbShng </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>Av </td><td>BLQ </td><td>50.0 </td><td>Unf </td><td>0.0 </td><td>430.0 </td><td>480.0 </td><td>GasA </td><td>TA </td><td>N </td><td>FuseA </td><td>480.0 </td><td>0.0 </td><td>0.0 </td><td>480.0 </td><td>1.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>1.0 </td><td>TA </td><td>4.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>Detchd </td><td>1958.0 </td><td>Unf </td><td>1.0 </td><td>308.0 </td><td>TA </td><td>TA </td><td>Y </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>10.0 </td><td>2006.0 </td><td>WD </td><td>Abnorml </td><td>35311.0 </td></tr>\n",
"<tr><td>6 </td><td>977.0 </td><td>11.193205027446796 </td><td>30.0 </td><td>RL </td><td>51.0 </td><td>5900.0 </td><td>Pave </td><td>nan </td><td>IR1 </td><td>Bnk </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>BrkSide </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>4.0 </td><td>7.0 </td><td>1923.0 </td><td>1958.0 </td><td>Gable </td><td>CompShg </td><td>Wd Sdng </td><td>Wd Sdng </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>PConc </td><td>Gd </td><td>TA </td><td>No </td><td>Unf </td><td>0.0 </td><td>Unf </td><td>0.0 </td><td>440.0 </td><td>440.0 </td><td>GasA </td><td>TA </td><td>Y </td><td>FuseA </td><td>869.0 </td><td>0.0 </td><td>0.0 </td><td>869.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>Fa </td><td>4.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>nan </td><td>1978.5061638868744</td><td>nan </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>Y </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>8.0 </td><td>2006.0 </td><td>WD </td><td>Normal </td><td>85500.0 </td></tr>\n",
"<tr><td>7 </td><td>1327.0 </td><td>11.201453663593002 </td><td>30.0 </td><td>RH </td><td>70.0 </td><td>4270.0 </td><td>Pave </td><td>nan </td><td>Reg </td><td>Bnk </td><td>AllPub </td><td>Inside </td><td>Mod </td><td>Edwards </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>3.0 </td><td>6.0 </td><td>1931.0 </td><td>2006.0 </td><td>Gable </td><td>CompShg </td><td>MetalSd </td><td>MetalSd </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>BrkTil </td><td>TA </td><td>TA </td><td>No </td><td>Rec </td><td>544.0 </td><td>Unf </td><td>0.0 </td><td>0.0 </td><td>544.0 </td><td>GasA </td><td>Ex </td><td>Y </td><td>SBrkr </td><td>774.0 </td><td>0.0 </td><td>0.0 </td><td>774.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>3.0 </td><td>1.0 </td><td>Gd </td><td>6.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>nan </td><td>1978.5061638868744</td><td>nan </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>Y </td><td>0.0 </td><td>0.0 </td><td>286.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>5.0 </td><td>2007.0 </td><td>WD </td><td>Normal </td><td>79000.0 </td></tr>\n",
"<tr><td>8 </td><td>1324.0 </td><td>11.213968403112121 </td><td>30.0 </td><td>RL </td><td>50.0 </td><td>5330.0 </td><td>Pave </td><td>nan </td><td>Reg </td><td>HLS </td><td>AllPub </td><td>Inside </td><td>Gtl </td><td>BrkSide </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>4.0 </td><td>7.0 </td><td>1940.0 </td><td>1950.0 </td><td>Hip </td><td>CompShg </td><td>VinylSd </td><td>VinylSd </td><td>None </td><td>0.0 </td><td>Fa </td><td>TA </td><td>CBlock </td><td>TA </td><td>TA </td><td>No </td><td>LwQ </td><td>280.0 </td><td>Unf </td><td>0.0 </td><td>140.0 </td><td>420.0 </td><td>GasA </td><td>Gd </td><td>Y </td><td>SBrkr </td><td>708.0 </td><td>0.0 </td><td>0.0 </td><td>708.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>Fa </td><td>5.0 </td><td>Typ </td><td>0.0 </td><td>nan </td><td>nan </td><td>1978.5061638868744</td><td>nan </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>Y </td><td>164.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>12.0 </td><td>2009.0 </td><td>WD </td><td>Normal </td><td>82500.0 </td></tr>\n",
"<tr><td>9 </td><td>1001.0 </td><td>11.253681686666216 </td><td>20.0 </td><td>RL </td><td>74.0 </td><td>10206.0 </td><td>Pave </td><td>nan </td><td>Reg </td><td>Lvl </td><td>AllPub </td><td>Corner </td><td>Gtl </td><td>Edwards </td><td>Norm </td><td>Norm </td><td>1Fam </td><td>1Story </td><td>3.0 </td><td>3.0 </td><td>1952.0 </td><td>1952.0 </td><td>Flat </td><td>Tar&Grv </td><td>BrkComm </td><td>Brk Cmn </td><td>None </td><td>0.0 </td><td>TA </td><td>TA </td><td>Slab </td><td>nan </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>nan </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>GasW </td><td>Fa </td><td>N </td><td>FuseF </td><td>944.0 </td><td>0.0 </td><td>0.0 </td><td>944.0 </td><td>0.0 </td><td>0.0 </td><td>1.0 </td><td>0.0 </td><td>2.0 </td><td>1.0 </td><td>Fa </td><td>4.0 </td><td>Min1 </td><td>0.0 </td><td>nan </td><td>Detchd </td><td>1956.0 </td><td>Unf </td><td>2.0 </td><td>528.0 </td><td>TA </td><td>Fa </td><td>Y </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>0.0 </td><td>nan </td><td>nan </td><td>nan </td><td>0.0 </td><td>7.0 </td><td>2009.0 </td><td>WD </td><td>Normal </td><td>82000.0 </td></tr>\n",
"</tbody>\n",
"</table>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"local_frame = preds.cbind(valid.drop(['Id'])).as_data_frame()\n",
"local_frame.sort_values('predict', axis=0, inplace=True)\n",
"local_frame = local_frame.iloc[0: local_frame.shape[0]//10, :]\n",
"local_frame = h2o.H2OFrame(local_frame)\n",
"local_frame['predict'] = local_frame['predict'].log()\n",
"local_frame.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train penalized linear model in local region "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"glm Model Build progress: |███████████████████████████████████████████████| 100%\n",
"glm prediction progress: |████████████████████████████████████████████████| 100%\n",
"\n",
"Local GLM R-square:\n",
"0.87\n",
"\n",
"Local GLM Coefficients:\n",
"FullBath: -0.10560466842690448\n",
"HalfBath: -0.1011673167208127\n",
"MoSold: -0.008566732387828832\n",
"YrSold: -0.002307356382236561\n",
"EnclosedPorch: -0.00047646418589632216\n",
"MiscVal: -7.659111768804664e-05\n",
"BsmtFinSF2: -4.009348055311646e-06\n",
"2ndFlrSF: 2.760992025667719e-07\n",
"BsmtFinSF1: 1.0698104588444621e-05\n",
"LowQualFinSF: 4.571947416837975e-05\n",
"WoodDeckSF: 5.7321738441605914e-05\n",
"GarageArea: 0.00017678332091345634\n",
"GrLivArea: 0.0003117252960306305\n",
"LotFrontage: 0.00095008001255757\n",
"YearRemodAdd: 0.001492563117669517\n",
"YearBuilt: 0.002536554911375407\n",
"BedroomAbvGr: 0.023509735677891618\n",
"OverallQual: 0.03550114547540719\n",
"BsmtFullBath: 0.038584610439521966\n",
"OverallCond: 0.04580958970433358\n",
"Fireplaces: 0.11432285083629967\n",
"BsmtHalfBath: 0.1795995633777401\n",
"Intercept: 7.506954243761411\n"
]
},
{
"data": {
"image/png": 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V9ElYWBjt27endOnSfPbZZxQvXpzr16+zcePGHD9vhUKheBlQTkIBp3///owfP57o6GgK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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115507940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# initialize\n",
"local_glm = H2OGeneralizedLinearEstimator(lambda_search=True)\n",
"\n",
"# train \n",
"local_glm.train(x=X_reals_decorr, y='predict', training_frame=local_frame)\n",
"\n",
"# ranked predictions plot\n",
"pred_frame = local_frame.cbind(local_glm.predict(local_frame))\\\n",
" .as_data_frame()[['predict', 'predict0']]\n",
"pred_frame.columns = ['ML Preds.', 'Surrogate Preds.']\n",
"pred_frame.sort_values(by='ML Preds.', inplace=True)\n",
"pred_frame.reset_index(inplace=True, drop=True)\n",
"_ = pred_frame.plot(title='Ranked Predictions Plot')\n",
"\n",
"# r2\n",
"print('\\nLocal GLM R-square:\\n%.2f' % local_glm.r2())\n",
"\n",
"# coefs\n",
"print('\\nLocal GLM Coefficients:')\n",
"for c_name, c_val in sorted(local_glm.coef().items(), key=operator.itemgetter(1)):\n",
" if c_val != 0.0:\n",
" print('%s %s' % (str(c_name + ':').ljust(25), c_val))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here the R<sup>2</sup> and ranked predictions plot show a slightly less accurate fit in the local sample. So the regression coefficients and reason codes may be a bit more approximate than those in the first example."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create explanations (or 'reason codes') for a row in the local set"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mZmYt4+RvZmbWMk7+ZmZmLePkb2Zm1jJO/mZmZi3j5G9mZtYyTv5mZmYt4+Rv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\nzKzH/B+oeo7i0b8nywAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c7dfcf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"row = 30 # select a row to describe\n",
"local_contrib_frame = pd.DataFrame(columns=['Name', 'Local Contribution', 'Sign'])\n",
"\n",
"# multiply values in row by local glm coefficients\n",
"for name in local_frame[row, :].columns:\n",
" contrib = 0.0\n",
" try:\n",
" contrib = local_frame[row, name]*local_glm.coef()[name]\n",
" except:\n",
" pass\n",
" if contrib != 0.0:\n",
" local_contrib_frame = local_contrib_frame.append({'Name':name,\n",
" 'Local Contribution': contrib,\n",
" 'Sign': contrib > 0}, \n",
" ignore_index=True)\n",
"# plot \n",
"_ = local_contrib_frame.plot(x = 'Name',\n",
" y = 'Local Contribution',\n",
" kind='bar', \n",
" title='Local Contributions for Row ' + str(row) + '\\n', \n",
" color=local_contrib_frame.Sign.map({True: 'r', False: 'b'}),\n",
" legend=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Shutdown H2O"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Are you sure you want to shutdown the H2O instance running at http://127.0.0.1:54321 (Y/N)? y\n",
"H2O session _sid_bca1 closed.\n"
]
}
],
"source": [
"h2o.cluster().shutdown(prompt=True)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"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 |
coolharsh55/advent-of-code | 2016/python3/Day16.ipynb | 1 | 9936 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 16: Dragon Checksum\n",
"\n",
"author: Harshvardhan Pandit\n",
"\n",
"license: [MIT](https://opensource.org/licenses/MIT)\n",
"\n",
"[link to problem statement](http://adventofcode.com/2016/day/16)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You're done scanning this part of the network, but you've left traces of your presence. You need to overwrite some disks with random-looking data to cover your tracks and update the local security system with a new checksum for those disks.\n",
"\n",
"For the data to not be suspiscious, it needs to have certain properties; purely random data will be detected as tampering. To generate appropriate random data, you'll need to use a modified [dragon curve](https://en.wikipedia.org/wiki/Dragon_curve).\n",
"\n",
"Start with an appropriate initial state (your puzzle input). Then, so long as you don't have enough data yet to fill the disk, repeat the following steps:\n",
"\n",
" - Call the data you have at this point \"a\".\n",
" - Make a copy of \"a\"; call this copy \"b\".\n",
" - Reverse the order of the characters in \"b\".\n",
" - In \"b\", replace all instances of 0 with 1 and all 1s with 0.\n",
" - The resulting data is \"a\", then a single 0, then \"b\".\n",
"\n",
"For example, after a single step of this process,\n",
"\n",
" - `1` becomes `100`.\n",
" - `0` becomes `001`.\n",
" - `11111` becomes `11111000000`.\n",
" - `111100001010` becomes `1111000010100101011110000`.\n",
" \n",
"Repeat these steps until you have enough data to fill the desired disk.\n",
"\n",
"Once the data has been generated, you also need to create a checksum of that data. Calculate the checksum only for the data that fits on the disk, even if you generated more data than that in the previous step.\n",
"\n",
"The checksum for some given data is created by considering each non-overlapping pair of characters in the input data. If the two characters match (00 or 11), the next checksum character is a 1. If the characters do not match (01 or 10), the next checksum character is a 0. This should produce a new string which is exactly half as long as the original. If the length of the checksum is even, repeat the process until you end up with a checksum with an odd length.\n",
"\n",
"For example, suppose we want to fill a disk of length `12`, and when we finally generate a string of at least length 12, the first 12 characters are `110010110100`. To generate its checksum:\n",
"\n",
" - Consider each pair: `11, 00, 10, 11, 01, 00`.\n",
" - These are same, same, different, same, different, same, producing 110101.\n",
" - The resulting string has length 6, which is even, so we repeat the process.\n",
" - The pairs are 11 (same), 01 (different), 01 (different).\n",
" - This produces the checksum 100, which has an odd length, so we stop.\n",
"\n",
"Therefore, the checksum for `110010110100` is `100`.\n",
"\n",
"Combining all of these steps together, suppose you want to fill a disk of length 20 using an initial state of `10000`:\n",
"\n",
" - Because `10000` is too short, we first use the modified dragon curve to make it longer.\n",
" - After one round, it becomes `10000011110` (11 characters), still too short.\n",
" - After two rounds, it becomes `10000011110010000111110` (23 characters), which is enough.\n",
" - Since we only need 20, but we have 23, we get rid of all but the first 20 characters: `10000011110010000111`.\n",
" - Next, we start calculating the checksum; after one round, we have `0111110101`, which 10 characters long (even), so we continue.\n",
" - After two rounds, we have 01100, which is 5 characters long (odd), so we are done.\n",
"\n",
"In this example, the correct checksum would therefore be 01100.\n",
"\n",
"The first disk you have to fill has length `272`. Using the initial state in your puzzle input, what is the correct checksum?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Solution Logic\n",
"\n",
"At first look, this looks to be too trivial to be a problem for day 16. Surely, it is _far_ too easy to take a string, then create a reverse of it, flip its bits, and join it together with '0' in between. And continue doing so until it is at least of the specified length, after which we discard the remaining characters. The checksum is merely a '1' or '0' depending on whether the two characters are same are not. Oh, and once you've calculated that, you also need to check if the checksum is _even_, because if it is, then calculate it again.\n",
"\n",
"Since this is so _trivial_, I'll let the code speak for itself."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DISK_LENGTH = 272"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def invert(string):\n",
" return ''.join(('1' if x == '0' else '0' for x in string))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"000011110101\n"
]
}
],
"source": [
"print(invert('111100001010'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def fill_disk(string):\n",
" while(len(string) < DISK_LENGTH):\n",
" string = string + '0' + invert(string[::-1])\n",
" return string[:DISK_LENGTH]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open('../inputs/day16.txt', 'r') as f:\n",
" input_data = f.readline().strip()\n",
" string = fill_disk(input_data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"string length: 272\n",
"11110010111001001001101100010110000011110010111001001101101100010110000011110010111001001001101100010110000111110010111001001101101100010110000011110010111001001001101100010110000011110010111001001101101100010110000111110010111001001001101100010110000111110010111001001101\n"
]
}
],
"source": [
"print('string length: ', len(string))\n",
"print(string)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_checksum(string):\n",
" checksum = []\n",
" for index in range(0, len(string), 2):\n",
" if string[index] == string[index + 1]:\n",
" checksum.append('1')\n",
" else:\n",
" checksum.append('0')\n",
" if len(checksum) % 2 == 0:\n",
" return create_checksum(checksum)\n",
" return ''.join(checksum)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"checksum = create_checksum(string)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checksum length 17\n",
"01110011101111011\n"
]
}
],
"source": [
"print('checksum length', len(checksum))\n",
"print(checksum)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Part Two\n",
"\n",
"The second disk you have to fill has length 35651584. Again using the initial state in your puzzle input, what is the correct checksum for this disk?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DISK_LENGTH = 35651584"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"string = fill_disk(input_data)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"string length: 35651584\n"
]
}
],
"source": [
"print('string length: ', len(string))"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"checksum = create_checksum(string)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"checksum length 17\n",
"11001111011000111\n"
]
}
],
"source": [
"print('checksum length', len(checksum))\n",
"print(checksum)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"== END =="
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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"file_extension": ".py",
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| mit |
tdhopper/notes-on-dirichlet-processes | pages/2015-10-14-collapsed-gibbs-sampling-for-mixture-models.ipynb | 1 | 102320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Collapsed Gibbs Sampling for Bayesian Mixture Models (with a Nonparametric Extension)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[In an earlier notebook](/mixture-model/), I showed how we can fit the parameters of a Bayesian mixture model using a Gibbs sampler. The sampler defines a Markov chain that, in steady state, samples from the posterior distribution of the mixture model. To move the chain forward by one step we:\n",
"\n",
"* Sample the cluster assignment $z_i$.\n",
"* Sample the mixture weights $\\pi$\n",
"* Sample the cluster means $\\mu_n$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It turns out that we can derive a Gibbs sampler that _just_ samples the assignments instead of the mixture weights and cluster means. This is known as a _collapsed_ Gibbs sampler. If we integrate out the cluster means $\\theta_k$ and mixture weights from the margial distribution of cluster assignment \n",
"$$p(z_i=k \\,|\\, \n",
" z_{\\neg i}, \\pi,\n",
" \\theta_1, \\theta_2, \\theta_3, \\sigma, \\mathbf{x}, \\alpha\n",
" )$$ we are left with \n",
"$$p(z_i\\,|\\, z_{\\neg i}, \\sigma, \\mathbf{x}, \\alpha).$$\n",
" \n",
"By the conditional independence, we can factorize this marginal distribution \n",
"$$\n",
"\\begin{align}\n",
"p(z_i=k\\,|\\, z_{\\neg i}, \\sigma, \\mathbf{x}, \\alpha)\n",
" &\\propto\n",
" p(z_i=k\\,|\\, x_i, z_{\\neg i}, \\sigma, \\mathbf{x}_{\\neg i}, \\alpha)\\\\\n",
" &=\n",
" p(z_i=k\\,|\\, z_{\\neg i}, \\sigma, \\mathbf{x}_{\\neg i}, \\alpha)\n",
" p(x_i \\,|\\, z, \\sigma, \\mathbf{x}_{\\neg i}, \\alpha)\\\\\n",
" &=\n",
" p(z_i=k \\,|\\, z_{\\neg i}, \\alpha) p(x_i \\,|\\, z, \\mathbf{x}_{\\neg i}, \\sigma)\\\\\n",
" &=\n",
" p(z_i=k \\,|\\, z_{\\neg i}, \\alpha)p(x_i \\,|\\, z_i=k, z_{\\neg i}, x_{\\neg_i}, \\sigma)\\\\\n",
" &=\n",
" p(z_i=k \\,|\\, z_{\\neg i}, \\alpha)p(x_i \\,|\\, \\left\\{x_j \\,|\\, z_j=k, j\\neq i\\right\\}, \\sigma).\n",
"\\end{align}\n",
"$$\n",
"\n",
"The two terms have intuitive explanations. $p(z_i = k \\,|\\, z_{\\neg i}, \\alpha)$ is the probability point $x_i$ will be assigned to component $k$ given the current assignments. Because we are using a symmetric Dirichlet prior, this is the predictive likelihood of a Dirichlet-categorical distribution. This is given by:\n",
"$$p(z_i=k \\,|\\, z_{\\not i}, \\alpha)=\n",
" \\frac{N_k^{-i}+\\alpha / K}{N-1+\\alpha}$$\n",
" where $N_k^{-i}=\\sum_{j\\neq i} \\delta(z_j, k)$ is the number of observation assigned to $k$ (except $x_i$). We also need to define $\\bar{x}_k^{-i}$ to be the mean of all observations assigned to component $k$ (except $x_i$).\n",
" \n",
"The second term is the predictive likelihood that point $x_i$ is distributed according to cluster $k$ (given the data currently in cluster $k$). For our example, we are assuming unknown cluster means are distributed according to a normal distribution with hyperparameter mean $\\lambda_1$ and variance $\\lambda_2^2$ and known cluster variance $\\sigma^2$.\n",
"\n",
"Thus, \n",
"$$\n",
"\\begin{align}\n",
"p(x_i \\,|\\, \\left\\{x_j \\,|\\, z_j=k, j\\neq i\\right\\}, \\sigma)\n",
" &= \\mathcal{N}(x_i \\,|\\, \\mu_k, \\sigma_k^2+\\sigma^2)\n",
"\\end{align}\n",
" $$\n",
"where\n",
"$$\\sigma_k^2 = \\left( \\frac{N_k^{-i}}{\\sigma^2} + \\frac{1}{\\lambda_2^2} \\right)^{-1}$$\n",
"and\n",
"$$\\mu_k = \\sigma_k^2 \\left( \n",
" \\frac{\\lambda_1}{\\lambda_2^2}+\\frac{N_k^{-i}\\cdot \\bar{x}_k^{-i}}{\\sigma^2}\n",
"\\right).$$\n",
"This is derived in Kevin Murphey's fantastic article [Conjugate Bayesian analysis of the Gaussian distribution](http://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf). \n",
"\n",
"\n",
"At each step of the collapsed sampler, we sample each $z_i$ as follows:\n",
"\n",
"* For each cluster $k$, compute $$f_k(x_i) =p(x_i \\,|\\, \\left\\{x_j \\,|\\, x_j=k, j\\neq i\\right\\}, \\lambda).$$ This is the predictive probability that $x_i$ is in cluster $k$ given the data currently assigned to that cluster.\n",
"* Sample $$z_i\\sim \\frac{1}{Z_i}\\sum_{k=1}^K(N_k^{-i}+\\alpha/K)f_k(x_i)\\delta(z_i,k)$$\n",
"\n",
"where the normalizing constant is $Z_i=\\sum_{k=1}^K(N_k^{-i}+\\alpha/K)f_k(x_i)$.\n",
"\n",
"Let's write code for this Gibbs sampler!"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from collections import namedtuple, Counter\n",
"from scipy import stats\n",
"from numpy import random"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(12345)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, load the same dataset we used [previously](/mixture-model/):"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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KO7zc6/XG2r+L5V6vl1WeQbnkaev4jXv+jb6/t8b+q+uaPd80H7+iKFheXgY4OF820bSu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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10af51050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = pd.Series.from_csv(\"clusters.csv\")\n",
"_=data.hist(bins=20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, we want to define a state object and a function for updating the sufficient statistics of the state."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"SuffStat = namedtuple('SuffStat', 'theta N')\n",
"\n",
"def initial_state(num_clusters=3, alpha=1.0):\n",
" cluster_ids = range(num_clusters)\n",
"\n",
" state = {\n",
" 'cluster_ids_': cluster_ids,\n",
" 'data_': data,\n",
" 'num_clusters_': num_clusters,\n",
" 'cluster_variance_': .01,\n",
" 'alpha_': alpha,\n",
" 'hyperparameters_': {\n",
" \"mean\": 0,\n",
" \"variance\": 1,\n",
" },\n",
" 'suffstats': {cid: None for cid in cluster_ids},\n",
" 'assignment': [random.choice(cluster_ids) for _ in data],\n",
" 'pi': {cid: alpha / num_clusters for cid in cluster_ids},\n",
" }\n",
" update_suffstats(state)\n",
" return state\n",
"\n",
"def update_suffstats(state):\n",
" for cluster_id, N in Counter(state['assignment']).iteritems():\n",
" points_in_cluster = [x \n",
" for x, cid in zip(state['data_'], state['assignment'])\n",
" if cid == cluster_id\n",
" ]\n",
" mean = np.array(points_in_cluster).mean()\n",
" \n",
" state['suffstats'][cluster_id] = SuffStat(mean, N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we define functions to compute the two terms of our marginal distribution over cluster assignments (as we derived above)."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"def log_predictive_likelihood(data_id, cluster_id, state):\n",
" \"\"\"Predictive likelihood of the data at data_id is generated\n",
" by cluster_id given the currenbt state.\n",
" \n",
" From Section 2.4 of \n",
" http://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf\n",
" \"\"\"\n",
" ss = state['suffstats'][cluster_id]\n",
" hp_mean = state['hyperparameters_']['mean']\n",
" hp_var = state['hyperparameters_']['variance']\n",
" param_var = state['cluster_variance_']\n",
" x = state['data_'][data_id]\n",
" return _log_predictive_likelihood(ss, hp_mean, hp_var, param_var, x)\n",
"\n",
"\n",
"def _log_predictive_likelihood(ss, hp_mean, hp_var, param_var, x):\n",
" posterior_sigma2 = 1 / (ss.N * 1. / param_var + 1. / hp_var)\n",
" predictive_mu = posterior_sigma2 * (hp_mean * 1. / hp_var + ss.N * ss.theta * 1. / param_var)\n",
" predictive_sigma2 = param_var + posterior_sigma2\n",
" predictive_sd = np.sqrt(predictive_sigma2)\n",
" return stats.norm(predictive_mu, predictive_sd).logpdf(x)\n",
"\n",
"\n",
"def log_cluster_assign_score(cluster_id, state):\n",
" \"\"\"Log-likelihood that a new point generated will\n",
" be assigned to cluster_id given the current state.\n",
" \"\"\"\n",
" current_cluster_size = state['suffstats'][cluster_id].N\n",
" num_clusters = state['num_clusters_']\n",
" alpha = state['alpha_']\n",
" return np.log(current_cluster_size + alpha * 1. / num_clusters)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given these two functions, we can compute the posterior probability distribution for assignment of a given datapoint. This is the core of our collapsed Gibbs sampler.\n",
"\n",
"To simplify the computation of things like $N_k^{-i}$ (where we remove point $i$ from the summary statistics), we create two simple functions to add and remove a point from the summary statistics for a given cluster. "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cluster_assignment_distribution(data_id, state):\n",
" \"\"\"Compute the marginal distribution of cluster assignment\n",
" for each cluster.\n",
" \"\"\"\n",
" scores = {}\n",
" for cid in state['suffstats'].keys():\n",
" scores[cid] = log_predictive_likelihood(data_id, cid, state)\n",
" scores[cid] += log_cluster_assign_score(cid, state)\n",
" scores = {cid: np.exp(score) for cid, score in scores.iteritems()}\n",
" normalization = 1.0/sum(scores.values())\n",
" scores = {cid: score*normalization for cid, score in scores.iteritems()}\n",
" return scores\n",
"\n",
"def add_datapoint_to_suffstats(x, ss):\n",
" \"\"\"Add datapoint to sufficient stats for normal component\n",
" \"\"\"\n",
" return SuffStat((ss.theta*(ss.N)+x)/(ss.N+1), ss.N+1)\n",
"\n",
"\n",
"def remove_datapoint_from_suffstats(x, ss):\n",
" \"\"\"Remove datapoint from sufficient stats for normal component\n",
" \"\"\"\n",
" return SuffStat((ss.theta*(ss.N)-x*1.0)/(ss.N-1), ss.N-1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we're ready to create a function that takes a Gibbs step on the state. For each datapoint, it\n",
"1. Removes the datapoint from its current cluster.\n",
"2. Computes the posterior probability of the point being assigned to each cluster (given the other current assignments).\n",
"3. Assigns the datapoint to a cluster sampled from this probability distribution."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"def gibbs_step(state):\n",
" pairs = zip(state['data_'], state['assignment'])\n",
" for data_id, (datapoint, cid) in enumerate(pairs):\n",
"\n",
" state['suffstats'][cid] = remove_datapoint_from_suffstats(datapoint, \n",
" state['suffstats'][cid])\n",
" scores = cluster_assignment_distribution(data_id, state).items()\n",
" labels, scores = zip(*scores)\n",
" cid = random.choice(labels, p=scores)\n",
" state['assignment'][data_id] = cid\n",
" state['suffstats'][cid] = add_datapoint_to_suffstats(state['data_'][data_id], state['suffstats'][cid])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's our old function to plot the assignments."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_clusters(state):\n",
" gby = pd.DataFrame({\n",
" 'data': state['data_'], \n",
" 'assignment': state['assignment']}\n",
" ).groupby(by='assignment')['data']\n",
" hist_data = [gby.get_group(cid).tolist() \n",
" for cid in gby.groups.keys()]\n",
" plt.hist(hist_data, \n",
" bins=20,\n",
" histtype='stepfilled', alpha=.5 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Randomly assign the datapoints to a cluster to start."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
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rwC53v23al+4CtkTLW4A7Z44VEZHFUc6W+VuAPwB+ZWY7o/s+A3wR+J6Z3QQcBG6MJaGI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\niEgCqMxFRBJAZS4ikgAqcxGRBPj/1ebIh1tZkMEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b3607d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"state = initial_state()\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look what happens to the assignments after just one Gibbs step!"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEACAYAAABWLgY0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAG7xJREFUeJzt3X2QXXWd5/H3px/y0HkkJOQZE5EIUVBRwEFdriO6DOMG\ny6lRnBEz4rgzWD6O60iwSjs1JePjDKtTbtWgoWBWI4gMG1xHCbhXcUGIPEMIJIFAOjGd585DJ+mH\n+90/7oFtOp1On9P39O2cfF5VXbn33PO795Pum09O/8655ygiMDOzYmqodwAzM8uPS97MrMBc8mZm\nBeaSNzMrMJe8mVmBueTNzAps0JKXtEJSu6Qn+i3/lKSnJT0p6et9li+TtF7SOknvySu0mZkNTdNx\nHr8R+C5w80sLJL0TWAKcGxHdkmYkyxcDHwQWA3OBuyUtiohKLsnNzOy4Bt2Sj4h7gT39Fl8N/GNE\ndCfr7EiWXw6sjIjuiNgEbAAuqG1cMzNLI8uc/JnAf5L0O0llSW9Jls8B2vqs10Z1i97MzOrkeNM1\nxxpzSkS8VdL5wK3Aq4+xrs+ZYGZWR1lKvg24HSAi1kiqSJoObAHm91lvXrLsFSS5+M3MMogIpR2T\nZbrmDuCPASQtAsZExE5gFXCFpDGSFlKd1nnwGEFH3ddXvvKVumdwJmc6GXM509C+shp0S17SSuBi\n4FRJm4EvAyuAFclhlV3AR5LiXivpVmAt0AN8IoaTzMzMhm3Qko+IDx3joSuPsf51wHXDDWVmZrXh\nT7wmSqVSvSMcxZmGxpmGbjTmcqZ8aaRnVCR5FsfMLCVJxAjteDUzsxOES97MrMBc8mZmBeaSNzMr\nMJe8mVmBueTNzArMJW9mVmAueTOzAnPJm5kVmEvezKzAXPJmZgXmkjczKzCXvJlZgbnkzcwKzCVv\nZlZgWS7kbWY2JDt37uTB3/4WKpX0gxsauODtb2f69Om1D3YSccmbWW7279/Pxp/8hAtbWlKPfeDg\nQV57zjku+WE63oW8VwB/CmyPiHP6PfZ54JvA9IjYnSxbBlwF9AKfjoi7ckltZieMyePHc8HcuanH\nrdu8OYc0J5/jzcnfCFzaf6Gk+cC7gRf6LFsMfBBYnIz5niTP+ZuZ1dGgJRwR9wJ7Bnjon4C/77fs\ncmBlRHRHxCZgA3BBLUKamVk2qbe0JV0OtEXE4/0emgO09bnfBqT/Hc3MzGom1Y5XSS3AtVSnal5e\nPMiQyBLKzMxqI+3RNWcAC4DHJAHMAx6SdCGwBZjfZ915ybKjtLa2vny7VCpRKpVSxjAzK7ZyuUy5\nXB728yhi8I1tSQuAO/sfXZM89jzw5ojYnex4/RHVefi5wN3Aa6LfC0jqv8jMCur555/nN1/9Kkvn\nzUs99ubNm3nbtddyxhln5JDsxCOJiBhs5mRAg87JS1oJ3AcskrRZ0kf7rfJyW0fEWuBWYC3wH8An\n3OZmZvU16HRNRHzoOI+/ut/964DrapDLzMxqwMexm5kVmEvezKzAXPJmZgXmkjczKzCXvJlZgbnk\nzcwKzCVvZlZgLnkzswJzyZuZFZhL3syswFzyZmYF5pI3Myswl7yZWYG55M3MCswlb2ZWYC55M7MC\nc8mbmRWYS97MrMBc8mZmBXa8C3mvkNQu6Yk+y74p6WlJj0m6XdKUPo8tk7Re0jpJ78kzuJmZHd/x\ntuRvBC7tt+wu4HUR8QbgWWAZgKTFwAeBxcmY70nybwpmZnU0aAlHxL3Ann7LVkdEJbn7ADAvuX05\nsDIiuiNiE7ABuKC2cc3MLI3hbmlfBfw8uT0HaOvzWBswd5jPb2Zmw9CUdaCkLwFdEfGjQVaLgRa2\ntra+fLtUKlEqlbLGMDMrpHK5TLlcHvbzZCp5SX8FXAa8q8/iLcD8PvfnJcuO0rfkzczsaP03gJcv\nX57peVJP10i6FPgCcHlEHO7z0CrgCkljJC0EzgQezJTKzMxqYtAteUkrgYuB6ZI2A1+hejTNGGC1\nJID7I+ITEbFW0q3AWqAH+EREDDhdY2ZmI2PQko+IDw2weMUg618HXDfcUGZmVhs+jt3MrMBc8mZm\nBeaSNzMrMJe8mVmBueTNzArMJW9mVmAueTOzAnPJm5kVmEvezKzAXPJmZgXmkjczKzCXvJlZgbnk\nzcwKLPOVoczs5LHiu99l6+OPpx4XEZze2ZlDIhsql7yZHVdvRwdXTpjAnEmTUo9NrjthdeKSN7Mh\naWpooLmxsd4xLCXPyZuZFZhL3syswDxdY2aj1oYNG+jo6Eg9rqWlhbPOOiuHRCee413IewXwp8D2\niDgnWTYNuAV4FbAJ+EBE7E0eWwZcBfQCn46Iu/KLbmZF9pqGBnbccgubU47r7Opi/9lnc9aXvpRL\nrhPN8bbkbwS+C9zcZ9k1wOqI+IakLyb3r5G0GPggsBiYC9wtaVFEVHLIbWYFd9HcuZnGbd2/n59V\nXDsvGXROPiLuBfb0W7wEuCm5fRPwvuT25cDKiOiOiE3ABuCC2kU1M7O0sux4nRkR7cntdmBmcnsO\n0NZnvTaqW/RmZlYnw9rxGhEhKQZbZaCFra2tL98ulUqUSqXhxDAzK5xyuUy5XB7282Qp+XZJsyJi\nm6TZwPZk+RZgfp/15iXLjtK35M3M7Gj9N4CXL1+e6XmyTNesApYmt5cCd/RZfoWkMZIWAmcCD2ZK\nZWZmNXG8QyhXAhcD0yVtBr4MfA24VdLHSA6hBIiItZJuBdYCPcAnImKwqRwzM8vZoCUfER86xkOX\nHGP964DrhhvKzMxqw6c1MDMrMJe8mVmBueTNzArMJW9mVmAueTOzAnPJm5kVmEvezKzAXPJmZgXm\nkjczKzCXvJlZgbnkzcwKzCVvZlZgLnkzswJzyZuZFZhL3syswFzyZmYF5pI3MyuwLBfytow6Ojr4\n1rdW0NmZfmxjI1x55SWcc845tQ9mZoXlkh9BlUqFtrZg5syrUo/duvVuurq6ckhlZkWWueQlLQM+\nDFSAJ4CPAhOAW4BXkVzkOyL2Dj9mcTQ0NDJu3NTU45qaxuaQxsyKLtOcvKQFwMeB8yLiHKARuAK4\nBlgdEYuAe5L7ZmZWJ1l3vO4DuoEWSU1AC7AVWALclKxzE/C+YSc0M7PMMpV8ROwGvg28SLXc90bE\namBmRLQnq7UDM2uS0szMMsk0Jy/pDOCzwAKgA/iJpA/3XSciQlIMNL61tfXl26VSiVKplCWGmVlh\nlctlyuXysJ8n647XtwD3RcQuAEm3A38EbJM0KyK2SZoNbB9ocN+SNzOzo/XfAF6+fHmm58k6J78O\neKuk8ZIEXAKsBe4ElibrLAXuyPj8ZmZWA5m25CPiMUk3A7+negjlw8C/ApOAWyV9jOQQyhrlNDOz\nDDIfJx8R3wC+0W/xbqpb9WZmNgr43DVmZgXmkjczKzCXvJlZgbnkzcwKzCVvZlZgLnkzswJzyZuZ\nFZhL3syswFzyZmYF5pI3Myswl7yZWYG55M3MCswlb2ZWYC55M7MCc8mbmRWYS97MrMBc8mZmBeaS\nNzMrMJe8mVmBZS55SVMl3SbpaUlrJV0oaZqk1ZKelXSXpKm1DGtmZukMZ0v+vwM/j4izgXOBdcA1\nwOqIWATck9w3M7M6yVTykqYA74iIFQAR0RMRHcAS4KZktZuA99UkpZmZZZJ1S34hsEPSjZIelnSD\npAnAzIhoT9ZpB2bWJKWZmWXSNIxx5wGfjIg1kq6n39RMRISkGGhwa2vry7dLpRKlUiljDDOzYiqX\ny5TL5WE/T9aSbwPaImJNcv82YBmwTdKsiNgmaTawfaDBfUvezMyO1n8DePny5ZmeJ9N0TURsAzZL\nWpQsugR4CrgTWJosWwrckSmVmZnVRNYteYBPAT+UNAbYCHwUaARulfQxYBPwgWEntJft2bOHtra2\n1OMaGhqYM2dODonMbLTLXPIR8Rhw/gAPXZI9jh2LNJWVK9cBz6caF1Fh2rS9fOtbX8wnmJmNasPZ\nkrcRNH/+O4B3pB7X3d3J3r3frX0gMzsh+LQGZmYF5pI3Myswl7yZWYG55M3MCswlb2ZWYC55M7MC\nc8mbmRWYS97MrMD8YagMuru7qVQqqccdOXIkhzRmZsfmks/gxz++g7vvfoaGhsbUYzs7fUVEMxs5\nLvkMurpgwoT3cdppr693FDOzQXlO3syswFzyZmYF5pI3Myswl7yZWYG55M3MCswlb2ZWYC55M7MC\nG1bJS2qU9IikO5P70yStlvSspLsk+ZM/ZmZ1NNwt+c8Aa4FI7l8DrI6IRcA9yX0zM6uTzJ94lTQP\nuAz4KvB3yeIlwMXJ7ZuAMi56s1Fh/fr1rLrhBujuTj324M6dMGNGDqnyse2ZZ/j25z6XfmBzM//l\nr/+aRYsW1T5UnQzntAb/DHwBmNxn2cyIaE9utwMzh/H8ZlZDPT09zNy+nSWzZ6cfPG8eE5qbax8q\nBzMnTOCz8+dnGnvnH/5AT09PjRPVV6aSl/ReYHtEPCKpNNA6ERGSYqDHWltbX75dKpUolQZ8CjOr\nsabGRiaPHVvvGLlqbGjI/Hdsbkx/0sG8lMtlyuXysJ8n65b8RcASSZcB44DJkv4NaJc0KyK2SZoN\nbB9ocN+SNzOzo/XfAF6+fHmm58m04zUiro2I+RGxELgC+FVEXAmsApYmqy0F7siUyszMaqJWx8m/\nNC3zNeDdkp4F/ji5b2ZmdTLs88lHxK+BXye3dwOXDPc5zcysNvyJVzOzAnPJm5kVmC//d1II9uzZ\nk2nk+PHjGTduXI3z5GPv3r1EDHjU7nGNHTuWlpaWGicyqz+XfOGJnTvH8fnP35R6ZE/PIf72by/m\noosuyiFX7S3/5nI6ejtA6cZ1d3Xz5+/8c5a8d0k+wczqyCVfcM3N4zn77M9mGvvCC3fVOE2+DnYd\nZO5/nktjU7oPtLStbaMSlZxSmdWX5+TNzArMJW9mVmAueTOzAvOcvI0qGzdu5Olnns40tqe3WGcP\nNKsFl7yNKm1tbfzg3h8wbe601GP1aiGlPLTGrOBc8jbqTJ4xmfmvz3Y+cDN7Jc/Jm5kVmLfkreYi\ngscffzzT2A0bNnBo36EaJzI7ebnkreYigu9859+JODf12Oeea6P5rI4cUpmdnE7akt+2bRvXXfd9\nDmXYaDx8uJdJk86ufagC6e1tYMGC96cet3VrFxse/g272h5LPXZsyxEu+a9vTv2JV7MiO2lLHuDg\nwVOZO/fjmcZK3p2Rh5kzL2BrbxOTpp2SemzH9u/lkMjsxHZSlzyIhoaT/FswykgNqKERNWTYGvfh\nk2ZH8eaomVmBZdqMlTQfuBk4jer1Xf81Ir4jaRpwC/AqYBPwgYjYW6OsZrm59/f38uSGJ1OPG988\nnquvutrnordRK+tcRTfwuYh4VNJE4CFJq4GPAqsj4huSvghck3yZjVqnLTyNwzMOs5vdqcfufmA3\nvb29OaQyq41MJR8R24Btye0Dkp4G5gJLgIuT1W4CyrjkbZQbM34MY8aPyTR2b4N/UbXRbdhz8pIW\nAG8CHgBmRkR78lA7MHO4z29mZtkNq+STqZqfAp+JiP19H4vqxTazXXDTzMxqIvPxg5KaqRb8v0XE\nHcnidkmzImKbpNnA9oHGtra2vny7VCpRKpWyxjAzK6RyuUy5XB7282Q9ukbAD4C1EXF9n4dWAUuB\nryd/3jHA8FeUvJmZHa3/BvDy5cszPU/WLfm3AR8GHpf0SLJsGfA14FZJHyM5hDLj85uZWQ1kPbrm\ntxx7Pv+S7HFstNm8eTNr1qxJNaZSqeSUxszS8mf67ZhaWubxy192c4xdK8cUEfT2nueyNxsFXPJ2\nTDNmLAYWpx4XEfzi7l/wxPpV2V54SrZhZnY0l7zl4kjvEaaeNXVEX7PS08CTv3o+03VeT1swkVln\n+mMdJ71KhQfLZZ595JHjr9vPhFNO4d2XXZZDqOFxyVthjBn/HjanP/0Mh/ZvYcz4jcw6s/aZ7MRy\n/uTJdGS4qtmBri4enjHDJW+Wp4nTXpNpXKW3C9hY2zB2Qlp4SvrrGADs6uzk4RpnqRWfatjMrMBc\n8mZmBebpmhEUEcM6rFASDQ3+f9nMhs4lP4IOHz7MPffeQ0/0pB4rxPnnnM+cOXNySGZmReWSH2G9\njb1MPSP9oYUdWzpySGMnol/+7Gcc2Lkz9biOffto8QfUTjouebMTzPr77uO89nYmjUl/oZPJU/xJ\ns5ONS97sBLTo1FOZ7uvK2hC45E8Qlajw+LrHeWrDUyP2mkK87szXMXv27BF7zRPNwZ6D/MP1/4Aa\n0n/K9uI3X8xll46+D89YsbjkM9i5cyednZ2px3V3d2d+zckzJ4/4Cb/2bd/Hjh07UueuXhTs5LDg\nnQsyXf9sxws72LNvT63jmB3FJZ/Bi1tfZOOujTSPbU49VhPTb/EBNDY30khjprFZjZs8jucOPAcH\n0o+NySdH0Y+fND7TuKwXDh+O3kqFrt7ezOPHNjXRkOG8QFZfLvksAlqmtjDhlAn1TpKrlikttEzx\nvG9RPLtrF99aez/RmP4/4OaeBr583sXMnTw5h2SWpxO+5O+559ds2LAt9bg9e3bz9DMbaN/1+9Rj\n93bsBb/X7UR0anD669MfYfP8Q3u57bm1jGtKXxkLJ5zCexaekXqc1cYJX/JPPbWZhx6aw8SJs1KN\n6+zsZPch6KY9/YtOgbEtY9OPs1wdOnQo07gjR45w+PDhGqcplsYZsKZrS+pxnZ09vOqFKTRGtmme\n0yZMYHxz+mnRsY2NzPfhokABSh5gypTTmZbyDIQHDhxg/MQtTJha7CmXk8n2tjbG9vamPp/8gY6t\n7M7w4aKTyYsvNrJjx2tobExXuN3dvTyxv4efbzqY+jUrlXbedNYeJk9Mt0HV3dPL2TGDa9789tSv\nWUQ1L3lJlwLXA43A9yPi67V+DbMBVSqcNnZs6p2DlYaR3aF9IqpUmmhpeRtjx2Y4Fe+p2V6zo+M/\nmHVWN9Ompdu5vX//Ebqfzb6DuWhqWvKSGoF/oXox7y3AGkmrIuLpWr5OHvZue5Sps95Y7xiv4ExD\nU4tMhzo62fTopkxj5y2eR9OYV/5T2vToJha8ccGwMtXankOHeHL9dnonpt/x2tWV34nx9u5dx9Sp\nZ+X2/FmUN22itGBBvWPURK235C8ANkTEJgBJPwYuB1zyGfTNVKlU6nL8ef8zX+b9fcry99y99WEm\nzTgn82s2j5nCjufezUM70+9Nj3iAWWf2pC753t5eDh0+xIED6Y9PrWR8H+w+dIhnn5/AYd6Tafyk\nSeMyjTuewUq+p6dCV1e6rfKurl6O9PRwoKsrU55xTU0u+UHMBTb3ud8GXFjj1zgp7d61i4O7dtLA\nyB2nXCGYOnsOU0ZwB9a+ffvo+MMfUk257N+9m63r19PQ2wsZzucydtw0DlVOoXN/6qEcPLidrq7T\nGTchXQHu2bOHX/zkZvaW70v/op2d6PTT048DGhrGcMop6S/OXg+9vZO5//5Jqcd1dzcwpqORrdte\nSD1WdPL5i4p1plfVcutQ0p8Bl0bEx5P7HwYujIhP9VknavmaV199DbffvhEp/T/uIxyhYVy1TDr3\nPkXL1NfVLFctvCJTAEeO0Nw0cvPH3T29MO6VO71G5Pt0ON3f88C+p5k4+ewcAx1bV2UDY6bsOGr5\ngR0HmDhj4jHHdR/qZsJBmJByR+ZwbepoYMqpo2u7q9Y/v97uCmN7mmjKcE2k7t4tTB73HDsOHGDG\nxGP//PqLCM4+91z+ffXq1K85VJKISH+YUq1L/q1Aa0RcmtxfBlT67nyVdHJ8FNLMrMZGQ8k3Ac8A\n7wK2Ag8CHzoRdryamRVRTefkI6JH0ieBX1I9hPIHLngzs/qp6Za8mZmNLrlfFVrSNEmrJT0r6S5J\nA177TtIySU9JekLSjyTldt6AFJmmSrpN0tOS1ib7HHIz1FzJuo2SHpF0Z70zSZov6f8kP78nJX06\npyyXSlonab2kLx5jne8kjz8m6U155EiTSdJfJlkel/R/JZ1b70x91jtfUo+k94+GTJJKyXv6SUnl\nvDMNJZek6ZJ+IenRJNdf5ZxnhaR2SU8Msk6693hE5PoFfAP4++T2F4GvDbDOAuA5YGxy/xZgaT0z\nJY/dBFyV3G4CptT7e9Vn3b8DfgisqncmYBbwxuT2RKr7Zc6ucY5GYEPyXmkGHu3/GsBlwM+T2xcC\nv8v5ezOUTH/00vsGuHQ0ZOqz3q+AnwF/Vu9MwFTgKWBecn96nplS5GoF/vGlTMAuoCnHTO8A3gQ8\ncYzHU7/Hc9+SB5ZQLUuSP983wDr7gG6gJdl520L1E7N1yyRpCvCOiFgB1f0NEZH31bSH8r1C0jyq\nP+zvQ+4Hzh83U0Rsi4hHk9sHqH74rdYHG7/8QbuI6AZe+qDdgFkj4gFgqqSZNc6RKlNE3N/nffMA\nMC/HPEPKlPgUcBtw9PGf9cn0F8BPI6INICJG4mRCQ8n1B/7/OWcnA7sioievQBFxLzDY1WRSv8dH\nouRnRsRLp3psB44KFBG7gW8DL1I9KmdvRNxdz0zAQmCHpBslPSzpBkl5n1x9KLkA/hn4AjASl4oa\naiYAJC2guiXyQI1zDPRBu7lDWCfPUh1Kpr4+Bvw8xzwwhEyS5lIts/+RLMp7x9xQvk9nAtOSab/f\nS7oy50xDzXUD8DpJW4HHgM+MQK7BpH6P1+ToGkmrqf7K3t+X+t6JiBjoOHlJZwCfpfprUwfwE0l/\nGRE/rFcmqt+b84BPRsQaSdcD1wBfzpqpFrkkvRfYHhGPSCoNJ0utMvV5nolUtw4/k2zR19JQi6j/\nbzZ5FtiQn1vSO4GrgLflFwcYWqbrgWuSn6fI/7fBoWRqpvrv7V1Uf5O/X9LvImJ9nXNdCzwaEaWk\np1ZLekNEZPh8dM2keo/XpOQj4t3HTFPdiTArIrZJmg1sH2C1twD3RcSuZMztwEVU55zrlakNaIuI\nNcn926iW/LDUINdFwBJJlwHjgMmSbo6Ij9QxE5KagZ8C/zMi7siaZRBbgPl97s+n+jMabJ155Dvt\nN5RMJDtbb6D6afC8L+w6lExvBn5c7XemA38iqTsiVtUx02ZgZ0QcAg5J+g3wBiDPkh9KrouArwJE\nxEZJzwOvBdJfbag2Ur/HR2K6ZhWwNLm9FBioANYBb5U0PtmyuARYW89MEbEN2CxpUbLoEqo7hvI0\nlFzXRsT8iFgIXAH8ajgFX4tMyc/sB8DaiLg+pxy/B86UtEDVc1h8MMnWP+tHkkxvpTrtl+GqMLXL\nJOl04HbgwxGxIccsQ84UEa+OiIXJe+g24OocC35ImYD/Bbxd1aPGWqjuVMyzA4aaax3Vf/skc9+v\npXqQSL2kf4/ntZe4z97gacDdwLPAXcDUZPkc4H/3We/vqZboE1R3LDSPgkxvANZQnYu7nfyPrhlS\nrj7rX0z+R9ccNxPwdqr7Bx4FHkm+Ls0hy59QPXJnA7AsWfY3wN/0WedfkscfA84bgff3oJmo7hzf\n1ef78mC9M/Vb90bg/aMhE/Df+nTAp/PONMSf33TgzuT99ATwFznnWUl1v2QX1d9urhrue9wfhjIz\nK7CRmK4xM7M6ccmbmRWYS97MrMBc8mZmBeaSNzMrMJe8mVmBueTNzArMJW9mVmD/D6/iAGl7dGkt\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b839f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gibbs_step(state)\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Two:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10b82e0d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gibbs_step(state)\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After just two steps, our assignments look _really_ good. We can run it a few more times and see the assignments again."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10b3cb7d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for _ in range(20): gibbs_step(state)\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nonparametric Mixture Models!\n",
"\n",
"It turns out, the collapsed Gibbs sampler for mixture models is almost identical in the context of a _nonparametric_ model. This model uses a _Dirichlet process prior_ instead of a _Dirichlet distribution prior_. It doesn't require us to specify how many clusters we are looking for in our data.\n",
"\n",
"The cluster assignment score changes slightly. It is proportional to $N_k^{-i}$ for each known cluster. We assign a datapoint to a _new_ cluster with probability proportional to $\\alpha$ (which is now the DP dispersion parameter)."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def log_cluster_assign_score_dp(cluster_id, state):\n",
" \"\"\"Log-likelihood that a new point generated will\n",
" be assigned to cluster_id given the current state.\n",
" \"\"\"\n",
" if cluster_id == \"new\":\n",
" return np.log(state[\"alpha_\"])\n",
" else:\n",
" return np.log(state['suffstats'][cluster_id].N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The predictive likelihood remains the same for known clusters. However, we need to know the likelihood of assigning a datapoint to a new cluster. In this case, we fall back on the hyperparameters to get:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"p(x_i \\,|\\, z, x_{\\neg_i}, \\sigma)\n",
" &= \\mathcal{N}(x_i \\,|\\, \\lambda_1, \\lambda_2^2+\\sigma^2)\n",
"\\end{align}\n",
" $$"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def log_predictive_likelihood_dp(data_id, cluster_id, state):\n",
" \"\"\"Predictive likelihood of the data at data_id is generated\n",
" by cluster_id given the currenbt state.\n",
" \n",
" From Section 2.4 of \n",
" http://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf\n",
" \"\"\"\n",
" if cluster_id == \"new\":\n",
" ss = SuffStat(0, 0)\n",
" else:\n",
" ss = state['suffstats'][cluster_id]\n",
" \n",
" hp_mean = state['hyperparameters_']['mean']\n",
" hp_var = state['hyperparameters_']['variance']\n",
" param_var = state['cluster_variance_']\n",
" x = state['data_'][data_id]\n",
" return _log_predictive_likelihood(ss, hp_mean, hp_var, param_var, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Given this, we can define the marginal distribution over cluster assignment. The only change is that the \"`new`\" state enters in the distribution."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def cluster_assignment_distribution_dp(data_id, state):\n",
" \"\"\"Compute the marginal distribution of cluster assignment\n",
" for each cluster.\n",
" \"\"\"\n",
" scores = {}\n",
" cluster_ids = state['suffstats'].keys() + ['new']\n",
" for cid in cluster_ids:\n",
" scores[cid] = log_predictive_likelihood_dp(data_id, cid, state)\n",
" scores[cid] += log_cluster_assign_score_dp(cid, state)\n",
" scores = {cid: np.exp(score) for cid, score in scores.iteritems()}\n",
" normalization = 1.0/sum(scores.values())\n",
" scores = {cid: score*normalization for cid, score in scores.iteritems()}\n",
" return scores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also need to be able to create a new cluster when \"`new`\" is drawn, and destroy a cluster if its emptied."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_cluster(state):\n",
" state[\"num_clusters_\"] += 1\n",
" cluster_id = max(state['suffstats'].keys()) + 1\n",
" state['suffstats'][cluster_id] = SuffStat(0, 0)\n",
" state['cluster_ids_'].append(cluster_id)\n",
" return cluster_id\n",
"\n",
"def destroy_cluster(state, cluster_id):\n",
" state[\"num_clusters_\"] = 1\n",
" del state['suffstats'][cluster_id]\n",
" state['cluster_ids_'].remove(cluster_id)\n",
" \n",
"def prune_clusters(state):\n",
" for cid in state['cluster_ids_']:\n",
" if state['suffstats'][cid].N == 0:\n",
" destroy_cluster(state, cid)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we can define the `gibbs_step_dp` function. It's nearly identical to the earlier `gibbs_step` function except\n",
"* It uses `cluster_assignment_distribution_dp`\n",
"* It creates a new cluster when the sampled assignment is \"`new`\".\n",
"* It destroys a cluster any time it is emptied.\n",
"\n",
"For clarity, I split out the code for sampling assignment to its own function. "
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"def sample_assignment(data_id, state):\n",
" \"\"\"Sample new assignment from marginal distribution.\n",
" If cluster is \"`new`\", create a new cluster.\n",
" \"\"\"\n",
" scores = cluster_assignment_distribution_dp(data_id, state).items()\n",
" labels, scores = zip(*scores)\n",
" cid = random.choice(labels, p=scores)\n",
" if cid == \"new\":\n",
" return create_cluster(state)\n",
" else:\n",
" return int(cid)\n",
"\n",
"def gibbs_step_dp(state):\n",
" \"\"\"Collapsed Gibbs sampler for Dirichlet Process Mixture Model\n",
" \"\"\"\n",
" pairs = zip(state['data_'], state['assignment'])\n",
" for data_id, (datapoint, cid) in enumerate(pairs):\n",
" state['suffstats'][cid] = remove_datapoint_from_suffstats(datapoint, state['suffstats'][cid])\n",
" prune_clusters(state)\n",
" cid = sample_assignment(data_id, state)\n",
" state['assignment'][data_id] = cid\n",
" state['suffstats'][cid] = add_datapoint_to_suffstats(state['data_'][data_id], state['suffstats'][cid])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This time, we will start by randomly assigning our data to two clusters."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
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l4f5DEMoglWXNZW6WQ7tehmMt76po7Jz2BVVOY6eCy9wsp+YurGA3i81YLnObktvvvJ1H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\nB1zmZmY54DI3M8sBl7mZWQ78f0vr+1GshcH3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b3cead0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"state = initial_state(num_clusters=2, alpha=0.1)\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what happens when we run our Gibbs sampler once.\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10af45190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gibbs_step_dp(state)\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We went from 2 to 4 clusters!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After 100 iterations:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10b442a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for _ in range(99): gibbs_step_dp(state)\n",
"plot_clusters(state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After 100 iterations, our assignment looks correct! We went back to 3 clusters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can sample the mixture weights, if we need them, using the \"Conditional Distribution of Mixture Weights\" derived [here](/mixture-model/)."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.21330625, 0.29838101, 0.48831275])"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss = state['suffstats']\n",
"alpha = [ss[cid].N + state['alpha_'] / state['num_clusters_'] \n",
" for cid in state['cluster_ids_']]\n",
"stats.dirichlet(alpha).rvs(size=1).flatten()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also sample the cluster means using [the method we derived earlier](/mixture-model/):"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cluster_id: 1 mean -0.0176257860235\n",
"cluster_id: 2 mean -0.400581819532\n",
"cluster_id: 3 mean 0.600302879661\n"
]
}
],
"source": [
"for cluster_id in state['cluster_ids_']:\n",
" cluster_var = state['cluster_variance_']\n",
" hp_mean = state['hyperparameters_']['mean']\n",
" hp_var = state['hyperparameters_']['variance']\n",
" ss = state['suffstats'][cluster_id]\n",
"\n",
" numerator = hp_mean / hp_var + ss.theta * ss.N / cluster_var\n",
" denominator = (1.0 / hp_var + ss.N / cluster_var)\n",
" posterior_mu = numerator / denominator\n",
" posterior_var = 1.0 / denominator\n",
"\n",
" mean = stats.norm(posterior_mu, np.sqrt(posterior_var)).rvs()\n",
" print(\"cluster_id:\", cluster_id, \"mean\", mean)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much thanks to Erik Sudderth's excellent introduction to nonparametric Bayes in [Chapter 2 of his dissertation](http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf). Algorithms 2.2 and 2.3 in that piece are the clearest formulation of collapsed Gibbs sampling for mixture models that I have come across."
]
}
],
"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"
},
"nikola": {
"date": "2015-10-14",
"slug": "collapsed-gibbs",
"title": "Collapsed Gibbs Sampling for Mixture Models"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
CGATOxford/CGATPipelines | CGATPipelines/pipeline_docs/pipeline_bamstats/Jupyter_report/CGAT_context_stats_report.ipynb | 1 | 1323691 | null | mit |
ibm-cds-labs/pixiedust | notebook/DSX/Welcome to PixieDust.ipynb | 1 | 446209 | {"cells": [{"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "# Welcome to PixieDust\n\nThis notebook features an introduction to [PixieDust](https://pixiedust.github.io/pixiedust/index.html), the Python library that makes data visualization easy. \n\nThis notebook runs on Python 2.7 and 3.5, with Spark 1.6 and 2.0.\n\n## <a id=\"toc\"></a>Table of Contents\n\n * [Get started](#part_one)\n * [Load text data from remote sources](#part_two)\n * [Mix Scala and Python on the same notebook](#part_three)\n * [Add Spark packages and run inside your notebook](#part_four)\n * [Stash your data](#part_five)\n * [Contribute](#contribute)\n\n\n<hr>\n\n# <a id=\"part_one\"></a>Get started\n\nThis introduction is pretty straightforward, but it wouldn't hurt to load up the [PixieDust documentation](https://pixiedust.github.io/pixiedust/) so it's handy. \n\nNew to notebooks? Don't worry. Here's all you need to know to run this introduction:\n\n1. Make sure this notebook is in Edit mode\n1. To run code cells, put your cursor in the cell and press **Shift + Enter**.\n1. The cell number will change to **[\\*]** to indicate that it is currently executing. (When starting with notebooks, it's best to run cells in order, one at a time.)"}, {"cell_type": "code", "metadata": {"scrolled": true, "collapsed": false}, "outputs": [], "source": "# To confirm you have the latest version of PixieDust on your system, run this cell\n!pip install --user --upgrade pixiedust", "execution_count": null}, {"cell_type": "markdown", "metadata": {}, "source": "Now that you have PixieDust installed and up-to-date on your system, you need to import it into this notebook. This is the last dependency before you can play with PixieDust."}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "Pixiedust database opened successfully\n", "output_type": "stream", "name": "stdout"}, {"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "\n <div style=\"margin:10px\">\n <a href=\"https://github.com/ibm-watson-data-lab/pixiedust\" target=\"_new\">\n <img src=\"https://github.com/ibm-watson-data-lab/pixiedust/raw/master/docs/_static/pd_icon32.png\" style=\"float:left;margin-right:10px\"/>\n </a>\n <span>Pixiedust version 1.0.2</span>\n </div>\n "}, "metadata": {}}], "source": "import pixiedust", "execution_count": 1}, {"cell_type": "markdown", "metadata": {}, "source": "If you get a message telling you that you're not running the latest version of PixieDust, restart the kernel from the **Kernel** menu and rerun the `import pixiedust` command. (Any time you restart the kernel, rerun the `import pixiedust` command.)\n\nWhen you see the message `Pixiedust version upgraded from 0.60 to 1.0.2`, or `Pixiedust version 1.0.2`, you're all set."}, {"cell_type": "markdown", "metadata": {}, "source": "## Behold, display()\n\nIn the next cell, build a simple dataset and store it in a variable. "}, {"cell_type": "code", "metadata": {"collapsed": true}, "outputs": [], "source": "# Build the SQL context required to create a Spark dataframe \nsqlContext=SQLContext(sc) \n# Create the Spark dataframe, passing in some data, and assign it to a variable\ndf = sqlContext.createDataFrame(\n[(\"Green\", 75),\n (\"Blue\", 25)],\n[\"Colors\",\"%\"])", "execution_count": 3}, {"cell_type": "markdown", "metadata": {}, "source": "The data in the variable `df` is ready to be visualized, without any further code other than the call to `display()`."}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "pieChart", "rowCount": "100", "title": "Colors in this pie chart, by %", "keyFields": "Colors", "valueFields": "%", "aggregation": "SUM"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n Colors in this pie chart, by %\n </div>\n <div id=\"chartFigure51755a6b\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n \n \n <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n \n \n \n </div>"}, "metadata": {}}], "source": "# display the dataframe above as a pie chart\ndisplay(df)", "execution_count": 4}, {"cell_type": "markdown", "metadata": {}, "source": "After running the cell above, you should see a Spark DataFrame displayed as a **pie chart**, along with some controls to tweak the display. All that came from passing the DataFrame variable to `display()`.\n\nIn the next cell, you'll pass more interesting data to `display()`, which will also offer more advanced controls."}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "barChart", "title": "Customers by Category clustered by Year", "mpld3": "false", "valueFields": "unique_customers", "aggregation": "SUM", "rowCount": "100", "keyFields": "category", "rendererId": "matplotlib", "clusterby": "year"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n Customers by Category clustered by Year\n </div>\n <div id=\"chartFigure6c2a96a6\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n \n \n <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n \n \n \n </div>"}, "metadata": {}}], "source": "# create another DataFrame, in a new variable\ndf2 = sqlContext.createDataFrame(\n[(2010, 'Camping Equipment', 3),\n (2010, 'Golf Equipment', 1),\n (2010, 'Mountaineering Equipment', 1),\n (2010, 'Outdoor Protection', 2),\n (2010, 'Personal Accessories', 2),\n (2011, 'Camping Equipment', 4),\n (2011, 'Golf Equipment', 5),\n (2011, 'Mountaineering Equipment',2),\n (2011, 'Outdoor Protection', 4),\n (2011, 'Personal Accessories', 2),\n (2012, 'Camping Equipment', 5),\n (2012, 'Golf Equipment', 5),\n (2012, 'Mountaineering Equipment', 3),\n (2012, 'Outdoor Protection', 5),\n (2012, 'Personal Accessories', 3),\n (2013, 'Camping Equipment', 8),\n (2013, 'Golf Equipment', 5),\n (2013, 'Mountaineering Equipment', 3),\n (2013, 'Outdoor Protection', 8),\n (2013, 'Personal Accessories', 4)],\n[\"year\",\"category\",\"unique_customers\"])\n\n# This time, we've combined the dataframe and display() call in the same cell\n# Run this cell \ndisplay(df2)", "execution_count": 4}, {"cell_type": "markdown", "metadata": {}, "source": "## display() controls\n\n### Renderers\nThe chart above, like the first one, is rendered by matplotlib. With PixieDust, you have other options. To toggle between renderers, use the `Renderers` control at top right of the display output:\n1. [Bokeh](http://bokeh.pydata.org/en/0.10.0/index.html) is interactive; play with the controls along the top of the chart, for example, zoom and save.\n1. [Matplotlib](http://matplotlib.org/) is static; you can save the image as a PNG\n\n### Chart options\n\n1. **Chart types**: At top left, you should see an option to display the dataframe as a table. You should also see a dropdown menu with other chart options, including bar charts, pie charts, scatter plots, and so on.\n1. **Options**: Click the `Options` button to explore other display configurations; for example, clustering and aggregation.\n\nHere's more on [customizing `display()` output](https://pixiedust.github.io/pixiedust/displayapi.html)."}, {"cell_type": "markdown", "metadata": {}, "source": "## Load External Data\nSo far, you've worked with data hard-coded into our notebook. Now, load external data (CSV) from a URL."}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "scatterPlot", "title": "Distribution of MPG per Horsepower", "valueFields": "mpg", "rowCount": "1000", "color": "origin", "keyFields": "horsepower", "rendererId": "matplotlib", "kind": "hex"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n Distribution of MPG per Horsepower\n </div>\n <div id=\"chartFiguree49cca0f\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n \n \n <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n \n \n \n </div>"}, "metadata": {}}], "source": "# load a CSV with pixiedust.sampledata()\ndf3 = pixiedust.sampleData(\"https://github.com/ibm-watson-data-lab/open-data/raw/master/cars/cars.csv\")\ndisplay(df3)", "execution_count": 5}, {"cell_type": "markdown", "metadata": {}, "source": "You should see a scatterplot above, rendered again by matplotlib. Find the `Renderer` menu at top-right. You should see options for **Bokeh** and **Seaborn**. If you don't see Seaborn, it's not installed on your system. No problem, just install it by running the next cell."}, {"cell_type": "code", "metadata": {"collapsed": true}, "outputs": [], "source": "# To install Seaborn, uncomment the next line, and then run this cell\n#!pip install --user seaborn", "execution_count": 7}, {"cell_type": "markdown", "metadata": {}, "source": "*If you installed Seaborn, you'll need to also restart your notebook kernel, and run the cell to `import pixiedust` again. Find **Restart** in the **Kernel** menu above.*"}, {"cell_type": "markdown", "metadata": {}, "source": "End of chapter. [Return to table of contents](#toc)\n<hr>"}, {"cell_type": "markdown", "metadata": {}, "source": "\n# <a id=\"part_two\"></a>Load text data from remote sources\n"}, {"cell_type": "markdown", "metadata": {}, "source": "Data files commonly reside in remote sources, such as such as public or private market places or GitHub repositories. You can load comma separated value (csv) data files using Pixiedust's `sampleData` method. "}, {"cell_type": "markdown", "metadata": {}, "source": "## Prerequisites"}, {"cell_type": "markdown", "metadata": {}, "source": "If you haven't already, import PixieDust. Follow the instructions in [Get started](#part_one)."}, {"cell_type": "markdown", "metadata": {}, "source": "When you run a notebook cell (that loads or processes data) it might trigger execution of one or more Spark jobs. \n\n## Enable Apache Spark job monitoring"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "Succesfully enabled Spark Job Progress Monitor\n", "output_type": "stream", "name": "stdout"}], "source": "pixiedust.enableJobMonitor()", "execution_count": 6}, {"cell_type": "markdown", "metadata": {}, "source": "## Load data\n\nTo load a data set, run `pixiedust.sampleData` and specify the data set URL:"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [], "source": "homes = pixiedust.sampleData(\"https://openobjectstore.mybluemix.net/misc/milliondollarhomes.csv\")", "execution_count": null}, {"cell_type": "markdown", "metadata": {}, "source": "The `pixiedust.sampleData` method loads the data into an [Apache Spark DataFrame](https://spark.apache.org/docs/latest/sql-programming-guide.html#datasets-and-dataframes), which you can inspect and visualize using `display()`."}, {"cell_type": "markdown", "metadata": {}, "source": "## Inspect and preview the loaded data\n\nTo inspect the automatically inferred schema and preview a small subset of the data, you can use the _DataFrame Table_ view, as shown in this preconfigured example: "}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "dataframe"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div><style type=\"text/css\" class=\"pd_save is-viewer-good\">\n .df-table-wrapper .panel-heading {\n border-radius: 0;\n padding: 0px;\n }\n .df-table-wrapper .panel-heading:hover {\n border-color: #008571;\n }\n .df-table-wrapper .panel-title a {\n background-color: #f9f9fb;\n color: #333333;\n display: block;\n outline: none;\n padding: 10px 15px;\n text-decoration: none;\n }\n .df-table-wrapper .panel-title a:hover {\n background-color: #337ab7;\n border-color: #2e6da4;\n color: #ffffff;\n display: block;\n padding: 10px 15px;\n text-decoration: none;\n }\n .df-table-wrapper {\n font-size: small;\n font-weight: 300;\n letter-spacing: 0.5px;\n line-height: normal;\n }\n .df-table-search-count {\n display: inline-block;\n margin: 20px 0;\n }\n .df-table-container {\n max-height: 50vh;\n max-width: 100%;\n overflow-x: auto;\n position: relative;\n }\n .df-table-wrapper table {\n border: 0 none #ffffff;\n border-collapse: collapse;\n margin: 0;\n min-width: 100%;\n padding: 0;\n table-layout: fixed;\n }\n .df-table-wrapper tr.hidden {\n display: none;\n }\n .df-table-wrapper tr:nth-child(even) {\n background-color: #f9f9fb;\n }\n .df-table-wrapper tr.even {\n background-color: #f9f9fb;\n }\n .df-table-wrapper tr.odd {\n background-color: #ffffff;\n }\n .df-table-wrapper td + td {\n border-left: 1px solid #e0e0e0;\n }\n\n .df-table-wrapper thead,\n .fixed-header {\n color: #337ab7;\n font-family: monospace;\n }\n .df-table-wrapper tr,\n .fixed-row {\n border: 0 none #ffffff;\n margin: 0;\n padding: 0;\n }\n .df-table-wrapper th,\n .df-table-wrapper td,\n .fixed-cell {\n border: 0 none #ffffff;\n margin: 0;\n min-width: 50px;\n padding: 5px 20px 5px 10px;\n text-align: left;\n word-wrap: break-word;\n }\n .df-table-wrapper th {\n padding-bottom: 0;\n padding-top: 0;\n }\n .df-table-wrapper th div {\n max-height: 1px;\n visibility: hidden;\n }\n\n .df-schema-field {\n margin-left: 10px;\n }\n\n .fixed-header-container {\n overflow: hidden;\n position: relative;\n }\n .fixed-header {\n border-bottom: 2px solid #2e6da4;\n display: table;\n position: relative;\n }\n .fixed-row {\n display: table-row;\n }\n .fixed-cell {\n display: table-cell;\n }\n</style><div class=\"df-table-wrapper df-table-wrapper-f6fe6655 panel-group pd_save is-viewer-good\">\n <!-- dataframe schema -->\n <div class=\"panel panel-default\">\n <div class=\"panel-heading\">\n <h4 class=\"panel-title\" style=\"margin: 0px;\">\n <a data-toggle=\"collapse\" href=\"#df-schema-f6fe6655\" data-parent=\"#df-table-wrapper-f6fe6655\">Schema</a>\n </h4>\n </div>\n <div id=\"df-schema-f6fe6655\" class=\"panel-collapse collapse\">\n <div class=\"panel-body\" style=\"font-family: monospace;\">\n <div class=\"df-schema-type\">\n <span>type: </span><span>struct</span>\n </div>\n <div class=\"df-schema-fields\">\n <div>field:</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'PROPERTY TYPE', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'ADDRESS', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'CITY', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'STATE', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'ZIP', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'PRICE', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'BEDS', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'double', 'name': 'BATHS', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'LOCATION', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'SQFT', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'LOT SIZE', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'YEAR BUILT', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'DAYS ON MARKET', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'URL', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'SOURCE', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'integer', 'name': 'LISTING ID', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'double', 'name': 'LATITUDE', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'double', 'name': 'LONGITUDE', 'nullable': True}</div>\n \n </div>\n </div>\n </div>\n </div>\n <!-- dataframe table -->\n <div class=\"panel panel-default\">\n <div class=\"panel-heading\">\n <h4 class=\"panel-title\" style=\"margin: 0px;\">\n <a data-toggle=\"collapse\" href=\"#df-table-f6fe6655\" data-parent=\"#df-table-wrapper-f6fe6655\">Table</a>\n </h4>\n </div>\n <div id=\"df-table-f6fe6655\" class=\"panel-collapse collapse in\">\n <div class=\"panel-body\">\n \n <input class=\"df-table-search form-control input-sm\" placeholder=\"Search table\" type=\"text\">\n <div>\n <span class=\"df-table-search-count\">Showing 100 of 500</span>\n </div>\n <!-- fixed header for when dataframe table scrolls -->\n <div class=\"fixed-header-container\">\n <div class=\"fixed-header\">\n <div class=\"fixed-row\">\n \n <div class=\"fixed-cell\">PROPERTY TYPE</div>\n \n <div class=\"fixed-cell\">ADDRESS</div>\n \n <div class=\"fixed-cell\">CITY</div>\n \n <div class=\"fixed-cell\">STATE</div>\n \n <div class=\"fixed-cell\">ZIP</div>\n \n <div class=\"fixed-cell\">PRICE</div>\n \n <div class=\"fixed-cell\">BEDS</div>\n \n <div class=\"fixed-cell\">BATHS</div>\n \n <div class=\"fixed-cell\">LOCATION</div>\n \n <div class=\"fixed-cell\">SQFT</div>\n \n <div class=\"fixed-cell\">LOT SIZE</div>\n \n <div class=\"fixed-cell\">YEAR BUILT</div>\n \n <div class=\"fixed-cell\">DAYS ON MARKET</div>\n \n <div class=\"fixed-cell\">URL</div>\n \n <div class=\"fixed-cell\">SOURCE</div>\n \n <div class=\"fixed-cell\">LISTING ID</div>\n \n <div class=\"fixed-cell\">LATITUDE</div>\n \n <div class=\"fixed-cell\">LONGITUDE</div>\n \n </div>\n </div>\n </div>\n <div class=\"df-table-container\">\n <table class=\"df-table\">\n <thead>\n <tr>\n \n <th><div>PROPERTY TYPE</div></th>\n \n <th><div>ADDRESS</div></th>\n \n <th><div>CITY</div></th>\n \n <th><div>STATE</div></th>\n \n <th><div>ZIP</div></th>\n \n <th><div>PRICE</div></th>\n \n <th><div>BEDS</div></th>\n \n <th><div>BATHS</div></th>\n \n <th><div>LOCATION</div></th>\n \n <th><div>SQFT</div></th>\n \n <th><div>LOT SIZE</div></th>\n \n <th><div>YEAR BUILT</div></th>\n \n <th><div>DAYS ON MARKET</div></th>\n \n <th><div>URL</div></th>\n \n <th><div>SOURCE</div></th>\n \n <th><div>LISTING ID</div></th>\n \n <th><div>LATITUDE</div></th>\n \n <th><div>LONGITUDE</div></th>\n \n </tr>\n </thead>\n <tbody>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>4 Newbury Road Rd</td>\n \n <td>Windham</td>\n \n <td>NH</td>\n \n <td>3087</td>\n \n <td>2450000</td>\n \n <td>5</td>\n \n <td>7.5</td>\n \n <td>Windham</td>\n \n <td>13461</td>\n \n <td>139392</td>\n \n <td>2008</td>\n \n <td>84</td>\n \n <td>http://www.redfin.com/NH/Windham/4-Newbury-Rd-03087/home/96548208</td>\n \n <td>NEREN</td>\n \n <td>58467283</td>\n \n <td>42.83153747</td>\n \n <td>-71.27639808</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>25 Marshall Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2482</td>\n \n <td>1909847</td>\n \n <td>5</td>\n \n <td>4.5</td>\n \n <td>Wellesley</td>\n \n <td>4900</td>\n \n <td>12228</td>\n \n <td>2016</td>\n \n <td>71</td>\n \n <td>http://www.redfin.com/MA/Wellesley/25-Marshall-Rd-02482/home/105557102</td>\n \n <td>MLS PIN</td>\n \n <td>61782463</td>\n \n <td>42.2997542</td>\n \n <td>-71.3088256</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>15 E Meadow Ln</td>\n \n <td>Middleton</td>\n \n <td>MA</td>\n \n <td>1949</td>\n \n <td>1177500</td>\n \n <td>None</td>\n \n <td>2.5</td>\n \n <td></td>\n \n <td>4263</td>\n \n <td>40281</td>\n \n <td>2015</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Middleton/15-E-Meadow-Ln-01949/home/67981805</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.585715</td>\n \n <td>-71.012888</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>983 Memorial Dr #302</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2138</td>\n \n <td>1100000</td>\n \n <td>3</td>\n \n <td>2.0</td>\n \n <td>Harvard Square</td>\n \n <td>1606</td>\n \n <td>None</td>\n \n <td>1920</td>\n \n <td>74</td>\n \n <td>http://www.redfin.com/MA/Cambridge/983-Memorial-Dr-02138/unit-302/home/105594755</td>\n \n <td>MLS PIN</td>\n \n <td>61690710</td>\n \n <td>42.3722656</td>\n \n <td>-71.1252212</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Franklin St Ph 2E</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>8950000</td>\n \n <td>3</td>\n \n <td>4.5</td>\n \n <td>Midtown</td>\n \n <td>3435</td>\n \n <td>None</td>\n \n <td>2016</td>\n \n <td>86</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Franklin-St-02108/unit-2E/home/102070369</td>\n \n <td>MLS PIN</td>\n \n <td>55818606</td>\n \n <td>42.35631</td>\n \n <td>-71.05945</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>18 Yarmouth St #1</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>2600000</td>\n \n <td>3</td>\n \n <td>3.5</td>\n \n <td>South End</td>\n \n <td>2522</td>\n \n <td>None</td>\n \n <td>1880</td>\n \n <td>88</td>\n \n <td>http://www.redfin.com/MA/Boston/18-Yarmouth-St-02116/unit-1/home/9313347</td>\n \n <td>MLS PIN</td>\n \n <td>59168291</td>\n \n <td>42.3458731</td>\n \n <td>-71.0767967</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>128 Lowell St</td>\n \n <td>Lexington</td>\n \n <td>MA</td>\n \n <td>2420</td>\n \n <td>1185000</td>\n \n <td>5</td>\n \n <td>3.5</td>\n \n <td>Lexington</td>\n \n <td>3275</td>\n \n <td>6300</td>\n \n <td>2016</td>\n \n <td>88</td>\n \n <td>http://www.redfin.com/MA/Lexington/128-Lowell-St-02420/home/8553025</td>\n \n <td>MLS PIN</td>\n \n <td>59375875</td>\n \n <td>42.436932</td>\n \n <td>-71.190511</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>20 Jackson Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2481</td>\n \n <td>2165000</td>\n \n <td>4</td>\n \n <td>4.5</td>\n \n <td>Wellesley</td>\n \n <td>5199</td>\n \n <td>16321</td>\n \n <td>2016</td>\n \n <td>88</td>\n \n <td>http://www.redfin.com/MA/Wellesley/20-Jackson-Rd-02481/home/8964864</td>\n \n <td>MLS PIN</td>\n \n <td>51221892</td>\n \n <td>42.307657</td>\n \n <td>-71.252257</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>30 Winchester St #3</td>\n \n <td>Brookline</td>\n \n <td>MA</td>\n \n <td>2446</td>\n \n <td>1400000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Coolidge Corner</td>\n \n <td>1504</td>\n \n <td>None</td>\n \n <td>1915</td>\n \n <td>66</td>\n \n <td>http://www.redfin.com/MA/Brookline/30-Winchester-St-02446/unit-3/home/105251020</td>\n \n <td>MLS PIN</td>\n \n <td>58480309</td>\n \n <td>42.3420632</td>\n \n <td>-71.1257602</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>30 Winchester St #4</td>\n \n <td>Brookline</td>\n \n <td>MA</td>\n \n <td>2446</td>\n \n <td>1500000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Coolidge Corner</td>\n \n <td>1584</td>\n \n <td>None</td>\n \n <td>1915</td>\n \n <td>66</td>\n \n <td>http://www.redfin.com/MA/Brookline/30-Winchester-St-02446/unit-4/home/105251022</td>\n \n <td>MLS PIN</td>\n \n <td>58480311</td>\n \n <td>42.3420632</td>\n \n <td>-71.1257602</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>30 Winchester St #5</td>\n \n <td>Brookline</td>\n \n <td>MA</td>\n \n <td>2446</td>\n \n <td>1600000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Coolidge Corner</td>\n \n <td>1686</td>\n \n <td>None</td>\n \n <td>1915</td>\n \n <td>66</td>\n \n <td>http://www.redfin.com/MA/Brookline/30-Winchester-St-02446/unit-5/home/105251023</td>\n \n <td>MLS PIN</td>\n \n <td>58480312</td>\n \n <td>42.3420632</td>\n \n <td>-71.1257602</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>576 Washington St #206</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2482</td>\n \n <td>2500000</td>\n \n <td>2</td>\n \n <td>2.5</td>\n \n <td>Wellesley</td>\n \n <td>2221</td>\n \n <td>None</td>\n \n <td>2015</td>\n \n <td>59</td>\n \n <td>http://www.redfin.com/MA/Wellesley/576-Washington-St-02482/unit-206/home/109083144</td>\n \n <td>MLS PIN</td>\n \n <td>62060226</td>\n \n <td>42.295421</td>\n \n <td>-71.292739</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>2 Wellington Way</td>\n \n <td>Bedford</td>\n \n <td>MA</td>\n \n <td>1730</td>\n \n <td>1150000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Wellington Way</td>\n \n <td>3531</td>\n \n <td>43560</td>\n \n <td>2012</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Bedford/2-Wellington-Way-01730/home/41363649</td>\n \n <td>MLS PIN</td>\n \n <td>59806880</td>\n \n <td>42.5029123</td>\n \n <td>-71.2849657</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>267 Humphrey St #1</td>\n \n <td>Swampscott</td>\n \n <td>MA</td>\n \n <td>1907</td>\n \n <td>1700000</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td>Swampscott</td>\n \n <td>2140</td>\n \n <td>None</td>\n \n <td>2016</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Swampscott/267-Humphrey-St-01907/unit-1/home/105944789</td>\n \n <td>MLS PIN</td>\n \n <td>61004497</td>\n \n <td>42.466738</td>\n \n <td>-70.913681</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Franklin St #3602</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>2825000</td>\n \n <td>2</td>\n \n <td>2.5</td>\n \n <td>Midtown</td>\n \n <td>1486</td>\n \n <td>None</td>\n \n <td>2016</td>\n \n <td>59</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Franklin-St-02108/unit-3602/home/108795642</td>\n \n <td>MLS PIN</td>\n \n <td>61519341</td>\n \n <td>42.35631</td>\n \n <td>-71.05945</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>33 Monument Sq #1</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2129</td>\n \n <td>1250000</td>\n \n <td>2</td>\n \n <td>3.0</td>\n \n <td>Charlestown</td>\n \n <td>1923</td>\n \n <td>None</td>\n \n <td>1896</td>\n \n <td>59</td>\n \n <td>http://www.redfin.com/MA/Boston/33-Monument-Sq-02129/unit-1/home/105149354</td>\n \n <td>MLS PIN</td>\n \n <td>58437300</td>\n \n <td>42.3764066</td>\n \n <td>-71.0618387</td>\n \n </tr>\n \n <tr>\n \n <td>Townhouse</td>\n \n <td>343 Commercial St Unit TH20</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2109</td>\n \n <td>3100000</td>\n \n <td>2</td>\n \n <td>2.5</td>\n \n <td>Waterfront</td>\n \n <td>2290</td>\n \n <td>2290</td>\n \n <td>1978</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Boston/343-Commercial-St-02109/unit-TH20/home/105711342</td>\n \n <td>MLS PIN</td>\n \n <td>59226234</td>\n \n <td>42.3658992</td>\n \n <td>-71.0511488</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>46 Shepard St #24</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2138</td>\n \n <td>1120000</td>\n \n <td>3</td>\n \n <td>2.0</td>\n \n <td>Harvard Square</td>\n \n <td>1524</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Cambridge/46-Shepard-St-02138/unit-24/home/11585901</td>\n \n <td>MLS PIN</td>\n \n <td>61655663</td>\n \n <td>42.3807083</td>\n \n <td>-71.123601</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Franklin St #1808</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>2038888</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>Midtown</td>\n \n <td>1566</td>\n \n <td>None</td>\n \n <td>2016</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Franklin-St-02108/unit-1808/home/109247975</td>\n \n <td>MLS PIN</td>\n \n <td>62369735</td>\n \n <td>42.35631</td>\n \n <td>-71.05945</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>117 Beacon St Unit A</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>4750000</td>\n \n <td>4</td>\n \n <td>4.5</td>\n \n <td>Back Bay</td>\n \n <td>3052</td>\n \n <td>None</td>\n \n <td>1864</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Boston/117-Beacon-St-02116/unit-A/home/108973963</td>\n \n <td>MLS PIN</td>\n \n <td>61672595</td>\n \n <td>42.354921</td>\n \n <td>-71.073407</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Franklin St #1008</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>2049000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>Midtown</td>\n \n <td>1476</td>\n \n <td>None</td>\n \n <td>2016</td>\n \n <td>59</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Franklin-St-02108/unit-1008/home/109481369</td>\n \n <td>MLS PIN</td>\n \n <td>62725868</td>\n \n <td>42.35631</td>\n \n <td>-71.05945</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>341 Marlborough St #4</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>3005000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Back Bay</td>\n \n <td>1618</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>67</td>\n \n <td>http://www.redfin.com/MA/Boston/341-Marlborough-St-02115/unit-4/home/56787595</td>\n \n <td>MLS PIN</td>\n \n <td>61970237</td>\n \n <td>42.351212</td>\n \n <td>-71.085754</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>485-495 Harrison Ave #401</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2118</td>\n \n <td>1390000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>South End</td>\n \n <td>1409</td>\n \n <td>None</td>\n \n <td>1914</td>\n \n <td>67</td>\n \n <td>http://www.redfin.com/MA/Boston/495-Harrison-Ave-02118/unit-401/home/108978383</td>\n \n <td>MLS PIN</td>\n \n <td>61691728</td>\n \n <td>42.3417737</td>\n \n <td>-71.0667754</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>415 Concord Rd</td>\n \n <td>Weston</td>\n \n <td>MA</td>\n \n <td>2493</td>\n \n <td>1725000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Weston</td>\n \n <td>3185</td>\n \n <td>217687</td>\n \n <td>1974</td>\n \n <td>59</td>\n \n <td>http://www.redfin.com/MA/Weston/415-Concord-Rd-02493/home/8779249</td>\n \n <td>MLS PIN</td>\n \n <td>47855399</td>\n \n <td>42.386891</td>\n \n <td>-71.320689</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>18 Lorena Rd</td>\n \n <td>Winchester</td>\n \n <td>MA</td>\n \n <td>1890</td>\n \n <td>2375000</td>\n \n <td>6</td>\n \n <td>7.0</td>\n \n <td>Winchester</td>\n \n <td>5812</td>\n \n <td>11988</td>\n \n <td>2016</td>\n \n <td>67</td>\n \n <td>http://www.redfin.com/MA/Winchester/18-Lorena-Rd-01890/home/103985838</td>\n \n <td>MLS PIN</td>\n \n <td>56019944</td>\n \n <td>42.4458374</td>\n \n <td>-71.1285051</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>1 Wilshire Rd</td>\n \n <td>Newbury</td>\n \n <td>MA</td>\n \n <td>1951</td>\n \n <td>2225000</td>\n \n <td>4</td>\n \n <td>5.5</td>\n \n <td>Wilshire Road</td>\n \n <td>4214</td>\n \n <td>18138</td>\n \n <td>2014</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Newbury/1-Wilshire-Rd-01951/home/105539600</td>\n \n <td>MLS PIN</td>\n \n <td>59011440</td>\n \n <td>42.7796754</td>\n \n <td>-70.8476708</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>25 Lorena Rd</td>\n \n <td>Winchester</td>\n \n <td>MA</td>\n \n <td>1890</td>\n \n <td>2075000</td>\n \n <td>5</td>\n \n <td>4.5</td>\n \n <td>Winchester</td>\n \n <td>5600</td>\n \n <td>16820</td>\n \n <td>2016</td>\n \n <td>66</td>\n \n <td>http://www.redfin.com/MA/Winchester/25-Lorena-Rd-01890/home/108267308</td>\n \n <td>MLS PIN</td>\n \n <td>60054516</td>\n \n <td>42.4456575</td>\n \n <td>-71.1279728</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>Lot 1 Monsen Rd</td>\n \n <td>Concord</td>\n \n <td>MA</td>\n \n <td>1742</td>\n \n <td>1454900</td>\n \n <td>4</td>\n \n <td>4.5</td>\n \n <td>Monsen Farm</td>\n \n <td>4555</td>\n \n <td>21509</td>\n \n <td>2015</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Concord/1-Monsen-Rd-01742/home/102153489</td>\n \n <td>MLS PIN</td>\n \n <td>51940855</td>\n \n <td>42.4693342</td>\n \n <td>-71.3266466</td>\n \n </tr>\n \n <tr>\n \n <td>Townhouse</td>\n \n <td>170 Harvard St Unit 1</td>\n \n <td>Newton</td>\n \n <td>MA</td>\n \n <td>2460</td>\n \n <td>1100000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Newtonville</td>\n \n <td>2388</td>\n \n <td>10089</td>\n \n <td>1910</td>\n \n <td>66</td>\n \n <td>http://www.redfin.com/MA/Newton/170-Harvard-St-02460/unit-1/home/109313528</td>\n \n <td>MLS PIN</td>\n \n <td>62550577</td>\n \n <td>42.3468986</td>\n \n <td>-71.2005455</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>Zero Worcester Sq Ph 2</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2118</td>\n \n <td>1665000</td>\n \n <td>2</td>\n \n <td>2.5</td>\n \n <td>South End</td>\n \n <td>1515</td>\n \n <td>None</td>\n \n <td>2013</td>\n \n <td>58</td>\n \n <td>http://www.redfin.com/MA/Boston/0-Worcester-Sq-02118/unit-2/home/109286549</td>\n \n <td>MLS PIN</td>\n \n <td>62470232</td>\n \n <td>42.3372471</td>\n \n <td>-71.0753519</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>1 Jerusalem Ln</td>\n \n <td>Cohasset</td>\n \n <td>MA</td>\n \n <td>2025</td>\n \n <td>1437000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Jerusalem Road/Atlantic Avenue/Jerusalem Lane Cul De Sac</td>\n \n <td>2724</td>\n \n <td>9443</td>\n \n <td>2000</td>\n \n <td>66</td>\n \n <td>http://www.redfin.com/MA/Cohasset/1-Jerusalem-Ln-02025/home/8835487</td>\n \n <td>MLS PIN</td>\n \n <td>60777396</td>\n \n <td>42.259862</td>\n \n <td>-70.811424</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>183 Massachusetts Ave #803</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2115</td>\n \n <td>1082500</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>Back Bay</td>\n \n <td>1220</td>\n \n <td>None</td>\n \n <td>2002</td>\n \n <td>74</td>\n \n <td>http://www.redfin.com/MA/Boston/183-Massachusetts-Ave-02115/unit-803/home/12402725</td>\n \n <td>MLS PIN</td>\n \n <td>62071287</td>\n \n <td>42.3455392</td>\n \n <td>-71.0871356</td>\n \n </tr>\n \n <tr>\n \n <td>Townhouse</td>\n \n <td>31 Day St Unit B</td>\n \n <td>Somerville</td>\n \n <td>MA</td>\n \n <td>2144</td>\n \n <td>1351000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Davis Square</td>\n \n <td>1874</td>\n \n <td>None</td>\n \n <td>2012</td>\n \n <td>88</td>\n \n <td>http://www.redfin.com/MA/Somerville/31-Day-St-02144/unit-B/home/40314152</td>\n \n <td>MLS PIN</td>\n \n <td>61798074</td>\n \n <td>42.3951863</td>\n \n <td>-71.1242988</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>83 Newton St</td>\n \n <td>Somerville</td>\n \n <td>MA</td>\n \n <td>2143</td>\n \n <td>1240000</td>\n \n <td>8</td>\n \n <td>4.0</td>\n \n <td></td>\n \n <td>4034</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Somerville/83-Newton-St-02143/home/8730062</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3771375</td>\n \n <td>-71.0976651</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>34 Crestwood Rd</td>\n \n <td>Marblehead</td>\n \n <td>MA</td>\n \n <td>1945</td>\n \n <td>2997000</td>\n \n <td>1</td>\n \n <td>5.0</td>\n \n <td></td>\n \n <td>8509</td>\n \n <td>34400</td>\n \n <td>2012</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Marblehead/34-Crestwood-Rd-01945/home/11768413</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.501777</td>\n \n <td>-70.877002</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>78 Bonad Rd</td>\n \n <td>Brookline</td>\n \n <td>MA</td>\n \n <td>2467</td>\n \n <td>1070000</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td>South Brookline</td>\n \n <td>1806</td>\n \n <td>4982</td>\n \n <td>2016</td>\n \n <td>71</td>\n \n <td>http://www.redfin.com/MA/Brookline/78-Bonad-Rd-02467/home/109052310</td>\n \n <td>MLS PIN</td>\n \n <td>61948749</td>\n \n <td>42.3178198</td>\n \n <td>-71.1626756</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>217 Forest St</td>\n \n <td>Winchester</td>\n \n <td>MA</td>\n \n <td>1890</td>\n \n <td>1155000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Muraco School District</td>\n \n <td>3779</td>\n \n <td>9234</td>\n \n <td>2013</td>\n \n <td>72</td>\n \n <td>http://www.redfin.com/MA/Winchester/217-Forest-St-01890/home/110057079</td>\n \n <td>MLS PIN</td>\n \n <td>56198065</td>\n \n <td>42.4714541</td>\n \n <td>-71.1156268</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>31 Mussell Point Way</td>\n \n <td>Gloucester</td>\n \n <td>MA</td>\n \n <td>1930</td>\n \n <td>2075000</td>\n \n <td>3</td>\n \n <td>3.5</td>\n \n <td></td>\n \n <td>2878</td>\n \n <td>39400</td>\n \n <td>2001</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Gloucester/31-Mussell-Point-Way-01930/home/11301633</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.586906</td>\n \n <td>-70.687225</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>25 Piedmont St #2</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>1660000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>South End</td>\n \n <td>1328</td>\n \n <td>None</td>\n \n <td>2015</td>\n \n <td>70</td>\n \n <td>http://www.redfin.com/MA/Boston/25-Piedmont-St-02116/unit-2/home/109296598</td>\n \n <td>MLS PIN</td>\n \n <td>62502554</td>\n \n <td>42.3498964</td>\n \n <td>-71.0684372</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>1 Denny St</td>\n \n <td>Westborough</td>\n \n <td>MA</td>\n \n <td>1581</td>\n \n <td>1100000</td>\n \n <td>6</td>\n \n <td>3.5</td>\n \n <td>Westborough</td>\n \n <td>3394</td>\n \n <td>239580</td>\n \n <td>1917</td>\n \n <td>79</td>\n \n <td>http://www.redfin.com/MA/Westborough/1-Denny-St-01581/home/16634032</td>\n \n <td>MLS PIN</td>\n \n <td>60054100</td>\n \n <td>42.259306</td>\n \n <td>-71.611866</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>192 Claybrook Rd</td>\n \n <td>Dover</td>\n \n <td>MA</td>\n \n <td>2030</td>\n \n <td>3780000</td>\n \n <td>5</td>\n \n <td>7.5</td>\n \n <td>Dover</td>\n \n <td>11500</td>\n \n <td>110016</td>\n \n <td>2000</td>\n \n <td>81</td>\n \n <td>http://www.redfin.com/MA/Dover/192-Claybrook-Rd-02030/home/11706025</td>\n \n <td>MLS PIN</td>\n \n <td>57585881</td>\n \n <td>42.2640769</td>\n \n <td>-71.3030216</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>36 Mount Vernon St #36</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2140</td>\n \n <td>1460000</td>\n \n <td>4</td>\n \n <td>2.0</td>\n \n <td>Cambridge</td>\n \n <td>2006</td>\n \n <td>None</td>\n \n <td>1873</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Cambridge/36-Mount-Vernon-St-02140/unit-36/home/109048192</td>\n \n <td>MLS PIN</td>\n \n <td>61935510</td>\n \n <td>42.3869743</td>\n \n <td>-71.1206802</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>67 Harvard Ave #67</td>\n \n <td>Brookline</td>\n \n <td>MA</td>\n \n <td>2446</td>\n \n <td>1789000</td>\n \n <td>3</td>\n \n <td>3.5</td>\n \n <td></td>\n \n <td>3584</td>\n \n <td>None</td>\n \n <td>1909</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Brookline/67-Harvard-Ave-02446/unit-67/home/26768959</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3370986</td>\n \n <td>-71.1238246</td>\n \n </tr>\n \n <tr>\n \n <td>Townhouse</td>\n \n <td>7 Marlborough St #1</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>2570000</td>\n \n <td>3</td>\n \n <td>3.5</td>\n \n <td>Back Bay</td>\n \n <td>2179</td>\n \n <td>2179</td>\n \n <td>1900</td>\n \n <td>88</td>\n \n <td>http://www.redfin.com/MA/Boston/7-Marlborough-St-02116/unit-1/home/9299190</td>\n \n <td>MLS PIN</td>\n \n <td>59800618</td>\n \n <td>42.354279</td>\n \n <td>-71.072681</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>78 Waltham St #3</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2118</td>\n \n <td>1135000</td>\n \n <td>2</td>\n \n <td>1.0</td>\n \n <td></td>\n \n <td>850</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/78-Waltham-St-02118/unit-3/home/9310881</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.343133</td>\n \n <td>-71.0706771</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>32 Union Park #4</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2118</td>\n \n <td>1235000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td></td>\n \n <td>1028</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/32-Union-Park-02118/unit-4/home/9282029</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3429262</td>\n \n <td>-71.0717198</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>109 Chandler St #1</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>1925000</td>\n \n <td>3</td>\n \n <td>3.5</td>\n \n <td></td>\n \n <td>1803</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/109-Chandler-St-02116/unit-1/home/9322251</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3464393</td>\n \n <td>-71.0736695</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>141 Dorchester Ave #119</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2127</td>\n \n <td>1150000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>South Boston</td>\n \n <td>1701</td>\n \n <td>1701</td>\n \n <td>2006</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Boston/141-Dorchester-Ave-02127/unit-119/home/18982472</td>\n \n <td>MLS PIN</td>\n \n <td>59722577</td>\n \n <td>42.3419516</td>\n \n <td>-71.0574969</td>\n \n </tr>\n \n <tr>\n \n <td>Townhouse</td>\n \n <td>113 Commonwealth Ave #4</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>2330000</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td>Back Bay</td>\n \n <td>1884</td>\n \n <td>1884</td>\n \n <td>1930</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Boston/113-Commonwealth-Ave-02116/unit-4/home/9250390</td>\n \n <td>MLS PIN</td>\n \n <td>61017855</td>\n \n <td>42.3524353</td>\n \n <td>-71.0770045</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>75 Brookline St</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2139</td>\n \n <td>1399900</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td>Cambridgeport</td>\n \n <td>1810</td>\n \n <td>3016</td>\n \n <td>1854</td>\n \n <td>79</td>\n \n <td>http://www.redfin.com/MA/Cambridge/75-Brookline-St-02139/home/11558783</td>\n \n <td>MLS PIN</td>\n \n <td>59593814</td>\n \n <td>42.3620583</td>\n \n <td>-71.1033835</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>23 Laurel Hill Ln</td>\n \n <td>Winchester</td>\n \n <td>MA</td>\n \n <td>1890</td>\n \n <td>1475000</td>\n \n <td>5</td>\n \n <td>4.0</td>\n \n <td>Winchester</td>\n \n <td>4037</td>\n \n <td>12475</td>\n \n <td>2014</td>\n \n <td>80</td>\n \n <td>http://www.redfin.com/MA/Winchester/23-Laurel-Hill-Ln-01890/home/11439968</td>\n \n <td>MLS PIN</td>\n \n <td>58047231</td>\n \n <td>42.4724111</td>\n \n <td>-71.1196572</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>14 Hillside Ave</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2140</td>\n \n <td>5350000</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td></td>\n \n <td>4557</td>\n \n <td>15526</td>\n \n <td>1905</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Cambridge/14-Hillside-Ave-02140/home/110093849</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3853181</td>\n \n <td>-71.1238296</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>22 Garfield Rd</td>\n \n <td>Belmont</td>\n \n <td>MA</td>\n \n <td>2478</td>\n \n <td>1560000</td>\n \n <td>5</td>\n \n <td>3.5</td>\n \n <td>Belmont</td>\n \n <td>3137</td>\n \n <td>12614</td>\n \n <td>1936</td>\n \n <td>80</td>\n \n <td>http://www.redfin.com/MA/Belmont/22-Garfield-Rd-02478/home/8452209</td>\n \n <td>MLS PIN</td>\n \n <td>61669441</td>\n \n <td>42.4033462</td>\n \n <td>-71.1745363</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>721 Pleasant St</td>\n \n <td>Belmont</td>\n \n <td>MA</td>\n \n <td>2478</td>\n \n <td>1480000</td>\n \n <td>7</td>\n \n <td>5.0</td>\n \n <td>Belmont Center</td>\n \n <td>6000</td>\n \n <td>43900</td>\n \n <td>1837</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Belmont/721-Pleasant-St-02478/home/11774971</td>\n \n <td>MLS PIN</td>\n \n <td>61594748</td>\n \n <td>42.39572</td>\n \n <td>-71.179743</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>129 I St</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2127</td>\n \n <td>1290000</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td>South Boston</td>\n \n <td>2385</td>\n \n <td>None</td>\n \n <td>1890</td>\n \n <td>77</td>\n \n <td>http://www.redfin.com/MA/Boston/129-I-St-02127/home/9187855</td>\n \n <td>MLS PIN</td>\n \n <td>59630321</td>\n \n <td>42.3337549</td>\n \n <td>-71.040101</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>20 Authors Rd</td>\n \n <td>Concord</td>\n \n <td>MA</td>\n \n <td>1742</td>\n \n <td>1935000</td>\n \n <td>4</td>\n \n <td>4.0</td>\n \n <td>Concord</td>\n \n <td>5010</td>\n \n <td>25102</td>\n \n <td>2016</td>\n \n <td>80</td>\n \n <td>http://www.redfin.com/MA/Concord/20-Authors-Rd-01742/home/11594499</td>\n \n <td>MLS PIN</td>\n \n <td>57898084</td>\n \n <td>42.461323</td>\n \n <td>-71.338927</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>297 Heaths Bridge Rd</td>\n \n <td>Concord</td>\n \n <td>MA</td>\n \n <td>1742</td>\n \n <td>1400000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Conantum</td>\n \n <td>5135</td>\n \n <td>61344</td>\n \n <td>1995</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Concord/297-Heaths-Bridge-Rd-01742/home/11600408</td>\n \n <td>MLS PIN</td>\n \n <td>61928887</td>\n \n <td>42.434909</td>\n \n <td>-71.362825</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>73 Monument St</td>\n \n <td>Concord</td>\n \n <td>MA</td>\n \n <td>1742</td>\n \n <td>2250000</td>\n \n <td>5</td>\n \n <td>4.0</td>\n \n <td>Concord</td>\n \n <td>3799</td>\n \n <td>40075</td>\n \n <td>1880</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Concord/73-Monument-St-01742/home/11590552</td>\n \n <td>MLS PIN</td>\n \n <td>57869520</td>\n \n <td>42.463184</td>\n \n <td>-71.348692</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>162 Highland St</td>\n \n <td>Weston</td>\n \n <td>MA</td>\n \n <td>2493</td>\n \n <td>4800000</td>\n \n <td>5</td>\n \n <td>5.0</td>\n \n <td>South side Estate Area</td>\n \n <td>6741</td>\n \n <td>129260</td>\n \n <td>2001</td>\n \n <td>80</td>\n \n <td>http://www.redfin.com/MA/Weston/162-Highland-St-02493/home/8775137</td>\n \n <td>MLS PIN</td>\n \n <td>58298305</td>\n \n <td>42.355043</td>\n \n <td>-71.319191</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>12 Thoreau Rd</td>\n \n <td>Lexington</td>\n \n <td>MA</td>\n \n <td>2420</td>\n \n <td>1403000</td>\n \n <td>4</td>\n \n <td>2.0</td>\n \n <td>Burnham Farms Estates</td>\n \n <td>2263</td>\n \n <td>38712</td>\n \n <td>1959</td>\n \n <td>81</td>\n \n <td>http://www.redfin.com/MA/Lexington/12-Thoreau-Rd-02420/home/8577537</td>\n \n <td>MLS PIN</td>\n \n <td>61594796</td>\n \n <td>42.463883</td>\n \n <td>-71.211304</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>229 Manning St</td>\n \n <td>Needham</td>\n \n <td>MA</td>\n \n <td>2492</td>\n \n <td>1660706</td>\n \n <td>4</td>\n \n <td>1.5</td>\n \n <td></td>\n \n <td>1678</td>\n \n <td>10454</td>\n \n <td>1949</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Needham/229-Manning-St-02492/home/8914592</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.2863162</td>\n \n <td>-71.2283038</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>252 Manning St</td>\n \n <td>Needham</td>\n \n <td>MA</td>\n \n <td>2492</td>\n \n <td>1649000</td>\n \n <td>5</td>\n \n <td>5.5</td>\n \n <td>Needham</td>\n \n <td>6163</td>\n \n <td>9583</td>\n \n <td>2016</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Needham/252-Manning-St-02492/home/8914358</td>\n \n <td>MLS PIN</td>\n \n <td>61580545</td>\n \n <td>42.285505</td>\n \n <td>-71.227655</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>228 Upham St</td>\n \n <td>Melrose</td>\n \n <td>MA</td>\n \n <td>2176</td>\n \n <td>1500000</td>\n \n <td>7</td>\n \n <td>4.5</td>\n \n <td>East Side</td>\n \n <td>6639</td>\n \n <td>23418</td>\n \n <td>1908</td>\n \n <td>79</td>\n \n <td>http://www.redfin.com/MA/Melrose/228-Upham-St-02176/home/11783107</td>\n \n <td>MLS PIN</td>\n \n <td>61488166</td>\n \n <td>42.458383</td>\n \n <td>-71.054305</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>26 Coolidge Hill Rd</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2138</td>\n \n <td>1225000</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td></td>\n \n <td>2682</td>\n \n <td>4615</td>\n \n <td>1925</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Cambridge/26-Coolidge-Hill-Rd-02138/home/11594928</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.374227</td>\n \n <td>-71.1395469</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>221 Mount Auburn St #302</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2138</td>\n \n <td>1110000</td>\n \n <td>1</td>\n \n <td>1.0</td>\n \n <td>Harvard Square</td>\n \n <td>988</td>\n \n <td>None</td>\n \n <td>1960</td>\n \n <td>79</td>\n \n <td>http://www.redfin.com/MA/Cambridge/221-Mount-Auburn-St-02138/unit-302/home/11587449</td>\n \n <td>MLS PIN</td>\n \n <td>59440749</td>\n \n <td>42.3749516</td>\n \n <td>-71.129983</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>197 Countryside Rd</td>\n \n <td>Newton</td>\n \n <td>MA</td>\n \n <td>2459</td>\n \n <td>1415000</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td>Newton Center</td>\n \n <td>2764</td>\n \n <td>25878</td>\n \n <td>1979</td>\n \n <td>80</td>\n \n <td>http://www.redfin.com/MA/Newton/197-Countryside-Rd-02459/home/11490231</td>\n \n <td>MLS PIN</td>\n \n <td>61707285</td>\n \n <td>42.304059</td>\n \n <td>-71.2010034</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>321 Central St</td>\n \n <td>Newton</td>\n \n <td>MA</td>\n \n <td>2466</td>\n \n <td>1030000</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td>Newton</td>\n \n <td>2724</td>\n \n <td>30033</td>\n \n <td>1874</td>\n \n <td>81</td>\n \n <td>http://www.redfin.com/MA/Newton/321-Central-St-02466/home/11440793</td>\n \n <td>MLS PIN</td>\n \n <td>59477712</td>\n \n <td>42.3428608</td>\n \n <td>-71.2532636</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>13 Utica Rd</td>\n \n <td>Needham</td>\n \n <td>MA</td>\n \n <td>2494</td>\n \n <td>1131250</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td>Needham</td>\n \n <td>3923</td>\n \n <td>9000</td>\n \n <td>2005</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Needham/13-Utica-Rd-02494/home/11714925</td>\n \n <td>MLS PIN</td>\n \n <td>61553852</td>\n \n <td>42.2994493</td>\n \n <td>-71.2294273</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>6 Sawyer Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2481</td>\n \n <td>2745000</td>\n \n <td>5</td>\n \n <td>8.0</td>\n \n <td>Cliff Estates</td>\n \n <td>7000</td>\n \n <td>20000</td>\n \n <td>2014</td>\n \n <td>81</td>\n \n <td>http://www.redfin.com/MA/Wellesley/6-Sawyer-Rd-02481/home/8976461</td>\n \n <td>MLS PIN</td>\n \n <td>59710146</td>\n \n <td>42.311576</td>\n \n <td>-71.280358</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>22 Leighton Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2482</td>\n \n <td>1530000</td>\n \n <td>5</td>\n \n <td>3.5</td>\n \n <td>Dana Hall</td>\n \n <td>3574</td>\n \n <td>10000</td>\n \n <td>1916</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Wellesley/22-Leighton-Rd-02482/home/8986596</td>\n \n <td>MLS PIN</td>\n \n <td>59747229</td>\n \n <td>42.290604</td>\n \n <td>-71.295376</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>123 Abbott Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2481</td>\n \n <td>2184000</td>\n \n <td>6</td>\n \n <td>5.5</td>\n \n <td>Country Club</td>\n \n <td>6119</td>\n \n <td>34050</td>\n \n <td>1905</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Wellesley/123-Abbott-Rd-02481/home/11724812</td>\n \n <td>MLS PIN</td>\n \n <td>56084352</td>\n \n <td>42.303269</td>\n \n <td>-71.267922</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>46 White Oak Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2481</td>\n \n <td>1630000</td>\n \n <td>5</td>\n \n <td>3.5</td>\n \n <td>Wellesley</td>\n \n <td>2777</td>\n \n <td>27440</td>\n \n <td>1946</td>\n \n <td>78</td>\n \n <td>http://www.redfin.com/MA/Wellesley/46-White-Oak-Rd-02481/home/8978449</td>\n \n <td>MLS PIN</td>\n \n <td>60874162</td>\n \n <td>42.323326</td>\n \n <td>-71.286699</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>94 Albion Rd</td>\n \n <td>Wellesley</td>\n \n <td>MA</td>\n \n <td>2481</td>\n \n <td>1457500</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Cliff Estates</td>\n \n <td>2918</td>\n \n <td>26021</td>\n \n <td>1978</td>\n \n <td>81</td>\n \n <td>http://www.redfin.com/MA/Wellesley/94-Albion-Rd-02481/home/8984365</td>\n \n <td>MLS PIN</td>\n \n <td>58888275</td>\n \n <td>42.315699</td>\n \n <td>-71.291925</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>79 Overlook Dr</td>\n \n <td>Carlisle</td>\n \n <td>MA</td>\n \n <td>1741</td>\n \n <td>1080000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Carlisle</td>\n \n <td>3800</td>\n \n <td>182516</td>\n \n <td>1997</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Carlisle/79-Overlook-Dr-01741/home/8467988</td>\n \n <td>MLS PIN</td>\n \n <td>60090312</td>\n \n <td>42.544212</td>\n \n <td>-71.330903</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>3 Grey Ln</td>\n \n <td>Lynnfield</td>\n \n <td>MA</td>\n \n <td>1940</td>\n \n <td>1050000</td>\n \n <td>5</td>\n \n <td>3.5</td>\n \n <td>King James Grant</td>\n \n <td>4500</td>\n \n <td>40140</td>\n \n <td>1960</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Lynnfield/3-Grey-Ln-01940/home/11330976</td>\n \n <td>MLS PIN</td>\n \n <td>61233631</td>\n \n <td>42.542049</td>\n \n <td>-71.031766</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>6 Bennett Rd</td>\n \n <td>Wayland</td>\n \n <td>MA</td>\n \n <td>1778</td>\n \n <td>1100000</td>\n \n <td>4</td>\n \n <td>3.0</td>\n \n <td>Wayland</td>\n \n <td>3768</td>\n \n <td>35840</td>\n \n <td>1893</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Wayland/6-Bennett-Rd-01778/home/11685599</td>\n \n <td>MLS PIN</td>\n \n <td>59097036</td>\n \n <td>42.36189</td>\n \n <td>-71.353183</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>10 Bowdoin St #417</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2114</td>\n \n <td>1050000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>Beacon Hill</td>\n \n <td>1139</td>\n \n <td>1306</td>\n \n <td>2003</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Boston/10-Bowdoin-St-02114/unit-417/home/11837836</td>\n \n <td>MLS PIN</td>\n \n <td>61320832</td>\n \n <td>42.3607583</td>\n \n <td>-71.062671</td>\n \n </tr>\n \n <tr>\n \n <td>Townhouse</td>\n \n <td>9 Joy St #3</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2114</td>\n \n <td>1175000</td>\n \n <td>2</td>\n \n <td>1.0</td>\n \n <td></td>\n \n <td>1250</td>\n \n <td>1250</td>\n \n <td>1890</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/9-Joy-St-02114/unit-3/home/9236297</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3587067</td>\n \n <td>-71.0649545</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Garden St Unit 12</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2114</td>\n \n <td>1592500</td>\n \n <td>3</td>\n \n <td>2.0</td>\n \n <td></td>\n \n <td>1435</td>\n \n <td>None</td>\n \n <td>1899</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Garden-St-02114/unit-12/home/9329550</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3611202</td>\n \n <td>-71.066975</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>210 Meadowbrook Rd</td>\n \n <td>Weston</td>\n \n <td>MA</td>\n \n <td>2493</td>\n \n <td>3938000</td>\n \n <td>4</td>\n \n <td>5.0</td>\n \n <td>Weston</td>\n \n <td>6300</td>\n \n <td>60000</td>\n \n <td>1961</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Weston/210-Meadowbrook-Rd-02493/home/8785940</td>\n \n <td>MLS PIN</td>\n \n <td>59777195</td>\n \n <td>42.358451</td>\n \n <td>-71.282983</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>8 Hidden Rd</td>\n \n <td>Weston</td>\n \n <td>MA</td>\n \n <td>2493</td>\n \n <td>1700000</td>\n \n <td>5</td>\n \n <td>4.0</td>\n \n <td>Country Club</td>\n \n <td>4160</td>\n \n <td>71326</td>\n \n <td>1926</td>\n \n <td>85</td>\n \n <td>http://www.redfin.com/MA/Weston/8-Hidden-Rd-02493/home/8784603</td>\n \n <td>MLS PIN</td>\n \n <td>59640638</td>\n \n <td>42.362089</td>\n \n <td>-71.286346</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>12 Museum Way #2304</td>\n \n <td>Cambridge</td>\n \n <td>MA</td>\n \n <td>2141</td>\n \n <td>1175000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td></td>\n \n <td>1266</td>\n \n <td>None</td>\n \n <td>1998</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Cambridge/12-Museum-Way-02141/unit-2304/home/11602082</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3703968</td>\n \n <td>-71.0712157</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Franklin St Ph 1A</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>7995000</td>\n \n <td>3</td>\n \n <td>4.5</td>\n \n <td>Midtown</td>\n \n <td>3172</td>\n \n <td>None</td>\n \n <td>2016</td>\n \n <td>60</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Franklin-St-02108/unit-1A/home/101613053</td>\n \n <td>MLS PIN</td>\n \n <td>50956978</td>\n \n <td>42.35631</td>\n \n <td>-71.05945</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>41 Milford St #2</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2118</td>\n \n <td>2175000</td>\n \n <td>3</td>\n \n <td>2.5</td>\n \n <td>South End</td>\n \n <td>2150</td>\n \n <td>2150</td>\n \n <td>1890</td>\n \n <td>84</td>\n \n <td>http://www.redfin.com/MA/Boston/41-Milford-St-02118/unit-2/home/28465481</td>\n \n <td>MLS PIN</td>\n \n <td>61799764</td>\n \n <td>42.344312</td>\n \n <td>-71.069765</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>250 Boylston St Unit 5</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>11500000</td>\n \n <td>4</td>\n \n <td>4.5</td>\n \n <td></td>\n \n <td>4841</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/250-Boylston-St-02116/unit-5/home/9313437</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.351883</td>\n \n <td>-71.0693583</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>137 Marlborough St #6</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>2950000</td>\n \n <td>3</td>\n \n <td>3.0</td>\n \n <td>Back Bay</td>\n \n <td>2128</td>\n \n <td>2128</td>\n \n <td>1930</td>\n \n <td>84</td>\n \n <td>http://www.redfin.com/MA/Boston/137-Marlborough-St-02116/unit-6/home/9250255</td>\n \n <td>MLS PIN</td>\n \n <td>62113842</td>\n \n <td>42.3531759</td>\n \n <td>-71.078579</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>128 Beacon St Unit G</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>3395000</td>\n \n <td>2</td>\n \n <td>2.5</td>\n \n <td>Back Bay</td>\n \n <td>2170</td>\n \n <td>None</td>\n \n <td>1899</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Boston/128-Beacon-St-02116/unit-G/home/11838759</td>\n \n <td>MLS PIN</td>\n \n <td>61916911</td>\n \n <td>42.35518</td>\n \n <td>-71.074644</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>65 E India Row Apt 17A</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>2050000</td>\n \n <td>1</td>\n \n <td>1.0</td>\n \n <td></td>\n \n <td>754</td>\n \n <td>None</td>\n \n <td>1972</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/65-E-India-Row-02110/unit-17A/home/9260829</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3576699</td>\n \n <td>-71.0504859</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>110 Stuart St Unit 25J</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2116</td>\n \n <td>1110000</td>\n \n <td>1</td>\n \n <td>1.5</td>\n \n <td>Midtown</td>\n \n <td>1103</td>\n \n <td>None</td>\n \n <td>2009</td>\n \n <td>88</td>\n \n <td>http://www.redfin.com/MA/Boston/110-Stuart-St-02116/unit-25J/home/39913276</td>\n \n <td>MLS PIN</td>\n \n <td>60206455</td>\n \n <td>42.3509321</td>\n \n <td>-71.0653685</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>165 Tremont St Unit 1502</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2111</td>\n \n <td>2365000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td></td>\n \n <td>1054</td>\n \n <td>None</td>\n \n <td>2003</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Boston/165-Tremont-St-02111/unit-1502/home/9329174</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.3539223</td>\n \n <td>-71.0636921</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>2 Avery St Unit 18E</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2111</td>\n \n <td>3055000</td>\n \n <td>3</td>\n \n <td>3.5</td>\n \n <td>Midtown</td>\n \n <td>2344</td>\n \n <td>None</td>\n \n <td>2000</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Boston/2-Avery-St-02111/unit-18E/home/11736816</td>\n \n <td>MLS PIN</td>\n \n <td>54707422</td>\n \n <td>42.3527843</td>\n \n <td>-71.0630991</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>1 Franklin St #1108</td>\n \n <td>Boston</td>\n \n <td>MA</td>\n \n <td>2110</td>\n \n <td>1750000</td>\n \n <td>2</td>\n \n <td>2.0</td>\n \n <td>Midtown</td>\n \n <td>1566</td>\n \n <td>999</td>\n \n <td>2016</td>\n \n <td>60</td>\n \n <td>http://www.redfin.com/MA/Boston/1-Franklin-St-02108/unit-1108/home/108154689</td>\n \n <td>MLS PIN</td>\n \n <td>62285782</td>\n \n <td>42.35631</td>\n \n <td>-71.05945</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>142 Farm St</td>\n \n <td>Dover</td>\n \n <td>MA</td>\n \n <td>2030</td>\n \n <td>1125000</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td></td>\n \n <td>3273</td>\n \n <td>89734</td>\n \n <td>1859</td>\n \n <td>None</td>\n \n <td>http://www.redfin.com/MA/Dover/142-Farm-St-02030/home/11708174</td>\n \n <td></td>\n \n <td>None</td>\n \n <td>42.221839</td>\n \n <td>-71.321119</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>143 Pine St</td>\n \n <td>Dover</td>\n \n <td>MA</td>\n \n <td>2030</td>\n \n <td>1077000</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td>Dover</td>\n \n <td>3736</td>\n \n <td>87466</td>\n \n <td>1995</td>\n \n <td>86</td>\n \n <td>http://www.redfin.com/MA/Dover/143-Pine-St-02030/home/11708952</td>\n \n <td>MLS PIN</td>\n \n <td>60627103</td>\n \n <td>42.219392</td>\n \n <td>-71.284986</td>\n \n </tr>\n \n <tr>\n \n <td>Condo/Co-op</td>\n \n <td>223 Morrison Ave #223</td>\n \n <td>Somerville</td>\n \n <td>MA</td>\n \n <td>2144</td>\n \n <td>1375000</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Davis Square</td>\n \n <td>2923</td>\n \n <td>None</td>\n \n <td>1900</td>\n \n <td>60</td>\n \n <td>http://www.redfin.com/MA/Somerville/223-Morrison-Ave-02144/unit-223/home/109084304</td>\n \n <td>MLS PIN</td>\n \n <td>62063986</td>\n \n <td>42.3978318</td>\n \n <td>-71.1204787</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>85 CHANDLER St</td>\n \n <td>Somerville</td>\n \n <td>MA</td>\n \n <td>2144</td>\n \n <td>1000000</td>\n \n <td>5</td>\n \n <td>1.0</td>\n \n <td>Somerville</td>\n \n <td>2054</td>\n \n <td>4500</td>\n \n <td>1900</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Somerville/85-Chandler-St-02144/home/8692230</td>\n \n <td>MLS PIN</td>\n \n <td>61671952</td>\n \n <td>42.4004857</td>\n \n <td>-71.1201065</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>32 Hancock Rd</td>\n \n <td>Weston</td>\n \n <td>MA</td>\n \n <td>2493</td>\n \n <td>1070000</td>\n \n <td>4</td>\n \n <td>3.0</td>\n \n <td>Weston</td>\n \n <td>2403</td>\n \n <td>64792</td>\n \n <td>1971</td>\n \n <td>84</td>\n \n <td>http://www.redfin.com/MA/Weston/32-Hancock-Rd-02493/home/8776937</td>\n \n <td>MLS PIN</td>\n \n <td>59921546</td>\n \n <td>42.397893</td>\n \n <td>-71.284612</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>294 Central Ave</td>\n \n <td>Needham</td>\n \n <td>MA</td>\n \n <td>2494</td>\n \n <td>1049900</td>\n \n <td>4</td>\n \n <td>2.5</td>\n \n <td>Needham</td>\n \n <td>2938</td>\n \n <td>10019</td>\n \n <td>2011</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Needham/294-Central-Ave-02494/home/8923881</td>\n \n <td>MLS PIN</td>\n \n <td>61048054</td>\n \n <td>42.306322</td>\n \n <td>-71.237639</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>46 Sudbury Rd</td>\n \n <td>Concord</td>\n \n <td>MA</td>\n \n <td>1742</td>\n \n <td>1562500</td>\n \n <td>4</td>\n \n <td>3.5</td>\n \n <td>Concord</td>\n \n <td>2646</td>\n \n <td>7840</td>\n \n <td>1812</td>\n \n <td>85</td>\n \n <td>http://www.redfin.com/MA/Concord/46-Sudbury-Rd-01742/home/11593642</td>\n \n <td>MLS PIN</td>\n \n <td>61869457</td>\n \n <td>42.458425</td>\n \n <td>-71.353752</td>\n \n </tr>\n \n <tr>\n \n <td>Single Family Residential</td>\n \n <td>18 Calumet Rd</td>\n \n <td>Winchester</td>\n \n <td>MA</td>\n \n <td>1890</td>\n \n <td>1705000</td>\n \n <td>6</td>\n \n <td>2.5</td>\n \n <td>Winchester</td>\n \n <td>3860</td>\n \n <td>14850</td>\n \n <td>1896</td>\n \n <td>87</td>\n \n <td>http://www.redfin.com/MA/Winchester/18-Calumet-Rd-01890/home/11457397</td>\n \n <td>MLS PIN</td>\n \n <td>61460482</td>\n \n <td>42.448535</td>\n \n <td>-71.150249</td>\n \n </tr>\n \n </tbody>\n </table>\n </div>\n </div>\n </div>\n </div>\n</div><script class=\"pd_save is-viewer-good\">\n $(function() {\n var tableWrapper = $('.df-table-wrapper-f6fe6655');\n var fixedHeader = $('.fixed-header', tableWrapper);\n var tableContainer = $('.df-table-container', tableWrapper);\n var table = $('.df-table', tableContainer);\n var rows = $('tbody > tr', table);\n var total = 500;\n\n fixedHeader\n .css('width', table.width())\n .find('.fixed-cell')\n .each(function(i, e) {\n $(this).css('width', $('.df-table-wrapper-f6fe6655 th:nth-child(' + (i+1) + ')').css('width'));\n });\n\n tableContainer.scroll(function() {\n fixedHeader.css({ left: table.position().left });\n });\n\n $('.df-table-search', tableWrapper).keyup(function() {\n var val = '^(?=.*\\\\b' + $.trim($(this).val()).split(/\\s+/).join('\\\\b)(?=.*\\\\b') + ').*$';\n var reg = RegExp(val, 'i');\n var index = 0;\n \n rows.each(function(i, e) {\n if (!reg.test($(this).text().replace(/\\s+/g, ' '))) {\n $(this).attr('class', 'hidden');\n }\n else {\n $(this).attr('class', (++index % 2 == 0 ? 'even' : 'odd'));\n }\n });\n\n $('.df-table-search-count', tableWrapper).html('Showing ' + index + ' of ' + total);\n });\n });\n</script>"}, "metadata": {}}], "source": "display(homes)", "execution_count": 8}, {"cell_type": "markdown", "metadata": {}, "source": "## Simple visualization using bar charts\n\nWith PixieDust `display()`, you can visually explore the loaded data using built-in charts, such as, bar charts, line charts, scatter plots, or maps.\n\nTo explore a data set:\n* choose the desired chart type from the drop down\n* configure chart options\n* configure display options\n\nYou can analyze the average home price for each city by choosing: \n* chart type: bar chart\n* chart options\n * _Options > Keys_: `CITY`\n * _Options > Values_: `PRICE` \n * _Options > Aggregation_: `AVG`\n \nRun the next cell to review the results. "}, {"cell_type": "code", "metadata": {"scrolled": false, "collapsed": false, "pixiedust": {"displayParams": {"handlerId": "barChart", "legend": "true", "title": "Average home price by city", "mpld3": "false", "valueFields": "PRICE", "stretch": "true", "aggregation": "AVG", "rowCount": "100", "keyFields": "CITY", "rendererId": "matplotlib"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n Average home price by city\n </div>\n <div id=\"chartFigurefda7c3bb\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n \n \n <center><img style=\"max-width:initial !important\" 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class=\"pd_save\"></center>\n \n \n \n </div>"}, "metadata": {}}], "source": "display(homes)", "execution_count": 9}, {"cell_type": "markdown", "metadata": {}, "source": "## Explore the data\n\nYou can change the display **Options** so you can continue to explore the loaded data set without having to pre-process the data. \n\nFor example, change: \n* _Options > Key_ to `YEAR_BUILT` and \n* _Options > aggregation_ to `COUNT` \n\nNow you can find out how old the listed properties are:"}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "barChart", "legend": "false", "title": "Property age", "valueFields": "PRICE", "stretch": "true", "aggregation": "COUNT", "rowCount": "100", "keyFields": "YEAR BUILT"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n Property age\n </div>\n <div id=\"chartFigure551724ad\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n \n \n <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n \n \n \n </div>"}, "metadata": {}}], "source": "display(homes)", "execution_count": 10}, {"cell_type": "markdown", "metadata": {}, "source": "## Use sample data sets\n\nPixieDust comes with a set of curated data sets that you can use get familiar with the different chart types and options. \n\nType `pixiedust.sampleData()` to display those data sets."}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<div id=\"pm_overallContainerc023bcd6\" style=\"display:none\">\n <table width=\"100%\" style=\"border:0px\">\n <tr style=\"border:0px\">\n <td width=\"20px\" style=\"border:0px\"><span id=\"twistiec023bcd6\" style=\"color:blue;font-size:x-large;cursor:pointer\">▸</span></td>\n <td width=\"130px\" style=\"text-align:left;border:0px\"><span id=\"pm_overallJobNamec023bcd6\"></span>:</td>\n <td width=\"calc(100% - 150px)\" style=\"border:0px\"><progress id=\"pm_overallProgressc023bcd6\" max=\"100\" value=\"0\" style=\"width:100%\"></progress></td>\n </tr>\n </table>\n</div>\n<div id=\"pm_containerc023bcd6\" style=\"display:none\">\n <ul class=\"nav nav-tabs\" id=\"progressMonitorsc023bcd6\">\n </ul>\n <div class=\"tab-content\" id=\"tabContentc023bcd6\">\n </div>\n</div>\n\n<script>\n$(\"#twistiec023bcd6\").click(function(){\n visible = $(\"#pm_containerc023bcd6\").is(':visible');\n $(\"#pm_containerc023bcd6\").slideToggle(\"slow\");\n $(this).html(visible?\"▸\":\"▾\")\n});\n</script>"}, "metadata": {}}, {"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<table class=\"table table-hover pd_save\" style=\"font-size:small\">\n <thead>\n <tr>\n <th align=\"center\" valign=\"middle\">Id</th>\n <th>Name</th>\n <th>Topic</th>\n <th>Publisher</th>\n </tr>\n </thead>\n <tbody>\n \n <tr>\n <td>1</td>\n <td>Car performance data</td>\n <td>transportation</td>\n <td>IBM</td>\n </tr>\n \n <tr>\n <td>2</td>\n <td>Sample retail sales transactions, January 2009</td>\n <td>Economy & Business</td>\n <td>IBM Cloud Data Services</td>\n </tr>\n \n <tr>\n <td>3</td>\n <td>Total population by country</td>\n <td>Society</td>\n <td>IBM Cloud Data Services</td>\n </tr>\n \n <tr>\n <td>4</td>\n <td>GoSales Transactions for Naive Bayes Model</td>\n <td>Leisure</td>\n <td>IBM</td>\n </tr>\n \n <tr>\n <td>5</td>\n <td>Election results by County</td>\n <td>Society</td>\n <td>IBM</td>\n </tr>\n \n <tr>\n <td>6</td>\n <td>Million dollar home sales in NE Mass late 2016</td>\n <td>Economy & Business</td>\n <td>Redfin.com</td>\n </tr>\n \n <tr>\n <td>7</td>\n <td>Boston Crime data, 2-week sample</td>\n <td>Society</td>\n <td>City of Boston</td>\n </tr>\n \n </tbody>\n</table>"}, "metadata": {}}], "source": "pixiedust.sampleData()", "execution_count": 11}, {"cell_type": "markdown", "metadata": {}, "source": "The homes sales data set you loaded earlier is one of the samples. Therefore, you could have loaded it by specifying the displayed data set id as parameter: `home = pixiedust.sampleData(6)`"}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "If your data isn't stored in csv files, you can load it into a DataFrame from any supported Spark [data source](https://spark.apache.org/docs/latest/sql-programming-guide.html#data-sources). See [these Python code snippets](https://apsportal.ibm.com/docs/content/analyze-data/python_load.html) for more information."}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "End of chapter. [Return to table of contents](#toc)\n<hr>"}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "# <a id=\"part_three\"></a>Mix Scala and Python on the same notebook\n\nPython has a rich ecosystem of modules including plotting with matplotlib, data structure and analysis with pandas, machine learning, and natural language processing. However, data scientists working with Spark might occasionally need to call out code written in Scala or Java, for example, one of the hundreds of libraries available on `spark-packages.org`. Unfortunately, Jupyter Python notebooks do not currently provide a way to call out Scala or Java code. As a result, a typical workaround is to first use a Scala notebook to run the Scala code, persist the output somewhere like a Hadoop Distributed File System, create another Python notebook, and re-load the data. This is obviously inefficent and awkward.\n\nAs you'll see in this notebook, PixieDust provides a solution to this problem by letting users write and run scala code directly in its own cell. It also lets variables be shared between Python and Scala and vice-versa."}, {"cell_type": "markdown", "metadata": {}, "source": "## Define a few simple variables in Python"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<div id=\"pm_overallContainer1d5e488d\" style=\"display:none\">\n <table width=\"100%\" style=\"border:0px\">\n <tr style=\"border:0px\">\n <td width=\"20px\" style=\"border:0px\"><span id=\"twistie1d5e488d\" style=\"color:blue;font-size:x-large;cursor:pointer\">▸</span></td>\n <td width=\"130px\" style=\"text-align:left;border:0px\"><span id=\"pm_overallJobName1d5e488d\"></span>:</td>\n <td width=\"calc(100% - 150px)\" style=\"border:0px\"><progress id=\"pm_overallProgress1d5e488d\" max=\"100\" value=\"0\" style=\"width:100%\"></progress></td>\n </tr>\n </table>\n</div>\n<div id=\"pm_container1d5e488d\" style=\"display:none\">\n <ul class=\"nav nav-tabs\" id=\"progressMonitors1d5e488d\">\n </ul>\n <div class=\"tab-content\" id=\"tabContent1d5e488d\">\n </div>\n</div>\n\n<script>\n$(\"#twistie1d5e488d\").click(function(){\n visible = $(\"#pm_container1d5e488d\").is(':visible');\n $(\"#pm_container1d5e488d\").slideToggle(\"slow\");\n $(this).html(visible?\"▸\":\"▾\")\n});\n</script>"}, "metadata": {}}], "source": "pythonString = \"Hello From Python\"\npythonInt = 20", "execution_count": 12}, {"cell_type": "markdown", "metadata": {}, "source": "## Import the PixieDust module"}, {"cell_type": "markdown", "metadata": {"collapsed": false}, "source": "If you haven't already, import PixieDust. Follow the instructions in [Get started](#part_one)."}, {"cell_type": "markdown", "metadata": {}, "source": "## Use the Python variables in Scala code\nPixieDust makes all variables defined in the Python scope available to Scala using the following rules:\n\n* Primitive types are mapped to the Scala equivalent: for example, Python Strings become Scala Strings, Python Integer become Scala Integer, and so on.\n* Some complex types are mapped as follows: PySpark SQLContext, DataFrame, RDD are mapped to their Scala Spark equivalents. Python GraphFrames mapped to their Scala equivalents. PixieDust will add more mapping as needed.\n* Python classes are currently not converted and therefore cannot be used in Scala.\n\nThe PixieDust Scala Bridge requires the environment variable SCALA_HOME to be defined and pointing at a Scala install:"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<div id=\"pm_overallContainer5557daf2\" style=\"display:none\">\n <table width=\"100%\" style=\"border:0px\">\n <tr style=\"border:0px\">\n <td width=\"20px\" style=\"border:0px\"><span id=\"twistie5557daf2\" style=\"color:blue;font-size:x-large;cursor:pointer\">▸</span></td>\n <td width=\"130px\" style=\"text-align:left;border:0px\"><span id=\"pm_overallJobName5557daf2\"></span>:</td>\n <td width=\"calc(100% - 150px)\" style=\"border:0px\"><progress id=\"pm_overallProgress5557daf2\" max=\"100\" value=\"0\" style=\"width:100%\"></progress></td>\n </tr>\n </table>\n</div>\n<div id=\"pm_container5557daf2\" style=\"display:none\">\n <ul class=\"nav nav-tabs\" id=\"progressMonitors5557daf2\">\n </ul>\n <div class=\"tab-content\" id=\"tabContent5557daf2\">\n </div>\n</div>\n\n<script>\n$(\"#twistie5557daf2\").click(function(){\n visible = $(\"#pm_container5557daf2\").is(':visible');\n $(\"#pm_container5557daf2\").slideToggle(\"slow\");\n $(this).html(visible?\"▸\":\"▾\")\n});\n</script>"}, "metadata": {}}, {"text": "Hello From Python\n30\n", "output_type": "stream", "name": "stdout"}], "source": "%%scala\nprint(pythonString)\nprint(pythonInt + 10)", "execution_count": 13}, {"cell_type": "markdown", "metadata": {}, "source": "## Define a variable in Scala and use it in Python\nIn this section, you'll create a Spark DataFrame in Scala and use it in Python with the PixieDust `display` method.\n\n**Note:** only variables that are prefixed with two underscores ( `__` ) are available for use in Python."}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<div id=\"pm_overallContainer02293b98\" style=\"display:none\">\n <table width=\"100%\" style=\"border:0px\">\n <tr style=\"border:0px\">\n <td width=\"20px\" style=\"border:0px\"><span id=\"twistie02293b98\" style=\"color:blue;font-size:x-large;cursor:pointer\">▸</span></td>\n <td width=\"130px\" style=\"text-align:left;border:0px\"><span id=\"pm_overallJobName02293b98\"></span>:</td>\n <td width=\"calc(100% - 150px)\" style=\"border:0px\"><progress id=\"pm_overallProgress02293b98\" max=\"100\" value=\"0\" style=\"width:100%\"></progress></td>\n </tr>\n </table>\n</div>\n<div id=\"pm_container02293b98\" style=\"display:none\">\n <ul class=\"nav nav-tabs\" id=\"progressMonitors02293b98\">\n </ul>\n <div class=\"tab-content\" id=\"tabContent02293b98\">\n </div>\n</div>\n\n<script>\n$(\"#twistie02293b98\").click(function(){\n visible = $(\"#pm_container02293b98\").is(':visible');\n $(\"#pm_container02293b98\").slideToggle(\"slow\");\n $(this).html(visible?\"▸\":\"▾\")\n});\n</script>"}, "metadata": {}}, {"text": "[year: int, zone: string, unique_customers: int, revenue: int]\n", "output_type": "stream", "name": "stdout"}], "source": "%%scala\n//Reuse the sqlContext object available in the python scope\nval c = sqlContext.asInstanceOf[org.apache.spark.sql.SQLContext]\nimport c.implicits._\n\nval __dfFromScala = Seq(\n(2010, \"Camping Equipment\", 3, 200),\n(2010, \"Golf Equipment\", 1, 240),\n(2010, \"Mountaineering Equipment\", 1, 348),\n(2010, \"Outdoor Protection\", 2, 200),\n(2010, \"Personal Accessories\", 2, 200),\n(2011, \"Camping Equipment\", 4, 489),\n(2011, \"Golf Equipment\", 5, 234),\n(2011, \"Mountaineering Equipment\",2, 123),\n(2011, \"Outdoor Protection\", 4, 654),\n(2011, \"Personal Accessories\", 2, 234),\n(2012, \"Camping Equipment\", 5, 876),\n(2012, \"Golf Equipment\", 5, 200),\n(2012, \"Mountaineering Equipment\", 3, 156),\n(2012, \"Outdoor Protection\", 5, 200),\n(2012, \"Personal Accessories\", 3, 345),\n(2013, \"Camping Equipment\", 8, 987),\n(2013, \"Golf Equipment\", 5, 434),\n(2013, \"Mountaineering Equipment\", 3, 278),\n(2013, \"Outdoor Protection\", 8, 134),\n(2013, \"Personal Accessories\", 4, 200)).toDF(\"year\", \"zone\", \"unique_customers\", \"revenue\")\n\nprint(__dfFromScala)", "execution_count": 14}, {"cell_type": "markdown", "metadata": {}, "source": "## Display a Scala DataFrame\nInvoke the PixieDust display API on `__dfFromScala` to visualize a Scala DataFrame:"}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "barChart", "rowCount": "100", "keyFields": "zone", "clusterby": "year", "valueFields": "unique_customers", "aggregation": "AVG"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n \n </div>\n <div id=\"chartFiguree9657d89\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n \n \n <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n \n \n \n </div>"}, "metadata": {}}], "source": "display(__dfFromScala)", "execution_count": 15}, {"cell_type": "markdown", "metadata": {}, "source": "In this chapter, you've seen how easy it is to intersperse Scala and Python in the same notebook.\nContinue exploring this powerful functionality by using more complex Scala libraries!"}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "End of chapter. [Return to table of contents](#toc)\n<hr>"}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "# <a id=\"part_four\"></a> Add Spark packages and run inside your notebook\n\nPixieDust PackageManager helps you install spark packages inside your notebook. This is especially useful when you're working in a hosted cloud environment without access to configuration files. Use PixieDust Package Manager to install:\n\n- a spark package from `spark-packages.org`\n- a package from the Maven search repository\n- a jar file directly from URL\n\n> **Note:** After you install a package, you must restart the kernel and import Pixiedust again.\n"}, {"cell_type": "markdown", "metadata": {}, "source": "## View list of packages\nTo see the packages installed on your system, run the following command:"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "direct.download:https://github.com/ibm-watson-data-lab/spark.samples/raw/master/dist/streaming-twitter-assembly-1.6.jar:1.0 => /gpfs/fs01/user/s2b7-790f27d2e466b6-772f4e1cd93d/data/libs/streaming-twitter-assembly-1.6.jar\ngraphframes:graphframes:0.1.0-spark1.6 => /gpfs/fs01/user/s2b7-790f27d2e466b6-772f4e1cd93d/data/libs/graphframes-0.1.0-spark1.6.jar\n", "output_type": "stream", "name": "stdout"}], "source": "import pixiedust\npixiedust.printAllPackages()", "execution_count": 2}, {"cell_type": "markdown", "metadata": {}, "source": "## Add a package from spark-packages.org\n\nThe command you use to install GraphFrames depends on your Spark version."}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "Package already installed: graphframes:graphframes:0\n", "output_type": "stream", "name": "stdout"}, {"metadata": {}, "data": {"text/plain": "<pixiedust.packageManager.package.Package at 0x7fecd1c5da50>"}, "execution_count": 3, "output_type": "execute_result"}], "source": "# For Spark 2.0, uncomment and run the next line\n#pixiedust.installPackage(\"graphframes:graphframes:0\")\n\n# For Spark 1.6, uncomment and run the next line\n#pixiedust.installPackage(\"graphframes:graphframes:0.1.0-spark1.6\")", "execution_count": 3}, {"cell_type": "markdown", "metadata": {}, "source": "## View the updated list of packages\n\nRun `printAllPackages` again to see that GraphFrames is now in your list:"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "direct.download:https://github.com/ibm-watson-data-lab/spark.samples/raw/master/dist/streaming-twitter-assembly-1.6.jar:1.0 => /gpfs/fs01/user/s2b7-790f27d2e466b6-772f4e1cd93d/data/libs/streaming-twitter-assembly-1.6.jar\ngraphframes:graphframes:0.1.0-spark1.6 => /gpfs/fs01/user/s2b7-790f27d2e466b6-772f4e1cd93d/data/libs/graphframes-0.1.0-spark1.6.jar\n", "output_type": "stream", "name": "stdout"}], "source": "pixiedust.printAllPackages()", "execution_count": 7}, {"cell_type": "markdown", "metadata": {}, "source": "## Display a GraphFrames data sample\n\nGraphGrames comes with sample data sets. Even if GraphFrames is already installed, running the install command loads the Python that comes along with the package and enables features like the one you're about to see. Run the following cell and PixieDust displays a sample graph data set called **friends**. On the upper left of the display, click the table dropdown and switch between views of nodes and edges. "}, {"cell_type": "code", "metadata": {"scrolled": true, "collapsed": false, "pixiedust": {"displayParams": {"handlerId": "edges"}}}, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! 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border-collapse: collapse;\n margin: 0;\n min-width: 100%;\n padding: 0;\n table-layout: fixed;\n }\n .df-table-wrapper tr.hidden {\n display: none;\n }\n .df-table-wrapper tr:nth-child(even) {\n background-color: #f9f9fb;\n }\n .df-table-wrapper tr.even {\n background-color: #f9f9fb;\n }\n .df-table-wrapper tr.odd {\n background-color: #ffffff;\n }\n .df-table-wrapper td + td {\n border-left: 1px solid #e0e0e0;\n }\n\n .df-table-wrapper thead,\n .fixed-header {\n color: #337ab7;\n font-family: monospace;\n }\n .df-table-wrapper tr,\n .fixed-row {\n border: 0 none #ffffff;\n margin: 0;\n padding: 0;\n }\n .df-table-wrapper th,\n .df-table-wrapper td,\n .fixed-cell {\n border: 0 none #ffffff;\n margin: 0;\n min-width: 50px;\n padding: 5px 20px 5px 10px;\n text-align: left;\n word-wrap: break-word;\n }\n .df-table-wrapper th {\n padding-bottom: 0;\n padding-top: 0;\n }\n .df-table-wrapper th div {\n max-height: 1px;\n visibility: hidden;\n }\n\n .df-schema-field {\n margin-left: 10px;\n }\n\n .fixed-header-container {\n overflow: hidden;\n position: relative;\n }\n .fixed-header {\n border-bottom: 2px solid #2e6da4;\n display: table;\n position: relative;\n }\n .fixed-row {\n display: table-row;\n }\n .fixed-cell {\n display: table-cell;\n }\n</style><div class=\"df-table-wrapper df-table-wrapper-cdea5370 panel-group pd_save is-viewer-good\">\n <!-- dataframe schema -->\n <div class=\"panel panel-default\">\n <div class=\"panel-heading\">\n <h4 class=\"panel-title\" style=\"margin: 0px;\">\n <a data-toggle=\"collapse\" href=\"#df-schema-cdea5370\" data-parent=\"#df-table-wrapper-cdea5370\">Schema</a>\n </h4>\n </div>\n <div id=\"df-schema-cdea5370\" class=\"panel-collapse collapse\">\n <div class=\"panel-body\" style=\"font-family: monospace;\">\n <div class=\"df-schema-type\">\n <span>type: </span><span>struct</span>\n </div>\n <div class=\"df-schema-fields\">\n <div>field:</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'src', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'dst', 'nullable': True}</div>\n \n <div class=\"df-schema-field\">{'metadata': {}, 'type': 'string', 'name': 'relationship', 'nullable': True}</div>\n \n </div>\n </div>\n </div>\n </div>\n <!-- dataframe table -->\n <div class=\"panel panel-default\">\n <div class=\"panel-heading\">\n <h4 class=\"panel-title\" style=\"margin: 0px;\">\n <a data-toggle=\"collapse\" href=\"#df-table-cdea5370\" data-parent=\"#df-table-wrapper-cdea5370\">Table</a>\n </h4>\n </div>\n <div id=\"df-table-cdea5370\" class=\"panel-collapse collapse in\">\n <div class=\"panel-body\">\n \n <input class=\"df-table-search form-control input-sm\" placeholder=\"Search table\" type=\"text\">\n <div>\n <span class=\"df-table-search-count\">Showing 7 of 7</span>\n </div>\n <!-- fixed header for when dataframe table scrolls -->\n <div class=\"fixed-header-container\">\n <div class=\"fixed-header\">\n <div class=\"fixed-row\">\n \n <div class=\"fixed-cell\">src</div>\n \n <div class=\"fixed-cell\">dst</div>\n \n <div class=\"fixed-cell\">relationship</div>\n \n </div>\n </div>\n </div>\n <div class=\"df-table-container\">\n <table class=\"df-table\">\n <thead>\n <tr>\n \n <th><div>src</div></th>\n \n <th><div>dst</div></th>\n \n <th><div>relationship</div></th>\n \n </tr>\n </thead>\n <tbody>\n \n <tr>\n \n <td>a</td>\n \n <td>b</td>\n \n <td>friend</td>\n \n </tr>\n \n <tr>\n \n <td>b</td>\n \n <td>c</td>\n \n <td>follow</td>\n \n </tr>\n \n <tr>\n \n <td>c</td>\n \n <td>b</td>\n \n <td>follow</td>\n \n </tr>\n \n <tr>\n \n <td>f</td>\n \n <td>c</td>\n \n <td>follow</td>\n \n </tr>\n \n <tr>\n \n <td>e</td>\n \n <td>f</td>\n \n <td>follow</td>\n \n </tr>\n \n <tr>\n \n <td>e</td>\n \n <td>d</td>\n \n <td>friend</td>\n \n </tr>\n \n <tr>\n \n <td>d</td>\n \n <td>a</td>\n \n <td>friend</td>\n \n </tr>\n \n </tbody>\n </table>\n </div>\n </div>\n </div>\n </div>\n</div><script class=\"pd_save is-viewer-good\">\n $(function() {\n var tableWrapper = $('.df-table-wrapper-cdea5370');\n var fixedHeader = $('.fixed-header', tableWrapper);\n var tableContainer = $('.df-table-container', tableWrapper);\n var table = $('.df-table', tableContainer);\n var rows = $('tbody > tr', table);\n var total = 7;\n\n fixedHeader\n .css('width', table.width())\n .find('.fixed-cell')\n .each(function(i, e) {\n $(this).css('width', $('.df-table-wrapper-cdea5370 th:nth-child(' + (i+1) + ')').css('width'));\n });\n\n tableContainer.scroll(function() {\n fixedHeader.css({ left: table.position().left });\n });\n\n $('.df-table-search', tableWrapper).keyup(function() {\n var val = '^(?=.*\\\\b' + $.trim($(this).val()).split(/\\s+/).join('\\\\b)(?=.*\\\\b') + ').*$';\n var reg = RegExp(val, 'i');\n var index = 0;\n \n rows.each(function(i, e) {\n if (!reg.test($(this).text().replace(/\\s+/g, ' '))) {\n $(this).attr('class', 'hidden');\n }\n else {\n $(this).attr('class', (++index % 2 == 0 ? 'even' : 'odd'));\n }\n });\n\n $('.df-table-search-count', tableWrapper).html('Showing ' + index + ' of ' + total);\n });\n });\n</script>"}, "metadata": {}}], "source": "#import the Graphs example\nfrom graphframes.examples import Graphs\n#create the friends example graph\ng=Graphs(sqlContext).friends()\n#use the pixiedust display\ndisplay(g)", "execution_count": 8}, {"cell_type": "markdown", "metadata": {}, "source": "## Install from Maven\nTo install a package from the [Apache Maven search repository](https://maven.apache.org/), visit the project and find the `groupId` and `artifactId` for the package that you want. Enter them in the following installation command. [See instructions for the installPackage command](https://pixiedust.github.io/pixiedust/packagemanager.html#install-from-maven-search-repository). For example, the following cell installs Apache Commons: "}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "Downloading package org.apache.commons:commons-csv:1.4 to /gpfs/fs01/user/s2b7-790f27d2e466b6-772f4e1cd93d/data/libs/commons-csv-1.4.jar\n", "output_type": "stream", "name": "stdout"}, {"output_type": "display_data", "data": {"text/plain": "<IPython.core.display.HTML object>", "text/html": "\n <div>\n <span id=\"pm_label8632d483\">Starting download...</span>\n <progress id=\"pm_progress8632d483\" max=\"100\" value=\"0\" style=\"width:200px\"></progress>\n </div>"}, "metadata": {}}, {"output_type": "display_data", "data": {"application/javascript": "\n $(\"#pm_label8632d483\").text(\"Downloaded 8192 of 39978 bytes\");\n $(\"#pm_progress8632d483\").attr(\"value\", 20.49);\n ", "text/plain": "<IPython.core.display.Javascript object>"}, "metadata": {}}, {"output_type": "display_data", "data": {"application/javascript": "\n $(\"#pm_label8632d483\").text(\"Downloaded 16384 of 39978 bytes\");\n $(\"#pm_progress8632d483\").attr(\"value\", 40.98);\n ", "text/plain": "<IPython.core.display.Javascript object>"}, "metadata": {}}, {"output_type": "display_data", "data": {"application/javascript": "\n $(\"#pm_label8632d483\").text(\"Downloaded 24576 of 39978 bytes\");\n $(\"#pm_progress8632d483\").attr(\"value\", 61.47);\n ", "text/plain": "<IPython.core.display.Javascript object>"}, "metadata": {}}, {"output_type": "display_data", "data": {"application/javascript": "\n $(\"#pm_label8632d483\").text(\"Downloaded 32768 of 39978 bytes\");\n $(\"#pm_progress8632d483\").attr(\"value\", 81.97);\n ", "text/plain": "<IPython.core.display.Javascript object>"}, "metadata": {}}, {"output_type": "display_data", "data": {"application/javascript": "\n $(\"#pm_label8632d483\").text(\"Downloaded 39978 of 39978 bytes\");\n $(\"#pm_progress8632d483\").attr(\"value\", 100.0);\n ", "text/plain": "<IPython.core.display.Javascript object>"}, "metadata": {}}, {"text": "Package org.apache.commons:commons-csv:1.4 downloaded successfully\n\u001b[31mPlease restart Kernel to complete installation of the new package\u001b[0m\nSuccessfully added package org.apache.commons:commons-csv:1.4\n", "output_type": "stream", "name": "stdout"}, {"metadata": {}, "data": {"text/plain": "<pixiedust.packageManager.package.Package at 0x7fe7d856ef50>"}, "execution_count": 7, "output_type": "execute_result"}], "source": "pixiedust.installPackage(\"org.apache.commons:commons-csv:0\")", "execution_count": 7}, {"cell_type": "markdown", "metadata": {}, "source": "## Install a jar file directly from a URL \n \nTo install a jar file that is not packaged in a maven repository, provide its URL. "}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "Package already installed: https://github.com/ibm-watson-data-lab/spark.samples/raw/master/dist/streaming-twitter-assembly-1.6.jar\n", "output_type": "stream", "name": "stdout"}, {"metadata": {}, "data": {"text/plain": "<pixiedust.packageManager.package.Package at 0x7feccf9ab6d0>"}, "output_type": "execute_result", "execution_count": 9}], "source": "pixiedust.installPackage(\"https://github.com/ibm-watson-data-lab/spark.samples/raw/master/dist/streaming-twitter-assembly-1.6.jar\")", "execution_count": 9}, {"cell_type": "markdown", "metadata": {}, "source": "## Follow the tutorial\n\nTo understand what you can do with this jar file, read David Taieb's latest [Realtime Sentiment Analysis of Twitter Hashtags with Spark](https://medium.com/ibm-watson-data-lab/real-time-sentiment-analysis-of-twitter-hashtags-with-spark-7ee6ca5c1585#.2iblfu58c) tutorial."}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "## Uninstall a package\n\nIt's just as easy to get rid of a package you installed. Just run the command `pixiedust.uninstallPackage(\"<<mypackage>>\")`. For example, you can uninstall Apache Commons:"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [{"text": "Successfully deleted package org.apache.commons:commons-csv:1.4\n", "output_type": "stream", "name": "stdout"}], "source": "pixiedust.uninstallPackage(\"org.apache.commons:commons-csv:0\")", "execution_count": 3}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "End of chapter. [Return to table of contents](#toc)\n<hr>"}, {"cell_type": "markdown", "metadata": {}, "source": "# <a id=\"part_five\"></a> Stash Your Data\n\nWith PixieDust, you also have the option to export the data from your notebook to external sources.\nThe output of the `display` API includes a toolbar that contains a **Download** button.\n\n<img style=\"margin:10px 0\" src=\"https://pixiedust.github.io/pixiedust/_images/downloadfile.png\">\n\n"}, {"cell_type": "markdown", "metadata": {}, "source": "\n## Stash to Cloudant\n\nYou save the data directly into a [Cloudant](https://cloudant.com/) or [CouchDB](https://couchdb.apache.org/) database.\n\n**Prerequisite:** Collect your database connection information: the database host, user name, and password. \n \nIf your Cloudant instance was provisioned in [Bluemix](https://console.ng.bluemix.net/catalog/services/cloudant-nosql-db/), you can find the connectivity information in the **Service Credentials** tab.\n\nTo stash to Cloudant:\n\n1. From the toolbar in the `display` output, click the **Download** button. \n2. Choose **Stash to Cloudant** from the menu. \n3. Click the dropdown to see the list of available connections and select an existing connection or add a new connection: \n 1. Click the **`+`** plus button to add a new connection.\n 1. Enter your Cloudant database credentials in JSON format. \n 1. If you are stashing to CouchDB, include the protocol. See the [sample credentials format](#Sample-Credentials-Format) below.\n 1. Click **OK**.\n 1. Select the new connection.\n4. Click **Submit**.\n\n\n### Sample Credentials Format \n\n#### CouchDB\n```\n{\n \"name\": \"local-couchdb-connection\",\n \"credentials\": {\n \"username\": \"couchdbuser\",\n \"password\": \"password\",\n \"protocol\": \"http\",\n \"host\": \"127.0.0.1:5984\",\n \"port\": 5984,\n \"url\": \"http://couchdbuser:[email protected]:5984\"\n }\n}\n```\n\n#### Cloudant\n```\n{\n \"name\": \"remote-cloudant-connection\",\n \"credentials\": {\n \"username\": \"username-bluemix\",\n \"password\": \"password\",\n \"host\": \"host-bluemix.cloudant.com\",\n \"port\": 443,\n \"url\": \"https://username-bluemix:[email protected]\"\n }\n}\n```\n"}, {"cell_type": "markdown", "metadata": {"collapsed": true}, "source": "## Download as a file\n\nAlternatively, you can choose to save the data set to various file formats (for example, CSV, JSON, XML, and so on).\n\nTo save a data set as a file:\n\n1. From the toolbar in the **`display`** output, click the **Download** button.\n1. Choose **Download as File**.\n1. Choose the desired format.\n1. Specify the number of records to download.\n <img style=\"margin:10px 0\" src=\"https://pixiedust.github.io/pixiedust/_images/save_as.png\">\n1. Click **OK**.\n"}, {"cell_type": "markdown", "metadata": {}, "source": "End of chapter. [Return to table of contents](#toc)\n<hr>"}, {"cell_type": "markdown", "metadata": {}, "source": "# <a id=\"contribute\"></a>Contribute\n\nBy now, you've walked through PixieDust's intro notebooks and seen PixieDust in action. If you like what you saw, join [the project](https://github.com/ibm-watson-data-lab/pixiedust)! \n\nAnyone can get involved. Here are some ways you can [contribute](https://pixiedust.github.io/pixiedust/contribute.html):\n\n - [Write a visualization](#Write-a-visualization)\n - [Build a renderer](#Build-a-renderer)\n - [Enter an issue](#Enter-an-issue)\n - [Share PixieDust](#Share-PixieDust)\n - [Learn more](#Learn-more)\n"}, {"cell_type": "markdown", "metadata": {}, "source": "## Write a visualization\n\nContribute your own custom visualization. Here's a taste of how it works. \n\nRun the next 4 cells to do the following:\n\n1. Import PixieDust. \n2. Generate a sample DataFrame. \n3. Create a custom table display option called **NewSample**. \n4. Display the DataFrame and see your new custom option under the **Table** dropdown menu.\n\nThis is just one small example you can quickly do within this notebook. [Read how to create a custom visualization](https://pixiedust.github.io/pixiedust/writeviz.html).\n"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [], "source": "import pixiedust", "execution_count": null}, {"cell_type": "markdown", "metadata": {}, "source": "Now, create a simple DataFrame:"}, {"cell_type": "code", "metadata": {"collapsed": true}, "outputs": [], "source": "sqlContext=SQLContext(sc)\nd1 = sqlContext.createDataFrame(\n[(2010, 'Camping Equipment', 3),\n (2010, 'Golf Equipment', 1),\n (2010, 'Mountaineering Equipment', 1),\n (2010, 'Outdoor Protection', 2),\n (2010, 'Personal Accessories', 2),\n (2011, 'Camping Equipment', 4),\n (2011, 'Golf Equipment', 5),\n (2011, 'Mountaineering Equipment',2),\n (2011, 'Outdoor Protection', 4),\n (2011, 'Personal Accessories', 2),\n (2012, 'Camping Equipment', 5),\n (2012, 'Golf Equipment', 5),\n (2012, 'Mountaineering Equipment', 3),\n (2012, 'Outdoor Protection', 5),\n (2012, 'Personal Accessories', 3),\n (2013, 'Camping Equipment', 8),\n (2013, 'Golf Equipment', 5),\n (2013, 'Mountaineering Equipment', 3),\n (2013, 'Outdoor Protection', 8),\n (2013, 'Personal Accessories', 4)],\n[\"year\",\"zone\",\"unique_customers\"])", "execution_count": null}, {"cell_type": "markdown", "metadata": {}, "source": "The following cell creates a new custom table visualization plugin called **NewSample**:"}, {"cell_type": "code", "metadata": {"collapsed": false}, "outputs": [], "source": "from pixiedust.display.display import *\n\nclass TestDisplay(Display):\n def doRender(self, handlerId):\n self._addHTMLTemplateString(\n\"\"\"\nNewSample Plugin\n<table class=\"table table-striped\">\n <thead> \n {%for field in entity.schema.fields%}\n <th>{{field.name}}</th>\n {%endfor%}\n </thead>\n <tbody>\n {%for row in entity.take(100)%}\n <tr>\n {%for field in entity.schema.fields%}\n <td>{{row[field.name]}}</td>\n {%endfor%}\n </tr>\n {%endfor%}\n </tbody>\n</table>\n\"\"\"\n )\n\n@PixiedustDisplay()\nclass TestPluginMeta(DisplayHandlerMeta):\n @addId\n def getMenuInfo(self,entity,dataHandler):\n if entity.__class__.__name__ == \"DataFrame\":\n return [\n {\"categoryId\": \"Table\", \"title\": \"NewSample Table\", \"icon\": \"fa-table\", \"id\": \"newsampleTest\"}\n ]\n else:\n return []\n def newDisplayHandler(self,options,entity):\n return TestDisplay(options,entity)", "execution_count": null}, {"cell_type": "markdown", "metadata": {}, "source": "Next, run `display()` to show the data. Click the **Table** dropdown. You now see **NewSample Table** option, the custom visualization you just created!"}, {"cell_type": "code", "metadata": {"collapsed": false, "pixiedust": {"displayParams": {"handlerId": "dataframe"}}}, "outputs": [], "source": "display(d1)", "execution_count": null}, {"cell_type": "markdown", "metadata": {}, "source": "**Error?** If you changed the name yourself in cell 3, you might get an error when you try to display. You can fix this by updating metadata in the display() cell. To do so, go to the Jupyter menu above the notebook and choose **View > Cell Toolbar > Edit Metadata**. Then scroll down to the `display(dl)` cell, click its **Edit Metadata** button and change the `handlerID`."}, {"cell_type": "markdown", "metadata": {}, "source": "## Build a renderer\n\nPixieDust lets you switch between renderers for charts and maps. We'd love to add more to the list. It's easy to get started. Try the `generate` tool to create a boilerplate renderer using a quick CLI wizard. [Read how to build a renderer](https://pixiedust.github.io/pixiedust/renderer.html)."}, {"cell_type": "markdown", "metadata": {}, "source": "## Enter an issue\n\nFound a bug? Thought of great enhancement? [Enter an issue](https://github.com/ibm-watson-data-lab/pixiedust/issues) to let us know. Tell us what you think."}, {"cell_type": "markdown", "metadata": {}, "source": "## Share PixieDust\n\nIf you think someone you know would be interested in PixieDust, spread the word:\n\n - <a href=\"https://twitter.com/home?status=Happy%20to%20find%20PixieDust.%20Data%20notebook%20visualizations%20for%20everyone%3A%20https%3A//github.com/ibm-watson-data-lab/pixiedust%0A\">Tweet</a>\n - <a href=\"https://www.linkedin.com/shareArticle?mini=true&url=https%3A//github.com/ibm-watson-data-lab/pixiedust&title=PixieDust%3A%20Data%20notebook%20visualizations%20for%20everyone&summary=Happy%20to%20find%20PixieDust,%20a%20new%20helper%20library%20for%20python%20and%20scala%3A%20https%3A//github.com/ibm-watson-data-lab/pixiedust%0A&source=\">Share on LinkedIn</a>\n - <a href=\"mailto:?&subject=PixieDust: Data notebook visualizations for everyone&body=I%20found%20a%20new%20helper%20library%20for%20notebooks%3A%20https%3A//github.com/ibm-watson-data-lab/pixiedust\">Send email</a>"}, {"cell_type": "markdown", "metadata": {}, "source": "## Learn more\n\nReady to pitch in? We can't wait to see what you share. [More on how to contribute](https://pixiedust.github.io/pixiedust/contribute.html). "}, {"cell_type": "markdown", "metadata": {}, "source": "End of chapter. [Return to table of contents](#toc)\n\n## Authors\n* Jose Barbosa\n* Mike Broberg\n* Inge Halilovic\n* Jess Mantaro\n* Brad Noble\n* David Taieb\n* Patrick Titzler\n\n<hr>\nCopyright © IBM Corp. 2017. This notebook and its source code are released under the terms of the MIT License."}, {"cell_type": "code", "metadata": {"collapsed": true}, "outputs": [], "source": "", "execution_count": null}], "nbformat": 4, "metadata": {"celltoolbar": "Raw Cell Format", "kernelspec": {"display_name": "Python 2 with Spark 1.6", "name": "python2", "language": "python"}, "language_info": {"version": "2.7.11", "nbconvert_exporter": "python", "mimetype": "text/x-python", "codemirror_mode": {"version": 2, "name": "ipython"}, "file_extension": ".py", "name": "python", "pygments_lexer": "ipython2"}}, "nbformat_minor": 0} | apache-2.0 |
anandha2017/udacity | nd101 Deep Learning Nanodegree Foundation/DockerImages/11_miniflow/07_gradient_descent/notebooks/gradient descent.ipynb | 1 | 4977 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def gradient_descent_update(x, gradx, learning_rate):\n",
" \"\"\"\n",
" Performs a gradient descent update.\n",
" \"\"\"\n",
"\n",
" x = x - learning_rate * gradx\n",
" \n",
" return x\n",
"\n",
"#import f"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"Given the starting point of any `x` gradient descent\n",
"should be able to find the minimum value of x for the\n",
"cost function `f` defined below.\n",
"\"\"\"\n",
"def f(x):\n",
" \"\"\"\n",
" Quadratic function.\n",
"\n",
" It's easy to see the minimum value of the function\n",
" is 5 when is x=0.\n",
" \"\"\"\n",
" return x**2 + 5"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"def df(x):\n",
" \"\"\"\n",
" Derivative of `f` with respect to `x`.\n",
" \"\"\"\n",
" return 2*x"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
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"text": [
"EPOCH 0: Cost = 72250005.000, x = 17000.000\n",
"EPOCH 25: Cost = 65367962.601, x = 16170.091\n",
"EPOCH 50: Cost = 59141456.639, x = 15380.696\n",
"EPOCH 75: Cost = 53508045.182, x = 14629.838\n",
"EPOCH 100: Cost = 48411234.093, x = 13915.636\n",
"EPOCH 125: Cost = 43799910.477, x = 13236.299\n",
"EPOCH 150: Cost = 39627830.109, x = 12590.127\n",
"EPOCH 175: Cost = 35853153.672, x = 11975.500\n",
"EPOCH 200: Cost = 32438027.176, x = 11390.877\n",
"EPOCH 225: Cost = 29348202.347, x = 10834.795\n",
"EPOCH 250: Cost = 26552693.163, x = 10305.860\n",
"EPOCH 275: Cost = 24023465.125, x = 9802.747\n",
"EPOCH 300: Cost = 21735154.106, x = 9324.194\n",
"EPOCH 325: Cost = 19664811.992, x = 8869.004\n",
"EPOCH 350: Cost = 17791676.553, x = 8436.035\n",
"EPOCH 375: Cost = 16096963.225, x = 8024.203\n",
"EPOCH 400: Cost = 14563676.733, x = 7632.476\n",
"EPOCH 425: Cost = 13176440.659, x = 7259.872\n",
"EPOCH 450: Cost = 11921343.235, x = 6905.458\n",
"EPOCH 475: Cost = 10785797.835, x = 6568.346\n",
"EPOCH 500: Cost = 9758416.747, x = 6247.691\n",
"EPOCH 525: Cost = 8828896.977, x = 5942.690\n",
"EPOCH 550: Cost = 7987916.922, x = 5652.579\n",
"EPOCH 575: Cost = 7227042.894, x = 5376.630\n",
"EPOCH 600: Cost = 6538644.538, x = 5114.153\n",
"EPOCH 625: Cost = 5915818.316, x = 4864.489\n",
"EPOCH 650: Cost = 5352318.274, x = 4627.013\n",
"EPOCH 675: Cost = 4842493.404, x = 4401.131\n",
"EPOCH 700: Cost = 4381230.974, x = 4186.276\n",
"EPOCH 725: Cost = 3963905.259, x = 3981.909\n",
"EPOCH 750: Cost = 3586331.146, x = 3787.520\n",
"EPOCH 775: Cost = 3244722.169, x = 3602.620\n",
"EPOCH 800: Cost = 2935652.534, x = 3426.746\n",
"EPOCH 825: Cost = 2656022.765, x = 3259.459\n",
"EPOCH 850: Cost = 2403028.622, x = 3100.338\n",
"EPOCH 875: Cost = 2174132.975, x = 2948.985\n",
"EPOCH 900: Cost = 1967040.368, x = 2805.021\n",
"EPOCH 925: Cost = 1779673.990, x = 2668.085\n",
"EPOCH 950: Cost = 1610154.856, x = 2537.834\n",
"EPOCH 975: Cost = 1456782.958, x = 2413.941\n",
"EPOCH 1000: Cost = 1318020.222, x = 2296.097\n"
]
}
],
"source": [
"# Random number between 0 and 10,000. Feel free to set x whatever you like.\n",
"x = random.randint(0, 10000)\n",
"\n",
"# TODO: Set the learning rate\n",
"learning_rate = 0.001\n",
"epochs = 1000\n",
"\n",
"for i in range(epochs+1):\n",
" cost = f(x)\n",
" gradx = df(x)\n",
" if i % 25 == 0:\n",
" print(\"EPOCH {}: Cost = {:.3f}, x = {:.3f}\".format(i, cost, gradx))\n",
" x = gradient_descent_update(x, gradx, learning_rate)"
]
},
{
"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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
computational-class/cjc | code/06.data_cleaning_Tweets.ipynb | 1 | 120844 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"***\n",
"***\n",
"# 数据清洗之推特数据\n",
"***\n",
"***\n",
"\n",
"王成军\n",
"\n",
"[email protected]\n",
"\n",
"计算传播网 http://computational-communication.com"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 数据清洗（data cleaning）\n",
"是数据分析的重要步骤，其主要目标是将混杂的数据清洗为可以被直接分析的数据，一般需要将数据转化为数据框（data frame）的样式。\n",
"\n",
"本章将以推特文本的清洗作为例子，介绍数据清洗的基本逻辑。\n",
"\n",
"- 清洗错误行\n",
"- 正确分列\n",
"- 提取所要分析的内容\n",
"- 介绍通过按行、chunk的方式对大规模数据进行预处理\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 1. 抽取tweets样本做实验\n",
"此节学生略过"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2752\n"
]
}
],
"source": [
"bigfile = open('/Users/chengjun/百度云同步盘/Writing/OWS/ows-raw.txt', 'r')\n",
"chunkSize = 1000000\n",
"chunk = bigfile.readlines(chunkSize)\n",
"print(len(chunk))\n",
"with open(\"/Users/chengjun/GitHub/cjc/data/ows_tweets_sample.txt\", 'w') as f:\n",
" for i in chunk:\n",
" f.write(i) "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Lazy Method for Reading Big File in Python?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:31:51.644484Z",
"start_time": "2019-06-08T07:30:56.170308Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 262665\n",
"1 525130\n",
"2 787344\n",
"3 1049351\n",
"4 1312571\n",
"5 1574666\n",
"6 1835628\n",
"7 2097136\n",
"8 2358494\n",
"9 2619723\n",
"10 2880857\n",
"11 3140945\n",
"12 3404775\n",
"13 3665565\n",
"14 3927996\n",
"15 4189419\n",
"16 4449078\n",
"17 4709001\n",
"18 4969877\n",
"19 5230937\n",
"20 5492578\n",
"21 5756613\n",
"22 6022478\n",
"23 6286119\n",
"24 6549476\n",
"25 6602141\n"
]
}
],
"source": [
"# https://stackoverflow.com/questions/519633/lazy-method-for-reading-big-file-in-python?lq=1\n",
"import csv\n",
"bigfile = open('/Users/datalab/bigdata/cjc/ows-raw.txt', 'r')\n",
"\n",
"chunkSize = 10**8\n",
"chunk = bigfile.readlines(chunkSize)\n",
"num, num_lines = 0, 0\n",
"while chunk:\n",
" lines = csv.reader((line.replace('\\x00','') for line in chunk), \n",
" delimiter=',', quotechar='\"')\n",
" #do sth.\n",
" num_lines += len(list(lines))\n",
" print(num, num_lines)\n",
" num += 1\n",
" chunk = bigfile.readlines(chunkSize) # read another chunk"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 字节（Byte /bait/）\n",
"\n",
"计算机信息技术用于计量存储容量的一种计量单位，通常情况下一字节等于有八位， [1] 也表示一些计算机编程语言中的数据类型和语言字符。\n",
"- 1B（byte，字节）= 8 bit；\n",
"- 1KB=1000B；1MB=1000KB=1000×1000B。其中1000=10^3。\n",
"- 1KB（kilobyte，千字节）=1000B= 10^3 B；\n",
"- 1MB（Megabyte，兆字节，百万字节，简称“兆”）=1000KB= 10^6 B；\n",
"- 1GB（Gigabyte，吉字节，十亿字节，又称“千兆”）=1000MB= 10^9 B；"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## 用Pandas的get_chunk功能来处理亿级数据\n",
"\n",
"> 只有在超过5TB数据量的规模下，Hadoop才是一个合理的技术选择。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"f = open('../bigdata/OWS/ows-raw.txt',encoding='utf-8')\n",
"reader = pd.read_table(f, sep=',', iterator=True, error_bad_lines=False) #跳过报错行\n",
"loop = True\n",
"chunkSize = 100000\n",
"data = []\n",
"\n",
"while loop:\n",
" try:\n",
" chunk = reader.get_chunk(chunkSize)\n",
" dat = data_cleaning_funtion(chunk) # do sth.\n",
" data.append(dat) \n",
" except StopIteration:\n",
" loop = False\n",
" print(\"Iteration is stopped.\")\n",
"\n",
"df = pd.concat(data, ignore_index=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 2. 清洗错行的情况"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:42:24.661108Z",
"start_time": "2019-06-08T07:42:24.648304Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"with open(\"../data/ows_tweets_sample.txt\", 'r') as f:\n",
" lines = f.readlines() "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:42:28.452634Z",
"start_time": "2019-06-08T07:42:28.441018Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2753"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 总行数\n",
"len(lines)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:42:32.821269Z",
"start_time": "2019-06-08T07:42:32.816918Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'121813245488140288,\"@HumanityCritic i\\'m worried that the #ows sells out to the hamsher-norquist spitefuck, and tries to unite with the teahad.\",http://a2.twimg.com/profile_images/627683576/flytits_normal.jpg,2011-10-06,5,5,\"2011-10-06 05:05:15\",N;,fucentarmal,27480502,en,HumanityCritic,230431,\"<a href="http://www.tweetdeck.com" rel="nofollow">TweetDeck</a>\"\\n'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 查看第一行\n",
"lines[15]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function split:\n",
"\n",
"split(...) method of builtins.str instance\n",
" S.split(sep=None, maxsplit=-1) -> list of strings\n",
" \n",
" Return a list of the words in S, using sep as the\n",
" delimiter string. If maxsplit is given, at most maxsplit\n",
" splits are done. If sep is not specified or is None, any\n",
" whitespace string is a separator and empty strings are\n",
" removed from the result.\n",
"\n"
]
}
],
"source": [
"help(lines[1].split)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 问题： 第一行是变量名\n",
"> ## 1. 如何去掉换行符？\n",
"> ## 2. 如何获取每一个变量名？\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:43:39.363547Z",
"start_time": "2019-06-08T07:43:39.358317Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['\"Twitter ID\"',\n",
" 'Text',\n",
" '\"Profile Image URL\"',\n",
" 'Day',\n",
" 'Hour',\n",
" 'Minute',\n",
" '\"Created At\"',\n",
" 'Geo',\n",
" '\"From User\"',\n",
" '\"From User ID\"',\n",
" 'Language',\n",
" '\"To User\"',\n",
" '\"To User ID\"',\n",
" 'Source']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"varNames = lines[0].replace('\\n', '').split(',')\n",
"varNames"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:43:49.131388Z",
"start_time": "2019-06-08T07:43:49.127319Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"14"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(varNames)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:43:53.979866Z",
"start_time": "2019-06-08T07:43:53.975920Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'121818600490283009,\"RT @chachiTHEgr8: RT @TheNewDeal: First they ignore you, then they laugh at you, then they fight you, then you win. - Gandhi #OccupyWallStreet #OWS #p2\",http://a0.twimg.com/profile_images/326662126/Photo_233_normal.jpg,2011-10-06,5,26,\"2011-10-06 05:26:32\",N;,k_l_h_j,382233343,en,,0,\"<a href="http://twitter.com/#!/download/iphone" rel="nofollow">Twitter for iPhone</a>\"\\n'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lines[1344]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 如何来处理错误换行情况？"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T10:57:03.746530Z",
"start_time": "2018-04-28T10:57:03.727339Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"with open(\"../data/ows_tweets_sample_clean.txt\", 'w') as f:\n",
" right_line = '' # 正确的行，它是一个空字符串\n",
" blocks = [] # 确认为正确的行会被添加到blocks里面\n",
" for line in lines:\n",
" right_line += line.replace('\\n', ' ')\n",
" line_length = len(right_line.split(','))\n",
" if line_length >= 14:\n",
" blocks.append(right_line)\n",
" right_line = '' \n",
" for i in blocks:\n",
" f.write(i + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T10:57:07.915900Z",
"start_time": "2018-04-28T10:57:07.911441Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2627"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(blocks)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T10:57:16.586149Z",
"start_time": "2018-04-28T10:57:16.582151Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'121818879105310720,\"RT @Min_Reyes: RT @The99Percenters: New video to go viral. From We Are Change http://t.co/6Ff718jk Listen to the guy begging... #ows #cdnpoli\",http://a3.twimg.com/sticky/default_profile_images/default_profile_0_normal.png,2011-10-06,5,27,\"2011-10-06 05:27:38\",N;,MiyazakiMegu,260948518,en,,0,\"<a href="http://www.tweetdeck.com" rel="nofollow">TweetDeck</a>\" '"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"blocks[1344]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 同时考虑分列符和引用符\n",
"\n",
"- 分列符🔥分隔符：sep, delimiter\n",
"- 引用符☁️：quotechar\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:51:20.459071Z",
"start_time": "2019-06-08T07:51:20.453871Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['121813245488140288',\n",
" \"@HumanityCritic i'm worried that the #ows sells out to the hamsher-norquist spitefuck, and tries to unite with the teahad.\",\n",
" 'http://a2.twimg.com/profile_images/627683576/flytits_normal.jpg,2011-10-06,5,5',\n",
" '2011-10-06 05:05:15',\n",
" 'N;,fucentarmal,27480502,en,HumanityCritic,230431',\n",
" '<a href="http://www.tweetdeck.com" rel="nofollow">TweetDeck</a>\"\\n']"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"re.split(',\"|\",', lines[15])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:52:33.453629Z",
"start_time": "2019-06-08T07:52:33.441462Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"line = 35 length = 6\n",
"line = 36 length = 6\n",
"line = 37 length = 6\n",
"line = 38 length = 6\n",
"line = 39 length = 6\n",
"line = 40 length = 6\n",
"line = 41 length = 2\n",
"line = 42 length = 5\n",
"line = 43 length = 6\n",
"line = 44 length = 6\n",
"line = 45 length = 6\n",
"line = 46 length = 6\n",
"line = 47 length = 6\n",
"line = 48 length = 2\n",
"line = 49 length = 5\n"
]
}
],
"source": [
"import re\n",
"\n",
"with open(\"../data/ows_tweets_sample.txt\",'r') as f:\n",
" lines = f.readlines()\n",
" \n",
"for i in range(35,50):\n",
" i_ = re.split(',\"|\",', lines[i])\n",
" print('line =',i,' length =', len(i_))\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:54:54.976462Z",
"start_time": "2019-06-08T07:54:54.944533Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"with open(\"../data/ows_tweets_sample_clean4.txt\", 'w') as f:\n",
" right_line = '' # 正确的行，它是一个空字符串\n",
" blocks = [] # 确认为正确的行会被添加到blocks里面\n",
" for line in lines:\n",
" right_line += line.replace('\\n', ' ').replace('\\r', ' ')\n",
" #line_length = len(right_line.split(','))\n",
" i_ = re.split(',\"|\",', right_line)\n",
" line_length = len(i_)\n",
" if line_length >= 6:\n",
" blocks.append(right_line)\n",
" right_line = ''\n",
"# for i in blocks:\n",
"# f.write(i + '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:54:59.860355Z",
"start_time": "2019-06-08T07:54:59.856381Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2626"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(blocks)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 3. 读取数据、正确分列"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:55:54.719495Z",
"start_time": "2019-06-08T07:55:54.712843Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"# 提示：你可能需要修改以下路径名\n",
"with open(\"../data/ows_tweets_sample.txt\", 'r') as f:\n",
" chunk = f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:55:57.501462Z",
"start_time": "2019-06-08T07:55:57.497278Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2753"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(chunk)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:56:00.549021Z",
"start_time": "2019-06-08T07:56:00.544656Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['\"Twitter ID\",Text,\"Profile Image URL\",Day,Hour,Minute,\"Created At\",Geo,\"From User\",\"From User ID\",Language,\"To User\",\"To User ID\",Source\\n',\n",
" '121813144174727168,\"RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE RT !!HELP!!!!\",http://a2.twimg.com/profile_images/1539375713/Twitter_normal.jpg,2011-10-06,5,4,\"2011-10-06 05:04:51\",N;,Anonops_Cop,401240477,en,,0,\"<a href="http://twitter.com/">web</a>\"\\n',\n",
" '121813146137657344,\"@jamiekilstein @allisonkilkenny Interesting interview (never aired, wonder why??) by Fox with #ows protester http://t.co/Fte55Kh7\",http://a2.twimg.com/profile_images/1574715503/Kate6_normal.jpg,2011-10-06,5,4,\"2011-10-06 05:04:51\",N;,KittyHybrid,34532053,en,jamiekilstein,2149053,\"<a href="http://twitter.com/">web</a>\"\\n']"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chunk[:3]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:56:05.677057Z",
"start_time": "2019-06-08T07:56:05.656929Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2627\n"
]
}
],
"source": [
"import csv\n",
"lines_csv = csv.reader(chunk, delimiter=',', quotechar='\"') \n",
"print(len(list(lines_csv)))\n",
"# next(lines_csv)\n",
"# next(lines_csv)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-29T01:12:38.678653Z",
"start_time": "2018-04-29T01:12:38.611535Z"
},
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"import re\n",
"import csv\n",
"\n",
"from collections import defaultdict\n",
"\n",
"def extract_rt_user(tweet):\n",
" rt_patterns = re.compile(r\"(RT|via)((?:\\b\\W*@\\w+)+)\", re.IGNORECASE)\n",
" rt_user_name = rt_patterns.findall(tweet)\n",
" if rt_user_name:\n",
" rt_user_name = rt_user_name[0][1].strip(' @')\n",
" else:\n",
" rt_user_name = None\n",
" return rt_user_name\n",
"\n",
"rt_network = defaultdict(int)\n",
"f = open(\"../data/ows_tweets_sample.txt\", 'r')\n",
"chunk = f.readlines(100000)\n",
"while chunk: \n",
" #lines = csv.reader(chunk, delimiter=',', quotechar='\"') \n",
" lines = csv.reader((line.replace('\\x00','') for line in chunk), delimiter=',', quotechar='\"')\n",
" for line in lines:\n",
" tweet = line[1]\n",
" from_user = line[8]\n",
" rt_user = extract_rt_user(tweet)\n",
" rt_network[(from_user, rt_user)] += 1 \n",
" chunk = f.readlines(100000)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:56:22.886245Z",
"start_time": "2019-06-08T07:56:22.198448Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"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>Twitter ID</th>\n",
" <th>Text</th>\n",
" <th>Profile Image URL</th>\n",
" <th>Day</th>\n",
" <th>Hour</th>\n",
" <th>Minute</th>\n",
" <th>Created At</th>\n",
" <th>Geo</th>\n",
" <th>From User</th>\n",
" <th>From User ID</th>\n",
" <th>Language</th>\n",
" <th>To User</th>\n",
" <th>To User ID</th>\n",
" <th>Source</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>121813144174727168</td>\n",
" <td>RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLIN...</td>\n",
" <td>http://a2.twimg.com/profile_images/1539375713/...</td>\n",
" <td>2011-10-06</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2011-10-06 05:04:51</td>\n",
" <td>N;</td>\n",
" <td>Anonops_Cop</td>\n",
" <td>401240477</td>\n",
" <td>en</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>&lt;a href=&quot;http://twitter.com/&quot;&gt;...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>121813146137657344</td>\n",
" <td>@jamiekilstein @allisonkilkenny Interesting in...</td>\n",
" <td>http://a2.twimg.com/profile_images/1574715503/...</td>\n",
" <td>2011-10-06</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2011-10-06 05:04:51</td>\n",
" <td>N;</td>\n",
" <td>KittyHybrid</td>\n",
" <td>34532053</td>\n",
" <td>en</td>\n",
" <td>jamiekilstein</td>\n",
" <td>2149053</td>\n",
" <td>&lt;a href=&quot;http://twitter.com/&quot;&gt;...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>121813150000619521</td>\n",
" <td>@Seductivpancake Right! Those guys have a vict...</td>\n",
" <td>http://a1.twimg.com/profile_images/1241412831/...</td>\n",
" <td>2011-10-06</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>2011-10-06 05:04:52</td>\n",
" <td>N;</td>\n",
" <td>nerdsherpa</td>\n",
" <td>95067344</td>\n",
" <td>en</td>\n",
" <td>Seductivpancake</td>\n",
" <td>19695580</td>\n",
" <td>&lt;a href=&quot;http://www.echofon.com/&quot;...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Twitter ID Text \\\n",
"0 121813144174727168 RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLIN... \n",
"1 121813146137657344 @jamiekilstein @allisonkilkenny Interesting in... \n",
"2 121813150000619521 @Seductivpancake Right! Those guys have a vict... \n",
"\n",
" Profile Image URL Day Hour \\\n",
"0 http://a2.twimg.com/profile_images/1539375713/... 2011-10-06 5 \n",
"1 http://a2.twimg.com/profile_images/1574715503/... 2011-10-06 5 \n",
"2 http://a1.twimg.com/profile_images/1241412831/... 2011-10-06 5 \n",
"\n",
" Minute Created At Geo From User From User ID Language \\\n",
"0 4 2011-10-06 05:04:51 N; Anonops_Cop 401240477 en \n",
"1 4 2011-10-06 05:04:51 N; KittyHybrid 34532053 en \n",
"2 4 2011-10-06 05:04:52 N; nerdsherpa 95067344 en \n",
"\n",
" To User To User ID \\\n",
"0 NaN 0 \n",
"1 jamiekilstein 2149053 \n",
"2 Seductivpancake 19695580 \n",
"\n",
" Source \n",
"0 <a href="http://twitter.com/">... \n",
"1 <a href="http://twitter.com/">... \n",
"2 <a href="http://www.echofon.com/"... "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv(\"../data/ows_tweets_sample.txt\",\n",
" sep = ',', quotechar='\"')\n",
"df[:3]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:57:21.705488Z",
"start_time": "2019-06-08T07:57:21.701307Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2626"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:57:25.595447Z",
"start_time": "2019-06-08T07:57:25.588512Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE RT !!HELP!!!!'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.Text[0]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:06:31.097919Z",
"start_time": "2018-04-28T11:06:31.092038Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0 Anonops_Cop\n",
"1 KittyHybrid\n",
"2 nerdsherpa\n",
"3 hamudistan\n",
"4 kl_knox\n",
"5 vickycrampton\n",
"6 burgerbuilders\n",
"7 neverfox\n",
"8 davidgaliel\n",
"9 AnonOws\n",
"Name: From User, dtype: object"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['From User'][:10]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 4. 统计数量\n",
"### 统计发帖数量所对应的人数的分布\n",
"> 人数在发帖数量方面的分布情况"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:59:11.081963Z",
"start_time": "2019-06-08T07:59:11.076747Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"data_dict = defaultdict(int)\n",
"for i in df['From User']:\n",
" data_dict[i] +=1 "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:59:11.737607Z",
"start_time": "2019-06-08T07:59:11.706495Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[('MiranHosny', 1),\n",
" ('BradMarston', 1),\n",
" ('Sir_Richard_311', 1),\n",
" ('elChepi', 1),\n",
" ('jboy', 1)]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(data_dict.items())[:5]\n",
"#data_dict"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:59:23.202541Z",
"start_time": "2019-06-08T07:59:22.945172Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### 安装微软雅黑字体\n",
"为了在绘图时正确显示中文，需要安装/data/文件夹中的微软雅黑字体（msyh.ttf）\n",
"\n",
"详见[common questions](0.common_questions.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:59:28.182593Z",
"start_time": "2019-06-08T07:59:27.976785Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data_dict.values())\n",
"#plt.yscale('log')\n",
"#plt.xscale('log')\n",
"plt.xlabel(u'发帖数', fontsize = 20)\n",
"plt.ylabel(u'人数', fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T07:59:53.302817Z",
"start_time": "2019-06-08T07:59:52.510526Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"tweet_dict = defaultdict(int)\n",
"for i in data_dict.values():\n",
" tweet_dict[i] += 1\n",
" \n",
"plt.loglog(tweet_dict.keys(), tweet_dict.values(), 'ro')#linewidth=2) \n",
"plt.xlabel(u'推特数', fontsize=20)\n",
"plt.ylabel(u'人数', fontsize=20 )\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:00:01.767550Z",
"start_time": "2019-06-08T08:00:00.760854Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"import statsmodels.api as sm\n",
"\n",
"def powerPlot(d_value, d_freq, color, marker):\n",
" d_freq = [i + 1 for i in d_freq]\n",
" d_prob = [float(i)/sum(d_freq) for i in d_freq]\n",
" #d_rank = ss.rankdata(d_value).astype(int)\n",
" x = np.log(d_value)\n",
" y = np.log(d_prob)\n",
" xx = sm.add_constant(x, prepend=True)\n",
" res = sm.OLS(y,xx).fit()\n",
" constant,beta = res.params\n",
" r2 = res.rsquared\n",
" plt.plot(d_value, d_prob, linestyle = '',\\\n",
" color = color, marker = marker)\n",
" plt.plot(d_value, np.exp(constant+x*beta),\"red\")\n",
" plt.xscale('log'); plt.yscale('log')\n",
" plt.text(max(d_value)/2,max(d_prob)/10,\n",
" r'$\\beta$ = ' + str(round(beta,2)) +'\\n' + r'$R^2$ = ' + str(round(r2, 2)), fontsize = 20)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:13:27.176717Z",
"start_time": "2018-04-28T11:13:26.420464Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"histo, bin_edges = np.histogram(list(data_dict.values()), 15)\n",
"bin_center = 0.5*(bin_edges[1:] + bin_edges[:-1])\n",
"powerPlot(bin_center,histo, 'r', '^')\n",
"#lg=plt.legend(labels = [u'Tweets', u'Fit'], loc=3, fontsize=20)\n",
"plt.ylabel(u'概率', fontsize=20)\n",
"plt.xlabel(u'推特数', fontsize=20) \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:14:19.219105Z",
"start_time": "2018-04-28T11:14:19.171044Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"import statsmodels.api as sm\n",
"from collections import defaultdict\n",
"import numpy as np\n",
"\n",
"def powerPlot2(data):\n",
" d = sorted(data, reverse = True )\n",
" d_table = defaultdict(int)\n",
" for k in d:\n",
" d_table[k] += 1\n",
" d_value = sorted(d_table)\n",
" d_value = [i+1 for i in d_value]\n",
" d_freq = [d_table[i]+1 for i in d_value]\n",
" d_prob = [float(i)/sum(d_freq) for i in d_freq]\n",
" x = np.log(d_value)\n",
" y = np.log(d_prob)\n",
" xx = sm.add_constant(x, prepend=True)\n",
" res = sm.OLS(y,xx).fit()\n",
" constant,beta = res.params\n",
" r2 = res.rsquared\n",
" plt.plot(d_value, d_prob, 'ro')\n",
" plt.plot(d_value, np.exp(constant+x*beta),\"red\")\n",
" plt.xscale('log'); plt.yscale('log')\n",
" plt.text(max(d_value)/2,max(d_prob)/5,\n",
" 'Beta = ' + str(round(beta,2)) +'\\n' + 'R squared = ' + str(round(r2, 2)))\n",
" plt.title('Distribution')\n",
" plt.ylabel('P(K)')\n",
" plt.xlabel('K')\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:14:26.818914Z",
"start_time": "2018-04-28T11:14:26.499569Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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tSHk2ZswYTjnlFFq3bp1/W29BEydOpGnTpjRr1oxZs2blb9+6dSt9+/alQYMGnHHGGdEsOSQxERxmVtnMzjWzPkHXIiFo3Rrefdd34/3oI2jTBm69FfbtC7oyiZK8FiIbNmzg+OOP529/+1vQJUXc/Pnzee+999iwYQMPPPBAoXklCxYsYNasWaxatYq5c+dy/fXX5wfqZZddRkZGBps3b2b+/PlBlF8iUQsOM0sys6alfHtroANQeKaOlG8JCX6sIzsbBg+G8eOhVSuYMyfoyiSKzIwePXqwefPmQq9NmTKFRo0akZKSwptvvgnAE088QZMmTWjfvj1XXnklV111FeDbgLz33nsAbNiwgSZNmgD+v9S7d+9O48aN6dSpU357kp49e3LDDTeQkpLCmjVrimyuCDBhwgQaNmxI586dy7zOxsyZMxk6dCiVKlWid+/ebN++na8K9HnLysqiV69eJCUl0aBBA9q0acPSpUtZtmwZzjkuv/xyAJKSkspURyRFPDjM7HgzewX4L/D7AtsvNbP1ZrbOzIYf6RjOueXAvyJcqkRS7drw7LMwfz5Urgx9+/og+dvf/NyPhAT/NTMz6EolAvbt28c///lPevfuXei1m266Kb9fVZcuXcjOzmbChAksWrSIpUuXsmXLlqMe38x46qmn+OKLL+jevTtPPPFE/mt79+5l06ZNNGvWrMjminPnzmXmzJmsWbOGuXPnsraYtWhmzJhRqKFiUWMhmzdvJjU1Nf95/fr1+fLLL/Oft2zZkrfeeovdu3fz5ZdfsmLFCrZv387KlSupX79+fg+vSZMmHfXnDkqlKHzGQeAR4HWgC4CZVQPuz32eA6w0s9eAROD5Au+d4JxTT+94cvbZfsLgvffCXXfBCy/8/NrGjTBihP8+b6EpiWl5bdI3b97MpEmTuPjiiwvtc9ZZZzFq1CjuuOMOWrZsSWZmJgMGDKB27doADBw4kKVLlx7xc04++WQyMzO57777WLRoEV26dMl/7Ze//CUAa9euzW+uCD7MOnXqxLZt27jiiivy26b379+/yM8YOHAgAwcOPOrP/OOPPx7SSTchIYHExMT85+effz6LFy+mY8eOtGjRgjZt2lCjRg3Wr19PdnY2CxYsICcnh86dO9O7d2/atm171M+MtoifcTjndjvn5gEHCmzuAyx0zm11zn0FzAfOdc595ZzrWeCh0IhHVav6sY5atQq/tncvjBsX/ZokIvLGOGbMmMFjjz3GviLGt1599VUGDBhA//79efHFF9m/fz9Vq1bNf/2nAhNKK1WqRE5ud+aC28eNG8e8efO44YYbGDly5CENEY877jiAYpsr7t+/n8qVK+fv/8MPPxT5s7z44ouFGirecccdhfarW7fuIZ18t23bximnnHLIPnfddRfZ2dnMnDmTLVu2kJ6eTu3atenevTsnnngiNWvW5Mwzz+Szzz4r+hcbsKAGxxsAGws83wLULW5nM2sLTADOMbObitlnhJllmVnW9u3bw1qsREiB0/dDbNxY9HaJWf3796dXr17ceeedhV5bt24dGRkZjBo1ivfff59OnToxa9Ysdu3axb59+3juuefy901LS2PlypUAhwwer1mzhr59+9K0aVPmFDN+Vlxzxc6dO/Pcc8/xww8/sH37dmbOnFnk+wcNGlSooeJtt91WaL8LLriAZ555hpycHN5++22aNm3KSSedlP/6gQMH2LNnDwCTJ0+mYcOGNGjQgN69ezNv3jx27drFzp07WbJkCe3btz/arzYQ0bhUVZQq+EtYeQ7iL1kVyTm3CjjitQvn3GRgMvheVWGoUSItJaXokEhMhBkz4JJL/EJSEhfGjx9P69atGTx48CGXXwYNGsSePXuoU6cOzz77LI0aNeLiiy+mZcuWnHzyyZx11ll8n9uFefTo0QwePJj58+fTvHnz/GNce+21DB8+nHvuuYeuXbsecjaSJykpiWeffZahQ4dy4MABGjVqxMyZMxk8eDALFiygcePGNGrUqMhxmFAMGDCAhQsX0qhRI2rUqJEffGPHjuW8887jtNNOo0OHDuzbt4+2bdvy1FNPAZCSksLo0aM5/fTTcc4xZsyY/MH/8iZqTQ7NbCjQzTl3lZn9GujpnBue+9p04CXn3Mvh+Cw1OYwRmZl+TGPv3p+3Va0Kder4QDn/fD943rBhcDVK4J5++mnee+89nnzyyaBLiXvlvcnhHKCPmdU2szpAV+CtgGqRoGRk+Dkeqan+zCI1FaZMgXXr4MEH4d//9o0T774bfvwx6GpFJFc0bsetZmbrgL8Ag3K/bwGMAxYD7wM3O+f2hOGz+pvZ5LxFYSQGZGT4haEOHvRfMzJ848QbbvCNE88/H8aOhfbt/WRCEQmc1uOQ8u+NN+Daa/3lq2HD/K28NWsGXZVI3Cnvl6pESu6CC+Djj33L9mnTfOPEqVPVOFEkIAoOiQ3HHgv33OMbJ6anw/Dh0KOHDxQRiaq4Cg6NcVQArVr5QfMnn/Sh0a6dHwMpeGeWiERUXAWHc+4159yI6tWrB12KRFJCAlx5pW+cOGSIv+uqVSuYPTvoykQqhLgKDqlgatXyYx3vvOPnf5x/PgwaBAXaPYhI+Ck4JPb16OEbJ44fD6+/Ds2bw8MPQ06xzQhEpAwUHBIfqlTxzRHXrIGuXeF3v4NOnUC3ZYuEXVwFhwbHhcaN/VjHCy/4JoqdOsF118Hh/yYyM7UOiEgpxVVwaHBcAN++5NJL/czza6+Fv//d38L7wgt+7kdej6yNG/3zvHVAFB4iJaKZ4xL/srLgN7+B5cuhTx9YvRq2bSu8X2qqb3siUkFp5rhIno4d4YMP4KGHYNGiokMDYNOm6NYlEqMUHFIxJCbC9df7y1fJyUXvk5IS3ZpEYpSCQyqW+vV9K/cCS5MCkJQEEyYEU5NIjImr4NBdVVIiGRl+3Y8GDX7elpAA+/b59u4ickRxFRy6q0pKLCPDj2k453tenXYaXH01dO/u54KISLHiKjhESqVFC1i4EJ56yve/at/et3DfU+a1xUTikoJDBPzcj2HDfHD8+td+saiWLf0iUiJyCAWHSEE1a/rxj4UL/Rog/frBJZfAli2hH0uz0yVOKThEitK9u1806u67fQuT5s3hwQfhwIGSvV+z0yWOxVVw6K4qCasqVWDMGD94ftZZcNNNcPrpfjLh0YwbV3hxqb17/XaRGBdXwaG7qiQiGjb0Yx0zZsDXX0OXLnDNNbBzZ/HvKW4WumanSxyIq+AQiRgzP9bx6ad+Bvrjj/vLV88/7y9FHa64WeianS5xQMEhEorjj4e//hU+/BBOOQUuu8w3Tly37tD9Jkwo3NokOVmz0yUuKDhESuO002DJEvjb32DpUr/m+Z13wg8/+NczMnxrk9RUf7aSmuqfZ2QEW7dIGKitukhZbdvmB85feAGaNoXHHoOzzw66KpGQqa26SLTUq+fHOt58069zfs45fhLh118HXZlIRCg4RMIlb5GoW2/1QZKe7i9PqXGixJm4Cg7N45DAJSX5sY6PPoK2bf3Kg926+ecicaLEwWFmDcxssJndbGajzSzDzBpGsrhQaR6HlBvp6TB/PjzzDHz+uR9Mv+UWNU6UuHDU4DCzVmY2B5gGtAb2AbuBZsAUM5trZm0jW6ZIDDLzYx1r18Lw4TBpku/E++qrQVcmUiaVSrDPXcD1zrm1Rb1oZo2Bu4FLw1mYSNw46SQ/1nHFFTByJFx0kX88/LAmBEpMKsmlqgeOEBq3OOe+cM4pNESO5swzYfly37L97bf92cf998NPPwVdmUhIShIcU8zsyoIbzKyKmU0DNJtJJBSVK/uxjk8+8bftjh4NHTv6yYQiMaIkwXEG8P/M7CEzSzCzusC7gOW+JiKhSk2FWbPg5Zfhf/+Drl39Zaxvvw26MpGjOmpwOOe+AfriB8XnAouA6c65Ic65fRGuTyR+mcHFF/uzjxtvhCef9HdjTZ9edONEkXKiJHdVjQXGAN8DxwAfA9XMbGzuayJSFtWq+bGOrCy/UuDll0OvXvDZZ0FXJlKkklyqqpz7qATMAT4ssK1y5EoTqWDatYNFi+DRR2HZMmjdGm6/HfbvD7oykUMctcmhmaU45464+oyZ1XfObQ1rZWWgJocS8776Cm6+GZ57Dk49Ff7xD38WIhJB4WxyONnMxhc1Szx3NvntwBOlqDHs1HJE4kadOn598rff9s979/Yt2b/6Kti6RChZcPwCWAM8YWafm9liM1tkZp/jZ5OvA/pFssiSUssRiTu9evk+V7fd5peuTU/3l7JycoKuTCqwkNbjMLMEoAb+VtxvnHPl8l+vLlVJXPrsM/jtb30PrM6d/bof7doFXZXEkbBdqjKzE8xskpnNAK52zm13zn1dXkNDJG41bQrDhkGNGn7Vwfbt4Re/gO+/D/1YmZn+Dq6EBP81MzO675eYVqKZ4/i7p54CepjZHyJbkogUKTPTt2n/5puft735pv/D/fLLJZ/7kZkJI0bAxo3+PRs3+ucl/eNf1vdLzCvJXVXrnXMNc79PAhY559pHo7jS0qUqiUtpaf6P9OEqV/b9rvr182ugp6aW7jipqbBhQ+nrKOn7pdwK511V+R3YcmeKVy1LYSJSSpuKuSv+p598y/YFC3zjxHvvPXLjxOKOU9z2cL9fYl5JgqOJmf2Y9wDSc7//Kfe5iERDcS3YU1P9nI9PPoHzzoM//MEvHPX++6Edp6Qt3sv6fol5JelVleCcq1Lgkfe8snOuSjSKFBFgwgRITj50W3Ky3w7+D/fLL/vmid9955esvfpq30QxlOOUtQ6Je3G15rhIXMvI8AtCpab6Bompqf55xmGrG1x4oT/7GD0apk6FZs3g2Wd/Hjwv6XHKWofErZDmccQKDY6L5ProI9+uffFi6NnTTx5MTw+6Kimnwjk4LiKxqk0beO89ePxxWLnSP7/1VtinFRGk9BQcIvEuIcHPs1i7FgYPhvHjfefdt94KujKJUQoOkYqidm0/1jFvHiQmQp8+Pki+/DLoyiTGxFVwqDuuSAmcc44f+7jjDnjlFT/m8fe/q3GilFhcBYe644qUUNWq8Oc/w+rV0KkTjBoFZ5wBy5cHXZnEgLgKDhEJ0amn+rGO557zM79PPx1uuAF27Qq6MinHFBwiFZ0ZXHYZZGf7W3cffhiaN/frf8Th7fpSdgoOEfFOOMGPdSxe7AfSBw3yjRPXrw+6MilnFBwicqjOneHDD+HBB+Hf/4aWLeHuu+FHtaYTT8EhIoVVquTHOj791C8WNXasXzjq3XeDrkzKAQWHiBTvlFPgpZfgtddgzx7o3h2GD4cdO4KuTAKk4BCRo+vXDz7+2LdsnzbNz/2YOlWD5xWUgkNESubYY+Gee/xcj/R0f+bRo4fvxCsVioJDRELTurUfNH/iCX8W0ratHwPZuzfoyiRKFBwiErqEBLjqKj/3IyPD33XVqhXMnh10ZRIFCg4RKb1ateDpp/1651WqwPnn+/kfW7cGXZlEkIJDRMquZ09Ytcq3bH/9dT/z/OGH1TgxTik4RCQ8qlaFceNgzRrfMPF3v/OTCQuuxpmZCWlp/lJXWpp/LjFHwSEi4dW4Mbz5Jjz/vL9k1akTXHedH0wfMQI2bvS38W7c6J8rPGKO1hwXkcj57jv40598D6yEhKIvXaWmwoYNUS9NCtOa4yISvOrV4ZFHYOnS4sc7Nm2Kbk1SZgoOEYm800+HlJSiXytuu5RbCg4RiY6JEyE5+dBtZjBkSDD1SKkpOEQkOjIyYPJkP6Zh5tf8qFkTJkyAoUNh+/agK5QSilpwmFmSmTUt5Xs7mtkjZvaKmQ0Md20iEiUZGX4g/OBB+O9//fd//KO/syo9HaZM8a9JuRbx4DCz483sFeC/wO8LbL/UzNab2TozG36Uw6xwzl0HDAf6R7BcEYmm5GR/CWvlSr9g1FVX+caJH38cdGVyBNE44zgIPALclLfBzKoB9wPdch8TzayWmdUxs3cKPHoDOOfybse4BXgoCjWLSDS1bAkLF8JTT/nFo9q182ciapxYLkU8OJxzu51z84ADBTb3ARY657Y6574C5gPnOue+cs71LPB4G8DMKpnZfcBrzrnlka5ZRAJgBsOG+caJv/61b+HesiV9akf6AAAKGUlEQVS88UbQlclhghocbwBsLPB8C1D3CPtPBM4ARpjZiKJ2MLMRZpZlZlnbNcgmErtq1vRjHQsX+ktZ/frBJZfAli1BVya5ggqOKvhLWHkOAsV2Q3PO/d451805N9Q5N7mYfSY75zo65zrWqlUrzOWKSNR17w4rVviW7bNn+8aJf/0rHDhw9PdKRAUVHF8C9Qs8PwXYHFAtIlJeVakCY8b4wfKzzoIbb/S9rz74IOjKKrSggmMO0MfMaptZHaAr8FZAtYhIedewoR/rePFFfxtvly5w7bWwc2fQlVVI0bgdt5qZrQP+AgzK/b4FMA5YDLwP3Oyc2xOGz+pvZpO/++67sh5KRMobMxg40N91df318Nhj/vLV88/7brsSNeqOKyKxadkyGDnSr/fRuzf84x/QpEnQVcU0dccVkfjWoQMsWeK77y5Z4tc8v/NO+OGHoCuLewoOEYldiYkwapSf+3HxxXDbbdCmDcyfH3RlcS2ugkNjHCIVVL16fqxj9mx/u+6558Lll8PXXwddWVyKq+Bwzr3mnBtRvXr1oEsRkSD07evXPB83Dl54wTdOfOIJNU4Ms7gKDhERkpJg/HhYtcpfthoxArp1g48+CrqyuKHgEJH41Lw5LFgATz8Nn38Op50Gt9wCe8p853+Fp+AQkfhlBldc4QfPhw2DSZOgRQt49dWgK4tpcRUcGhwXkSLVqOHHOt59F6pVg4su8ndhbdoUdGUxKa6CQ4PjInEoMxPS0iAhwX/NzCz9vt26+caJf/kLvPWWP/u4/3746afI1R8uofweIs05F3ePDh06OBGJA9OnO5ec7JxvKuIfycl+e1n2dc659eudu+ACv1+bNs4tXhzRH6VMQv3ZSgnIciX4G6uWIyJSfqWlwcaNhbenpvr1yku7bx7n4JVX4LrrYNs2fwfW3XfDiSeWre5wK83PVgpqOSIisa+4MYiitoeybx4zGDDAN0684QY/DpKe7i8Dlaf/qC7NzxZBCg4RKb9SUkq+PZR9D1etGjzwgG+YmJYGQ4b4xomffVbiUiOqLD9bBMRVcOiuKpE4M2GCXz62oORkv70s+xanfXtYtMh32s3Kgtat4Y47YP/+0GsPp3D8bOFUkoGQWHtocFwkjkyf7lxqqnNm/uuRBoRD2fdovvzSucsu8wPRp57q3Ny5pT9WOITzZysGGhzX4LiIhMHbb8M118C6dZCR4W/fPfnkoKuKCA2Oi4iEQ+/esHo1/PnPfuna9HR4/PEK3ThRwSEicjTHHOPHOj76yPe8GjkSzjzTN1KsgBQcIiIl1awZzJ0L06bBF1/4VQhHj4bdu4OuLKriKjh0V5WIRJyZv1137Vq46io/5tG8uZ9IWEHEVXA49aoSkWg58UR47DF/++6JJ/qJhBddVPQM7zgTV8EhIhJ1Z5wBy5bBfff5y1gtWvjvY6FxYikpOESkYippt9mS7Fe5MtSt68889u6F3/8eGjf2ZyOhHKc8dcA9kpJM9oi1hyYAisgRlbTbbFn2M/NfR4xw7rHHjn6cKHXAPRI0AVATAEWkGCXtNlvW/Y4/3i9V61zR8z4KHidKHXCPRBMARUSKU9Jus2Xd7/vv/fhHcZMFC76vnHXAPRIFh4hUPCXtNhuO/dq2LdlxylkH3CNRcIhIxVPSbrPh2m/ixMKvm8HgwaF/VnlQkoGQWHkA/YHJTZo0Ccc4kYjEs5J2mw3XfgVfr13buTp1/AD44MHObdsW2mdFCBoc1+C4iJRj+/fDvff6s5GqVf2Stb/5DSQmBlaSBsdFRMqzY47xHXdXr4ZOneDaa/1kwuXLg67sqBQcIiJBOvVUeOstP9lv40Y4/XS//vn33wddWbEUHCIiQTODX/0KsrNhxAh4+GHfOPGll/wckHJGwSEiUl6ceCI8+qhvVVKzJgwcCP36RW0CYEkpOEREypsuXSArCx54ABYu9I0T//KXctM4UcEhIlIeVaoEN94In34KffrAmDHQvj28917QlSk4RETKtQYN4OWXYdYsP2B+1ll+AalvvgmsJAWHiEgsuPBC+OQTuOUWePppSE+HZ54JZPA8roJDS8eKSFw79lg/aXDFCmjaFIYOhbPP9pezoiiugsNp6VgRqQhat4Z334UnnoCPPvKNFP/0J9i3LyofH1fBISJSYSQk+LGO7Gy47DLfDLFVq0NXHYzUR0f8E0REJHJq1/ZjHfPn+0tZJ5wQ8Y+sFPFPEBGRyDv7bFi1ys9CjzCdcYiIxIsohAYoOEREJEQKDhERCYmCQ0REQqLgEBGJB5mZkJbmb9NNS/PPI0R3VYmIxLrMTL+Ox969/vnGjf45QEZG2D9OZxwiIrFu3LifQyPP3r1+ewQoOEREYt2mTaFtLyMFh4hIrEtJCW17GSk4RERi3YQJkJx86LbkZL89AuIqONRWXUQqpIwMmDwZUlP97PHUVP88AgPjAOYCWAQk0jp27OiysrKCLkNEJKaY2TLnXMej7RdXZxwiIhJ5Cg4REQmJgkNEREKi4BARkZAoOEREJCRxeVeVmW0HNpby7dWBeL2ftzz/bEHVFo3PjcRnhOuYZT1Oad9fE9hRhs+V0JXkf6tU51ytox0oLoOjLMxssnNuRNB1REJ5/tmCqi0anxuJzwjXMct6nNK+38yySnLbp4RPOP8d6lJVYa8FXUAEleefLajaovG5kfiMcB2zrMcpz/+m5FBh+99KZxwiEnU644htOuMQkSBMDroAKT2dcYiISEh0xiEiIiFRcIiISEi05riIBMrMKgPdgUrOuTlB1yNHpzMOEYkYM0sys6ZH2a010AH4RRRKkjBQcIhI2JnZ8Wb2CvBf4PcFtl9qZuvNbJ2ZDQdwzi0H/hVQqVIKulQlIpFwEHgEeB3oAmBm1YD7c5/nACvN7DXn3PbAqpRS0RmHiISdc263c24ecKDA5j7AQufcVufcV8B84NxACpQyUXCISLQ04NDmo1uAumbWFpgAnGNmNwVSmYREl6pEJFqq4C9h5TkI5DjnVgEZwZQkpaEzDhGJli+B+gWenwJsDqgWKQMFh4hEyxygj5nVNrM6QFfgrYBrklLQpSoRCbvcO6hWANWAY8ysJ3A1MA5YnLvbzc65PcFUKGWhJociIhISXaoSEZGQKDhERCQkCg4REQmJgkNEREKi4BARkZAoOEREJCQKDhERCYmCQ0REQqLgEIkCMztQ4PtbzGy2mSUGWZNIaanliEgUmdk5wK+Bbs65nKDrESkNtRwRiYLcM46GwFygn3Pu84BLEik1XaoSiZ4ZwHUKDYl1Cg6R6NkOnBl0ESJlpeAQiZ7LgQwzGxh0ISJloTEOkSgwswPOuUq562vPBXrlLpkqEnN0xiESRblhcRMwy8xqBV2PSGnojENEREKiMw4REQmJgkNEREKi4BARkZAoOEREJCQKDhERCYmCQ0REQqLgEBGRkCg4REQkJAoOEREJyf8H65s7sC01H0sAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"powerPlot2(data_dict.values())"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:14:50.947967Z",
"start_time": "2018-04-28T11:14:50.933308Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import powerlaw\n",
"def plotPowerlaw(data,ax,col,xlab):\n",
" fit = powerlaw.Fit(data,xmin=2)\n",
" #fit = powerlaw.Fit(data)\n",
" fit.plot_pdf(color = col, linewidth = 2)\n",
" a,x = (fit.power_law.alpha,fit.power_law.xmin)\n",
" fit.power_law.plot_pdf(color = col, linestyle = 'dotted', ax = ax, \\\n",
" label = r\"$\\alpha = %d \\:\\:, x_{min} = %d$\" % (a,x))\n",
" ax.set_xlabel(xlab, fontsize = 20)\n",
" ax.set_ylabel('$Probability$', fontsize = 20)\n",
" plt.legend(loc = 0, frameon = False)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:14:53.968210Z",
"start_time": "2018-04-28T11:14:53.962880Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"from collections import defaultdict\n",
"data_dict = defaultdict(int)\n",
"\n",
"for i in df['From User']:\n",
" data_dict[i] += 1"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2018-04-28T11:14:57.469192Z",
"start_time": "2018-04-28T11:14:56.922983Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"image/png": 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te4DhR8j8RXWvgZo4EDkRAvuATgpWCGErVtsZ84033jAmlpycHAA0TUOn0xESEsLAgQMZMGAAnTp1Ys6cOSxYsIB169aRmJhImzZtzP9NhGPQ6VSCAbUj55q3YPQMVa4mciLs+EmN1xzPhl9mQ7OOEHEbBA1SZW2EEHbJ7JaMXq/+evTw8KBPnz4MGDCAAQMG0L9/f3x9fS87/7PPPuPBBx/ktttu4/PPP7dM1BYkLRk7pGlwZIdqyeh0cPIA+LZTLZydK9Xamj9PqnObtFLlarpFg7ObTcMWwpGY+t1pdpKZP38+AwcOJDIyEmdn0/6CfOSRR/j66685efKkObeqF5Jk7FxpMSz9q5ruHP13dayiTHWnpX4Npy/MQPPwgV6xEDoa3LxsF68QDsJq3WVt2rShXbt2JicYgM6dO8vUZlE7rh4w7HHVYgEoOw9oquUSPERtA536FeTvhT8+VYkndLRKOJ6Xt6yFEPXL7JHTKVOm8OGHH5r1mcmTJ/Pee++ZeyshlIDe0Lyjer5lqdpCoKxYzT7r3B8mvq42T2vbE0rPqaTzaRxsWARnjtk0dCEcndktGVN611asWEF6ejovv/wyAB06dOCRRx4xP7paKCsrY8OGDZSXlxMTE1Mv9xT1qFNfcG9SvWVAZYVKNh3C1eNYNqSsgANbIONnVYizyyCImFidqIQQ9caklswXX3zBnj17TEowABkZGbzyyiu1Dqq4uJjs7OxafdZgMJCcnMzPP/9c6/sLO9a2B0RNUs9PH1YVnY/uqn6/dbCalXbn26o7DdT6my8fh59fqXmuEMLqTEoy99xzD127dsXX1xedTsfKlSv54IMPSElJoazs8nLtf/75J25u5s/0OXPmDOPHj6d169bMmTPHeHzZsmV06tSJoKAgFi9efM1rREZGcscdd5h9b9EAVZSpac9N/C5/r3lHNVHgnnehxxhwclWtm2+ehu/+pXbulIWdQlidSd1lr7/+OqmpqSQnJ5OVlcVvv/3G5s2bAXBxcaF79+5ERkYSERFB06ZN+fzzzwkICDA7GL1ez9SpUxk3bhxJSUkAFBUVMX36dJKSknByciI8PJzY2FgqKiq46667jJ+dOXMmI0aMMPueogFrEQi3zqp+ve4daNdLdY9VadoaBsWpbaG3J6gutCM71MOvM4SNhxv6yVobIazEpP+zpk2bZnyu1+t54IEHjGVjUlJSMBgMpKWl1fjMf//7X7OD8fb2Jjo6miVLlhiPJSYmMnjwYNq1awfAsGHDWL16NXfddRfr1q0z+x6ikSothoJDauvnK/H0hZumQMQENU6T/r2akbZqHni3hJ7jIGQ4uHnXb9xCNHJm//n21FNP0bt3b26//XbjsYqKCjIzM0lLS+PkyZNERUUxaNCga1zFdDk5OTVaRe3btycvL++q56enpzNnzhwMBgPz58+vkSCrxMfHEx8fD0B+vpSVbxRcPVQ15yp5mXDyIITG1Cw/4+alEk3PsbBrnWrdnM5V+9ps+0Lt4tlrHDSV6hRCWMJ1k8yXX35JVFQUQUGqCu6rr7562TlOTk707NmTnj17WjzA0tJSY5UBUC0pJ6er7w8fFhbG0qVLr3nNuLg44uLiALWgSDQS+ov+u8heBzlp0HUouFyhbp6zm0pA3UfAoRTVsjm8XZWu2fETBPaFsFuhTTdVdUAIUSvXTTJ33303er2ezMxMgoODeeaZZ4zjL126dLF6gP7+/jW6xXJzc7nxxhutfl/RwN38/9TOmy7uappz9jo120x/yR8oOr1ahxPQG07sh+3fw+6NsD9JPVp1Ucnmhn6Xf1YIcV3XTTKvv/46ycnJeHp6AjBnzhx0F/6y8/b2Jjw83Jh0IiMj6d69e42WR13FxMTw7LPPcvz4cSorK9m8eTOLFi2q83UTEhJISEigsLDQAlEKu6PTgVdz9fzgVrUJmnsT1UK5mpadYNgTcOMUNUEg4xc4vhtWvg7efqqLLWSElK0Rwgxm1y5bt26dccA/JSWF7OxsKisrjYnH3d2dnj17EhUVxTvvvGNWMEVFRURERFBUVERJSQl+fn68//77HDx40Liw8/XXX+e2224z67rXIrXLHICmQV4G+Ieq5HNiPzRrD04u1/5c2XnVAtr+fXWNNBf3C+M2sWrmmhAOymoFMi917tw50tPTaySezMxMysvLqaioqMul64UkGQdTVgyf/hU6RkD0P0z7jFZ5YdzmO7VxGqhutk43qq601l1l3EY4nHpLMldSWlrKjh07iIyMtPSlLaaqu2zt2rXs3r3b1uGI+nQoRU1bbt7xQsHNyuoyNddzYh+kJ8CejVBZro61CoawW2TcRjgUiyWZL774gsjISIKDgy0WnD2RloyD++1DVQngjjdNTzSgdvHccWHc5nyROubtp6Y/dxsu4zai0bNYktHr9eh0OuMgf1RUFJGRkURGRhISEmIci2moJMk4uLxMyNupdt+E6oKbpio7D9lr1RTowiPqmIuHWtjZc5yM24hGy2JJZv78+aSmppKSksKuXbtqDPJ7eHgQFhZWI/GEhoZecx2LvZDuMnGZU7nw479h+DS1PsYcWiUcTFaTBGqM29ykutLMvZ4Qds4qYzLnzp0jLS2N5ORk4yD/zp07KS8vNyYeNzc34+yyhQsX1v43qCfSkhFGBYdg4/swYhp4Nqv9dWTcRjiAehv4P3/+POnp6TUSz44dO2R2mWj41r2jthYIHly7z/9ZoKoHZCRWj9s0aaWmP4cMN28MSAg7Y7Htly8tK3MpNzc3+vbtS9++1YvcysrKMBgMZoQrhJ0pK1bdZ1cruGkKr+Zw470QeTvsWqvqpBUeUZMNtn4BoaPUAs+qRaNCNEImDfxfWlamanV/fZSVsTZpyYir0irVQk69ExzJgIKDKjHoalnRorJCVR9I+w6O7lTH9M7Q5Wa13qaF+dtjCGErFmvJ2LqsjLVIWRlxXTo9VE2e3L0BctOgazS4mL8hH6CSVaeb1OPoLrW4c38S7FqjHh0iIHy82hOngc/aFKKKXZWVsQVpyQiTaJoquOnVXLVIdq2B4KF13+ysME91o2WthvLz6liLQJVsOg+UzdSE3ZKyMiaSJCPMtn8L/PIKjJ5x7YKb5igpUgs7DT9C8Wl1zKuFWmvTfaQs7hR2R8rKmEiSjKiVvExoE6K6tfL3qYKbzq51v25FGWSvV11pp3LUMRcPte9Nz1ho4lf3ewhhATZNMg2BLMYUFlFWDJ/GQcdI0wtumkKrhEOpkP5tzcWdnQeorjS/zpa7lxC1YNUkc+bMGRYuXIjBYKCkpISAgABGjhzJyJEjG8Sg/8WkJSPqLCdNdW017wBlJWr8xtWCa2Dy96qWzZ5NKvkAtO0J4beq5Fbb2W5C1IHVkkxGRgbR0dHk5+dz8Ud1Oh3BwcG8/fbbjBgxwvyIbUSSjLCoTR+ogpt3vmX5xZZF+Wp76MxfVQsKVDdd2K3QZbBluuuEMJGp351m/wk0bdo0jh8/zr333ktSUhIHDhxg/fr1PP744xw6dIgxY8Y0iFllQlhF0EC1lqYqwVRacPJLEz/o/xeY8gH0e0C1nk7lqsoEn8ZB8nIoOWO5+wlhAWa3ZLy9vYmKimL9+vWXvZeXl8d9993H2rVrWbt2LYMGDbJYoNYiLRlhNady4ceXIHoa+IdY/voV5bD3NzVuc2K/OubsBt2iVemaulQrEOI6rNaScXd3p1+/fld8z9/fn2+//RZ/f39mz55t7qWFaFw0DXzagk8b61zfyVnVVbt9PsS+BB0i1VqbHT/BZ3+DxNfgaJZ17i2Eicxe6TV48GD27dt31fe9vLyYMGECH330UZ0CszZZ8S+srnkH9eUPKuGsXQDtekLXoZa9j04H7cPU4+RBtd1A9nrY97t6tOkGYeMhsI9UgBb17rotmSVLlmAwGKisVLNann/+eX766Se2b99+1c+4udWy7EY9io2NJT4+Hh8fH1uHIhxBeQmcOQrnTlv3Pi0CYOhUuDceIiaCq5dqzSS+Cl88plo5ZSXWjUGIi5i8M6a7uzu9evUiKiqKAwcOsGXLFubOncuUKVNqTFs+d+4c4eHh9OrVixUrVlj9F6grGZMR9aZGwc0dahylxxjrti7KimHnatW6KTqujrl4qAkK3YZD62CpkyZqxWJTmBcuXEhqairJyclkZGRQVlZW/WGdjoCAAGJjYwkMDKSgoIDPP/8cV1dXVq9ejb+//Q88SpIRNrFhEeSkwB1v177gpjkqK1QxzvTv4diu6uPNOqiJAsFDwNPX+nGIRsMq62RKS0sxGAwkJycbNykzGAyUlpaqi134iygiIoI+ffrw7rvv1jL8+iNJRtiEpqkaZZ7NVALIWg1dh9VPQcxTOap1k70Wii+MSeqdIKC3Sjgdo2TsRlxXvZWVKS8vx2AwkJKSYkw+BoOB8+fPS4FMIUxhjYKbpqgoh0PJsHOV+llVTcCzmZqc0DUamrWrv3hEg2LT2mUVFRVkZGTQq1cvS1/a4iTJCLtwNAtad71QcHOP6sZyrscJNH8WQPY6lXAKj1QfbxOitoru3F+2ixY1SIFME0mSEXalquBmhwgYPq3+769pKuFlrYI9v6lZcQAu7mp/m5Dh1clQODSL7Yx5JXPnzuV///sfBQUFtG/fnrCwMHr37k1UVBTh4eG4utp/DSVZJyPskosHjHhSbY4GFwpuVoKrZ/3cX6dT1Qn8Q2DAw6qiQNZqtV101ir18G2nxm66DlVda0Jcg9ktmSVLlvDggw/i7e1N27Ztyc7OVhe68JeNs7Mz3bt3p0+fPsTHx1s+YguTloywa5s+gP1/wF1v27a76tRhlWB2ra3eVE2nrzlZQHbxdChW6y7r27cvBw4cwGAw0Lp1a/R6Pffffz8dO3Zk8eLFHD58GL1ej6ZpMvAvRF0d3aVaEeHj1euKctt+mVeUq6nXWavh4LbqAqAevtB1iEo4zTrYLj5Rb6xWu2zPnj3ceuuttG7d2nisU6dOvPTSSxgMBkaOHMnEiRNJTk4299JCiEu16VqdYE7lwGf/B3k7bRePk7OaATfqWZjyoaoG7dtetW7SvoUvpsLXT8POlVBabLs4hd0w+0+i8vJyWrVqZXyt1+uNCzR9fX1ZunQp3bp145ZbbiE8PNxykQoh1P4x9lJd2dNXJcCwW9UCz6zVsGejen5sl+rq6zxAtW78u8tkAQdldkvG39+f48ePG183bdqUgoIC4+sWLVowevToBrEQU4gGpVkHGPei+nLXNFjzFmStsXVUKnm06QZDHoX7l6jaaf7dVUXoXWvgu5nw+aOQ8pWaKi0citktmT59+pCZmWl83bVrV1JSUmqc4+/vz3fffVf36IQQV1ZeomqR2dsmZS7uquXSLRpOH1ZJcNcatfbmj09gy1K1ZXS34RAQBU4uto5YWJnZLZkxY8awdetWY2tmzJgxbNmyhY0bNwJqIeaqVatwd3e3bKRCiGouHnDLy2pzMoDDBlWXzJI7cdaVbzu4aYrayXPMv6DTTarVc3Cbqgr98cOweTEUHLJ1pMKKarUY8+zZs+h0Ory8vDh9+jTdunXjzJkzDBkyhH379rF7926mTJnCkiVLrBCyZcnsMtEobIyHQylwx1v1U3Czts6dht3rVWWBUznVx1sFQ0i0WvDp5mW7+ITJ6nXF//bt25kyZQoGgwGAYcOG8eWXX9KiRYu6XtrqJMmIRkHTVLFLT1/Vmtm5UnVZ2Wt3lKbB8d1q7c3ujarSAYCzK9wwQCUc/1CZLGDHrLri/1K9evUiPT2d3Nxc3N3dadmypSUuK4QwlU5XXar/UDJseE9VDajPgpvm0OnUXjatg6H/g2oHz6xVcCRDVYfOXgutuqiJDtKyadActnZZVVmZtWvXsnv3bluHI4RlHctWX9I6nWoxNOto391oVQrz1GSBnSvV2psOEWo8R7YesDtW7S47c+YMCxcuxGAwUFJSQkBAACNHjmTkyJE1dslsCKS7TDRqti64WVtnjsJXT6nZcz3HwsBHbB2RuITVussyMjKIjo4mPz+fi/PTW2+9RXBwMG+//TYjRoww97JCCGtw8YCRT1UXsiwrhspK+++CatoGRj0D3z8Phh/VTLUeY2wdlagFs5sd06ZN4/jvA9NHAAAb4UlEQVTx49x7770kJSVx4MAB1q9fz+OPP86hQ4cYM2YM77zzjjViFULURrueqlIAwB+fwrInGkbJF//uaoEnqOoBOWm2jUfUitndZd7e3kRFRbF+/frL3svLy+O+++5j7dq1rF27lkGDBlksUGuR7jLhUI5lQ17mRQU3y+x3BlqVpE8g9Su13cGE16QAp52wWoFMd3d3+vXrd8X3/P39+fbbb/H392f27NnmXloIYW2tg2sW3Fz6f2pGlz27cbJayFl6Dn76DxTbWZUDcU1mJ5nBgwezb9++q77v5eXFhAkT2Lx5c50CE0JYmx5aBKjxDnum00P036HlDXDmGCS+plpgokG4bpJZsmQJBoOByspKAJ5//nl++ukntm/fftXPuLk1gKmSQji6Zu1g7PPVBTdXv6EqKdsjF3cYPVOt/cnLUOuAHHP1RYNz3STz4IMPEh4eTpMmTejXrx/vv/8+Q4YMYfjw4Xz00UfG5FPl3LlzfPvttwwfPtxqQQshLKy8RFVILimydSRX590CRs9QVQGyVkPaN7aOSJjgugP/CxcuJDU1leTkZDIyMox7x4DacjkgIIDY2FgCAwMpKCjg888/x9XVldWrV+Pvbyf7XlyDDPwLcYGmgVapFj4eNkD+XlWA094WQu77XXWZoYNRT6vxGlHvrLIYs7S0FIPBQHJyMsnJyaSkpGAwGCgtLVUXu1BnKCIigj59+jSIPWUkyQhxBZveVwU3J71pn5UCkperbQOc3WD8bPC7wdYROZx6K5BZXl6OwWAgJSXFmHwMBgPnz5+nosKOyo5fhSQZIa5A09Rqew8fVXAzMxFCRtjPdOeqTduy14FXC5g4V43XiHpTbwUynZ2diYiIICIigoceeghQe8pkZNj5tEghxNXpdCrBgGrRbIwH75b2U3BTp1MLNc8cg6M74ZfZcMt/7LPV5eBqXWissLCQtWvXsmrVKrKzs2u85+TkRK9eveocnBDCDgT2US2FgD7q9bFsKCuxbUygWlWjnoEmrVQR0LVvqTElYVdqlWReffVV/P39GT58ODExMYSEhODv78/MmTMpKrLj2SlCiNqpquhcVgw/vgzr7KR0lIePqtLs4gF7N8PWL2wdkbiE2Unm448/ZsaMGXh6ejJlyhT+/ve/c+edd6LX65k9ezYRERHs37+/TkEVFxdf1joy1bZt25g6dSrjx49nxYoVdYpDCHEJFw/Veuh9l3pdVgznz9o2puYdYeQ/1aLN5GWQfXnJK2FDmpkiIiK0tm3baidOnKhxvKKiQvvggw80b29vrXPnztrZs2fNvbRWWFio3XrrrVqTJk20hx56yHj8yy+/1AIDA7XOnTtrH3744TWvUV5ermmapp08eVK77777rnvPqKgos+MUQlywYZGmffSgpp0/Z+tING17gqYtvFXTFt2uaXk7bR1No2fqd6fZLZmsrCxuu+22y7ZW1uv1PPTQQ3z77bfs37+fefPmmZ3w9Ho9U6dOZf78+cZjRUVFTJ8+nU2bNrFp0yZmzJhBfn4+R48eZciQIcbHypUrATUeBDB37lyeeOIJs2MQQpih2zAIuxVcPdRrW5Z76TEWQkepGH6ZDUXHbReLMDI7yXh6euLu7n7V96Ojo4mJieGrr74yOxhvb2+io6Nxdq6e9JaYmMjgwYNp164dbdq0YdiwYaxevZo2bdqwbt0646NqD5vy8nL++c9/EhsbS2RkpNkxCCHM4BcEYbeo5wU5aoM0WxXc1OlgwMPQPgyKC1UxzdJztolFGJmdZHr06MHq1deubxQWFlbncZkqOTk5BAQEGF+3b9+evLy8q54/Y8YMfv/9d+Lj44mPj7/iOfHx8fTu3ZvevXuTn59vkTiFcHh6Pfh1tm3BTSdnNT7j2x4KDsGq+Wqdj7AZs5PMAw88QHp6Oq+99tpVzzly5EidgrpYaWlpjS2d9Xq9sUvsSubMmcOmTZtYsmQJcXFxVzwnLi6Obdu2sW3bNvz8/CwWqxAOzbedmulVVXBz1RuQubL+43DzhjEzwa0JHNwGv39U/zEIo1olmREjRjBjxgzuvvtu0tJq7la3Zs0ali1bRt++llm05e/vz+HDh42vc3Nz6dCh7psWJSQkEBcXR2FhYZ2vJYS4RPl5KD4NZTbqrvLxV3XN9M6w/XtVsUDYRK3KypSUlHD33Xfz3XffodPpaNq0KYGBgZw6dYqcnBz0ej1r1qyp9c6YS5YsYdOmTXzwwQccO3aMyMhIUlNTqayspH///hgMBry8LLNHuZSVEcJKNA3Q1NTi3O2Qv0dNEqjPgptZq2HtAnXPsS9Ae1kkbilW2xlz586duLm58c033/D9998zduxYNE0jPT2d3Nxc+vbtS2JiYq0STFFREUFBQTz99NMsX76coKAgMjMzmTVrFv369WPAgAHMmzfPYglGCGFFOp1KMAAHtsDOVVBZXr8xdItWO4FWVqjKzacPX/8zwqLMbsno9XruvfdePv744xrHz549i5ubGy4udlJAz0TSkhGinpScAfem6gs/42cIGan2hrG2qgRzYAv4tIUJr4F7E+vft5GzWkumWbNmVxwT8fb2blAJRsZkhKhn7k3Vz0MpsOkDyE279vmWoneC4f+AFoFQeAQS50BFPbeoHJjZSWbQoEFkZWVZI5Z6FRsbS3x8PD4+PrYORQjHEtgHbn+9uqLzsWxVnsaaXDzUjDPPZnDEABsXyfbN9cTsJDNz5kx+/PFHtm7dao14hBCOwC9I/SwrgZ9ehnULrX9Pbz+1fbOTK+xcqWadCaszO8msWLGCYcOGMXz4cD76qOHOP5fuMiHsgIs7jHoW+lwouFlq5YKbrbrAsMfV881L4ID8sWxttRr41+l0aJqGTqejVatWjB07lhtvvJHevXvTs2fPGmVh7J0M/AthRzbGw/4/4K7/VtdDs4ZtX8LWz1WSu+1VNV4jzGK17ZfXrVtHSkqK8ZGdnU1lZSU6nQ4AV1dXevbsSe/evVm4sB6awHUkSUYIO5K/D/IyoFesel1RZp0tnzUNVr8BuzeobrSJc1WlAmEyqyWZS507d4709PQaiSczM5Py8nIqKuy/ZpAkGSHsVMEhSHgBRkyHtj0sf/3yUvj+OTi2C1p3hVv+Dc6yfbOpTP3urHO/lqenJ/369aNfv37GY6WlpezYsaOul7aqhIQEEhISZExGCHvl5KLGUJrVvYzUFTm7qvGgr/6pEs3a/8LwaWoRqbAYkwf+X3zxRfz9/XF1daVLly785z//oazsyntHuLq62n2ZfZnCLISd8/FXs8E8fFT31sp5kPmrZe/h6aumNru4w56NamdNYVEmJZnFixfz73//m2PHjlFeXs7evXt54YUXuP32260dnxBCqIKb58+qKc+W1iIQRjypSuBs/Rz2bLL8PRyYSUnmvffew9XVlaVLl5Kbm8uqVauIjIzkhx9+YPny5daOUQjh6FzcYezz0Gucep27HVJWWG7lfkBv6PeAer7mbbVAVFiESUlm79693H777dx99920bduWYcOGsXLlSpo1a3ZZDTMhhLCKiwtuHtwGWWtAs+Dkol6xEDICKkrh51fgrGxoaAkmJZlTp04RFBRU45ivry9jx44lJSXFKoFZmyzGFKIBG/CgKnTp7KYKYG5PULPF6kKng0F/hXY91V44P82yfrkbB2DywP/Fu1NW6dixIydPnrRoQPVFBv6FaOCqKinnpMJvH0Juet2v6eQMI59S1ZpPHlC7e8r2zXVidlmZizk7O191hpkQQtSLgN5w+3xVeBPgaJYqT1Nb7k0ubN/srbYH+OMTy8TpoExOMi+//DI9e/bk4YcfJj4+nrS0NMrLpVy2EMIO+N2gfpaVqG6u9XWsNuLbDmKeVtsEpH2rCmqKWjFpxf+IESNITU2loKBAfeiSxUr/+Mc/CA8PJyIigpCQkCt2rdkrWfEvRCNzNEvtXePbVrVoKsuq97IxV+avKmHpnSD2JetUHmigrFJWZt++fWzbts34SE1NNQ6cVyUeNzc3evToQUREBIsWLapl+PVHkowQjdiGRbA/Ce5eWPuCm5sXQ/r34NYEJs5Ri0RF/dUuy87OrpF40tLSOHv2LDqdzq5rl1WVlVm7di27d++2dThCCGs4sV8V3Ox5YX1Nean5Wz5XVsAvs9W0ad92alabm7flY21g6i3JXErTNHbu3ElycjJTpkyx5KWtQloyQjiIkwch4Xm1ur9dT/M+W1oM3zwDBQdVhYDAPmrjNb/O4NXCIeud1VuBzEvpdDq6d+9O9+7dLX1pIYSoPWdXaNMNmnc0/7OuF7Zv/vppNbX55IHq9zx8qhOOX2f13Ku5QyaeK2k4u4sJIURd+PirqsugCm6umqcG8kNHmfb5Jq3grgWqpE3+HsjfC8f3QHEhHEpWjyoevtUJx68ztLqQeByQJBkhhOMpPw+l58yvEuDmDZ37qweoZFV0rDrh5O9Vj+LTlycez2YXtXYuavE0cpJkhBCOx8UdxjwHXBiSzk1Xe8qET1Cr/k2l00HTNurReYA6pmlw5mh1wqlq9Zw7pSYPHLxoHKNG4qka42lciUeSjBDCMel0wIVxk0Mp6ss/bLxlruvjrx5BA9WxGolnDxzfCyeulXgu6mbz66yONVCSZIQQov9fIGqSmhxQUQ47flRjNZbajvmKiacSzhyr2c1mTDxb1aOKV3O1RfTIpxrchAKHTTKy/bIQooaqtS+5abD5f6pIZlU9NGvQ6asTT5dB6phWCYVHa3az5e+FPwtUQmpgCQassE6moZF1MkKIy5zYDy07qedHs9S0Z1dP28SiVUJhHpz/E1oH2yaGKzD1u7PhFBkTQoj6UpVgykrgZwsU3KwLnV5VGrCjBGMOh+0uE0KI66qahVbVlVZ6To3ZeNSy4KYDkpaMEEJcS+tgVdEZIOkTWPZE3farcTDSkhFCCFOFxkDzDtUVncvPW24GWiMlLRkhhDBVi0DoMUY9P3kAPnlElZkRVyVJRgghasPFQ9U+axGoXjv2RN2rkiQjhBC10bQ1xDylJgFoGvw6F3b8ZOuo7I4kGSGEqKvy81BRqjY4EzXIwL8QQtSVizuMnkmNgpt5OyFyIji52DQ0W3PYlkxCQgJxcXFSVkYIYRk6nVo4CZCTCns2yjgNUlZGysoIIazj/J/g5qUWbxp+gNDR4NJ4pjtLWRkhhLAlNy/1Mzcdfl8Chx1zqrMkGSGEsKaAKLjjreqKzkcyVCvHQUiSEUIIa2sRoH6WlcAvr8KGd20bTz2S2WVCCFFfXNxh3POXFNwsAw8f28ZlRdKSEUKI+tSqi9qoDCDpY/jyCZVsGilpyQghhK30GAPNA6o3RCs736hmoIG0ZIQQwnaad4Qeo9Xzkwfg04cbXcFNSTJCCGEPXDygXS9oGaheN5IljJJkhBDCHjRtDSP/Ce4XCm4mvqYWcTZwkmSEEMLelJeCVmkshdaQycC/EELYGxc3GPVs9eucVFVwM2pSgyu4KS0ZIYSwRzqdegAcNsDezQ1ynMYuk0xxcTHZ2dm1+mxWVhZTp05l1KhRZGZmWjgyIYSwgZvug4lzwNlVFdxM/VpVD2gA7CrJnDlzhvHjx9O6dWvmzJljPL5s2TI6depEUFAQixcvvuY1unXrxoIFC3jggQdITU21dshCCFE/qtbS5KarRZxHdtg2HhPZ1ZiMXq9n6tSpjBs3jqSkJACKioqYPn06SUlJODk5ER4eTmxsLBUVFdx1113Gz86cOZMRI0YAMH36dLZu3cqyZcts8nsIIYTVBETBnQugeQf1+kiGqo1WVarGzthVS8bb25vo6GicnatzX2JiIoMHD6Zdu3a0adOGYcOGsXr1atq0acO6deuMj6oEAzBv3jzef/99XnvtNVv8GkIIYV1VCabsPCS+Cuvtt+CmXbVkriQnJ4eAgADj6/bt25OXl3fV83/44Qd+/PFHTpw4wfTp0694Tnx8PPHx8QDk5+dbNmAhhKgvLm4w7gVwvbB3zfk/VcFNT1/bxnURu08ypaWl6PXVDS69Xo+Tk9NVzx83bhzjxo275jXj4uKIi4sD1O5uQgjRYPkFVT9P+gT2/w73vFs9hmNjdtVddiX+/v4cPnzY+Do3N5cOHTrYMCIhhLBTvcZCn3suKrhp+xlodp9kYmJiSExM5Pjx4xw9epTNmzczcuTIOl83ISGBuLg4CgsLLRClEELYgWYdIDRGPT+xHz55WM1GsyG76i4rKioiIiKCoqIiSkpKWLduHe+//z6zZs2iX79+gBrU9/LyqvO9YmNjiY2Nle4yIUTj5OYFHSKgZSf1WtOqF3fWI52mNcAlpBbUu3dvtm3bZuswhBDCeqoKbrbtAb2uPWZtKlO/O+2qJVOfEhISSEhIkO4yIUTjV16qfkpLpv5JS0YI4TCquswOpcLxbOh9Z60vZep3p90P/AshhLCQqpbMkR1qK4F64LDdZUII4bBumlJvFZ0dNsnImIwQwqHV0/iMw3aXxcbGEh8fj4+Pj61DEUKIRsthk4wQQgjrkyQjhBDCamRMRsZkhBDCahy2JSNjMkIIYX0Om2SEEEJYnyQZIYQQVuPwZWVatmxJYGBgrT5bWFjYqLvb7P33s2V89XFva93DUte1xHVqe438/Hz8/PzqdG9Re4WFhZw6dYoTJ05c/2RN1Nojjzxi6xCsyt5/P1vGVx/3ttY9LHVdS1yntteIioqq871F7Znz7026y+ogNjbW1iFYlb3/fraMrz7uba17WOq6lriOvf83Jq7MnH9vDt9dJoRoeKR6esMhLRkhRIMTFxdn6xCEiaQlI4QQwmqkJSOEEMJqJMkIIYSwGkkyQgghrMZhC2QKIRqHsrIyNmzYQHl5OTExMbYOR1xCWjJCCLtVXFxMdnb2Nc8xGAwkJyfz888/11NUwhySZIQQdufMmTOMHz+e1q1bM2fOHOPxZcuW0alTJ4KCgli8eDEAkZGR3HHHHbYKVVyHdJcJIeyOXq9n6tSpjBs3jqSkJACKioqYPn06SUlJODk5ER4eTmxsrNQws3PSkhFC2B1vb2+io6Nxdq7+OzgxMZHBgwfTrl072rRpw7Bhw1i9erUNoxSmkCQjhGgQcnJyCAgIML5u3749eXl5pKenM3PmTNasWcP8+fNtGKG4EukuE0I0CKWlpej11X8X6/V6nJycCAsLY+nSpTaMTFyLtGSEEA2Cv78/hw8fNr7Ozc2lQ4cONoxImEKSjBCiQYiJiSExMZHjx49z9OhRNm/ezMiRI20dlrgO6S4TQtidoqIiIiIiKCoqoqSkhHXr1vH+++8za9Ys+vXrB8C8efPw8vKycaTieqQKsxBCCKuR7jIhhBBWI0lGCCGE1UiSEUIIYTWSZIQQQliNJBkhhBBWI0lGCCGE1UiSEUIIYTWSZIQQQliNJBkhhBBWI0lGiEv4+fmh0+lMfrz33nu2Dtlk8+fPR6fT8dlnn9k6FOEgpHaZEBf5888/efTRR2scKy8vZ9asWbi6uvLss89e9plRo0bVV3h1lpKSAkBUVJSNIxGOQmqXCXEd6enphIeHExUVxbZt22wdTp2EhISQm5tLYWFhjb1ZhLAW+a9MiOuoSixX++t/1apV6HQ6nn/++RrH//jjD2OX2oEDB2q8N3nyZPR6Pbt27apx/KuvvmL06NG0bNkSV1dXunTpwiuvvEJFRcUV723q+U8//TQ6nY6srCzOnj2Lk5OTMbZPP/3UeN7GjRuZMGECnTt3xt3dnVatWtG3b19mzJhh0j8rIS4l3WVCXEdycjIAvXv3vuL7zZs3B1R5+ou99tprxucFBQUEBgYCcOTIEZYvX05sbCxdu3YFoKKignvvvZcvvviCoKAgJk2ahJubGz///DMzZ85k165dfPTRR8brmXt+VFQU999/Px999BH9+/dnxIgRxvcGDx4MwCuvvMLMmTPp2LEjMTExtGzZkmPHjrFt2zZ++eUXXnnlldr+IxSOTBNCXFPfvn01QEtOTr7i+/v27dMA7aGHHjIey87O1vR6vTZ+/HgN0FatWmV8b8aMGRqgbdy40Xjs0Ucf1QDtmWee0crKyozHS0tLtf79+2uAlpGRUevzNU3TFi1apAHaokWLLvsdjh49qjk5OWkDBw7Uzp8/f9n7+fn51/pHJMRVSXeZENdQXl7O9u3bcXV1pUePHlc8p1mzZkDNlszcuXNp0qQJTz31FKBaMgAlJSXEx8dz4403MnDgQEB1qy1cuJBbb72V2bNn4+xc3cHg4uLC/fffbzyvNudXqRr0j4yMvOx3yMrKoqKiguDgYFxdXS97v2XLllf9ZyTEtUh3mRDXkJGRQUlJCVFRUVf88gXw8fFBr9cbk8yxY8f4+OOPeeKJJ4x70FclmU8++YQTJ07w7rvvGj+/YMECNE3D09OTF1988bLr79ixAwDtwhwdc8+vkpKSgouLCz179rzsM6Ghofj4+LB48WLy8/OZPHkyI0eONCZQIWrNtg0pIezbBx98oAFaXFzcNc9r1qyZNnDgQE3TNO3ZZ5/VXF1dtcOHD2tnzpzRAG327NmapmlaaGio1rlzZ62iosL4WT8/Pw247mPlypW1Ol/TNK2srExzd3fXwsLCrvo7GAwG7fbbb9c8PT01QHNyctJGjRp11W5CIUwhLRkhruF6g/5VmjVrRlFREWfPnuW9997jnnvuoW3btgA4OTlRUFDAr7/+SkZGBv/973+N04dLSkrIz8/n5ptvZv369deNx9zzq2RmZlJSUnLFrrIqPXr0YPny5ZSWlrJhwwbi4+NZvnw5W7du5fDhw7i5uZl8PyGqyJiMENdQlWSut3ixKsnEx8dz+vRpnnzySeN7TZs2paCggDfffJMWLVrwl7/8xfiedqFL68SJEybFY+75VdLS0gCIiIi47rmurq4MHz6cZcuWMXDgQE6ePMmxY8fMup8QVSTJCHEVpgz6V2nWrBmnTp3izTffZMyYMYSGhhrf8/Hx4Y8//uCXX37hb3/7G56ensb3PDw86NWrF5mZmXz99ddXvPamTZuM617MPb/KyZMnAZXwLpWamsrevXsvO75nzx527NhBx44dad++/TV/fyGuRrrLhLgKUwb9q1QlmVOnTvHJJ5/UeM/X15e0tDTc3d157LHHLvvs3LlzGTt2LBMnTmT48OH06tWLyspKDh8+THJyMmVlZRw6dKjW50N1S2zmzJns2LEDLy8vQkNDmTRpEm+//TYfffQRffv2JTQ0lFatWrF//36+//57ABYvXizVAUTt2XpQSAh79eGHH5o06K9pmhYXF6cBWp8+fS57b/DgwRqgPfLII1f9/JYtW7SJEydqrVu31pydnbUWLVpoPXr00OLi4mqssant+ZqmaQsWLNCCg4M1Nzc3DdBmzJihaZqmffPNN9q9996rBQcHa02aNNFcXFy0gIAA7aGHHtKys7Ov+7sLcS1Su0wIIYTVSBtYCCGE1UiSEUIIYTWSZIQQQliNJBkhhBBWI0lGCCGE1UiSEUIIYTWSZIQQQliNJBkhhBBWI0lGCCGE1UiSEUIIYTX/H0ud0ZRl1TTuAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.cm as cm\n",
"cmap = cm.get_cmap('rainbow_r',6)\n",
"\n",
"fig = plt.figure(figsize=(6, 4),facecolor='white')\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"plotPowerlaw(list(data_dict.values()), ax,cmap(1), \n",
" '$Tweets$')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 5. 清洗tweets文本"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-01T03:46:46.498846Z",
"start_time": "2019-04-01T03:46:46.496236Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"tweet = '''RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:02:19.500334Z",
"start_time": "2019-06-08T08:02:19.259536Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import re\n",
"\n",
"import twitter_text\n",
"# https://github.com/dryan/twitter-text-py/issues/21\n",
"#Macintosh HD ▸ 用户 ▸ datalab ▸ 应用程序 ▸ anaconda ▸ lib ▸ python3.5 ▸ site-packages"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 安装twitter_text\n",
"\n",
"[twitter-text-py](https://github.com/dryan/twitter-text-py/issues/21) could not be used for python 3\n",
"\n",
"\n",
"> ### <del>pip install twitter-text</del>\n",
"\n",
"Glyph debug the problem, and make [a new repo of twitter-text-py3](https://github.com/glyph/twitter-text-py).\n",
"\n",
"> ## pip install twitter-text\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 无法正常安装的同学\n",
"## 可以在spyder中打开terminal安装\n",
"\n",
"pip install twitter-text"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:04:37.675542Z",
"start_time": "2019-06-08T08:04:37.668241Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'AnonKitsu: @who'"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"\n",
"tweet = '''RT @AnonKitsu: @who ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''\n",
"\n",
"rt_patterns = re.compile(r\"(RT|via)((?:\\b\\W*@\\w+)+)\", \\\n",
" re.IGNORECASE)\n",
"rt_user_name = rt_patterns.findall(tweet)[0][1].strip(' @')#.split(':')[0]\n",
"rt_user_name"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"ExecuteTime": {
"end_time": "2019-04-01T03:59:45.727956Z",
"start_time": "2019-04-01T03:59:45.720369Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'AnonKitsu'"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import re\n",
"\n",
"tweet = '''RT @AnonKitsu: @who ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''\n",
"\n",
"rt_patterns = re.compile(r\"(RT|via)((?:\\b\\W*@\\w+)+)\", \\\n",
" re.IGNORECASE)\n",
"rt_user_name = rt_patterns.findall(tweet)[0][1].strip(' @').split(':')[0]\n",
"rt_user_name"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:05:00.196880Z",
"start_time": "2019-06-08T08:05:00.188010Z"
},
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[]\n",
"None\n"
]
}
],
"source": [
"import re\n",
"\n",
"tweet = '''@chengjun:@who ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''\n",
"\n",
"rt_patterns = re.compile(r\"(RT|via)((?:\\b\\W*@\\w+)+)\", re.IGNORECASE)\n",
"rt_user_name = rt_patterns.findall(tweet)\n",
"print(rt_user_name)\n",
"\n",
"if rt_user_name:\n",
" print('it exits.')\n",
"else:\n",
" print('None')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:05:27.804540Z",
"start_time": "2019-06-08T08:05:27.795572Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import re\n",
"\n",
"def extract_rt_user(tweet):\n",
" rt_patterns = re.compile(r\"(RT|via)((?:\\b\\W*@\\w+)+)\", re.IGNORECASE)\n",
" rt_user_name = rt_patterns.findall(tweet)\n",
" if rt_user_name:\n",
" rt_user_name = rt_user_name[0][1].strip(' @').split(':')[0]\n",
" else:\n",
" rt_user_name = None\n",
" return rt_user_name"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:05:31.592897Z",
"start_time": "2019-06-08T08:05:31.587624Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"'chengjun'"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tweet = '''RT @chengjun: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''\n",
"\n",
"extract_rt_user(tweet) "
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:05:42.978825Z",
"start_time": "2019-06-08T08:05:42.975151Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"None\n"
]
}
],
"source": [
"tweet = '''@chengjun: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''\n",
"\n",
"print(extract_rt_user(tweet) )"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:06:01.060683Z",
"start_time": "2019-06-08T08:06:01.032491Z"
},
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[('RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE RT !!HELP!!!!',\n",
" 'Anonops_Cop'),\n",
" ('@jamiekilstein @allisonkilkenny Interesting interview (never aired, wonder why??) by Fox with #ows protester http://t.co/Fte55Kh7',\n",
" 'KittyHybrid'),\n",
" (\"@Seductivpancake Right! Those guys have a victory condition: regime change. #ows doesn't seem to have a goal I can figure out.\",\n",
" 'nerdsherpa')]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import csv\n",
"\n",
"with open(\"../data/ows_tweets_sample.txt\", 'r') as f:\n",
" chunk = f.readlines()\n",
" \n",
"rt_network = []\n",
"lines = csv.reader(chunk[1:], delimiter=',', quotechar='\"')\n",
"tweet_user_data = [(i[1], i[8]) for i in lines]\n",
"tweet_user_data[:3]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:07:37.624179Z",
"start_time": "2019-06-08T08:07:37.588574Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[(('OccupyNCGBORO', 'angela0328'), 1),\n",
" (('evlance', 'KeithOlbermann'), 1),\n",
" (('Lusho0487', 'anonops'), 1)]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import defaultdict\n",
"\n",
"rt_network = []\n",
"rt_dict = defaultdict(int)\n",
"for k, i in enumerate(tweet_user_data):\n",
" tweet,user = i\n",
" rt_user = extract_rt_user(tweet)\n",
" if rt_user:\n",
" rt_network.append((user, rt_user)) #(rt_user,' ', user, end = '\\n')\n",
" rt_dict[(user, rt_user)] += 1\n",
"#rt_network[:5]\n",
"list(rt_dict.items())[:3]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 获得清洗过的推特文本\n",
"\n",
"不含人名、url、各种符号（如RT @等）"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:08:42.317807Z",
"start_time": "2019-06-08T08:08:42.309193Z"
},
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def extract_tweet_text(tweet, at_names, urls):\n",
" for i in at_names:\n",
" tweet = tweet.replace(i, '')\n",
" for j in urls:\n",
" tweet = tweet.replace(j, '')\n",
" marks = ['RT @', '@', '"', '#', '\\n', '\\t', ' ']\n",
" for k in marks:\n",
" tweet = tweet.replace(k, '')\n",
" return tweet"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:09:07.984224Z",
"start_time": "2019-06-08T08:09:07.973948Z"
},
"scrolled": true,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['AnonKitsu', 'chengjun', 'mili'] ['http://computational-communication.com', 'http://ccc.nju.edu.cn'] ['OCCUPYWALLSTREET', 'OWS', 'OCCUPYNY'] AnonKitsu -------->\n"
]
}
],
"source": [
"import twitter_text\n",
"\n",
"tweet = '''RT @AnonKitsu: ALERT!!!!!!!!!!COPS ARE KETTLING PROTESTERS IN PARK W HELICOPTERS AND PADDYWAGONS!!!! \n",
" #OCCUPYWALLSTREET #OWS #OCCUPYNY PLEASE @chengjun @mili http://computational-communication.com \n",
" http://ccc.nju.edu.cn RT !!HELP!!!!'''\n",
"\n",
"ex = twitter_text.Extractor(tweet)\n",
"at_names = ex.extract_mentioned_screen_names()\n",
"urls = ex.extract_urls()\n",
"hashtags = ex.extract_hashtags()\n",
"rt_user = extract_rt_user(tweet)\n",
"#tweet_text = extract_tweet_text(tweet, at_names, urls)\n",
"\n",
"print(at_names, urls, hashtags, rt_user,'-------->')#, tweet_text)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:10:11.740636Z",
"start_time": "2019-06-08T08:10:11.722855Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"import csv\n",
"\n",
"lines = csv.reader(chunk,delimiter=',', quotechar='\"')\n",
"tweets = [i[1] for i in lines]"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2019-06-08T08:10:16.517097Z",
"start_time": "2019-06-08T08:10:16.506944Z"
},
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[] [] [] None\n",
"['AnonKitsu'] [] ['OCCUPYWALLSTREET', 'OWS', 'OCCUPYNY'] AnonKitsu\n",
"['jamiekilstein', 'allisonkilkenny'] ['http://t.co/Fte55Kh7'] ['ows'] None\n",
"['Seductivpancake'] [] ['ows'] None\n",
"['bembel'] ['http://j.mp/rhHavq'] ['OccupyWallStreet', 'OWS'] bembel\n"
]
}
],
"source": [
"for tweet in tweets[:5]:\n",
" ex = twitter_text.Extractor(tweet)\n",
" at_names = ex.extract_mentioned_screen_names()\n",
" urls = ex.extract_urls()\n",
" hashtags = ex.extract_hashtags()\n",
" rt_user = extract_rt_user(tweet)\n",
" #tweet_text = extract_tweet_text(tweet, at_names, urls)\n",
"\n",
" print(at_names, urls, hashtags, rt_user)\n",
" #print(tweet_text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# 思考：\n",
"\n",
"### 提取出raw tweets中的rtuser与user的转发网络\n",
"\n",
"## 格式：\n",
"rt_user1, user1, 3\n",
"\n",
"rt_user2, user3, 2\n",
"\n",
"rt_user2, user4, 1\n",
"\n",
"...\n",
"\n",
"数据保存为csv格式"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"# 阅读文献"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"celltoolbar": "Slideshow",
"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": "3.5.4"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": false,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {
"height": "1260px",
"left": "1835px",
"top": "224px",
"width": "512px"
},
"toc_section_display": false,
"toc_window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
james-prior/cohpy | 20171212-dojo-fizzbuzz-revisited.ipynb | 1 | 14861 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, repeat selected cells from 20170921-dojo-fizzbuzz-revisited."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# This is for rerunning cells without restarting kernel.\n",
"\n",
"try:\n",
" del fizzbuzz\n",
"except NameError:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def test():\n",
" \"\"\"Ad-hoc testing\"\"\"\n",
" for input_, expected_output in expected_outputs.items():\n",
" actual_output = fizzbuzz(input_)\n",
" assert expected_output == actual_output\n",
" return 'All tests passed.'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'expected_outputs' 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-3-1d807cdc2f7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m# since the code they test does not exist (yet).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-2-b7bd15dcdadf>\u001b[0m in \u001b[0;36mtest\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest\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 2\u001b[0m \u001b[0;34m\"\"\"Ad-hoc testing\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexpected_outputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[0m\u001b[1;32m 4\u001b[0m \u001b[0mactual_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfizzbuzz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mactual_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'expected_outputs' is not defined"
]
}
],
"source": [
"# Tests should crash,\n",
"# since the code they test does not exist (yet).\n",
"\n",
"test()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"# This is written to provoke test failures.\n",
"\n",
"def fizzbuzz(i):\n",
" return 'hello world'"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'expected_outputs' 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-5-5cf5fd05a164>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Should crash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-2-b7bd15dcdadf>\u001b[0m in \u001b[0;36mtest\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtest\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 2\u001b[0m \u001b[0;34m\"\"\"Ad-hoc testing\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexpected_outputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[0m\u001b[1;32m 4\u001b[0m \u001b[0mactual_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfizzbuzz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32massert\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mactual_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'expected_outputs' is not defined"
]
}
],
"source": [
"# Should crash\n",
"test()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-71552858fb78>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;31m# Tests should fail.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-2-b7bd15dcdadf>\u001b[0m in \u001b[0;36mtest\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexpected_outputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[0mactual_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfizzbuzz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mactual_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'All tests passed.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"# Let's modify fizzbuzz for FizzBuzzBurr,\n",
"# where Burr is for multiples of 7.\n",
"\n",
"expected_outputs = {\n",
" 1*3: 'Fizz',\n",
" 2*3: 'Fizz',\n",
" 1*5: 'Buzz',\n",
" 2*5: 'Buzz',\n",
" 1*7: 'Burr',\n",
" 2*7: 'Burr',\n",
" 3*5: 'FizzBuzz',\n",
" 3*7: 'FizzBurr',\n",
" 5*7: 'BuzzBurr',\n",
" 3*5*7: 'FizzBuzzBurr',\n",
" 16: str(16),\n",
"}\n",
"\n",
"def fizzbuzz(i):\n",
" return None\n",
"\n",
"# Tests should fail.\n",
"test()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'All tests passed.'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Play with trying list comprehension instead of for loop.\n",
"modulus_words = (\n",
" (3, 'Fizz'),\n",
" (5, 'Buzz'),\n",
" (7, 'Burr'),\n",
")\n",
"\n",
"def fizzbuzz(i):\n",
" terms = [\n",
" word\n",
" for modulus, word in modulus_words\n",
" if i % modulus == 0\n",
" ]\n",
" if not terms:\n",
" terms.append(str(i))\n",
" return ''.join(terms)\n",
"\n",
"test()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Now we add new stuff."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'All tests passed.'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use \"or\" operator like Neil Ludbad used at monthly meeting\n",
"# to eliminate an if statement.\n",
"modulus_words = (\n",
" (3, 'Fizz'),\n",
" (5, 'Buzz'),\n",
" (7, 'Burr'),\n",
")\n",
"\n",
"def fizzbuzz(i):\n",
" terms = [\n",
" word\n",
" for modulus, word in modulus_words\n",
" if i % modulus == 0\n",
" ]\n",
" return ''.join(terms) or str(i)\n",
"\n",
"test()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'All tests passed.'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Consolidate to one big expression.\n",
"# It is hard to read, so I do not like it.\n",
"modulus_words = (\n",
" (3, 'Fizz'),\n",
" (5, 'Buzz'),\n",
" (7, 'Burr'),\n",
")\n",
"\n",
"def fizzbuzz(i):\n",
" return ''.join(\n",
" word\n",
" for modulus, word in modulus_words\n",
" if i % modulus == 0\n",
" ) or str(i)\n",
"\n",
"test()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's try multiples of non-primes 4, 6, and 15."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-0c97865e82ac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# Tests should fail.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-2-b7bd15dcdadf>\u001b[0m in \u001b[0;36mtest\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0minput_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexpected_outputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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[0mactual_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfizzbuzz\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mexpected_output\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mactual_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0;34m'All tests passed.'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"expected_outputs = {\n",
" 1*4: 'Fizz',\n",
" 2*4: 'Fizz',\n",
" 1*6: 'Buzz',\n",
" 2*6: 'FizzBuzz',\n",
" 1*15: 'Burr',\n",
" 2*15: 'BuzzBurr',\n",
" 4*6: 'FizzBuzz',\n",
" 4*15: 'FizzBuzzBurr',\n",
" 6*15: 'BuzzBurr',\n",
" 4*6*15: 'FizzBuzzBurr',\n",
" 5: str(5),\n",
"}\n",
"\n",
"def fizzbuzz(i):\n",
" return None\n",
"\n",
"# Tests should fail.\n",
"test()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'All tests passed.'"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"modulus_words = (\n",
" (4, 'Fizz'),\n",
" (6, 'Buzz'),\n",
" (15, 'Burr'),\n",
")\n",
"\n",
"def fizzbuzz(i):\n",
" terms = [\n",
" word\n",
" for modulus, word in modulus_words\n",
" if i % modulus == 0\n",
" ]\n",
" if not terms:\n",
" terms.append(str(i))\n",
" return ''.join(terms)\n",
"\n",
"test()"
]
}
],
"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 |
smharper/openmc | examples/jupyter/pandas-dataframes.ipynb | 1 | 169178 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook demonstrates how systematic analysis of tally scores is possible using Pandas dataframes. A dataframe can be automatically generated using the `Tally.get_pandas_dataframe(...)` method. Furthermore, by linking the tally data in a statepoint file with geometry and material information from a summary file, the dataframe can be shown with user-supplied labels."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import glob\n",
"\n",
"from IPython.display import Image\n",
"import matplotlib.pyplot as plt\n",
"import scipy.stats\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import openmc\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Input Files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we need to define materials that will be used in the problem. We will create three materials for the fuel, water, and cladding of the fuel pin."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# 1.6 enriched fuel\n",
"fuel = openmc.Material(name='1.6% Fuel')\n",
"fuel.set_density('g/cm3', 10.31341)\n",
"fuel.add_nuclide('U235', 3.7503e-4)\n",
"fuel.add_nuclide('U238', 2.2625e-2)\n",
"fuel.add_nuclide('O16', 4.6007e-2)\n",
"\n",
"# borated water\n",
"water = openmc.Material(name='Borated Water')\n",
"water.set_density('g/cm3', 0.740582)\n",
"water.add_nuclide('H1', 4.9457e-2)\n",
"water.add_nuclide('O16', 2.4732e-2)\n",
"water.add_nuclide('B10', 8.0042e-6)\n",
"\n",
"# zircaloy\n",
"zircaloy = openmc.Material(name='Zircaloy')\n",
"zircaloy.set_density('g/cm3', 6.55)\n",
"zircaloy.add_nuclide('Zr90', 7.2758e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With our three materials, we can now create a materials file object that can be exported to an actual XML file."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate a Materials collection\n",
"materials_file = openmc.Materials([fuel, water, zircaloy])\n",
"\n",
"# Export to \"materials.xml\"\n",
"materials_file.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's move on to the geometry. This problem will be a square array of fuel pins for which we can use OpenMC's lattice/universe feature. The basic universe will have three regions for the fuel, the clad, and the surrounding coolant. The first step is to create the bounding surfaces for fuel and clad, as well as the outer bounding surfaces of the problem."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Create cylinders for the fuel and clad\n",
"fuel_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.39218)\n",
"clad_outer_radius = openmc.ZCylinder(x0=0.0, y0=0.0, r=0.45720)\n",
"\n",
"# Create boundary planes to surround the geometry\n",
"# Use both reflective and vacuum boundaries to make life interesting\n",
"min_x = openmc.XPlane(x0=-10.71, boundary_type='reflective')\n",
"max_x = openmc.XPlane(x0=+10.71, boundary_type='vacuum')\n",
"min_y = openmc.YPlane(y0=-10.71, boundary_type='vacuum')\n",
"max_y = openmc.YPlane(y0=+10.71, boundary_type='reflective')\n",
"min_z = openmc.ZPlane(z0=-10.71, boundary_type='reflective')\n",
"max_z = openmc.ZPlane(z0=+10.71, boundary_type='reflective')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the surfaces defined, we can now construct a fuel pin cell from cells that are defined by intersections of half-spaces created by the surfaces."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Create fuel Cell\n",
"fuel_cell = openmc.Cell(name='1.6% Fuel', fill=fuel,\n",
" region=-fuel_outer_radius)\n",
"\n",
"# Create a clad Cell\n",
"clad_cell = openmc.Cell(name='1.6% Clad', fill=zircaloy)\n",
"clad_cell.region = +fuel_outer_radius & -clad_outer_radius\n",
"\n",
"# Create a moderator Cell\n",
"moderator_cell = openmc.Cell(name='1.6% Moderator', fill=water,\n",
" region=+clad_outer_radius)\n",
"\n",
"# Create a Universe to encapsulate a fuel pin\n",
"pin_cell_universe = openmc.Universe(name='1.6% Fuel Pin', cells=[\n",
" fuel_cell, clad_cell, moderator_cell\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the pin cell universe, we can construct a 17x17 rectangular lattice with a 1.26 cm pitch."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Create fuel assembly Lattice\n",
"assembly = openmc.RectLattice(name='1.6% Fuel - 0BA')\n",
"assembly.pitch = (1.26, 1.26)\n",
"assembly.lower_left = [-1.26 * 17. / 2.0] * 2\n",
"assembly.universes = [[pin_cell_universe] * 17] * 17"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OpenMC requires that there is a \"root\" universe. Let us create a root cell that is filled by the pin cell universe and then assign it to the root universe."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Create root Cell\n",
"root_cell = openmc.Cell(name='root cell', fill=assembly)\n",
"\n",
"# Add boundary planes\n",
"root_cell.region = +min_x & -max_x & +min_y & -max_y & +min_z & -max_z\n",
"\n",
"# Create root Universe\n",
"root_universe = openmc.Universe(name='root universe')\n",
"root_universe.add_cell(root_cell)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now must create a geometry that is assigned a root universe and export it to XML."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Create Geometry and export to \"geometry.xml\"\n",
"geometry = openmc.Geometry(root_universe)\n",
"geometry.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the geometry and materials finished, we now just need to define simulation parameters. In this case, we will use 5 inactive batches and 15 minimum active batches each with 2500 particles. We also tell OpenMC to turn tally triggers on, which means it will keep running until some criterion on the uncertainty of tallies is reached."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# OpenMC simulation parameters\n",
"min_batches = 20\n",
"max_batches = 200\n",
"inactive = 5\n",
"particles = 2500\n",
"\n",
"# Instantiate a Settings object\n",
"settings = openmc.Settings()\n",
"settings.batches = min_batches\n",
"settings.inactive = inactive\n",
"settings.particles = particles\n",
"settings.output = {'tallies': False}\n",
"settings.trigger_active = True\n",
"settings.trigger_max_batches = max_batches\n",
"\n",
"# Create an initial uniform spatial source distribution over fissionable zones\n",
"bounds = [-10.71, -10.71, -10, 10.71, 10.71, 10.]\n",
"uniform_dist = openmc.stats.Box(bounds[:3], bounds[3:], only_fissionable=True)\n",
"settings.source = openmc.Source(space=uniform_dist)\n",
"\n",
"# Export to \"settings.xml\"\n",
"settings.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us also create a plot file that we can use to verify that our pin cell geometry was created successfully."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Instantiate a Plot\n",
"plot = openmc.Plot(plot_id=1)\n",
"plot.filename = 'materials-xy'\n",
"plot.origin = [0, 0, 0]\n",
"plot.width = [21.5, 21.5]\n",
"plot.pixels = [250, 250]\n",
"plot.color_by = 'material'\n",
"\n",
"# Show plot\n",
"openmc.plot_inline(plot)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we can see from the plot, we have a nice array of pin cells with fuel, cladding, and water! Before we run our simulation, we need to tell the code what we want to tally. The following code shows how to create a variety of tallies."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate an empty Tallies object\n",
"tallies = openmc.Tallies()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate a fission rate mesh Tally"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate a tally Mesh\n",
"mesh = openmc.RegularMesh(mesh_id=1)\n",
"mesh.dimension = [17, 17]\n",
"mesh.lower_left = [-10.71, -10.71]\n",
"mesh.width = [1.26, 1.26]\n",
"\n",
"# Instantiate tally Filter\n",
"mesh_filter = openmc.MeshFilter(mesh)\n",
"\n",
"# Instantiate energy Filter\n",
"energy_filter = openmc.EnergyFilter([0, 0.625, 20.0e6])\n",
"\n",
"# Instantiate the Tally\n",
"tally = openmc.Tally(name='mesh tally')\n",
"tally.filters = [mesh_filter, energy_filter]\n",
"tally.scores = ['fission', 'nu-fission']\n",
"\n",
"# Add mesh and Tally to Tallies\n",
"tallies.append(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instantiate a cell Tally with nuclides"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate tally Filter\n",
"cell_filter = openmc.CellFilter(fuel_cell)\n",
"\n",
"# Instantiate the tally\n",
"tally = openmc.Tally(name='cell tally')\n",
"tally.filters = [cell_filter]\n",
"tally.scores = ['scatter']\n",
"tally.nuclides = ['U235', 'U238']\n",
"\n",
"# Add mesh and tally to Tallies\n",
"tallies.append(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a \"distribcell\" Tally. The distribcell filter allows us to tally multiple repeated instances of the same cell throughout the geometry."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# Instantiate tally Filter\n",
"distribcell_filter = openmc.DistribcellFilter(moderator_cell)\n",
"\n",
"# Instantiate tally Trigger for kicks\n",
"trigger = openmc.Trigger(trigger_type='std_dev', threshold=5e-5)\n",
"trigger.scores = ['absorption']\n",
"\n",
"# Instantiate the Tally\n",
"tally = openmc.Tally(name='distribcell tally')\n",
"tally.filters = [distribcell_filter]\n",
"tally.scores = ['absorption', 'scatter']\n",
"tally.triggers = [trigger]\n",
"\n",
"# Add mesh and tally to Tallies\n",
"tallies.append(tally)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# Export to \"tallies.xml\"\n",
"tallies.export_to_xml()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we a have a complete set of inputs, so we can go ahead and run our simulation."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" %%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" %%%%%%%%%%%%%%%%%%%%%%%%\n",
" ############### %%%%%%%%%%%%%%%%%%%%%%%%\n",
" ################## %%%%%%%%%%%%%%%%%%%%%%%\n",
" ################### %%%%%%%%%%%%%%%%%%%%%%%\n",
" #################### %%%%%%%%%%%%%%%%%%%%%%\n",
" ##################### %%%%%%%%%%%%%%%%%%%%%\n",
" ###################### %%%%%%%%%%%%%%%%%%%%\n",
" ####################### %%%%%%%%%%%%%%%%%%\n",
" ####################### %%%%%%%%%%%%%%%%%\n",
" ###################### %%%%%%%%%%%%%%%%%\n",
" #################### %%%%%%%%%%%%%%%%%\n",
" ################# %%%%%%%%%%%%%%%%%\n",
" ############### %%%%%%%%%%%%%%%%\n",
" ############ %%%%%%%%%%%%%%%\n",
" ######## %%%%%%%%%%%%%%\n",
" %%%%%%%%%%%\n",
"\n",
" | The OpenMC Monte Carlo Code\n",
" Copyright | 2011-2019 MIT and OpenMC contributors\n",
" License | http://openmc.readthedocs.io/en/latest/license.html\n",
" Version | 0.11.0-dev\n",
" Git SHA1 | 61c911cffdae2406f9f4bc667a9a6954748bb70c\n",
" Date/Time | 2019-07-18 22:46:04\n",
" OpenMP Threads | 4\n",
"\n",
" Reading settings XML file...\n",
" Reading cross sections XML file...\n",
" Reading materials XML file...\n",
" Reading geometry XML file...\n",
" Reading U235 from /opt/data/hdf5/nndc_hdf5_v15/U235.h5\n",
" Reading U238 from /opt/data/hdf5/nndc_hdf5_v15/U238.h5\n",
" Reading O16 from /opt/data/hdf5/nndc_hdf5_v15/O16.h5\n",
" Reading H1 from /opt/data/hdf5/nndc_hdf5_v15/H1.h5\n",
" Reading B10 from /opt/data/hdf5/nndc_hdf5_v15/B10.h5\n",
" Reading Zr90 from /opt/data/hdf5/nndc_hdf5_v15/Zr90.h5\n",
" Maximum neutron transport energy: 20000000.000000 eV for U235\n",
" Reading tallies XML file...\n",
" Writing summary.h5 file...\n",
" Initializing source particles...\n",
"\n",
" ====================> K EIGENVALUE SIMULATION <====================\n",
"\n",
" Bat./Gen. k Average k\n",
" ========= ======== ====================\n",
" 1/1 0.55921\n",
" 2/1 0.63816\n",
" 3/1 0.68834\n",
" 4/1 0.71192\n",
" 5/1 0.67935\n",
" 6/1 0.68254\n",
" 7/1 0.65804 0.67029 +/- 0.01225\n",
" 8/1 0.66225 0.66761 +/- 0.00756\n",
" 9/1 0.66336 0.66655 +/- 0.00545\n",
" 10/1 0.70686 0.67461 +/- 0.00910\n",
" 11/1 0.71753 0.68176 +/- 0.01031\n",
" 12/1 0.66967 0.68004 +/- 0.00889\n",
" 13/1 0.67800 0.67978 +/- 0.00770\n",
" 14/1 0.65634 0.67718 +/- 0.00727\n",
" 15/1 0.66891 0.67635 +/- 0.00656\n",
" 16/1 0.66281 0.67512 +/- 0.00606\n",
" 17/1 0.68160 0.67566 +/- 0.00556\n",
" 18/1 0.63835 0.67279 +/- 0.00586\n",
" 19/1 0.66200 0.67202 +/- 0.00548\n",
" 20/1 0.67156 0.67199 +/- 0.00510\n",
" Triggers unsatisfied, max unc./thresh. is 68.3537 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 70089 --- greater than max batches\n",
" Creating state point statepoint.020.h5...\n",
" 21/1 0.67469 0.67216 +/- 0.00478\n",
" Triggers unsatisfied, max unc./thresh. is 63.9814 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 65503 --- greater than max batches\n",
" 22/1 0.69218 0.67334 +/- 0.00464\n",
" Triggers unsatisfied, max unc./thresh. is 64.4829 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 70692 --- greater than max batches\n",
" 23/1 0.72838 0.67639 +/- 0.00534\n",
" Triggers unsatisfied, max unc./thresh. is 65.1347 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 76371 --- greater than max batches\n",
" 24/1 0.68472 0.67683 +/- 0.00507\n",
" Triggers unsatisfied, max unc./thresh. is 61.6163 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 72140 --- greater than max batches\n",
" 25/1 0.66664 0.67632 +/- 0.00483\n",
" Triggers unsatisfied, max unc./thresh. is 59.0208 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 69675 --- greater than max batches\n",
" 26/1 0.65315 0.67522 +/- 0.00473\n",
" Triggers unsatisfied, max unc./thresh. is 56.5216 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 67094 --- greater than max batches\n",
" 27/1 0.63865 0.67356 +/- 0.00480\n",
" Triggers unsatisfied, max unc./thresh. is 53.8991 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 63918 --- greater than max batches\n",
" 28/1 0.68053 0.67386 +/- 0.00460\n",
" Triggers unsatisfied, max unc./thresh. is 51.504 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61017 --- greater than max batches\n",
" 29/1 0.71585 0.67561 +/- 0.00474\n",
" Triggers unsatisfied, max unc./thresh. is 49.3115 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58364 --- greater than max batches\n",
" 30/1 0.67268 0.67549 +/- 0.00455\n",
" Triggers unsatisfied, max unc./thresh. is 47.3457 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56046 --- greater than max batches\n",
" 31/1 0.67027 0.67529 +/- 0.00437\n",
" Triggers unsatisfied, max unc./thresh. is 48.2456 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60524 --- greater than max batches\n",
" 32/1 0.67324 0.67522 +/- 0.00421\n",
" Triggers unsatisfied, max unc./thresh. is 47.1077 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59922 --- greater than max batches\n",
" 33/1 0.66398 0.67481 +/- 0.00408\n",
" Triggers unsatisfied, max unc./thresh. is 45.4352 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57807 --- greater than max batches\n",
" 34/1 0.66373 0.67443 +/- 0.00395\n",
" Triggers unsatisfied, max unc./thresh. is 44.8243 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58273 --- greater than max batches\n",
" 35/1 0.68412 0.67476 +/- 0.00383\n",
" Triggers unsatisfied, max unc./thresh. is 43.7412 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57404 --- greater than max batches\n",
" 36/1 0.66026 0.67429 +/- 0.00374\n",
" Triggers unsatisfied, max unc./thresh. is 43.0549 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57471 --- greater than max batches\n",
" 37/1 0.67283 0.67424 +/- 0.00362\n",
" Triggers unsatisfied, max unc./thresh. is 42.9634 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59073 --- greater than max batches\n",
" 38/1 0.69507 0.67487 +/- 0.00356\n",
" Triggers unsatisfied, max unc./thresh. is 41.6527 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57259 --- greater than max batches\n",
" 39/1 0.68681 0.67522 +/- 0.00347\n",
" Triggers unsatisfied, max unc./thresh. is 40.4174 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55547 --- greater than max batches\n",
" 40/1 0.65886 0.67476 +/- 0.00340\n",
" Triggers unsatisfied, max unc./thresh. is 39.424 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54404 --- greater than max batches\n",
" 41/1 0.63736 0.67372 +/- 0.00347\n",
" Triggers unsatisfied, max unc./thresh. is 40.094 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57877 --- greater than max batches\n",
" 42/1 0.71800 0.67491 +/- 0.00358\n",
" Triggers unsatisfied, max unc./thresh. is 39.0603 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56457 --- greater than max batches\n",
" 43/1 0.67193 0.67484 +/- 0.00348\n",
" Triggers unsatisfied, max unc./thresh. is 38.8448 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57344 --- greater than max batches\n",
" 44/1 0.66680 0.67463 +/- 0.00340\n",
" Triggers unsatisfied, max unc./thresh. is 38.227 for absorption in tally 3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" WARNING: The estimated number of batches is 56996 --- greater than max batches\n",
" 45/1 0.65956 0.67425 +/- 0.00334\n",
" Triggers unsatisfied, max unc./thresh. is 37.2591 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55535 --- greater than max batches\n",
" 46/1 0.64705 0.67359 +/- 0.00332\n",
" Triggers unsatisfied, max unc./thresh. is 37.802 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58594 --- greater than max batches\n",
" 47/1 0.67729 0.67368 +/- 0.00324\n",
" Triggers unsatisfied, max unc./thresh. is 36.9727 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57419 --- greater than max batches\n",
" 48/1 0.68259 0.67389 +/- 0.00317\n",
" Triggers unsatisfied, max unc./thresh. is 36.3752 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56901 --- greater than max batches\n",
" 49/1 0.64395 0.67320 +/- 0.00317\n",
" Triggers unsatisfied, max unc./thresh. is 35.7676 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56296 --- greater than max batches\n",
" 50/1 0.68839 0.67354 +/- 0.00312\n",
" Triggers unsatisfied, max unc./thresh. is 34.977 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55058 --- greater than max batches\n",
" 51/1 0.71108 0.67436 +/- 0.00316\n",
" Triggers unsatisfied, max unc./thresh. is 34.453 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54608 --- greater than max batches\n",
" 52/1 0.66286 0.67411 +/- 0.00310\n",
" Triggers unsatisfied, max unc./thresh. is 33.9781 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54268 --- greater than max batches\n",
" 53/1 0.62666 0.67313 +/- 0.00319\n",
" Triggers unsatisfied, max unc./thresh. is 33.4946 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53856 --- greater than max batches\n",
" 54/1 0.67124 0.67309 +/- 0.00313\n",
" Triggers unsatisfied, max unc./thresh. is 32.8639 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 52927 --- greater than max batches\n",
" 55/1 0.67741 0.67317 +/- 0.00306\n",
" Triggers unsatisfied, max unc./thresh. is 32.2922 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 52145 --- greater than max batches\n",
" 56/1 0.67182 0.67315 +/- 0.00300\n",
" Triggers unsatisfied, max unc./thresh. is 31.9136 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 51948 --- greater than max batches\n",
" 57/1 0.68764 0.67343 +/- 0.00296\n",
" Triggers unsatisfied, max unc./thresh. is 31.3059 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50969 --- greater than max batches\n",
" 58/1 0.72310 0.67436 +/- 0.00305\n",
" Triggers unsatisfied, max unc./thresh. is 30.8841 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50558 --- greater than max batches\n",
" 59/1 0.67689 0.67441 +/- 0.00299\n",
" Triggers unsatisfied, max unc./thresh. is 30.5895 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50534 --- greater than max batches\n",
" 60/1 0.65890 0.67413 +/- 0.00295\n",
" Triggers unsatisfied, max unc./thresh. is 30.0567 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49693 --- greater than max batches\n",
" 61/1 0.69128 0.67443 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 29.8144 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49784 --- greater than max batches\n",
" 62/1 0.65469 0.67409 +/- 0.00288\n",
" Triggers unsatisfied, max unc./thresh. is 29.3138 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 48986 --- greater than max batches\n",
" 63/1 0.71839 0.67485 +/- 0.00293\n",
" Triggers unsatisfied, max unc./thresh. is 28.9465 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 48604 --- greater than max batches\n",
" 64/1 0.69556 0.67520 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 29.1602 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50174 --- greater than max batches\n",
" 65/1 0.70067 0.67563 +/- 0.00289\n",
" Triggers unsatisfied, max unc./thresh. is 28.9248 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50204 --- greater than max batches\n",
" 66/1 0.67994 0.67570 +/- 0.00284\n",
" Triggers unsatisfied, max unc./thresh. is 28.7841 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50545 --- greater than max batches\n",
" 67/1 0.74539 0.67682 +/- 0.00301\n",
" Triggers unsatisfied, max unc./thresh. is 28.4946 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50346 --- greater than max batches\n",
" 68/1 0.67753 0.67683 +/- 0.00296\n",
" Triggers unsatisfied, max unc./thresh. is 28.1166 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49810 --- greater than max batches\n",
" 69/1 0.69595 0.67713 +/- 0.00293\n",
" Triggers unsatisfied, max unc./thresh. is 28.0441 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50340 --- greater than max batches\n",
" 70/1 0.70621 0.67758 +/- 0.00292\n",
" Triggers unsatisfied, max unc./thresh. is 27.708 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49908 --- greater than max batches\n",
" 71/1 0.71027 0.67807 +/- 0.00292\n",
" Triggers unsatisfied, max unc./thresh. is 27.2979 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 49187 --- greater than max batches\n",
" 72/1 0.63710 0.67746 +/- 0.00294\n",
" Triggers unsatisfied, max unc./thresh. is 27.3359 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 50071 --- greater than max batches\n",
" 73/1 0.70979 0.67794 +/- 0.00294\n",
" Triggers unsatisfied, max unc./thresh. is 29.5308 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59306 --- greater than max batches\n",
" 74/1 0.65957 0.67767 +/- 0.00291\n",
" Triggers unsatisfied, max unc./thresh. is 29.2344 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58976 --- greater than max batches\n",
" 75/1 0.66611 0.67751 +/- 0.00287\n",
" Triggers unsatisfied, max unc./thresh. is 28.8289 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58183 --- greater than max batches\n",
" 76/1 0.66033 0.67726 +/- 0.00284\n",
" Triggers unsatisfied, max unc./thresh. is 28.4986 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57670 --- greater than max batches\n",
" 77/1 0.68535 0.67738 +/- 0.00280\n",
" Triggers unsatisfied, max unc./thresh. is 28.2548 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57486 --- greater than max batches\n",
" 78/1 0.71920 0.67795 +/- 0.00282\n",
" Triggers unsatisfied, max unc./thresh. is 28.2853 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58410 --- greater than max batches\n",
" 79/1 0.67645 0.67793 +/- 0.00278\n",
" Triggers unsatisfied, max unc./thresh. is 27.9534 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57829 --- greater than max batches\n",
" 80/1 0.68300 0.67800 +/- 0.00275\n",
" Triggers unsatisfied, max unc./thresh. is 27.5813 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57060 --- greater than max batches\n",
" 81/1 0.69810 0.67826 +/- 0.00272\n",
" Triggers unsatisfied, max unc./thresh. is 27.2164 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56301 --- greater than max batches\n",
" 82/1 0.68213 0.67831 +/- 0.00269\n",
" Triggers unsatisfied, max unc./thresh. is 26.8628 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55570 --- greater than max batches\n",
" 83/1 0.68745 0.67843 +/- 0.00265\n",
" Triggers unsatisfied, max unc./thresh. is 26.5172 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54852 --- greater than max batches\n",
" 84/1 0.65239 0.67810 +/- 0.00264\n",
" Triggers unsatisfied, max unc./thresh. is 26.2016 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54241 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 85/1 0.64990 0.67775 +/- 0.00263\n",
" Triggers unsatisfied, max unc./thresh. is 25.9705 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53963 --- greater than max batches\n",
" 86/1 0.68586 0.67785 +/- 0.00260\n",
" Triggers unsatisfied, max unc./thresh. is 25.7908 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53884 --- greater than max batches\n",
" 87/1 0.63453 0.67732 +/- 0.00262\n",
" Triggers unsatisfied, max unc./thresh. is 25.5271 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53439 --- greater than max batches\n",
" 88/1 0.65402 0.67704 +/- 0.00261\n",
" Triggers unsatisfied, max unc./thresh. is 25.321 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 53221 --- greater than max batches\n",
" 89/1 0.69063 0.67720 +/- 0.00258\n",
" Triggers unsatisfied, max unc./thresh. is 25.8769 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56253 --- greater than max batches\n",
" 90/1 0.65729 0.67697 +/- 0.00256\n",
" Triggers unsatisfied, max unc./thresh. is 25.7648 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56431 --- greater than max batches\n",
" 91/1 0.72355 0.67751 +/- 0.00259\n",
" Triggers unsatisfied, max unc./thresh. is 25.5034 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55942 --- greater than max batches\n",
" 92/1 0.63010 0.67696 +/- 0.00262\n",
" Triggers unsatisfied, max unc./thresh. is 25.2708 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55565 --- greater than max batches\n",
" 93/1 0.68610 0.67707 +/- 0.00259\n",
" Triggers unsatisfied, max unc./thresh. is 24.9941 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54980 --- greater than max batches\n",
" 94/1 0.67618 0.67706 +/- 0.00256\n",
" Triggers unsatisfied, max unc./thresh. is 24.7139 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 54365 --- greater than max batches\n",
" 95/1 0.68946 0.67719 +/- 0.00253\n",
" Triggers unsatisfied, max unc./thresh. is 25.4371 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58240 --- greater than max batches\n",
" 96/1 0.70557 0.67751 +/- 0.00252\n",
" Triggers unsatisfied, max unc./thresh. is 25.5082 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59216 --- greater than max batches\n",
" 97/1 0.64689 0.67717 +/- 0.00252\n",
" Triggers unsatisfied, max unc./thresh. is 25.2374 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58603 --- greater than max batches\n",
" 98/1 0.70194 0.67744 +/- 0.00251\n",
" Triggers unsatisfied, max unc./thresh. is 25.393 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59972 --- greater than max batches\n",
" 99/1 0.68278 0.67750 +/- 0.00248\n",
" Triggers unsatisfied, max unc./thresh. is 25.5651 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61441 --- greater than max batches\n",
" 100/1 0.67066 0.67742 +/- 0.00246\n",
" Triggers unsatisfied, max unc./thresh. is 25.3552 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61079 --- greater than max batches\n",
" 101/1 0.64907 0.67713 +/- 0.00245\n",
" Triggers unsatisfied, max unc./thresh. is 25.3463 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61679 --- greater than max batches\n",
" 102/1 0.69810 0.67735 +/- 0.00243\n",
" Triggers unsatisfied, max unc./thresh. is 25.1877 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61544 --- greater than max batches\n",
" 103/1 0.70659 0.67764 +/- 0.00242\n",
" Triggers unsatisfied, max unc./thresh. is 24.9371 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60948 --- greater than max batches\n",
" 104/1 0.64152 0.67728 +/- 0.00243\n",
" Triggers unsatisfied, max unc./thresh. is 24.6848 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60330 --- greater than max batches\n",
" 105/1 0.68117 0.67732 +/- 0.00240\n",
" Triggers unsatisfied, max unc./thresh. is 24.4368 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59721 --- greater than max batches\n",
" 106/1 0.71963 0.67774 +/- 0.00242\n",
" Triggers unsatisfied, max unc./thresh. is 24.2091 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59200 --- greater than max batches\n",
" 107/1 0.69488 0.67790 +/- 0.00240\n",
" Triggers unsatisfied, max unc./thresh. is 23.9711 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58616 --- greater than max batches\n",
" 108/1 0.65697 0.67770 +/- 0.00238\n",
" Triggers unsatisfied, max unc./thresh. is 23.8071 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58384 --- greater than max batches\n",
" 109/1 0.70032 0.67792 +/- 0.00237\n",
" Triggers unsatisfied, max unc./thresh. is 23.5788 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57825 --- greater than max batches\n",
" 110/1 0.66571 0.67780 +/- 0.00235\n",
" Triggers unsatisfied, max unc./thresh. is 23.5035 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58009 --- greater than max batches\n",
" 111/1 0.69676 0.67798 +/- 0.00234\n",
" Triggers unsatisfied, max unc./thresh. is 23.3157 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57629 --- greater than max batches\n",
" 112/1 0.68219 0.67802 +/- 0.00231\n",
" Triggers unsatisfied, max unc./thresh. is 23.1525 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57361 --- greater than max batches\n",
" 113/1 0.69025 0.67813 +/- 0.00230\n",
" Triggers unsatisfied, max unc./thresh. is 23.0036 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57156 --- greater than max batches\n",
" 114/1 0.69241 0.67826 +/- 0.00228\n",
" Triggers unsatisfied, max unc./thresh. is 22.792 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56628 --- greater than max batches\n",
" 115/1 0.68646 0.67834 +/- 0.00226\n",
" Triggers unsatisfied, max unc./thresh. is 22.6864 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56620 --- greater than max batches\n",
" 116/1 0.69601 0.67850 +/- 0.00224\n",
" Triggers unsatisfied, max unc./thresh. is 22.5007 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 56203 --- greater than max batches\n",
" 117/1 0.68761 0.67858 +/- 0.00222\n",
" Triggers unsatisfied, max unc./thresh. is 22.3093 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 55749 --- greater than max batches\n",
" 118/1 0.71356 0.67889 +/- 0.00223\n",
" Triggers unsatisfied, max unc./thresh. is 22.6651 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58054 --- greater than max batches\n",
" 119/1 0.69850 0.67906 +/- 0.00221\n",
" Triggers unsatisfied, max unc./thresh. is 22.4712 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57570 --- greater than max batches\n",
" 120/1 0.70957 0.67933 +/- 0.00221\n",
" Triggers unsatisfied, max unc./thresh. is 22.3266 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57331 --- greater than max batches\n",
" 121/1 0.69643 0.67947 +/- 0.00220\n",
" Triggers unsatisfied, max unc./thresh. is 22.6029 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59269 --- greater than max batches\n",
" 122/1 0.67717 0.67945 +/- 0.00218\n",
" Triggers unsatisfied, max unc./thresh. is 22.4667 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59062 --- greater than max batches\n",
" 123/1 0.68419 0.67949 +/- 0.00216\n",
" Triggers unsatisfied, max unc./thresh. is 22.3764 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59089 --- greater than max batches\n",
" 124/1 0.69221 0.67960 +/- 0.00214\n",
" Triggers unsatisfied, max unc./thresh. is 22.3341 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59364 --- greater than max batches\n",
" 125/1 0.73940 0.68010 +/- 0.00218\n",
" Triggers unsatisfied, max unc./thresh. is 22.1478 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58868 --- greater than max batches\n",
" 126/1 0.66908 0.68001 +/- 0.00217\n",
" Triggers unsatisfied, max unc./thresh. is 22.0085 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58615 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 127/1 0.66041 0.67985 +/- 0.00216\n",
" Triggers unsatisfied, max unc./thresh. is 21.8274 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58131 --- greater than max batches\n",
" 128/1 0.69395 0.67996 +/- 0.00214\n",
" Triggers unsatisfied, max unc./thresh. is 21.6537 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57678 --- greater than max batches\n",
" 129/1 0.68665 0.68002 +/- 0.00212\n",
" Triggers unsatisfied, max unc./thresh. is 21.7739 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58794 --- greater than max batches\n",
" 130/1 0.64849 0.67976 +/- 0.00212\n",
" Triggers unsatisfied, max unc./thresh. is 21.7492 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59134 --- greater than max batches\n",
" 131/1 0.69734 0.67990 +/- 0.00211\n",
" Triggers unsatisfied, max unc./thresh. is 21.59 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58738 --- greater than max batches\n",
" 132/1 0.69482 0.68002 +/- 0.00210\n",
" Triggers unsatisfied, max unc./thresh. is 21.4249 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58302 --- greater than max batches\n",
" 133/1 0.68884 0.68009 +/- 0.00208\n",
" Triggers unsatisfied, max unc./thresh. is 21.2587 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57853 --- greater than max batches\n",
" 134/1 0.63042 0.67971 +/- 0.00210\n",
" Triggers unsatisfied, max unc./thresh. is 21.1851 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57902 --- greater than max batches\n",
" 135/1 0.69209 0.67980 +/- 0.00209\n",
" Triggers unsatisfied, max unc./thresh. is 21.0525 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57623 --- greater than max batches\n",
" 136/1 0.69873 0.67995 +/- 0.00208\n",
" Triggers unsatisfied, max unc./thresh. is 20.9996 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57774 --- greater than max batches\n",
" 137/1 0.70270 0.68012 +/- 0.00207\n",
" Triggers unsatisfied, max unc./thresh. is 20.8455 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 57364 --- greater than max batches\n",
" 138/1 0.67295 0.68006 +/- 0.00205\n",
" Triggers unsatisfied, max unc./thresh. is 21.3716 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60752 --- greater than max batches\n",
" 139/1 0.63853 0.67975 +/- 0.00206\n",
" Triggers unsatisfied, max unc./thresh. is 21.2124 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60301 --- greater than max batches\n",
" 140/1 0.66645 0.67966 +/- 0.00205\n",
" Triggers unsatisfied, max unc./thresh. is 21.1279 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60268 --- greater than max batches\n",
" 141/1 0.70730 0.67986 +/- 0.00204\n",
" Triggers unsatisfied, max unc./thresh. is 20.9845 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59893 --- greater than max batches\n",
" 142/1 0.68838 0.67992 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 20.8774 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59719 --- greater than max batches\n",
" 143/1 0.64900 0.67970 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 21.3772 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 63069 --- greater than max batches\n",
" 144/1 0.64490 0.67945 +/- 0.00203\n",
" Triggers unsatisfied, max unc./thresh. is 21.2531 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62791 --- greater than max batches\n",
" 145/1 0.69221 0.67954 +/- 0.00201\n",
" Triggers unsatisfied, max unc./thresh. is 21.2049 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62956 --- greater than max batches\n",
" 146/1 0.69481 0.67965 +/- 0.00200\n",
" Triggers unsatisfied, max unc./thresh. is 21.0645 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62569 --- greater than max batches\n",
" 147/1 0.70394 0.67982 +/- 0.00200\n",
" Triggers unsatisfied, max unc./thresh. is 20.9156 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62125 --- greater than max batches\n",
" 148/1 0.69482 0.67992 +/- 0.00198\n",
" Triggers unsatisfied, max unc./thresh. is 20.7699 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61694 --- greater than max batches\n",
" 149/1 0.63886 0.67964 +/- 0.00199\n",
" Triggers unsatisfied, max unc./thresh. is 20.6366 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61331 --- greater than max batches\n",
" 150/1 0.69377 0.67973 +/- 0.00198\n",
" Triggers unsatisfied, max unc./thresh. is 20.5819 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61430 --- greater than max batches\n",
" 151/1 0.71045 0.67994 +/- 0.00198\n",
" Triggers unsatisfied, max unc./thresh. is 20.5417 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61612 --- greater than max batches\n",
" 152/1 0.66093 0.67982 +/- 0.00197\n",
" Triggers unsatisfied, max unc./thresh. is 20.4124 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61256 --- greater than max batches\n",
" 153/1 0.68564 0.67985 +/- 0.00196\n",
" Triggers unsatisfied, max unc./thresh. is 20.3025 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61010 --- greater than max batches\n",
" 154/1 0.66961 0.67979 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 20.2239 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60948 --- greater than max batches\n",
" 155/1 0.67099 0.67973 +/- 0.00193\n",
" Triggers unsatisfied, max unc./thresh. is 20.0962 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60584 --- greater than max batches\n",
" 156/1 0.72742 0.68004 +/- 0.00194\n",
" Triggers unsatisfied, max unc./thresh. is 19.9753 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60256 --- greater than max batches\n",
" 157/1 0.66458 0.67994 +/- 0.00193\n",
" Triggers unsatisfied, max unc./thresh. is 19.8852 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60109 --- greater than max batches\n",
" 158/1 0.69052 0.68001 +/- 0.00192\n",
" Triggers unsatisfied, max unc./thresh. is 19.7963 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59965 --- greater than max batches\n",
" 159/1 0.70643 0.68018 +/- 0.00192\n",
" Triggers unsatisfied, max unc./thresh. is 19.6991 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59766 --- greater than max batches\n",
" 160/1 0.68576 0.68022 +/- 0.00191\n",
" Triggers unsatisfied, max unc./thresh. is 19.6197 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59670 --- greater than max batches\n",
" 161/1 0.69854 0.68034 +/- 0.00190\n",
" Triggers unsatisfied, max unc./thresh. is 19.8287 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61341 --- greater than max batches\n",
" 162/1 0.65983 0.68020 +/- 0.00189\n",
" Triggers unsatisfied, max unc./thresh. is 20.0243 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62958 --- greater than max batches\n",
" 163/1 0.66316 0.68010 +/- 0.00188\n",
" Triggers unsatisfied, max unc./thresh. is 19.8975 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62560 --- greater than max batches\n",
" 164/1 0.66179 0.67998 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 19.895 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62940 --- greater than max batches\n",
" 165/1 0.70881 0.68016 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 19.8013 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62740 --- greater than max batches\n",
" 166/1 0.70729 0.68033 +/- 0.00187\n",
" Triggers unsatisfied, max unc./thresh. is 19.6876 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62410 --- greater than max batches\n",
" 167/1 0.71073 0.68052 +/- 0.00186\n",
" Triggers unsatisfied, max unc./thresh. is 19.5695 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 62046 --- greater than max batches\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 168/1 0.69610 0.68061 +/- 0.00185\n",
" Triggers unsatisfied, max unc./thresh. is 19.4797 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61857 --- greater than max batches\n",
" 169/1 0.67141 0.68056 +/- 0.00184\n",
" Triggers unsatisfied, max unc./thresh. is 19.438 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61970 --- greater than max batches\n",
" 170/1 0.67727 0.68054 +/- 0.00183\n",
" Triggers unsatisfied, max unc./thresh. is 19.3208 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61599 --- greater than max batches\n",
" 171/1 0.64150 0.68030 +/- 0.00184\n",
" Triggers unsatisfied, max unc./thresh. is 19.2066 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61242 --- greater than max batches\n",
" 172/1 0.68758 0.68035 +/- 0.00183\n",
" Triggers unsatisfied, max unc./thresh. is 19.114 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61018 --- greater than max batches\n",
" 173/1 0.67126 0.68029 +/- 0.00182\n",
" Triggers unsatisfied, max unc./thresh. is 19.1545 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61644 --- greater than max batches\n",
" 174/1 0.65933 0.68017 +/- 0.00181\n",
" Triggers unsatisfied, max unc./thresh. is 19.0415 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 61281 --- greater than max batches\n",
" 175/1 0.70572 0.68032 +/- 0.00181\n",
" Triggers unsatisfied, max unc./thresh. is 18.9347 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60954 --- greater than max batches\n",
" 176/1 0.66175 0.68021 +/- 0.00180\n",
" Triggers unsatisfied, max unc./thresh. is 18.8337 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60660 --- greater than max batches\n",
" 177/1 0.68714 0.68025 +/- 0.00179\n",
" Triggers unsatisfied, max unc./thresh. is 18.7329 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60364 --- greater than max batches\n",
" 178/1 0.70181 0.68037 +/- 0.00178\n",
" Triggers unsatisfied, max unc./thresh. is 18.6297 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 60048 --- greater than max batches\n",
" 179/1 0.66700 0.68030 +/- 0.00177\n",
" Triggers unsatisfied, max unc./thresh. is 18.5239 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59711 --- greater than max batches\n",
" 180/1 0.68980 0.68035 +/- 0.00176\n",
" Triggers unsatisfied, max unc./thresh. is 18.4186 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59374 --- greater than max batches\n",
" 181/1 0.69586 0.68044 +/- 0.00176\n",
" Triggers unsatisfied, max unc./thresh. is 18.3816 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59473 --- greater than max batches\n",
" 182/1 0.68689 0.68048 +/- 0.00175\n",
" Triggers unsatisfied, max unc./thresh. is 18.2781 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59139 --- greater than max batches\n",
" 183/1 0.69257 0.68054 +/- 0.00174\n",
" Triggers unsatisfied, max unc./thresh. is 18.1773 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58819 --- greater than max batches\n",
" 184/1 0.69926 0.68065 +/- 0.00173\n",
" Triggers unsatisfied, max unc./thresh. is 18.2191 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59422 --- greater than max batches\n",
" 185/1 0.67801 0.68063 +/- 0.00172\n",
" Triggers unsatisfied, max unc./thresh. is 18.1184 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59096 --- greater than max batches\n",
" 186/1 0.67049 0.68058 +/- 0.00171\n",
" Triggers unsatisfied, max unc./thresh. is 18.0484 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58965 --- greater than max batches\n",
" 187/1 0.68164 0.68058 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 17.9808 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58848 --- greater than max batches\n",
" 188/1 0.66856 0.68052 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 17.9146 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58736 --- greater than max batches\n",
" 189/1 0.71850 0.68073 +/- 0.00170\n",
" Triggers unsatisfied, max unc./thresh. is 17.8551 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58665 --- greater than max batches\n",
" 190/1 0.67095 0.68067 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.8953 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59250 --- greater than max batches\n",
" 191/1 0.70857 0.68082 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.8197 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59068 --- greater than max batches\n",
" 192/1 0.65322 0.68067 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.8199 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59387 --- greater than max batches\n",
" 193/1 0.67888 0.68066 +/- 0.00168\n",
" Triggers unsatisfied, max unc./thresh. is 17.8072 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59620 --- greater than max batches\n",
" 194/1 0.72890 0.68092 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.7152 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59319 --- greater than max batches\n",
" 195/1 0.64688 0.68074 +/- 0.00169\n",
" Triggers unsatisfied, max unc./thresh. is 17.6252 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59029 --- greater than max batches\n",
" 196/1 0.68906 0.68078 +/- 0.00168\n",
" Triggers unsatisfied, max unc./thresh. is 17.5465 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58810 --- greater than max batches\n",
" 197/1 0.69381 0.68085 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 17.4939 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58764 --- greater than max batches\n",
" 198/1 0.70057 0.68095 +/- 0.00167\n",
" Triggers unsatisfied, max unc./thresh. is 17.4414 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58717 --- greater than max batches\n",
" 199/1 0.67868 0.68094 +/- 0.00166\n",
" Triggers unsatisfied, max unc./thresh. is 17.4394 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 59008 --- greater than max batches\n",
" 200/1 0.69190 0.68100 +/- 0.00165\n",
" Triggers unsatisfied, max unc./thresh. is 17.3511 for absorption in tally 3\n",
" WARNING: The estimated number of batches is 58712 --- greater than max batches\n",
" Creating state point statepoint.200.h5...\n",
"\n",
" =======================> TIMING STATISTICS <=======================\n",
"\n",
" Total time for initialization = 9.3777e-01 seconds\n",
" Reading cross sections = 8.7757e-01 seconds\n",
" Total time in simulation = 4.0652e+01 seconds\n",
" Time in transport only = 3.9022e+01 seconds\n",
" Time in inactive batches = 9.1120e-01 seconds\n",
" Time in active batches = 3.9741e+01 seconds\n",
" Time synchronizing fission bank = 4.0496e-02 seconds\n",
" Sampling source sites = 3.3700e-02 seconds\n",
" SEND/RECV source sites = 6.4404e-03 seconds\n",
" Time accumulating tallies = 2.0272e-03 seconds\n",
" Total time for finalization = 4.0896e-03 seconds\n",
" Total time elapsed = 4.1621e+01 seconds\n",
" Calculation Rate (inactive) = 13718.1 particles/second\n",
" Calculation Rate (active) = 12267.1 particles/second\n",
"\n",
" ============================> RESULTS <============================\n",
"\n",
" k-effective (Collision) = 0.68122 +/- 0.00150\n",
" k-effective (Track-length) = 0.68100 +/- 0.00165\n",
" k-effective (Absorption) = 0.68224 +/- 0.00159\n",
" Combined k-effective = 0.68162 +/- 0.00134\n",
" Leakage Fraction = 0.34047 +/- 0.00082\n",
"\n"
]
}
],
"source": [
"# Remove old HDF5 (summary, statepoint) files\n",
"!rm statepoint.*\n",
"\n",
"# Run OpenMC!\n",
"openmc.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tally Data Processing"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"# We do not know how many batches were needed to satisfy the \n",
"# tally trigger(s), so find the statepoint file(s)\n",
"statepoints = glob.glob('statepoint.*.h5')\n",
"\n",
"# Load the last statepoint file\n",
"sp = openmc.StatePoint(statepoints[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the mesh fission rate tally**"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t1\n",
"\tName =\tmesh tally\n",
"\tFilters =\tMeshFilter, EnergyFilter\n",
"\tNuclides =\ttotal \n",
"\tScores =\t['fission', 'nu-fission']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"# Find the mesh tally with the StatePoint API\n",
"tally = sp.get_tally(name='mesh tally')\n",
"\n",
"# Print a little info about the mesh tally to the screen\n",
"print(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.16617932]]\n",
"\n",
" [[0.06455926]]\n",
"\n",
" [[0.32266365]]\n",
"\n",
" [[0.13355528]]]\n"
]
}
],
"source": [
"# Get the relative error for the thermal fission reaction \n",
"# rates in the four corner pins \n",
"data = tally.get_values(scores=['fission'],\n",
" filters=[openmc.MeshFilter, openmc.EnergyFilter], \\\n",
" filter_bins=[((1,1),(1,17), (17,1), (17,17)), \\\n",
" ((0., 0.625),)], value='rel_err')\n",
"print(data)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
" <th>energy low [eV]</th>\n",
" <th>energy high [eV]</th>\n",
" <th>score</th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</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>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>1.76e-04</td>\n",
" <td>2.92e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>4.28e-04</td>\n",
" <td>7.12e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>6.67e-05</td>\n",
" <td>6.94e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.75e-04</td>\n",
" <td>1.71e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>2.04e-04</td>\n",
" <td>3.80e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>4.96e-04</td>\n",
" <td>9.27e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>5.76e-05</td>\n",
" <td>6.97e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.52e-04</td>\n",
" <td>1.91e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>1.80e-04</td>\n",
" <td>3.15e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>4.38e-04</td>\n",
" <td>7.68e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>7.19e-05</td>\n",
" <td>9.68e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.89e-04</td>\n",
" <td>2.49e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>1.91e-04</td>\n",
" <td>3.67e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>4.66e-04</td>\n",
" <td>8.93e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>6.78e-05</td>\n",
" <td>9.81e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.76e-04</td>\n",
" <td>2.44e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>fission</td>\n",
" <td>1.56e-04</td>\n",
" <td>2.32e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.00e+00</td>\n",
" <td>6.25e-01</td>\n",
" <td>nu-fission</td>\n",
" <td>3.81e-04</td>\n",
" <td>5.65e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>fission</td>\n",
" <td>6.28e-05</td>\n",
" <td>8.06e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6.25e-01</td>\n",
" <td>2.00e+07</td>\n",
" <td>nu-fission</td>\n",
" <td>1.62e-04</td>\n",
" <td>2.05e-05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mesh 1 energy low [eV] energy high [eV] score mean \\\n",
" x y z \n",
"0 1 1 1 0.00e+00 6.25e-01 fission 1.76e-04 \n",
"1 1 1 1 0.00e+00 6.25e-01 nu-fission 4.28e-04 \n",
"2 1 1 1 6.25e-01 2.00e+07 fission 6.67e-05 \n",
"3 1 1 1 6.25e-01 2.00e+07 nu-fission 1.75e-04 \n",
"4 2 1 1 0.00e+00 6.25e-01 fission 2.04e-04 \n",
"5 2 1 1 0.00e+00 6.25e-01 nu-fission 4.96e-04 \n",
"6 2 1 1 6.25e-01 2.00e+07 fission 5.76e-05 \n",
"7 2 1 1 6.25e-01 2.00e+07 nu-fission 1.52e-04 \n",
"8 3 1 1 0.00e+00 6.25e-01 fission 1.80e-04 \n",
"9 3 1 1 0.00e+00 6.25e-01 nu-fission 4.38e-04 \n",
"10 3 1 1 6.25e-01 2.00e+07 fission 7.19e-05 \n",
"11 3 1 1 6.25e-01 2.00e+07 nu-fission 1.89e-04 \n",
"12 4 1 1 0.00e+00 6.25e-01 fission 1.91e-04 \n",
"13 4 1 1 0.00e+00 6.25e-01 nu-fission 4.66e-04 \n",
"14 4 1 1 6.25e-01 2.00e+07 fission 6.78e-05 \n",
"15 4 1 1 6.25e-01 2.00e+07 nu-fission 1.76e-04 \n",
"16 5 1 1 0.00e+00 6.25e-01 fission 1.56e-04 \n",
"17 5 1 1 0.00e+00 6.25e-01 nu-fission 3.81e-04 \n",
"18 5 1 1 6.25e-01 2.00e+07 fission 6.28e-05 \n",
"19 5 1 1 6.25e-01 2.00e+07 nu-fission 1.62e-04 \n",
"\n",
" std. dev. \n",
" \n",
"0 2.92e-05 \n",
"1 7.12e-05 \n",
"2 6.94e-06 \n",
"3 1.71e-05 \n",
"4 3.80e-05 \n",
"5 9.27e-05 \n",
"6 6.97e-06 \n",
"7 1.91e-05 \n",
"8 3.15e-05 \n",
"9 7.68e-05 \n",
"10 9.68e-06 \n",
"11 2.49e-05 \n",
"12 3.67e-05 \n",
"13 8.93e-05 \n",
"14 9.81e-06 \n",
"15 2.44e-05 \n",
"16 2.32e-05 \n",
"17 5.65e-05 \n",
"18 8.06e-06 \n",
"19 2.05e-05 "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a pandas dataframe for the mesh tally data\n",
"df = tally.get_pandas_dataframe(nuclides=False)\n",
"\n",
"# Set the Pandas float display settings\n",
"pd.options.display.float_format = '{:.2e}'.format\n",
"\n",
"# Print the first twenty rows in the dataframe\n",
"df.head(20)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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": [
"# Create a boxplot to view the distribution of\n",
"# fission and nu-fission rates in the pins\n",
"bp = df.boxplot(column='mean', by='score')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x15494902cf28>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Extract thermal nu-fission rates from pandas\n",
"fiss = df[df['score'] == 'nu-fission']\n",
"fiss = fiss[fiss['energy low [eV]'] == 0.0]\n",
"\n",
"# Extract mean and reshape as 2D NumPy arrays\n",
"mean = fiss['mean'].values.reshape((17,17))\n",
"\n",
"plt.imshow(mean, interpolation='nearest')\n",
"plt.title('fission rate')\n",
"plt.xlabel('x')\n",
"plt.ylabel('y')\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the cell+nuclides scatter-y2 rate tally**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t2\n",
"\tName =\tcell tally\n",
"\tFilters =\tCellFilter\n",
"\tNuclides =\tU235 U238 \n",
"\tScores =\t['scatter']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"# Find the cell Tally with the StatePoint API\n",
"tally = sp.get_tally(name='cell tally')\n",
"\n",
"# Print a little info about the cell tally to the screen\n",
"print(tally)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>cell</th>\n",
" <th>nuclide</th>\n",
" <th>score</th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>U235</td>\n",
" <td>scatter</td>\n",
" <td>3.80e-02</td>\n",
" <td>1.33e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>U238</td>\n",
" <td>scatter</td>\n",
" <td>2.33e+00</td>\n",
" <td>8.12e-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cell nuclide score mean std. dev.\n",
"0 1 U235 scatter 3.80e-02 1.33e-04\n",
"1 1 U238 scatter 2.33e+00 8.12e-03"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a pandas dataframe for the cell tally data\n",
"df = tally.get_pandas_dataframe()\n",
"\n",
"# Print the first twenty rows in the dataframe\n",
"df.head(20)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.00811746]\n",
" [0.00013266]]]\n"
]
}
],
"source": [
"# Get the standard deviations the total scattering rate\n",
"data = tally.get_values(scores=['scatter'], \n",
" nuclides=['U238', 'U235'], value='std_dev')\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Analyze the distribcell tally**"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tally\n",
"\tID =\t3\n",
"\tName =\tdistribcell tally\n",
"\tFilters =\tDistribcellFilter\n",
"\tNuclides =\ttotal \n",
"\tScores =\t['absorption', 'scatter']\n",
"\tEstimator =\ttracklength\n",
"\n"
]
}
],
"source": [
"# Find the distribcell Tally with the StatePoint API\n",
"tally = sp.get_tally(name='distribcell tally')\n",
"\n",
"# Print a little info about the distribcell tally to the screen\n",
"print(tally)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use the new Tally data retrieval API with pure NumPy"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[0.04347272]]\n",
"\n",
" [[0.04671736]]\n",
"\n",
" [[0.04878286]]\n",
"\n",
" [[0.03059582]]\n",
"\n",
" [[0.04548096]]\n",
"\n",
" [[0.04288085]]\n",
"\n",
" [[0.02557663]]\n",
"\n",
" [[0.0419826 ]]\n",
"\n",
" [[0.05878954]]\n",
"\n",
" [[0.04217666]]]\n"
]
}
],
"source": [
"# Get the relative error for the scattering reaction rates in\n",
"# the first 10 distribcell instances \n",
"data = tally.get_values(scores=['scatter'], filters=[openmc.DistribcellFilter],\n",
" filter_bins=[tuple(range(10))], value='rel_err')\n",
"print(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Print the distribcell tally dataframe"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"2\" halign=\"left\">level 1</th>\n",
" <th colspan=\"3\" halign=\"left\">level 2</th>\n",
" <th colspan=\"2\" halign=\"left\">level 3</th>\n",
" <th>distribcell</th>\n",
" <th>score</th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>univ</th>\n",
" <th>cell</th>\n",
" <th colspan=\"3\" halign=\"left\">lat</th>\n",
" <th>univ</th>\n",
" <th>cell</th>\n",
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" <tr>\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>id</th>\n",
" <th>id</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>id</th>\n",
" <th>id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>558</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>279</td>\n",
" <td>absorption</td>\n",
" <td>6.26e-04</td>\n",
" <td>4.62e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>559</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>279</td>\n",
" <td>scatter</td>\n",
" <td>8.73e-02</td>\n",
" <td>2.14e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>560</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>280</td>\n",
" <td>absorption</td>\n",
" <td>6.15e-04</td>\n",
" <td>3.12e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>561</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
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" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>280</td>\n",
" <td>scatter</td>\n",
" <td>8.06e-02</td>\n",
" <td>1.85e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>562</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>281</td>\n",
" <td>absorption</td>\n",
" <td>6.36e-04</td>\n",
" <td>4.24e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>563</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>281</td>\n",
" <td>scatter</td>\n",
" <td>7.59e-02</td>\n",
" <td>1.93e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>564</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>282</td>\n",
" <td>absorption</td>\n",
" <td>5.30e-04</td>\n",
" <td>2.75e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>565</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>282</td>\n",
" <td>scatter</td>\n",
" <td>6.82e-02</td>\n",
" <td>1.02e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>566</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>283</td>\n",
" <td>absorption</td>\n",
" <td>4.67e-04</td>\n",
" <td>2.84e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>567</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>283</td>\n",
" <td>scatter</td>\n",
" <td>6.42e-02</td>\n",
" <td>1.81e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>568</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>284</td>\n",
" <td>absorption</td>\n",
" <td>4.52e-04</td>\n",
" <td>2.13e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>569</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>284</td>\n",
" <td>scatter</td>\n",
" <td>5.64e-02</td>\n",
" <td>1.20e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>570</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>285</td>\n",
" <td>absorption</td>\n",
" <td>3.85e-04</td>\n",
" <td>1.99e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>571</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>285</td>\n",
" <td>scatter</td>\n",
" <td>4.86e-02</td>\n",
" <td>1.58e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>572</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>286</td>\n",
" <td>absorption</td>\n",
" <td>2.84e-04</td>\n",
" <td>2.16e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>573</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>286</td>\n",
" <td>scatter</td>\n",
" <td>3.91e-02</td>\n",
" <td>1.66e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>574</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>287</td>\n",
" <td>absorption</td>\n",
" <td>2.17e-04</td>\n",
" <td>2.15e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>575</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>287</td>\n",
" <td>scatter</td>\n",
" <td>3.02e-02</td>\n",
" <td>1.71e-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>576</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>288</td>\n",
" <td>absorption</td>\n",
" <td>1.50e-04</td>\n",
" <td>1.42e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>577</th>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>288</td>\n",
" <td>scatter</td>\n",
" <td>1.89e-02</td>\n",
" <td>9.31e-04</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" level 1 level 2 level 3 distribcell score \\\n",
" univ cell lat univ cell \n",
" id id id x y id id \n",
"558 3 4 2 7 16 1 3 279 absorption \n",
"559 3 4 2 7 16 1 3 279 scatter \n",
"560 3 4 2 8 16 1 3 280 absorption \n",
"561 3 4 2 8 16 1 3 280 scatter \n",
"562 3 4 2 9 16 1 3 281 absorption \n",
"563 3 4 2 9 16 1 3 281 scatter \n",
"564 3 4 2 10 16 1 3 282 absorption \n",
"565 3 4 2 10 16 1 3 282 scatter \n",
"566 3 4 2 11 16 1 3 283 absorption \n",
"567 3 4 2 11 16 1 3 283 scatter \n",
"568 3 4 2 12 16 1 3 284 absorption \n",
"569 3 4 2 12 16 1 3 284 scatter \n",
"570 3 4 2 13 16 1 3 285 absorption \n",
"571 3 4 2 13 16 1 3 285 scatter \n",
"572 3 4 2 14 16 1 3 286 absorption \n",
"573 3 4 2 14 16 1 3 286 scatter \n",
"574 3 4 2 15 16 1 3 287 absorption \n",
"575 3 4 2 15 16 1 3 287 scatter \n",
"576 3 4 2 16 16 1 3 288 absorption \n",
"577 3 4 2 16 16 1 3 288 scatter \n",
"\n",
" mean std. dev. \n",
" \n",
" \n",
"558 6.26e-04 4.62e-05 \n",
"559 8.73e-02 2.14e-03 \n",
"560 6.15e-04 3.12e-05 \n",
"561 8.06e-02 1.85e-03 \n",
"562 6.36e-04 4.24e-05 \n",
"563 7.59e-02 1.93e-03 \n",
"564 5.30e-04 2.75e-05 \n",
"565 6.82e-02 1.02e-03 \n",
"566 4.67e-04 2.84e-05 \n",
"567 6.42e-02 1.81e-03 \n",
"568 4.52e-04 2.13e-05 \n",
"569 5.64e-02 1.20e-03 \n",
"570 3.85e-04 1.99e-05 \n",
"571 4.86e-02 1.58e-03 \n",
"572 2.84e-04 2.16e-05 \n",
"573 3.91e-02 1.66e-03 \n",
"574 2.17e-04 2.15e-05 \n",
"575 3.02e-02 1.71e-03 \n",
"576 1.50e-04 1.42e-05 \n",
"577 1.89e-02 9.31e-04 "
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a pandas dataframe for the distribcell tally data\n",
"df = tally.get_pandas_dataframe(nuclides=False)\n",
"\n",
"# Print the last twenty rows in the dataframe\n",
"df.tail(20)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>std. dev.</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2.89e+02</td>\n",
" <td>2.89e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.15e-04</td>\n",
" <td>2.29e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.33e-04</td>\n",
" <td>9.14e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.84e-05</td>\n",
" <td>3.31e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.08e-04</td>\n",
" <td>1.58e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>4.10e-04</td>\n",
" <td>2.24e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.25e-04</td>\n",
" <td>2.93e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>8.87e-04</td>\n",
" <td>5.06e-05</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean std. dev.\n",
" \n",
" \n",
"count 2.89e+02 2.89e+02\n",
"mean 4.15e-04 2.29e-05\n",
"std 2.33e-04 9.14e-06\n",
"min 1.84e-05 3.31e-06\n",
"25% 2.08e-04 1.58e-05\n",
"50% 4.10e-04 2.24e-05\n",
"75% 6.25e-04 2.93e-05\n",
"max 8.87e-04 5.06e-05"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show summary statistics for absorption distribcell tally data\n",
"absorption = df[df['score'] == 'absorption']\n",
"absorption[['mean', 'std. dev.']].dropna().describe()\n",
"\n",
"# Note that the maximum standard deviation does indeed\n",
"# meet the 5e-5 threshold set by the tally trigger"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Perform a statistical test comparing the tally sample distributions for two categories of fuel pins."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mann-Whitney Test p-value: 0.3531165056829588\n"
]
}
],
"source": [
"# Extract tally data from pins in the pins divided along y=-x diagonal \n",
"multi_index = ('level 2', 'lat',)\n",
"lower = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] < 16]\n",
"upper = df[df[multi_index + ('x',)] + df[multi_index + ('y',)] > 16]\n",
"lower = lower[lower['score'] == 'absorption']\n",
"upper = upper[upper['score'] == 'absorption']\n",
"\n",
"# Perform non-parametric Mann-Whitney U Test to see if the \n",
"# absorption rates (may) come from same sampling distribution\n",
"u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
"print('Mann-Whitney Test p-value: {0}'.format(p))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the symmetry implied by the y=-x diagonal ensures that the two sampling distributions are identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **not reject** the null hypothesis that the two sampling distributions are identical.\n",
"\n",
"Next, perform the same test but with two groupings of pins which are not symmetrically identical to one another."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mann-Whitney Test p-value: 2.835784441937541e-42\n"
]
}
],
"source": [
"# Extract tally data from pins in the pins divided along y=x diagonal\n",
"multi_index = ('level 2', 'lat',)\n",
"lower = df[df[multi_index + ('x',)] > df[multi_index + ('y',)]]\n",
"upper = df[df[multi_index + ('x',)] < df[multi_index + ('y',)]]\n",
"lower = lower[lower['score'] == 'absorption']\n",
"upper = upper[upper['score'] == 'absorption']\n",
"\n",
"# Perform non-parametric Mann-Whitney U Test to see if the \n",
"# absorption rates (may) come from same sampling distribution\n",
"u, p = scipy.stats.mannwhitneyu(lower['mean'], upper['mean'])\n",
"print('Mann-Whitney Test p-value: {0}'.format(p))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the asymmetry implied by the y=x diagonal ensures that the two sampling distributions are *not* identical. Indeed, as illustrated by the test above, for any reasonable significance level (*e.g.*, $\\alpha$=0.05) one would **reject** the null hypothesis that the two sampling distributions are identical."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/romano/.pyenv/versions/3.7.0/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" after removing the cwd from sys.path.\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x154948ffb160>"
]
},
"execution_count": 32,
"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": [
"# Extract the scatter tally data from pandas\n",
"scatter = df[df['score'] == 'scatter']\n",
"\n",
"scatter['rel. err.'] = scatter['std. dev.'] / scatter['mean']\n",
"\n",
"# Show a scatter plot of the mean vs. the std. dev.\n",
"scatter.plot(kind='scatter', x='mean', y='rel. err.', title='Scattering Rates')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x154948f326a0>"
]
},
"execution_count": 33,
"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": [
"# Plot a histogram and kernel density estimate for the scattering rates\n",
"scatter['mean'].plot(kind='hist', bins=25)\n",
"scatter['mean'].plot(kind='kde')\n",
"plt.title('Scattering Rates')\n",
"plt.xlabel('Mean')\n",
"plt.legend(['KDE', 'Histogram'])"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
OSGeo-live/CesiumWidget | GSOC/notebooks/Projects/GRASS/Introduction to GRASS GIS/igrass/Command_parsing.ipynb | 1 | 16468 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Well use an utility script with few lines of code to parse the output of GRASS commands and make new functions that use the parsed output.\n",
"\n",
"The script use ipython specific syntax like **```!system_command```** which allows to run any command available in the user **```$PATH```**. The code is saved in a file with **```.ipy```** extension and is imported using the ipython magic function **```%run```**:\n",
"\n",
"**```%run file.ipy```** \n",
"\n",
"the source code available in grassutil.ipy in the current directory and can be loaded in the notebook using the magic function **```%load```**:\n",
"\n",
"**```%load grassutil.ipy```** \n",
"\n",
"It is also possible to save a new file or to overwrite an existing one, from the content of a ```code cell``` using the magic function:\n",
"\n",
"**```%%file filename```**.\n",
"\n",
"\n",
"Running the ```grassutil.py``` code, the following functions will be immediatly available in the current notebook:\n",
" \n",
" getLayerList\n",
" list2dict\n",
" vlayerInfo\n",
" rlayerInfo\n",
" region2dict\n",
" makeImage"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the IPython notebook is possinle to execute any command available in the ```$USER``` ```$PATH``` environment, such all the grass command after exporting the grass environment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use of the [g.gisenv](../../../../files/notebooks/projects/GRASS/docs/html/g.gisenv.html)\n"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GISDBASE=/home/main/notebooks/data/grass7data\r\n",
"LOCATION_NAME=nc_basic_spm_grass7\r\n",
"MAPSET=user1\r\n",
"LOCATION=/home/main/notebooks/data/grass7data/nc_basic_spm_grass7\r\n"
]
}
],
"source": [
"!g.gisenv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use of the [g.mapset](../../../../files/notebooks/projects/GRASS/docs/html/g.mapset.html)\n",
"* Set the GRASS WORKSPACE to : \n",
" - LOCATION : spearfish \n",
" - MAPSET : user1"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING: <user1> is already the current mapset\r\n",
"\u0007"
]
}
],
"source": [
"!g.mapset location=nc_basic_spm_grass7 mapset=user1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* print projection info with [g.proj](../../../../files/notebooks/projects/GRASS/docs/html/g.proj.html)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-PROJ_INFO-------------------------------------------------\r\n",
"name : Lambert Conformal Conic\r\n",
"proj : lcc\r\n",
"datum : nad83\r\n",
"a : 6378137.0\r\n",
"es : 0.006694380022900787\r\n",
"lat_1 : 36.16666666666666\r\n",
"lat_2 : 34.33333333333334\r\n",
"lat_0 : 33.75\r\n",
"lon_0 : -79\r\n",
"x_0 : 609601.22\r\n",
"y_0 : 0\r\n",
"no_defs : defined\r\n",
"-PROJ_EPSG-------------------------------------------------\r\n",
"epsg : 3358\r\n",
"-PROJ_UNITS------------------------------------------------\r\n",
"unit : Meter\r\n",
"units : Meters\r\n",
"meters : 1\r\n"
]
}
],
"source": [
"!g.proj -p"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* list vector and raster layers with [g.list](../../../../files/notebooks/projects/GRASS/docs/html/g.list.html)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"basin_50K\r\n",
"basins\r\n",
"elevation@PERMANENT\r\n",
"elevation@user1\r\n",
"elevation_shade\r\n",
"geology\r\n",
"lakes\r\n",
"landuse\r\n",
"soils\r\n"
]
}
],
"source": [
"!g.list rast"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* use the getLayerList function to store the g.list output in a python list"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rasterlist = getLayerList(type='rast')\n",
"vectorlist = getLayerList(type='vect')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['basin_50K',\n",
" 'basins',\n",
" 'elevation@PERMANENT',\n",
" 'elevation@user1',\n",
" 'elevation_shade',\n",
" 'geology',\n",
" 'lakes',\n",
" 'landuse',\n",
" 'soils']"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rasterlist"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['boundary_region',\n",
" 'boundary_state',\n",
" 'census',\n",
" 'elev_points',\n",
" 'firestations',\n",
" 'geology',\n",
" 'geonames',\n",
" 'hospitals',\n",
" 'points_of_interest',\n",
" 'railroads',\n",
" 'roadsmajor',\n",
" 'schools',\n",
" 'streams',\n",
" 'streets',\n",
" 'zipcodes']"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vectorlist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* print info for a raster layer with [r.info](../../../../files/notebooks/projects/GRASS/docs/html/r.info.html)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" +----------------------------------------------------------------------------+\r\n",
" | Map: elevation@PERMANENT Date: Tue Nov 7 01:09:51 2006 |\r\n",
" | Mapset: PERMANENT Login of Creator: helena |\r\n",
" | Location: nc_basic_spm_grass7 |\r\n",
" | DataBase: /home/main/notebooks/data/grass7data |\r\n",
" | Title: South-West Wake county: Elevation NED 10m ( elev_ned10m ) |\r\n",
" | Timestamp: none |\r\n",
" |----------------------------------------------------------------------------|\r\n",
" | |\r\n",
" | Type of Map: raster Number of Categories: 255 |\r\n",
" | Data Type: FCELL |\r\n",
" | Rows: 1350 |\r\n",
" | Columns: 1500 |\r\n",
" | Total Cells: 2025000 |\r\n",
" | Projection: Lambert Conformal Conic |\r\n",
" | N: 228500 S: 215000 Res: 10 |\r\n",
" | E: 645000 W: 630000 Res: 10 |\r\n",
" | Range of data: min = 55.57879 max = 156.3299 |\r\n",
" | |\r\n",
" | Data Description: |\r\n",
" | generated by r.proj |\r\n",
" | |\r\n",
" | Comments: |\r\n",
" | r.proj input=\"ned03arcsec\" location=\"northcarolina_latlong\" mapset=\"\\ |\r\n",
" | helena\" output=\"elev_ned10m\" method=\"cubic\" resolution=10 |\r\n",
" | |\r\n",
" +----------------------------------------------------------------------------+\r\n",
"\r\n"
]
}
],
"source": [
"!r.info elevation@PERMANENT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* use the 'r/v'layerInfo function to store the [r.info](../../../../files/notebooks/projects/GRASS/docs/html/r.info.html) / [v.info](../../../../files/notebooks/projects/GRASS/docs/html/v.info.html) output in a python dictionary"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mapset parameter not specified, using mapset PERMANENT as default\n",
"['north=228500', 'south=215000', 'east=645000', 'west=630000', 'nsres=10', 'ewres=10', 'rows=1350', 'cols=1500', 'cells=2025000', 'datatype=FCELL', 'ncats=255']\n"
]
}
],
"source": [
"rasterlayerinfo = rlayerInfo(map='elevation')\n",
"vectorlayerinfo = vlayerInfo(map='geology')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['rows',\n",
" 'north',\n",
" 'datatype',\n",
" 'west',\n",
" 'cells',\n",
" 'cols',\n",
" 'range',\n",
" 'ewres',\n",
" 'ncats',\n",
" 'east',\n",
" 'nsres',\n",
" 'south',\n",
" 'history']"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rasterlayerinfo.keys()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mapset parameter not specified, using mapset PERMANENT as default\n",
"['north=228500', 'south=215000', 'east=645000', 'west=630000', 'nsres=10', 'ewres=10', 'rows=1350', 'cols=1500', 'cells=2025000', 'datatype=FCELL', 'ncats=255']\n"
]
},
{
"data": {
"text/plain": [
"{'cells': '2025000',\n",
" 'cols': '1500',\n",
" 'datatype': 'FCELL',\n",
" 'east': '645000',\n",
" 'ewres': '10',\n",
" 'history': 'Data Source:\\n \\n \\nData Description:\\n generated by r.proj\\nComments:\\n r.proj input=\"ned03arcsec\" location=\"northcarolina_latlong\" mapset=\"\\\\\\n helena\" output=\"elev_ned10m\" method=\"cubic\" resolution=10',\n",
" 'ncats': '255',\n",
" 'north': '228500',\n",
" 'nsres': '10',\n",
" 'range': {'max': '156.3299', 'min': '55.57879'},\n",
" 'rows': '1350',\n",
" 'south': '215000',\n",
" 'west': '630000'}"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rlayerInfo('elevation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* use of the makeImage function to display raster and/or vector maps \n",
"(a wrapper around [display commands](../../../../files/notebooks/projects/GRASS/docs/html/display.html) in GRASS using the CAIRO DRIVER) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!g.mapset location=nc_basic_spm_grass7 mapset=user1\n",
"inputlayer={\n",
" 'raster': ['elevation'], \n",
" 'vector':['points_of_interest']\n",
"}\n",
"\n",
"makeImage(basemap='elevation', inputlayer=inputlayer, maptype='overlay', \n",
" vsize=10, maptitle='points_of_interest', gridsize=4000, outputimagename='test.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.core.display import Image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example on how to repoject raster and vector data between 2 different GRASS LOCATION:\n",
"* set the GRASS environment to the previously generated lonlat LOCATION (see GRASS_init.ipynb) \n",
"* reproject GRASS layers : \n",
" - raster : elevation\n",
" - vector : bugsites "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!g.proj -c epsg=4326 location=lonlat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!g.mapset -c location=lonlat mapset=PERMANENT"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"region = !r.proj input=elevation location=nc_basic_spm_grass7 -g"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"region"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"newregion = dict([(i.split('=')[0],i.split('=')[1]) for i in region[-1].split()])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!g.region -p n={newregion['n']} s={newregion['s']} e={newregion['e']} w={newregion['w']} res=0.0001"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!r.proj input=elevation location=nc_basic_spm_grass7 output=elevation method=bicubic --o --q"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!v.proj input=points_of_interest location=nc_basic_spm_grass7 output=points_of_interest --o --q"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!g.region -p n={newregion['n']} s={newregion['s']} e={newregion['e']} w={newregion['w']} res=0.0001\n",
"inputlayer={\n",
" 'raster': ['elevation'], \n",
" 'vector':['points_of_interest']\n",
"}\n",
"\n",
"makeImage(basemap='elevation', inputlayer=inputlayer, maptype='overlay', \n",
" vsize=10, maptitle='points_of_interest', outputimagename='test.png')"
]
},
{
"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
}
| apache-2.0 |
marxav/hello-world | multiple_outputs_with_cnn.ipynb | 1 | 179484 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"import random\n",
"import numpy as np\n",
"import random\n",
"import keras\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"import tensorflow as tf\n",
"from keras import models\n",
"from keras import layers\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# number of characters in a word.\n",
"# for instance abccba has nb_chars = 6\n",
"nb_chars = 4\n",
"\n",
"# number of possible characters used during the encoding.\n",
"# for instance abcde leads to 01234 has nb_letters = 5\n",
"nb_letters = 26\n",
"\n",
"# number of words samples to be generated \n",
"nb_words = 40000\n",
"\n",
"# percentage of words that will be used for validation\n",
"percentage_split = 0.9\n",
"\n",
"# number of epochs for fitting the model training step\n",
"nb_epochs = 200"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"456976"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# total number of combinations\n",
"nb_letters**nb_chars"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def create_inputs(nb_words, nb_chars, nb_letters):\n",
" '''Create a numpy array of nb_words rows with nb_chars columns each element\n",
" being a random letter of nb_letters (a, b...)'''\n",
" words = np.zeros((nb_words, nb_chars, 1), dtype=int)\n",
" \n",
" for w in range(nb_words):\n",
" optim_tentative = False\n",
" if optim_tentative == True and w%10 != 0:\n",
" i = random.randint(0, nb_letters-1)\n",
" for c in range(nb_chars):\n",
" words[w, c, 0] = ord('a') + i\n",
" else:\n",
" for c in range(nb_chars):\n",
" i = random.randint(0, nb_letters-1)\n",
" words[w, c, 0] = ord('a') + i\n",
" \n",
" return words\n",
"\n",
"\n",
"def encrypt(words, nb_words, nb_chars):\n",
" '''Encrypt each element of a numpy array of nb_words rows with nb_chars \n",
" columns each item with a secret algorithm'''\n",
" \n",
" encrypted_words = words.copy()\n",
" encrypted_words_probs = np.zeros((nb_words, nb_chars, nb_chars))\n",
" \n",
" #val_max = -1\n",
" \n",
" for w in range(nb_words):\n",
" for c in range(nb_chars): # 0,1,2,3,4\n",
" \n",
" encrypted_words[w,c] = int(words[w,c]) - 49\n",
" val = encrypted_words[w,c] - 48\n",
" \n",
" #if val > val_max:\n",
" # val_max = val\n",
" \n",
" # add entropy (i.e. mistakes in the encryption)\n",
" #epsilon = random.randint(0, 100)\n",
" #if epsilon == 5 and val != val_max:\n",
" #val +=1\n",
" \n",
" #print('w:',w,', c:',c,', [wc]:', val)\n",
" #encrypted_words_probs[w, c, val ] = 1.0\n",
" encrypted_words[w,c] = val\n",
" return encrypted_words"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def normalize(x):\n",
" # normalize x_test data\n",
" mean = x.mean(axis=0)\n",
" x_normalized = x - mean\n",
" std = x_normalized.std(axis=0)\n",
" x_normalized /= std\n",
" return x_normalized"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def build_model(nb_chars, nb_letters):\n",
"\n",
" # This returns a tensor\n",
" inputs = layers.Input(shape=(nb_chars,1,), dtype='float32', name='main_input')\n",
"\n",
" # a layer instance is callable on a tensor, and returns a tensor\n",
" x = layers.Dense(1024, activation='relu', name='hl_1')(inputs)\n",
" #x = layers.Dropout(0.4)(x)\n",
" x = layers.Conv1D(256, 3, activation='relu', padding='same', name='conv_1d')(x)\n",
" x = layers.MaxPooling1D(3)(x)\n",
" #x = layers.AveragePooling1D(3)(x)\n",
" x = layers.Dense(64, activation='relu', name='hl_2')(x)\n",
"\n",
" outputs = []\n",
" losses = {}\n",
" for o in range(nb_chars):\n",
" name_i = 'output_'+str(o)\n",
" output_i = layers.Dense(nb_letters, activation='softmax', dtype='float32', name=name_i)(x)\n",
" outputs.append(output_i)\n",
" losses[name_i] = 'categorical_crossentropy'\n",
"\n",
" model = keras.models.Model(inputs=inputs, outputs=outputs)\n",
"\n",
" rmsprop = keras.optimizers.RMSprop(lr=0.001)\n",
"\n",
" model.compile(optimizer=rmsprop,\n",
" loss=losses,\n",
" metrics=['accuracy']) \n",
" return model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def print_readable_inputs(x):\n",
" words = []\n",
" for w in x:\n",
" word = []\n",
" for c in w:\n",
" word.append(c)\n",
" words.append(word)\n",
" \n",
" print(words)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def print_readable_outputs_(outputs, nb_words, nb_chars):\n",
" \n",
" # outputs are listed : first, per char, second by sample, third by letter probability\n",
" words = [''] * nb_words\n",
" \n",
" c_i = 0\n",
" for char in outputs:\n",
"\n",
" s_i = 0\n",
" for sample in char:\n",
"\n",
" l_i = 0\n",
" best_value = -float('inf')\n",
" best_letter = -1\n",
" for letter_probs in sample:\n",
" if letter_probs > best_value:\n",
" best_value = letter_probs\n",
" best_letter = l_i\n",
" l_i += 1\n",
" words[s_i] += str(best_letter)\n",
" if c_i != nb_chars - 1:\n",
" words[s_i] += ' '\n",
" s_i += 1\n",
" c_i += 1\n",
" print(words)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def print_readable_outputs(outputs, nb_words, nb_chars):\n",
" \n",
" # outputs are listed : first, per char, second by sample, third by letter probability\n",
" words = [''] * nb_words\n",
" c_i = 0\n",
" for char in outputs:\n",
" s_i = 0\n",
" for sample in char:\n",
" best_letter = np.argmax(sample)\n",
" words[s_i] += str(best_letter)\n",
" if c_i != nb_chars - 1:\n",
" words[s_i] += ' '\n",
" s_i += 1\n",
" c_i += 1\n",
" print(words)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x: (as readable inputs)\n",
"x (partial):\n",
" [[[108]\n",
" [100]\n",
" [117]\n",
" [101]]\n",
"\n",
" [[114]\n",
" [105]\n",
" [116]\n",
" [ 98]]] out of 40000\n",
"\n",
"x_train:\n",
" [[[-0.19803652]\n",
" [-1.2741154 ]\n",
" [ 0.99438854]\n",
" [-1.13152572]]\n",
"\n",
" [[ 0.60293358]\n",
" [-0.60525155]\n",
" [ 0.86124776]\n",
" [-1.53095823]]] out of 40000\n",
"\n",
"y (readable):\n",
" [[[11]\n",
" [ 3]\n",
" [20]\n",
" [ 4]]\n",
"\n",
" [[17]\n",
" [ 8]\n",
" [19]\n",
" [ 1]]\n",
"\n",
" [[10]\n",
" [ 8]\n",
" [23]\n",
" [20]]\n",
"\n",
" ...\n",
"\n",
" [[13]\n",
" [ 4]\n",
" [ 9]\n",
" [ 0]]\n",
"\n",
" [[17]\n",
" [ 0]\n",
" [24]\n",
" [19]]\n",
"\n",
" [[19]\n",
" [14]\n",
" [25]\n",
" [ 5]]]\n",
"\n",
"y (less readable):\n",
" [[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.]\n",
" [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.]\n",
" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.\n",
" 0. 0. 0.]\n",
" [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.]]\n",
"\n",
" [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.]\n",
" [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.]\n",
" [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.\n",
" 0. 0. 0.]\n",
" [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
" 0. 0. 0.]]] out of 40000\n",
"\n",
"y.shape (40000, 4, 1)\n",
"y_train):\n",
"[array([[[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" ...,\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]]]), array([[[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" ...,\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[1., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[0., 0., 0., ..., 0., 0., 0.]]])]\n",
"y_train[0].shape= (40000, 1, 26)\n"
]
}
],
"source": [
"x = create_inputs(nb_words, nb_chars, nb_letters)\n",
"print('x: (as readable inputs)')\n",
"\n",
"first_n_samples = 2\n",
"\n",
"#print_readable_inputs(x[:first_n_samples])\n",
"print('x (partial):\\n', x[:first_n_samples], 'out of ',len(x))\n",
"print()\n",
"\n",
"\n",
"# Normalize the data\n",
"x_train = normalize(x)\n",
"\n",
"print('x_train:\\n', x_train[:first_n_samples], 'out of ',len(x_train))\n",
"print()\n",
"\n",
"# create output data for training\n",
"y = encrypt(x, nb_words, nb_chars)\n",
"print('y (readable):\\n', y)\n",
"print()\n",
"\n",
"# process the y data as useful ANN output data\n",
"y_train0 = keras.utils.to_categorical(y, nb_letters)\n",
"print('y (less readable):\\n', y_train0[:first_n_samples], 'out of ',len(y_train0))\n",
"print('')\n",
"\n",
"\n",
"print('y.shape',y.shape)\n",
"\n",
"y_train = []\n",
"for c in range(nb_chars):\n",
" # extract each 'char' colomn from the global y_train0 tensor\n",
" # in order to have multiplue yi_train outputs tensors \n",
" yi_train = np.ndarray(shape=(nb_words, 1, nb_letters))\n",
" for w in range(nb_words):\n",
" yi_train[w,:] = y_train0[w,c,:]\n",
" y_train.append(yi_train)\n",
"\n",
"# Not really displayable, henced commented\n",
"print('y_train):')\n",
"print(y_train[:first_n_samples])\n",
"\n",
"print('y_train[0].shape=',y_train[0].shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"__________________________________________________________________________________________________\n",
"Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
"main_input (InputLayer) (None, 4, 1) 0 \n",
"__________________________________________________________________________________________________\n",
"hl_1 (Dense) (None, 4, 1024) 2048 main_input[0][0] \n",
"__________________________________________________________________________________________________\n",
"conv_1d (Conv1D) (None, 4, 256) 786688 hl_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"max_pooling1d_1 (MaxPooling1D) (None, 1, 256) 0 conv_1d[0][0] \n",
"__________________________________________________________________________________________________\n",
"hl_2 (Dense) (None, 1, 64) 16448 max_pooling1d_1[0][0] \n",
"__________________________________________________________________________________________________\n",
"output_0 (Dense) (None, 1, 26) 1690 hl_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"output_1 (Dense) (None, 1, 26) 1690 hl_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"output_2 (Dense) (None, 1, 26) 1690 hl_2[0][0] \n",
"__________________________________________________________________________________________________\n",
"output_3 (Dense) (None, 1, 26) 1690 hl_2[0][0] \n",
"==================================================================================================\n",
"Total params: 811,944\n",
"Trainable params: 811,944\n",
"Non-trainable params: 0\n",
"__________________________________________________________________________________________________\n",
"None\n"
]
}
],
"source": [
"coding_model = build_model(nb_chars, nb_letters)\n",
"print(coding_model.summary())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 3999 samples, validate on 36001 samples\n",
"Epoch 1/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 12.4148 - output_0_loss: 3.0764 - output_1_loss: 3.0669 - output_2_loss: 3.1250 - output_3_loss: 3.1466 - output_0_acc: 0.0863 - output_1_acc: 0.0853 - output_2_acc: 0.0733 - output_3_acc: 0.0718 - val_loss: 11.8018 - val_output_0_loss: 2.8391 - val_output_1_loss: 2.8489 - val_output_2_loss: 3.0544 - val_output_3_loss: 3.0593 - val_output_0_acc: 0.1034 - val_output_1_acc: 0.1064 - val_output_2_acc: 0.0829 - val_output_3_acc: 0.0876\n",
"Epoch 2/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 11.0227 - output_0_loss: 2.6465 - output_1_loss: 2.6416 - output_2_loss: 2.8240 - output_3_loss: 2.9107 - output_0_acc: 0.1368 - output_1_acc: 0.1460 - output_2_acc: 0.1178 - output_3_acc: 0.1055 - val_loss: 10.3224 - val_output_0_loss: 2.4270 - val_output_1_loss: 2.3827 - val_output_2_loss: 2.6989 - val_output_3_loss: 2.8138 - val_output_0_acc: 0.1602 - val_output_1_acc: 0.1874 - val_output_2_acc: 0.1349 - val_output_3_acc: 0.1152\n",
"Epoch 3/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 9.8441 - output_0_loss: 2.3245 - output_1_loss: 2.2573 - output_2_loss: 2.5320 - output_3_loss: 2.7302 - output_0_acc: 0.1870 - output_1_acc: 0.2138 - output_2_acc: 0.1610 - output_3_acc: 0.1383 - val_loss: 9.8310 - val_output_0_loss: 2.3370 - val_output_1_loss: 2.2331 - val_output_2_loss: 2.4987 - val_output_3_loss: 2.7622 - val_output_0_acc: 0.1974 - val_output_1_acc: 0.2172 - val_output_2_acc: 0.1600 - val_output_3_acc: 0.1260\n",
"Epoch 4/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 9.0691 - output_0_loss: 2.1494 - output_1_loss: 2.0190 - output_2_loss: 2.3402 - output_3_loss: 2.5604 - output_0_acc: 0.2133 - output_1_acc: 0.2571 - output_2_acc: 0.1838 - output_3_acc: 0.1683 - val_loss: 8.9815 - val_output_0_loss: 2.1775 - val_output_1_loss: 1.9704 - val_output_2_loss: 2.2648 - val_output_3_loss: 2.5689 - val_output_0_acc: 0.2153 - val_output_1_acc: 0.2378 - val_output_2_acc: 0.2153 - val_output_3_acc: 0.1542\n",
"Epoch 5/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 8.4451 - output_0_loss: 1.9770 - output_1_loss: 1.8383 - output_2_loss: 2.1856 - output_3_loss: 2.4442 - output_0_acc: 0.2688 - output_1_acc: 0.3088 - output_2_acc: 0.2196 - output_3_acc: 0.1928 - val_loss: 8.2068 - val_output_0_loss: 1.9173 - val_output_1_loss: 1.8074 - val_output_2_loss: 2.1295 - val_output_3_loss: 2.3526 - val_output_0_acc: 0.2634 - val_output_1_acc: 0.3194 - val_output_2_acc: 0.2287 - val_output_3_acc: 0.1947\n",
"Epoch 6/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 7.9636 - output_0_loss: 1.8393 - output_1_loss: 1.7222 - output_2_loss: 2.0600 - output_3_loss: 2.3420 - output_0_acc: 0.3003 - output_1_acc: 0.3353 - output_2_acc: 0.2543 - output_3_acc: 0.2036 - val_loss: 7.8378 - val_output_0_loss: 1.7851 - val_output_1_loss: 1.7397 - val_output_2_loss: 2.0133 - val_output_3_loss: 2.2998 - val_output_0_acc: 0.3276 - val_output_1_acc: 0.3202 - val_output_2_acc: 0.2606 - val_output_3_acc: 0.1994\n",
"Epoch 7/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 7.5474 - output_0_loss: 1.7329 - output_1_loss: 1.6285 - output_2_loss: 1.9673 - output_3_loss: 2.2187 - output_0_acc: 0.3411 - output_1_acc: 0.3703 - output_2_acc: 0.2683 - output_3_acc: 0.2236 - val_loss: 7.4820 - val_output_0_loss: 1.6651 - val_output_1_loss: 1.6329 - val_output_2_loss: 1.9684 - val_output_3_loss: 2.2156 - val_output_0_acc: 0.3418 - val_output_1_acc: 0.3502 - val_output_2_acc: 0.2505 - val_output_3_acc: 0.2203\n",
"Epoch 8/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 7.2004 - output_0_loss: 1.6164 - output_1_loss: 1.5561 - output_2_loss: 1.8825 - output_3_loss: 2.1454 - output_0_acc: 0.3723 - output_1_acc: 0.3936 - output_2_acc: 0.2986 - output_3_acc: 0.2506 - val_loss: 7.3822 - val_output_0_loss: 1.6674 - val_output_1_loss: 1.5135 - val_output_2_loss: 1.9483 - val_output_3_loss: 2.2529 - val_output_0_acc: 0.3537 - val_output_1_acc: 0.3813 - val_output_2_acc: 0.2595 - val_output_3_acc: 0.1993\n",
"Epoch 9/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 6.8687 - output_0_loss: 1.5402 - output_1_loss: 1.4650 - output_2_loss: 1.8106 - output_3_loss: 2.0529 - output_0_acc: 0.3853 - output_1_acc: 0.4124 - output_2_acc: 0.3063 - output_3_acc: 0.2588 - val_loss: 6.9277 - val_output_0_loss: 1.4783 - val_output_1_loss: 1.5106 - val_output_2_loss: 1.8435 - val_output_3_loss: 2.0952 - val_output_0_acc: 0.4000 - val_output_1_acc: 0.3753 - val_output_2_acc: 0.3059 - val_output_3_acc: 0.2445\n",
"Epoch 10/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 6.6137 - output_0_loss: 1.4660 - output_1_loss: 1.3943 - output_2_loss: 1.7424 - output_3_loss: 2.0111 - output_0_acc: 0.4056 - output_1_acc: 0.4414 - output_2_acc: 0.3338 - output_3_acc: 0.2613 - val_loss: 6.9549 - val_output_0_loss: 1.5596 - val_output_1_loss: 1.5239 - val_output_2_loss: 1.7660 - val_output_3_loss: 2.1054 - val_output_0_acc: 0.3713 - val_output_1_acc: 0.4068 - val_output_2_acc: 0.3249 - val_output_3_acc: 0.2600\n",
"Epoch 11/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 6.3587 - output_0_loss: 1.4053 - output_1_loss: 1.3405 - output_2_loss: 1.6763 - output_3_loss: 1.9367 - output_0_acc: 0.4236 - output_1_acc: 0.4574 - output_2_acc: 0.3506 - output_3_acc: 0.2851 - val_loss: 6.3195 - val_output_0_loss: 1.4491 - val_output_1_loss: 1.2919 - val_output_2_loss: 1.6364 - val_output_3_loss: 1.9421 - val_output_0_acc: 0.3999 - val_output_1_acc: 0.4778 - val_output_2_acc: 0.3635 - val_output_3_acc: 0.2806\n",
"Epoch 12/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 6.1285 - output_0_loss: 1.3461 - output_1_loss: 1.3028 - output_2_loss: 1.6083 - output_3_loss: 1.8713 - output_0_acc: 0.4589 - output_1_acc: 0.4666 - output_2_acc: 0.3746 - output_3_acc: 0.3026 - val_loss: 6.1862 - val_output_0_loss: 1.3691 - val_output_1_loss: 1.2706 - val_output_2_loss: 1.6477 - val_output_3_loss: 1.8989 - val_output_0_acc: 0.4527 - val_output_1_acc: 0.4759 - val_output_2_acc: 0.3620 - val_output_3_acc: 0.2878\n",
"Epoch 13/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 5.8751 - output_0_loss: 1.2982 - output_1_loss: 1.2205 - output_2_loss: 1.5301 - output_3_loss: 1.8263 - output_0_acc: 0.4669 - output_1_acc: 0.5114 - output_2_acc: 0.3946 - output_3_acc: 0.3053 - val_loss: 5.9366 - val_output_0_loss: 1.2348 - val_output_1_loss: 1.1987 - val_output_2_loss: 1.6808 - val_output_3_loss: 1.8223 - val_output_0_acc: 0.4776 - val_output_1_acc: 0.4938 - val_output_2_acc: 0.3420 - val_output_3_acc: 0.2965\n",
"Epoch 14/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 5.7124 - output_0_loss: 1.2389 - output_1_loss: 1.1940 - output_2_loss: 1.4990 - output_3_loss: 1.7805 - output_0_acc: 0.5021 - output_1_acc: 0.5049 - output_2_acc: 0.3978 - output_3_acc: 0.3246 - val_loss: 6.0132 - val_output_0_loss: 1.2872 - val_output_1_loss: 1.2692 - val_output_2_loss: 1.5425 - val_output_3_loss: 1.9143 - val_output_0_acc: 0.4373 - val_output_1_acc: 0.4785 - val_output_2_acc: 0.3975 - val_output_3_acc: 0.2997\n",
"Epoch 15/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 5.4734 - output_0_loss: 1.1882 - output_1_loss: 1.1169 - output_2_loss: 1.4520 - output_3_loss: 1.7164 - output_0_acc: 0.5026 - output_1_acc: 0.5324 - output_2_acc: 0.4136 - output_3_acc: 0.3446 - val_loss: 6.1227 - val_output_0_loss: 1.2098 - val_output_1_loss: 1.2496 - val_output_2_loss: 1.4686 - val_output_3_loss: 2.1947 - val_output_0_acc: 0.4803 - val_output_1_acc: 0.4925 - val_output_2_acc: 0.4044 - val_output_3_acc: 0.2250\n",
"Epoch 16/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 5.3102 - output_0_loss: 1.1487 - output_1_loss: 1.0832 - output_2_loss: 1.3886 - output_3_loss: 1.6896 - output_0_acc: 0.5304 - output_1_acc: 0.5581 - output_2_acc: 0.4389 - output_3_acc: 0.3561 - val_loss: 6.4033 - val_output_0_loss: 1.4472 - val_output_1_loss: 1.3943 - val_output_2_loss: 1.6478 - val_output_3_loss: 1.9140 - val_output_0_acc: 0.4186 - val_output_1_acc: 0.4429 - val_output_2_acc: 0.3695 - val_output_3_acc: 0.2905\n"
]
},
{
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"text": [
"Epoch 17/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 5.1109 - output_0_loss: 1.1050 - output_1_loss: 1.0418 - output_2_loss: 1.3357 - output_3_loss: 1.6283 - output_0_acc: 0.5396 - output_1_acc: 0.5649 - output_2_acc: 0.4506 - output_3_acc: 0.3751 - val_loss: 5.7096 - val_output_0_loss: 1.2544 - val_output_1_loss: 1.0783 - val_output_2_loss: 1.4472 - val_output_3_loss: 1.9296 - val_output_0_acc: 0.4718 - val_output_1_acc: 0.5346 - val_output_2_acc: 0.4277 - val_output_3_acc: 0.3062\n",
"Epoch 18/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 4.9748 - output_0_loss: 1.0631 - output_1_loss: 1.0151 - output_2_loss: 1.3114 - output_3_loss: 1.5852 - output_0_acc: 0.5499 - output_1_acc: 0.5761 - output_2_acc: 0.4636 - output_3_acc: 0.3858 - val_loss: 5.1643 - val_output_0_loss: 1.0275 - val_output_1_loss: 1.0541 - val_output_2_loss: 1.3543 - val_output_3_loss: 1.7283 - val_output_0_acc: 0.5752 - val_output_1_acc: 0.5529 - val_output_2_acc: 0.4323 - val_output_3_acc: 0.3267\n",
"Epoch 19/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 4.8046 - output_0_loss: 1.0312 - output_1_loss: 0.9714 - output_2_loss: 1.2636 - output_3_loss: 1.5384 - output_0_acc: 0.5676 - output_1_acc: 0.5946 - output_2_acc: 0.4891 - output_3_acc: 0.3931 - val_loss: 5.3738 - val_output_0_loss: 1.1708 - val_output_1_loss: 1.0978 - val_output_2_loss: 1.4813 - val_output_3_loss: 1.6238 - val_output_0_acc: 0.4893 - val_output_1_acc: 0.5389 - val_output_2_acc: 0.3945 - val_output_3_acc: 0.3539\n",
"Epoch 20/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 4.6446 - output_0_loss: 0.9604 - output_1_loss: 0.9347 - output_2_loss: 1.2353 - output_3_loss: 1.5142 - output_0_acc: 0.6064 - output_1_acc: 0.6214 - output_2_acc: 0.5004 - output_3_acc: 0.4116 - val_loss: 5.2430 - val_output_0_loss: 1.2790 - val_output_1_loss: 1.0517 - val_output_2_loss: 1.3406 - val_output_3_loss: 1.5717 - val_output_0_acc: 0.4795 - val_output_1_acc: 0.5342 - val_output_2_acc: 0.4530 - val_output_3_acc: 0.3720\n",
"Epoch 21/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 4.5227 - output_0_loss: 0.9470 - output_1_loss: 0.8968 - output_2_loss: 1.1816 - output_3_loss: 1.4973 - output_0_acc: 0.6127 - output_1_acc: 0.6252 - output_2_acc: 0.5116 - output_3_acc: 0.4091 - val_loss: 4.6977 - val_output_0_loss: 0.9378 - val_output_1_loss: 0.9596 - val_output_2_loss: 1.2053 - val_output_3_loss: 1.5950 - val_output_0_acc: 0.5905 - val_output_1_acc: 0.6059 - val_output_2_acc: 0.4884 - val_output_3_acc: 0.3647\n",
"Epoch 22/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 4.3900 - output_0_loss: 0.9326 - output_1_loss: 0.8665 - output_2_loss: 1.1608 - output_3_loss: 1.4302 - output_0_acc: 0.6129 - output_1_acc: 0.6469 - output_2_acc: 0.5184 - output_3_acc: 0.4319 - val_loss: 4.5963 - val_output_0_loss: 0.9732 - val_output_1_loss: 0.9109 - val_output_2_loss: 1.1756 - val_output_3_loss: 1.5366 - val_output_0_acc: 0.5609 - val_output_1_acc: 0.6328 - val_output_2_acc: 0.5007 - val_output_3_acc: 0.3924\n",
"Epoch 23/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 4.2156 - output_0_loss: 0.8775 - output_1_loss: 0.8278 - output_2_loss: 1.1233 - output_3_loss: 1.3870 - output_0_acc: 0.6219 - output_1_acc: 0.6639 - output_2_acc: 0.5261 - output_3_acc: 0.4501 - val_loss: 4.6990 - val_output_0_loss: 0.9681 - val_output_1_loss: 0.9457 - val_output_2_loss: 1.2069 - val_output_3_loss: 1.5783 - val_output_0_acc: 0.5797 - val_output_1_acc: 0.5988 - val_output_2_acc: 0.4825 - val_output_3_acc: 0.3759\n",
"Epoch 24/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 4.1149 - output_0_loss: 0.8490 - output_1_loss: 0.8034 - output_2_loss: 1.0880 - output_3_loss: 1.3745 - output_0_acc: 0.6407 - output_1_acc: 0.6657 - output_2_acc: 0.5544 - output_3_acc: 0.4509 - val_loss: 4.4119 - val_output_0_loss: 0.9925 - val_output_1_loss: 0.9304 - val_output_2_loss: 1.0728 - val_output_3_loss: 1.4162 - val_output_0_acc: 0.5940 - val_output_1_acc: 0.6053 - val_output_2_acc: 0.5472 - val_output_3_acc: 0.4403\n",
"Epoch 25/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 4.0181 - output_0_loss: 0.8516 - output_1_loss: 0.7790 - output_2_loss: 1.0663 - output_3_loss: 1.3212 - output_0_acc: 0.6452 - output_1_acc: 0.6859 - output_2_acc: 0.5534 - output_3_acc: 0.4734 - val_loss: 4.7839 - val_output_0_loss: 0.8580 - val_output_1_loss: 1.0064 - val_output_2_loss: 1.3975 - val_output_3_loss: 1.5220 - val_output_0_acc: 0.6430 - val_output_1_acc: 0.5794 - val_output_2_acc: 0.4567 - val_output_3_acc: 0.4136\n",
"Epoch 26/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.9131 - output_0_loss: 0.8018 - output_1_loss: 0.7513 - output_2_loss: 1.0480 - output_3_loss: 1.3119 - output_0_acc: 0.6537 - output_1_acc: 0.6949 - output_2_acc: 0.5611 - output_3_acc: 0.4881 - val_loss: 4.0937 - val_output_0_loss: 0.7944 - val_output_1_loss: 0.7472 - val_output_2_loss: 1.0669 - val_output_3_loss: 1.4851 - val_output_0_acc: 0.6636 - val_output_1_acc: 0.6876 - val_output_2_acc: 0.5396 - val_output_3_acc: 0.4078\n",
"Epoch 27/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.7703 - output_0_loss: 0.7598 - output_1_loss: 0.7143 - output_2_loss: 1.0143 - output_3_loss: 1.2818 - output_0_acc: 0.6849 - output_1_acc: 0.7057 - output_2_acc: 0.5804 - output_3_acc: 0.4876 - val_loss: 3.9624 - val_output_0_loss: 0.8158 - val_output_1_loss: 0.8515 - val_output_2_loss: 1.0715 - val_output_3_loss: 1.2237 - val_output_0_acc: 0.6569 - val_output_1_acc: 0.6486 - val_output_2_acc: 0.5326 - val_output_3_acc: 0.4937\n",
"Epoch 28/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.6992 - output_0_loss: 0.7485 - output_1_loss: 0.6999 - output_2_loss: 0.9974 - output_3_loss: 1.2534 - output_0_acc: 0.6854 - output_1_acc: 0.7179 - output_2_acc: 0.5881 - output_3_acc: 0.4899 - val_loss: 3.9309 - val_output_0_loss: 0.7361 - val_output_1_loss: 0.6766 - val_output_2_loss: 1.1219 - val_output_3_loss: 1.3963 - val_output_0_acc: 0.6835 - val_output_1_acc: 0.7276 - val_output_2_acc: 0.5346 - val_output_3_acc: 0.4398\n",
"Epoch 29/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.5541 - output_0_loss: 0.7082 - output_1_loss: 0.6654 - output_2_loss: 0.9627 - output_3_loss: 1.2178 - output_0_acc: 0.6987 - output_1_acc: 0.7264 - output_2_acc: 0.6014 - output_3_acc: 0.5141 - val_loss: 4.4173 - val_output_0_loss: 1.0308 - val_output_1_loss: 0.8977 - val_output_2_loss: 1.0073 - val_output_3_loss: 1.4815 - val_output_0_acc: 0.5911 - val_output_1_acc: 0.6639 - val_output_2_acc: 0.5831 - val_output_3_acc: 0.4386\n",
"Epoch 30/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.4702 - output_0_loss: 0.6725 - output_1_loss: 0.6422 - output_2_loss: 0.9544 - output_3_loss: 1.2010 - output_0_acc: 0.7312 - output_1_acc: 0.7424 - output_2_acc: 0.5929 - output_3_acc: 0.5146 - val_loss: 3.7916 - val_output_0_loss: 0.7404 - val_output_1_loss: 0.6916 - val_output_2_loss: 0.9242 - val_output_3_loss: 1.4355 - val_output_0_acc: 0.6820 - val_output_1_acc: 0.7228 - val_output_2_acc: 0.6078 - val_output_3_acc: 0.4185\n",
"Epoch 31/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.4111 - output_0_loss: 0.6554 - output_1_loss: 0.6374 - output_2_loss: 0.9114 - output_3_loss: 1.2069 - output_0_acc: 0.7239 - output_1_acc: 0.7364 - output_2_acc: 0.6222 - output_3_acc: 0.5181 - val_loss: 3.4074 - val_output_0_loss: 0.6036 - val_output_1_loss: 0.6248 - val_output_2_loss: 0.9918 - val_output_3_loss: 1.1871 - val_output_0_acc: 0.7575 - val_output_1_acc: 0.7431 - val_output_2_acc: 0.5881 - val_output_3_acc: 0.5267\n",
"Epoch 32/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.2740 - output_0_loss: 0.6410 - output_1_loss: 0.6031 - output_2_loss: 0.8736 - output_3_loss: 1.1562 - output_0_acc: 0.7449 - output_1_acc: 0.7572 - output_2_acc: 0.6387 - output_3_acc: 0.5299 - val_loss: 3.5060 - val_output_0_loss: 0.6898 - val_output_1_loss: 0.6985 - val_output_2_loss: 0.9780 - val_output_3_loss: 1.1397 - val_output_0_acc: 0.7042 - val_output_1_acc: 0.6980 - val_output_2_acc: 0.5933 - val_output_3_acc: 0.5199\n",
"Epoch 33/200\n"
]
},
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"text": [
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.2092 - output_0_loss: 0.6419 - output_1_loss: 0.5669 - output_2_loss: 0.8762 - output_3_loss: 1.1241 - output_0_acc: 0.7289 - output_1_acc: 0.7737 - output_2_acc: 0.6419 - output_3_acc: 0.5464 - val_loss: 3.6649 - val_output_0_loss: 0.6735 - val_output_1_loss: 0.6310 - val_output_2_loss: 1.0601 - val_output_3_loss: 1.3003 - val_output_0_acc: 0.7052 - val_output_1_acc: 0.7257 - val_output_2_acc: 0.5586 - val_output_3_acc: 0.4756\n",
"Epoch 34/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 3.1295 - output_0_loss: 0.5944 - output_1_loss: 0.5762 - output_2_loss: 0.8502 - output_3_loss: 1.1087 - output_0_acc: 0.7572 - output_1_acc: 0.7752 - output_2_acc: 0.6427 - output_3_acc: 0.5516 - val_loss: 3.6848 - val_output_0_loss: 0.5966 - val_output_1_loss: 0.7276 - val_output_2_loss: 1.0442 - val_output_3_loss: 1.3164 - val_output_0_acc: 0.7587 - val_output_1_acc: 0.6860 - val_output_2_acc: 0.5758 - val_output_3_acc: 0.4661\n",
"Epoch 35/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 3.0440 - output_0_loss: 0.5659 - output_1_loss: 0.5517 - output_2_loss: 0.8499 - output_3_loss: 1.0765 - output_0_acc: 0.7727 - output_1_acc: 0.7802 - output_2_acc: 0.6562 - output_3_acc: 0.5656 - val_loss: 3.0173 - val_output_0_loss: 0.5786 - val_output_1_loss: 0.4879 - val_output_2_loss: 0.8231 - val_output_3_loss: 1.1277 - val_output_0_acc: 0.7476 - val_output_1_acc: 0.8115 - val_output_2_acc: 0.6570 - val_output_3_acc: 0.5508\n",
"Epoch 36/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.9370 - output_0_loss: 0.5535 - output_1_loss: 0.5226 - output_2_loss: 0.7978 - output_3_loss: 1.0630 - output_0_acc: 0.7749 - output_1_acc: 0.7917 - output_2_acc: 0.6639 - output_3_acc: 0.5619 - val_loss: 3.3285 - val_output_0_loss: 0.6739 - val_output_1_loss: 0.5818 - val_output_2_loss: 0.9703 - val_output_3_loss: 1.1026 - val_output_0_acc: 0.7191 - val_output_1_acc: 0.7513 - val_output_2_acc: 0.6082 - val_output_3_acc: 0.5452\n",
"Epoch 37/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.8844 - output_0_loss: 0.5669 - output_1_loss: 0.4952 - output_2_loss: 0.7929 - output_3_loss: 1.0294 - output_0_acc: 0.7652 - output_1_acc: 0.8030 - output_2_acc: 0.6742 - output_3_acc: 0.5754 - val_loss: 3.4227 - val_output_0_loss: 0.5338 - val_output_1_loss: 0.6551 - val_output_2_loss: 1.0108 - val_output_3_loss: 1.2230 - val_output_0_acc: 0.7891 - val_output_1_acc: 0.7458 - val_output_2_acc: 0.5948 - val_output_3_acc: 0.5100\n",
"Epoch 38/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 2.8574 - output_0_loss: 0.5439 - output_1_loss: 0.5048 - output_2_loss: 0.7706 - output_3_loss: 1.0380 - output_0_acc: 0.7794 - output_1_acc: 0.7949 - output_2_acc: 0.6822 - output_3_acc: 0.5834 - val_loss: 2.9494 - val_output_0_loss: 0.5120 - val_output_1_loss: 0.4996 - val_output_2_loss: 0.8766 - val_output_3_loss: 1.0613 - val_output_0_acc: 0.7880 - val_output_1_acc: 0.7985 - val_output_2_acc: 0.6509 - val_output_3_acc: 0.5667\n",
"Epoch 39/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 2.7437 - output_0_loss: 0.4913 - output_1_loss: 0.4838 - output_2_loss: 0.7530 - output_3_loss: 1.0155 - output_0_acc: 0.8032 - output_1_acc: 0.8062 - output_2_acc: 0.6934 - output_3_acc: 0.5939 - val_loss: 3.1692 - val_output_0_loss: 0.6804 - val_output_1_loss: 0.4578 - val_output_2_loss: 0.9191 - val_output_3_loss: 1.1119 - val_output_0_acc: 0.7240 - val_output_1_acc: 0.8195 - val_output_2_acc: 0.6165 - val_output_3_acc: 0.5423\n",
"Epoch 40/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.6946 - output_0_loss: 0.4782 - output_1_loss: 0.4476 - output_2_loss: 0.7677 - output_3_loss: 1.0010 - output_0_acc: 0.8080 - output_1_acc: 0.8215 - output_2_acc: 0.6874 - output_3_acc: 0.5909 - val_loss: 3.5027 - val_output_0_loss: 0.5756 - val_output_1_loss: 0.5703 - val_output_2_loss: 1.0680 - val_output_3_loss: 1.2889 - val_output_0_acc: 0.7758 - val_output_1_acc: 0.7619 - val_output_2_acc: 0.5672 - val_output_3_acc: 0.5058\n",
"Epoch 41/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.6279 - output_0_loss: 0.4807 - output_1_loss: 0.4335 - output_2_loss: 0.7326 - output_3_loss: 0.9810 - output_0_acc: 0.8087 - output_1_acc: 0.8270 - output_2_acc: 0.6947 - output_3_acc: 0.6107 - val_loss: 2.7927 - val_output_0_loss: 0.4104 - val_output_1_loss: 0.4683 - val_output_2_loss: 0.7729 - val_output_3_loss: 1.1412 - val_output_0_acc: 0.8476 - val_output_1_acc: 0.8051 - val_output_2_acc: 0.6741 - val_output_3_acc: 0.5505\n",
"Epoch 42/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.5102 - output_0_loss: 0.4562 - output_1_loss: 0.4381 - output_2_loss: 0.7228 - output_3_loss: 0.8931 - output_0_acc: 0.8235 - output_1_acc: 0.8275 - output_2_acc: 0.7022 - output_3_acc: 0.6384 - val_loss: 3.3353 - val_output_0_loss: 0.5580 - val_output_1_loss: 0.5384 - val_output_2_loss: 1.0460 - val_output_3_loss: 1.1929 - val_output_0_acc: 0.7577 - val_output_1_acc: 0.8103 - val_output_2_acc: 0.6073 - val_output_3_acc: 0.5465\n",
"Epoch 43/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.4775 - output_0_loss: 0.4546 - output_1_loss: 0.4103 - output_2_loss: 0.6988 - output_3_loss: 0.9137 - output_0_acc: 0.8152 - output_1_acc: 0.8392 - output_2_acc: 0.7147 - output_3_acc: 0.6204 - val_loss: 3.8617 - val_output_0_loss: 0.7389 - val_output_1_loss: 0.7594 - val_output_2_loss: 1.1345 - val_output_3_loss: 1.2289 - val_output_0_acc: 0.7195 - val_output_1_acc: 0.7132 - val_output_2_acc: 0.5662 - val_output_3_acc: 0.5042\n",
"Epoch 44/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 2.4319 - output_0_loss: 0.4081 - output_1_loss: 0.4205 - output_2_loss: 0.6837 - output_3_loss: 0.9196 - output_0_acc: 0.8415 - output_1_acc: 0.8440 - output_2_acc: 0.7144 - output_3_acc: 0.6294 - val_loss: 2.7254 - val_output_0_loss: 0.4721 - val_output_1_loss: 0.5440 - val_output_2_loss: 0.7638 - val_output_3_loss: 0.9455 - val_output_0_acc: 0.8071 - val_output_1_acc: 0.7856 - val_output_2_acc: 0.6854 - val_output_3_acc: 0.6101\n",
"Epoch 45/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.3270 - output_0_loss: 0.3890 - output_1_loss: 0.3782 - output_2_loss: 0.6565 - output_3_loss: 0.9033 - output_0_acc: 0.8505 - output_1_acc: 0.8582 - output_2_acc: 0.7334 - output_3_acc: 0.6397 - val_loss: 2.7700 - val_output_0_loss: 0.3888 - val_output_1_loss: 0.4473 - val_output_2_loss: 0.7695 - val_output_3_loss: 1.1644 - val_output_0_acc: 0.8432 - val_output_1_acc: 0.8204 - val_output_2_acc: 0.6958 - val_output_3_acc: 0.5627\n",
"Epoch 46/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 2.3179 - output_0_loss: 0.4146 - output_1_loss: 0.3665 - output_2_loss: 0.6555 - output_3_loss: 0.8813 - output_0_acc: 0.8325 - output_1_acc: 0.8530 - output_2_acc: 0.7297 - output_3_acc: 0.6487 - val_loss: 2.9803 - val_output_0_loss: 0.4697 - val_output_1_loss: 0.4629 - val_output_2_loss: 0.8902 - val_output_3_loss: 1.1575 - val_output_0_acc: 0.8055 - val_output_1_acc: 0.8104 - val_output_2_acc: 0.6461 - val_output_3_acc: 0.5622\n",
"Epoch 47/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 2.2500 - output_0_loss: 0.3830 - output_1_loss: 0.3564 - output_2_loss: 0.6642 - output_3_loss: 0.8464 - output_0_acc: 0.8475 - output_1_acc: 0.8582 - output_2_acc: 0.7347 - output_3_acc: 0.6674 - val_loss: 2.7158 - val_output_0_loss: 0.3863 - val_output_1_loss: 0.5858 - val_output_2_loss: 0.7712 - val_output_3_loss: 0.9724 - val_output_0_acc: 0.8447 - val_output_1_acc: 0.7718 - val_output_2_acc: 0.7042 - val_output_3_acc: 0.6040\n",
"Epoch 48/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 2.2142 - output_0_loss: 0.3804 - output_1_loss: 0.3517 - output_2_loss: 0.6231 - output_3_loss: 0.8590 - output_0_acc: 0.8532 - output_1_acc: 0.8635 - output_2_acc: 0.7537 - output_3_acc: 0.6557 - val_loss: 2.4995 - val_output_0_loss: 0.3843 - val_output_1_loss: 0.3390 - val_output_2_loss: 0.7176 - val_output_3_loss: 1.0586 - val_output_0_acc: 0.8541 - val_output_1_acc: 0.8706 - val_output_2_acc: 0.7223 - val_output_3_acc: 0.5987\n",
"Epoch 49/200\n"
]
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"Epoch 50/200\n",
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"Epoch 51/200\n",
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"Epoch 52/200\n",
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"Epoch 53/200\n",
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"Epoch 54/200\n",
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"Epoch 55/200\n",
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"Epoch 56/200\n",
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"Epoch 57/200\n",
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"Epoch 58/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.7430 - output_0_loss: 0.2702 - output_1_loss: 0.2497 - output_2_loss: 0.5258 - output_3_loss: 0.6973 - output_0_acc: 0.8957 - output_1_acc: 0.9090 - output_2_acc: 0.7982 - output_3_acc: 0.7222 - val_loss: 2.4838 - val_output_0_loss: 0.3295 - val_output_1_loss: 0.2958 - val_output_2_loss: 0.7711 - val_output_3_loss: 1.0874 - val_output_0_acc: 0.8617 - val_output_1_acc: 0.8878 - val_output_2_acc: 0.7055 - val_output_3_acc: 0.5893\n",
"Epoch 59/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.7355 - output_0_loss: 0.2731 - output_1_loss: 0.2373 - output_2_loss: 0.5249 - output_3_loss: 0.7003 - output_0_acc: 0.8970 - output_1_acc: 0.9175 - output_2_acc: 0.7939 - output_3_acc: 0.7269 - val_loss: 2.3728 - val_output_0_loss: 0.3459 - val_output_1_loss: 0.3103 - val_output_2_loss: 0.7898 - val_output_3_loss: 0.9269 - val_output_0_acc: 0.8584 - val_output_1_acc: 0.8781 - val_output_2_acc: 0.7169 - val_output_3_acc: 0.6500\n",
"Epoch 60/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.7118 - output_0_loss: 0.2705 - output_1_loss: 0.2330 - output_2_loss: 0.4948 - output_3_loss: 0.7136 - output_0_acc: 0.9062 - output_1_acc: 0.9162 - output_2_acc: 0.8095 - output_3_acc: 0.7219 - val_loss: 2.2350 - val_output_0_loss: 0.4673 - val_output_1_loss: 0.2853 - val_output_2_loss: 0.5726 - val_output_3_loss: 0.9098 - val_output_0_acc: 0.8248 - val_output_1_acc: 0.8958 - val_output_2_acc: 0.7717 - val_output_3_acc: 0.6482\n",
"Epoch 61/200\n",
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"Epoch 62/200\n",
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"Epoch 63/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.6222 - output_0_loss: 0.2359 - output_1_loss: 0.2412 - output_2_loss: 0.4718 - output_3_loss: 0.6732 - output_0_acc: 0.9160 - output_1_acc: 0.9107 - output_2_acc: 0.8112 - output_3_acc: 0.7479 - val_loss: 2.1595 - val_output_0_loss: 0.2877 - val_output_1_loss: 0.2251 - val_output_2_loss: 0.7099 - val_output_3_loss: 0.9369 - val_output_0_acc: 0.8857 - val_output_1_acc: 0.9197 - val_output_2_acc: 0.7247 - val_output_3_acc: 0.6296\n",
"Epoch 64/200\n",
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"Epoch 65/200\n"
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"Epoch 66/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.5357 - output_0_loss: 0.2146 - output_1_loss: 0.2228 - output_2_loss: 0.4692 - output_3_loss: 0.6291 - output_0_acc: 0.9205 - output_1_acc: 0.9147 - output_2_acc: 0.8260 - output_3_acc: 0.7622 - val_loss: 1.7616 - val_output_0_loss: 0.2352 - val_output_1_loss: 0.2809 - val_output_2_loss: 0.5281 - val_output_3_loss: 0.7174 - val_output_0_acc: 0.9173 - val_output_1_acc: 0.8964 - val_output_2_acc: 0.8052 - val_output_3_acc: 0.7290\n",
"Epoch 67/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.4696 - output_0_loss: 0.1999 - output_1_loss: 0.1839 - output_2_loss: 0.4477 - output_3_loss: 0.6382 - output_0_acc: 0.9275 - output_1_acc: 0.9322 - output_2_acc: 0.8320 - output_3_acc: 0.7562 - val_loss: 1.9371 - val_output_0_loss: 0.1935 - val_output_1_loss: 0.1764 - val_output_2_loss: 0.7666 - val_output_3_loss: 0.8005 - val_output_0_acc: 0.9345 - val_output_1_acc: 0.9379 - val_output_2_acc: 0.7135 - val_output_3_acc: 0.6899\n",
"Epoch 68/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.4461 - output_0_loss: 0.2150 - output_1_loss: 0.1968 - output_2_loss: 0.4110 - output_3_loss: 0.6234 - output_0_acc: 0.9225 - output_1_acc: 0.9287 - output_2_acc: 0.8375 - output_3_acc: 0.7624 - val_loss: 1.8098 - val_output_0_loss: 0.1653 - val_output_1_loss: 0.3445 - val_output_2_loss: 0.5502 - val_output_3_loss: 0.7499 - val_output_0_acc: 0.9446 - val_output_1_acc: 0.8742 - val_output_2_acc: 0.7862 - val_output_3_acc: 0.7003\n",
"Epoch 69/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.4584 - output_0_loss: 0.2237 - output_1_loss: 0.1993 - output_2_loss: 0.4346 - output_3_loss: 0.6008 - output_0_acc: 0.9207 - output_1_acc: 0.9257 - output_2_acc: 0.8357 - output_3_acc: 0.7722 - val_loss: 1.7672 - val_output_0_loss: 0.1881 - val_output_1_loss: 0.2623 - val_output_2_loss: 0.5759 - val_output_3_loss: 0.7409 - val_output_0_acc: 0.9356 - val_output_1_acc: 0.9044 - val_output_2_acc: 0.7762 - val_output_3_acc: 0.7154\n",
"Epoch 70/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.4309 - output_0_loss: 0.1834 - output_1_loss: 0.2049 - output_2_loss: 0.4347 - output_3_loss: 0.6079 - output_0_acc: 0.9345 - output_1_acc: 0.9242 - output_2_acc: 0.8350 - output_3_acc: 0.7679 - val_loss: 2.1940 - val_output_0_loss: 0.2455 - val_output_1_loss: 0.4378 - val_output_2_loss: 0.7507 - val_output_3_loss: 0.7601 - val_output_0_acc: 0.9051 - val_output_1_acc: 0.8462 - val_output_2_acc: 0.7343 - val_output_3_acc: 0.7155\n",
"Epoch 71/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.4088 - output_0_loss: 0.2097 - output_1_loss: 0.1897 - output_2_loss: 0.4342 - output_3_loss: 0.5751 - output_0_acc: 0.9222 - output_1_acc: 0.9327 - output_2_acc: 0.8350 - output_3_acc: 0.7822 - val_loss: 2.5918 - val_output_0_loss: 0.5882 - val_output_1_loss: 0.2938 - val_output_2_loss: 0.6879 - val_output_3_loss: 1.0218 - val_output_0_acc: 0.7991 - val_output_1_acc: 0.8887 - val_output_2_acc: 0.7621 - val_output_3_acc: 0.6751\n",
"Epoch 72/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.3931 - output_0_loss: 0.1956 - output_1_loss: 0.1929 - output_2_loss: 0.4174 - output_3_loss: 0.5872 - output_0_acc: 0.9267 - output_1_acc: 0.9365 - output_2_acc: 0.8412 - output_3_acc: 0.7764 - val_loss: 2.0535 - val_output_0_loss: 0.2627 - val_output_1_loss: 0.3364 - val_output_2_loss: 0.4982 - val_output_3_loss: 0.9562 - val_output_0_acc: 0.9084 - val_output_1_acc: 0.8762 - val_output_2_acc: 0.8149 - val_output_3_acc: 0.6865\n",
"Epoch 73/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.3929 - output_0_loss: 0.2025 - output_1_loss: 0.1927 - output_2_loss: 0.4037 - output_3_loss: 0.5940 - output_0_acc: 0.9285 - output_1_acc: 0.9305 - output_2_acc: 0.8450 - output_3_acc: 0.7679 - val_loss: 1.6200 - val_output_0_loss: 0.2166 - val_output_1_loss: 0.2574 - val_output_2_loss: 0.4770 - val_output_3_loss: 0.6690 - val_output_0_acc: 0.9189 - val_output_1_acc: 0.9075 - val_output_2_acc: 0.8128 - val_output_3_acc: 0.7401\n",
"Epoch 74/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.3168 - output_0_loss: 0.1835 - output_1_loss: 0.1646 - output_2_loss: 0.3912 - output_3_loss: 0.5775 - output_0_acc: 0.9407 - output_1_acc: 0.9400 - output_2_acc: 0.8577 - output_3_acc: 0.7934 - val_loss: 1.9765 - val_output_0_loss: 0.3003 - val_output_1_loss: 0.2715 - val_output_2_loss: 0.6094 - val_output_3_loss: 0.7954 - val_output_0_acc: 0.8866 - val_output_1_acc: 0.9072 - val_output_2_acc: 0.7668 - val_output_3_acc: 0.7030\n",
"Epoch 75/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.2833 - output_0_loss: 0.1749 - output_1_loss: 0.1656 - output_2_loss: 0.3689 - output_3_loss: 0.5740 - output_0_acc: 0.9395 - output_1_acc: 0.9417 - output_2_acc: 0.8620 - output_3_acc: 0.7842 - val_loss: 1.7700 - val_output_0_loss: 0.2227 - val_output_1_loss: 0.1716 - val_output_2_loss: 0.5555 - val_output_3_loss: 0.8202 - val_output_0_acc: 0.9163 - val_output_1_acc: 0.9406 - val_output_2_acc: 0.7942 - val_output_3_acc: 0.6978\n",
"Epoch 76/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.3074 - output_0_loss: 0.1899 - output_1_loss: 0.1604 - output_2_loss: 0.4004 - output_3_loss: 0.5567 - output_0_acc: 0.9400 - output_1_acc: 0.9430 - output_2_acc: 0.8542 - output_3_acc: 0.7859 - val_loss: 1.9419 - val_output_0_loss: 0.3461 - val_output_1_loss: 0.3927 - val_output_2_loss: 0.5560 - val_output_3_loss: 0.6471 - val_output_0_acc: 0.8639 - val_output_1_acc: 0.8568 - val_output_2_acc: 0.8006 - val_output_3_acc: 0.7551\n",
"Epoch 77/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.2631 - output_0_loss: 0.1873 - output_1_loss: 0.1659 - output_2_loss: 0.3853 - output_3_loss: 0.5246 - output_0_acc: 0.9340 - output_1_acc: 0.9387 - output_2_acc: 0.8615 - output_3_acc: 0.8027 - val_loss: 2.2959 - val_output_0_loss: 0.2095 - val_output_1_loss: 0.2099 - val_output_2_loss: 1.0976 - val_output_3_loss: 0.7789 - val_output_0_acc: 0.9245 - val_output_1_acc: 0.9275 - val_output_2_acc: 0.6665 - val_output_3_acc: 0.7105\n",
"Epoch 78/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.2374 - output_0_loss: 0.1646 - output_1_loss: 0.1731 - output_2_loss: 0.3766 - output_3_loss: 0.5231 - output_0_acc: 0.9442 - output_1_acc: 0.9412 - output_2_acc: 0.8595 - output_3_acc: 0.8030 - val_loss: 1.5957 - val_output_0_loss: 0.2933 - val_output_1_loss: 0.1649 - val_output_2_loss: 0.4534 - val_output_3_loss: 0.6841 - val_output_0_acc: 0.8972 - val_output_1_acc: 0.9420 - val_output_2_acc: 0.8342 - val_output_3_acc: 0.7684\n",
"Epoch 79/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.2428 - output_0_loss: 0.1609 - output_1_loss: 0.1591 - output_2_loss: 0.3775 - output_3_loss: 0.5453 - output_0_acc: 0.9440 - output_1_acc: 0.9475 - output_2_acc: 0.8690 - output_3_acc: 0.7924 - val_loss: 1.8450 - val_output_0_loss: 0.1334 - val_output_1_loss: 0.1662 - val_output_2_loss: 0.8965 - val_output_3_loss: 0.6490 - val_output_0_acc: 0.9549 - val_output_1_acc: 0.9401 - val_output_2_acc: 0.6912 - val_output_3_acc: 0.7563\n",
"Epoch 80/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 1.2380 - output_0_loss: 0.1668 - output_1_loss: 0.1485 - output_2_loss: 0.3955 - output_3_loss: 0.5272 - output_0_acc: 0.9445 - output_1_acc: 0.9517 - output_2_acc: 0.8615 - output_3_acc: 0.8087 - val_loss: 1.7110 - val_output_0_loss: 0.1881 - val_output_1_loss: 0.2719 - val_output_2_loss: 0.4213 - val_output_3_loss: 0.8297 - val_output_0_acc: 0.9331 - val_output_1_acc: 0.9019 - val_output_2_acc: 0.8476 - val_output_3_acc: 0.7190\n",
"Epoch 81/200\n"
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"Epoch 82/200\n",
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"Epoch 83/200\n",
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"Epoch 84/200\n",
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"Epoch 85/200\n",
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"Epoch 86/200\n",
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"Epoch 87/200\n",
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"Epoch 88/200\n",
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"Epoch 89/200\n",
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"Epoch 90/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.0917 - output_0_loss: 0.1452 - output_1_loss: 0.1276 - output_2_loss: 0.3166 - output_3_loss: 0.5023 - output_0_acc: 0.9552 - output_1_acc: 0.9577 - output_2_acc: 0.8855 - output_3_acc: 0.8235 - val_loss: 1.6552 - val_output_0_loss: 0.1589 - val_output_1_loss: 0.2468 - val_output_2_loss: 0.5286 - val_output_3_loss: 0.7209 - val_output_0_acc: 0.9411 - val_output_1_acc: 0.9071 - val_output_2_acc: 0.8121 - val_output_3_acc: 0.7510\n",
"Epoch 91/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.0521 - output_0_loss: 0.1400 - output_1_loss: 0.1298 - output_2_loss: 0.3051 - output_3_loss: 0.4772 - output_0_acc: 0.9507 - output_1_acc: 0.9560 - output_2_acc: 0.8932 - output_3_acc: 0.8252 - val_loss: 1.6416 - val_output_0_loss: 0.2392 - val_output_1_loss: 0.2251 - val_output_2_loss: 0.5012 - val_output_3_loss: 0.6762 - val_output_0_acc: 0.9161 - val_output_1_acc: 0.9164 - val_output_2_acc: 0.8138 - val_output_3_acc: 0.7520\n",
"Epoch 92/200\n",
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"Epoch 93/200\n",
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"Epoch 94/200\n",
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"Epoch 95/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.0398 - output_0_loss: 0.1243 - output_1_loss: 0.1010 - output_2_loss: 0.3222 - output_3_loss: 0.4922 - output_0_acc: 0.9567 - output_1_acc: 0.9630 - output_2_acc: 0.8895 - output_3_acc: 0.8270 - val_loss: 1.4561 - val_output_0_loss: 0.1018 - val_output_1_loss: 0.1607 - val_output_2_loss: 0.5225 - val_output_3_loss: 0.6711 - val_output_0_acc: 0.9673 - val_output_1_acc: 0.9467 - val_output_2_acc: 0.8145 - val_output_3_acc: 0.7866\n",
"Epoch 96/200\n",
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"Epoch 97/200\n"
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"Epoch 98/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 1.0080 - output_0_loss: 0.1272 - output_1_loss: 0.1242 - output_2_loss: 0.2737 - output_3_loss: 0.4829 - output_0_acc: 0.9560 - output_1_acc: 0.9535 - output_2_acc: 0.9060 - output_3_acc: 0.8320 - val_loss: 1.4208 - val_output_0_loss: 0.1476 - val_output_1_loss: 0.1619 - val_output_2_loss: 0.5381 - val_output_3_loss: 0.5732 - val_output_0_acc: 0.9513 - val_output_1_acc: 0.9448 - val_output_2_acc: 0.8324 - val_output_3_acc: 0.8078\n",
"Epoch 99/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.9862 - output_0_loss: 0.1346 - output_1_loss: 0.1194 - output_2_loss: 0.2944 - output_3_loss: 0.4378 - output_0_acc: 0.9512 - output_1_acc: 0.9605 - output_2_acc: 0.9000 - output_3_acc: 0.8527 - val_loss: 2.3790 - val_output_0_loss: 0.4554 - val_output_1_loss: 0.1987 - val_output_2_loss: 0.7053 - val_output_3_loss: 1.0196 - val_output_0_acc: 0.8609 - val_output_1_acc: 0.9273 - val_output_2_acc: 0.7798 - val_output_3_acc: 0.7083\n",
"Epoch 100/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.9673 - output_0_loss: 0.1211 - output_1_loss: 0.1157 - output_2_loss: 0.2919 - output_3_loss: 0.4386 - output_0_acc: 0.9620 - output_1_acc: 0.9595 - output_2_acc: 0.8955 - output_3_acc: 0.8432 - val_loss: 1.4693 - val_output_0_loss: 0.1319 - val_output_1_loss: 0.3313 - val_output_2_loss: 0.4274 - val_output_3_loss: 0.5787 - val_output_0_acc: 0.9549 - val_output_1_acc: 0.8956 - val_output_2_acc: 0.8536 - val_output_3_acc: 0.8052\n",
"Epoch 101/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.9395 - output_0_loss: 0.1186 - output_1_loss: 0.0975 - output_2_loss: 0.2993 - output_3_loss: 0.4242 - output_0_acc: 0.9582 - output_1_acc: 0.9657 - output_2_acc: 0.8975 - output_3_acc: 0.8625 - val_loss: 1.4723 - val_output_0_loss: 0.1129 - val_output_1_loss: 0.1600 - val_output_2_loss: 0.3997 - val_output_3_loss: 0.7997 - val_output_0_acc: 0.9616 - val_output_1_acc: 0.9446 - val_output_2_acc: 0.8609 - val_output_3_acc: 0.7636\n",
"Epoch 102/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.9460 - output_0_loss: 0.1194 - output_1_loss: 0.1058 - output_2_loss: 0.3100 - output_3_loss: 0.4108 - output_0_acc: 0.9610 - output_1_acc: 0.9647 - output_2_acc: 0.8912 - output_3_acc: 0.8457 - val_loss: 1.6965 - val_output_0_loss: 0.2364 - val_output_1_loss: 0.1637 - val_output_2_loss: 0.6264 - val_output_3_loss: 0.6700 - val_output_0_acc: 0.9180 - val_output_1_acc: 0.9443 - val_output_2_acc: 0.8115 - val_output_3_acc: 0.7873\n",
"Epoch 103/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.9190 - output_0_loss: 0.1015 - output_1_loss: 0.1115 - output_2_loss: 0.3006 - output_3_loss: 0.4053 - output_0_acc: 0.9672 - output_1_acc: 0.9602 - output_2_acc: 0.8917 - output_3_acc: 0.8462 - val_loss: 1.6249 - val_output_0_loss: 0.1789 - val_output_1_loss: 0.1426 - val_output_2_loss: 0.6092 - val_output_3_loss: 0.6942 - val_output_0_acc: 0.9344 - val_output_1_acc: 0.9508 - val_output_2_acc: 0.8017 - val_output_3_acc: 0.7823\n",
"Epoch 104/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.9345 - output_0_loss: 0.1163 - output_1_loss: 0.0951 - output_2_loss: 0.3038 - output_3_loss: 0.4193 - output_0_acc: 0.9605 - output_1_acc: 0.9712 - output_2_acc: 0.8947 - output_3_acc: 0.8557 - val_loss: 1.7832 - val_output_0_loss: 0.1509 - val_output_1_loss: 0.1894 - val_output_2_loss: 0.6603 - val_output_3_loss: 0.7826 - val_output_0_acc: 0.9484 - val_output_1_acc: 0.9383 - val_output_2_acc: 0.8035 - val_output_3_acc: 0.7674\n",
"Epoch 105/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.9715 - output_0_loss: 0.1347 - output_1_loss: 0.1143 - output_2_loss: 0.2941 - output_3_loss: 0.4283 - output_0_acc: 0.9532 - output_1_acc: 0.9615 - output_2_acc: 0.8940 - output_3_acc: 0.8507 - val_loss: 2.0314 - val_output_0_loss: 0.1342 - val_output_1_loss: 0.1552 - val_output_2_loss: 1.0733 - val_output_3_loss: 0.6686 - val_output_0_acc: 0.9557 - val_output_1_acc: 0.9458 - val_output_2_acc: 0.6981 - val_output_3_acc: 0.7712\n",
"Epoch 106/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.9411 - output_0_loss: 0.1011 - output_1_loss: 0.1292 - output_2_loss: 0.2939 - output_3_loss: 0.4168 - output_0_acc: 0.9662 - output_1_acc: 0.9597 - output_2_acc: 0.9015 - output_3_acc: 0.8552 - val_loss: 1.3437 - val_output_0_loss: 0.1377 - val_output_1_loss: 0.1594 - val_output_2_loss: 0.3166 - val_output_3_loss: 0.7300 - val_output_0_acc: 0.9529 - val_output_1_acc: 0.9447 - val_output_2_acc: 0.8891 - val_output_3_acc: 0.7447\n",
"Epoch 107/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.9031 - output_0_loss: 0.1087 - output_1_loss: 0.1231 - output_2_loss: 0.2690 - output_3_loss: 0.4023 - output_0_acc: 0.9650 - output_1_acc: 0.9572 - output_2_acc: 0.9047 - output_3_acc: 0.8660 - val_loss: 2.2220 - val_output_0_loss: 0.3116 - val_output_1_loss: 0.2472 - val_output_2_loss: 0.5032 - val_output_3_loss: 1.1600 - val_output_0_acc: 0.9006 - val_output_1_acc: 0.9146 - val_output_2_acc: 0.8184 - val_output_3_acc: 0.6808\n",
"Epoch 108/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8932 - output_0_loss: 0.1107 - output_1_loss: 0.1002 - output_2_loss: 0.2585 - output_3_loss: 0.4238 - output_0_acc: 0.9640 - output_1_acc: 0.9667 - output_2_acc: 0.9097 - output_3_acc: 0.8602 - val_loss: 1.3881 - val_output_0_loss: 0.0873 - val_output_1_loss: 0.2218 - val_output_2_loss: 0.5031 - val_output_3_loss: 0.5759 - val_output_0_acc: 0.9732 - val_output_1_acc: 0.9264 - val_output_2_acc: 0.8280 - val_output_3_acc: 0.8107\n",
"Epoch 109/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8803 - output_0_loss: 0.0969 - output_1_loss: 0.1082 - output_2_loss: 0.2712 - output_3_loss: 0.4041 - output_0_acc: 0.9675 - output_1_acc: 0.9647 - output_2_acc: 0.9052 - output_3_acc: 0.8615 - val_loss: 1.0874 - val_output_0_loss: 0.0828 - val_output_1_loss: 0.1391 - val_output_2_loss: 0.3198 - val_output_3_loss: 0.5457 - val_output_0_acc: 0.9735 - val_output_1_acc: 0.9562 - val_output_2_acc: 0.8879 - val_output_3_acc: 0.8088\n",
"Epoch 110/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.9538 - output_0_loss: 0.1258 - output_1_loss: 0.1023 - output_2_loss: 0.2926 - output_3_loss: 0.4330 - output_0_acc: 0.9615 - output_1_acc: 0.9690 - output_2_acc: 0.9062 - output_3_acc: 0.8530 - val_loss: 1.2595 - val_output_0_loss: 0.2234 - val_output_1_loss: 0.1367 - val_output_2_loss: 0.3479 - val_output_3_loss: 0.5513 - val_output_0_acc: 0.9239 - val_output_1_acc: 0.9564 - val_output_2_acc: 0.8837 - val_output_3_acc: 0.8205\n",
"Epoch 111/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8878 - output_0_loss: 0.1084 - output_1_loss: 0.1086 - output_2_loss: 0.2543 - output_3_loss: 0.4166 - output_0_acc: 0.9655 - output_1_acc: 0.9630 - output_2_acc: 0.9095 - output_3_acc: 0.8627 - val_loss: 1.4578 - val_output_0_loss: 0.1625 - val_output_1_loss: 0.1919 - val_output_2_loss: 0.5061 - val_output_3_loss: 0.5973 - val_output_0_acc: 0.9484 - val_output_1_acc: 0.9327 - val_output_2_acc: 0.8474 - val_output_3_acc: 0.8111\n",
"Epoch 112/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8951 - output_0_loss: 0.1208 - output_1_loss: 0.0846 - output_2_loss: 0.2885 - output_3_loss: 0.4012 - output_0_acc: 0.9587 - output_1_acc: 0.9757 - output_2_acc: 0.9020 - output_3_acc: 0.8730 - val_loss: 3.9064 - val_output_0_loss: 0.6025 - val_output_1_loss: 0.3647 - val_output_2_loss: 1.3815 - val_output_3_loss: 1.5578 - val_output_0_acc: 0.8606 - val_output_1_acc: 0.8781 - val_output_2_acc: 0.7180 - val_output_3_acc: 0.6488\n",
"Epoch 113/200\n"
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"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8900 - output_0_loss: 0.1077 - output_1_loss: 0.1121 - output_2_loss: 0.2627 - output_3_loss: 0.4074 - output_0_acc: 0.9670 - output_1_acc: 0.9627 - output_2_acc: 0.9122 - output_3_acc: 0.8645 - val_loss: 1.5537 - val_output_0_loss: 0.3122 - val_output_1_loss: 0.1446 - val_output_2_loss: 0.4954 - val_output_3_loss: 0.6015 - val_output_0_acc: 0.9142 - val_output_1_acc: 0.9508 - val_output_2_acc: 0.8310 - val_output_3_acc: 0.7951\n",
"Epoch 114/200\n",
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"Epoch 115/200\n",
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"Epoch 116/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.8712 - output_0_loss: 0.1016 - output_1_loss: 0.1070 - output_2_loss: 0.2643 - output_3_loss: 0.3984 - output_0_acc: 0.9677 - output_1_acc: 0.9652 - output_2_acc: 0.9130 - output_3_acc: 0.8670 - val_loss: 1.1706 - val_output_0_loss: 0.2226 - val_output_1_loss: 0.1598 - val_output_2_loss: 0.2820 - val_output_3_loss: 0.5062 - val_output_0_acc: 0.9248 - val_output_1_acc: 0.9486 - val_output_2_acc: 0.9028 - val_output_3_acc: 0.8417\n",
"Epoch 117/200\n",
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"Epoch 118/200\n",
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"Epoch 119/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.8326 - output_0_loss: 0.0996 - output_1_loss: 0.0789 - output_2_loss: 0.2797 - output_3_loss: 0.3743 - output_0_acc: 0.9645 - output_1_acc: 0.9735 - output_2_acc: 0.9097 - output_3_acc: 0.8680 - val_loss: 1.3373 - val_output_0_loss: 0.1286 - val_output_1_loss: 0.2423 - val_output_2_loss: 0.4400 - val_output_3_loss: 0.5264 - val_output_0_acc: 0.9544 - val_output_1_acc: 0.9328 - val_output_2_acc: 0.8704 - val_output_3_acc: 0.8372\n",
"Epoch 120/200\n",
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"Epoch 121/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8488 - output_0_loss: 0.1061 - output_1_loss: 0.0901 - output_2_loss: 0.2745 - output_3_loss: 0.3782 - output_0_acc: 0.9652 - output_1_acc: 0.9745 - output_2_acc: 0.9087 - output_3_acc: 0.8707 - val_loss: 1.5317 - val_output_0_loss: 0.1739 - val_output_1_loss: 0.2425 - val_output_2_loss: 0.4807 - val_output_3_loss: 0.6345 - val_output_0_acc: 0.9438 - val_output_1_acc: 0.9247 - val_output_2_acc: 0.8565 - val_output_3_acc: 0.8122\n",
"Epoch 122/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8310 - output_0_loss: 0.0842 - output_1_loss: 0.1076 - output_2_loss: 0.2687 - output_3_loss: 0.3706 - output_0_acc: 0.9712 - output_1_acc: 0.9687 - output_2_acc: 0.9067 - output_3_acc: 0.8762 - val_loss: 1.7628 - val_output_0_loss: 0.1436 - val_output_1_loss: 0.2188 - val_output_2_loss: 0.4150 - val_output_3_loss: 0.9854 - val_output_0_acc: 0.9510 - val_output_1_acc: 0.9377 - val_output_2_acc: 0.8713 - val_output_3_acc: 0.7484\n",
"Epoch 123/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.8192 - output_0_loss: 0.1055 - output_1_loss: 0.1307 - output_2_loss: 0.2201 - output_3_loss: 0.3628 - output_0_acc: 0.9682 - output_1_acc: 0.9582 - output_2_acc: 0.9260 - output_3_acc: 0.8847 - val_loss: 1.1976 - val_output_0_loss: 0.0912 - val_output_1_loss: 0.1055 - val_output_2_loss: 0.2729 - val_output_3_loss: 0.7280 - val_output_0_acc: 0.9717 - val_output_1_acc: 0.9641 - val_output_2_acc: 0.9104 - val_output_3_acc: 0.7923\n",
"Epoch 124/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8162 - output_0_loss: 0.0918 - output_1_loss: 0.0863 - output_2_loss: 0.2646 - output_3_loss: 0.3735 - output_0_acc: 0.9682 - output_1_acc: 0.9687 - output_2_acc: 0.9142 - output_3_acc: 0.8815 - val_loss: 1.4543 - val_output_0_loss: 0.3333 - val_output_1_loss: 0.2237 - val_output_2_loss: 0.4338 - val_output_3_loss: 0.4636 - val_output_0_acc: 0.8998 - val_output_1_acc: 0.9296 - val_output_2_acc: 0.8679 - val_output_3_acc: 0.8492\n",
"Epoch 125/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.8012 - output_0_loss: 0.1034 - output_1_loss: 0.0748 - output_2_loss: 0.2615 - output_3_loss: 0.3615 - output_0_acc: 0.9657 - output_1_acc: 0.9747 - output_2_acc: 0.9125 - output_3_acc: 0.8832 - val_loss: 1.6052 - val_output_0_loss: 0.2075 - val_output_1_loss: 0.2016 - val_output_2_loss: 0.4490 - val_output_3_loss: 0.7470 - val_output_0_acc: 0.9422 - val_output_1_acc: 0.9391 - val_output_2_acc: 0.8572 - val_output_3_acc: 0.7919\n",
"Epoch 126/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.8094 - output_0_loss: 0.0975 - output_1_loss: 0.1061 - output_2_loss: 0.2459 - output_3_loss: 0.3599 - output_0_acc: 0.9660 - output_1_acc: 0.9660 - output_2_acc: 0.9220 - output_3_acc: 0.8792 - val_loss: 1.5902 - val_output_0_loss: 0.3005 - val_output_1_loss: 0.1331 - val_output_2_loss: 0.5037 - val_output_3_loss: 0.6530 - val_output_0_acc: 0.9055 - val_output_1_acc: 0.9548 - val_output_2_acc: 0.8451 - val_output_3_acc: 0.8031\n",
"Epoch 127/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.8023 - output_0_loss: 0.0948 - output_1_loss: 0.0923 - output_2_loss: 0.2410 - output_3_loss: 0.3743 - output_0_acc: 0.9692 - output_1_acc: 0.9687 - output_2_acc: 0.9232 - output_3_acc: 0.8747 - val_loss: 1.2046 - val_output_0_loss: 0.1462 - val_output_1_loss: 0.0990 - val_output_2_loss: 0.3618 - val_output_3_loss: 0.5976 - val_output_0_acc: 0.9482 - val_output_1_acc: 0.9681 - val_output_2_acc: 0.8902 - val_output_3_acc: 0.8312\n",
"Epoch 128/200\n",
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"Epoch 129/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7699 - output_0_loss: 0.0896 - output_1_loss: 0.0846 - output_2_loss: 0.2546 - output_3_loss: 0.3411 - output_0_acc: 0.9707 - output_1_acc: 0.9757 - output_2_acc: 0.9190 - output_3_acc: 0.8880 - val_loss: 1.2340 - val_output_0_loss: 0.1355 - val_output_1_loss: 0.1595 - val_output_2_loss: 0.2708 - val_output_3_loss: 0.6683 - val_output_0_acc: 0.9531 - val_output_1_acc: 0.9479 - val_output_2_acc: 0.9189 - val_output_3_acc: 0.8067\n",
"Epoch 130/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7929 - output_0_loss: 0.1035 - output_1_loss: 0.0871 - output_2_loss: 0.2394 - output_3_loss: 0.3630 - output_0_acc: 0.9672 - output_1_acc: 0.9685 - output_2_acc: 0.9220 - output_3_acc: 0.8787 - val_loss: 1.0990 - val_output_0_loss: 0.0667 - val_output_1_loss: 0.1516 - val_output_2_loss: 0.3324 - val_output_3_loss: 0.5483 - val_output_0_acc: 0.9780 - val_output_1_acc: 0.9540 - val_output_2_acc: 0.8984 - val_output_3_acc: 0.8368\n",
"Epoch 131/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7466 - output_0_loss: 0.0765 - output_1_loss: 0.0977 - output_2_loss: 0.2212 - output_3_loss: 0.3512 - output_0_acc: 0.9755 - output_1_acc: 0.9692 - output_2_acc: 0.9237 - output_3_acc: 0.8835 - val_loss: 1.7473 - val_output_0_loss: 0.2204 - val_output_1_loss: 0.2994 - val_output_2_loss: 0.5830 - val_output_3_loss: 0.6446 - val_output_0_acc: 0.9328 - val_output_1_acc: 0.9144 - val_output_2_acc: 0.8399 - val_output_3_acc: 0.8073\n",
"Epoch 132/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7793 - output_0_loss: 0.0877 - output_1_loss: 0.0831 - output_2_loss: 0.2312 - output_3_loss: 0.3774 - output_0_acc: 0.9715 - output_1_acc: 0.9732 - output_2_acc: 0.9257 - output_3_acc: 0.8817 - val_loss: 1.2413 - val_output_0_loss: 0.2115 - val_output_1_loss: 0.1215 - val_output_2_loss: 0.2905 - val_output_3_loss: 0.6178 - val_output_0_acc: 0.9351 - val_output_1_acc: 0.9608 - val_output_2_acc: 0.9091 - val_output_3_acc: 0.8238\n",
"Epoch 133/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7889 - output_0_loss: 0.1040 - output_1_loss: 0.0956 - output_2_loss: 0.2367 - output_3_loss: 0.3527 - output_0_acc: 0.9692 - output_1_acc: 0.9702 - output_2_acc: 0.9255 - output_3_acc: 0.8870 - val_loss: 1.2494 - val_output_0_loss: 0.2001 - val_output_1_loss: 0.1261 - val_output_2_loss: 0.3970 - val_output_3_loss: 0.5262 - val_output_0_acc: 0.9366 - val_output_1_acc: 0.9604 - val_output_2_acc: 0.8789 - val_output_3_acc: 0.8488\n",
"Epoch 134/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7638 - output_0_loss: 0.0852 - output_1_loss: 0.0779 - output_2_loss: 0.2539 - output_3_loss: 0.3467 - output_0_acc: 0.9712 - output_1_acc: 0.9732 - output_2_acc: 0.9212 - output_3_acc: 0.8807 - val_loss: 1.3072 - val_output_0_loss: 0.1376 - val_output_1_loss: 0.1406 - val_output_2_loss: 0.3629 - val_output_3_loss: 0.6661 - val_output_0_acc: 0.9561 - val_output_1_acc: 0.9551 - val_output_2_acc: 0.8824 - val_output_3_acc: 0.8115\n",
"Epoch 135/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7616 - output_0_loss: 0.0796 - output_1_loss: 0.1059 - output_2_loss: 0.2270 - output_3_loss: 0.3491 - output_0_acc: 0.9735 - output_1_acc: 0.9705 - output_2_acc: 0.9232 - output_3_acc: 0.8937 - val_loss: 1.1512 - val_output_0_loss: 0.0603 - val_output_1_loss: 0.0939 - val_output_2_loss: 0.4519 - val_output_3_loss: 0.5450 - val_output_0_acc: 0.9799 - val_output_1_acc: 0.9717 - val_output_2_acc: 0.8832 - val_output_3_acc: 0.8434\n",
"Epoch 136/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7531 - output_0_loss: 0.0696 - output_1_loss: 0.0997 - output_2_loss: 0.2398 - output_3_loss: 0.3440 - output_0_acc: 0.9785 - output_1_acc: 0.9715 - output_2_acc: 0.9207 - output_3_acc: 0.8895 - val_loss: 1.3541 - val_output_0_loss: 0.0808 - val_output_1_loss: 0.2091 - val_output_2_loss: 0.4014 - val_output_3_loss: 0.6628 - val_output_0_acc: 0.9729 - val_output_1_acc: 0.9418 - val_output_2_acc: 0.8844 - val_output_3_acc: 0.8215\n",
"Epoch 137/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7934 - output_0_loss: 0.0897 - output_1_loss: 0.0928 - output_2_loss: 0.2647 - output_3_loss: 0.3462 - output_0_acc: 0.9715 - output_1_acc: 0.9702 - output_2_acc: 0.9145 - output_3_acc: 0.8922 - val_loss: 1.3773 - val_output_0_loss: 0.0773 - val_output_1_loss: 0.1518 - val_output_2_loss: 0.4692 - val_output_3_loss: 0.6790 - val_output_0_acc: 0.9737 - val_output_1_acc: 0.9534 - val_output_2_acc: 0.8616 - val_output_3_acc: 0.8173\n",
"Epoch 138/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7648 - output_0_loss: 0.0932 - output_1_loss: 0.1009 - output_2_loss: 0.2471 - output_3_loss: 0.3236 - output_0_acc: 0.9712 - output_1_acc: 0.9677 - output_2_acc: 0.9225 - output_3_acc: 0.8992 - val_loss: 1.0441 - val_output_0_loss: 0.0743 - val_output_1_loss: 0.1420 - val_output_2_loss: 0.4511 - val_output_3_loss: 0.3767 - val_output_0_acc: 0.9771 - val_output_1_acc: 0.9552 - val_output_2_acc: 0.8748 - val_output_3_acc: 0.8883\n",
"Epoch 139/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7313 - output_0_loss: 0.0842 - output_1_loss: 0.0791 - output_2_loss: 0.2378 - output_3_loss: 0.3301 - output_0_acc: 0.9755 - output_1_acc: 0.9737 - output_2_acc: 0.9260 - output_3_acc: 0.8945 - val_loss: 1.4328 - val_output_0_loss: 0.0462 - val_output_1_loss: 0.2052 - val_output_2_loss: 0.5457 - val_output_3_loss: 0.6356 - val_output_0_acc: 0.9852 - val_output_1_acc: 0.9379 - val_output_2_acc: 0.8486 - val_output_3_acc: 0.8280\n",
"Epoch 140/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7392 - output_0_loss: 0.0846 - output_1_loss: 0.0861 - output_2_loss: 0.1955 - output_3_loss: 0.3730 - output_0_acc: 0.9757 - output_1_acc: 0.9730 - output_2_acc: 0.9390 - output_3_acc: 0.8832 - val_loss: 1.3747 - val_output_0_loss: 0.1135 - val_output_1_loss: 0.1231 - val_output_2_loss: 0.2980 - val_output_3_loss: 0.8400 - val_output_0_acc: 0.9623 - val_output_1_acc: 0.9656 - val_output_2_acc: 0.9080 - val_output_3_acc: 0.7935\n",
"Epoch 141/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7184 - output_0_loss: 0.0922 - output_1_loss: 0.0676 - output_2_loss: 0.2288 - output_3_loss: 0.3298 - output_0_acc: 0.9722 - output_1_acc: 0.9807 - output_2_acc: 0.9295 - output_3_acc: 0.8972 - val_loss: 1.5553 - val_output_0_loss: 0.0989 - val_output_1_loss: 0.1877 - val_output_2_loss: 0.4814 - val_output_3_loss: 0.7873 - val_output_0_acc: 0.9663 - val_output_1_acc: 0.9448 - val_output_2_acc: 0.8634 - val_output_3_acc: 0.7985\n",
"Epoch 142/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7759 - output_0_loss: 0.1014 - output_1_loss: 0.0970 - output_2_loss: 0.2701 - output_3_loss: 0.3074 - output_0_acc: 0.9702 - output_1_acc: 0.9685 - output_2_acc: 0.9212 - output_3_acc: 0.8995 - val_loss: 1.6349 - val_output_0_loss: 0.3224 - val_output_1_loss: 0.1838 - val_output_2_loss: 0.3025 - val_output_3_loss: 0.8262 - val_output_0_acc: 0.9076 - val_output_1_acc: 0.9447 - val_output_2_acc: 0.9021 - val_output_3_acc: 0.7942\n",
"Epoch 143/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7269 - output_0_loss: 0.0763 - output_1_loss: 0.0748 - output_2_loss: 0.2281 - output_3_loss: 0.3477 - output_0_acc: 0.9752 - output_1_acc: 0.9767 - output_2_acc: 0.9317 - output_3_acc: 0.8932 - val_loss: 1.2911 - val_output_0_loss: 0.0840 - val_output_1_loss: 0.1521 - val_output_2_loss: 0.4391 - val_output_3_loss: 0.6160 - val_output_0_acc: 0.9740 - val_output_1_acc: 0.9561 - val_output_2_acc: 0.8778 - val_output_3_acc: 0.8393\n",
"Epoch 144/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7321 - output_0_loss: 0.0889 - output_1_loss: 0.0770 - output_2_loss: 0.2510 - output_3_loss: 0.3151 - output_0_acc: 0.9720 - output_1_acc: 0.9747 - output_2_acc: 0.9232 - output_3_acc: 0.8995 - val_loss: 1.3985 - val_output_0_loss: 0.1093 - val_output_1_loss: 0.3384 - val_output_2_loss: 0.3753 - val_output_3_loss: 0.5755 - val_output_0_acc: 0.9678 - val_output_1_acc: 0.9065 - val_output_2_acc: 0.8839 - val_output_3_acc: 0.8286\n"
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"Epoch 145/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7749 - output_0_loss: 0.1130 - output_1_loss: 0.0729 - output_2_loss: 0.2384 - output_3_loss: 0.3506 - output_0_acc: 0.9637 - output_1_acc: 0.9742 - output_2_acc: 0.9240 - output_3_acc: 0.8942 - val_loss: 1.1880 - val_output_0_loss: 0.1499 - val_output_1_loss: 0.1703 - val_output_2_loss: 0.3321 - val_output_3_loss: 0.5357 - val_output_0_acc: 0.9494 - val_output_1_acc: 0.9514 - val_output_2_acc: 0.8993 - val_output_3_acc: 0.8460\n",
"Epoch 146/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7217 - output_0_loss: 0.1010 - output_1_loss: 0.0797 - output_2_loss: 0.1988 - output_3_loss: 0.3422 - output_0_acc: 0.9715 - output_1_acc: 0.9740 - output_2_acc: 0.9415 - output_3_acc: 0.8915 - val_loss: 1.1092 - val_output_0_loss: 0.1255 - val_output_1_loss: 0.1295 - val_output_2_loss: 0.3049 - val_output_3_loss: 0.5494 - val_output_0_acc: 0.9606 - val_output_1_acc: 0.9594 - val_output_2_acc: 0.9171 - val_output_3_acc: 0.8437\n",
"Epoch 147/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7814 - output_0_loss: 0.1005 - output_1_loss: 0.0884 - output_2_loss: 0.2444 - output_3_loss: 0.3481 - output_0_acc: 0.9695 - output_1_acc: 0.9755 - output_2_acc: 0.9195 - output_3_acc: 0.8897 - val_loss: 1.4046 - val_output_0_loss: 0.1182 - val_output_1_loss: 0.1372 - val_output_2_loss: 0.5000 - val_output_3_loss: 0.6492 - val_output_0_acc: 0.9608 - val_output_1_acc: 0.9626 - val_output_2_acc: 0.8626 - val_output_3_acc: 0.8319\n",
"Epoch 148/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6516 - output_0_loss: 0.0805 - output_1_loss: 0.0770 - output_2_loss: 0.2101 - output_3_loss: 0.2840 - output_0_acc: 0.9755 - output_1_acc: 0.9765 - output_2_acc: 0.9327 - output_3_acc: 0.9025 - val_loss: 1.4254 - val_output_0_loss: 0.0973 - val_output_1_loss: 0.1675 - val_output_2_loss: 0.4037 - val_output_3_loss: 0.7570 - val_output_0_acc: 0.9694 - val_output_1_acc: 0.9514 - val_output_2_acc: 0.8857 - val_output_3_acc: 0.8086\n",
"Epoch 149/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7021 - output_0_loss: 0.0896 - output_1_loss: 0.0900 - output_2_loss: 0.2032 - output_3_loss: 0.3194 - output_0_acc: 0.9727 - output_1_acc: 0.9707 - output_2_acc: 0.9320 - output_3_acc: 0.8970 - val_loss: 1.1107 - val_output_0_loss: 0.0863 - val_output_1_loss: 0.2507 - val_output_2_loss: 0.3420 - val_output_3_loss: 0.4317 - val_output_0_acc: 0.9739 - val_output_1_acc: 0.9307 - val_output_2_acc: 0.9015 - val_output_3_acc: 0.8736\n",
"Epoch 150/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7832 - output_0_loss: 0.1068 - output_1_loss: 0.0797 - output_2_loss: 0.2366 - output_3_loss: 0.3601 - output_0_acc: 0.9660 - output_1_acc: 0.9750 - output_2_acc: 0.9307 - output_3_acc: 0.8915 - val_loss: 1.2298 - val_output_0_loss: 0.1223 - val_output_1_loss: 0.1564 - val_output_2_loss: 0.3919 - val_output_3_loss: 0.5592 - val_output_0_acc: 0.9612 - val_output_1_acc: 0.9520 - val_output_2_acc: 0.8892 - val_output_3_acc: 0.8416\n",
"Epoch 151/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7040 - output_0_loss: 0.0637 - output_1_loss: 0.0727 - output_2_loss: 0.2230 - output_3_loss: 0.3446 - output_0_acc: 0.9822 - output_1_acc: 0.9767 - output_2_acc: 0.9302 - output_3_acc: 0.8922 - val_loss: 0.8603 - val_output_0_loss: 0.0484 - val_output_1_loss: 0.1181 - val_output_2_loss: 0.2888 - val_output_3_loss: 0.4050 - val_output_0_acc: 0.9867 - val_output_1_acc: 0.9665 - val_output_2_acc: 0.9134 - val_output_3_acc: 0.8824\n",
"Epoch 152/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6649 - output_0_loss: 0.0814 - output_1_loss: 0.0789 - output_2_loss: 0.2014 - output_3_loss: 0.3032 - output_0_acc: 0.9790 - output_1_acc: 0.9752 - output_2_acc: 0.9327 - output_3_acc: 0.9042 - val_loss: 1.2269 - val_output_0_loss: 0.1485 - val_output_1_loss: 0.1091 - val_output_2_loss: 0.2439 - val_output_3_loss: 0.7254 - val_output_0_acc: 0.9518 - val_output_1_acc: 0.9684 - val_output_2_acc: 0.9264 - val_output_3_acc: 0.8243\n",
"Epoch 153/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6939 - output_0_loss: 0.0828 - output_1_loss: 0.0733 - output_2_loss: 0.2468 - output_3_loss: 0.2909 - output_0_acc: 0.9722 - output_1_acc: 0.9735 - output_2_acc: 0.9287 - output_3_acc: 0.9052 - val_loss: 1.0778 - val_output_0_loss: 0.1072 - val_output_1_loss: 0.1789 - val_output_2_loss: 0.3738 - val_output_3_loss: 0.4179 - val_output_0_acc: 0.9667 - val_output_1_acc: 0.9499 - val_output_2_acc: 0.8911 - val_output_3_acc: 0.8809\n",
"Epoch 154/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7133 - output_0_loss: 0.0899 - output_1_loss: 0.0964 - output_2_loss: 0.2405 - output_3_loss: 0.2866 - output_0_acc: 0.9742 - output_1_acc: 0.9707 - output_2_acc: 0.9345 - output_3_acc: 0.9122 - val_loss: 1.4313 - val_output_0_loss: 0.2550 - val_output_1_loss: 0.1113 - val_output_2_loss: 0.3099 - val_output_3_loss: 0.7552 - val_output_0_acc: 0.9152 - val_output_1_acc: 0.9667 - val_output_2_acc: 0.9093 - val_output_3_acc: 0.8160\n",
"Epoch 155/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6892 - output_0_loss: 0.0909 - output_1_loss: 0.0639 - output_2_loss: 0.2063 - output_3_loss: 0.3281 - output_0_acc: 0.9717 - output_1_acc: 0.9795 - output_2_acc: 0.9360 - output_3_acc: 0.9025 - val_loss: 1.2385 - val_output_0_loss: 0.1216 - val_output_1_loss: 0.1537 - val_output_2_loss: 0.3542 - val_output_3_loss: 0.6091 - val_output_0_acc: 0.9641 - val_output_1_acc: 0.9591 - val_output_2_acc: 0.8959 - val_output_3_acc: 0.8356\n",
"Epoch 156/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7054 - output_0_loss: 0.0864 - output_1_loss: 0.0696 - output_2_loss: 0.2338 - output_3_loss: 0.3156 - output_0_acc: 0.9765 - output_1_acc: 0.9770 - output_2_acc: 0.9320 - output_3_acc: 0.9052 - val_loss: 1.1120 - val_output_0_loss: 0.1115 - val_output_1_loss: 0.1597 - val_output_2_loss: 0.3198 - val_output_3_loss: 0.5209 - val_output_0_acc: 0.9664 - val_output_1_acc: 0.9529 - val_output_2_acc: 0.8997 - val_output_3_acc: 0.8594\n",
"Epoch 157/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7110 - output_0_loss: 0.1049 - output_1_loss: 0.0717 - output_2_loss: 0.2212 - output_3_loss: 0.3133 - output_0_acc: 0.9692 - output_1_acc: 0.9757 - output_2_acc: 0.9320 - output_3_acc: 0.9035 - val_loss: 1.8318 - val_output_0_loss: 0.1138 - val_output_1_loss: 0.2740 - val_output_2_loss: 0.3638 - val_output_3_loss: 1.0802 - val_output_0_acc: 0.9662 - val_output_1_acc: 0.9282 - val_output_2_acc: 0.8976 - val_output_3_acc: 0.7340\n",
"Epoch 158/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6910 - output_0_loss: 0.0851 - output_1_loss: 0.0749 - output_2_loss: 0.2382 - output_3_loss: 0.2929 - output_0_acc: 0.9772 - output_1_acc: 0.9737 - output_2_acc: 0.9232 - output_3_acc: 0.9110 - val_loss: 1.0166 - val_output_0_loss: 0.1088 - val_output_1_loss: 0.0944 - val_output_2_loss: 0.3081 - val_output_3_loss: 0.5053 - val_output_0_acc: 0.9640 - val_output_1_acc: 0.9708 - val_output_2_acc: 0.9145 - val_output_3_acc: 0.8760\n",
"Epoch 159/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6803 - output_0_loss: 0.0727 - output_1_loss: 0.0713 - output_2_loss: 0.2223 - output_3_loss: 0.3140 - output_0_acc: 0.9745 - output_1_acc: 0.9787 - output_2_acc: 0.9315 - output_3_acc: 0.9010 - val_loss: 1.3526 - val_output_0_loss: 0.0973 - val_output_1_loss: 0.1228 - val_output_2_loss: 0.4708 - val_output_3_loss: 0.6617 - val_output_0_acc: 0.9665 - val_output_1_acc: 0.9602 - val_output_2_acc: 0.8681 - val_output_3_acc: 0.8309\n",
"Epoch 160/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6387 - output_0_loss: 0.0717 - output_1_loss: 0.0613 - output_2_loss: 0.2112 - output_3_loss: 0.2945 - output_0_acc: 0.9802 - output_1_acc: 0.9802 - output_2_acc: 0.9357 - output_3_acc: 0.9082 - val_loss: 2.1830 - val_output_0_loss: 0.3319 - val_output_1_loss: 0.1813 - val_output_2_loss: 0.7337 - val_output_3_loss: 0.9361 - val_output_0_acc: 0.9170 - val_output_1_acc: 0.9489 - val_output_2_acc: 0.8071 - val_output_3_acc: 0.7691\n"
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"Epoch 161/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6848 - output_0_loss: 0.0779 - output_1_loss: 0.0849 - output_2_loss: 0.2105 - output_3_loss: 0.3114 - output_0_acc: 0.9770 - output_1_acc: 0.9722 - output_2_acc: 0.9315 - output_3_acc: 0.9015 - val_loss: 1.5658 - val_output_0_loss: 0.1712 - val_output_1_loss: 0.2685 - val_output_2_loss: 0.5414 - val_output_3_loss: 0.5847 - val_output_0_acc: 0.9475 - val_output_1_acc: 0.9231 - val_output_2_acc: 0.8742 - val_output_3_acc: 0.8493\n",
"Epoch 162/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7004 - output_0_loss: 0.0999 - output_1_loss: 0.0758 - output_2_loss: 0.2048 - output_3_loss: 0.3199 - output_0_acc: 0.9697 - output_1_acc: 0.9770 - output_2_acc: 0.9405 - output_3_acc: 0.9090 - val_loss: 2.3400 - val_output_0_loss: 0.1089 - val_output_1_loss: 0.3946 - val_output_2_loss: 1.0296 - val_output_3_loss: 0.8069 - val_output_0_acc: 0.9687 - val_output_1_acc: 0.8854 - val_output_2_acc: 0.8080 - val_output_3_acc: 0.7870\n",
"Epoch 163/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6569 - output_0_loss: 0.0867 - output_1_loss: 0.0745 - output_2_loss: 0.1969 - output_3_loss: 0.2987 - output_0_acc: 0.9745 - output_1_acc: 0.9777 - output_2_acc: 0.9375 - output_3_acc: 0.9075 - val_loss: 1.3047 - val_output_0_loss: 0.1392 - val_output_1_loss: 0.1576 - val_output_2_loss: 0.2832 - val_output_3_loss: 0.7247 - val_output_0_acc: 0.9599 - val_output_1_acc: 0.9588 - val_output_2_acc: 0.9195 - val_output_3_acc: 0.8203\n",
"Epoch 164/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6981 - output_0_loss: 0.0889 - output_1_loss: 0.0751 - output_2_loss: 0.2116 - output_3_loss: 0.3225 - output_0_acc: 0.9767 - output_1_acc: 0.9780 - output_2_acc: 0.9345 - output_3_acc: 0.9085 - val_loss: 1.0526 - val_output_0_loss: 0.0793 - val_output_1_loss: 0.2146 - val_output_2_loss: 0.2729 - val_output_3_loss: 0.4857 - val_output_0_acc: 0.9747 - val_output_1_acc: 0.9421 - val_output_2_acc: 0.9210 - val_output_3_acc: 0.8757\n",
"Epoch 165/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6889 - output_0_loss: 0.0897 - output_1_loss: 0.0956 - output_2_loss: 0.2018 - output_3_loss: 0.3018 - output_0_acc: 0.9710 - output_1_acc: 0.9765 - output_2_acc: 0.9377 - output_3_acc: 0.9120 - val_loss: 1.0828 - val_output_0_loss: 0.1122 - val_output_1_loss: 0.2121 - val_output_2_loss: 0.2944 - val_output_3_loss: 0.4641 - val_output_0_acc: 0.9659 - val_output_1_acc: 0.9433 - val_output_2_acc: 0.9148 - val_output_3_acc: 0.8740\n",
"Epoch 166/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6988 - output_0_loss: 0.1057 - output_1_loss: 0.0805 - output_2_loss: 0.2372 - output_3_loss: 0.2754 - output_0_acc: 0.9727 - output_1_acc: 0.9770 - output_2_acc: 0.9307 - output_3_acc: 0.9160 - val_loss: 2.8682 - val_output_0_loss: 0.6431 - val_output_1_loss: 0.2797 - val_output_2_loss: 0.6629 - val_output_3_loss: 1.2826 - val_output_0_acc: 0.8629 - val_output_1_acc: 0.9356 - val_output_2_acc: 0.8375 - val_output_3_acc: 0.7387\n",
"Epoch 167/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6899 - output_0_loss: 0.0686 - output_1_loss: 0.0727 - output_2_loss: 0.1879 - output_3_loss: 0.3607 - output_0_acc: 0.9802 - output_1_acc: 0.9777 - output_2_acc: 0.9407 - output_3_acc: 0.9022 - val_loss: 2.1004 - val_output_0_loss: 0.1552 - val_output_1_loss: 0.6091 - val_output_2_loss: 0.8333 - val_output_3_loss: 0.5029 - val_output_0_acc: 0.9473 - val_output_1_acc: 0.8624 - val_output_2_acc: 0.8126 - val_output_3_acc: 0.8672\n",
"Epoch 168/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6684 - output_0_loss: 0.1048 - output_1_loss: 0.0624 - output_2_loss: 0.1920 - output_3_loss: 0.3092 - output_0_acc: 0.9692 - output_1_acc: 0.9795 - output_2_acc: 0.9382 - output_3_acc: 0.9137 - val_loss: 1.6154 - val_output_0_loss: 0.1841 - val_output_1_loss: 0.0890 - val_output_2_loss: 0.8392 - val_output_3_loss: 0.5032 - val_output_0_acc: 0.9446 - val_output_1_acc: 0.9722 - val_output_2_acc: 0.8062 - val_output_3_acc: 0.8699\n",
"Epoch 169/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6715 - output_0_loss: 0.0846 - output_1_loss: 0.0528 - output_2_loss: 0.2207 - output_3_loss: 0.3134 - output_0_acc: 0.9740 - output_1_acc: 0.9827 - output_2_acc: 0.9407 - output_3_acc: 0.9132 - val_loss: 3.3525 - val_output_0_loss: 0.1960 - val_output_1_loss: 0.6641 - val_output_2_loss: 1.5428 - val_output_3_loss: 0.9497 - val_output_0_acc: 0.9398 - val_output_1_acc: 0.8635 - val_output_2_acc: 0.7309 - val_output_3_acc: 0.7766\n",
"Epoch 170/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6452 - output_0_loss: 0.0749 - output_1_loss: 0.0845 - output_2_loss: 0.1937 - output_3_loss: 0.2921 - output_0_acc: 0.9770 - output_1_acc: 0.9775 - output_2_acc: 0.9407 - output_3_acc: 0.9110 - val_loss: 1.7861 - val_output_0_loss: 0.1707 - val_output_1_loss: 0.2599 - val_output_2_loss: 0.5351 - val_output_3_loss: 0.8204 - val_output_0_acc: 0.9498 - val_output_1_acc: 0.9329 - val_output_2_acc: 0.8599 - val_output_3_acc: 0.8165\n",
"Epoch 171/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.7212 - output_0_loss: 0.1033 - output_1_loss: 0.1080 - output_2_loss: 0.2001 - output_3_loss: 0.3098 - output_0_acc: 0.9717 - output_1_acc: 0.9677 - output_2_acc: 0.9407 - output_3_acc: 0.9145 - val_loss: 1.2554 - val_output_0_loss: 0.0952 - val_output_1_loss: 0.1459 - val_output_2_loss: 0.3875 - val_output_3_loss: 0.6267 - val_output_0_acc: 0.9713 - val_output_1_acc: 0.9585 - val_output_2_acc: 0.8949 - val_output_3_acc: 0.8533\n",
"Epoch 172/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6380 - output_0_loss: 0.0859 - output_1_loss: 0.0888 - output_2_loss: 0.1770 - output_3_loss: 0.2862 - output_0_acc: 0.9737 - output_1_acc: 0.9745 - output_2_acc: 0.9445 - output_3_acc: 0.9192 - val_loss: 1.0188 - val_output_0_loss: 0.1894 - val_output_1_loss: 0.0848 - val_output_2_loss: 0.3148 - val_output_3_loss: 0.4298 - val_output_0_acc: 0.9395 - val_output_1_acc: 0.9733 - val_output_2_acc: 0.9154 - val_output_3_acc: 0.8844\n",
"Epoch 173/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.7420 - output_0_loss: 0.0915 - output_1_loss: 0.1077 - output_2_loss: 0.2267 - output_3_loss: 0.3161 - output_0_acc: 0.9732 - output_1_acc: 0.9697 - output_2_acc: 0.9375 - output_3_acc: 0.9102 - val_loss: 1.0342 - val_output_0_loss: 0.0967 - val_output_1_loss: 0.1512 - val_output_2_loss: 0.3484 - val_output_3_loss: 0.4378 - val_output_0_acc: 0.9704 - val_output_1_acc: 0.9580 - val_output_2_acc: 0.9079 - val_output_3_acc: 0.8796\n",
"Epoch 174/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6684 - output_0_loss: 0.0716 - output_1_loss: 0.0810 - output_2_loss: 0.1956 - output_3_loss: 0.3201 - output_0_acc: 0.9780 - output_1_acc: 0.9742 - output_2_acc: 0.9387 - output_3_acc: 0.9027 - val_loss: 1.1518 - val_output_0_loss: 0.1002 - val_output_1_loss: 0.1441 - val_output_2_loss: 0.4634 - val_output_3_loss: 0.4442 - val_output_0_acc: 0.9682 - val_output_1_acc: 0.9580 - val_output_2_acc: 0.8879 - val_output_3_acc: 0.8854\n",
"Epoch 175/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6243 - output_0_loss: 0.0886 - output_1_loss: 0.0574 - output_2_loss: 0.1620 - output_3_loss: 0.3163 - output_0_acc: 0.9745 - output_1_acc: 0.9825 - output_2_acc: 0.9467 - output_3_acc: 0.9120 - val_loss: 1.4588 - val_output_0_loss: 0.1714 - val_output_1_loss: 0.2498 - val_output_2_loss: 0.4333 - val_output_3_loss: 0.6043 - val_output_0_acc: 0.9434 - val_output_1_acc: 0.9326 - val_output_2_acc: 0.8770 - val_output_3_acc: 0.8444\n",
"Epoch 176/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6481 - output_0_loss: 0.0773 - output_1_loss: 0.0635 - output_2_loss: 0.2087 - output_3_loss: 0.2986 - output_0_acc: 0.9785 - output_1_acc: 0.9790 - output_2_acc: 0.9375 - output_3_acc: 0.9135 - val_loss: 1.4945 - val_output_0_loss: 0.1839 - val_output_1_loss: 0.1405 - val_output_2_loss: 0.6173 - val_output_3_loss: 0.5528 - val_output_0_acc: 0.9474 - val_output_1_acc: 0.9597 - val_output_2_acc: 0.8555 - val_output_3_acc: 0.8616\n"
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"Epoch 177/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6696 - output_0_loss: 0.0805 - output_1_loss: 0.0845 - output_2_loss: 0.2123 - output_3_loss: 0.2925 - output_0_acc: 0.9777 - output_1_acc: 0.9732 - output_2_acc: 0.9420 - output_3_acc: 0.9125 - val_loss: 1.1026 - val_output_0_loss: 0.0748 - val_output_1_loss: 0.1118 - val_output_2_loss: 0.3193 - val_output_3_loss: 0.5967 - val_output_0_acc: 0.9804 - val_output_1_acc: 0.9712 - val_output_2_acc: 0.9197 - val_output_3_acc: 0.8494\n",
"Epoch 178/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6658 - output_0_loss: 0.0678 - output_1_loss: 0.0841 - output_2_loss: 0.2307 - output_3_loss: 0.2833 - output_0_acc: 0.9742 - output_1_acc: 0.9770 - output_2_acc: 0.9355 - output_3_acc: 0.9192 - val_loss: 1.0448 - val_output_0_loss: 0.0964 - val_output_1_loss: 0.2132 - val_output_2_loss: 0.3197 - val_output_3_loss: 0.4155 - val_output_0_acc: 0.9707 - val_output_1_acc: 0.9486 - val_output_2_acc: 0.9165 - val_output_3_acc: 0.8901\n",
"Epoch 179/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6753 - output_0_loss: 0.0824 - output_1_loss: 0.0802 - output_2_loss: 0.1932 - output_3_loss: 0.3196 - output_0_acc: 0.9762 - output_1_acc: 0.9760 - output_2_acc: 0.9427 - output_3_acc: 0.9092 - val_loss: 2.5132 - val_output_0_loss: 0.2629 - val_output_1_loss: 0.1932 - val_output_2_loss: 0.8956 - val_output_3_loss: 1.1616 - val_output_0_acc: 0.9243 - val_output_1_acc: 0.9468 - val_output_2_acc: 0.8074 - val_output_3_acc: 0.7846\n",
"Epoch 180/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6466 - output_0_loss: 0.1028 - output_1_loss: 0.0532 - output_2_loss: 0.1682 - output_3_loss: 0.3224 - output_0_acc: 0.9685 - output_1_acc: 0.9827 - output_2_acc: 0.9485 - output_3_acc: 0.9047 - val_loss: 1.2513 - val_output_0_loss: 0.1563 - val_output_1_loss: 0.2757 - val_output_2_loss: 0.3470 - val_output_3_loss: 0.4724 - val_output_0_acc: 0.9556 - val_output_1_acc: 0.9289 - val_output_2_acc: 0.9111 - val_output_3_acc: 0.8774\n",
"Epoch 181/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6467 - output_0_loss: 0.0986 - output_1_loss: 0.0537 - output_2_loss: 0.2105 - output_3_loss: 0.2839 - output_0_acc: 0.9745 - output_1_acc: 0.9817 - output_2_acc: 0.9410 - output_3_acc: 0.9137 - val_loss: 1.5042 - val_output_0_loss: 0.0793 - val_output_1_loss: 0.1949 - val_output_2_loss: 0.4175 - val_output_3_loss: 0.8125 - val_output_0_acc: 0.9793 - val_output_1_acc: 0.9469 - val_output_2_acc: 0.8985 - val_output_3_acc: 0.8278\n",
"Epoch 182/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6497 - output_0_loss: 0.0785 - output_1_loss: 0.0556 - output_2_loss: 0.2385 - output_3_loss: 0.2770 - output_0_acc: 0.9752 - output_1_acc: 0.9822 - output_2_acc: 0.9377 - output_3_acc: 0.9242 - val_loss: 1.3147 - val_output_0_loss: 0.0615 - val_output_1_loss: 0.3216 - val_output_2_loss: 0.4254 - val_output_3_loss: 0.5062 - val_output_0_acc: 0.9809 - val_output_1_acc: 0.9152 - val_output_2_acc: 0.8916 - val_output_3_acc: 0.8766\n",
"Epoch 183/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6636 - output_0_loss: 0.0847 - output_1_loss: 0.0649 - output_2_loss: 0.2022 - output_3_loss: 0.3118 - output_0_acc: 0.9777 - output_1_acc: 0.9770 - output_2_acc: 0.9405 - output_3_acc: 0.9150 - val_loss: 1.6335 - val_output_0_loss: 0.0528 - val_output_1_loss: 0.1575 - val_output_2_loss: 0.4405 - val_output_3_loss: 0.9828 - val_output_0_acc: 0.9844 - val_output_1_acc: 0.9551 - val_output_2_acc: 0.8938 - val_output_3_acc: 0.7921\n",
"Epoch 184/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6316 - output_0_loss: 0.0640 - output_1_loss: 0.0631 - output_2_loss: 0.2048 - output_3_loss: 0.2997 - output_0_acc: 0.9790 - output_1_acc: 0.9795 - output_2_acc: 0.9412 - output_3_acc: 0.9170 - val_loss: 1.3459 - val_output_0_loss: 0.2363 - val_output_1_loss: 0.1506 - val_output_2_loss: 0.3721 - val_output_3_loss: 0.5869 - val_output_0_acc: 0.9389 - val_output_1_acc: 0.9604 - val_output_2_acc: 0.9023 - val_output_3_acc: 0.8410\n",
"Epoch 185/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6856 - output_0_loss: 0.1023 - output_1_loss: 0.0822 - output_2_loss: 0.2267 - output_3_loss: 0.2744 - output_0_acc: 0.9735 - output_1_acc: 0.9777 - output_2_acc: 0.9360 - output_3_acc: 0.9207 - val_loss: 1.2202 - val_output_0_loss: 0.1571 - val_output_1_loss: 0.1678 - val_output_2_loss: 0.4571 - val_output_3_loss: 0.4382 - val_output_0_acc: 0.9533 - val_output_1_acc: 0.9545 - val_output_2_acc: 0.9026 - val_output_3_acc: 0.8891\n",
"Epoch 186/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6710 - output_0_loss: 0.0804 - output_1_loss: 0.0632 - output_2_loss: 0.2295 - output_3_loss: 0.2980 - output_0_acc: 0.9740 - output_1_acc: 0.9792 - output_2_acc: 0.9380 - output_3_acc: 0.9177 - val_loss: 1.0061 - val_output_0_loss: 0.0677 - val_output_1_loss: 0.1403 - val_output_2_loss: 0.3314 - val_output_3_loss: 0.4666 - val_output_0_acc: 0.9784 - val_output_1_acc: 0.9628 - val_output_2_acc: 0.9126 - val_output_3_acc: 0.8840\n",
"Epoch 187/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6687 - output_0_loss: 0.1051 - output_1_loss: 0.0762 - output_2_loss: 0.1882 - output_3_loss: 0.2993 - output_0_acc: 0.9735 - output_1_acc: 0.9805 - output_2_acc: 0.9462 - output_3_acc: 0.9212 - val_loss: 1.0227 - val_output_0_loss: 0.0813 - val_output_1_loss: 0.1006 - val_output_2_loss: 0.3482 - val_output_3_loss: 0.4926 - val_output_0_acc: 0.9753 - val_output_1_acc: 0.9716 - val_output_2_acc: 0.9067 - val_output_3_acc: 0.8766\n",
"Epoch 188/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6325 - output_0_loss: 0.0914 - output_1_loss: 0.0569 - output_2_loss: 0.1905 - output_3_loss: 0.2938 - output_0_acc: 0.9732 - output_1_acc: 0.9820 - output_2_acc: 0.9450 - output_3_acc: 0.9162 - val_loss: 1.3308 - val_output_0_loss: 0.1571 - val_output_1_loss: 0.1194 - val_output_2_loss: 0.3513 - val_output_3_loss: 0.7030 - val_output_0_acc: 0.9529 - val_output_1_acc: 0.9667 - val_output_2_acc: 0.9146 - val_output_3_acc: 0.8396\n",
"Epoch 189/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6286 - output_0_loss: 0.0799 - output_1_loss: 0.0740 - output_2_loss: 0.2059 - output_3_loss: 0.2689 - output_0_acc: 0.9792 - output_1_acc: 0.9780 - output_2_acc: 0.9422 - output_3_acc: 0.9255 - val_loss: 1.4320 - val_output_0_loss: 0.1146 - val_output_1_loss: 0.1928 - val_output_2_loss: 0.3272 - val_output_3_loss: 0.7975 - val_output_0_acc: 0.9653 - val_output_1_acc: 0.9508 - val_output_2_acc: 0.9181 - val_output_3_acc: 0.8210\n",
"Epoch 190/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6323 - output_0_loss: 0.0965 - output_1_loss: 0.0609 - output_2_loss: 0.1953 - output_3_loss: 0.2796 - output_0_acc: 0.9725 - output_1_acc: 0.9800 - output_2_acc: 0.9420 - output_3_acc: 0.9197 - val_loss: 1.7946 - val_output_0_loss: 0.2810 - val_output_1_loss: 0.3480 - val_output_2_loss: 0.4271 - val_output_3_loss: 0.7385 - val_output_0_acc: 0.9481 - val_output_1_acc: 0.9197 - val_output_2_acc: 0.9050 - val_output_3_acc: 0.8288\n",
"Epoch 191/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6370 - output_0_loss: 0.0946 - output_1_loss: 0.0661 - output_2_loss: 0.1734 - output_3_loss: 0.3029 - output_0_acc: 0.9720 - output_1_acc: 0.9785 - output_2_acc: 0.9487 - output_3_acc: 0.9140 - val_loss: 1.1440 - val_output_0_loss: 0.0989 - val_output_1_loss: 0.1374 - val_output_2_loss: 0.2279 - val_output_3_loss: 0.6799 - val_output_0_acc: 0.9721 - val_output_1_acc: 0.9599 - val_output_2_acc: 0.9408 - val_output_3_acc: 0.8447\n",
"Epoch 192/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6972 - output_0_loss: 0.0845 - output_1_loss: 0.0861 - output_2_loss: 0.1889 - output_3_loss: 0.3377 - output_0_acc: 0.9780 - output_1_acc: 0.9732 - output_2_acc: 0.9505 - output_3_acc: 0.9097 - val_loss: 2.1242 - val_output_0_loss: 0.1417 - val_output_1_loss: 0.2800 - val_output_2_loss: 0.8580 - val_output_3_loss: 0.8445 - val_output_0_acc: 0.9600 - val_output_1_acc: 0.9307 - val_output_2_acc: 0.8239 - val_output_3_acc: 0.8198\n"
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"Epoch 193/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6037 - output_0_loss: 0.0815 - output_1_loss: 0.0643 - output_2_loss: 0.1912 - output_3_loss: 0.2667 - output_0_acc: 0.9747 - output_1_acc: 0.9795 - output_2_acc: 0.9447 - output_3_acc: 0.9207 - val_loss: 1.1933 - val_output_0_loss: 0.2884 - val_output_1_loss: 0.1317 - val_output_2_loss: 0.3037 - val_output_3_loss: 0.4694 - val_output_0_acc: 0.9303 - val_output_1_acc: 0.9682 - val_output_2_acc: 0.9242 - val_output_3_acc: 0.8875\n",
"Epoch 194/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6349 - output_0_loss: 0.0823 - output_1_loss: 0.0823 - output_2_loss: 0.1977 - output_3_loss: 0.2727 - output_0_acc: 0.9750 - output_1_acc: 0.9757 - output_2_acc: 0.9460 - output_3_acc: 0.9230 - val_loss: 1.4164 - val_output_0_loss: 0.1990 - val_output_1_loss: 0.3339 - val_output_2_loss: 0.3161 - val_output_3_loss: 0.5674 - val_output_0_acc: 0.9499 - val_output_1_acc: 0.9186 - val_output_2_acc: 0.9148 - val_output_3_acc: 0.8569\n",
"Epoch 195/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6305 - output_0_loss: 0.0685 - output_1_loss: 0.0708 - output_2_loss: 0.1752 - output_3_loss: 0.3160 - output_0_acc: 0.9800 - output_1_acc: 0.9792 - output_2_acc: 0.9505 - output_3_acc: 0.9142 - val_loss: 0.8842 - val_output_0_loss: 0.0819 - val_output_1_loss: 0.0830 - val_output_2_loss: 0.2400 - val_output_3_loss: 0.4793 - val_output_0_acc: 0.9773 - val_output_1_acc: 0.9781 - val_output_2_acc: 0.9342 - val_output_3_acc: 0.8860\n",
"Epoch 196/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6502 - output_0_loss: 0.0763 - output_1_loss: 0.0655 - output_2_loss: 0.2276 - output_3_loss: 0.2807 - output_0_acc: 0.9790 - output_1_acc: 0.9797 - output_2_acc: 0.9352 - output_3_acc: 0.9220 - val_loss: 1.0835 - val_output_0_loss: 0.1127 - val_output_1_loss: 0.1080 - val_output_2_loss: 0.4137 - val_output_3_loss: 0.4491 - val_output_0_acc: 0.9651 - val_output_1_acc: 0.9728 - val_output_2_acc: 0.9016 - val_output_3_acc: 0.8896\n",
"Epoch 197/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6170 - output_0_loss: 0.0716 - output_1_loss: 0.0722 - output_2_loss: 0.1771 - output_3_loss: 0.2960 - output_0_acc: 0.9787 - output_1_acc: 0.9800 - output_2_acc: 0.9442 - output_3_acc: 0.9190 - val_loss: 1.4295 - val_output_0_loss: 0.1345 - val_output_1_loss: 0.1450 - val_output_2_loss: 0.3166 - val_output_3_loss: 0.8334 - val_output_0_acc: 0.9589 - val_output_1_acc: 0.9628 - val_output_2_acc: 0.9202 - val_output_3_acc: 0.8231\n",
"Epoch 198/200\n",
"3999/3999 [==============================] - 4s 1ms/step - loss: 0.6600 - output_0_loss: 0.0858 - output_1_loss: 0.0701 - output_2_loss: 0.1931 - output_3_loss: 0.3110 - output_0_acc: 0.9762 - output_1_acc: 0.9820 - output_2_acc: 0.9487 - output_3_acc: 0.9155 - val_loss: 1.1306 - val_output_0_loss: 0.0481 - val_output_1_loss: 0.1196 - val_output_2_loss: 0.3791 - val_output_3_loss: 0.5838 - val_output_0_acc: 0.9859 - val_output_1_acc: 0.9684 - val_output_2_acc: 0.9141 - val_output_3_acc: 0.8700\n",
"Epoch 199/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6274 - output_0_loss: 0.0849 - output_1_loss: 0.0626 - output_2_loss: 0.1957 - output_3_loss: 0.2841 - output_0_acc: 0.9795 - output_1_acc: 0.9797 - output_2_acc: 0.9445 - output_3_acc: 0.9177 - val_loss: 1.2552 - val_output_0_loss: 0.0699 - val_output_1_loss: 0.0865 - val_output_2_loss: 0.3381 - val_output_3_loss: 0.7607 - val_output_0_acc: 0.9787 - val_output_1_acc: 0.9735 - val_output_2_acc: 0.9130 - val_output_3_acc: 0.8439\n",
"Epoch 200/200\n",
"3999/3999 [==============================] - 5s 1ms/step - loss: 0.6479 - output_0_loss: 0.0912 - output_1_loss: 0.0703 - output_2_loss: 0.1727 - output_3_loss: 0.3137 - output_0_acc: 0.9762 - output_1_acc: 0.9820 - output_2_acc: 0.9487 - output_3_acc: 0.9140 - val_loss: 0.9785 - val_output_0_loss: 0.0773 - val_output_1_loss: 0.1324 - val_output_2_loss: 0.2612 - val_output_3_loss: 0.5076 - val_output_0_acc: 0.9783 - val_output_1_acc: 0.9676 - val_output_2_acc: 0.9329 - val_output_3_acc: 0.8804\n"
]
}
],
"source": [
"history = coding_model.fit(x_train, y_train, validation_split=percentage_split, batch_size=32, epochs=nb_epochs, verbose=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"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": [
"# Plot training & validation accuracy values (of first char only)\n",
"plt.plot(history.history['output_0_acc'])\n",
"plt.plot(history.history['val_output_0_acc'])\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",
"\n",
"\n",
"# Plot training & validation loss values (of first char only)\n",
"plt.plot(history.history['output_0_loss'])\n",
"plt.plot(history.history['val_output_0_loss'])\n",
"plt.title('Model loss')\n",
"plt.ylabel('Loss')\n",
"plt.xlabel('Epoch')\n",
"plt.legend(['Train', 'Test'], loc='upper left')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"# Evaluate on test data\n",
"100000/100000 [==============================] - 4s 40us/step\n",
"loss : 0.39301084247589113\n",
"output_0_loss : 0.03150831361413002\n",
"output_1_loss : 0.051943262299895286\n",
"output_2_loss : 0.10636471690177918\n",
"output_3_loss : 0.20319454999923706\n",
"output_0_acc : 0.41352\n",
"output_1_acc : 0.41009\n",
"output_2_acc : 0.39454\n",
"output_3_acc : 0.37301\n"
]
}
],
"source": [
"nb_words_to_test = 100000\n",
"\n",
"x_test = create_inputs(nb_words_to_test, nb_chars, nb_letters)\n",
"\n",
"# normalize x_test data\n",
"x_test_normalized = normalize(x_test)\n",
"\n",
"y_test_raw = encrypt(x_test, nb_words_to_test, nb_chars)\n",
"y_test_raw_cate = keras.utils.to_categorical(y_test_raw, nb_letters)\n",
"\n",
"# process the y data as useful ANN multiple-outputs data\n",
"y_test = []\n",
"for c in range(nb_chars):\n",
" # extract each 'char' colomn from the global y_train0 tensor\n",
" # in order to have multiplue yi_train outputs tensors\n",
" yi_test = np.ndarray(shape=(nb_words_to_test, 1, nb_letters))\n",
" for w in range(nb_words):\n",
" yi_test[w,:] = y_test_raw_cate[w,c,:]\n",
" y_test.append(yi_test)\n",
"\n",
"print('\\n# Evaluate on test data')\n",
"results = coding_model.evaluate(x_test_normalized, y_test, batch_size=128)\n",
"for r in range(len(results)):\n",
" print(coding_model.metrics_names[r],':',results[r])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[array([121]), array([101]), array([104]), array([114])], [array([99]), array([102]), array([116]), array([111])], [array([117]), array([121]), array([116]), array([112])]]\n",
"x_test=\n",
" [[[121]\n",
" [101]\n",
" [104]\n",
" [114]]\n",
"\n",
" [[ 99]\n",
" [102]\n",
" [116]\n",
" [111]]\n",
"\n",
" [[117]\n",
" [121]\n",
" [116]\n",
" [112]]]\n",
"x_test_scaled=\n",
" [[[ 0.90575292]\n",
" [-0.76074973]\n",
" [-1.41421356]\n",
" [ 1.33630621]]\n",
"\n",
" [[-1.39346603]\n",
" [-0.6520712 ]\n",
" [ 0.70710678]\n",
" [-1.06904497]]\n",
"\n",
" [[ 0.48771311]\n",
" [ 1.41282093]\n",
" [ 0.70710678]\n",
" [-0.26726124]]]\n",
"-->\n",
"prediction\n",
"['19 7 2 23', '2 7 18 5', '16 23 18 10']\n",
"check prediction\n",
"y_test=\n",
" [[[24]\n",
" [ 4]\n",
" [ 7]\n",
" [17]]\n",
"\n",
" [[ 2]\n",
" [ 5]\n",
" [19]\n",
" [14]]\n",
"\n",
" [[20]\n",
" [24]\n",
" [19]\n",
" [15]]]\n"
]
}
],
"source": [
"nb_words_to_test = 3\n",
"\n",
"x_test = create_inputs(nb_words_to_test, nb_chars, nb_letters)\n",
"print_readable_inputs(x_test)\n",
"print(\"x_test=\\n\", x_test)\n",
"\n",
"x_test_scaled = normalize(x_test)\n",
"print(\"x_test_scaled=\\n\", x_test_scaled)\n",
"print('-->')\n",
"\n",
"prediction = coding_model.predict(x_test_scaled)\n",
"#print(prediction)\n",
"print('prediction')\n",
"print_readable_outputs(prediction, nb_words_to_test, nb_chars)\n",
"\n",
"print('check prediction')\n",
"y_test = encrypt(x_test, nb_words_to_test, nb_chars)\n",
"print(\"y_test=\\n\", y_test)"
]
},
{
"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": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
waveform80/presentations | generators/generators.ipynb | 1 | 21580 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Pretend we're in Python 3 ... as much as possible\n",
"from __future__ import (\n",
" unicode_literals,\n",
" absolute_import,\n",
" print_function,\n",
" division,\n",
" )\n",
"str = type('')\n",
"try:\n",
" from itertools import izip as zip\n",
"except ImportError:\n",
" pass\n",
"\n",
"import io\n",
"import random\n",
"import time\n",
"import datetime as dt\n",
"import operator as op\n",
"import matplotlib.pyplot as plt\n",
"import requests\n",
"from collections import deque, defaultdict, namedtuple\n",
"from itertools import count, islice, tee, chain\n",
"from bs4 import BeautifulSoup\n",
"\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sample(iterable, num=10):\n",
" return [j for i, j in zip(range(num), iterable)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def squares():\n",
" for i in count(1):\n",
" yield i * i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def powers():\n",
" for i in count(1):\n",
" yield i ** i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def triangles():\n",
" i = 0\n",
" for inc in count(1):\n",
" i += inc\n",
" yield i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def fib():\n",
" i = 0\n",
" j = 1\n",
" while True:\n",
" yield j\n",
" i += j\n",
" i, j = j, i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sliding_window(iterable, size=2):\n",
" d = deque(maxlen=size)\n",
" for i in iterable:\n",
" d.append(i)\n",
" if len(d) == size:\n",
" yield tuple(d)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def moving_average(iterable, size):\n",
" for win in sliding_window(iterable, size):\n",
" yield sum(win) / size"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def derive(iterable, func=op.sub):\n",
" for win in sliding_window(iterable):\n",
" yield func(win[1], win[0])\n",
"\n",
"differences = lambda it: derive(it, op.sub)\n",
"ratios = lambda it: derive(it, op.truediv)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def converges(iterable):\n",
" deltas = (abs(d) for d in differences(iterable))\n",
" for win in sliding_window(deltas):\n",
" if win[1] > win[0]:\n",
" return False\n",
" if win[0] == win[1] == 0.0:\n",
" return True"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def converges_on(iterable):\n",
" it1, it2 = tee(iterable, 2)\n",
" deltas = (abs(d) for d in differences(it2))\n",
" for v, win in zip(it1, sliding_window(deltas)):\n",
" if win[1] > win[0]:\n",
" raise ValueError(\"Series does not converge\")\n",
" if win[0] == win[1] == 0.0:\n",
" return v"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 48
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sample(ratios(fib()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
"[1.0,\n",
" 2.0,\n",
" 1.5,\n",
" 1.6666666666666667,\n",
" 1.6,\n",
" 1.625,\n",
" 1.6153846153846154,\n",
" 1.619047619047619,\n",
" 1.6176470588235294,\n",
" 1.6181818181818182]"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"converges_on(ratios(fib()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 50,
"text": [
"1.618033988749895"
]
}
],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(sample(ratios(fib()), 20))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 51,
"text": [
"[<matplotlib.lines.Line2D at 0x7fef7ce5ad10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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3pmktzF9+2R62OGtW8MsEgHAIe5iHqotFktLS7Iuc585dWHf+vN0qf+gh+1asAGCCsId5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\n/wPdNTI5hcNpYgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fef7cc33cd0>"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"Stock = namedtuple('Stock', ('price', 'change'))\n",
"\n",
"def market_states(url=\"http://mrkrabs.waveform.org.uk/presentations/generators/stocks/market.html\"):\n",
" last_date = None\n",
" while True:\n",
" doc = requests.get(url).content\n",
" #doc = io.open(url, 'rb').read()\n",
" soup = BeautifulSoup(doc, 'html5lib')\n",
" date = dt.datetime.strptime(\n",
" soup.find(id=\"market-date\").text,\n",
" 'Current market date: %A, %B %d %Y')\n",
" if date != last_date:\n",
" state = {\n",
" symbol.text.strip(): Stock(float(price.text), float(change.text))\n",
" for table in soup.find_all('table')\n",
" for tbody in table.find_all('tbody')\n",
" for row in tbody.find_all('tr')\n",
" for symbol, name, price, change, volume in (row.find_all('td'),)\n",
" if change.text\n",
" }\n",
" yield date, state\n",
" last_date = date\n",
" time.sleep(1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sell_symbols(window, portfolio, threshold=2.0):\n",
" return {\n",
" symbol\n",
" for symbol in portfolio\n",
" if window[-1][symbol].price > threshold * portfolio[symbol].bought_at\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def buy_symbols(window, portfolio, threshold=0.05):\n",
" return {\n",
" symbol\n",
" for symbol in window[-1].viewkeys() - portfolio.viewkeys()\n",
" if symbol in window[0]\n",
" for old_price in (window[0][symbol].price,)\n",
" for new_price in (window[-1][symbol].price,)\n",
" for delta in (new_price - old_price,)\n",
" if delta > threshold * new_price\n",
" }"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"PortfolioStock = namedtuple('PortfolioStock', ('count', 'bought_at'))\n",
"\n",
"def decisions(states, balance, portfolio={}):\n",
" for window in sliding_window(states, 5):\n",
" date, window = window[-1][0], tuple(s[1] for s in window)\n",
" for symbol in sell_symbols(window, portfolio):\n",
" price = window[-1][symbol].price\n",
" count = portfolio[symbol].count\n",
" balance += price * count\n",
" del portfolio[symbol]\n",
" yield date, 'SELL', symbol, count, price\n",
" for symbol in buy_symbols(window, portfolio):\n",
" price = window[-1][symbol].price\n",
" count = int(balance // price)\n",
" if count > 0:\n",
" portfolio[symbol] = PortfolioStock(count, price)\n",
" balance -= count * price\n",
" yield date, 'BUY', symbol, count, price"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot([state['SDR.L'].price for date, state in sample(market_states())])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-56-c9af6c048db2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SDR.L'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprice\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmarket_states\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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[0m",
"\u001b[0;32m<ipython-input-39-07993a03add7>\u001b[0m in \u001b[0;36msample\u001b[0;34m(iterable, num)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-52-48e5f0b49fc7>\u001b[0m in \u001b[0;36mmarket_states\u001b[0;34m(url)\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mlast_date\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\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[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | cc0-1.0 |
Cyb3rWard0g/HELK | docker/helk-jupyter/notebooks/sigma/win_malware_dtrack.ipynb | 1 | 2815 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DTRACK Process Creation\n",
"Detects specific process parameters as seen in DTRACK infections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rule Content\n",
"```\n",
"- title: DTRACK Process Creation\n",
" id: f1531fa4-5b84-4342-8f68-9cf3fdbd83d4\n",
" status: experimental\n",
" description: Detects specific process parameters as seen in DTRACK infections\n",
" author: Florian Roth\n",
" date: 2019/10/30\n",
" references:\n",
" - https://securelist.com/my-name-is-dtrack/93338/\n",
" - https://app.any.run/tasks/4bc9860d-ab51-4077-9e09-59ad346b92fd/\n",
" - https://app.any.run/tasks/ce4deab5-3263-494f-93e3-afb2b9d79f14/\n",
" logsource:\n",
" category: process_creation\n",
" product: windows\n",
" service: null\n",
" detection:\n",
" selection:\n",
" CommandLine: '* echo EEEE > *'\n",
" condition: selection\n",
" fields:\n",
" - CommandLine\n",
" - ParentCommandLine\n",
" falsepositives:\n",
" - Unlikely\n",
" level: critical\n",
"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Querying Elasticsearch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from elasticsearch import Elasticsearch\n",
"from elasticsearch_dsl import Search\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialize Elasticsearch client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"es = Elasticsearch(['http://helk-elasticsearch:9200'])\n",
"searchContext = Search(using=es, index='logs-*', doc_type='doc')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run Elasticsearch Query"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"s = searchContext.query('query_string', query='process_command_line.keyword:*\\ echo\\ EEEE\\ \\ *')\n",
"response = s.execute()\n",
"if response.success():\n",
" df = pd.DataFrame((d.to_dict() for d in s.scan()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Show Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.head()"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
fmalazemi/CheatSheets | python3/zip.ipynb | 1 | 2601 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# zip(*iterables)\n",
"Creates an tuple of all iterables items and return an **iterator** of the tuple. "
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(zip())"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(1,), (2,), (3,)]"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = [1,2,3]\n",
"it = zip(x)\n",
"list(it)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(1, 'a'), (2, 'b'), (3, 'c')]\n"
]
}
],
"source": [
"x = [1, 2, 3, 4, 5, 6]\n",
"y = [\"a\", \"b\", \"c\"]\n",
"it = zip(x,y) # Tuple length is always min of all input iterables. \n",
"ls = list(it)\n",
"print(ls)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## unzip\n",
"To unzip a list of tuples add \\*. \n",
"If tuples have different lengths, the smallest is used for unzipping. "
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 2, 3)\n",
"('a', 'b', 'c')\n"
]
}
],
"source": [
"x, y = zip(*ls)\n",
"print(x)\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 3)\n",
"(2, 4)\n"
]
}
],
"source": [
"x = [(1,2, 4), (3,4)]\n",
"x, y = list(zip(*x))\n",
"print(x)\n",
"print(y)"
]
}
],
"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
}
| gpl-3.0 |
xysmas/microsoft_malware_challenge | src/analysis/feat_extraction/Get_time_series.ipynb | 2 | 2282 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pymongo\n",
"import numpy as np\n",
"username = 'populator'\n",
"password = 'malware_challenge'\n",
"cm = pymongo.MongoClient('afruizc-office.cs.unm.edu')\n",
"cm.malware.authenticate(username, password)\n",
"\n",
"data = [(x['id'], x['asm_info']['seq']) \n",
" for x in cm.malware.test_samples.find({\n",
" \"id\": {\"$exists\": True}, # The id field exists\n",
" \"asm_info.seq\": {\"$ne\": []}}) if x.get('asm_info', '') \\\n",
" and x['asm_info'].get('seq', '')]\n",
"final = [(f[0], len(f[1]), ','.join(f[1])) for f in data]\n",
"with open('time_series_test.txt', 'w') as f:\n",
" f.writelines([','.join(map(str, x)) for x in final])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'n': 1, 'ok': 1}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/home/afruizc/Documents/hacks/microsoft_malware_challenge/analysis/Notebooks'"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pwd"
]
},
{
"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
}
| apache-2.0 |
BuzzFeedNews/2015-11-refugees-in-the-united-states | notebooks/us-refugee-analysis.ipynb | 1 | 385793 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis of U.S. Refugee Data\n",
"\n",
"The refugee data in this notebook comes from the [Department of State's Refugee Processing Center](https://www.wrapsnet.org/)'s [data portal](http://www.wrapsnet.org/Reports/InteractiveReporting/tabid/393/Default.aspx). It was retrieved on November 18, 2015."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib as mpl\n",
"import seaborn as sb\n",
"import itertools\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"by_destination = pd.read_csv(\"../data/WRAPS-arrivals-by-destination-2005-2015-clean.csv\")\n",
"by_religion = pd.read_csv(\"../data/WRAPS-arrivals-by-religion-2005-2015-clean.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"672518\n",
"672522\n"
]
}
],
"source": [
"# The totals for the two data-reports are off by just four arrivals.\n",
"print(by_destination[\"arrivals\"].sum())\n",
"print(by_religion[\"arrivals\"].sum())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"state_populations = pd.read_csv(\"../data/census-state-populations.csv\").set_index(\"state\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"colors = itertools.cycle([ \"#3498db\",\"#2ecc71\", \"#9b59b6\" ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up a few data-processing and chart-making functions"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def arrivals_by_year(origin=None, dest_state=None, dest_city=None):\n",
" return by_destination[\n",
" (by_destination[\"origin\"] == (origin if origin else by_destination[\"origin\"])) &\n",
" (by_destination[\"dest_state\"] == (dest_state if dest_state else by_destination[\"dest_state\"])) &\n",
" (by_destination[\"dest_city\"] == (dest_city if dest_city else by_destination[\"dest_city\"]))\n",
" ].groupby(\"year\")[\"arrivals\"].sum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_vbar(series, title):\n",
" ax = series.plot(kind=\"bar\", width=0.85,\n",
" figsize=(10, 6),\n",
" fontsize=14,\n",
" color=next(colors))\n",
" ax.figure.set_facecolor(\"white\")\n",
" ax.set_ylim(0, series.max() * 1.05)\n",
" ax.set_yticklabels([ \"{0:,.0f}\".format(y) for y in ax.get_yticks() ])\n",
" mpl.pyplot.setp(ax.get_children()[len(series) + 1], alpha=0.5)\n",
" mpl.pyplot.setp(ax.get_xticklabels(), rotation=0)\n",
" ax.set_xlabel(\"\")\n",
" ax.set_title(title, fontsize=16, fontweight=\"bold\", alpha=0.75)\n",
" return ax"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_hbar(series, title, width=9, height=20):\n",
" ax = series.plot(kind=\"barh\", width=0.75,\n",
" figsize=(width, height),\n",
" fontsize=14,\n",
" color=next(colors))\n",
" ax.figure.set_facecolor(\"white\")\n",
" ax.set_xlim(0, series.max() * 1.05)\n",
" ax.set_xticklabels([ \"{0:,.0f}\".format(x) for x in ax.get_xticks() ])\n",
" ax.invert_yaxis()\n",
" ax.set_ylabel(\"\")\n",
" ax.set_title(title, fontsize=16, fontweight=\"bold\", alpha=0.75)\n",
" return ax"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refugee arrivals by year"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NREREJAgotImIiIgEAYU2ERERkSCg0CYiIiISBBTaRERERIKAQpuIiIhIEFBo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\nABtBHF6I9cliAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1097f80f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = make_vbar(arrivals_by_year(), \"Refugee Arrivals in U.S., 2005 – 2015\")\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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GIAMAALAYgQwAAMBiBDIAAACLEcgAAAAsRiADAACwGIEMAADAYgQyAAAAixHI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\nFvv/HWhVbbH3kZoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1039d9c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = make_vbar(arrivals_by_year(origin=\"Syria\"), \"Syrian Refugee Arrivals in U.S., 2005 – 2015\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refugee arrivals by origin"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Erm8k9sIXu4tLuSx3KnobJNHTQyuH78DeIustioQQQgiR96ITI+jr05GKFd/+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103a26320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"origin_counts = by_destination.groupby(\"origin\")[\"arrivals\"].sum()\n",
"ax = make_hbar(origin_counts.sort_values(ascending=False).head(19),\n",
" \"Top Origins For U.S. Refugee Arrivals, 2005 – 2015\",\n",
" height=(20 * 19 / 51))\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refugee arrivals by religion"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"religion\n",
"Christian 166421\n",
"Moslem 117270\n",
"Moslem Suni 56752\n",
"Hindu 52266\n",
"Catholic 51353\n",
"Buddhist 44522\n",
"Moslem Shiite 35131\n",
"Pentecostalist 23412\n",
"Baptist 16606\n",
"No Religion 15072\n",
"Orthodox 14486\n",
"Protestant 12864\n",
"Jehovah Witness 10009\n",
"Bahai 8569\n",
"Kirat 7413\n",
"Seventh Day Adventist 6901\n",
"Ancestral Worship 6773\n",
"Sabeans-Mandean 5424\n",
"Animist 4910\n",
"Jewish 3854\n",
"Methodist 2821\n",
"Zoroastrian 2275\n",
"Evangelical Christian 2140\n",
"Other Religion 1094\n",
"Unknown 913\n",
"Yazidi 726\n",
"Lutheran 543\n",
"Ahmadiyya 488\n",
"Chaldean 389\n",
"Uniate 330\n",
"Atheist 220\n",
"Cao Dai 211\n",
"Moslem Ismaici 99\n",
"Kaaka'i 96\n",
"Hoa Hao 87\n",
"Russian Orthodox 23\n",
"Ukr Orthodox 20\n",
"Coptic 18\n",
"Greek Orthodox 8\n",
"Old Believer 5\n",
"Mennonite 5\n",
"Hare Krishna 3\n",
"Name: arrivals, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_religion.groupby(\"religion\")[\"arrivals\"].sum().sort_values(ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: The code below groups four separate designations — \"Moslem,\" \"Moslem Suni,\" \"Moslem Shiite,\" and \"Moslem Ismaici\" (likely a typo for \"Moslem Ismaili\") — into a single \"Muslim\" grouping."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"muslim_arrivals = by_religion[\n",
" by_religion[\"religion\"].str.contains(\"Moslem\")\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"year\n",
"2005 19768\n",
"2006 17928\n",
"2007 11706\n",
"2008 15421\n",
"2009 17598\n",
"2010 19331\n",
"2011 8871\n",
"2012 19271\n",
"2013 25291\n",
"2014 30113\n",
"2015 23954\n",
"Name: arrivals, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"muslim_arrivals_by_year = muslim_arrivals.groupby(\"year\")[\"arrivals\"].sum()\n",
"muslim_arrivals_by_year"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Since 2005, about 31% of arriving refugees have been Muslim.\n"
]
}
],
"source": [
"print(\"Since 2005, about {0:.0f}% of arriving refugees have been Muslim.\".format(\n",
" muslim_arrivals[\"arrivals\"].sum() * 100.0 / by_religion[\"arrivals\"].sum())\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ef/55BQcH68knn9Ts2bN1/PhxPf/88+bp2//4xz9qpiqgFli0aJGcTqeaNGli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\n/gFe+sj0FNC2FAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109d374a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"muslim_ratio = muslim_arrivals_by_year / by_religion.groupby(\"year\")[\"arrivals\"].sum()\n",
"ax = make_vbar(muslim_ratio, \"Muslim Refugees, as Pct. of All U.S. Refugee Arrivals\")\n",
"ax.set_yticklabels([ \"{0:.0f}%\".format(y * 100) for y in ax.get_yticks() ])\n",
"ax.set_ylim(0, 0.5)\n",
"pass"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"muslim_origins = muslim_arrivals.groupby(\"origin\")[\"arrivals\"].sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8GbVazdSpU2nTpg2NGjWiW7duREZGKvslJSUxYsQI/Pz86Nu3L2vXrqVDhw7l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\nYowxxhjTES60GGOMMcZ05H998royFxjSLwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103a5af98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = make_hbar(muslim_origins.sort_values(ascending=False).head(10),\n",
" \"Muslim Refugees Arrivals By Origin, 2005 – 2015\", height=10*20/51)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Refugees by U.S. destination"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"large_single_populations = by_destination.groupby([ \"origin\", \"dest_state\", \"dest_city\" ])[\"arrivals\"].sum()\\\n",
" .sort_values(ascending=False).head(15)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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yCi0nISGBadOm8cYbb+Dk5ETPnj35/vvvi12P7du30759+/+8P8WVkZHBihUr\n6Nq1K05OTri5ubFo0SLS0tKKtf61a9ews7MjPj4eADs7O44dOwZAUlISHh4eODo6snTp0jKvu5eX\nV7mUK4QQQojnywvdU65SqSq6CqVy+PBhKlXSHnKouPty9epVPD09cXZ2ZsmSJVhaWvLHH3/g7+/P\njRs3+Pjjj4sso1u3bnTo0KFUdS+pjIwMvL29efjwIZ9++imNGzcmLi6OefPm8ffff7N+/fp8bVGU\no0ePUq1aNQB27drFtWvX2LlzJ+bm5mVe/+XLl6Onp1fm5QohhBDi+fJCB+XPqurVq6OjU7qbHNOn\nT8fOzo7AwEBlWu3atVGpVEyZMgVPT09q1nxyDrOBgQEGBgal2n5JrVmzhvj4ePbu3YuJiQkA1tbW\nrFq1ii5duhAWFsbbb79dojItLCyUn1NTU7GxsaF+/fplWm8NTfAvhBBCCPEkL2z6Sl7bt2+nX79+\n+Pr64uLiwtatW0lNTWXy5Mm89tpr2NvbKwGgRkJCAh9++CHOzs64u7uzZcsWOnbsWIF7UbRbt27x\n22+/4ePjk29et27dWL9+vdJbfOLECQYMGICTkxPOzs4MGTKEhIQEIH/6SkxMDIMHD6ZVq1a8/vrr\nBAYGolarAVi2bBljx45l1qxZuLi40KZNG1avXl3sOoeGhtKnTx8lINewsbFh06ZNvP766wAkJibi\n6+tL69atcXBwwN3dncjIyALL1KSvTJo0icDAQE6cOIGdnR03btwgPT2dhQsX8sYbb+Ds7Mzw4cO5\nceMG8G8azIoVK2jdujV+fn4EBgY+cf/ypkllZmYyf/582rdvj729PR07diQoKKjYbSGEEEKI55cE\n5f/v1KlT1K9fn61bt9KhQwfmzZtHXFwca9euZe/evbz88stMnTqVzMxMAEaNGkVWVhYhISEMHjyY\npUuX/ud0mCflgeelCXhL6vz586jVahwcHPLN09PTw8XFBT09PVJTUxk2bBht27Zlz549Sm/1qlWr\n8q2XlJTEgAEDqFWrFiEhIcyYMYMtW7awdu1aZZnw8HD09PQIDQ1lyJAhLF68mJiYmCLr++jRI65e\nvVpgfQFatmyJsbExABMmTCAnJ4fg4GB27NhBrVq1mD59+hPLnzJlCj4+Pjg6OnL06FFlnfDwcD7/\n/HO+++47srOzGTFihNax+fPPP9m2bRtDhw5FrVYXuX+a8+Lrr7/m4MGDLFu2jH379uHu7s6cOXO4\nfft2kW2oZFNmAAAgAElEQVQhhBBCiOebBOV5DB8+nHr16mFhYYGLiwszZ87Ezs6OOnXq4OPjQ0pK\nComJiURHR3P69Glmz55Nw4YN6d69O3369Cl1sAy5vahjx44lNbXosT1dXFxwdnbW+rdo0aIi17t/\n/z4AVatWfeJy//zzDyNGjOCjjz7C2tqali1b0rlzZy5dupRv2d27d1OlShVmzZpFgwYN6NSpE2PG\njOGbb75RljExMWHSpEnY2NgwePBgTExMOHPmTLHrqwm8n6RTp05MnTqVBg0aYGtry4ABA4oM/I2N\njalSpQq6urpYWFjw4MEDdu3axZQpU2jdujWNGzdm4cKFXL16lV9++UVZz9vbGxsbG+rVq1ei/Wvc\nuDFz5szB0dGR2rVrM2zYMLKysoiLiyty/4QQQgjxfJOc8v9namqKoaGh8rl3796Eh4fz3XffERcX\nx5kzZ1CpVOTk5BAbG4uxsTG1a/87VJ2zszM//fRTqbevp6dHnz598Pb2ZunSpdjY2BS6bGhoaL6H\nG4sKtAHMzMwASElJUX4uSPXq1enVqxfr1q0jOjqaS5cucf78eVq0aJFv2ZiYGJo2bapVHycnJ5KT\nk0lOTgbAyspK6y6CkZERWVlZxa6vJjh/Eg8PD/bs2cNff/1FXFwcZ8+eVY5XcV2+fJmcnByt/TQx\nMaF+/frExsZia2sL5Oa051Xc/XNzc+Po0aMEBAQodQTIzs4udh2FEEIIUb7MzY2xtCw6riprEpT/\nv8cfXPz00085ceIEvXv3xtPTE0tLS/r37w+AoaFhvl7xJ735yd/fn3PnzhWrHhcvXsTLy4vdu3cX\n2kNcp06dUj3o2bx5c3R0dDh16lS+IQ0zMzMZOnQoo0ePxtramj59+tC8eXPatWtHv379+Pnnn/nz\nzz/zlVm5cuV8baEJhDXTCxp9pDh3FfT19WnSpAknT57krbfeyjd/xowZNG3alH79+uHj48P9+/fp\n1q0bnTp1IjMzk1GjRhW5jbwKe3g1OztbK3B+fLni7t+SJUv4/vvveffdd+nVqxfTp09/6p9DEEII\nIV40SUmp3L79oNzKLyzgl6C8AKmpqezZs4fg4GCl1/TQoUNAbrBla2tLWloacXFxyqgdTwq6p0yZ\nUqzt7t27l23btrFgwYJipWyUlJmZGa6urqxfvz5fUL5z506OHz9OQEAA4eHhGBsb89VXXynzN27c\nWGCZDRo0YN++fWRlZaGrm3s6nThxAlNT0yf2xhdXr169WL16NcOGDdN62DMmJoZt27Yxb948Ll68\nSGRkJEeOHKF69eoAbNmyBShZ/n2dOnXQ1dXl77//xtXVFYDk5GSuXLlSJqOzBAcHM336dLp27QpQ\nYDqQEEIIIV5MklNeAH19fQwNDfnpp5+4du0aR44cYd68eUDuuNl16tShc+fO+Pn5ER0dTUREBOvX\nr/9PD3qmp6dz9OhRvvrqqyLHy759+3aB/4qTBjFx4kTOnTvHqFGjOHnyJJcvX2bjxo3MmTOHUaNG\nUbNmTczMzEhISODXX38lPj6e1atX8/PPP5Oenp6vvB49epCdnc20adOIiYnhwIEDBAYG4unpWaz2\n+Oeff574oOPAgQOxtrbGy8uLQ4cOER8fT1hYGB9++CGtW7ema9euVKtWDR0dHfbs2cP169fZt2+f\nckGRkZFRZB00qlSpgoeHB3PmzOH48eOcP3+eCRMmULNmTWWUl//CzMyMiIgI4uPjiYyMZOLEiejq\n6hbYrkIIIYR4sUhQ/v/yBpD6+vosWLCA/fv307VrV5YvX868efOwtrZWesT9/f2pWbMmHh4eLFmy\nhHffffc/PehpYGDAnDlzlN7mJ9Wxffv2vP7661r/XF1dlQcGnxQM169fn+DgYAwMDBg1ahS9e/cm\nNDSUadOmMXLkSADefvttevXqxccff0yfPn24du0aS5YsIS4uLl+QW6VKFb755hvi4+Nxd3fH39+f\nQYMGMWbMGKUuT6pPcHDwEwNefX19NmzYQNu2bZk9ezbdu3dn4cKF9OrVi+XLl6Ojo0OtWrWYMWMG\na9eupWvXrmzdupXVq1djYGCgHK/C6vB4/T799FPatWuHr68vnp6eVK5cmY0bNyrpSY+XU9T+5TV3\n7lwuXLhAt27d8Pf3x9fXl5YtWxIVFVWs9YUQQgjx/FKp/0skKRTbt28nMDCQiIiIiq5KuQsJCeGr\nr75i//79ZVKeh4cHwcHBZVLW88a/+9fUqla76AWFEEII8Z/dun8NtymtsLVtVG7bKCynXHrKRYnE\nxcURGRlZ5Fs/i+vQoUM0adKkTMoSQgghhHhWyYOeZaQkaQzPsvHjx5OYmIi/v3+ZlNe2bdt8D52K\nf91JS6joKgghhBAvjIr83pX0FSGeYhcuXCApqegXSomSMTc3lnYtB9Ku5UPatXxIu5aP56FdbWzq\nPnGo6/+qsPQVCcqFeMqV51ipLypLy6rSruVA2rV8SLuWD2nX8iHtWjTJKRdCCCGEEOIpJUG5EEII\nIYQQFUwe9BTiKSY55eUjOfnZz3l8Gkm7lg9p1/Ih7Vo65Z1v/SKToFyIp9iKYd9T3ahshp8UQggh\n/os7aQl4zOtarmN4v8gkKBfiKVbdqKa8PEgIIYR4AUhOuRBCCCGEEBXshQrKvby8+OKLLyq6GqVy\n/vx5xo0bh6urKw4ODnTq1ImAgAAePPh32KFJkybx6aeflsv2d+7cSceOHYu17LJlyxgwYECZbTsn\nJ4fNmzfTq1cvnJ2deeONN5g2bRp3794tdhl2dnYcO3YMgI4dO7J161YA0tPTGTZsGI6OjkycOLHM\n6qxRnsdECCGEEM+PFy595Vl86+Zvv/3G8OHD6dGjB8uXL8fS0pKYmBi++OILfHx8+O6776hUqdJz\n+1bRjz/+mDNnzjB+/HgcHBxITExk4cKFDBo0iODgYIyNjUtU3rZt26hSpQoAv/zyC0ePHmXr1q3U\nrFn2udtTpkwp8zKFEEII8fx54YLyZ01GRgZ+fn707NmTWbNmKdNr1aql9JgfPHgQNzc31Go1z9u7\noHbt2sXBgwfZu3cvNjY2ANjY2LB69Wo6depEcHAwQ4YMKVGZZmZmys8PHjzA3NwcOzu7Mq23Rkkv\nGIQQQgjxYnqh0lfy2r59O/369cPX1xcXFxe2bt1KamoqkydP5rXXXsPe3p4uXboQFhamrGNnZ8eO\nHTvo0aMHjo6OeHp6Eh8fX671/PXXX7l58ya+vr755lWrVo0dO3bg5uZW4Lr79++nW7duODk58c47\n7/DLL78o87y8vFixYgWDBw+mRYsWdO7cmUOHDinzExMTGTp0KM7Ozri7u3PlyhWtsg8ePIi7uzuO\njo64uLgwduxYUlMLHloqMjKSd999lxYtWtC9e3d27txZ7P0PDQ2lc+fOSkCuUbVqVdauXcs777wD\nUOSxy6tjx46EhIQQGBjIZ599RmJiInZ2dvzxxx+o1Wq++eYb3nzzTVq0aIGXlxfR0dHKunZ2dixd\nupQ2bdrg4+NDaGgonp6eBAYG0qZNG1xcXJgzZ45ycfR4+srq1atxc3PD3t6edu3a8eWXXxa7LYQQ\nQgjx/Hphg3KAU6dOUb9+fbZu3UqHDh2YN28ecXFxrF27lr179/Lyyy8zdepUMjMzlXVWrFjB5MmT\n2bZtGykpKSxevPg/1SEnJ+eJ80+cOEH9+vWpXr16gfOtra0LnB4dHc2ECRMYNmwYP/zwA/369WPU\nqFFaAebq1avp0aMHu3fvplmzZkydOlUJJn19fcnMzCQkJIThw4ezceNGJTUmPj4eX19fBgwYwL59\n+1i6dCm//fYbwcHB+epx+/Zthg0bRq9evdi9ezcjR47E39+fgwcPFqt9zp8/j4ODQ4Hz7O3tMTc3\nByjWsctLpVLxwQcf4Ofnh6WlJUePHsXJyYnAwEDWrVuHn58foaGh1K5dmyFDhvDw4UNl3YiICIKC\ngpg8eTJqtZrTp08TFxdHUFAQ06ZNY8uWLcoFUN6Uop07d7Ju3Tr8/f0JCwtj1KhRrFixglOnThWr\nLYQQQgjx/Hqhg3KA4cOHU69ePSwsLHBxcWHmzJnY2dlRp04dfHx8SElJITExUVl+0KBBvPrqqzRq\n1AhPT09Onz5d6m1nZmY+sYcZIDk5GRMTE61pc+bMwdnZWfk3ffp0ZZ4mAFyzZg19+vShZ8+e2NjY\n4OHhQdeuXdm0aZOyrKurK71798bGxoYRI0aQmJhIQkICFy9e5O+//2b27Nk0bNiQt956i4EDByoB\ne05ODlOmTKFv375YWVnRtm1b2rRpQ0xMTL76b9myhVdeeQUvLy9sbGzo2rUrgwYNYsOGDcVqo/v3\n7xcrBaQ4x+5xVapUwdjYGB0dHSwsLNDV1WXz5s2MHj2aDh060KBBA2bPno2enh47duxQ1uvXrx/1\n6tWjYcOGAGRnZzNz5kzq1atHz549sbOz48yZMwBa6US1atUiICCAV199FSsrKzw8PKhevXqB7SaE\nEEKIF8sLnVNuamqKoaGh8rl3796Eh4fz3XffERcXpwRWeXuz86ZRGBkZkZWVVert6+np0adPH7y9\nvVm6dGm+FA3ITVHJO8IKwIgRI/D29katVjN//nwyMjLyrRcTE8PFixeVUUYAsrKyaNGihfK5Tp06\nWvsCuRcKly5dwtjYmNq1/x0f297enl27dgFQt25d9PT0WLlyJZcuXeLixYtcunSJ7t2756tHbGws\nhw8fxtnZWZmWnZ2NhYVFke0Dufnf9+/fL3K54hy7oty9e5eUlBStNtLV1cXe3p7Y2FhlWt520dQx\n74XD4+eFJjB/5ZVXOHnyJIsWLSI2NpaoqCju3LlDdnZ2sesohBBCVCRzc2MsLas+cZmi5ouCvdBB\nuYGBgdbnTz/9lBMnTtC7d288PT2xtLSkf//+Wsvo6elpfS7swUp/f3/OnTtXrHpcvHgRLy8vdu/e\nna9X2MnJiXXr1pGSkqL0mJubmytpG3kvKvLKyclh8ODB9OnTR6uueV+N+/i+PGm/dHX/PVWio6Px\n9PSkY8eOuLi44OPjw/r167WW1/TYZ2Vl0aNHD0aOHKlVto5O8W7SODg4cPLkyQLnBQYGolKp+Oij\nj4p17IpSuXLlAqdnZWVpBc6Pv164oHbM236atggJCWHu3Ln069ePzp07M3HiRLy9vUtURyGEEKIi\nJSWlcvv2g0LnW1pWfeJ8UfhFywsdlOeVmprKnj17CA4OVnpKNQ8+lmZEk+IOhbd37162bdvGggUL\nCkzTcHV1pWbNmixfvhw/Pz+teWq1mtu3b2NlZZVvvfr16xMfH6/V+x4YGIiJiQleXl5PrFPjxo1J\nS0sjLi6O+vXrA2hdYOzcuZNWrVqxaNEiZdrly5eVZTV1A2jQoAGRkZFa9QgKCuLWrVuMHTv2ifUA\n6NWrF+PHj+fKlSvUrVtXmX737l02bdrEBx98UGbHztjYGEtLS/7++2+aNm0K5N45OHv2LG3atCl2\nOYUJCgpixIgRDB06FMhNzblz585zN2KOEEIIIUruhc8p19DX18fQ0JCffvqJa9euceTIEebNmwdQ\nYHpIWUhPT+fo0aN89dVXSs93QfVasGAB27dvZ8KECfz1119cv36dQ4cO4eXlxZ9//knLli2V5TUB\n3vvvv8++fftYv349V65cITg4mFWrVmkFtoUFg7a2trz22mv4+fkRHR1NRESE1oOeZmZmXLhwgVOn\nTnH58mUCAgI4f/486enp+coaMGAAUVFRLF68mMuXL7Nv3z4+//xzXnrpJSC3bW/fvl1omslbb71F\n27Zt8fHx4ccffyQ+Pp4jR47wwQcf8NJLL+Ht7V2mx+6DDz4gMDCQiIgIYmJimDZtGhkZGQWm5hSX\npp3NzMw4duyYkl4zduxYVCpVuZ1fQgghhHh2vNBBed4X7WiC3/3799O1a1eWL1/OvHnzsLa2LjQN\n5b++rMfAwIA5c+ZopYYUpFWrVoSGhlK5cmXGjx9Ply5dmDZtGjY2Nmzfvp2+ffvmq0+LFi1YsGAB\nISEhdO/enQ0bNjB37lxcXV0L3P/HPy9ZsgRLS0s8PT1ZuHAhPj4+yjwvLy9atmyJj48P7733Hrq6\nukydOlUZ2SVvPaysrFi1ahW//vorPXr04PPPP8fX1xcPDw8ADh8+zOuvv86tW7cK3f9ly5bRt29f\nvvzyS3r06MG0adNwcXFh/fr1GBoalurYFbbf77//Ph4eHkybNo0+ffpw69YtNm3aVOhFU1HnQN75\nkydP5tGjR/Tu3Ztx48bRvXt3unfvTlRUVJF1FEIIIcTzTaWWe+eigo0aNYqZM2cW++HPF4l/96+p\nVa120QsKIYQQ5ezW/Wu4TWmFrW2jQpeRnPKiFZZT/kL3lIuKFx0dTVZWlgTkQgghhHihyYOeokI1\nbtyYlStXVnQ1nlp30hIqugpCCCEEIN9J5U3SV4R4il24cIGkpMJfLiVKx9zcWNq1HEi7lg9p1/Ih\n7Vo6NjZ18w0NnJekrxRNhkQU4hnUuHFj+eNWDuRLo3xIu5YPadfyIe0qnjaSUy6EEEIIIUQFk6Bc\nCCGEEEKICibpK0I8xSSnvHwkJ0suaXmQdi0f0q7lo6LataicbPHikqBciKfYimHfU92oZkVXQwgh\nRBm4k5aAx7yuTxznW7y4JCgX4ilW3aimvDxICCGEeAFITrkQQgghhBAVTILycnD//n3mz5+Pm5sb\nTk5OdOnShdWrV5OVlVWs9Y8fP07z5s3LuZYl17FjR+zs7Pjtt9/yzTt8+DB2dnZ8+umnAGzfvp32\n7duXSz3s7Ow4duxYsZbV1Pnxfz169ABg0qRJSp1L6tq1a9jZ2REfH1/g/J07d9KxY8dSlS2EEEKI\nF4ukr5Sxe/fu0b9/fywtLfH398fGxoazZ8/i7+/PxYsXWbBgQUVX8T/R1dXl4MGDvPrqq1rT9+/f\nj0qlQqVSAdCtWzc6dOhQLnU4evQo1apVK/bykyZNUoJwDV3d3FM/b51LysrKiqNHj2JmZlaq9YUQ\nQgghNCQoL2MLFy5EX1+ftWvXKk9XW1tbY2ZmhpeXF15eXjg6OlZwLUvv5Zdf5uDBg3z22WfKNLVa\nTUREBC1atEDzglgDAwMMDAzKpQ4WFhYlWt7Y2LjQddRqNaV9qa2Ojk6J6yKEEEIIURBJXylDGRkZ\n7N27l4EDB+Yb7ujll19m48aNNGnSBMifglFQusf69et55ZVXaNOmDV988YXWvNWrV+Pm5oa9vT3t\n2rXjyy+/LKe90ubq6sqNGzeIiYlRpp08eRJTU1Pq16+vTHt8fw4ePIi7uzuOjo64uLgwduxYUlNz\nh6JatmwZ48aNw9/fH2dnZzp16sSxY8fYtGkTbdu25bXXXmPLli1KWSVJXykpTT1btGhB165d2bdv\nnzLPy8uLWbNm8eabb+Lq6sqZM2e00lcSExMZOnQozs7OuLu7c+XKlQLLLqgNhBBCCPFik6C8DF29\nepWHDx/i4OBQ4PzWrVsXu/c4OzubsLAwNm7ciL+/P99++y3btm0DcnOV161bh7+/P2FhYYwaNYoV\nK1Zw6tSpUtc9Ozu7WMsZGxvzyiuvcODAAWXagQMH6NSpU6HrxMfH4+vry4ABA9i3bx9Lly7lt99+\nIzg4WFkmLCwMIyMjdu3aRfPmzRkzZgy//fYbmzZton///sybN4/79++Xat+K6gnXpK8cO3aM0aNH\n4+7uzq5du+jXrx/jx4/XatfQ0FDmz5/PypUrMTU11SrH19eXzMxMQkJCGD58OBs3blTKLk4bCCGE\nEOLFJekrZUgTNFatWrVMyps/fz42NjY0adIEb29vvv32W/r06UOtWrUICAhQ8ro9PDwIDAwkJiam\n1Kkx27dvx8DAgJ49exa5bMeOHdm9ezdDhw4FcoPyBQsWsHnz5gLzs3NycpgyZQp9+/YFcnOx27Rp\no9XbbmJiwtixYwHo3bs3YWFh+Pn5YW1tzfvvv8/KlSu5evUq9vb2Jd632bNnM3fuXK1pBw4cwNzc\nXGvali1b6Ny5M97e3gC8//77nDp1ijVr1rB06VIg905By5YtgdwHPTUuXrzI33//zf79+6lduzYN\nGzYkKiqKXbt2PbENLl26VOL9EUII8ewyNzfG0rJs4oSn1fO+f+VFgvIypHngLyUlBRsbm/9UlomJ\niVYZzZo1Y82aNQC88sornDx5kkWLFhEbG0tUVBR37twpdm93Qd555x0GDRrE3bt38fHxKXQ5lUpF\np06dmDt3LsnJydy7d49Hjx7RvHnzQnuk69ati56eHitXruTSpUtcvHiRS5cu0b17d2UZa2tr5efK\nlStrTdN8zsjIKNW+jRo1irfffltr2uO93ACxsbH069dPa5qTkxMhISEF1jOvS5cuYWxsTO3a/44p\nbm9vrwTlxWkDIYQQz7+kpFRu335Q0dUoN5aWVZ/r/SsLhV20SFBehurUqYOJiQknT54ssEfX19eX\nnj174ubmlm/e4wF1pUqVtD7n5OSgp6cHQEhICHPnzqVfv3507tyZiRMnKr27j4uKimL27NnFqv/d\nu3eZP38+arWaDz74oNDlatWqRZMmTfj555+5c+dOgfuTV3R0NJ6ennTs2BEXFxd8fHxYv3691jKP\n729ZMjc3L9ZFkib4zysnJ4ecnBzl85PSjx6/KNGM8AKFt0FpHzIVQgghxPNFgvIyVKlSJbp3786W\nLVvo27ev1sOex44dIywsTOmF1tPTIy0tTZn/+FjXycnJJCQkULNm7ivWT548ia2tLQBBQUGMGDFC\nSR+5f/8+d+7cKTDAa9q0Kd9++22Rdc/IyMDb2xtfX1+6detW5PKdOnUiIiKCO3fuKGknhdm5cyet\nWrVi0aJFyrTLly9rPRj6NKhfvz4nT57UmnbixIli1bNx48akpaURFxenLH/u3Dll/rPSBkIIIYSo\nGPKgZxn76KOPSE9P54MPPuD48eNcvXqV0NBQPvnkE/r06YOzszMADg4ObNmyhStXrnDw4EFCQ0O1\n8rFVKhUTJ04kOjqavXv3snnzZqX32szMjGPHjhEXF8eZM2cYO3YsKpWq1OkdkBs0jh49ulgBOeQG\n5UeOHOHq1au8/PLLyvSCLgzMzMy4cOECp06d4vLlywQEBHD+/HnS09NLXV+N1NRUkpKS/lMZmjr7\n+PgQHh7Ohg0buHz5MuvXr2f//v289957+ZZ9nK2tLa+99hp+fn5ER0cTERGh9aBnebaBEEIIIZ59\nEpSXMXNzc4KCgmjQoAETJ06kR48erFmzhmHDhjFr1ixlualTp5KSkkL37t1ZvXo1Y8aM0SqnRo0a\nvPrqqwwcOJC5c+cyZswY3nzzTQAmT57Mo0eP6N27N+PGjaN79+50796dqKioUte7b9++tG3bttjL\n29nZYW5ujqurqxJ4Pv4iHs3PXl5etGzZEh8fH9577z10dXWZOnUq0dHRBa6Xd92iLF26lHfffbfY\n9X5c3m3b29uzcOFCvvvuO3r06EFoaChLly6lTZs2hdYr7+clS5ZgaWmJp6cnCxcu1MrNL6oNhBBC\nCPFiU6klqVU8w9RqNZ6ens/t0IL+3b+mVrXaRS8ohBDiqXfr/jXcprTC1rZRRVel3MiDnkWTBz3F\ncyk4OJiOHTtWdDXKzZ20hIqughBCiDIif9PFk0hPuXimZWVlaY1y8ry5cOECSUny1s+yZm5uLO1a\nDqRdy4e0a/moqHa1samb763fzxPpKS+a9JSL59LzHJBD7qgu8set7MmXRvmQdi0f0q7lQ9pVPG3k\nQU8hhBBCCCEqmATlQgghhBBCVDAJyoUQQgghhKhgz3dCrhDPOHnQs3wkJ8uDc+VB2rV8SLuWj7Jq\n1+f9wU3xvyNBuRBPsRXDvqe6Uc2KroYQQogC3ElLwGNe1+d63HHxv/PUBeV2dnZan01NTenUqRN+\nfn4YGRlVUK3+N+zs7Fi3bp3WGySLKzIykoEDBxIdHc21a9dwc3MjPDwcGxubcqhp2Th+/DiDBg3i\n3Llz3Lhxo8zq3LFjR0aMGEHfvn1LvO6tW7d44403iIiIwMrK6j8dk7JQ3aimvDxICCGEeAE8lTnl\nS5cu5ejRoxw+fJivvvqKM2fOEBAQUNHVemZYWVlx9OhRrK2tK7oqxVbWdVapVGVSztGjR3FxcSmT\nsoQQQgghCvNUBuXVqlXDwsKCGjVq0KJFC4YNG8bevXsrulrPDB0dHSwsLNDReSoPb4Ge1jpbWFig\np6dX0dUQQgghxHPu6YqAClG5cmWtz15eXnzxxRfK52vXrmFnZ0d8fDyQmwaydOlS2rRpg4+PD6Gh\noXh6erJq1Spat25Nu3bt2L17N3v37uWNN96gdevWLFmyRCkvMTERX19fWrdujYODA+7u7kRGRv5v\ndvb/TZo0CX9/fz755BOcnZ1p3749oaGhyvzU1FTGjRtHq1at6Ny5M6dOnVLmPd4eMTExDBkyhJYt\nW+Lo6MiAAQO4dOkSkJtC4urqyvfff4+rqyvOzs6MHz+e9PR0pbzVq1fj5uaGvb097dq148svv1Tm\neXl5ERgYiKenJ05OTgwYMICYmBhlfkpKClOnTqVt27a0atWK8ePHk5KSkm9/H6/zvn376Nq1K46O\njrz11lts3769VO24bNkyxo4dy6xZs3BxcaFNmzasXr1amZ+ZmYm/vz+tW7fG1dWVAwcOaK1vZ2fH\nsWPHgCefF5r6h4WF8eabb+Lo6MjQoUNJTk5Wytq2bRtvv/029vb2vPrqq8yYMYPs7OxS7ZcQQggh\nni9PfVCelJTEpk2b6NWrl9b0otITIiIiCAoKYvLkyajVas6cOcOVK1fYtm0bXbp0YerUqQQFBfH1\n11/zySef8NVXX3Hx4kUAJkyYQE5ODsHBwezYsYNatWoxffr0/7QfpQm+goODad68OT/88ANvvfUW\nM2bM4P79+wBMnz6dmJgYNm3axMyZM1m/fn2BbaJWqxk5ciS1a9dm586dBAcHk5OTw+eff64sk5SU\nxFQhI6wAACAASURBVI8//siaNWtYtmwZ+/fvV4LgnTt3sm7dOvz9/QkLC2PUqFGsWLGC06dPK+t/\n/fXXdOvWje3bt1OrVi0+/PBDMjIyABg1ahTnz59n1apVrF+/nri4OCZMmPDE/b579y7jx4/Hx8eH\nn376iWHDhjFlyhTi4uJK3IYA4eHh6OnpERoaypAhQ1i8eLFy4bBs2TIOHDjAihUrCAwM5Ntvvy20\nnOKcF6tXr2bRokVs3ryZs2fPsmbNGiA353/WrFmMGzeO8PBwZs6cyfbt2wkLCyvVPgkhhBDi+fJU\nBuXDhw/H2dkZZ2dnXnvtNaKionjvvfdKVEa/fv2oV68eDRs2BCAnJ4epU6diY2ND3759efToEaNH\nj6ZRo0Z4eHhgbGxMbGwsAJ06dWLq1Kk0aNAAW1vbfL2/pTFp0iQSEhJKtE6TJk0YPHgwtWvXxtfX\nl/T0dC5cuMCDBw/Yt28ffn5+NGvWjDZt2jB69GjUanW+Mv755x/69+/PhAkTsLGxoVmzZvTu3Vvp\nKQfIysrCz8+PRo0a0a5dO15//XUl6K5VqxYBAQG8+uqrWFlZ4eHhQfXq1bXW79ChAwMHDqRBgwbM\nnj2be/fucfjwYaKjo/njjz8ICAjAwcEBBwcHFixYwKFDh57YngkJCWRlZVGjRg1eeukl3nnnHdat\nW4eFhUWJ2k/DxMSESZMmYWNjw+DBgzExMeHMmTOo1WpCQkLw9fXFxcUFR0dHpkyZUmg5xTkvRo0a\nhaOjI46OjvTo0UNpR0NDQ+bOnYubmxsvvfQSb731Fs2aNfvP55UQQgghng9P3egrALNmzaJly5ZA\nbvrDrl278PDwICQkhHr16hWrjNq1tUesMDMzo0qVKsC/6TBWVlbK/MqVKyu9ux4eHuzZs4e//vqL\nuLg4zp49i0qlIicnp9Q5z4MHD2bQoEEsXryYZs2aFWudOnXqKD8bGxsDuQF0XFwc2dnZNG3aVJlv\nb29fYBmGhob079+fHTt2cObMGeLi4jh37hxmZmZP3FZWVhYAr7zyCidPnmTRokXExsYSFRXFnTt3\ntHr+NccKwMjIiHr16hEbG0tGRgZGRkY0aNBAmd+gQQNMTEyIiYnBxMSkwDo3a9aMjh07MmzYMOrU\nqUOHDh1wd3enWrVqRbZZQaysrLTuIhgZGZGVlUVycjLJycla7di8efNCy3nSeaGRd+QYzXY05f4f\ne3cellP+PnD8nUqWaFGYaGzDRGkh0chWaUhZxpYoGo0wNHZmZDf2LbLU2AYJEdnGvo5thu/YRkJI\nGCmZqEHr7w9Xz8+j7Qkp3K/rmutbZ/mc+9zPM/O9z+k+n6OlpcXChQu5ceMG165dIzo6ushmdRFC\nCCFE8VIsi/KKFSsqihtjY2PMzMw4duwYmzZtYtSoUdnaNHJqDXl9In91dfVs2+RUYGdmZuLl5cWT\nJ09o164dDg4OpKamMmjQoBxjDQwM5OjRoyqdV1xcHJ6enmzdulWlaf80NLJ/PK/eDX/155y2BUhO\nTqZLly7o6enh6OiIq6srN2/eVOqrBpQeZnx13NDQUKZNm0a3bt1wcnJi9OjReHp6Ku37em4zMjJQ\nV1fP9ixAlvT09HzbeZYsWUJERAQHDx7k0KFDrF+/nmXLltG0adM898tJTg9qFjSPGRkZKn0vXv/e\nZY19/Phxvv/+ezp27Ejz5s0ZNGgQkyZNKvC5CCGEKF709bUxNCxX1GEUK5KPN1Msi/KcZGZmKu5I\nampqkpT0/2/hyno48F24fv06Z8+e5ffff8fAwACA4OBgRQyv8/HxwcfHJ99xz507x8yZM5k3b162\nu/gFVbNmTTQ0NLh48SJ2dnYAXLlyJcdt//jjDx48eMDOnTsVxfPx48dzPJechISEMGDAAPr16wfA\nkydPiI+PV9r/1WM/ffqU6OhoTExM+Oyzz0hOTiYqKopatWoBcOPGDZKSkqhRo0aOD3xmjbd161bG\njh1L3bp1GTRoEF5eXuzfv/+NivLc6OvrY2BgwMWLFxV3y3PL440bNwr0vciSdQEZGhpKp06dFIV4\nWloa0dHRNGrU6J2djxBCiPcvISGJuLinRR1GsWFoWE7ykY/cLlqKZVGemJhIXFwc8LInesuWLdy5\nc4c2bdoAUL9+fbZu3Ur79u2Blw/rvat5qXV0dChRogS7du3C0dGRS5cuERgYCMCLFy8ULTAFFR4e\nzsqVKxVtKG9DW1ubTp068fPPPzN9+nRSU1OVZkR5la6uLs+fP2fv3r2Ym5tz6tQpQkNDc/zLwauy\nCk09PT1OnTpF69atSU5OZv78+aipqSlafQC2b99Oo0aNqF+/Pv7+/lStWhVbW1tKlChBy5YtGTNm\nDOPHjyczM5NJkyZhbW2NiYkJZ86cyfHY5cqVY8OGDejo6NChQwfu3btHZGQk7dq1A15eGGRmZuba\n/lIQvXr1YtGiRVStWhUdHR2mTZuW43bly5fP9Xvxai5el5VHXV1d/vrrLyIjIylRogSBgYEkJiYq\nzXIjhBBCiE9XsXzQc8iQITRr1oxmzZrh4uLC6dOnWbRoEZaWlgB4eXlhampKr169GDFiBP3798+z\nyFRTU8tWtOdWxFeqVImJEyeycuVKnJ2d2bx5M0FBQWhpaREREfHG5zR58uQCFeQ5xfyqcePG0ahR\nI7y9vRk9ejS9e/dW2j7rZysrK77//numTp2Kq6srJ06cICgoiMTERB48eKC0bU7HHjt2LM+ePaNj\nx44MHz4cFxcXXFxclHLh4uJCSEgInTt35tmzZ/zyyy+K1qCZM2dSrVo1+vTpg7e3N3Xq1GHp0qXZ\n4nz1Z2NjY/z9/dm3bx8uLi6MHj2anj170qVLFwB+/PHHXNuJCppHHx8fOnfuzPDhw/Hx8aF79+45\nbl+5cuVcvxdZd9fzyuPgwYMxNDTEzc2Nfv36UbduXXx8fLh69apK5yGEEEKIj5tapqp9DELkwMPD\ng8aNG6tcJL8LT58+ZejQoSxfvvy9HbOoTHX5hcrl367dSQghROF48OQujn4NqVWrdlGHUmxI+0r+\ncmtfKZZ3yoXIyy+//JJt3nohhBBCiA9ZsewpFyIvvr6+uc6SIoQQQgjxIZLKRryVtWvXvvdjfkoF\neXxywV44JYQQ4v2R/0aLd+nTqW6E+AANDOxGQkJS/huKAtHX15a8FgLJa+GQvBaOd5VXY+Nq7yAa\nIaQoF6JYq1OnjjwwUwjkQaTCIXktHJLXwiF5FcWNPOgphBBCCCFEEZOiXAghhBBCiCIm7StCFGPX\nrl2TXtJC8Pix9OgWho89r8bG1ShZsmRRhyGE+EhJUS5EMbbEZxMGZSsVdRhCfPLik2Nxm+4sL4kR\nQhQaKcqFKMYMylaSN3oKIYQQn4B8e8rT0tJYsmQJTk5O1K9fn+bNmzN+/HgSEhLeR3zZeHh4sGDB\nAgDGjBnDyJEjiySOLPb29piYmHD69Ols644dO4aJiYkixrCwMFq0aPG+Q3znCuMzGDNmDCYmJixc\nuDDbuqSkJMzMzBS5u3v3LiYmJsTExLz1cQtTTEwMR48eLeowhBBCCPEByPdO+dy5czl+/DiTJk2i\nevXq3Lt3jzlz5uDt7U1YWNj7iDEbNTU1APz8/Irk+K/T0NDg8OHDNGnSRGn5gQMHUFNTU8Tbrl07\nWrVqVRQhvnOF8Rlk5dHX11dp+dGjR0lPT1cc08jIiBMnTqCnp/fOjl0YfvrpJ6ytrT+KCzEhhBBC\nFK5875SHhYXh6+uLra0tn332GdbW1syZM4crV65w8eLF9xFjrrS1tdHW1i7SGAAaNWrE4cOHlZZl\nZmZy6NAhLCwsyMzMBEBLS6vYF5IF9a4+AzU1NRo2bEhkZCQPHjxQWnfgwAEsLCwUv5coUYIKFSpQ\nokTxnzwo67MXQgghhMhLvlWNmpoap06dIiMjQ7GsatWq7N69my+//BJ4WXgsX76c1q1bY2FhgYeH\nB1evXlVsb2Jiwu7du2nbti2WlpaMGDGCmJgYPDw8sLS0xMPDg7i4OMX2QUFBODo6YmZmhp2dXY4t\nDZC9dULV/d615s2bc//+faKiohTLLly4gK6uLjVq1FAse7V95cyZMzRv3pxNmzbRvHlzrKysGDFi\nBC9evABg0aJFDB06lMmTJ2NtbY2trS1BQUFKx12yZAnNmzfH2toab29voqOjFeuioqLw9vamQYMG\nmJub4+7uzo0bN5SOvWbNGho3bsxXX33FkiVLlMY+fPgwnTp1wsLCAmdnZ/bs2ZPjub/6GTx9+pQh\nQ4bQuHFjGjZsyODBg4mPj1c5jxUrVsTMzIxDhw4plqWkpHDixAkcHBwUBe7r7SsmJiZs27YNV1dX\nzM3N6dGjh2JdfnmGl0V/u3btsLS05JtvvuH48eOKdZGRkfTs2RMrKyvs7OyYOXMm6enpQN7f+zFj\nxvDnn3+ybNkyPD09Afjrr79wd3fH0tISKysrvL29iY2VVzQLIYQQQoWi3NPTk5CQEFq1asW4cePY\nvXs3T58+pWbNmmhpaQEQEBDAqlWr+Omnn9i6dStVq1bF29ub//77TzHOokWLmDlzJkuXLmXPnj24\nu7vj6enJ+vXruXfvHitXrgQgPDycVatWMXXqVPbt28egQYNYsmQJly5dyhbbq60hue33Nnfzs4qv\n/Ghra9O4cWMOHjyoWHbw4EEcHBzy3C8hIYHffvuNFStWsGjRIg4cOKDUErR//340NTXZunUr3t7e\nzJs3T1H4r127lvDwcGbPnk1oaCjVqlWjd+/evHjxgszMTAYOHEjVqlUJDw9nw4YNZGRkMGvWLKVj\n79y5k19//ZXJkyezcuVKQkJCADh16hSDBw+mU6dObN++nW7dujFixIhcc5n1Gfj7+3P//n3WrVvH\npk2bePToEdOnT1cph1kcHByUivIzZ85Qq1YtDAwM8txvyZIljB07li1btpCYmMi8efNUyvPVq1cZ\nNWoUPj4+7Nixg27dujFo0CBFcT1y5Ehq1arFjh07WLBgAeHh4WzZsgXI/XufnJyMn58flpaW9OnT\nh4CAAJKSkvDx8aFp06bs2rWLFStWEBMTw7JlywqUHyGEEEJ8nPItygcOHMi8efP4/PPPCQsLY9iw\nYdjZ2bFixQrg5d3CdevWMXjwYFq1akXNmjWZMmUKmpqabNu2TTGOp6cn5ubm2NraUqdOHezs7Gjd\nujX16tXDwcGBmzdvAlC5cmVmzJhBkyZNMDIyws3NDQMDA8Vd3le92hqQ236v3r0uqLCwMLZv367S\ntvb29kotLAcPHsTJyQn4/6L1dWlpafz000/Url0bOzs7mjVrxuXLlxXrdXR0GDNmDMbGxvTt2xcd\nHR3F+uXLlzNy5EgaN25MjRo18PPzQ0NDgz179vD8+XO6d+/OqFGjMDY2pl69enTs2FEph2lpaUyf\nPh0TExMcHR3p3bs3GzduBCA4OBgnJyc8PT2pVq0affr0wcnJSfGZ5+b+/fuUKVOGKlWqUKtWLWbN\nmoW3t7dK+cvKk4ODA3/88QfPnj0DXt7FzspjXnr37k2TJk2oXbs2PXr0ULqIyyvPK1asoHPnzrRv\n3x5jY2Pc3NxwdnZm7dq1inPS09PDyMgIa2trfvnlF+zs7PL93mtra6OpqUnp0qUpX748z58/Z8CA\nAXz//fdUqVKFBg0a4OTklOP3WgghhBCfHpWmRHR2dsbZ2ZmnT59y8uRJNm7cyOzZs6lRowbm5uYk\nJiYq9fxqaGhgZmamKLQBjI2NFT+XKlUKIyMjxe9aWlqkpKQA0LhxYy5cuMDcuXO5efMmERERxMfH\n53rXOqswL+h+qvjmm2/o3bs3jx49wsvLK9ftsorJadOm8fjxY/7991+ePXuGqalpvj3Fn3/+ueLn\nsmXLkpaWpvjdyMhIqaDPWp+cnExsbCwjRoxQWp+amkp0dDSlS5eme/fubNu2jcuXL3Pr1i2uXLmi\n1M9erlw5atWqpfjd1NRU0R5z8+ZNunXrphSnpaUloaGheZ5Lnz59GDBgALa2tjRu3JjWrVvToUOH\nPPfJkpWnL774gsqVK/P777/j6OjI4cOHCQ4O5s8//8xz/1e/X6/nEXLPc1RUFNevX2fz5s2K9Wlp\naYrv84ABA5g7dy4bN26kefPmtGvXDlNTU+Lj43P93t+6dStbfAYGBnTo0IFVq1Zx9epVbty4QWRk\npNL+QojiTV9fG0PDckVy7KI67sdO8lo4JK9vJs+i/OrVq2zbto0xY8YALwu5r7/+mq+//pouXbpw\n8uRJbGxsctw3LS1NqSBWV1dXWp/b3ePQ0FCmTZtGt27dcHJyYvTo0Yqe3JxkjVOQ/SIiIpgyZUru\nJ/6KR48eMXPmTDIzM/n2229z3a5y5cp8+eWXHDlyhPj4eBwdHVUaX1NTU+n3V4v419dlrc/K6/z5\n8/niiy+U1pUrV47k5GS6dOmCnp4ejo6OuLq6cvPmTaWe9Nc/j4yMDMWyUqVKZTtuRkaG0nMFOcVs\nY2PD0aNHOXz4MEePHmXGjBns2LGDNWvW5JmD12W1sFSqVAldXV2MjY3zLcrzymNe6zMyMujbty+d\nO3dWWpf11r6+ffvi7OzMwYMHOXLkCAMHDmTAgAH06dMnxzhe/95niY2NpXPnzpiammJnZ0e3bt04\ncuQI586dy/O8hBDFR0JCEnFxT9/7cQ0NyxXJcT92ktfCIXnNX24XLXkW5enp6axevRpXV1dMTU2V\n1pUpUwY9PT20tbUxNDTk/Pnz1K1bF3h5x/bvv//G1tZW5QCziuuQkBAGDBhAv379AHjy5Anx8fH5\n3nEuyH5169Zl/fr1+caUkpKCp6cnvr6+tGvXLt/ts4rJ+Ph4hg4dmu/2r8vtQuV15cuXp0KFCjx8\n+FAxxWJGRgbDhw+nW7duPH/+nAcPHrBz505FoX38+HGlXPz777/8888/fPbZZwBcunQJExMTAGrU\nqMGFCxeUjvnXX38pPbSaU9xLly7FwsICV1dXXF1dOXfuHD179iQhIQF9fX2Vz93BwQFfX18MDQ1V\nal15GzVq1CAmJkbpTntAQAA6Ojp07NiR+fPn069fP3r16kWvXr1YunQpO3bsYNCgQfl+7189p/37\n96OtrU1gYKBiWUEvVoQQQgjx8cqzp9zU1JSWLVsycOBAwsPDiYmJ4dKlS8yZM4dr167RpUsXAL79\n9lsCAgI4dOgQUVFRjB8/npSUFFxcXHIcN6dCOWuZnp4ep06d4tatW1y+fJmhQ4eipqamaG95V/up\nIjw8nMGDB6tUkMPLYvL333/nzp07NGrUKFuM+cnMzFR52z59+uDv78+BAweIjo5m4sSJnDx5ki++\n+AJdXV2eP3/O3r17uXv3LqGhoYSGhmbLxdixY7l+/Tp79+5l3bp1eHh4AODl5cX+/fv59ddfuX37\nNqtXr+bAgQP07Nkz17jhZf/1lClT+Ouvv4iJiWH79u0YGRmhp6dHeno6cXFxpKam5nvuVlZWpKen\nExwcrPJfHAoq61h9+vRhz549rF69mujoaDZs2MCyZcuoVq0a5cqV48SJE0yZMoWoqCgiIyM5evQo\nZmZmQP7f+zJlyhAdHU1CQgJ6enrExsZy8uRJYmJiCAoK4siRI0qzwAghhBDi05VvT7m/vz9BQUEs\nW7aM+/fvU7JkSWxsbAgODqZSpUrAy8ImKSmJ8ePHk5SUhJWVFWvXrs317ujrd4RfnUVl7Nix/PTT\nT3Ts2JHKlSvTv39/KlasSERERI7jvMl+quratWuBtjcxMUFfXx8bGxtFXK/GmPV7Tj+/vu3r+72u\nb9++PHv2jMmTJ/PkyRPq1avH8uXLMTQ0xNDQkO+//56pU6fy7NkzWrRoQVBQEO7u7kpzgDdr1gx3\nd3fKli3LsGHDFBcfZmZmzJkzh4ULFzJnzhxq1qyJv79/jn/5eDXO0aNHM3XqVAYOHMh///2HpaUl\ngYGBqKmpce3aNTp16sTatWuVLlhyGkddXR17e3v+/PNPxd37vPKY11j55dnCwoLZs2ezePFi5s6d\nS9WqVZk2bRrNmzcHXs7qMnXqVLp160aJEiVwcHBg7NixQP7fezc3N0aPHo23tzebN2/mzz//ZMiQ\nIQC0adOG+fPnM2LECFJSUhTtMkIIIYT4NKllyttNPjlnzpyhd+/eSnPJvw/Tp0+nXbt2mJubv9fj\nfsimuvxC5fJVizoMIT55D57cxdGvIbVq1X7vx5Ye3cIheS0cktf85dZTXvxfiSg+CvHx8Vy8eJF6\n9eoVdShCCCGEEMWOSlMiio+Pqg+VvisGBgasXbsWDQ35yhVEfLK88VOI4kD+XRRCFDZpXxGiGLt2\n7RoJCUlFHcZHR19fW/JaCD72vBobVyuS5z+kHaBwSF4Lh+Q1f280JaIQomjVqVNH/uNWCOT/NAqH\n5FUIId6c9JQLIYQQQghRxKQoF0IIIYQQoohJUS6EEEIIIUQRk55yIYoxedCzcDx+/HE/kFhUPvS8\nFtWDnEIIAVKUC1GsLfHZhEHZSkUdhhAfvfjkWNymOxfJy4GEEAKkKBeiWDMoW0ne6CmEEEJ8At57\nT7mJiYnSP02aNGHs2LEkJye/71Deu4SEBPz8/LCzs6N+/fq0bduWZcuWkZaWptL+Z86cwcTEhIyM\nDJW237NnD/Hx8TmuCwsLU/oc6tatS8OGDfH29ubmzZsqn1NePDw8WLBggcrbmpiYsGXLlmzroqKi\nMDExwd3dHSh4HopKREQEZ8+eLeowhBBCCPEBKJIHPf39/Tlx4gTHjh0jMDCQy5cvM2PGjKII5b3q\n168fiYmJBAYGsnfvXoYOHcq6deuYPXu2Svs3aNCAEydOUKJE/h/bvXv3GDJkCM+ePct1G0NDQ06c\nOMGJEyc4fvw4GzZsICUlhQEDBvCu3ilVkDeHamhocPjw4WzLDx48iJqammKsguShKH3//ffcvn27\nqMMQQgghxAegSKqa8uXLU6FCBSpWrIiFhQU+Pj7s3r27KEJ5byIjI7l8+TJTpkzB1NQUIyMjnJyc\nGDZsGJs2bVJpDE1NTSpUqKDStllFdV7FdYkSJahQoQIVKlTAwMCA2rVrM3z4cKKjo4mMjFTpOO9S\no0aNOHHiBCkpKUrLDxw4gIWFheJcCpKHoiYvzBVCCCGEKorFrcZSpUop/f5628Pdu3cxMTEhJiYG\neNkC4+/vj62tLV5eXmzdupUePXqwbNkybGxssLOzY+fOnezevZuWLVtiY2PD/PnzFeM9fPgQX19f\nbGxsqF+/Pp06dSr0NoOsu7wnTpxQWu7s7MzWrVsVv0dFReHt7U2DBg0wNzfH3d2dGzduAMptG1k5\n2bdvH61bt8bc3Jx+/frx+PFjABwdHQFwcnJi27ZtKseZdfdZU1MTgMOHD9OpUyfMzc2xtrZm6NCh\nJCW9nF1h0aJFipaSLPb29mzevFnl473K3NycsmXLcurUKcWyhw8fEh0dTePGjRXLCpKHsLAwevTo\nQUBAALa2tlhbW/Pzzz8rFcsbN27EwcEBKysr3N3duXTpktKxvvnmGywsLGjVqhVBQUGKdS9evGDO\nnDm0bNkSKysr+vfvz/3794GX3+H79+8zbtw4fvzxx3xzKYQQQohPW5EX5QkJCaxdu5YOHTooLc+v\n7eHQoUOEhIQwduxYMjMzuXz5MtHR0WzZsoU2bdowbtw4QkJC+OWXXxg2bBiBgYFcv34dgFGjRpGR\nkcGGDRvYtm0blStXZsKECW91Hunp6Xmur1OnDra2tgwfPpwOHTowe/ZsTpw4gYaGBtWrVwde3lUd\nOHAgVatWJTw8nA0bNpCRkcGsWbNyHTcoKIi5c+eybt06/v77b1asWAFAaGgoAJs2baJt27YqnUNs\nbCz+/v7UqlWLmjVrEhMTg6+vL+7u7uzZswd/f39Onz7Nhg0bVBqvoNTU1LC3t+fQoUOKZYcOHaJZ\ns2aKi4Tc5JYHgEuXLnHr1i1CQkIYP348wcHBHD9+XDH+woULGTt2LOHh4TRv3pzevXsTHx9Peno6\nvr6+2Nvb89tvvzF+/HgWL16suLCaMGEC+/fvZ9asWWzcuJH09HQGDBhARkYGAQEBVK5cmTFjxjB2\n7Nj3nkshhBBCfFiKpCjv378/VlZWWFlZ8dVXXxEREUHPnj0LNEa3bt2oXr06X3zxBQAZGRmMGzcO\nY2NjunbtyrNnzxg8eDC1a9fGzc0NbW1txQOMDg4OjBs3jpo1a1KrVi3c3d2Jiop6q3MaM2YMsbGx\neW4TGBjIsGHDSE9PZ8WKFfTt25dWrVopirznz5/TvXt3Ro0ahbGxMfXq1aNjx46KO+U5GTRoEObm\n5pibm+Pq6qq4y6unp6f4Xy0trRz3ffjwoeJzsLCwoEWLFiQkJDB37lzU1NTIyMjAz8+Prl27YmRk\nRNOmTbG1tX3rXOVGTU0NBwcHjhw5olh28OBBnJyc8t03tzzAywumSZMmUb16ddq3b4+JiQmXL18G\nYPny5Xz33XfY29vz+eef079/f8zMzNi0aRNJSUkkJiZSoUIFjIyMaNWqFb/++ismJiYkJiayfft2\n/Pz8sLGxoU6dOsyZM4c7d+5w/PhxdHR0KFGiBNra2mhra+eay7w+WyGEEEJ8OopkSsTJkyfToEED\nAEVx4+bmRmhoqOKucX6qVlWeJk5PT48yZcoA/98OY2RkpFhfqlQpRa+ym5sbu3bt4n//+x+3bt3i\n77//VhShb/rwYN++fenduzfz5s2jXr16OW5TsmRJ+vXrR79+/fjnn384evQoq1ev5vvvv+fAgQMY\nGBjQvXt3tm3bxuXLl7l16xZXrlxRFNg5MTY2VvxctmxZlWdyAahQoQIhISHAy4JYV1cXbW1txfpq\n1aqhqanJ0qVLuXHjBtevX+fGjRu4uLiofIyCsrW15cmTJ1y5coXPP/+c8+fPs3Dhwnx73PPKg56e\nntJ5vbo+KiqK+fPn4+/vr1ifmprKZ599ho6ODr169WLSpEksXbqUli1b0qFDBypUqMCFCxfI4YUq\nKgAAIABJREFUyMjAwsJCsZ+Ojg41atTg5s2btGjRQim+osilEKJg9PW1MTQsV9Rh5Ki4xvWhk7wW\nDsnrmymSorxixYqKIsrY2BgzMzOOHTvGpk2bGDVqVLbWlZxaQ15/65q6unq2bXIqsDMzM/Hy8uLJ\nkye0a9cOBwcHUlNTGTRoUI6xBgYGcvToUZXOKy4uDk9PT7Zu3apUJALs3buXhIQEevToAcBnn32G\nm5sbX3/9NS1atOB///sfTZs2pUuXLujp6eHo6Iirqys3b95U6mN+3et5KMiDherq6tnifNXVq1fp\n0aMH9vb2WFtb4+XlxerVqxXrc2oxyq+NJz8lS5akWbNmHDp0iJo1a9KoUSNKly6t0n6vejUPObW+\nZK3PyMhgzJgx2NnZKa3LusDz8/OjV69eHDhwgMOHD+Ph4cHUqVNzvfBKT0/PMQe55VIeBBWi+EhI\nSCIu7mlRh5GNoWG5YhnXh07yWjgkr/nL7aKl2Lw8KDMzUzHvtKamptIDcFkPeL4L169f5+zZs/z+\n++8YGBgAEBwcrIjhdT4+Pvj4+OQ77rlz55g5cybz5s3Ldhcf4P79+6xYsYKOHTsqFZmlS5dGXV0d\nfX19/vjjDx48eMDOnTsVFxnHjx9/o8KtIFMR5iY8PJyGDRsyd+5cxbLbt29To0YN4OXn9Or88v/9\n9x+PHj166+Pa29uzZs0abt++TevWrd96vLzUqFGDf/75R+niZNKkSTRq1AgLCwsCAwPx8/PD29sb\nb29v/Pz82LNnD23btkVDQ4Pz58/TvHlzAB4/fkx0dLQiP69+BvnlUgghhBCftiLpKU9MTCQuLo64\nuDhiYmJYsGABd+7coU2bNgDUr1+fffv2cenSJS5dusSiRYveSZEJKHp9d+3axb1799izZw+BgYHA\ny9k03lR4eDgrV67MsSAH+OabbyhZsiReXl6cOHGCu3fvcubMGYYMGcKXX36JtbU1urq6PH/+nL17\n93L37l1CQ0MJDQ3NNkWgKrLu9EZERPDff/+90Tnp6elx7do1Ll68yO3bt5kxYwaRkZGKPNWvX5/r\n16/z22+/cfv2bcaPH5/tLxZZFxQpKSnExcXl+cKfrG1btmxJZGQkx48fp1WrVm8Uu6r69OnD2rVr\n2bZtG3fu3CEgIIDNmzdTs2ZNdHR02Lt3L1OnTiU6OpqLFy9y9uxZTE1NKV26NG5ubvz888+cOXOG\nyMhIRo0aRaVKlWjWrBnw8jOIiooiMTEx31wKIYQQ4tNWJHfKhwwZovhZS0uLunXrsmjRIiwtLQHw\n8vLi2rVr9OrVSzGDxeDBg3Md79UXy7y6LCeVKlVi4sSJLF68mHnz5tGoUSOCgoLo3r07ERERNGzY\n8I3OafLkyXmu19HRISQkBH9/f3766ScePXqEnp4eTk5OipcHWVlZ8f333zN16lSePXtGixYtCAoK\nwt3dnQcPHmQ7r5zOOWuZnp4enTp1Yvjw4YwcORJPT89ct82Nh4cHV65cwcvLi1KlStGpUyfGjRvH\n8uXLAfjqq6/w8vJiwoQJqKur07t3bxISErIdB+DYsWMMGjSIQ4cOKfX657Strq4uDRo0UPS55xSv\nqnnIaf2rnJ2dSUhIICAggIcPH1KrVi2WLFmCiYkJAEuXLmXmzJmKv3C0a9eOgQMHAjBy5EgyMzPx\n9fUlNTWVpk2bsmbNGkUrTa9evZg5cyb37t1j5syZeeZSCCGEEJ82tUxpahXvyaBBg5g0adIH8+Kf\n4mCqyy9ULp/zX1+EEO/Ogyd3cfRrSK1atYs6lGykR7dwSF4Lh+Q1f7n1lBf5POXi03D16lXS0tKk\nIBdCCCGEyEGxedBTfNzq1KnD0qVLizqMD058ct5z3wsh3g35d00IUdSkKBfvxZvO//6pGxjYjYSE\npPw3FAWir68teS0EH3pejY2rFXUIQohPmBTlQhRjderUkd68QiA9j4VD8iqEEG9Obl8KIYQQQghR\nxKQoF0IIIYQQoohJ+4oQxdi1a9c+6B7d4urx4w+797m4+pDzamxcTfGOASGEKApSlAtRjC3x2YRB\n2UpFHYYQH7X45FjcpjsXyznKhRCfDinKhSjGDMpWkpcHCSGEEJ8A6SkXQgghhBCiiMmd8nfAxMRE\n6XddXV0cHBz46aefKFu2bBFFVfju3r2Lo6Oj4vcSJUpQrlw5rK2tGTVqFNWqqTbn76JFizh16hTr\n168nLCwMf39/jh49Wlhhs2jRIhYvXpzr+kGDBjFo0KBCO74QQgghxOukKH9H/P39sba2Jj09nX/+\n+Yfx48czY8YMpkyZUtShFbpNmzZRpUoV0tLSiI2NZdGiRfTq1YuwsDAMDQ1VGkNNTa2Qo/x/ffv2\nxd3dHXh5YdG9e3c2b97MZ599BkDp0qXfWyxCCCGEECDtK+9M+fLlqVChAhUrVsTCwgIfHx92795d\n1GG9F3p6elSoUIFKlSphbm7O4sWLKVOmDIGBgSqPkZmZWYgRKitTpgwVKlSgQoUK6OrqAqCvr69Y\nVqZMmfcWixBCCCEESFFeaEqVKqX0u4eHBwsWLFD8fvfuXUxMTIiJiQFetsD4+/tja2uLl5cXW7du\npUePHixbtgwbGxvs7OzYuXMnu3fvpmXLltjY2DB//nzFeA8fPsTX1xcbGxvq169Pp06dOHv27Ps5\n2deULFmSDh06sH//fsWyw4cP06lTJ8zNzbG2tmbo0KEkJeU/dVpe+z19+pQhQ4bQuHFjGjZsyODB\ng4mPj3/r+FNSUvj555+xtbWlcePGDBkyhEePHgGwefNmzMzMuHnzJgAxMTFYWlqybds2AP766y/c\n3d2xtLTEysoKb29vYmNjAUhNTWXChAl89dVXWFpa8u2333Lr1q23jlcIIYQQHz4pygtBQkICa9eu\npUOHDkrL82vROHToECEhIYwdO5bMzEwuX75MdHQ0W7ZsoU2bNowbN46QkBB++eUXhg0bRmBgINev\nXwdg1KhRZGRksGHDBrZt20blypWZMGHCW51Henr6G+9bq1YtYmNjSU5OJiYmBl9fX9zd3dmzZw/+\n/v6cPn2aDRs25DlGfvv5+/tz//591q1bx6ZNm3j06BHTp09/45izzJs3j4sXLxIYGEhwcDAZGRn4\n+PgA0KVLFxo2bMjPP/8MwPjx42nSpAkdO3YkKSkJHx8fmjZtyq5du1ixYgUxMTEsW7YMgODgYE6e\nPElQUBDbt2+nbNmy/Pjjj28drxBCCCE+fNJT/o7079+fEiVeXuM8e/YMXV1d/Pz8CjRGt27dqF69\nOgAXL14kIyODcePGUaZMGbp27cq6desYPHgwtWvXpnbt2syZM4ebN29Su3ZtHBwccHJyolKll3Na\nu7u78913373VOY0ZM4YRI0YoxiyIcuXKAZCcnExGRgZ+fn507doVACMjI2xtbYmKispzjPz2u3//\nPmXKlKFKlSqUKVOGWbNm8fTp0wLH+qpnz54RHBzMpk2bqFu3LgCzZs2iSZMmnD17Fmtra6ZMmYKr\nqyvDhw/nypUr7NixA4Dnz58zYMAAvLy8AKhSpQpOTk6cP38eePnXkVKlSmFkZIS+vj4TJ04kOjr6\nreIVQrwb+vraGBqWK+owclWcY/uQSV4Lh+T1zUhR/o5MnjyZBg0aAJCYmMj27dtxc3MjNDRUUWjn\np2pV5fmo9fT0FP3NWe0wRkZGivWlSpUiJSUFADc3N3bt2sX//vc/bt26xd9//42amhoZGRmKi4WC\n6tu3L71792bevHnUq1evQPtmtZiULVuWihUroqmpydKlS7lx4wbXr1/nxo0buLi45DlGtWrV8tyv\nT58+DBgwQNFm0rp162x/nSiomJgYUlNTFQ+CZklJSSE6Ohpra2s+//xz+vfvj7+/PxMmTKBixYoA\nGBgY0KFDB1atWsXVq1e5ceMGkZGRWFhYANCjRw/27NlD8+bNadCgAQ4ODnTu3Pmt4hVCvBsJCUnE\nxb3dRX1hMTQsV2xj+5BJXguH5DV/uV20SFH+jlSsWBFjY2MAjI2NMTMz49ixY2zatIlRo0Zla13J\nqTXk9Vc8q6urZ9smpwI7MzMTLy8vnjx5Qrt27XBwcCA1NTXXaf0CAwNVnnIwLi4OT09Ptm7dqjg/\nVURGRmJkZETZsmW5evUqPXr0wN7eHmtra7y8vFi9enW+Y+S2X9ZDoTY2Nhw9epTDhw9z9OhRZsyY\nwY4dO1izZo3Kcb4u63MJDg5W3O2HlznW19dX/B4REYG6ujqnT5+mR48eAMTGxtK5c2dMTU2xs7Oj\nW7duHDlyhHPnzgEvW3oOHTrEsWPHOHLkCMuWLWPTpk2EhYWhpaX1xjELIYQQ4sMnRXkhyszMJCMj\nAwBNTU2lBxuzHvB8F65fv87Zs2f5/fffMTAwAF4WlVkxvM7Hx0fRI52Xc+fOMXPmTObNm5ftLn5e\nUlJS2L59O23atAEgPDychg0bMnfuXMU2t2/fpkaNGnmOk99+S5cuxcLCAldXV1xdXTl37hw9e/Yk\nISFBqYAuCGNjY9TV1UlISFD8dSApKYmRI0cydOhQ6tSpw6FDhzh69ChBQUH079+fQ4cOYW9vz/79\n+9HW1laadebVC4SQkBDKly9Pu3btcHR0ZPDgwbRo0YLIyEjMzc3fKF4hhBBCfBykKH9HEhMTiYuL\nA172Fm/ZsoU7d+4oCtP69euzdetW2rdvD7x8gc27mptbR0eHEiVKsGvXLhwdHbl06ZKiMHzx4sUb\nT/EXHh7OypUr0dbWznO7hIQESpUqRUZGBvfv3ycwMJAXL14oetr19PS4du0aFy9epHz58mzYsEFx\nJz0v+e13//59tm/fzrRp0zAwMGD79u0YGRmhp6dHeno6CQkJ6OrqoqmpqfI5a2tr07VrV6ZMmcKk\nSZMwNDRk3rx5XLt2jerVq5OUlMSkSZPo168fTZs2pW/fvkyaNAkbGxv09PSIjY3l5MmTGBsb89tv\nv3HkyBG++OIL4OV3JCAgAF1dXapVq0Z4eDhly5bN9+JECCGEEB8/mX3lHRkyZAjNmjWjWbNmuLi4\ncPr0aRYtWoSlpSUAXl5emJqa0qtXL0aMGEH//v1zbE/Joqamlq1oz62Ir1SpEhMnTmTlypU4Ozuz\nefNmgoKC0NLSIiIi4o3PafLkyfkW5ADdu3enWbNm2NvbM2zYMCpUqMCGDRvQ09MDXk4H2aBBA7y8\nvOjZsycaGhqMGzeOq1ev5niuWT/nt9/o0aOxsLBg4MCBuLi4cPv2bQIDA1FTU+PatWs0a9ZM8ZBl\nXl7P65gxY2jatClDhw6la9euvHjxgpUrV1KyZEnmz5+PlpaW4oJjwIABaGhoMG/ePNq2bUuHDh0Y\nMmQInTt35u7du8yfP59bt26RkpKCt7c3Li4ujBkzBmdnZ44cOUJgYKBSm4wQQgghPk1qme/zrS1C\nvEfTp0+nXbt2H3RryFSXX6hcXvXWISFEwT14chdHv4bUqlW7qEPJkTw4Vzgkr4VD8po/edBTfFLi\n4+O5ePEiI0eOLOpQ3kp8cmxRhyDER0/+PRNCFAdyp1x8tNLS0tDQ+LCvO69du0ZCQv5vPhUFo6+v\nLXktBB9yXo2Nq2WbAau4kDuPhUPyWjgkr/mTO+Xik/OhF+QAderUkf+4FQL5P43CIXkVQog3Jw96\nCiGEEEIIUcSkKBdCCCGEEKKISVEuhBBCCCFEEfvwm26F+IjJg56F4/HjD/eBxOLsQ8hrcX6gUwjx\naZOiXIhibInPJgzKVirqMIT4KMQnx+I23bnYzkcuhPi0SVEuRDFmULaSvDxICCGE+AR81D3laWlp\nLFmyBCcnJ+rXr0/z5s0ZP348CQkJRRKPh4cHCxYsAF6+yr04vNgmJSWFJUuW4OzsjKWlJY6Ojsyd\nO5fk5GSV9r979y4mJibExMS889g8PDxYunTpOx9XVadPn8bLy4uGDRtibW2Nh4cHR44cUdomv8/R\n3t6ezZs3F3KkQgghhPjQfdR3yufOncvx48eZNGkS1atX5969e8yZMwdvb2/CwsKKJCY1NTUA/Pz8\niuT4r0pJScHT05P//vuPkSNHUqdOHW7dusX06dM5f/48q1evRl1dvUhjzMrX+7Zt2zb8/Pz47rvv\n8PPzQ11dnQMHDvDDDz/g6+tL3759FfHlFeOWLVsoU6bM+wpbCCGEEB+oj7ooDwsLY8qUKdja2gLw\n2WefMWfOHBwdHbl48SLm5uZFFpu2tnaRHTvLihUriImJYffu3ejo6ABQpUoVli1bRps2bdi3bx9t\n27Yt4ijfv7i4OCZMmMD48ePp1q2bYrm3tzdVqlRh5MiRtGjRgi+++IL8Xoirp6dX2OEKIYQQ4iPw\nUbevqKmpcerUKTIyMhTLqlatyu7du/nyyy8ByMzMZPny5bRu3RoLCws8PDy4evWqYnsTExN2795N\n27ZtsbS0ZMSIEcTExODh4YGlpSUeHh7ExcUptg8KCsLR0REzMzPs7OxYuHBhjrG93vag6n7v0tat\nW+ncubOiIM9ibGzM2rVradasGQAvXrxgzpw5tGzZEisrK/r378/9+/dzHNPExIRTp04pfg8LC6NF\nixYAnDlzhubNmxMWFkbTpk2xsbFh1apVnDlzhjZt2tCgQQN+/PFHpUL3/v37uLu7Y25ujpubG5GR\nkSodC8Df35/mzZsr9j1//rxKedm+fTvlypWja9eu2da1bduW6tWrs2XLFsWypKQkhg8fjpWVFa1a\ntSI8PFyxzt7entDQUAAyMjLy/K4JIYQQ4tP1URflnp6ehISE0KpVK8aNG8fu3bt5+vQpNWvWREtL\nC4CAgABWrVrFTz/9xNatW6latSre3t78999/inEWLVrEzJkzWbp0KXv27MHd3R1PT0/Wr1/PvXv3\nWLlyJQDh4eGsWrWKqVOnsm/fPgYNGsSSJUu4dOlStthebXvIbb+LFy++8bmnp6fnuf7Zs2fcuXOH\n+vXr57i+QYMGirv5EyZMYP/+/cyaNYuNGzeSnp7OgAEDlC52VJWQkMC+fftYt24d3333HbNnz2bW\nrFmKf3bs2MHRo0cV24eFhdG+fXu2bdtGlSpVGDRokErH3b9/P+vXr2fu3Ln89ttv1KtXD19fX5Vi\nvHTpEmZmZrm2pTRs2FCpwD906BAmJibs2LGDtm3b4ufnx5MnTxTrs8ZZvHhxjt81Vfv3hRBCCPHx\n+qiL8oEDBzJv3jw+//xzwsLCGDZsGHZ2dqxYsQJ4eZd83bp1DB48mFatWlGzZk2mTJmCpqYm27Zt\nU4zj6emJubk5tra21KlTBzs7O1q3bk29evVwcHDg5s2bAFSuXJkZM2bQpEkTjIyMcHNzw8DAgBs3\nbmSL7dW7wbntFxUV9cbnHhYWxvbt23Ndn1U05tdGk5iYyPbt2/Hz88PGxoY6deowZ84c7ty5w/Hj\nxwscV1paGiNHjqRGjRr06NGDjIwMevXqhbm5OY6OjtSqVUuRT4B27drh5uZGzZo1mTx5Mo8ePVLp\nuPfu3UNDQ4PPPvuMKlWqMHz4cGbPnp3vxQq8zE358uVzXV++fHn+/fdfxe/m5uZ89913VK1alYED\nB5Kamprts1P1uyaEEEKIT9NH3VMO4OzsjLOzM0+fPuXkyZNs3LiR2bNnU6NGDczNzUlMTMTCwkKx\nvYaGBmZmZkqFobGxseLnUqVKYWRkpPhdS0uLlJQUABo3bsyFCxeYO3cuN2/eJCIigvj4+FwLwazC\nvKD7qeKbb76hd+/ePHr0CC8vr2zrs3qdX72jm5Pbt2+TkZGhlCMdHR1q1KjBzZs3qVWrVoFjy8pn\nqVKlAJTyWapUKUU+AaW+/7Jly1K9enWioqKU2lRy4uLiQkhICK1bt6Z+/frY29vTpUsXlR5c1dHR\nUWpJet3Dhw/R1dXNdj7w/xc5L168UNrn0aNHKn3XhBCFS19fG0PDckUdRoF9iDF/CCSvhUPy+mY+\n2qL86tWrbNu2jTFjxgBQrlw5vv76a77++mu6dOnCyZMnsbGxyXHftLQ0pYL49UIut7aG0NBQpk2b\nRrdu3XBycmL06NF4enrmGmPWOAXZLyIigilTpuR+4q949OgRM2fOJDMzk2+//VZpXcmSJfnyyy+5\ncOECX3/9dbZ9J06cSN26dbG0tMxx7PT0dJUuGnLaRkND+WtXokTuf7B5fV1GRgaampr5HsvAwIDd\nu3dz6tQpjhw5wsaNGwkODmbLli1UrFgxz5gtLS0JCgoiNTU127EyMzP5+++/sbOzyzP+1x8AzboA\neV1aWtobtQEJId5MQkIScXFPizqMAjE0LPfBxfwhkLwWDslr/nK7aPloi/L09HRWr16Nq6srpqam\nSuvKlCmDnp4e2traGBoacv78eerWrQtAamoqf//9t2LGFlVkFdchISEMGDCAfv36AS/vQsfHx+c7\nQ0dB9qtbty7r16/PN6as6Q59fX1p165djtt06NCBoKAgfHx8lB72jIqKYsuWLUyfPp3PP/8cDQ0N\nzp8/T/PmzQF4/Pgx0dHR1KhRI9uYmpqaSj3Sbzt/eUREhOLnxMREoqOjFXfn8zrWb7/9xqNHj+jV\nqxd2dnaMGjWKJk2acO7cuXxnlHF1dSUgIIC1a9dmu5jZvXs3t27dYv78+QU6j3f1XRNCCCHEx+mj\nLcpNTU1p2bIlAwcOZNiwYTRo0IB///2XvXv3cu3aNWbPng3At99+S0BAAJUqVaJatWosX76clJQU\nXFxcchw3p0I5a5menh6nTp2idevWJCcnM3/+fNTU1JTaMd7FfqoIDw9n8ODBNG3aNNdtevXqxa5d\nu/Dw8GD48OHUrFmTiIgIZsyYgY2NDc7OzpQoUQI3Nzd+/vlntLS00NXVZc6cOVSqVIlmzZrx8OFD\npTHr169PcHAwtWvX5ubNm2zdurVAc52/nt9t27ZhaWmJubk5c+fOpVq1anz11Vf5His1NZU5c+Zg\naGiIqakpp06dIiUlRVEQP3r0iNKlS+c4h7i+vj6TJk1izJgxJCUl0a5dOzQ0NDh06BALFy5k2LBh\n1K5d8Nd0F/S7JoQQQohPx0dblMPLKfGCgoJYtmwZ9+/fp2TJktjY2BAcHEylSpUA6NOnD0lJSYwf\nP56kpCSsrKxYu3Yt+vr6OY75euvKq7OojB07lp9++omOHTtSuXJl+vfvT8WKFZXu9r7tfqrKaTq/\n15UsWZJff/2VxYsXM2XKFOLi4qhUqRIdOnTAx8dH0ZYxcuRIMjMz8fX1JTU1laZNm7JmzRpKliyZ\nLSfjxo1j7NixuLi4YGZmxg8//EBAQIDSeefl9fV9+vRh/fr1TJw4kYYNGyqNldex2rdvz/3795k5\ncyZxcXFUr16d+fPnU716dQDatGlD7969GTRoUI5xODs7U7lyZZYtW0ZwcDDp6emYmpqyYMECpX72\n/F4e9Pq5FOS7JoQQQohPh1pmfr0VQnyEDh48SFRUlKJlqLia6vILlctXLeowhPgoPHhyF0e/htSq\nVfC/dBUl6dEtHJLXwiF5zV9uPeUf9ZSIQuRm/fr1ODo6FnUYQgghhBDAR96+IkRuAgMDs80CUxzF\nJ8cWdQhCfDTk3ychRHFW/KsSIQrBh1CQAwwM7EZCQlJRh/HR0dfXlrwWgg8hr8bG1Yo6BCGEyNGH\nUZkI8YmqU6eO9OYVAul5LBySVyGEeHPSUy6EEEIIIUQRk6JcCCGEEEKIIibtK0IUY9euXSv2Pbof\nosePi3/v84eoqPJqbFxN8d4EIYT4UElRLkQxtsRnEwZlKxV1GEIUW/HJsbhNd/7g5h4XQojXSVEu\nRDFmULaSvDxICCGE+ARIT7kQQgghhBBFTIryAnjy5AkzZ87E0dERS0tL2rRpQ1BQEGlpaSrtf+bM\nGUxNTQs5yoLz8PBgwYIF2ZZHR0djYmLC/fv3AbC3t2fz5s0qjWliYsKpU6feOrawsDBatGjxxvuP\nGTMGExOTbP/UrVuXx48fv/X4eeXkwYMHSvkTQgghhMiNtK+o6N9//6V79+4YGhoydepUjI2N+fvv\nv5k6dSrXr19n9uzZRR3iW1FTU8t3my1btlCmTJn3EM27o6amxtdff8348eOzrdPT03vr8T/EnAgh\nhBCi+JGiXEVz5syhZMmSrFy5UvGUf5UqVdDT08PDwwMPDw/Mzc2LOMrC9S6K2PctMzOTkiVLUqFC\nhUIZ/0PMiRBCCCGKH2lfUUFKSgq7d++mV69e2abdatSoEWvWrOHLL78Esrdt5NQesXr1aho3boyt\nrW22tpGgoCAcHR0xMzPDzs6OhQsXFtJZFZy9vT2hoaEAJCUlMXbsWL766ivMzMxo06YN+/bty3G/\nlJQUfv75Z2xtbWncuDFDhgzh0aNHivV//fUX7u7uWFpaYmVlhbe3N7GxsUpjLFmyBFtbWxo1asSM\nGTMKFLcqfwXIEhUVRd++fWnYsCHNmjUjICCAzMxMABYtWkT//v3x8PDAxsaG48ePK+UkNTWVqVOn\nYmNjQ/PmzTl48GC2sb29vWnQoAHm5ua4u7tz48aNAp2LEEIIIT5OUpSr4M6dO/z333/Ur18/x/U2\nNjZoaWmpNFZ6ejr79u1jzZo1TJ06lfXr17NlyxYAwsPDWbVqFVOnTmXfvn0MGjSIJUuWcPHixTeO\nPT09XaXtsgrP/GQVuNOnT+fWrVusXLmS3bt306hRI8aNG0dqamq2febNm8fFixcJDAwkODiYjIwM\nfHx8gJfFvY+PD02bNmXXrl2sWLGCmJgYli1bptg/NjaWGzduEBISwqRJk/j11185cuSISvEW5NwS\nEhJwd3encuXKhIaGMnHiRIKDg1m5cqVimyNHjtCmTRvWrVtHgwYNlHKyaNEiDh48yJIlSwgICGD9\n+vVKMQwcOJCqVasSHh7Ohg0byMjIYNasWSqfhxBCCCE+XtK+ooInT54AUK5cuXcy3syZMzE2NubL\nL7/E09OT9evX07lzZypXrsyMGTNo0qQJAG5ubgQEBBAVFfXGrTFhYWFoaWnRvn37PLewabCqAAAg\nAElEQVRbvnw5v/76q8rjWltb06dPH2rXfjk3sJeXF6GhoTx8+JAqVaootnv27BnBwcFs2rSJunXr\nAjBr1iyaNGnC2bNnqV69OgMGDMDLywt42RLk5OTE+fPnFWNoaGgwZcoUypYtS/Xq1fnll1+IjIyk\nZcuWKsX622+/ceDAAaVls2bNonXr1krLdu7cSZkyZZg8eTLq6urUrFmTuLg4/P396du3L/CyXaVn\nz57ZjpGZmUloaCijRo3C2toaAD8/P8V5PX/+nO7du+Pm5qboQe/YsSNBQUEqnYMQInf6+toYGr6b\n/z4XVx/7+RUVyWvhkLy+GSnKVZDVN5yYmIixsfFbjaWjo6M0Rr169VixYgUAjRs35sKFC8ydO5eb\nN28SERFBfHy8yne7c/LNN9/Qu3dvHj16pCgQc9K9e3f69OmjtOzevXvZlmXp2LEj+/fvZ+PGjdy6\ndYvLly8DkJGRobRdTEwMqampuLu7Ky1PSUkhOjoaa2trOnTowKpVq7h69So3btwgMjISCwsLxba6\nurqULVtW8bu2tjYvXrxQ5fQBaNmyJaNHj1ZallOPeVRUFHXr1kVdXV2xzNLSksePH/P48WMAjIyM\ncjxG1jZZFx6A0kw7pUuXpnv37mzbto3Lly9z69Ytrly5Ij3pQrwDCQlJxMU9LeowCo2hYbmP+vyK\niuS1cEhe85fbRYsU5Sr4/PPP0dHR4cKFC5iZmWVb7+vrS/v27XF0dMy27vWC+tWCD14WsZqamgCE\nhoYybdo0unXrhpOTE6NHj8bT0zPHmCIiIpgyZYpK8T969IiZM2eSmZnJt99+m+M25cuXz3bB8XqB\n/aqRI0fy119/0bFjR3r06IGhoSHdu3fPtl3W+QcHByv9pSEzMxN9fX1iY2Pp3Lkzpqam2NnZ0a1b\nN44cOcK5c+cU276es6z9VVW6dGmVLqZKlSqVbdysHGQtz69N6dX9NTT+/1+v5ORkunTpgp6eHo6O\njri6unLz5k25Uy6EEEIIQIpylairq+Pi4kJwcDBdu3ZVetjz1KlT7Nu3T3EXWlNTk+TkZMX6mJgY\npbEeP35MbGwslSq9fHX6hQsXqFWrFgAhISEMGDCAfv36AS/bZuLj43MsQOvWravUs5yblJQUPD09\n8fX1pV27dgU885wlJSWxa9cuNmzYoLijffToUSB7sWxsbIy6ujoJCQnUq1dPsf/IkSMZMmQIf/75\nJ9ra2gQGBir2WbNmzTuJEwr2kGfNmjXZs2cPaWlpioL6r7/+QldXN9872vr6+hgYGHDx4kXF3fIr\nV64o1v/xxx88ePCAnTt3Ki4yjh8/XqCLCyGEEEJ8vORBTxV9//33vHjxgm+//ZYzZ85w584dtm7d\nyrBhw+jcuTNWVlYA1K9fn+DgYKKjozl8+DBbt25VKgzV1NQYPXo0V69eZffu3axbt05x91pPT49T\np04p2kGGDh2KmpoaKSkpbxx3eHg4gwcPzrcgL0hxqKWlRenSpdm7dy93797l999/Z/r06QDZYtXW\n1qZr165MmTKF06dPExUVxejRo7l27Ro1atRAT0+P2NhYTp48SUxMDEFBQRw5ciTf9pSseFNSUoiL\ni8v1rn5BzsvV1ZX09HTGjx9PVFQUBw8eJCAggB49eqhU3Pfq1YtFixZx4sQJLl++zLRp0xTrdHV1\nef78uSJnoaGhhIaGvtVnK4QQQoiPhxTlKtLX1yckJISaNWsyevRoXF1dWbFiBT4+PkyePFmx3bhx\n40hMTMTFxYWgoCB++OEHpXEqVqxIkyZN6NWrF9OmTeOHH35QPHA4duxYnj17RseOHRk+fDguLi64\nuLgQERHxxnF37dqVpk2b5rtdbkVnTss1NTWZPXs2Bw4cwNnZmcWLFzN9+nSqVKmidHc4y5gxY2ja\ntClDhw6la9euvHjxQjHfe9u2benQoQNDhgyhc+fO3L17l/nz53Pr1i1FwZpTDFnLjh07RrNmzXjw\n4EGu8edVUL+6vkyZMixfvpyYmBg6der0f+zde1zO5//A8VfSoqKTaMgk/GIdaY6JlfO5OSVCi9XM\n2IwYckiNObdiag77jhYih6WZ05yaHZzmNIvEwiSrb1ZzqO7790ePPl+3osItq/fz8ejx6P4crs/7\nurrl/bl6f66bkJAQRo4cqfwMS2rL39+fAQMG8NFHH+Hv78+QIUOU452dnXnvvfcICQmhT58+JCYm\nEhUVRVZW1mNjF0IIIUTloaOWv5+Lf7lx48YxZ84crX1AUHkK6f0FljXrl3cYQry0bt65RucZLbGx\naVLeoWiNPDinHTKu2iHjWjJ50FNUSBcuXCAvL69CJuQAt3PSSj5IiEpM/o0IISoKmSkX/2oqlarE\nspJ/s6SkJDIysss7jArHzMxIxlULymtcraxeK/JpyxWJzDxqh4yrdsi4lkxmykWFVKVKxX4somnT\npvLLTQvkPw3tkHEVQoinV7EzGiGEEEIIIf4FJCkXQgghhBCinElSLoQQQgghRDmTmnIhXmLyoKd2\nZGbKg57a8DzHtaI/vCmEEI+SpFyIl9gK/03UMqxT3mEI8ULdzknDa17PCr32uBBCPKrCJ+U+Pj60\nbNmSDz74oLxDKZPU1FQWL17M0aNHuXfvHg0bNsTLy4uhQ4c+l/YfPHhAXFwcXl5eQMnjlJKSwvjx\n47l69SozZsxg5syZrF27lrZt2z7xOteuXaNz587s2bMHKyurIvu3b99OWFgY+/fvLzHmuLg4pk2b\nhouLC+vXry+yf/DgwZw+fVq5lru7O2PHjmXgwIFPbLekvtva2paqr9pQy7COfHiQEEIIUQlU+KQc\nHv8R8i+re/fuMWLECDp06MC6deswMDDgl19+ITg4mNzcXEaMGPHM19i5cyeff/65kpTDk8fp66+/\nRldXl4SEBExMTPDw8KBmzZrPHEdZVa1alVOnTpGVlYWxsbGy/datW5w9e1ajD1u2bMHAwKBU7T6p\n74mJieXSVyGEEEJUHpUiKf+3+eGHH7hz5w7BwcHKtvr163Pt2jU2bNjwXJLysn5mVHZ2No0bN6Z+\n/YJZWyMjo2eO4WmYm5tjaGjIgQMH6Nevn7J93759ODg4cOrUKWWbqanpc7umEEIIIYQ2VarVV+Li\n4hg8eDDjx4/HxcWFzZs3k52dzfTp02nXrh12dnZ0796d3bt3K+ekpaUxZswYnJ2d8fT0JDo6Gnd3\nd63GqaOjw927dzlx4oTG9lGjRhEVFaW8vnnzJhMmTKB169a0adOGuXPn8uDBA6WvHTt21Djfx8eH\nZcuW8fPPPzNt2jTS0tJo1qwZ169fBwpmm8eMGYODgwPdunXjyJEjynlbt24lPj6eZs2aAQUlHUeP\nHgUKSmFCQ0Np27YtrVu35oMPPuCvv/4qtm+3bt3inXfeUcbz6tWrZR4fd3d3vv/+e41t+/bto0uX\nLkWOi42NBSA/P5+wsDDc3Nxo2bIlY8eOJT09XSOu4vr+aF/v3bvH9OnTcXFxwc3NjdjYWJo3b86N\nGzcASE5OZvTo0bRo0QIHBwe8vb25dOkSAD/99BNubm5s2rQJNzc3nJ2dmTRpEvfv3y/zGAghhBCi\nYqlUSTnA6dOnsba2ZvPmzbz55pvMmzePlJQU1qxZQ0JCAm+88QZBQUHk5uYCMG7cOPLy8oiNjcXP\nz4+wsLBnLodRqVRP3N++fXtsbGwYNmwYXl5efPbZZxw7dgwjIyNlpvrBgweMHDmSe/fusW7dOsLC\nwjh06BDz589/Yts6Ojo4Ozszbdo0LCwsOHLkCK+++ipQUN/dvXt3du7cib29PYGBgQBERETQo0eP\nIslqoSVLlnD69GkiIyOJjo5GpVLh7+9f7PXHjx9Pbm4usbGxBAQE8NVXX5V5PD08PDhy5Ah5eXlA\nwSz+qVOncHNzK7a/AOHh4WzevJmQkBBiY2O5f/8+U6ZMUY57XN8fFRISwsmTJ1m9ejVLly5l1apV\nyl8d1Go1Y8eOpX79+mzfvp0NGzagUqlYsGCBcn5GRgbffvstq1evJjw8nL179xIXF1em/gshhBCi\n4ql0STlAQEAADRs2xNzcHBcXF+bMmYOtrS0NGjTA19eXrKwsbt26xYULFzhz5gxz586lcePG9O7d\nmwEDBpS59ONhubm5fPjhh2RnP37ZsFdeeYWYmBjGjBnD7du3WbFiBcOHD6dHjx6cO3cOgMOHD5OW\nlsbChQtp2rQprVu3ZubMmWzcuPGJbQPo6elhZGRElSpVMDc3Vz6qvkuXLgwYMAArKytGjx5NRkYG\nt27dwtjYGH19fV555ZUipRx3794lOjqa2bNn4+DgQOPGjVmwYAGXLl3i+PHjGsdevHiRU6dOKePZ\nrVs3hg8fXubxdHJyQl9fn59//hmAQ4cO4eLi8tj6cbVazcaNG5kwYQJubm40atSI2bNnY29vr1z7\ncX1/WE5ODtu3b2fGjBk4OjrSsmVLgoKClDbu3bvHkCFDCAwMxMrKiubNm9O/f39lphwgLy+PadOm\n0aRJE1xdXenQoQNnzpwpU/+FEEIIUfFUuppyExMTqlevrrzu378/e/bsYePGjaSkpCgPC6pUKi5f\nvqwxOw3g7OzMd99999TX19PTY8CAAYwYMYKwsLBiVySBgprtiRMnMnHiRFJSUjhw4ABr164lICCA\nffv2kZycTIMGDTQeQHR2diY/P58rV648VWwNGjTQuD5QYmlFamoqubm5eHt7a2x/8OABV65coU6d\n/y3nd+nSpSLjaWdnx44dO8oUp46ODm+++Sb79++nXbt27N27ly5dujw2uc/MzCQzMxM7Oztlm5WV\nFR9++KHyujR9v3z5Mrm5udjb2yvbnJyclO+rV6/OkCFD2LZtG2fPniUlJYXz588XqW1/9FqFM/5C\nCCGEqLwqXVKur6+v8Xry5MmcPHmS/v37M3ToUCwsLBgyZAhQkGQ9mug96cMsQkJCOH/+fKniuHjx\nIj4+PsTHxxd5aHLTpk3UqFGDHj16AGBtbY21tTWurq706dOHpKQkqlWrVqTN/Px8oKA8priSkML9\nj1M4Y14WhW1GR0dTo0YNZbtarcbMzIz//ve/Gsc/Op5Vqz7dW9Dd3Z2QkBCmTJlCYmIiQUFB5OTk\nFHusnp5eie2Vpu+FsT7ch4e/z8nJYeDAgZiamtK5c2f69OnD5cuXNZ4DeDQetVr9TH95EaKiMjMz\nwsKiRskHVhIyFtoh46odMq5Pp9Il5Q/Lzs5m586dbNiwAUdHRwAOHjwIFCRLNjY25OTkkJKSgrW1\nNcATk+4ZM2aU6roJCQls2bKFhQsXFruKSVJSEseOHaNbt24ayaKhoSFQsBpIo0aN+OOPPzSWBjx1\n6hS6urq89tpr/PHHHxpJqlqtJjU1ldatWwNlXybyccdbWVmhq6tLRkYGzZs3BwrGdfLkyXz44Yca\nJSVNmzYt03g+Sbt27cjMzCQ6OpqmTZtiamr62KS8Ro0amJmZce7cOWxtbQG4cuUKw4YNIyEhodTX\nbNCgAXp6epw9e5Z27doBcPbsWWX/zz//zM2bN4mPj0dXVxcoKDN6UtL9b1uuU4gXJSMjm/T0v8s7\njJeChUUNGQstkHHVDhnXkj3upqVS1pQXeuWVV6hevTrfffcd165d48iRI8ybNw8oKL9o0KABXbt2\nZdq0aVy4cIH9+/fz5ZdfPlMidf/+fRITE4mMjMTMzKzYY0aOHMmNGzcYO3Ysx44d49q1axw+fJiJ\nEyfSs2dPXn31Vdq3b0/Dhg0JDAzk999/56effiIkJIRevXphbGyMnZ0d2dnZfPXVV6SmprJgwQLu\n3LmjXMPAwIC///6bK1euKOUTT0oeH7fPyMiIQYMGMXfuXH788UeSk5OZMmUKSUlJNGzYUONYGxsb\n2rVrpzGejz7o+ddff/HPP/+UOI7VqlWjffv2hIeHF1l1pTgjRowgPDycH374geTkZIKDg3n99deV\nG5rSzFYbGhry1ltvMW/ePH799VdOnTpFaGgoOjo66OjoYGJiwr1795T3U2xsLLGxscqKOMWRWXIh\nhBBCQCVMyh9OAF955RUWLlzI3r176dmzJ8uXL2fevHnUq1dPmcENCQmhTp06eHl5sXTpUgYOHPhM\niZS+vj6hoaFPLNuwsrIiJiYGfX19JkyYQPfu3Zk1axZt2rRRVlfR0dFh+fLl6OjoMGTIED788EM8\nPDwICQkBoGHDhkyZMoXIyEj69+9PXl4ePXv2VK7Rtm1bGjVqRL9+/bhw4UKRsXn0dWHiWZypU6fS\nvn17PvzwQwYNGsT9+/dZs2aNUurz8HlLly7FwsKCoUOHsmjRInx9fTXa6t69O2vWrCn2Oo/G4OHh\nwT///EPnzp2LjflhY8aMoWfPnnz00UcMGTIEY2NjjZVqntT3h02ZMgVbW1tGjRrFhAkT6Nu3L2q1\nGj09PZydnXnvvfcICQmhT58+JCYmEhUVRVZWFjdv3nzsdWS2XAghhBA6apmqK5O4uDgiIiJK9bHw\nouwKH2J95513yjuUYu3du5d27dopZTmnT5/G29ubX3/9VSlZeZ5Cen+BZc36JR8oRAVy8841Os9o\niY1Nk/IO5aUg5QDaIeOqHTKuJZPyFfGv8PXXX2vMfL9sli9fTmhoKFevXuX8+fMsXLiQzp07ayUh\nF0IIIUTlIUl5GUm5gXZFRkbSqFGj8g7jsRYtWsT169fx9PTE19eXBg0aKCVDQgghhBBPq1KvvvI0\nPD098fT0LO8wKqynXSLxRbGxseHLL798Yde7nZP2wq4lxMtC3vdCiMro5c6AhKjkxkYOJiPjyZ/Q\nKsrOzMxIxlULnue4Wlm99lzaEUKIfwtJyoV4iTVt2lQemNECeRBJO2RchRDi6UlNuRBCCCGEEOVM\nknIhhBBCCCHKmZSvCPESS0pKktpnLcjMlJpybXhe42pl9Zry4WNCCFFZSFIuxEtshf8mahnWKe8w\nhHhhbuek4TWvp3xwkBCi0pGkXIiXWC3DOvKJnkIIIUQlIDXlQgghhBBClDNJyh9ha2ur8dWmTRum\nT59OTk5OeYemVdeuXcPW1pbU1NQi+5YuXYqPj4/WY8jJyWHr1q1av05cXFyRn/PDXx9//LHWYxBC\nCCGEeJiUrxQjLCwMFxcX8vPz+fPPP5k5cybz589n7ty55R1ahbZ27Vp++OEHrX9iaq9evejYsSMA\neXl5dOzYkYiICJydnQHQ19fX6vWFEEIIIR4lM+XFqFmzJubm5tSuXRtHR0f8/f1JSEgo77AqPLVa\n/UKuo6+vj7m5ufIFYGxsrLw2MjJ6IXEIIYQQQhSSpLwUqlWrpvHax8eHZcuWKa8fLf2wtbUlLCyM\ntm3b4uvry9atWxk6dCgrV66kVatWuLq6Eh8fT0JCAp06daJVq1YsXbpUae/WrVuMHz+eVq1aYW9v\nj6enJ8eOHXsxnS2F5ORk/Pz8aNmyJR06dCAiIkJJqMPDw/H29tY43t3dndjYWAB+//13hg0bhrOz\nM66urnz66afk5+cTFxfH8uXLOXHiBM2aNQPg/v37LFq0iE6dOuHs7ExAQAA3btwA/jfmu3fvpkuX\nLjg4OPDOO++QmZn5XPq4YsUK3NzccHFxYfTo0Vy9ehWAo0ePYmtry9GjRwG4c+cOrq6uLF++XBmb\n0aNH06JFCxwcHPD29ubSpUtKu2FhYbi5ueHg4ICXlxenTp16LvEKIYQQ4t9NkvISZGRksG7dOvr1\n66exXUdH54nn7d+/n5iYGKZPn45arebs2bNcvXqVLVu20L17d4KCgoiJieGLL75g4sSJREZGcvHi\nRQACAwNRqVRs2LCBbdu2YWlpyaxZs56pH/n5+aU6rqTZ6oyMDLy9vbG0tCQ2NpbZs2cTHR3NmjVr\nnnhe4XhNnjwZGxsbvvnmG5YtW8b27dvZsmULvXr1wtfXFwcHB44cOQLArFmz2LNnDwsWLGDjxo3k\n5+fz7rvvolKplHajoqJYvHgx69ev59y5c6xevbpU/XySdevWsX37dhYuXEhsbCyvvfYaI0eO5N69\ne7Rt2xZPT09CQ0PJz89nwYIFWFhYEBAQgFqtZuzYsdSvX5/t27ezYcMGVCoVCxYsAGDPnj18/fXX\nLF68mG+//ZbmzZszfvz4Z45XCCGEEP9+UlNejICAAKpUKbhfuXv3LiYmJsyYMaNMbQwePJiGDRsC\ncPr0aVQqFUFBQRgYGDBo0CDWr1/P+++/T5MmTWjSpAmLFi3i8uXLNGnSBA8PD7p27UqdOgXrU3t7\nezNmzJhn6tPUqVOZNGmS0ubjPHrzAZCbm0uLFi0AiI+Px8DAgODgYHR1dWnUqBHp6emEhYXh5+dX\nYhw3btzgzTffpG7dutSvX58vvvgCU1NT9PX1MTAwoGrVqpibm5OVlcWOHTuIjIykVatWAMqs+eHD\nh7GxsQFg3LhxODg4ANCnTx/OnDlTpnEpzqpVqwgKCqJ169YAzJgxg4MHD/Ldd9/Rr18/pk6dSs+e\nPQkMDGT37t3Exsaiq6vL3bt3GTJkCF5eXhgYGADQv39/oqKiALh+/TpVq1bl1VdfpV69enz00Ud0\n69aN/Px8dHV1nzluISoKMzMjLCxqlHcYLxUZD+2QcdUOGdenI0l5MYKDg5UktDA59PLyIjY2Vkm0\nS1K/vuba0qampkqiVlgOU7duXWV/tWrVePDgAQBeXl7s3LmTEydOkJKSwrlz59DR0UGlUik3C2Xl\n5+fHyJEjWbJkCc2bN3/scStXrtSIS61W8+WXXyolGMnJyTRr1kwjiXRyciIzM7NUpSPvvvsuixcv\nZuPGjbi5udGrVy9ef/31IsdduXIFlUqFo6Ojss3Y2Bhra2suX76sJOVWVlbKfkNDQ/Ly8kqM4Uly\ncnJIS0tj0qRJGn8Nyc3NVUpYjI2NmTJlCoGBgYwePRpbW1sAqlevzpAhQ9i2bRtnz54lJSWF8+fP\nY2pqCkDv3r2JiYmhS5cu2Nvb4+7uzsCBAyUhF+IRGRnZpKf/Xd5hvDQsLGrIeGiBjKt2yLiW7HE3\nLZKUF6N27dpKsmdlZYWdnR2HDh1i06ZNBAYGFildKa405NGPiC4u8SouwVar1fj6+nLnzh169eqF\nh4cHubm5jBs3rthYIyMjOXjwYKn6lZ6ezogRI9i6datGMvuwunXrFtlXo0YNpaylWrVqRUpcCstJ\n1Gp1sWU9D4+Pn58fPXv2ZN++fRw4cICxY8fy7rvvFunf41ZAyc/P12jv0XF+1odFC9teunQpjRs3\n1mi3Ro3//SP67bff0NXV5ZdfflH6nZOTw8CBAzE1NaVz58706dOHy5cvKzPltWrVIiEhgaNHj3Lg\nwAE2btxIdHQ0W7ZsoXbt2s8UtxBCCCH+3SQpLyW1Wq0kn3p6emRnZyv7ilvb+2ldvHiRY8eOceTI\nEWrVqgVAdHS0EsOj/P398ff3L7Hd48eP8+mnn7JkyZIis/hlYWNjw65du8jLy6Nq1YK3z8mTJzEx\nMcHU1BQ9PT2NNd3/+ecf/vrrLwD+/vtvli5dyjvvvMPw4cMZPnw4n3/+Od988w3jxo3TSOgbNGhA\n1apVOXXqFG5ubgBkZmZy9epVrK2tnzr+khSuvHPr1i3efPNNoOCm46OPPmLw4MG0bduW8+fPs27d\nOpYvX87UqVNZt24dI0aM4Oeff+bmzZvEx8crN2GHDx9W2v7222/566+/GD58OK6urgQGBtKmTRuO\nHz9Ojx49tNYnIYQQQrz85EHPYmRlZZGenk56ejqpqaksW7aMP/74g+7duwNgb2/P7t27OXPmDGfO\nnCE8PLzEBz9Ly9jYmCpVqrBz506uX7/Orl27iIyMBApWI3la27dvZ82aNc+UkENBCUZ+fj4zZ84k\nOTmZffv2ERERwdChQ9HR0cHe3p6LFy/y7bffcuXKFWbOnKkkqDVq1CAxMZG5c+eSnJzM77//zsGD\nB7GzswPAwMCA9PR0rl27hoGBAV5eXoSGhvLTTz/x+++/ExgYSJ06dejQoUOpYk1PT3+qMRs1ahRh\nYWHs3buXq1evMnv2bH744QcaN25Mfn4+M2bMoG/fvnTq1ImPPvqIZcuW8eeff2JiYsK9e/f47rvv\nuHbtGrGxscTGxiox5ObmsmjRImX/9u3befDggbLajBBCCCEqL5kpL8YHH3ygfK+vr0+zZs0IDw/H\nyckJAF9fX5KSkhg+fDiWlpZMnTqV999//7Ht6ejoFEnaH5fE16lTh9mzZ7N8+XKWLFnCG2+8QVRU\nFEOGDOG3336jZcuWT9Wn4ODgEo95XEwPx29gYMCqVasIDQ3F09MTc3NzRo4cqczWt2vXDl9fX2bN\nmoWuri4jR44kIyNDaWvFihWEhIQwePBgqlSpgoeHB9OnTwegW7dubNy4kT59+rBv3z4mT56MWq1m\n/Pjx5Obm0r59e7766iulZKW4MS3cdufOHTp06MD8+fPp379/mcbKz8+Pu3fvEhwczJ07d2jevDmr\nVq3CwsKCNWvWcP36dWWVl0GDBrFlyxbmzJnDypUree+99wgJCeHu3bt07NiRqKgovL29uXnzJn37\n9uXGjRt8+umnpKen07BhQ5YuXVrq5xSEEEIIUXHpqF/UJ7YI8YJ99dVX1KpVi549e5Z3KE8tpPcX\nWNZ8tr9uCPFvcvPONTrPaImNTZPyDuWlIQ/OaYeMq3bIuJZMHvQUlcqDBw9ISEjg888/L+9Qnsnt\nnLTyDkGIF0re80KIykpmykWFVRHW/05KSiIjI7vkA0WZmJkZybhqwfMaVyur14qsrFSZycyjdsi4\naoeMa8lkplxUOv/2hBygadOm8stNC+Q/De2QcRVCiKcnq68IIYQQQghRziQpF0IIIYQQopxJUi6E\nEEIIIUQ5k5pyIV5i8qCndmRmyoOe2vA8xlUe8hRCVFaSlAvxElvhv4lahnXKOwwhXojbOWl4zesp\na5QLISolScqFeInVMqwjHx4khBBCVAJSUy6EEEIIIUQ5q5BJua2trcZXmzZtmMQXksYAACAASURB\nVD59Ojk5OeUdmlb99NNPGv1+/fXXcXd3Jyoqqkzt7NmzB1dXV5ydnbl48SLLli3D2dkZd3f3Es/1\n8fFh2bJlT9sFDe7u7gwZMqTI9sJ+qlSq53KdR6WlpeHs7MyCBQuK7Ltx4wZOTk58+eWXZWrz2rVr\n2Nrakpqa+pyiFEIIIURFUiGTcoCwsDASExM5dOgQkZGRnD17lvnz55d3WC/EoUOHSExMZN++fUyf\nPp3IyEi++eabUp8fERFBx44d2blzJ+bm5qxcuZJp06YRExNTqvN1dHSeNvQifv31VzZt2vTc2iuN\nOnXqMH78eL766itSUlI09s2bN49GjRoxcuTIFxqTEEIIISq2CpuU16xZE3Nzc2rXro2joyP+/v4k\nJCSUd1gvRK1atTA3N8fS0hIPDw969+5dpr5nZ2fj5ORE3bp1+eeffwBo1aoVdeq8+AcO69aty+LF\ni8nMzHyh1x0xYgSNGjUiNDRU2ZaYmMj+/fsJCQl5rjceQgghhBAVNil/VLVq1TReP1pm8Wh5ga2t\nLWFhYbRt2xZfX1+2bt3K0KFDWblyJa1atcLV1ZX4+HgSEhLo1KkTrVq1YunSpUp7t27dYvz48bRq\n1Qp7e3s8PT05duzYi+nsI6pXr66RRNra2nL06FHldVxcHB07dlT2Xb9+naCgIHx8fOjcuTMA3bp1\nIyIiAoCoqCg6d+6MnZ0drq6ufPbZZ4+9dlxcHD179sTR0ZG33nqLn3/+uUyx+/r6YmhoyMKFCx97\nzN9//82UKVNwcXHB1dWVmTNnkpOTg0qlonXr1uzdu1c5tl+/fowdO1Z5vXbtWt5+++0iberq6jJr\n1iwSExP5/vvvyc/P55NPPmHEiBE0b94cgO+//x5PT08cHR3p2bMnu3btUs738fEhODiYLl264Obm\nxn//+1+N9jdu3EiLFi04ffp0mcZDCCGEEBVTpUjKMzIyWLduHf369dPYXtJs5/79+4mJiWH69Omo\n1WrOnj3L1atX2bJlC927dycoKIiYmBi++OILJk6cSGRkJBcvXgQgMDAQlUrFhg0b2LZtG5aWlsya\nNeuZ+pGfn1+q49RqtfL9pUuXSEhIoG/fvqU698iRI1haWjJ16lTCwsKIjY0FYNOmTbz99tts376d\ntWvXEhISwu7duxk3bhwrVqzgzJkzRdqKi4tj7ty5+Pv7s2PHDlxdXXnnnXf4888/SxULFNxQTJ8+\nna1bt3LixIlij5k2bRpZWVl8/fXXREZGkpKSwscff0yVKlVo166dciOQlZXFxYsXOXXqlHJuYmIi\nbm5uxbbbsmVL+vfvz6JFi4iJieHu3btMmDABgKNHj/L+++/j6enJjh07GDx4MJMmTdJIsrdu3cqn\nn37K559/jomJibJ93759zJ8/n+XLl+Pg4FDqsRBCCCFExVVhl0QMCAigSpWCe467d+9iYmLCjBkz\nytTG4MGDadiwIQCnT59GpVIRFBSEgYEBgwYNYv369bz//vs0adKEJk2asGjRIi5fvkyTJk3w8PCg\na9euSsmHt7c3Y8aMeaY+TZ06lUmTJpVYRuLi4gIUJPEPHjzAyckJV1fXUl2jVq1aVKlSBSMjI8zM\nzJTyFVNTUwwMDLC0tGT+/Pm0adMGAC8vLyIiIrh06RL29vYaba1bt47hw4crN0MTJ07k559/Zt26\ndQQGBpa63x4eHnTs2JE5c+YQFxense+PP/5g7969/PjjjxgbGwMwf/58PDw8uHnzJq6urqxbtw6A\nY8eO4eLiwpkzZ7hy5Qp169bl+PHjfPzxx4+99uTJk+nRo4eSRBf+xSU6OpquXbsyYsQIAEaNGsXp\n06dZvXo1YWFhALi5udGiRQug4C8xAMePH2fOnDksWLCAtm3blnoMhKgszMyMsLCoUd5hvHRkTLRD\nxlU7ZFyfToVNyoODg5WEKCsrix07duDl5UVsbKySaJekfn3N9aELE1P4XzlM3bp1lf3VqlXjwYMH\nQEGyunPnTk6cOEFKSgrnzp1DR0cHlUql3CyUlZ+fHyNHjmTJkiVKCUVxtm7diq6uLiqVilu3bvH5\n558zbNgwtmzZQtWqz/Yjb926Nb/++iuLFy/m8uXL/Pbbb9y+fbvYWfzLly/z3nvvaWxzcnLi8uXL\nZb5uUFAQvXr1Yt26dTRr1kzZnpycjFqtplOnThrH6+jocOXKFdq3b09QUBBZWVn88ssvtG7dGrVa\nzbFjx6hfvz6mpqbY2Ng89rpmZmYMGjSIo0ePKiU+hX0bPHhwkb4V/mUBoF69ekXamzlzJiqVSuN9\nI4T4n4yMbNLT/y7vMF4qFhY1ZEy0QMZVO2RcS/a4m5YKm5TXrl0bKysrAKysrLCzs+PQoUNs2rSJ\nwMDAIqUrxSWVj37Us66ubpFjikuw1Wo1vr6+3Llzh169euHh4UFubi7jxo0rNtbIyEgOHjxYqn6l\np6czYsQItm7dqvTvUQ0aNFDieu2112jYsCEdOnQgMTFRI7EsVNqyGIDY2Fg++eQTBg8eTNeuXZky\nZYoyW/yoR+v4AfLy8sp0vUL16tUjICCA8PBwZs+erRG7gYEB27dv1zherVZjYWFB9erVsbGx4Zdf\nfuGXX34hMDCQ3NxcTpw4QWpqKh06dCjx2vr6+ujr65fYN5VKpbFM46PnAIwfP56kpCRmz57Npk2b\n5IFRIYQQQgAVOCkvjlqtVpImPT09srOzlX3Pc/3oixcvcuzYMY4cOUKtWrWAgnKHwhge5e/vj7+/\nf4ntHj9+nE8//ZQlS5YUmcV/ksI+FybDenp6Gmu2l6XvMTExvPvuu7zzzjsA3Llzh9u3bxfbL2tr\na06dOqU8LAoFSxwW/gWjrPz8/Ni+fTtLly5Vkllra2v++ecf8vLysLa2VvrzySefEBISQvXq1XF1\ndWXfvn1cunQJJycncnNzmTt3LoaGhhoPfZaFtbU1v/76q8a2kydPKjE8Trdu3ejbty/du3dn06ZN\nxa7DLoQQQojKp8I+6JmVlUV6ejrp6emkpqaybNky/vjjD7p37w6Avb09u3fv5syZM5w5c4bw8PDn\nNmtpbGxMlSpV2LlzJ9evX2fXrl1ERkYCcP/+/adud/v27axZs6bEhLyw3+np6fz+++/Mnj0bMzMz\nWrduDRT0PTo6mqtXr/L999+zdevWUsdgamrK0aNHSUlJ4ezZs3z44Yfo6OgoZTsPe/vtt4mOjmbb\ntm2kpKSwePFikpKSlLKP7OxsMjIySn1tPT09Zs6cyY0bN5RtNjY2dOjQgcDAQE6fPs2FCxeYPHky\nmZmZyg2Rq6sr33zzDc2aNUNfXx9nZ2euXbvGpUuXaNeuXamv/zBfX1/27NnDf/7zH65cucKXX37J\n3r17GTZsmHJMcTcqUPBXnICAAJYsWfLCl3oUQgghxMupwiblH3zwAR06dKBDhw707t2bH3/8kfDw\ncJycnICCpOr1119n+PDhTJo0iYCAgGLLUwrp6OgUSdofl8TXqVOH2bNns2bNGnr27MnmzZuJiopC\nX1+f33777an7FBwcjJGR0RNjBOjYsaPS9+HDh1OlShXWrl2LoaEhgFJj3bt3b6KiopgwYcITb0ge\n3jd9+nTu3r1L//79+eijj+jduze9e/cutl9du3Zl0qRJfPbZZ/Tr149ffvmF1atXKzXcYWFhDBw4\nsExj0LZtW3r16qWxbcGCBbz22mu8/fbb+Pj4YGlpyYoVK5T9Li4uVK1aVXkA1tDQkGbNmuHk5KQ8\nI/Akxf3s7ezsWLRoERs3bqRPnz5s3bpVWULz4fMebafQqFGjMDY2ZtGiRaXvvBBCCCEqLB3146bz\nhNAytVrN0KFD2bBhQ3mH8tIK6f0FljVLX6okxL/ZzTvX6DyjJTY2Tco7lJeKPDinHTKu2iHjWrJK\n96CnePlt2LABd3f38g7jpXY7J628QxDihZH3uxCiMpOZclFu8vLynnmJxoouKSmJjIzskg8UZWJm\nZiTjqgXPY1ytrF4rsvJVZSczj9oh46odMq4lk5ly8dKRhLxkTZs2lV9uWiD/aWiHjKsQQjy9Cvug\npxBCCCGEEP8WkpQLIYQQQghRzqR+QIiXmNSUa0dmptSUa8OzjKvUkgshKjtJyoV4ia3w30Qtwzrl\nHYYQWnU7Jw2veT1lKUQhRKUmSbkQL7FahnVknXIhhBCiEpCaciGEEEIIIcpZpUrKbW1tNb7atGnD\n9OnTycnJKe/QtOratWtF+u7o6Mhbb73FgQMHlONsbW05evRoucT4PK+dlpZGs2bNCAgIKNXx7u7u\nbN68+blcWwghhBDiaVS68pWwsDBcXFzIz8/nzz//ZObMmcyfP5+5c+eWd2hat2nTJurVqwfA3bt3\n+c9//sO4ceP49ttvsbKyKtfYEhMTqVmz5nNpKyEhgQYNGnDkyBEyMjIwMzN74vFbtmzBwMDguVxb\nCCGEEOJpVKqZcoCaNWtibm5O7dq1cXR0xN/fn4SEhPIO64UwNTXF3Nwcc3Nz6tevz5QpU9DX1+f7\n778v79AwNzdHT0/vubQVHx/PoEGDMDc3Z8eOHSUeb2pqir6+/nO5thBCCCHE06h0SfmjqlWrpvHa\nx8eHZcuWKa8LSz9SU1OBgjKLsLAw2rZti6+vL1u3bmXo0KGsXLmSVq1a4erqSnx8PAkJCXTq1IlW\nrVqxdOlSpb1bt24xfvx4WrVqhb29PZ6enhw7duzFdPYRurq6ABrJ8IkTJ+jbty8ODg4MGzaMa9eu\nKfuSk5Px8/OjZcuWdOjQgYiICNRqtbJ/79699OrVCycnJ9566y0OHz6s7PPx8WHFihX4+fnh6OhI\n165dOXjwoLK/sHwlOTkZOzs7pZwkNzeXvn37Mm3atFL16cqVK5w7d47WrVvTsWNHtm3bprE/PDyc\ngIAAfHx8aNWqFYcPH8bd3Z3Y2FiuX79epMzH1taWESNGAHD//n0WLVpEp06dcHZ2JiAggBs3bgD/\ne5/s3r2bLl264ODgwDvvvENmZqZy7S1bttCjRw/s7Oxo06YNs2fPJj8/v1T9EkIIIUTFVqmT8oyM\nDNatW0e/fv00tuvo6DzxvP379xMTE8P06dNRq9WcPXuWq1evsmXLFrp3705QUBAxMTF88cUXTJw4\nkcjISC5evAhAYGAgKpWKDRs2sG3bNiwtLZk1a9Yz9aO0id3DCXROTg5Lly4lLy8PV1dXZXtsbCzT\np09n8+bN/P333yxYsAAoGCtvb28sLS2JjY1l9uzZREdHs2bNGgAuXLhAYGAg/v7+fPPNNwwePJhx\n48Zx4cIFpe2oqCj69OlDfHw8zZs3JygoSCMmABsbGwICAliyZAl///03q1atIisrq9RJeXx8PBYW\nFtjb2+Ph4cGFCxc0YgA4cOAA3bt3Z/369bRo0QIo+Jm/+uqrJCYmKl9r165FT0+Pt99+G4BZs2ax\nZ88eFixYwMaNG8nPz+fdd99FpVJp9HHx4sWsX7+ec+fOsXr1agCOHTtGcHAwH330EXv27GHOnDnE\nxcWxe/fuUvVLCCGEEBVbpaspDwgIoEqVgnuRu3fvYmJiwowZM8rUxuDBg2nYsCEAp0+fRqVSERQU\nhIGBAYMGDWL9+vW8//77NGnShCZNmrBo0SIuX75MkyZN8PDwoGvXrtSpU7D2tLe3N2PGjHmmPk2d\nOpVJkyYpbT7Owzcfd+/exdLSknnz5mnUk/v7+9O6dWsABg4cSHR0NFCQ7BoYGBAcHIyuri6NGjUi\nPT2dsLAw/Pz8WL16NQMGDKBv374AeHl58euvv7Ju3TpCQ0MBcHNzo3///gC8++679OvXj7S0NCwt\nLTXi9Pf3Z9euXUyfPp0DBw4QHh6OkZFRqcYiPj4ed3d3ANq1a4eRkRFbt27l448/Vo4xNTVl2LBh\nRc6tUqUK5ubmAGRnZzN79mxGjhxJp06dyMrKYseOHURGRtKqVSsAZdb88OHD2NjYADBu3DgcHBwA\n6NOnD2fOnAGgevXqfPLJJ3Tu3BmAV199lbVr15KcnFyqfglR0ZmZGWFhUaO8w3hpydhoh4yrdsi4\nPp1Kl5QHBwcrs6OFiZaXlxexsbFKol2S+vU11402NTVVHhQsLIepW7eusr9atWo8ePAAKEhWd+7c\nyYkTJ0hJSeHcuXPo6OigUqmUm4Wy8vPzY+TIkSxZsoTmzZs/9riVK1cqcRkaGhb7AGSDBg2U742M\njLh//z5QULrSrFkzpeQFwMnJiczMTDIzM0lOTubixYsaq5jk5eXh6OhYbNuGhoZAQXnKo/T09Jgz\nZw7Dhg2jR48edOzYscQxADh37hxXrlxRZtX19PTo2LEj8fHxTJkyRRnfh382j/Pxxx9jYWHBxIkT\ngYKyGJVKpdEfY2NjrK2tuXz5spKUP3yDY2hoSF5eHgCvv/46+vr6fPbZZ1y6dImkpCSuXr1K27Zt\nS9U3ISq6jIxs0tP/Lu8wXkoWFjVkbLRAxlU7ZFxL9riblkqXlNeuXVtJnKysrLCzs+PQoUNs2rSJ\nwMDAIqUrxZWGPPpR0A8nqoWKS7DVajW+vr7cuXOHXr164eHhQW5uLuPGjSs21sjISI266ydJT09n\nxIgRbN269bErqdStW7fEVVYejbuwvKRatWpFSk0KyzZUKhUqlQo/Pz8GDBigce7DY1WWBzkvXLiA\nrq4up0+f5u7du1SvXr3Ec+Lj44GCWfiHY1CpVBw4cECZQS/poc5Vq1Zx4sQJtm3bpozH487Jz8/X\neI88+t4oHLPDhw/z3nvv0b9/f9zc3Bg3bhxz5swpsU9CCCGEqBwqXVJenMLEDQoSx+zsbGVf4QOe\nz8PFixc5duwYR44coVatWgBKecijCS8UlHH4+/uX2O7x48f59NNPWbJkSZFZ/OelUaNG7Nq1i7y8\nPKpWLXjbnDx5EhMTE8zMzLC2tiY1NVUj6Y+IiMDY2BgfH58yXSstLY0lS5YQGhpKZGQky5Yt0yg/\nKY5arSYhIYE+ffpojJlKpeLtt99m27ZtSlL+JD/99BNhYWFERUVhYWGhbG/QoAFVq1bl1KlTuLm5\nAZCZmcnVq1extrZ+bHuFN3mxsbF4enoqiXheXh5Xr17ljTfeKDEmIYQQQlR8lS4pz8rKIj09HYB7\n9+6xZcsW/vjjD7p37w6Avb09W7duVWqjw8PDS3zws7SMjY2pUqUKO3fupHPnzpw5c4bIyEigYGWP\np10re/v27axZs6bUdddPo0+fPoSHhzNz5kz8/Py4cuUKERERDB06FB0dHUaNGoW3tzf29va8+eab\nHD16lJUrV7JixQqljeJuPIozZ84c7Ozs6N+/P7Vr12bMmDH07t0be3t7srOzefDgQZHSm2PHjpGW\nloaPjw+NGzfW2DdgwADWrFlDVlbWE6+blpbGxIkT8fX15f/+7/+U9wmAhYUFXl5ehIaGoq+vj4mJ\nCYsWLaJOnTp06NCBW7duFdtmYZ9NTEw4efIkv//+O1WqVCEyMpKsrCylPEgIIYQQlVulS8o/+OAD\n5Xt9fX2aNWtGeHg4Tk5OAPj6+pKUlMTw4cOxtLRk6tSpvP/++49tT0dHp0jS/rgkvk6dOsyePZvl\ny5ezZMkS3njjDaKiohgyZAi//fYbLVu2fKo+BQcHl3jM09xYPNw3AwMDVq1aRWhoKJ6enpibmzNy\n5EhlVtrR0ZGFCxeyfPlyFi9eTP369fnkk0+UWeXiYigupu+++47Dhw8rSxm2a9eOrl27EhQUxJYt\nWwgLC2Pfvn3s379f47ydO3fSpEkT5SHLhw0ZMoRVq1YRHx9f7M+r0A8//MBff/1FVFQUUVFRGnH+\n9ttvTJ48GbVazfjx48nNzaV9+/Z89dVXSslKcf0r3Pb+++8zdepUvLy8MDExYfjw4TRs2JDjx48X\nG4sQQgghKhcddWmnL4V4CajVaoYOHcqGDRvKO5QXIqT3F1jW1E5JkhAvi5t3rtF5RktsbJqUdygv\nJXlwTjtkXLVDxrVk8qCnqBA2bNhQqtrwiuJ2Tlp5hyCE1sn7XAghZKZc/Ms8/KBpZZCUlERGRnbJ\nB4oyMTMzknHVgmcZVyur14qsXiQKyMyjdsi4aoeMa8lkplxUCJUpIQdo2rSp/HLTAvlPQztkXIUQ\n4uk93afVCCGEEEIIIZ4bScqFEEIIIYQoZ5KUCyGEEEIIUc4qV4GuEP8y8qCndmRmyoOe2vA04yoP\neAohRAFJyoV4ia3w30QtwzrlHYYQWnE7Jw2veT1lfXIhhECSciFearUM68iHBwkhhBCVQIWoKffx\n8WHZsmXlHUaZpaam8sEHH9C6dWscHR3p168fMTExWrteeHg4tra2ypednR2dO3fms88+Iy8vTzlu\n6tSpTJ48WWtxlFVycjIfffQRrq6utGjRgkGDBrF79+5Snx8eHo63tzcAcXFxdOzYUdm3Z88eXF1d\ncXZ25tKlS8817mvXrmFra0tqaupzbVcIIYQQFU+FmSnX0dEp7xDK5N69e4wYMYIOHTqwbt06DAwM\n+OWXXwgODiY3N5cRI0Zo5bqOjo6sWLFCieHXX3/lk08+4c8//2TevHkAzJgxQyvXfhonT57Ez8+P\nXr16ERUVRY0aNdi3bx8TJ05k1qxZDBo0qEzt9erVizfffFN5HRERQceOHXnvvfeoU+f5lonUrVuX\nxMRETE1Nn2u7QgghhKh4KkxS/m/zww8/cOfOHYKDg5Vt9evX59q1a2zYsEFrSbmuri7m5ubK63r1\n6mFqaoqvry/Dhw/n9ddfx8jISCvXLiu1Ws3UqVPp2bMnc+fOVbaPGjWK7OxsFi9eTN++fdHX1y91\nm/r6+hrHZ2dn4+TkRN26dZ9r7ABVqlTRGGshhBBCiMepEOUrD4uLi2Pw4MGMHz8eFxcXNm/eTHZ2\nNtOnT6ddu3bY2dnRvXt3jfKHtLQ0xowZg7OzM56enkRHR+Pu7q7VOHV0dLh79y4nTpzQ2D5q1Cii\noqKU1ydPnsTb2xsnJyecnZ0ZPXo0aWlpSl+HDh1KREQEbdu2xcXFhdDQUNRqdZliadu2LQ0aNGDP\nnj1A0fKVvXv30qtXL5ycnHjrrbc4fPiwsk+lUrFo0SLatGlD69atWbFiBV26dOHnn38G4P79+yxa\ntIhOnTrh7OxMQEAAN27cKFVcJ06c4OrVq/j5+RXZN2LECKKiopRVG77//ns8PT1xcHDAxcWFDz/8\nkOzsoqtAPFy+Ymtry/Xr1wkKClJugpKTk/Hz86Nly5Z06NCBiIgIZTzDw8MJCAjAx8eHVq1acfjw\nYdzd3YmOjsbLywsHBwf69evHmTNngKLlK8nJyYwePZoWLVrg4OCAt7f3cy+ZEUIIIcS/U4VLygFO\nnz6NtbU1mzdv5s0332TevHmkpKSwZs0aEhISeOONNwgKCiI3NxeAcePGkZeXR2xsLH5+foSFhT1z\nOYxKpXri/vbt22NjY8OwYcPw8vLis88+49ixYxgZGVG/fsGDfdnZ2fj7+9O+fXt27tzJ6tWrSU1N\nZeXKlUo7Z86cISUlhZiYGGbOnEl0dLRG0lxajRo14vLly8rrwv5fuHCBwMBA/P39+eabbxg8eDDj\nxo3jwoULAERGRrJt2zYWL17Ml19+ycGDB7l27Zpy/qxZs9izZw8LFixg48aN5Ofn8+6775Y4PoXX\nNjQ0xNrausi+mjVr4uDggI6ODqmpqYwfPx5vb2927dpFWFgYP/74Ixs2bHhi+0eOHMHS0pKpU6cS\nERFBRkYG3t7eWFpaEhsby+zZs4mOjmbNmjXKOQcOHKB79+6sX7+eFi1aAAUlMGPGjGHHjh3UrFlT\nY1a/kFqtZuzYsdSvX5/t27ezYcMGVCoVCxYsKHEchBBCCFHxVcikHCAgIICGDRtibm6Oi4sLc+bM\nwdbWlgYNGuDr60tWVha3bt3iwoULnDlzhrlz59K4cWN69+7NgAEDyjzb/LDc3NzHztQWeuWVV4iJ\niWHMmDHcvn2bFStWMHz4cHr06MG5c+eAgprvd999l/fee4969erRokULunbtqjG7mp+fz5w5c2jY\nsCF9+/bF1taWs2fPljlmIyMjcnJyimxfvXo1AwYMoG/fvlhZWeHl5UXPnj1Zt24dAF9//TUTJkyg\nffv2NGvWjPnz5ytjl5WVxY4dO5gxYwatWrWiadOmLFq0iD/++KNUNw5///13qUppVCoVM2bMYNCg\nQdStW5f27dvTtm1bkpOTn3herVq1qFKlCkZGRtSsWZP4+HgMDAwIDg6mUaNGeHh4MGHCBFatWqWc\nY2pqyrBhw2jatCmGhoYA9O/fHw8PDxo2bIivr2+x43/v3j2GDBlCYGAgVlZWNG/enP79+8tMuRBC\nCCGAClpTbmJiQvXq1ZXX/fv3Z8+ePWzcuJGUlBTOnj2Ljo4OKpWKy5cva8xOAzg7O/Pdd9899fX1\n9PQYMGAAI0aMICwsDCsrq2KPMzIyYuLEiUycOJGUlBQOHDjA2rVrCQgIYN++fdSqVYt+/fqxdu1a\nLly4wKVLl/j9999xdHRU2jA1NdVIXA0NDTVWUimt7OxsJcl8WHJyMhcvXmTz5s3Ktry8PBwdHcnM\nzCQ9PR17e3tln7W1NcbGxgBcuXIFlUqlEa+xsTHW1tZcvnxZYxWU4piamnLnzp0SY3/ttdfQ09Pj\n888/59KlS1y8eJFLly7Ru3fvEs99tK/NmjVDV1dX2ebk5ERmZiaZmZkAxdaeN2jQQPne0NAQlUpV\n5KauevXqDBkyhG3btnH27FlSUlI4f/68PAQqKj0zMyMsLGqUdxgvPRkj7ZBx1Q4Z16dTIZPyRx/8\nmzx5MidPnqR///4MHToUCwsLhgwZAhQkS48mUE/6dLmQkBDOnz9fqjguXryIj48P8fHxRWZ8N23a\nRI0aNejRowdQkMxaW1vj6upKnz59uHjxIrVq1WLAgAG8/vrruLq6MnjwYA4cOMDx48eVdvT09Ipc\n92lm+ZOSknjrrbeKbFepVPj5+TFgwACN9l955RUleX30eoWvH/cAZn5+jEUYqwAAG0FJREFUPvn5\n+SXGZG9vz927d7l06RKNGzfW2Hf79m0mTpzInDlzuH//PkOHDsXd3R0XFxd8fX358ssvS2z/UdWq\nVSvSl8Iymyf1qTQ/g5ycHAYOHIipqSmdO3emT58+XL58WeP5ASEqo4yMbNLT/y7vMF5qFhY1ZIy0\nQMZVO2RcS/a4m5YKmZQ/LDs7m507d7JhwwZlxvbgwYNAQeJkY2NDTk4OKSkpSu3yk5Lu0i4XmJCQ\nwJYtW1i4cGGxJRhJSUkcO3aMbt26UaXK/6qICmerzczM2LNnD0ZGRkRGRir7v/rqq1JdvyyOHj3K\njRs36NatW5F91tbWpKamasz2R0REYGxsjI+PD7Vr1+bs2bM0a9YMKFh7vXB2u0GDBlStWpVTp07h\n5uYGQGZmJlevXi22TvxRzZs3p0mTJqxZs4ZPPvlEY190dDQXLlzg1VdfJSwsjJYtW7J48WJl/5Ur\nV0p1jYfZ2Niwa9cu8vLyqFq14J/GyZMnMTExeeYZ7Z9//pmbN28SHx+v3MwcPnz4mcqkhBBCCFFx\nVPik/JVXXqF69ep89913mJubc+XKFWU97gcPHtC4cWO6du3KtGnTmDVrFjdu3ODLL7+kRo2n/9PL\n/fv3SUxMJDIyUknuHjVy5Eh27NjB2LFjGT16NJaWlqSkpLB8+XJ69uzJq6++iqmpKWlpafzwww9Y\nWVnx7bffcuDAgSKzxo96UqKXl5fH7du3UavV3Lt3jzNnzvDpp58yePBgmjRpUqSNUaNG4e3tjb29\nPW+++SZHjx5l5cqVylrnw4cPJyIignr16mFmZkZISAhQ8KCogYEBXl5ehIaGoq+vj4mJCYsWLaJO\nnTp06NABKEjS9fT0Hls7PmvWLPz8/KhatSre3t7o6emRkJBAVFQUoaGhVKtWDVNTU5KSkjh9+jQ1\na9Zkw4YN/P7772Ve5rB379589tlnzJw5Ez8/P65cuUJERARDhw595gd/TUxMuHfvHt999x0ODg4c\nPXqU2NhYjVIZIYQQQlReFTIpfziBeuWVV1i4cCGffvop69ev5/XXX2fevHlMmjSJ8+fP07hxY0JC\nQpg5cyZeXl5YWVkxcOBAdu3a9dTX19fXJzQ09InHWFlZERMTw2effcaECRPIysqidu3a9O3bl7Fj\nxwLQo0cPfvnlFz744AMAunfvztKlS5k0aRIPHjwo0tfi+v/o9tOnT+Pq6goUlO40aNCAt99+W2Nd\ndB0dHaUNR0dHFi5cyPLly1m8eDH169fnk08+UWa+/fz8SE9PZ8KECejq6jJ69GhOnTqllHRMnjwZ\ntVrN+PHjyc3NpX379nz11VdKiZCvry/NmjVTbpQe5eLiwrp161ixYgVvv/029+7do2nTpkRERCgf\nAuTj48P58+fx9fWlWrVqeHp6EhQUpDyg+XB/njQ+BgYGrFq1itDQUDw9PTE3N2fkyJEEBAQU287j\nFHctZ2dn3nvvPUJCQrh79y4dO3YkKioKb29vbt68iaWlZYntCiGEEKLi0lHL38+LiIuLIyIigv37\n95d3KC+9Q4cOYWdnh5mZGQAZGRm0a9eO/fv3l2qm+sKFC8TExDBnzhxth/qvFNL7Cyxr1i/5QCH+\nhW7euUbnGS2xsWlS8sGVmNToaoeMq3bIuJbscTXlFXZJRPFibNq0iWnTppGcnExycjKzZ8/GwcGh\n1KUja9eupVevXlqOUgghhBDi5VYhy1eeVWnLFAQEBQURHByMl5cXarWadu3aERERUerzQ0NDH1t3\nL+B2Tlp5hyCE1sj7Wwgh/kfKV4R4iSUlJZGR8fgPoRJPx8zMSMZVC55mXK2sXnviMrRCygG0RcZV\nO2RcS1Zpl0QU4t+sadOm8stNC+Q/De2QcRVCiKcnNeVCCCGEEEKUM0nKhRBCCCGEKGdSviLES0xq\nyrUjM1NqyrWhcFylTlwIIcpOknIhXmIr/DdRy7BOeYchRKndzknDa15PWXtcCCHKSJJyIV5itQzr\nyIcHCSGEEJWA1JQ/BR8fH5YtW1beYZTJTz/9hK2trcaXs7Mz3t7enDp1CoBr165ha2tLamrqC4/v\neV/75MmT2NraEhISUqrjbW1tOXr06HO5thBCCCFEWUlS/pT+rR8udOjQIRITE0lMTGTr1q3UqVMH\nf39/srPLt762bt26JCYmUq9evefS3s6dO3nttdeIj48nNze3xOMTExNxcXF5LtcWQgghhCgrScor\nmVq1amFubo65uTkNGzZk+vTpZGVl8dNPP5VrXFWqVMHc3JwqVZ79LZmfn8+uXbt45513yMnJ4eDB\ngyWeY25ujp6e3jNfWwghhBDiaUhS/ozi4uIYPHgw48ePx8XFhc2bN5Odnc306dNp164ddnZ2dO/e\nnd27dyvnpKWlMWbMGJydnfH09CQ6Ohp3d/dyib8wCX44Id23bx9dunTB0dGRgIAA/vvf/yr7Tp48\nydChQ3F2dsbd3Z3o6GiN9jZu3IiHh4dSGnPmzBllX+HxXl5eODg40K9fP2X/w+UrR48e1SgnuXPn\nDq6urixfvrxUffrxxx/566+/6NSpE61atSIuLk5j/9SpU5kyZQr9+/enTZs2JCUlKdcrrszH1taW\njz/+GICsrCyCgoJo3749LVu2ZNKkSWRlZQEFJUJubv/f3r0HRV29fwB/8/VCeAlDLmaJgiamsCwo\nCHFR0HREItS8oCIaalTEjAkuKU6R6IKoZHkJw7TIscAEvFAmOua9EFbQFLGVyTTBRUpdFBZ2n98f\njp+f67KAXNqVntcMM3zOeXY/5zwo+3w+e/bgi4yMDPj6+sLFxQXR0dGora0Vzr1lyxaMHTsWjo6O\n8Pb2xqefftqsOTHGGGOsY+OivA0UFxfDzs4Ou3btgp+fH6RSKcrKyvDll18iNzcXbm5uWL58ubCM\nIjIyEvX19cjMzER4eDjWr1/f6uUwGo2mWXFEJHz/zz//IDk5GRYWFnB1dRXad+/ejXXr1iE9PR0X\nLlxAamoqAEAulyMsLAzu7u7Izs5GVFQU1qxZgx9//BEAcPjwYXz66adYtmwZcnJy4Ovri7CwMFRW\nVgrPvWHDBixYsAB79uzBs88+ixUrVuiM0dPTE5MmTcLKlSuhVquxevVqWFlZISIiollz3LdvH0Qi\nEXr37g1/f38cPXoUVVVVWjF79+5FZGQk0tLSMGjQIKHd1dVVWN5z4sQJrF69GqampggNDQXw4Gd3\n6dIlfP7559i+fTvKysqwZMkS4fFVVVX44YcfsHXrVnz22WfIy8sTLgpycnKwbds2JCQk4KeffkJk\nZCQ2bdqE4uLiZs2LMcYYYx0XF+VtJCIiAgMGDEDv3r0xYsQIxMfHY8iQIbC1tcW8efNw+/Zt3Lx5\nEyUlJTh37hxWrFiBQYMGITAwEFOmTNEqlp9UXV0dFi1a1Kx14SNGjICLiwvEYjE8PDxQWFiIdevW\noUePHkJMdHQ0nJycIBKJMGHCBFy6dAkAkJGRgZdffhmLFi1C//79ERwcjNmzZyMtLQ0AkJaWhgUL\nFsDf3x+2traIiIiAo6MjMjIyhOcODg7GmDFjMGDAAMybNw/nz59vcJyxsbH4+++/sWTJEuTk5EAq\nlaJTp05Nzk+lUuHgwYMYM2YMAGDcuHFQq9XYt2+fVtzQoUOFO9aPLpnp0qWLsLynrq4Oq1atQmxs\nLIYOHYqSkhLk5+cjMTERTk5OcHJyQnJyMn7++WfI5XIAQH19PZYuXYqXXnoJ3t7e8PHxEd4N6NOn\nDxITE+Hh4YG+fftixowZsLS0FB7LGGOMsf8u3hKxDfTq1QtmZmbCcXBwMA4ePIjvvvsOZWVlOH/+\nPExMTKDRaHDlyhX06NEDL774/9vcubi44MCBAy0+f5cuXTBlyhTMmTMH69evR79+/fTGZmVloVOn\nTjAxMUHPnj1hbm6uE2Nrayt836NHD6hUKgAP7pSLRCKtWLFYLCxhkcvlSElJwfr164X+uro6PP/8\n8w0+d/fu3aHRaBq8IDE3N4dEIsGSJUswf/58DBkypKk0AACOHDkCpVIpFOVWVlZwdnZGdnY25syZ\nI8Q19YFSlUqFqKgoeHl5ISQkBABw5coVdO/eHfb29kKcvb09zM3NIZfLhVw+nr/6+noAwMiRI1FU\nVIS1a9fiypUruHjxIiorK6FWq5s1N8aeFhYWPWBl1dPQw+hQOJ/tg/PaPjivLcNFeRswNTXVOo6J\niYFMJkNwcDBCQkJgZWWF6dOnAwDMzMx0itDG/vJdQkICLly40KxxXL58GaGhodi3b5/Wne9H2dra\nNvlhysfvSD8cb0Nj12g0QlGp0WgQGxsLb29vrcd269ZNOG7ow5T63iW4ePEiOnXqhPz8fBBRs5b4\nPLwjHhQUpDVGIkJpaSkGDx4MoPGcA4BUKoVSqdTaUvHxn/NDarVaq7B+dI6Pzi0zMxOrVq3CtGnT\nMG7cOEgkEq0LBcY6iqoqJRSKu4YeRodhZdWT89kOOK/tg/PaNH0XLVyUtzGlUon9+/fj22+/hbOz\nMwAIu38QEQYOHIjq6mqUlZXBzs4OABotuuPi4pp13tzcXHz//fdITk7WW5C31MPC0t7eXmcvb5lM\nJtw5trOzw40bN7Tu1MfHx8PNzQ0BAQFPdM4LFy4gPT0dGzduRGxsLNLT05ssYJVKJY4cOYLw8HBM\nmjRJaL937x5mzZqFrKwsSCSSJs+dk5ODrKwsZGZmar0DYmdnh+rqasjlcgwcOBAA8Pvvv0OpVMLO\nzk74wKc+O3fuxNtvv42FCxcCePAB1srKylYtXWKMMcZYx8BryttY165dYWZmhgMHDuDatWs4fvw4\npFIpgAdLImxtbTFu3DgsXboUJSUlOHz4MLZv396qD3rW1tbixIkTSE1NhYWFRVtNRcfMmTNRWlqK\nlJQUlJWVITs7Gzt37sSsWbMAAHPnzkV6ejqys7Nx9epVbNiwAbt27dJa7tEcarUacXFxCAoKwujR\no7F48WJ88sknuHHjBoAHxWxDBXBeXh7UajXCwsIwaNAg4UskEmH8+PHYu3dvk0tFLl26hA8//BAS\niQQWFhZQKBRQKBSoqqqCvb09Ro8ejdjYWJw7dw7FxcWQSCQYMWJEo8trHhbdzz33HE6dOiUsaVq0\naBFMTEyE5UGMMcYY++/iorwNPFpQd+3aFcnJycjLy0NAQAA2btwIqVSKF154QbgjnpCQABsbG8yY\nMQMpKSl44403WnW31NTUFCtXrkTnzo2/8dGcwv/xGBMTE6HNxsYGqampOHbsGIKCgrB582Z88MEH\neOONNwAAAQEBWLx4MTZs2IDAwEAcOnQImzZtarRgffR8D7//6quvcP36dcTExAAApk6disGDByM+\nPh7Agw+BRkZG6jzX/v374ePjA2tra52+kJAQ3Lp1C8eOHdOa0+PnPnjwIGpqahAfHw8vLy/4+PjA\nx8cHU6dOBQAkJSWhf//+mDt3LubPn4/Bgwdj8+bNzcrfsmXLcP/+fQQHB2Px4sUIDAxEYGAgLl68\nqDc/jDHGGPtvMCF+79zgdu/ejQ0bNuDw4cOGHspT4e7du1i0aJGw60tHlhD4Bfo8+2LTgYwZifI7\n1zA2bjgGDnzJ0EPpMHiNbvvgvLYPzmvT9K0p5zvl7KnzxRdf4PXXXzf0MBhjjDHG2gx/0NMINLSc\ngukXFRXV5FKdjqKyusLQQ2DsifC/WcYYaxlevsKYESstLUVVVdN/FIo9GQuLHpzXdvAwr/369W9y\n21HWfLwcoH1wXtsH57VpvCUiY0+hwYMH8y+3dsAvGu2D88oYYy3Ha8oZY4wxxhgzMC7KGWOMMcYY\nMzAuyhljjDHGGDMwLsoZY4wxxhgzMN59hTHGGGOMMQPjO+WMMcYYY4wZGBfljDHGGGOMGRgX5Ywx\nxhhjjBkYF+WMMcYYY4wZGBfljDHGGGOMGRgX5YwxxhhjjBkYF+WMMcYYY4wZGBfljBkhlUqF5cuX\nw93dHd7e3khLSzP0kBhjjDHWjrgoZ8wIrV69GkVFRdi+fTvi4+OxefNm5ObmGnpYRuPq1auIiIiA\nu7s7Ro0ahaSkJKhUqgZjS0pKMH36dIjFYkyePBnnzp3T6s/NzcWrr74KsViMd955B1VVVVr9KSkp\neOWVV+Du7o6kpCRoNJp2m5cxiYuLQ2hoqN5+zmvz1dXVQSqVwsPDAyNHjsRHH33E/17bgEKhwHvv\nvQc3Nzf4+vpi7dq1eufLeW2aSqVCYGAgTp06JbRdv34db775JlxcXBAQEICjR482+hytyeM///yD\nqKgoDB8+HP7+/sjOzm7bCT4NiDFmVKqrq0kkEtHJkyeFtk2bNlFISIgBR2U8amtracKECRQVFUVy\nuZx+/fVXGjt2LCUmJurEVldXk5eXF0mlUpLL5bRy5Ury9PQkpVJJRERFRUUkEokoKyuLSkpKKDQ0\nlMLDw4XHf/nll+Tr60v5+fn0yy+/kI+PD6Wmpv5rczWUkydPkoODA4WGhjbYz3l9MgkJCeTn50eF\nhYVUWFhIfn5+tG7dOp04zuuTCQ8Pp9DQULp8+TKdPn2avLy8KC0tTSeO89q0mpoaevfdd8nBwUF4\n7dFoNBQUFETvv/8+/f7775SamkrOzs70559/Nvgcrc3jW2+9RWFhYVRaWkq7du0iJycnKiwsbN+J\nGxkuyhkzMgUFBTRkyBCqra0V2k6fPk2Ojo6k0WgMODLjkJ+fT46OjnTv3j2hbe/eveTl5aUTm5mZ\nSX5+flpt48aNo4yMDCIiiomJoZiYGKHvxo0b5ODgQFevXiUiolGjRlFmZqbQn5OTQ6NGjWrL6Rid\n6upqGjNmDIWEhNDs2bMbjOG8Nt/t27fJ0dFR6yJ79+7d9Oabb+rEcl6fjFgspry8POFYKpXS/Pnz\ndeI4r427fPkyBQUFUVBQkFZRfvLkSRKJRFRdXS3Ezp07l1JSUhp8ntbk8Y8//tCKJSJatmwZRUdH\nt9k8nwa8fIUxI6NQKGBubo6uXbsKbZaWlqirq8OtW7cMODLjYG9vjy1btsDMzEyr/c6dOzqxRUVF\ncHV11WpzdXXF2bNnhX43Nzehr0+fPujbty9kMhkqKipQXl6u1e/q6ory8nJUVFS05ZSMSkpKCjw8\nPODu7q43hvPafAUFBTAzM4Onp6fQNmnSJGzdulUnlvP6ZBwdHbFnzx7U1NSgoqICx48fh6Ojo04c\n57Vx+fn58PT0xHfffafVXlRUhKFDh6Jbt25C2/Dhw4W8Pa6leSwvL0dRURGsrKzQr18/rX595+qo\nuChnzMjcv39fqyAHIBzrW4f6X2JhYaFV4Gg0GnzzzTd45ZVXdGIVCgWsra11Hv/wxbShfktLS5SX\nl0OhUACAVr+lpSUAoLy8vG0mY2RkMhkOHDgAiUQCItIbx3ltvqtXr6Jv377Yu3cvJk6cCH9/fyQl\nJaGurk4nlvP6ZNasWYPz58/D1dUVo0aNgpWVFSIjI3XiOK+NCwkJQWxsLJ555hmtdoVCASsrK602\nCwsLvfNuTR4bemzv3r07TI6bi4tyxoyMqampTvH98PjxX5oMkEqluHTpEmJiYnT6ampqGrzAeZjP\nxvpramqE40f7gI55caRSqRAXF4dly5ahZ8+ejcZyXpuvuroa165dw44dO7BixQp89NFHOHDgAJKT\nk3ViOa/NR0SIjo6GjY0NduzYgS1btuDatWtISkrSieW8toy+G0T65t3SPNbV1ek9V0MXrx0ZF+WM\nGRkbGxvcuXMH9fX1QptCoUDXrl3Rq1cvA47MuBAREhISsHPnTqxduxYDBw7UiTE1NUVtba1Wm0ql\nEpa+6LsAMjMzg6mpqXD8aB/QMS+ONm7ciP79+2P8+PFNxnJem69z585QKpVITk6Gq6srfH19IZFI\ndJYKAJzXJyGTyXDmzBmkpKTAxcUFvr6+SEhIwI4dO3R2/OC8tswzzzyjNy8NaU0e9T22o+f4cVyU\nM2ZkXn75ZXTp0gWFhYVCW0FBARwdHfG///F/WeDBkpWlS5fi22+/xSeffAJ/f/8G42xsbFBZWanV\nVllZKbwla21tLbyt+ni/jY2NcPxQQ2/BdhT79u3D8ePH4eLiAhcXF2zduhUFBQU6a3EBzuuTsLa2\nRufOnbXWyg4YMAC1tbU6xSPntfnKy8thbm4uzBsAhg0bBrVajevXr2vFcl5bRl/e9M27NXls6mf0\nX8Gv8IwZGTMzMwQHByM+Ph7FxcU4dOgQtm3bhjlz5hh6aEYjMTER+/fvx8aNGzF27Fi9cc7OzpDJ\nZMIxEaGwsBDOzs4AALFYjIKCAqH/xo0b+OuvvyAWi2FtbY2+ffvizJkzQn9BQQFsbGy0CoGOIj09\nHfv378eePXuQk5ODadOmwcnJCTk5OTqxnNfmE4vFqK+vR2lpqdAml8vRvXt3nXe+OK/NZ2trizt3\n7mgVgXK5HAC0LoAAzmtLiUQiXLx4Effv3xfaCgoKhLw9rjV5FIvFqKio0LqgKigogFgsboeZGTED\n7vzCGNPj/v37JJFISCwWk4+PD23bts3QQzIaMpmMHBwcaMuWLXTz5k2tLyKimzdvUk1NDRER3b17\nlzw9Penjjz+my5cv08qVK8nLy0vY4ksmk9GwYcMoIyND2Fd34cKFwrlSU1PJy8uLTp06Rb/88gv5\n+vo2uA9yR7Ru3TqtLRE5ry33zjvv0OTJk+n8+fOUn59P/v7+wr76nNeWmzlzJs2dO5dKSkpIJpPR\na6+9RhKJhIg4ry316JaIarWaJk6cSFFRUVRaWkqpqakkFovp+vXrRPTgb0bcvHmT1Go1EbU+j+Hh\n4TRr1iwqKSkR9ik/e/bsvzh7w+OinDH2VElMTCQHBwedryFDhlBVVRU5ODhQVlaWEF9cXEyTJk0i\nJycnmjp1Kv32229az5eVlUV+fn4kFospMjKSqqqqhD61Wk2JiYnk5uZGI0eOpNWrV/9r8zS0lJQU\n4Y8H3b59m/PaCkqlkj744AMaPnw4ubu7U2JiItXV1XFeW+nvv/+m6Oho8vDwIG9vb0pISKDa2lrO\nays8WpQTPdg/fPbs2eTk5ESBgYF04sQJoe/gwYPk4OAgFOlErcvjrVu3KCIigkQiEY0ZM4b27NnT\njjM1TiZEjex7xRhjT5mvv/4alpaWCAgIMPRQOhTOa/vgvLYPzuu/IzIyEvHx8ejdu7ehh9Ih8Jpy\nxliHoVKpkJubq7WPOWs9zmv74Ly2D87rv6OkpAT19fVckLchvlPOGOtQ1Go1OnXqZOhhdDic1/bB\neW0fnNf2p9FoYGJiAhMTE0MPpcPgopwxxhhjjDED4+UrjDHGGGOMGRgX5YwxxhhjjBkYF+WMMcYY\nY4wZGBfljDHGGGOMGdj/AXQ+0aojSz7JAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a667400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = make_hbar(large_single_populations, \"Refugee Country → Destination Flows, 2005 – 2015\", height=15*20/51)\n",
"ax.set_yticklabels([ \"{0} → {2}, {1}\".format(*y) for y in large_single_populations.index ])\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: State population data comes from the [Census Bureau's 2014 estimates](http://www.census.gov/popest/data/state/asrh/2014/index.html)."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": 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>pop_est_2014</th>\n",
" <th>arrivals</th>\n",
" <th>per_1k_residents</th>\n",
" </tr>\n",
" <tr>\n",
" <th>state</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>North Dakota</th>\n",
" <td>739482</td>\n",
" <td>4457</td>\n",
" <td>6.027192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Idaho</th>\n",
" <td>1634464</td>\n",
" <td>9527</td>\n",
" <td>5.828822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Dakota</th>\n",
" <td>853175</td>\n",
" <td>4703</td>\n",
" <td>5.512351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Minnesota</th>\n",
" <td>5457173</td>\n",
" <td>27259</td>\n",
" <td>4.995077</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vermont</th>\n",
" <td>626562</td>\n",
" <td>3110</td>\n",
" <td>4.963595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arizona</th>\n",
" <td>6731484</td>\n",
" <td>29825</td>\n",
" <td>4.430672</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nebraska</th>\n",
" <td>1881503</td>\n",
" <td>8123</td>\n",
" <td>4.317293</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Washington</th>\n",
" <td>7061530</td>\n",
" <td>27009</td>\n",
" <td>3.824809</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utah</th>\n",
" <td>2942902</td>\n",
" <td>10758</td>\n",
" <td>3.655575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kentucky</th>\n",
" <td>4413457</td>\n",
" <td>15695</td>\n",
" <td>3.556169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Hampshire</th>\n",
" <td>1326813</td>\n",
" <td>4423</td>\n",
" <td>3.333552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Michigan</th>\n",
" <td>9909877</td>\n",
" <td>30772</td>\n",
" <td>3.105185</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Colorado</th>\n",
" <td>5355866</td>\n",
" <td>16034</td>\n",
" <td>2.993727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Georgia</th>\n",
" <td>10097343</td>\n",
" <td>27252</td>\n",
" <td>2.698928</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Texas</th>\n",
" <td>26956958</td>\n",
" <td>65537</td>\n",
" <td>2.431172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Massachusetts</th>\n",
" <td>6745408</td>\n",
" <td>16214</td>\n",
" <td>2.403709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oregon</th>\n",
" <td>3970239</td>\n",
" <td>9404</td>\n",
" <td>2.368623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>North Carolina</th>\n",
" <td>9943964</td>\n",
" <td>22787</td>\n",
" <td>2.291541</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Indiana</th>\n",
" <td>6596855</td>\n",
" <td>13697</td>\n",
" <td>2.076292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tennessee</th>\n",
" <td>6549352</td>\n",
" <td>13576</td>\n",
" <td>2.072877</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New York</th>\n",
" <td>19746227</td>\n",
" <td>39359</td>\n",
" <td>1.993242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Missouri</th>\n",
" <td>6063589</td>\n",
" <td>11974</td>\n",
" <td>1.974738</td>\n",
" </tr>\n",
" <tr>\n",
" <th>California</th>\n",
" <td>38802500</td>\n",
" <td>76114</td>\n",
" <td>1.961575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ohio</th>\n",
" <td>11594163</td>\n",
" <td>22652</td>\n",
" <td>1.953742</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maine</th>\n",
" <td>1330089</td>\n",
" <td>2587</td>\n",
" <td>1.944983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Maryland</th>\n",
" <td>5976407</td>\n",
" <td>11438</td>\n",
" <td>1.913859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pennsylvania</th>\n",
" <td>12787209</td>\n",
" <td>24389</td>\n",
" <td>1.907297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Iowa</th>\n",
" <td>3107126</td>\n",
" <td>5901</td>\n",
" <td>1.899183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Illinois</th>\n",
" <td>12880580</td>\n",
" <td>23826</td>\n",
" <td>1.849761</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Florida</th>\n",
" <td>19893297</td>\n",
" <td>36568</td>\n",
" <td>1.838207</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Virginia</th>\n",
" <td>8326289</td>\n",
" <td>14994</td>\n",
" <td>1.800802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nevada</th>\n",
" <td>2839099</td>\n",
" <td>5065</td>\n",
" <td>1.784017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rhode Island</th>\n",
" <td>1055173</td>\n",
" <td>1868</td>\n",
" <td>1.770326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wisconsin</th>\n",
" <td>5757564</td>\n",
" <td>8778</td>\n",
" <td>1.524603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Connecticut</th>\n",
" <td>3596677</td>\n",
" <td>5065</td>\n",
" <td>1.408244</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alaska</th>\n",
" <td>736732</td>\n",
" <td>989</td>\n",
" <td>1.342415</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kansas</th>\n",
" <td>2904021</td>\n",
" <td>3894</td>\n",
" <td>1.340899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Mexico</th>\n",
" <td>2085572</td>\n",
" <td>1854</td>\n",
" <td>0.888965</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oklahoma</th>\n",
" <td>3878051</td>\n",
" <td>2732</td>\n",
" <td>0.704478</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Jersey</th>\n",
" <td>8938175</td>\n",
" <td>6210</td>\n",
" <td>0.694773</td>\n",
" </tr>\n",
" <tr>\n",
" <th>District of Columbia</th>\n",
" <td>658893</td>\n",
" <td>343</td>\n",
" <td>0.520570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Louisiana</th>\n",
" <td>4649676</td>\n",
" <td>2378</td>\n",
" <td>0.511433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South Carolina</th>\n",
" <td>4832482</td>\n",
" <td>1431</td>\n",
" <td>0.296121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alabama</th>\n",
" <td>4849377</td>\n",
" <td>1386</td>\n",
" <td>0.285810</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Delaware</th>\n",
" <td>935614</td>\n",
" <td>82</td>\n",
" <td>0.087643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>1850326</td>\n",
" <td>154</td>\n",
" <td>0.083229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hawaii</th>\n",
" <td>1419561</td>\n",
" <td>77</td>\n",
" <td>0.054242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas</th>\n",
" <td>2966369</td>\n",
" <td>111</td>\n",
" <td>0.037419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mississippi</th>\n",
" <td>2994079</td>\n",
" <td>81</td>\n",
" <td>0.027053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Montana</th>\n",
" <td>1023579</td>\n",
" <td>16</td>\n",
" <td>0.015631</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wyoming</th>\n",
" <td>584153</td>\n",
" <td>0</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pop_est_2014 arrivals per_1k_residents\n",
"state \n",
"North Dakota 739482 4457 6.027192\n",
"Idaho 1634464 9527 5.828822\n",
"South Dakota 853175 4703 5.512351\n",
"Minnesota 5457173 27259 4.995077\n",
"Vermont 626562 3110 4.963595\n",
"Arizona 6731484 29825 4.430672\n",
"Nebraska 1881503 8123 4.317293\n",
"Washington 7061530 27009 3.824809\n",
"Utah 2942902 10758 3.655575\n",
"Kentucky 4413457 15695 3.556169\n",
"New Hampshire 1326813 4423 3.333552\n",
"Michigan 9909877 30772 3.105185\n",
"Colorado 5355866 16034 2.993727\n",
"Georgia 10097343 27252 2.698928\n",
"Texas 26956958 65537 2.431172\n",
"Massachusetts 6745408 16214 2.403709\n",
"Oregon 3970239 9404 2.368623\n",
"North Carolina 9943964 22787 2.291541\n",
"Indiana 6596855 13697 2.076292\n",
"Tennessee 6549352 13576 2.072877\n",
"New York 19746227 39359 1.993242\n",
"Missouri 6063589 11974 1.974738\n",
"California 38802500 76114 1.961575\n",
"Ohio 11594163 22652 1.953742\n",
"Maine 1330089 2587 1.944983\n",
"Maryland 5976407 11438 1.913859\n",
"Pennsylvania 12787209 24389 1.907297\n",
"Iowa 3107126 5901 1.899183\n",
"Illinois 12880580 23826 1.849761\n",
"Florida 19893297 36568 1.838207\n",
"Virginia 8326289 14994 1.800802\n",
"Nevada 2839099 5065 1.784017\n",
"Rhode Island 1055173 1868 1.770326\n",
"Wisconsin 5757564 8778 1.524603\n",
"Connecticut 3596677 5065 1.408244\n",
"Alaska 736732 989 1.342415\n",
"Kansas 2904021 3894 1.340899\n",
"New Mexico 2085572 1854 0.888965\n",
"Oklahoma 3878051 2732 0.704478\n",
"New Jersey 8938175 6210 0.694773\n",
"District of Columbia 658893 343 0.520570\n",
"Louisiana 4649676 2378 0.511433\n",
"South Carolina 4832482 1431 0.296121\n",
"Alabama 4849377 1386 0.285810\n",
"Delaware 935614 82 0.087643\n",
"West Virginia 1850326 154 0.083229\n",
"Hawaii 1419561 77 0.054242\n",
"Arkansas 2966369 111 0.037419\n",
"Mississippi 2994079 81 0.027053\n",
"Montana 1023579 16 0.015631\n",
"Wyoming 584153 0 0.000000"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"all_by_state = pd.DataFrame(state_populations[\"pop_est_2014\"]).join(pd.DataFrame({\n",
" \"arrivals\": by_destination.groupby(\"dest_state\")[\"arrivals\"].sum(),\n",
"})).fillna(0)\n",
"all_by_state[\"per_1k_residents\"] = all_by_state[\"arrivals\"] * 1000.0 / all_by_state[\"pop_est_2014\"]\n",
"all_by_state.sort_values(\"per_1k_residents\", ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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XaYJOTk5Go9EQFhZGlSpVSEtLQ61W5+rjlZSUxPXr1/Mcpfno0SMuXLiASqVSaujefPNN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\n9wGJRl/sAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a66f390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = make_hbar(all_by_state[\"per_1k_residents\"].sort_values(ascending=False),\n",
" \"Refugee Arrivals Per 1,000 Residents, 2005 – 2015\")\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"---\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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
xpharry/Udacity-DLFoudation | tutorials/intro-to-tensorflow/intro_to_tensorflow.ipynb | 1 | 69687 | {
"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 [00:26<00:00, 8040.27files/s]\n",
"100%|██████████| 10001/10001 [00:01<00:00, 8131.13files/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",
" return 0.1 + (image_data - 0) * (0.9 - 0.1) / (255 - 0)\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": 8,
"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": "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": 9,
"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)\n",
"labels = tf.placeholder(tf.float32)\n",
"\n",
"# TODO: Set the weights and biases tensors\n",
"weights = tf.Variable(tf.truncated_normal([features_count, labels_count]))\n",
"biases = tf.Variable(tf.zeros(10))\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": 10,
"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": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 1/5: 100%|██████████| 1114/1114 [00:04<00:00, 245.50batches/s]\n",
"Epoch 2/5: 100%|██████████| 1114/1114 [00:04<00:00, 250.95batches/s]\n",
"Epoch 3/5: 100%|██████████| 1114/1114 [00:04<00:00, 247.05batches/s]\n",
"Epoch 4/5: 100%|██████████| 1114/1114 [00:04<00:00, 246.64batches/s]\n",
"Epoch 5/5: 100%|██████████| 1114/1114 [00:04<00:00, 245.65batches/s]\n"
]
},
{
"data": {
"image/png": 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1mUBubm4umZmZDHxkIGf2OJMHRz/YsBUVERGRo96cOXPIysqCgwRNDT5Pk5mlAz2B9dFu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\nBxo0KaWUUkr5QIMmpZRSSikf/D/sF47cDgZB8gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fedad4d2668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Validation accuracy at 0.781333327293396\n"
]
}
],
"source": [
"# Change if you have memory restrictions\n",
"batch_size = 128\n",
"\n",
"# TODO: Find the best parameters for each configuration\n",
"epochs = 5\n",
"learning_rate = 0.2\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": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Epoch 1/5: 100%|██████████| 1114/1114 [00:00<00:00, 1585.08batches/s]\n",
"Epoch 2/5: 100%|██████████| 1114/1114 [00:00<00:00, 1577.10batches/s]\n",
"Epoch 3/5: 100%|██████████| 1114/1114 [00:00<00:00, 1507.67batches/s]\n",
"Epoch 4/5: 100%|██████████| 1114/1114 [00:00<00:00, 1614.08batches/s]\n",
"Epoch 5/5: 100%|██████████| 1114/1114 [00:00<00:00, 1511.22batches/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Nice Job! Test Accuracy is 0.8389000296592712\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": 0
}
| mit |
prabodhprakash/problemsolving | spoj/WILLITST.ipynb | 1 | 3476 | {
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def will_it_stop(n):\n",
" while n > 1:\n",
" print n\n",
" if n%2 is 0:\n",
" n = n/2\n",
" else:\n",
" n = 3*n + 3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def is_power_of_two(n):\n",
" return n&(n-1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"0\n",
"0\n",
"0\n",
"0\n",
"0\n",
"0\n",
"2\n",
"4\n",
"6\n",
"12\n",
"8\n",
"16\n"
]
}
],
"source": [
"print is_power_of_two(2)\n",
"print is_power_of_two(4)\n",
"print is_power_of_two(8)\n",
"print is_power_of_two(16)\n",
"print is_power_of_two(32)\n",
"print is_power_of_two(64)\n",
"print is_power_of_two(128)\n",
"print is_power_of_two(3)\n",
"print is_power_of_two(6)\n",
"print is_power_of_two(7)\n",
"print is_power_of_two(14)\n",
"print is_power_of_two(9)\n",
"print is_power_of_two(18)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def will_it_ever_stop(n):\n",
" print \"TAK\" if (n&(n-1)) == 0 else \"NIE\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NIE\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"TAK\n",
"None\n",
"NIE\n",
"None\n",
"NIE\n",
"None\n",
"NIE\n",
"None\n",
"NIE\n",
"None\n",
"NIE\n",
"None\n",
"NIE\n",
"None\n"
]
}
],
"source": [
"print will_it_ever_stop(100000000000000)\n",
"print will_it_ever_stop(1)\n",
"print will_it_ever_stop(2)\n",
"print will_it_ever_stop(4)\n",
"print will_it_ever_stop(8)\n",
"print will_it_ever_stop(16)\n",
"print will_it_ever_stop(32)\n",
"print will_it_ever_stop(64)\n",
"print will_it_ever_stop(128)\n",
"print will_it_ever_stop(3)\n",
"print will_it_ever_stop(6)\n",
"print will_it_ever_stop(7)\n",
"print will_it_ever_stop(14)\n",
"print will_it_ever_stop(9)\n",
"print will_it_ever_stop(18)"
]
},
{
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},
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}
],
"metadata": {
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}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ethen8181/Business-Analytics | frequentist_statistics/correlation.ipynb | 1 | 105010 | {
"cells": [
{
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"execution_count": 1,
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{
"data": {
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"<style> @font-face {\n",
" font-family: 'Droid Sans Mono';\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" src: local('Droid Sans Mono'), url('fonts/droid-sans-mono.woff') format('woff');\n",
"}\n",
"@font-face {\n",
" font-family: 'Exo_2';\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" src: local('Exo_2'), url('fonts/exo-II-regular.ttf') format('truetype');\n",
"}\n",
"@font-face {\n",
" font-family: 'Fira Code';\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" src: local('Fira Code'), url('fonts/firacode.otf') format('opentype');\n",
"}\n",
"@font-face {\n",
" font-family: 'Lora';\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" src: local('Lora'), url('fonts/Lora-Regular.ttf') format('truetype');\n",
"}\n",
"div#notebook {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" line-height: 170%;\n",
" color: #303030;\n",
"}\n",
"body,\n",
"div.body {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" color: #303030;\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
"}\n",
"body.notebook_app {\n",
" padding: 0;\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
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"a {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" color: #303030;\n",
"}\n",
"a:hover,\n",
"a:focus {\n",
" color: #2f2f2f;\n",
"}\n",
".list_header,\n",
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" font-size: 14pt;\n",
" color: #2f2f2f;\n",
" background-color: #ffffff;\n",
"}\n",
"div#cluster_list_header.row.list_header,\n",
"div#running .row.list_header {\n",
" font-size: 14pt;\n",
" color: #303030;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-bottom: 2px solid rgba(180,180,180,.30);\n",
"}\n",
"div#cluster_list > div.list_item.row,\n",
"div#cluster_list > div.list_item.row:hover {\n",
" background: #f7f7f7;\n",
" background-color: #f7f7f7;\n",
"}\n",
"div#clusters.tab-pane.active {\n",
" font-size: 12.0pt;\n",
" padding: 4px 0 4px 0;\n",
"}\n",
"#running .panel-group .panel .panel-heading {\n",
" font-size: 14pt;\n",
" color: #303030;\n",
" padding: 8px 8px;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
"}\n",
"#running .panel-group .panel .panel-heading a {\n",
" font-size: 14pt;\n",
" color: #303030;\n",
"}\n",
"#running .panel-group .panel .panel-heading a:focus,\n",
"#running .panel-group .panel .panel-heading a:hover {\n",
" font-size: 14pt;\n",
" color: #303030;\n",
"}\n",
"#running .panel-group .panel .panel-body .list_container .list_item {\n",
" background: #f7f7f7;\n",
" background-color: #f7f7f7;\n",
" padding: 2px;\n",
" border-bottom: 2px solid rgba(180,180,180,.30);\n",
"}\n",
"#running .panel-group .panel .panel-body .list_container .list_item:hover {\n",
" background: #f7f7f7;\n",
" background-color: #f7f7f7;\n",
"}\n",
"#running .panel-group .panel .panel-body {\n",
" padding: 2px;\n",
"}\n",
"div.running_list_info.toolbar_info {\n",
" font-size: 12.0pt;\n",
" padding: 4px 0 4px 0;\n",
" height: inherit;\n",
" line-height: inherit;\n",
" text-shadow: none;\n",
"}\n",
".list_placeholder {\n",
" font-weight: normal;\n",
"}\n",
"#tree-selector {\n",
" padding: 0px;\n",
"}\n",
"#project_name > ul > li > a > i.fa.fa-home {\n",
" color: #ff7823;\n",
" font-size: 17pt;\n",
" display: inline-block;\n",
" position: static;\n",
" padding: 0px 0px;\n",
" font-weight: normal;\n",
" text-align: center;\n",
" vertical-align: text-top;\n",
"}\n",
"#project_name {\n",
" display: inline-flex;\n",
" padding-left: 7px;\n",
" margin-left: -2px;\n",
" margin-bottom: -20px;\n",
" text-align: -webkit-auto;\n",
" vertical-align: text-top;\n",
"}\n",
"div#notebook_toolbar div.dynamic-instructions {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
"}\n",
".toolbar_info {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" color: #303030;\n",
" text-shadow: none;\n",
" border: none;\n",
" height: inherit;\n",
" line-height: inherit;\n",
"}\n",
".list_container {\n",
" font-size: 12.0pt;\n",
" color: #303030;\n",
" border: none;\n",
" text-shadow: none !important;\n",
"}\n",
".list_container > div {\n",
" border-bottom: 1px solid rgba(180,180,180,.14);\n",
" font-size: 12.0pt;\n",
"}\n",
".list_header > div,\n",
".list_item > div {\n",
" padding-left: 0px;\n",
"}\n",
".list_header > div input,\n",
".list_item > div input {\n",
" top: 0px;\n",
"}\n",
".list_header > div .item_link,\n",
".list_item > div .item_link {\n",
" margin-left: -1px;\n",
" vertical-align: middle;\n",
" line-height: 22px;\n",
" font-size: 12.0pt;\n",
"}\n",
".item_icon {\n",
" font-size: 12.0pt;\n",
" vertical-align: middle;\n",
"}\n",
".list_item input:not([type=\"checkbox\"]) {\n",
" padding-right: 0px;\n",
" height: auto;\n",
" width: 20%;\n",
" margin: 6px 0 0;\n",
" margin-top: 1px;\n",
"}\n",
"#button-select-all {\n",
" height: auto;\n",
" font-size: 12.0pt;\n",
" padding: 5px;\n",
" min-width: 65px;\n",
" z-index: 0;\n",
"}\n",
"button#tree-selector-btn {\n",
" height: auto;\n",
" font-size: 12.0pt;\n",
" padding: 5px;\n",
"}\n",
"input#select-all.pull-left.tree-selector {\n",
" margin-left: 7px;\n",
" margin-right: 2px;\n",
" margin-top: 5px;\n",
"}\n",
"input[type=\"radio\"],\n",
"input[type=\"checkbox\"] {\n",
" margin: 6px 0 0;\n",
" margin-top: 1px;\n",
" line-height: normal;\n",
"}\n",
".list_container a {\n",
" font-size: 17px;\n",
" color: #303030;\n",
" border: none;\n",
" text-shadow: none !important;\n",
" font-weight: normal;\n",
" font-style: normal;\n",
"}\n",
"div.list_container a:hover {\n",
" color: #2f2f2f;\n",
"}\n",
"div.list_item:hover {\n",
" background-color: #fafafa;\n",
"}\n",
".breadcrumb > li {\n",
" font-size: 12.0pt;\n",
" color: #303030;\n",
" border: none;\n",
" text-shadow: none !important;\n",
"}\n",
"ul#tabs a {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" border-color: transparent;\n",
" text-shadow: none !important;\n",
"}\n",
".nav-tabs {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" background: #ffffff;\n",
" text-shadow: none !important;\n",
" border-color: transparent;\n",
" border-bottom-color: rgba(180,180,180,.30);\n",
"}\n",
".nav-tabs > li > a:hover {\n",
" color: #2f2f2f;\n",
" background-color: rgba(180,180,180,.14);\n",
"}\n",
".nav-tabs > li > a:active,\n",
".nav-tabs > li > a:focus,\n",
".nav-tabs > li.active > a,\n",
".nav-tabs > li.active > a:focus,\n",
".nav-tabs > li.active > a:hover,\n",
".nav-tabs > li.active > a,\n",
".nav-tabs > li.active > a:hover,\n",
".nav-tabs > li.active > a:focus {\n",
" color: #1c1c1c;\n",
" background-color: #eeeeee;\n",
" border: 1px solid transparent;\n",
" border-bottom-color: transparent;\n",
" cursor: default;\n",
"}\n",
".nav > li > a:hover,\n",
".nav > li > a:focus {\n",
" text-decoration: none;\n",
" background-color: rgba(180,180,180,.14);\n",
"}\n",
".nav > li.disabled > a,\n",
".nav > li.disabled > a:hover {\n",
" color: #aaaaaa;\n",
"}\n",
"div#notebook {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" padding-top: 4px;\n",
"}\n",
".notebook_app {\n",
" background-color: #ffffff;\n",
"}\n",
"#notebook-container {\n",
" padding: 13px;\n",
" background-color: #ffffff;\n",
" min-height: 0px;\n",
" box-shadow: none;\n",
" width: 980px;\n",
" margin-right: auto;\n",
" margin-left: auto;\n",
"}\n",
"div#ipython-main-app.container {\n",
" width: 980px;\n",
" margin-right: auto;\n",
" margin-left: auto;\n",
" margin-right: auto;\n",
" margin-left: auto;\n",
"}\n",
".container {\n",
" width: 980px;\n",
" margin-right: auto;\n",
" margin-left: auto;\n",
"}\n",
".notebook_app #header {\n",
" box-shadow: none !important;\n",
" background-color: #ffffff;\n",
" border-bottom: 2px solid rgba(180,180,180,.14);\n",
"}\n",
"#header {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" box-shadow: none;\n",
" background-color: #ffffff;\n",
"}\n",
"#header .header-bar {\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
"}\n",
"body > #header .header-bar {\n",
" width: 100%;\n",
" background: #ffffff;\n",
"}\n",
"#menubar {\n",
" background-color: #ffffff;\n",
"}\n",
"#menubar .navbar,\n",
".navbar-default {\n",
" background-color: #ffffff;\n",
" margin-bottom: 0px;\n",
"}\n",
".navbar {\n",
" border: none;\n",
"}\n",
".navbar-default {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" background-color: #ffffff;\n",
" border-color: rgba(180,180,180,.14);\n",
" line-height: 1.5em;\n",
" padding-bottom: 0px;\n",
"}\n",
".navbar-default .navbar-nav > li > a {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" color: #303030;\n",
" display: block;\n",
" line-height: 1.5em;\n",
" padding-top: 8px;\n",
" padding-bottom: 6px;\n",
"}\n",
".navbar-default .navbar-nav > li > a:hover,\n",
".navbar-default .navbar-nav > li > a:focus {\n",
" color: #2f2f2f;\n",
" background-color: rgba(180,180,180,.14);\n",
" border-color: rgba(180,180,180,.14);\n",
" line-height: 1.5em;\n",
"}\n",
".navbar-default .navbar-nav > .open > a,\n",
".navbar-default .navbar-nav > .open > a:hover,\n",
".navbar-default .navbar-nav > .open > a:focus {\n",
" color: #1c1c1c;\n",
" background-color: rgba(180,180,180,.14);\n",
" border-color: rgba(180,180,180,.14);\n",
" line-height: 1.5em;\n",
"}\n",
".edit_mode .modal_indicator:before {\n",
" font-size: 13pt;\n",
" color: #2c85f7;\n",
" content: \"\\f040\";\n",
"}\n",
".item_icon {\n",
" color: #126dce;\n",
"}\n",
".item_buttons .kernel-name {\n",
" font-size: 13pt;\n",
" color: #126dce;\n",
" line-height: 22px;\n",
"}\n",
".running_notebook_icon:before {\n",
" color: #009e07 !important;\n",
"}\n",
".item_buttons .running-indicator {\n",
" padding-top: 2px;\n",
" color: #009e07;\n",
"}\n",
"#modal_indicator {\n",
" float: right !important;\n",
" color: #126dce;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
"}\n",
"#kernel_indicator {\n",
" float: right !important;\n",
" color: #ff7823;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" font-size: 14.5pt;\n",
" border-left: 2px solid #ff7823;\n",
" padding-bottom: 2px;\n",
"}\n",
"#kernel_indicator .kernel_indicator_name {\n",
" color: #ff7823;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" font-size: 14.5pt;\n",
" padding-left: 5px;\n",
" padding-right: 5px;\n",
"}\n",
"div.notification_widget.info,\n",
".notification_widget.info,\n",
".notification_widget:active:hover,\n",
".notification_widget.active:hover,\n",
".open > .dropdown-toggle.notification_widget:hover,\n",
".notification_widget:active:focus,\n",
".notification_widget.active:focus,\n",
".open > .dropdown-toggle.notification_widget:focus,\n",
".notification_widget:active.focus,\n",
".notification_widget.active.focus,\n",
".open > .dropdown-toggle.notification_widget.focus,\n",
"div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn,\n",
"div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:hover,\n",
"div#notification_notebook.notification_widget.btn.btn-xs.navbar-btn:focus {\n",
" color: #126dce;\n",
" background-color: #ffffff;\n",
" border-color: #ffffff;\n",
"}\n",
"#notification_area,\n",
"div.notification_area {\n",
" float: right !important;\n",
" position: static;\n",
"}\n",
"#kernel_logo_widget,\n",
"#kernel_logo_widget .current_kernel_logo {\n",
" display: none;\n",
"}\n",
"div#ipython_notebook {\n",
" display: none;\n",
"}\n",
"i.fa.fa-icon {\n",
" -webkit-font-smoothing: antialiased;\n",
" -moz-osx-font-smoothing: grayscale;\n",
" text-rendering: auto;\n",
"}\n",
".fa {\n",
" display: inline-block;\n",
" font: normal normal normal 12pt/1 \"FontAwesome\", \"Exo_2\", sans-serif;\n",
" text-rendering: auto;\n",
" -webkit-font-smoothing: antialiased;\n",
" -moz-osx-font-smoothing: grayscale;\n",
"}\n",
".dropdown-menu {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" box-shadow: none;\n",
" padding: 0px;\n",
" text-align: left;\n",
" border: 2px solid rgba(180,180,180,.30);\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
" line-height: 1.3;\n",
" margin: 0px;\n",
"}\n",
".dropdown-menu:hover {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" border: 2px solid rgba(180,180,180,.30);\n",
" background-color: #ffffff;\n",
" box-shadow: none;\n",
" line-height: 1.3;\n",
"}\n",
".dropdown-header {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" display: block;\n",
" color: #ff7823;\n",
" text-decoration: underline;\n",
" white-space: nowrap;\n",
" padding: 8px 0px 0px 6px;\n",
" line-height: 1.3;\n",
"}\n",
".dropdown-menu > li > a {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" line-height: 1.3;\n",
" display: block;\n",
" padding: 10px 25px 10px 14px;\n",
" color: #303030;\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
"}\n",
".dropdown-menu > li > a:hover {\n",
" color: #2f2f2f;\n",
" background-color: rgba(180,180,180,.14);\n",
" background: rgba(180,180,180,.14);\n",
" border-color: rgba(180,180,180,.14);\n",
"}\n",
".dropdown-menu .divider {\n",
" height: 2px;\n",
" margin: 0px 0px;\n",
" overflow: hidden;\n",
" background-color: rgba(180,180,180,.30);\n",
"}\n",
".dropdown-submenu > .dropdown-menu {\n",
" top: 0;\n",
" left: 100%;\n",
" margin-top: -2px;\n",
" margin-left: 0px;\n",
" padding-top: 0px;\n",
"}\n",
".dropdown-menu > .disabled > a,\n",
".dropdown-menu > .disabled > a:hover,\n",
".dropdown-menu > .disabled > a:focus {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" font-weight: normal;\n",
" color: #aaaaaa;\n",
" padding: none;\n",
" display: block;\n",
" clear: both;\n",
" line-height: 1.2;\n",
" white-space: nowrap;\n",
"}\n",
".dropdown-submenu > a:after {\n",
" color: #303030;\n",
" margin-right: -16px;\n",
"}\n",
".dropdown-submenu:hover > a:after,\n",
".dropdown-submenu:active > a:after,\n",
".dropdown-submenu:focus > a:after,\n",
".dropdown-submenu:visited > a:after {\n",
" color: #ff7823;\n",
" margin-right: -16px;\n",
"}\n",
"div.kse-dropdown > .dropdown-menu,\n",
".kse-dropdown > .dropdown-menu {\n",
" min-width: 0;\n",
" top: 94%;\n",
"}\n",
".btn,\n",
".btn-default {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" color: #303030;\n",
" background: #ebebeb;\n",
" background-color: #ebebeb;\n",
" border: 2px solid #e8e8e8;\n",
" font-weight: normal;\n",
" box-shadow: none;\n",
" text-shadow: none;\n",
" border-radius: 2px;\n",
" font-size: inherit;\n",
"}\n",
".btn:hover,\n",
".btn:active:hover,\n",
".btn.active:hover,\n",
".btn-default:hover,\n",
".open > .dropdown-toggle.btn-default:hover,\n",
".open > .dropdown-toggle.btn:hover {\n",
" color: #2f2f2f;\n",
" background-color: #e4e4e4;\n",
" background: #e4e4e4;\n",
" border-color: #e4e4e4;\n",
" background-image: none;\n",
" box-shadow: none !important;\n",
" border-radius: 2px;\n",
"}\n",
".btn:active,\n",
".btn.active,\n",
".btn:active:focus,\n",
".btn.active:focus,\n",
".btn:active.focus,\n",
".btn.active.focus,\n",
".btn-default:focus,\n",
".btn-default.focus,\n",
".btn-default:active,\n",
".btn-default.active,\n",
".btn-default:active:hover,\n",
".btn-default.active:hover,\n",
".btn-default:active:focus,\n",
".btn-default.active:focus,\n",
".btn-default:active.focus,\n",
".btn-default.active.focus,\n",
".open > .dropdown-toggle.btn:focus,\n",
".open > .dropdown-toggle.btn.focus,\n",
".open > .dropdown-toggle.btn-default {\n",
" color: #1c1c1c;\n",
" background-color: #e4e4e4;\n",
" background: #e4e4e4;\n",
" border-color: #e4e4e4;\n",
" background-image: none;\n",
" box-shadow: none !important;\n",
" border-radius: 2px;\n",
"}\n",
".item_buttons > .btn,\n",
".item_buttons > .btn-group,\n",
".item_buttons > .input-group {\n",
" margin-left: 5px;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border: 2px solid #eeeeee;\n",
"}\n",
".item_buttons > .btn:hover,\n",
".item_buttons > .btn-group:hover,\n",
".item_buttons > .input-group:hover {\n",
" margin-left: 5px;\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border: 2px solid #e9e9e9;\n",
"}\n",
".btn-group > .btn-mini,\n",
".btn-sm,\n",
".btn-group-sm > .btn,\n",
".btn-xs,\n",
".btn-group-xs > .btn,\n",
".alternate_upload .btn-upload,\n",
".btn-group,\n",
".btn-group-vertical {\n",
" font-size: 12.0pt;\n",
" font-weight: normal;\n",
"}\n",
".btn-xs,\n",
".btn-group-xs > .btn {\n",
" font-size: 12.0pt;\n",
" background-image: none;\n",
" font-weight: normal;\n",
" text-shadow: none;\n",
" display: inline-table;\n",
"}\n",
".alternate_upload .btn-upload {\n",
" display: none;\n",
"}\n",
".alternate_upload input.fileinput {\n",
" display: none;\n",
"}\n",
"button.close {\n",
" border: 0px none;\n",
" font-family: sans-serif;\n",
" font-size: 25pt;\n",
"}\n",
".dynamic-buttons {\n",
" font-size: inherit;\n",
" padding-top: 0px;\n",
" display: inline-block;\n",
"}\n",
".close {\n",
" color: #de143d;\n",
" opacity: .5;\n",
" text-shadow: none;\n",
"}\n",
".close:hover {\n",
" color: #de143d;\n",
" opacity: 1;\n",
"}\n",
"div.btn.btn-default.output_collapsed {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-color: #eeeeee;\n",
"}\n",
"div.btn.btn-default.output_collapsed:hover {\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border-color: #e9e9e9;\n",
"}\n",
"div.nbext-enable-btns .btn[disabled],\n",
"div.nbext-enable-btns .btn[disabled]:hover,\n",
".btn-default.disabled,\n",
".btn-default[disabled],\n",
".btn-default.disabled:hover,\n",
".btn-default[disabled]:hover,\n",
"fieldset[disabled] .btn-default:hover,\n",
".btn-default.disabled:focus,\n",
".btn-default[disabled]:focus,\n",
"fieldset[disabled] .btn-default:focus,\n",
".btn-default.disabled.focus,\n",
".btn-default[disabled].focus,\n",
"fieldset[disabled] .btn-default.focus {\n",
" color: #4a4a4a;\n",
" background: #e8e8e8;\n",
" background-color: #e8e8e8;\n",
" border-color: #e8e8e8;\n",
"}\n",
".input-group-addon {\n",
" padding: 2px 5px;\n",
" font-size: 12.0pt;\n",
" font-weight: normal;\n",
" height: auto;\n",
" color: #303030;\n",
" text-align: center;\n",
" background-color: #ffffff;\n",
" border: none;\n",
"}\n",
".btn-group > .btn + .dropdown-toggle {\n",
" padding-left: 8px;\n",
" padding-right: 8px;\n",
" height: 100%;\n",
" border-left: 2px solid #ff7823 !important;\n",
"}\n",
".btn-group > .btn + .dropdown-toggle:hover {\n",
" border-left: 2px solid #ff7823 !important;\n",
"}\n",
".input-group-btn {\n",
" position: relative;\n",
" font-size: inherit;\n",
" white-space: nowrap;\n",
"}\n",
".input-group-btn:first-child > .btn,\n",
".input-group-btn:first-child > .btn-group {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border: 1px solid #e9e9e9;\n",
" margin: 2px;\n",
" font-size: inherit;\n",
"}\n",
".input-group-btn:first-child > .btn:hover,\n",
".input-group-btn:first-child > .btn-group:hover {\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border: 1px solid #e9e9e9;\n",
" margin: 2px;\n",
" font-size: inherit;\n",
"}\n",
"div.modal .btn-group > .btn:first-child {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border: 1px solid #e9e9e9;\n",
" margin-top: 0px !important;\n",
" margin-left: 0px;\n",
" margin-bottom: 2px;\n",
"}\n",
"div.modal .btn-group > .btn:first-child:hover {\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border: 1px solid #e9e9e9;\n",
"}\n",
"div.modal > button,\n",
"div.modal-footer > button {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-color: #eeeeee;\n",
"}\n",
"div.modal > button:hover,\n",
"div.modal-footer > button:hover {\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border-color: #e9e9e9;\n",
"}\n",
".modal-content {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" position: relative;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border: none;\n",
" border-radius: 1px;\n",
" background-clip: padding-box;\n",
" outline: none;\n",
"}\n",
".modal-header {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" color: #303030;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-color: rgba(180,180,180,.30);\n",
" padding: 12px;\n",
" min-height: 16.4286px;\n",
"}\n",
".modal-content h4 {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 16pt;\n",
" color: #303030;\n",
" padding: 5px;\n",
"}\n",
".modal-body {\n",
" background-color: #ffffff;\n",
" position: relative;\n",
" padding: 15px;\n",
"}\n",
".modal-footer {\n",
" padding: 10px;\n",
" text-align: right;\n",
" background-color: #f7f7f7;\n",
" border-top: 1px solid rgba(180,180,180,.30);\n",
"}\n",
".alert-info {\n",
" background-color: #fdfdfd;\n",
" border-color: rgba(180,180,180,.30);\n",
" color: #303030;\n",
"}\n",
".modal-header .close {\n",
" margin-top: -5px;\n",
" font-size: 25pt;\n",
"}\n",
".modal-backdrop,\n",
".modal-backdrop.in {\n",
" opacity: 0.75;\n",
" background-color: #eeeeee;\n",
"}\n",
"div.panel,\n",
"div.panel-default,\n",
".panel,\n",
".panel-default {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" background-color: #f7f7f7;\n",
" color: #303030;\n",
" margin-bottom: 14px;\n",
" border: 0;\n",
" box-shadow: none;\n",
"}\n",
"div.panel > .panel-heading,\n",
"div.panel-default > .panel-heading {\n",
" font-size: 14pt;\n",
" color: #303030;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border: 0;\n",
"}\n",
".modal .modal-dialog {\n",
" min-width: 950px;\n",
" margin: 50px auto;\n",
"}\n",
"div.container-fluid {\n",
" margin-right: auto;\n",
" margin-left: auto;\n",
" padding-left: 7px;\n",
" padding-right: 12px;\n",
"}\n",
"div.form-control,\n",
".form-control {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: inherit;\n",
" color: #303030;\n",
" background-color: #ffffff;\n",
" border: 2px solid #e7e7e7;\n",
" margin-left: 2px;\n",
" height: auto;\n",
" box-shadow: none;\n",
" padding: 6px 12px;\n",
" transition: border-color 0.15s ease-in-out 0s, box-shadow 0.15s ease-in-out 0s;\n",
"}\n",
".form-group.list-group-item {\n",
" color: #303030;\n",
" background-color: #f7f7f7;\n",
" border-color: rgba(180,180,180,.30);\n",
" margin-bottom: 0px;\n",
"}\n",
"input,\n",
"button,\n",
"select,\n",
"textarea {\n",
" background-color: #ffffff;\n",
" font-weight: normal;\n",
" border: 2px solid rgba(180,180,180,.30);\n",
"}\n",
"select.form-control.select-xs {\n",
" height: auto;\n",
"}\n",
"div.output.output_scroll {\n",
" box-shadow: none;\n",
"}\n",
"::-webkit-scrollbar-track {\n",
" -webkit-box-shadow: inset 0 0 6px rgba(0,0,0,0.11);\n",
" background-color: #d0d0d0;\n",
" border-radius: 6px;\n",
"}\n",
"::-webkit-scrollbar {\n",
" width: 14px;\n",
" height: 10px;\n",
" background-color: #d0d0d0;\n",
" border-radius: 6px;\n",
"}\n",
"::-webkit-scrollbar-thumb {\n",
" background-color: #ffffff;\n",
" background-image: -webkit-gradient(linear,40% 0%,75% 86%,from(#ff6b0f ),color-stop(0.5,#ff8b42 ),to(#ff6b0f ));\n",
" min-height: 60px;\n",
" border-radius: 2px;\n",
"}\n",
"div.input_area {\n",
" background-color: #efefef;\n",
" padding-right: 1.2em;\n",
" border: 0px;\n",
" border-top-left-radius: 0px;\n",
" border-top-right-radius: 2px;\n",
" border-bottom-left-radius: 0px;\n",
" border-bottom-right-radius: 0px;\n",
"}\n",
"div.cell {\n",
" padding: 0px;\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
" border: medium solid #ffffff;\n",
" border-top-right-radius: 2px;\n",
" border-top-left-radius: 2px;\n",
"}\n",
"div.cell.selected {\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
" border: medium solid #ff7823;\n",
" padding: 0px;\n",
" border-top-right-radius: 2px;\n",
" border-top-left-radius: 2px;\n",
"}\n",
".edit_mode div.cell.selected {\n",
" padding: 0px;\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
" border: medium solid #ffd5bb;\n",
" border-top-right-radius: 2px;\n",
" border-top-left-radius: 2px;\n",
"}\n",
"div.cell.edit_mode {\n",
" padding: 0px;\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
" border: medium solid #ffd5bb;\n",
" border-top-right-radius: 2px;\n",
" border-top-left-radius: 2px;\n",
"}\n",
"div.prompt,\n",
".prompt {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 9.5pt;\n",
" font-weight: normal;\n",
" color: #aaaaaa;\n",
" line-height: 170%;\n",
" padding: 0px;\n",
" padding-top: 4px;\n",
" padding-left: .25em;\n",
" text-align: left !important;\n",
" min-width: 12ex;\n",
" width: 12ex;\n",
"}\n",
"div.prompt.input_prompt {\n",
" background-color: #efefef;\n",
" border-right: 2px solid rgba(240,147,43,.50);\n",
" border-top-left-radius: 2px;\n",
" border-top-right-radius: 0px;\n",
" border-bottom-left-radius: 0px;\n",
" border-bottom-right-radius: 0px;\n",
" min-width: 12ex;\n",
" width: 12ex !important;\n",
"}\n",
"div.output_wrapper {\n",
" background-color: #ffffff;\n",
" border: 0px;\n",
" margin-bottom: 0em;\n",
" margin-top: 0em;\n",
" border-top-right-radius: 0px;\n",
" border-top-left-radius: 0px;\n",
" border-bottom-left-radius: 2px;\n",
" border-bottom-right-radius: 2px;\n",
"}\n",
"div.output_subarea.output_text.output_stream.output_stdout,\n",
"div.output_subarea.output_text {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 10.0pt;\n",
" line-height: 150% !important;\n",
" background-color: #ffffff;\n",
" color: #303030;\n",
" border-top-right-radius: 0px;\n",
" border-top-left-radius: 0px;\n",
" border-bottom-left-radius: 2px;\n",
" border-bottom-right-radius: 2px;\n",
"}\n",
"div.output_area pre {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 10.0pt;\n",
" line-height: 150% !important;\n",
" color: #303030;\n",
" border-top-right-radius: 0px;\n",
" border-top-left-radius: 0px;\n",
" border-bottom-left-radius: 2px;\n",
" border-bottom-right-radius: 2px;\n",
"}\n",
"div.output_area {\n",
" display: -webkit-box;\n",
"}\n",
"div.output_html {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 10.0pt;\n",
" color: #353535;\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
"}\n",
"div.output_subarea {\n",
" overflow-x: auto;\n",
" padding: .8em;\n",
" -webkit-box-flex: 1;\n",
" -moz-box-flex: 1;\n",
" box-flex: 1;\n",
" flex: 1;\n",
" max-width: 90%;\n",
"}\n",
"div.prompt.output_prompt {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 9.5pt;\n",
" background-color: #ffffff;\n",
" color: #ffffff;\n",
" border-bottom-left-radius: 2px;\n",
" border-top-right-radius: 0px;\n",
" border-top-left-radius: 0px;\n",
" border-bottom-right-radius: 0px;\n",
" min-width: 12ex;\n",
" width: 12ex;\n",
"}\n",
"div.out_prompt_overlay.prompt {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 9.5pt;\n",
" background-color: #ffffff;\n",
" border-bottom-left-radius: 2px;\n",
" border-top-right-radius: 0px;\n",
" border-top-left-radius: 0px;\n",
" border-bottom-right-radius: 0px;\n",
" min-width: 12ex;\n",
" width: 12ex;\n",
"}\n",
"div.out_prompt_overlay.prompt:hover {\n",
" background-color: #ffffff;\n",
" box-shadow: #e8e8e8 2px 1px 2px 2.5px inset;\n",
" border-bottom-left-radius: 2px;\n",
" -webkit-border-: 2px;\n",
" -moz-border-radius: 2px;\n",
" border-top-right-radius: 0px;\n",
" border-top-left-radius: 0px;\n",
" min-width: 12ex;\n",
" width: 12ex !important;\n",
"}\n",
"div.text_cell,\n",
"div.text_cell_render pre,\n",
"div.text_cell_render {\n",
" font-family: \"Lora\", serif;\n",
" font-size: 13pt;\n",
" line-height: 170% !important;\n",
" color: #353535;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" border-radius: 2px;\n",
"}\n",
"div.cell.text_cell.rendered.selected {\n",
" font-family: \"Lora\", serif;\n",
" border: medium solid #126dce;\n",
" line-height: 170% !important;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" border-radius: 2px;\n",
"}\n",
"div.cell.text_cell.unrendered.selected {\n",
" font-family: \"Lora\", serif;\n",
" line-height: 170% !important;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" border: medium solid #126dce;\n",
" border-radius: 2px;\n",
"}\n",
"div.cell.text_cell.selected {\n",
" font-family: \"Lora\", serif;\n",
" line-height: 170% !important;\n",
" border: medium solid #126dce;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" border-radius: 2px;\n",
"}\n",
".edit_mode div.cell.text_cell.selected {\n",
" font-family: \"Lora\", serif;\n",
" line-height: 170% !important;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" border: medium solid #87b0db;\n",
" border-radius: 2px;\n",
"}\n",
"div.text_cell.unrendered,\n",
"div.text_cell.unrendered.selected,\n",
"div.edit_mode div.text_cell.unrendered {\n",
" font-family: \"Lora\", serif;\n",
" line-height: 170% !important;\n",
" background: #ffffff;\n",
" background-color: #ffffff;\n",
" border-radius: 2px;\n",
"}\n",
"div.cell.text_cell.rendered .input_prompt {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 9.5pt;\n",
" font-weight: normal;\n",
" color: #aaaaaa;\n",
" text-align: left !important;\n",
" min-width: 0ex;\n",
" width: 0ex !important;\n",
" background-color: #ffffff;\n",
" border-right: 2px solid transparent;\n",
"}\n",
"div.cell.text_cell.unrendered .input_prompt {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 9.5pt;\n",
" font-weight: normal;\n",
" color: #aaaaaa;\n",
" text-align: left !important;\n",
" min-width: 0ex;\n",
" width: 0ex !important;\n",
" border-right: 2px solid transparent;\n",
"}\n",
"div.rendered_html code {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt;\n",
" padding-top: 3px;\n",
" color: #303030;\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
"}\n",
"pre,\n",
"code,\n",
"kbd,\n",
"samp {\n",
" white-space: pre-wrap;\n",
"}\n",
"code {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt !important;\n",
" line-height: 170% !important;\n",
" color: #353535;\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
"}\n",
"kbd {\n",
" padding: 4px;\n",
" font-size: 11pt;\n",
" color: #303030;\n",
" background-color: #efefef;\n",
" border: 0;\n",
" box-shadow: none;\n",
"}\n",
"pre {\n",
" display: block;\n",
" padding: 8.5px;\n",
" margin: 0 0 9px;\n",
" font-size: 12.0pt;\n",
" line-height: 1.42857143;\n",
" color: #303030;\n",
" background-color: #efefef;\n",
" border: 1px solid #e7e7e7;\n",
" border-radius: 2px;\n",
"}\n",
"div.rendered_html {\n",
" color: #353535;\n",
"}\n",
"div.rendered_html pre,\n",
"div.text_cell_render pre {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt !important;\n",
" line-height: 170% !important;\n",
" color: #353535;\n",
" background: #efefef;\n",
" background-color: #efefef;\n",
" border: 2px #e7e7e7 solid;\n",
" max-width: 86%;\n",
" border-radius: 2px;\n",
" padding: 5px;\n",
"}\n",
"div.text_cell_render h1,\n",
"div.rendered_html h1,\n",
"div.text_cell_render h2,\n",
"div.rendered_html h2,\n",
"div.text_cell_render h3,\n",
"div.rendered_html h3,\n",
"div.text_cell_render h4,\n",
"div.rendered_html h4,\n",
"div.text_cell_render h5,\n",
"div.rendered_html h5 {\n",
" font-family: \"Exo_2\", sans-serif;\n",
"}\n",
".rendered_html h1:first-child,\n",
".rendered_html h2:first-child,\n",
".rendered_html h3:first-child,\n",
".rendered_html h4:first-child,\n",
".rendered_html h5:first-child,\n",
".rendered_html h6:first-child {\n",
" margin-top: 0.2em;\n",
"}\n",
".rendered_html h1,\n",
".text_cell_render h1 {\n",
" color: #126dce;\n",
" font-size: 220%;\n",
" text-align: center;\n",
" font-weight: lighter;\n",
"}\n",
".rendered_html h2,\n",
".text_cell_render h2 {\n",
" text-align: left;\n",
" font-size: 170%;\n",
" color: #126dce;\n",
" font-style: normal;\n",
" font-weight: lighter;\n",
"}\n",
".rendered_html h3,\n",
".text_cell_render h3 {\n",
" font-size: 150%;\n",
" color: #126dce;\n",
" font-weight: lighter;\n",
" text-decoration: italic;\n",
" font-style: normal;\n",
"}\n",
".rendered_html h4,\n",
".text_cell_render h4 {\n",
" font-size: 120%;\n",
" color: #126dce;\n",
" font-weight: underline;\n",
" font-style: normal;\n",
"}\n",
".rendered_html h5,\n",
".text_cell_render h5 {\n",
" font-size: 100%;\n",
" color: #2f2f2f;\n",
" font-weight: lighter;\n",
" text-decoration: underline;\n",
"}\n",
".rendered_html table,\n",
".rendered_html tr,\n",
".rendered_html td {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 10.0pt !important;\n",
" line-height: 150% !important;\n",
" border: 1px solid #d6d6d6;\n",
" color: #353535;\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
"}\n",
"table.dataframe,\n",
".rendered_html tr,\n",
".dataframe * {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 10.0pt !important;\n",
" border: 1px solid #d6d6d6;\n",
"}\n",
".dataframe th,\n",
".rendered_html th {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 11pt !important;\n",
" font-weight: bold;\n",
" border: 1px solid #c4c4c4;\n",
" background: #eeeeee;\n",
"}\n",
".dataframe td,\n",
".rendered_html td {\n",
" font-family: \"Fira Code\", monospace;\n",
" font-size: 10.0pt !important;\n",
" color: #353535;\n",
" background: #ffffff;\n",
" border: 1px solid #d6d6d6;\n",
" text-align: left;\n",
" min-width: 4em;\n",
"}\n",
".dataframe-summary-row tr:last-child,\n",
".dataframe-summary-col td:last-child {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 11pt !important;\n",
" font-weight: bold;\n",
" color: #353535;\n",
" border: 1px solid #d6d6d6;\n",
" background: #eeeeee;\n",
"}\n",
"div.widget-area {\n",
" background-color: #ffffff;\n",
" background: #ffffff;\n",
" color: #303030;\n",
"}\n",
"div.widget-area a {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" color: #303030;\n",
" text-shadow: none !important;\n",
"}\n",
"div.widget-area a:hover,\n",
"div.widget-area a:focus {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12.0pt;\n",
" font-weight: normal;\n",
" font-style: normal;\n",
" color: #2f2f2f;\n",
" background: rgba(180,180,180,.14);\n",
" background-color: rgba(180,180,180,.14);\n",
" border-color: transparent;\n",
" background-image: none;\n",
" text-shadow: none !important;\n",
"}\n",
"div.widget_item.btn-group > button.btn.btn-default.widget-combo-btn,\n",
"div.widget_item.btn-group > button.btn.btn-default.widget-combo-btn:hover {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border: 2px solid #eeeeee !important;\n",
" font-size: inherit;\n",
" z-index: 0;\n",
"}\n",
"div.jupyter-widgets.widget-hprogress.widget-hbox,\n",
"div.widget-hbox,\n",
".widget-hbox {\n",
" display: inline-table;\n",
"}\n",
"div.jupyter-widgets.widget-hprogress.widget-hbox .widget-label,\n",
"div.widget-hbox .widget-label,\n",
".widget-hbox .widget-label {\n",
" font-size: 11pt;\n",
" min-width: 100%;\n",
" padding-top: 5px;\n",
" padding-right: 10px;\n",
" text-align: left;\n",
" vertical-align: text-top;\n",
"}\n",
".progress {\n",
" overflow: hidden;\n",
" height: 20px;\n",
" margin-bottom: 10px;\n",
" padding-left: 10px;\n",
" background-color: #c6c6c6;\n",
" border-radius: 4px;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
"}\n",
".rendered_html :link {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 100%;\n",
" color: #2c85f7;\n",
" text-decoration: underline;\n",
"}\n",
".rendered_html :visited,\n",
".rendered_html :visited:active,\n",
".rendered_html :visited:focus {\n",
" color: #2e6eb2;\n",
"}\n",
".rendered_html :visited:hover,\n",
".rendered_html :link:hover {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 100%;\n",
" color: #eb6a18;\n",
"}\n",
"a.anchor-link:link:hover {\n",
" font-size: inherit;\n",
" color: #eb6a18;\n",
"}\n",
"a.anchor-link:link {\n",
" font-size: inherit;\n",
" text-decoration: none;\n",
" padding: 0px 20px;\n",
" visibility: none;\n",
" color: #126dce;\n",
"}\n",
"div#nbextensions-configurator-container.container {\n",
" width: 980px;\n",
" margin-right: 0;\n",
" margin-left: 0;\n",
"}\n",
"div.nbext-selector > nav > .nav > li > a {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12pt;\n",
"}\n",
"div.nbext-readme > .nbext-readme-contents > .rendered_html {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 12pt;\n",
" line-height: 145%;\n",
" padding: 1em 1em;\n",
" color: #353535;\n",
" background-color: #ffffff;\n",
" -webkit-box-shadow: none;\n",
" -moz-box-shadow: none;\n",
" box-shadow: none;\n",
"}\n",
".nbext-icon,\n",
".nbext-desc,\n",
".nbext-compat-div,\n",
".nbext-enable-btns,\n",
".nbext-params {\n",
" margin-bottom: 8px;\n",
" font-size: 12pt;\n",
"}\n",
"div.nbext-readme > .nbext-readme-contents {\n",
" padding: 0;\n",
" overflow-y: hidden;\n",
"}\n",
"div.nbext-readme > .nbext-readme-contents:not(:empty) {\n",
" margin-top: 0.5em;\n",
" margin-bottom: 2em;\n",
" border: none;\n",
" border-top-color: rgba(180,180,180,.30);\n",
"}\n",
".nbext-showhide-incompat {\n",
" padding-bottom: 0.5em;\n",
" color: #4a4a4a;\n",
" font-size: 12.0pt;\n",
"}\n",
".shortcut_key,\n",
"span.shortcut_key {\n",
" display: inline-block;\n",
" width: 16ex;\n",
" text-align: right;\n",
" font-family: monospace;\n",
"}\n",
"mark,\n",
".mark {\n",
" background-color: #ffffff;\n",
" color: #353535;\n",
" padding: .15em;\n",
"}\n",
"a.text-warning,\n",
"a.text-warning:hover {\n",
" color: #aaaaaa;\n",
"}\n",
"a.text-warning.bg-warning {\n",
" background-color: #ffffff;\n",
"}\n",
"span.bg-success.text-success {\n",
" background-color: transparent;\n",
" color: #009e07;\n",
"}\n",
"span.bg-danger.text-danger {\n",
" background-color: #ffffff;\n",
" color: #de143d;\n",
"}\n",
".has-success .input-group-addon {\n",
" color: #009e07;\n",
" border-color: transparent;\n",
" background: inherit;\n",
" background-color: rgba(83,180,115,.10);\n",
"}\n",
".has-success .form-control {\n",
" border-color: #009e07;\n",
" -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
" box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
"}\n",
".has-error .input-group-addon {\n",
" color: #de143d;\n",
" border-color: transparent;\n",
" background: inherit;\n",
" background-color: rgba(192,57,67,.10);\n",
"}\n",
".has-error .form-control {\n",
" border-color: #de143d;\n",
" -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
" box-shadow: inset 0 1px 1px rgba(0,0,0,0.025);\n",
"}\n",
".kse-input-group-pretty > kbd {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" color: #303030;\n",
" font-weight: normal;\n",
" background: transparent;\n",
"}\n",
".kse-input-group-pretty > kbd {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" color: #303030;\n",
" font-weight: normal;\n",
" background: transparent;\n",
"}\n",
"div.nbext-enable-btns .btn[disabled],\n",
"div.nbext-enable-btns .btn[disabled]:hover,\n",
".btn-default.disabled,\n",
".btn-default[disabled] {\n",
" background: #e8e8e8;\n",
" background-color: #e8e8e8;\n",
" color: #282828;\n",
"}\n",
"label#Keyword-Filter {\n",
" display: none;\n",
"}\n",
".nav-pills > li.active > a,\n",
".nav-pills > li.active > a:hover,\n",
".nav-pills > li.active > a:focus {\n",
" color: #ffffff;\n",
" background-color: #126dce;\n",
"}\n",
".input-group .nbext-list-btn-add,\n",
".input-group-btn:last-child > .btn-group > .btn {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-color: #eeeeee;\n",
"}\n",
".input-group .nbext-list-btn-add:hover,\n",
".input-group-btn:last-child > .btn-group > .btn:hover {\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border-color: #e9e9e9;\n",
"}\n",
"#notebook-container > div.cell.code_cell.rendered.selected > div.widget-area > div.widget-subarea > div > div.widget_item.btn-group > button.btn.btn-default.dropdown-toggle.widget-combo-carrot-btn {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-color: #eeeeee;\n",
"}\n",
"#notebook-container > div.cell.code_cell.rendered.selected > div.widget-area > div.widget-subarea > div > div.widget_item.btn-group > button.btn.btn-default.dropdown-toggle.widget-combo-carrot-btn:hover {\n",
" background: #e9e9e9;\n",
" background-color: #e9e9e9;\n",
" border-color: #e9e9e9;\n",
"}\n",
"input.raw_input {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt !important;\n",
" color: #303030;\n",
" background-color: #efefef;\n",
" border-color: #ececec;\n",
" background: #ececec;\n",
" width: auto;\n",
" vertical-align: baseline;\n",
" padding: 0em 0.25em;\n",
" margin: 0em 0.25em;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
"}\n",
"audio,\n",
"video {\n",
" display: inline;\n",
" vertical-align: middle;\n",
" align-content: center;\n",
" margin-left: 20%;\n",
"}\n",
".cmd-palette .modal-body {\n",
" padding: 0px;\n",
" margin: 0px;\n",
"}\n",
".cmd-palette form {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
"}\n",
".typeahead-field input:last-child,\n",
".typeahead-hint {\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" z-index: 1;\n",
"}\n",
".typeahead-field input {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" color: #303030;\n",
" border: none;\n",
" font-size: 28pt;\n",
" display: inline-block;\n",
" line-height: inherit;\n",
" padding: 3px 10px;\n",
" height: 70px;\n",
"}\n",
".typeahead-select {\n",
" background-color: #eeeeee;\n",
"}\n",
"body > div.modal.cmd-palette.typeahead-field {\n",
" display: table;\n",
" border-collapse: separate;\n",
" background-color: #f7f7f7;\n",
"}\n",
".typeahead-container button {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 28pt;\n",
" background-color: #d0d0d0;\n",
" border: none;\n",
" display: inline-block;\n",
" line-height: inherit;\n",
" padding: 3px 10px;\n",
" height: 70px;\n",
"}\n",
".typeahead-search-icon {\n",
" min-width: 40px;\n",
" min-height: 55px;\n",
" display: block;\n",
" vertical-align: middle;\n",
" text-align: center;\n",
"}\n",
".typeahead-container button:focus,\n",
".typeahead-container button:hover {\n",
" color: #2f2f2f;\n",
" background-color: #ff7823;\n",
" border-color: #ff7823;\n",
"}\n",
".typeahead-list > li.typeahead-group.active > a,\n",
".typeahead-list > li.typeahead-group > a,\n",
".typeahead-list > li.typeahead-group > a:focus,\n",
".typeahead-list > li.typeahead-group > a:hover {\n",
" display: none;\n",
"}\n",
".typeahead-dropdown > li > a,\n",
".typeahead-list > li > a {\n",
" color: #303030;\n",
" text-decoration: none;\n",
"}\n",
".typeahead-dropdown,\n",
".typeahead-list {\n",
" font-family: \"Exo_2\", sans-serif;\n",
" font-size: 13pt;\n",
" color: #303030;\n",
" background-color: #ffffff;\n",
" border: none;\n",
" background-clip: padding-box;\n",
" margin-top: 0px;\n",
" padding: 3px 2px 3px 0px;\n",
" line-height: 1.7;\n",
"}\n",
".typeahead-dropdown > li.active > a,\n",
".typeahead-dropdown > li > a:focus,\n",
".typeahead-dropdown > li > a:hover,\n",
".typeahead-list > li.active > a,\n",
".typeahead-list > li > a:focus,\n",
".typeahead-list > li > a:hover {\n",
" color: #2f2f2f;\n",
" background-color: #f7f7f7;\n",
" border-color: #f7f7f7;\n",
"}\n",
".command-shortcut:before {\n",
" content: \"(command)\";\n",
" padding-right: 3px;\n",
" color: #aaaaaa;\n",
"}\n",
".edit-shortcut:before {\n",
" content: \"(edit)\";\n",
" padding-right: 3px;\n",
" color: #aaaaaa;\n",
"}\n",
"ul.typeahead-list i {\n",
" margin-left: 1px;\n",
" width: 18px;\n",
" margin-right: 10px;\n",
"}\n",
"ul.typeahead-list {\n",
" max-height: 50vh;\n",
" overflow: auto;\n",
"}\n",
".typeahead-list > li {\n",
" position: relative;\n",
" border: none;\n",
"}\n",
"div.input.typeahead-hint,\n",
"input.typeahead-hint,\n",
"body > div.modal.cmd-palette.in > div > div > div > form > div > div.typeahead-field > span.typeahead-query > input.typeahead-hint {\n",
" color: #aaaaaa !important;\n",
" background-color: transparent;\n",
" padding: 3px 10px;\n",
"}\n",
".typeahead-dropdown > li > a,\n",
".typeahead-list > li > a {\n",
" display: block;\n",
" padding: 5px;\n",
" clear: both;\n",
" font-weight: 400;\n",
" line-height: 1.7;\n",
" border: 1px solid #ffffff;\n",
" border-bottom-color: rgba(180,180,180,.30);\n",
"}\n",
"body > div.modal.cmd-palette.in > div {\n",
" min-width: 750px;\n",
" margin: 150px auto;\n",
"}\n",
".typeahead-container strong {\n",
" font-weight: bolder;\n",
" color: #ff7823;\n",
"}\n",
"#find-and-replace #replace-preview .match,\n",
"#find-and-replace #replace-preview .insert {\n",
" color: #ffffff;\n",
" background-color: #ff7823;\n",
" border-color: #ff7823;\n",
" border-style: solid;\n",
" border-width: 1px;\n",
" border-radius: 0px;\n",
"}\n",
"#find-and-replace #replace-preview .replace .match {\n",
" background-color: #de143d;\n",
" border-color: #de143d;\n",
" border-radius: 0px;\n",
"}\n",
"#find-and-replace #replace-preview .replace .insert {\n",
" background-color: #009e07;\n",
" border-color: #009e07;\n",
" border-radius: 0px;\n",
"}\n",
"div.CodeMirror,\n",
"div.CodeMirror pre {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt;\n",
" line-height: 170%;\n",
" color: #303030;\n",
"}\n",
"div.CodeMirror-lines {\n",
" padding-bottom: .6em;\n",
" padding-left: .5em;\n",
" padding-right: 1.5em;\n",
" padding-top: 4px;\n",
"}\n",
"span.ansiblack {\n",
" color: #dc4384;\n",
"}\n",
"span.ansiblue {\n",
" color: #009e07;\n",
"}\n",
"span.ansigray {\n",
" color: #ff7823;\n",
"}\n",
"span.ansigreen {\n",
" color: #333333;\n",
"}\n",
"span.ansipurple {\n",
" color: #653bc5;\n",
"}\n",
"span.ansicyan {\n",
" color: #055be0;\n",
"}\n",
"span.ansiyellow {\n",
" color: #ff7823;\n",
"}\n",
"span.ansired {\n",
" color: #de143d;\n",
"}\n",
"div.output-stderr {\n",
" background-color: #ebb5b7;\n",
"}\n",
"div.output-stderr pre {\n",
" color: #000000;\n",
"}\n",
"div.js-error {\n",
" color: #de143d;\n",
"}\n",
".ipython_tooltip {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt;\n",
" line-height: 170%;\n",
" border: 2px solid #dadada;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" border-radius: 2px;\n",
" overflow-x: visible;\n",
" overflow-y: visible;\n",
" box-shadow: none;\n",
" position: absolute;\n",
" z-index: 1000;\n",
"}\n",
".ipython_tooltip .tooltiptext pre {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt;\n",
" line-height: 170%;\n",
" background: #eeeeee;\n",
" background-color: #eeeeee;\n",
" color: #303030;\n",
" overflow-x: visible;\n",
" overflow-y: visible;\n",
" max-width: 900px;\n",
"}\n",
"div#tooltip.ipython_tooltip {\n",
" overflow-x: wrap;\n",
" overflow-y: visible;\n",
" max-width: 800px;\n",
"}\n",
"div.tooltiptext.bigtooltip {\n",
" overflow-x: visible;\n",
" overflow-y: scroll;\n",
" height: 400px;\n",
" max-width: 800px;\n",
"}\n",
".cm-s-ipython.CodeMirror {\n",
" font-family: \"Droid Sans Mono\", monospace;\n",
" font-size: 11pt;\n",
" background: #efefef;\n",
" color: #303030;\n",
" border-radius: 2px;\n",
" font-style: normal;\n",
" font-weight: normal;\n",
"}\n",
".cm-s-ipython div.CodeMirror-selected {\n",
" background: #e0e1e3;\n",
"}\n",
".cm-s-ipython .CodeMirror-gutters {\n",
" background: #e0e1e3;\n",
" border: none;\n",
" border-radius: 0px;\n",
"}\n",
".cm-s-ipython .CodeMirror-linenumber {\n",
" color: #aaaaaa;\n",
"}\n",
".cm-s-ipython .CodeMirror-cursor {\n",
" border-left: 2px solid #ff711a;\n",
"}\n",
".cm-s-ipython span.cm-comment {\n",
" color: #8d8d8d;\n",
" font-style: italic;\n",
"}\n",
".cm-s-ipython span.cm-atom {\n",
" color: #055be0;\n",
"}\n",
".cm-s-ipython span.cm-number {\n",
" color: #ff8132;\n",
"}\n",
".cm-s-ipython span.cm-property {\n",
" color: #e22978;\n",
"}\n",
".cm-s-ipython span.cm-attribute {\n",
" color: #de143d;\n",
"}\n",
".cm-s-ipython span.cm-keyword {\n",
" color: #713bc5;\n",
" font-weight: normal;\n",
"}\n",
".cm-s-ipython span.cm-string {\n",
" color: #009e07;\n",
"}\n",
".cm-s-ipython span.cm-meta {\n",
" color: #aa22ff;\n",
"}\n",
".cm-s-ipython span.cm-operator {\n",
" color: #055be0;\n",
"}\n",
".cm-s-ipython span.cm-builtin {\n",
" color: #e22978;\n",
"}\n",
".cm-s-ipython span.cm-variable {\n",
" color: #303030;\n",
"}\n",
".cm-s-ipython span.cm-variable-2 {\n",
" color: #de143d;\n",
"}\n",
".cm-s-ipython span.cm-variable-3 {\n",
" color: #aa22ff;\n",
"}\n",
".cm-s-ipython span.cm-def {\n",
" color: #e22978;\n",
" font-weight: normal;\n",
"}\n",
".cm-s-ipython span.cm-error {\n",
" background: rgba(191,97,106,.40);\n",
"}\n",
".cm-s-ipython span.cm-tag {\n",
" color: #e22978;\n",
"}\n",
".cm-s-ipython span.cm-link {\n",
" color: #ff7823;\n",
"}\n",
".cm-s-ipython span.cm-storage {\n",
" color: #055be0;\n",
"}\n",
".cm-s-ipython span.cm-entity {\n",
" color: #e22978;\n",
"}\n",
".cm-s-ipython span.cm-quote {\n",
" color: #009e07;\n",
"}\n",
"div.CodeMirror span.CodeMirror-matchingbracket {\n",
" color: #1c1c1c;\n",
" background-color: rgba(30,112,199,.30);\n",
"}\n",
"div.CodeMirror span.CodeMirror-nonmatchingbracket {\n",
" color: #1c1c1c;\n",
" background: rgba(191,97,106,.40) !important;\n",
"}\n",
"div.cell.text_cell .cm-s-default .cm-header {\n",
" color: #126dce;\n",
"}\n",
"div.cell.text_cell .cm-s-default span.cm-variable-2 {\n",
" color: #353535;\n",
"}\n",
"div.cell.text_cell .cm-s-default span.cm-variable-3 {\n",
" color: #aa22ff;\n",
"}\n",
".cm-s-default span.cm-comment {\n",
" color: #8d8d8d;\n",
"}\n",
".cm-s-default .cm-tag {\n",
" color: #009fb7;\n",
"}\n",
".cm-s-default .cm-builtin {\n",
" color: #e22978;\n",
"}\n",
".cm-s-default .cm-string {\n",
" color: #009e07;\n",
"}\n",
".cm-s-default .cm-keyword {\n",
" color: #713bc5;\n",
"}\n",
".cm-s-default .cm-number {\n",
" color: #ff8132;\n",
"}\n",
".cm-s-default .cm-error {\n",
" color: #055be0;\n",
"}\n",
".CodeMirror-cursor {\n",
" border-left: 2px solid #ff711a;\n",
" border-right: none;\n",
" width: 0;\n",
"}\n",
".cm-s-default div.CodeMirror-selected {\n",
" background: #e0e1e3;\n",
"}\n",
".cm-s-default .cm-selected {\n",
" background: #e0e1e3;\n",
"}\n",
"div#maintoolbar {\n",
" display: none !important;\n",
"}\n",
"#header-container {\n",
" display: none !important;\n",
"}\n",
"\n",
"/**********************************\n",
" MathJax Settings and Style Script\n",
"**********************************/\n",
".MathJax_Display,\n",
".MathJax nobr>span.math>span {\n",
" border: 0 !important;\n",
" font-size: 110% !important;\n",
" text-align: center !important;\n",
" margin: 0em !important;\n",
"}\n",
"/* Prevents MathJax from jittering */\n",
"/* cell position when cell is selected */\n",
".MathJax:focus, body :focus .MathJax {\n",
" display: inline-block !important;\n",
"}\n",
"\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" \"HTML-CSS\": {\n",
" preferredFont: \"TeX\",\n",
" availableFonts: [\"STIX\",\"TeX\"],\n",
" styles: {\n",
" scale: 110,\n",
" \".MathJax_Display\": {\n",
" \"font-size\": \"110%\",\n",
" }\n",
" }\n",
" }\n",
" });\n",
"</script>\n",
"\n",
" </style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from jupyterthemes import get_themes\n",
"from jupyterthemes.stylefx import set_nb_theme\n",
"themes = get_themes()\n",
"set_nb_theme(themes[1])"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ethen 2017-02-05 15:35:19 \n",
"\n",
"CPython 3.5.2\n",
"IPython 4.2.0\n",
"\n",
"numpy 1.12.0\n",
"matplotlib 2.0.0\n",
"jupyterthemes 0.13.9\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from scipy.stats import rankdata, spearmanr, pearsonr\n",
"\n",
"# 1. magic for inline plot\n",
"# 2. magic to print version\n",
"# 3. magic so that the notebook will reload external python modules\n",
"%matplotlib inline\n",
"%load_ext watermark\n",
"%load_ext autoreload \n",
"%autoreload 2\n",
"\n",
"%watermark -a 'Ethen' -d -t -v -p numpy,matplotlib,jupyterthemes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Correlation\n",
"\n",
"A correlation coefficient measures the extent to which two variables tend to change together. The coefficient describes both the strength and the direction of the relationship. The two most popular types of correlation are **Pearson correlation** and **Spearman correlation**.\n",
"\n",
"The Pearson correlation evaluates the linear relationship between two continuous variables. A relationship is linear when a change in one variable is associated with a constant proportional change in the other variable. On the other hand, the Spearman correlation evaluates the monotonic relationship between two continuous or ordinal variables. In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data.\n",
"\n",
"The Pearson and Spearman correlation coefficients can range in value from −1 to +1. For the Pearson correlation coefficient to be +1, when one variable increases then the other variable increases by a consistent amount. This relationship forms a perfect line. The Spearman correlation coefficient is also +1 in this case."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x106c972e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.rcParams['figure.figsize'] = 8, 6\n",
"\n",
"# y1 is simply 3 times x, so 1 unit\n",
"# increase in x increases y by 3 unit\n",
"x = np.arange(1, 101)\n",
"y1 = 3 * x\n",
"plt.scatter(x, y1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pearson: 1.0\n",
"spearman: 1.0\n"
]
}
],
"source": [
"print( 'pearson: ', pearsonr(x, x)[0] )\n",
"print( 'spearman: ', spearmanr(x, x)[0] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the relationship is that one variable increases when the other increases, but the amount is not consistent, the Pearson correlation coefficient is positive but less than +1. The Spearman coefficient still equals +1 in this case."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x106c82fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# y2 is the exponential of x,\n",
"# thus the despite the fact that\n",
"# y2 will increase when x is increased\n",
"# by a unit, but the relationship is not a constant\n",
"y2 = np.exp(x)\n",
"plt.scatter(x, y2)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pearson: 0.252032033904\n",
"spearman: 1.0\n"
]
}
],
"source": [
"print( 'pearson: ', pearsonr(x, y2)[0] )\n",
"print( 'spearman: ', spearmanr(x, y2)[0] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The idea can be generalized to negative relationships and when a relationship is random or non-existent, then both correlation coefficients are nearly zero.\n",
"\n",
"It is always a good idea to examine the relationship between variables with a scatterplot. Correlation coefficients only measure linear (Pearson) or monotonic (Spearman) relationships. Other relationships are possible."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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MTWdHr1Xf/1nSBjPbLukDkv6HpBskLTWznZIuiD7OtZEFQ1p8xoy219CrBlAUcXPTkycw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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c202198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create 1000 equally spaced points between -10 and 10\n",
"# calculate the y value for each element of the x vector\n",
"x3 = np.linspace(-10, 10, 1000)\n",
"y3 = x3 ** 2 + 2 * x3 + np.random.normal(loc = 0, scale = 5)\n",
"plt.scatter(x3, y3)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pearson: 0.360844075487\n",
"spearman: 0.269785169785\n"
]
}
],
"source": [
"print( 'pearson: ', pearsonr(x3, y3)[0] )\n",
"print( 'spearman: ', spearmanr(x3, y3)[0] )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the plot above, we can see an obvious parabola relationship, thus the Pearson coefficient and Spearman coefficient will not be very helpful in discovering the pattern.\n",
"\n",
"To understand how Spearman coefficient is calculated, we need to know that it is a rank correlation coefficient. Rank correlations are performed on ranks instead of the raw data itself. This can be very advantageous when dealing with data with outliers. For example, given two sets of data, say x = [5.05, 6.75, 3.21, 2.66] and y = [1.65, 26.5, -5.93, 7.96], with some ordering (here numerical) we can give them the ranks [3, 4, 2, 1] and [2, 4, 1, 3] respectively.\n",
"\n",
"Tied ranks are usually assigned using the midrank method, whereby those entries receive the mean of the ranks they would have received had they not been tied. Thus z = [1.65, 2.64, 2.64, 6.95] would yield ranks [1, 2.5, 2.5, 4] using the midrank method."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 3., 4., 2., 1.])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = [5.05, 6.75, 3.21, 2.66]\n",
"rankdata(x)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1. , 2.5, 2.5, 4. ])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the default method is average (tied rank)\n",
"z = [1.65, 2.64, 2.64, 6.95]\n",
"rankdata(z, method = 'average')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After obtaining the rank of the data, we can then use the following formula to perform the computation.\n",
"\n",
"\\begin{align}\n",
"\\rho &= 1 - \\frac{6 \\sum_i (d_i)^2 }{ n(n^2 - 1) }\n",
"\\end{align}\n",
"\n",
"Where $d$ is the difference between the two ranks and $n$ is the number of elements in the vector."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def compute_spearman(a, b):\n",
" \"\"\"pass in the two vectors a and b\"\"\"\n",
" rank1 = rankdata(a)\n",
" rank2 = rankdata(b)\n",
" n = rank1.shape[0]\n",
" spearman = 1 - 6 * np.sum( (rank2 - rank1) ** 2 ) / ( n * (n ** 2 - 1) )\n",
" return spearman"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.26978516978516975"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"compute_spearman(x3, y3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reference\n",
"\n",
"- [Blog: Using Python (and R) to calculate Rank Correlations](http://www2.warwick.ac.uk/fac/sci/moac/people/students/peter_cock/python/rank_correlations/)\n",
"- [Blog: Spearman Rank Correlation (Spearman’s Rho): Definition and How to Calculate it](http://www.statisticshowto.com/spearman-rank-correlation-definition-calculate/)\n",
"- [StackExchange: How to choose between Pearson and Spearman correlation?](http://stats.stackexchange.com/questions/8071/how-to-choose-between-pearson-and-spearman-correlation)\n",
"- [Minitab Support: A comparison of the Pearson and Spearman correlation methods](http://support.minitab.com/en-us/minitab-express/1/help-and-how-to/modeling-statistics/regression/supporting-topics/basics/a-comparison-of-the-pearson-and-spearman-correlation-methods/)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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 |
jinntrance/MOOC | coursera/ml-foundations/week1/Getting Started with SFrames.ipynb | 1 | 44795 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Fire up GraphLab Create\n",
"\n",
"We always start with this line before using any part of GraphLab Create"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphlab"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Load a tabular data set"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] This non-commercial license of GraphLab Create is assigned to [email protected] will expire on September 27, 2016. For commercial licensing options, visit https://dato.com/buy/.\n",
"\n",
"[INFO] Start server at: ipc:///tmp/graphlab_server-2994 - Server binary: /usr/local/lib/python2.7/site-packages/graphlab/unity_server - Server log: /tmp/graphlab_server_1443450134.log\n",
"[INFO] GraphLab Server Version: 1.6.1\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Finished parsing file /Users/joseph/workspaces/mmds/week1/people-example.csv\n",
"PROGRESS: Parsing completed. Parsed 7 lines in 0.023953 secs.\n",
"------------------------------------------------------\n",
"Inferred types from first line of file as \n",
"column_type_hints=[str,str,str,int]\n",
"If parsing fails due to incorrect types, you can correct\n",
"the inferred type list above and pass it to read_csv in\n",
"the column_type_hints argument\n",
"------------------------------------------------------\n",
"PROGRESS: Finished parsing file /Users/joseph/workspaces/mmds/week1/people-example.csv\n",
"PROGRESS: Parsing completed. Parsed 7 lines in 0.012013 secs.\n"
]
}
],
"source": [
"sf = graphlab.SFrame('people-example.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#SFrame basics"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">First Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Last Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Country</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">age</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bob</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Smith</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alice</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Williams</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Canada</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Malcolm</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Jone</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">England</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Felix</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">USA</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alex</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cooper</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Poland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Tod</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Campbell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Derek</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Ward</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Switzerland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25</td>\n",
" </tr>\n",
"</table>\n",
"[7 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tFirst Name\tstr\n",
"\tLast Name\tstr\n",
"\tCountry\tstr\n",
"\tage\tint\n",
"\n",
"Rows: 7\n",
"\n",
"Data:\n",
"+------------+-----------+---------------+-----+\n",
"| First Name | Last Name | Country | age |\n",
"+------------+-----------+---------------+-----+\n",
"| Bob | Smith | United States | 24 |\n",
"| Alice | Williams | Canada | 23 |\n",
"| Malcolm | Jone | England | 22 |\n",
"| Felix | Brown | USA | 23 |\n",
"| Alex | Cooper | Poland | 23 |\n",
"| Tod | Campbell | United States | 22 |\n",
"| Derek | Ward | Switzerland | 25 |\n",
"+------------+-----------+---------------+-----+\n",
"[7 rows x 4 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf #we can view first few lines of table"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">First Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Last Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Country</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">age</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bob</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Smith</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alice</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Williams</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Canada</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Malcolm</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Jone</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">England</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Felix</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">USA</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alex</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cooper</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Poland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Tod</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Campbell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Derek</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Ward</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Switzerland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25</td>\n",
" </tr>\n",
"</table>\n",
"[7 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tFirst Name\tstr\n",
"\tLast Name\tstr\n",
"\tCountry\tstr\n",
"\tage\tint\n",
"\n",
"Rows: 7\n",
"\n",
"Data:\n",
"+------------+-----------+---------------+-----+\n",
"| First Name | Last Name | Country | age |\n",
"+------------+-----------+---------------+-----+\n",
"| Bob | Smith | United States | 24 |\n",
"| Alice | Williams | Canada | 23 |\n",
"| Malcolm | Jone | England | 22 |\n",
"| Felix | Brown | USA | 23 |\n",
"| Alex | Cooper | Poland | 23 |\n",
"| Tod | Campbell | United States | 22 |\n",
"| Derek | Ward | Switzerland | 25 |\n",
"+------------+-----------+---------------+-----+\n",
"[7 rows x 4 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf.tail() # view end of the table"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#GraphLab Canvas"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Canvas is accessible via web browser at the URL: http://localhost:51700/index.html\n",
"Opening Canvas in default web browser.\n"
]
}
],
"source": [
"# .show() visualizes any data structure in GraphLab Create\n",
"sf.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# If you want Canvas visualization to show up on this notebook, \n",
"# rather than popping up a new window, add this line:\n",
"graphlab.canvas.set_target('ipynb')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"$(\"head\").append($(\"<link/>\").attr({\n",
" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
"}));\n",
"$(\"head\").append($(\"<link/>\").attr({\n",
" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//dato.com/files/canvas/1.5.2/css/canvas.css\"\n",
"}));\n",
"\n",
" (function(){\n",
"\n",
" var e = null;\n",
" if (typeof element == 'undefined') {\n",
" var scripts = document.getElementsByTagName('script');\n",
" var thisScriptTag = scripts[scripts.length-1];\n",
" var parentDiv = thisScriptTag.parentNode;\n",
" e = document.createElement('div');\n",
" parentDiv.appendChild(e);\n",
" } else {\n",
" e = element[0];\n",
" }\n",
"\n",
" require(['//dato.com/files/canvas/1.5.2/js/ipython_app.js'], function(IPythonApp){\n",
" var app = new IPythonApp();\n",
" app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 0.9897433186107858, \"complete\": true, \"min\": 22.0, \"max\": 25.0, \"quantile\": [22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 22.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 23.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 24.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0, 25.0], \"median\": 23.0, \"numeric\": true, \"num_unique\": 4, \"num_undefined\": 0, \"var\": 0.9795918367346915, \"progress\": 1.0, \"size\": 7, \"frequent_items\": {\"24\": {\"frequency\": 1, \"value\": 24}, \"25\": {\"frequency\": 1, \"value\": 25}, \"22\": {\"frequency\": 2, \"value\": 22}, \"23\": {\"frequency\": 3, \"value\": 23}}, \"mean\": 23.142857142857146}, \"selected_variable\": {\"name\": [\"<temporary SArray>\"], \"dtype\": \"int\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 7}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 25.016, \"bins\": [2, 0, 0, 0, 3, 0, 0, 1, 0, 0, 0, 1], \"min\": 21.992}, \"min\": 22, \"complete\": 1, \"max\": 25}}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sf['age'].show(view='Categorical')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Inspect columns of dataset"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: str\n",
"Rows: 7\n",
"['United States', 'Canada', 'England', 'USA', 'Poland', 'United States', 'Switzerland']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['Country']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: int\n",
"Rows: 7\n",
"[24, 23, 22, 23, 23, 22, 25]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['age']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some simple columnar operations"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"23.142857142857146"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['age'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['age'].max()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Create new columns in our SFrame"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">First Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Last Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Country</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">age</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bob</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Smith</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alice</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Williams</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Canada</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Malcolm</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Jone</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">England</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Felix</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">USA</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alex</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cooper</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Poland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Tod</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Campbell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Derek</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Ward</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Switzerland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25</td>\n",
" </tr>\n",
"</table>\n",
"[7 rows x 4 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tFirst Name\tstr\n",
"\tLast Name\tstr\n",
"\tCountry\tstr\n",
"\tage\tint\n",
"\n",
"Rows: 7\n",
"\n",
"Data:\n",
"+------------+-----------+---------------+-----+\n",
"| First Name | Last Name | Country | age |\n",
"+------------+-----------+---------------+-----+\n",
"| Bob | Smith | United States | 24 |\n",
"| Alice | Williams | Canada | 23 |\n",
"| Malcolm | Jone | England | 22 |\n",
"| Felix | Brown | USA | 23 |\n",
"| Alex | Cooper | Poland | 23 |\n",
"| Tod | Campbell | United States | 22 |\n",
"| Derek | Ward | Switzerland | 25 |\n",
"+------------+-----------+---------------+-----+\n",
"[7 rows x 4 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sf['Full Name'] = sf['First Name'] + ' ' + sf['Last Name']"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">First Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Last Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Country</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">age</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Full Name</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bob</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Smith</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bob Smith</td>\n",
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" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alice</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Williams</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Canada</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alice Williams</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Malcolm</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Jone</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">England</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Malcolm Jone</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Felix</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">USA</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Felix Brown</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alex</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cooper</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Poland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alex Cooper</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Tod</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Campbell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Tod Campbell</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Derek</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Ward</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Switzerland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Derek Ward</td>\n",
" </tr>\n",
"</table>\n",
"[7 rows x 5 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tFirst Name\tstr\n",
"\tLast Name\tstr\n",
"\tCountry\tstr\n",
"\tage\tint\n",
"\tFull Name\tstr\n",
"\n",
"Rows: 7\n",
"\n",
"Data:\n",
"+------------+-----------+---------------+-----+----------------+\n",
"| First Name | Last Name | Country | age | Full Name |\n",
"+------------+-----------+---------------+-----+----------------+\n",
"| Bob | Smith | United States | 24 | Bob Smith |\n",
"| Alice | Williams | Canada | 23 | Alice Williams |\n",
"| Malcolm | Jone | England | 22 | Malcolm Jone |\n",
"| Felix | Brown | USA | 23 | Felix Brown |\n",
"| Alex | Cooper | Poland | 23 | Alex Cooper |\n",
"| Tod | Campbell | United States | 22 | Tod Campbell |\n",
"| Derek | Ward | Switzerland | 25 | Derek Ward |\n",
"+------------+-----------+---------------+-----+----------------+\n",
"[7 rows x 5 columns]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: int\n",
"Rows: 7\n",
"[576, 529, 484, 529, 529, 484, 625]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['age'] * sf['age']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Use the apply function to do a advance transformation of our data"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype: str\n",
"Rows: 7\n",
"['United States', 'Canada', 'England', 'USA', 'Poland', 'United States', 'Switzerland']"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['Country']"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
"$(\"head\").append($(\"<link/>\").attr({\n",
" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
"}));\n",
"$(\"head\").append($(\"<link/>\").attr({\n",
" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//dato.com/files/canvas/1.5.2/css/canvas.css\"\n",
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" var app = new IPythonApp();\n",
" app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"complete\": true, \"numeric\": false, \"num_unique\": 6, \"num_undefined\": 0, \"progress\": 1.0, \"frequent_items\": {\"Canada\": {\"frequency\": 1, \"value\": \"Canada\"}, \"England\": {\"frequency\": 1, \"value\": \"England\"}, \"USA\": {\"frequency\": 1, \"value\": \"USA\"}, \"Poland\": {\"frequency\": 1, \"value\": \"Poland\"}, \"United States\": {\"frequency\": 2, \"value\": \"United States\"}, \"Switzerland\": {\"frequency\": 1, \"value\": \"Switzerland\"}}, \"size\": 7}, \"selected_variable\": {\"name\": [\"<temporary SArray>\"], \"dtype\": \"str\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 7}, \"type\": \"SArray\", \"view_components\": [\"Categorical\"]}, \"histogram\": null}, e);\n",
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"source": [
"sf['Country'].show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def transform_country(country):\n",
" if country == 'USA':\n",
" return 'United States'\n",
" else:\n",
" return country"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Brazil'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transform_country('Brazil')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'Brasil'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transform_country('Brasil')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'United States'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transform_country('USA')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PROGRESS: Using default 16 lambda workers.\n",
"PROGRESS: To maximize the degree of parallelism, add the following code to the beginning of the program:\n",
"PROGRESS: \"graphlab.set_runtime_config('GRAPHLAB_DEFAULT_NUM_PYLAMBDA_WORKERS', 32)\"\n",
"PROGRESS: Note that increasing the degree of parallelism also increases the memory footprint.\n"
]
},
{
"data": {
"text/plain": [
"dtype: str\n",
"Rows: 7\n",
"['United States', 'Canada', 'England', 'United States', 'Poland', 'United States', 'Switzerland']"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sf['Country'].apply(transform_country)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sf['Country'] = sf['Country'].apply(transform_country)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
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" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Last Name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Country</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">age</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Bob</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Smith</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">24</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alice</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Williams</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Canada</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Malcolm</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Jone</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">England</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Felix</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Brown</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">USA</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Alex</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Cooper</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Poland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">23</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Tod</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Campbell</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">United States</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">22</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Derek</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Ward</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Switzerland</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">25</td>\n",
" </tr>\n",
"</table>\n",
"[7 rows x 4 columns]<br/>\n",
"</div>"
],
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"Columns:\n",
"\tFirst Name\tstr\n",
"\tLast Name\tstr\n",
"\tCountry\tstr\n",
"\tage\tint\n",
"\n",
"Rows: 7\n",
"\n",
"Data:\n",
"+------------+-----------+---------------+-----+\n",
"| First Name | Last Name | Country | age |\n",
"+------------+-----------+---------------+-----+\n",
"| Bob | Smith | United States | 24 |\n",
"| Alice | Williams | Canada | 23 |\n",
"| Malcolm | Jone | England | 22 |\n",
"| Felix | Brown | USA | 23 |\n",
"| Alex | Cooper | Poland | 23 |\n",
"| Tod | Campbell | United States | 22 |\n",
"| Derek | Ward | Switzerland | 25 |\n",
"+------------+-----------+---------------+-----+\n",
"[7 rows x 4 columns]"
]
},
"execution_count": 8,
"metadata": {},
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],
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"sf"
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},
{
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"metadata": {
"collapsed": true
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}
],
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| cc0-1.0 |
jakobrunge/tigramite | neurips2020/river_discharge.ipynb | 1 | 726392 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An Application To Real Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font size = \"4\">\n",
"This notebook replicates the experiments that are being discussed in section 5 of the main paper.\n",
" \n",
"The river discharge data, here provided in the files `data_dillingen.csv`, `data_kempten.csv` and `data_lenggries.csv` in the folder `river_discharge_data` , has been downloaded from the Bavarian Environmental Agency (Bayerisches Landesamt für Umwelt, www.lfu.bayern.de) at https://www.gkd.bayern.de/en/rivers/discharge.\n",
"</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline \n",
"## use `%matplotlib notebook` for interactive figures\n",
"# plt.style.use('ggplot')\n",
"\n",
"from tigramite import data_processing as pp\n",
"from tigramite import plotting as tp\n",
"from tigramite.independence_tests import ParCorr\n",
"\n",
"from lpcmci import LPCMCI\n",
"from svarfci import SVARFCI"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load And Prepare Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# The measurement stations that we consider\n",
"stations = [\"dillingen\", \"kempten\", \"lenggries\"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Read the average daily discharges at each of these stations and combine them into a single pandas dataframe\n",
"average_discharges = None\n",
"for station in stations:\n",
" \n",
" new_frame = pd.read_csv(\"river_discharge_data/data_\" + station +\".csv\", sep = \";\", skiprows = range(10))\n",
" new_frame = new_frame[[\"Datum\", \"Mittelwert\"]]\n",
" \n",
" new_frame = new_frame.rename(columns = {\"Mittelwert\": station.capitalize(), \"Datum\": \"Date\"})\n",
" new_frame.replace({\",\": \".\"}, regex = True, inplace = True)\n",
" \n",
" new_frame[station.capitalize()] = new_frame[station.capitalize()].astype(float)\n",
"\n",
" if average_discharges is None:\n",
" average_discharges = new_frame\n",
" else:\n",
" average_discharges = average_discharges.merge(new_frame, on = \"Date\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>Date</th>\n",
" <th>Dillingen</th>\n",
" <th>Kempten</th>\n",
" <th>Lenggries</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-01-01</td>\n",
" <td>70.1</td>\n",
" <td>12.2</td>\n",
" <td>8.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-01-02</td>\n",
" <td>64.5</td>\n",
" <td>12.4</td>\n",
" <td>8.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-01-03</td>\n",
" <td>64.5</td>\n",
" <td>12.3</td>\n",
" <td>8.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-01-04</td>\n",
" <td>64.4</td>\n",
" <td>12.0</td>\n",
" <td>8.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-01-05</td>\n",
" <td>67.7</td>\n",
" <td>12.3</td>\n",
" <td>8.51</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Dillingen Kempten Lenggries\n",
"0 2017-01-01 70.1 12.2 8.64\n",
"1 2017-01-02 64.5 12.4 8.54\n",
"2 2017-01-03 64.5 12.3 8.47\n",
"3 2017-01-04 64.4 12.0 8.45\n",
"4 2017-01-05 67.7 12.3 8.51"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at the data\n",
"average_discharges.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Prepare a tigramite dataframe containing the discharge values only\n",
"var_names = [station.capitalize() for station in stations]\n",
"data = pp.DataFrame(average_discharges[var_names].values, var_names = var_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize The Data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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 the prepared time series dataset\n",
"tp.plot_timeseries(data, figsize=(15, 5))\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LPCMCI with \\\\( \\alpha = 0.01\\\\)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) <-> (0, 0)\n",
"(0,-2) <-> (0, 0)\n",
"(1,-1) --> (0, 0)\n",
"(0, 0) <-> (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(1,-2) --> (1, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.01\n",
"n_preliminary_iterations = 0\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.01\n",
"n_preliminary_iterations = 1\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.01\n",
"n_preliminary_iterations = 2\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(0, 0) <-- (2, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.01\n",
"n_preliminary_iterations = 3\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(0, 0) <-- (2, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.01\n",
"n_preliminary_iterations = 4\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## LPCMCI with \\\\( \\alpha = 0.05\\\\)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(2,-1) <-> (1, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.05\n",
"n_preliminary_iterations = 0\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(2,-1) <-> (1, 0)\n",
"(0, 0) <-- (2, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.05\n",
"n_preliminary_iterations = 1\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(2,-1) <-> (1, 0)\n",
"(0, 0) <-- (2, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.05\n",
"n_preliminary_iterations = 2\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(2,-1) <-> (1, 0)\n",
"(0, 0) <-- (2, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.05\n",
"n_preliminary_iterations = 3\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0,-2) --> (0, 0)\n",
"(1,-2) --> (0, 0)\n",
"(0, 0) <-- (1, 0)\n",
"(1,-1) --> (1, 0)\n",
"(2,-1) <-> (1, 0)\n",
"(0, 0) <-- (2, 0)\n",
"(1, 0) <-> (2, 0)\n",
"(2,-1) --> (2, 0)\n",
"(2,-2) --> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.05\n",
"n_preliminary_iterations = 4\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"lpcmci = LPCMCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = lpcmci.run_lpcmci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" n_preliminary_iterations = n_preliminary_iterations,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"lpcmci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = lpcmci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SVAR-FCI for various \\\\( \\alpha\\\\)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.01\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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6WrteJurVTO6yoFFsoYUXFp4BMBgEwXvS2KZIAxyZ1oYGZu/LU0e8qQDcEATBJ9ParkgdHY+bnTRNO3Hn4nN7GmrK57RjpUkN6nUEMNGbf9RiyoOVRBpoKpeGVmKoT70SqYd5pJevSiKmdrndtFfrP6x0hF6tQdgn8b601eNezrp7kTSTeuw8pzKuRaQBItKvA4b65JZpo9YE/0j8XG1GomPj52rn6KdqPel/EbrtqjSTtMvrIG7SHJFm8DzpJ/jZ8XZzq9YEf0v8fLExZsTvGGP2xs1DvBN3x6B6eBA3FWaawpS3J1JPD6e8vR2oDkjzWM3UJzlLGgJeSHmb00pNCd5auxY3e9WRwAcTL38adx5jWT2ugY/9hnRHT+4GVqa4PZF6uxV31J2W2cB9KW5PpG6CIHgRSPuWwmHeZ7KbyKCFPwM2Al8yxtxgjPmcMeZm3Cx2j+Jux1oXQRCsw01RmBaLuzRIpFn8N+km+MeCIHgmxe2J1Nt3SW/cyDbgP1Pa1rRVc4KPj+LPAL4FnI2b6vVo4EvAufWchz72NVy3YhrWBkHweErbEqm7IAhW42a3S8N23NTTIs3km0Ba16zPAK5LaVvT1oQuO7DWPm2tfa+19mBr7Sxr7WJr7V9YazfXK8Ay/447zz9V24GPpLAdkUb7KOmMRdmGayxFmkYQBPfjbhU71aP4ncCXgyDI/UDrVGaHa4QgCHaEYfhhXKIf99rFQrSLw7c9wNziy5hoEFuYyY6WfaJn5r76vhNOPWvUfYZFmsBPgVW4uziOOSalcvnflw3zT9leLMz5YBAEmrlRmtGHcIOux81d1erAM/NP2TlUmPOZ+oeavQndDz5rYRga3HmYSxhjZrtCtIvjtt5OYcHhmCPPg3kHwvZN2PW3Y7c8tdsUBxYX2rpyPXpS8ikMw4NxDdz+DE8wNcLY5f/poinuPlTlX5pVGIZX4E4xTTYHDJjiwBE+1IG0Zwaqq3jE45W4EfBVuyoP3/aA+2JPvhQzfxHGFNzzyZdiFhwxG9OiAXbSlIIgeA53s46XqDLobuzyf3iLyr80syAIvg38H8Y4ZTtODpjlSx1oqgQPEN8Y4G3AP+K+4FHnY+YWX8YceR7GjDzAMcZgFp8HpvD6RsQqUg9BEDyIuwHTg7jz6SOo/EveBUHwOeAy4BUqJHrVAafpEjxAEARREARXA6fh7t0+gLtGcjuww0SDrkumknkHQDRYsWtTpFnER/Jn4u5p/QjDdUDlX7wQBMFPcHOzfAG3o1t6qA7EmmaQXSVBEDwCXBaG4WzgtcCJQIstzPy62b7JMH/R6F/a/iIUZjbPwAORKoIgiIAfAj8Mw3Ah8Dpgocq/+CIIgs3AVWEYfhLX/p8OzLWFmV9THWjyBF8Sjwi+I34QrVzx+3b97W2cfOmILhprLfbJ28FGmsVOciUIgo3ADaDyL/6Jd3ZXxw+ilSt+T3WgSbvox2WLl7Plyd129fXYV17ARkX3vPp62PLkbmzx8qxDFKkblX/xneoA0GSXyU1E1NezCFO4A9PyKqJBKMyEaGgz2JN8uDxC/ObKf8s1mELbcPkvAtEHCm1d/5F1fCL1FueA/8W0nF5WB4YgOqrQ1rUh6/gaIZ9H8EChresFbPQOovhKomgQsPNI/650ItNOoa3rhcLrP34h0eA/AnH5jwAuzDAskYaJc8CZRIMbgVIdmIE7V++F3Cb42EPAE2XLs4ELMopFJAs/Tiy/Kerr8WIEsUihrSsC/iex+k1ZxJKFXCf4QluXxd3mtpwX1z+KxO5k5LXyBwLHZxSLSBa8zQG5TvCxvsSyN1+uSKGtawi4PbFadUB8khwx/9qor2d+JpE0mA8J/tbE8plRX0/VOYxFcijZwCnBizcKbV3PA4+WrZoBnJNROA2V+wRfaOt6Blhbtmomnny5IrFkgm/TeXjxzKg6kEkUDZb7BB/TEYz47FdA+e1hDwMWZxSLSBa8zAFK8CI5V2jr2g3clVitOiA+SeaAs6O+njmZRNJAvib4c6O+nlmZRCKSDe3kircKbV1PAk+VrZqNu1lTrvmS4NcB5TMXzQHOyCgWkSzoahLxnXc7uV4k+Ph6eO++XJEydwFDZcvHRn09B2cVjEgGvMsBXiT4mJejKEUACm1d24F7E6tz38CJlEnmgPOivp6ZmUTSID4n+POivp5c3C5XpEbeHcGIlHkU2Fi2PA84LaNYGsKnBL8G2FS2vDcQZBSLSBZG7eRmEoVIBqqcqs11HfAmwcdf7t2J1SdlEYtIRpKXyp0Q9fW0ZBKJSDaSdSDXOcCbBB97OLGc6y9XpFyhraufkb1Ys4GjMgpHJAvJHJDrW8f6nuBz/eWKVPBQYll1QHySLP8n5XnaZt8SvBo38Z12csVnTwE7ypb3AxZmFEvd+Zbg1ySWj837ZRIiCTpNJd4qtHVFwCOJ1bndyfUqwRfaul4Gni1bNQM4JqNwRLKgXizx3ahu+kyiaACvEnzMmy9XpIJRXfR5PgcpUoE3p6l8TPDefLkiFTwLbC1b3hs4NKNYRLLgTS+WEryO4MUj8XwQqgPiM2/Kv48J3pu9N5EqVAfEZ2uBwbLlg6O+ngVZBVNPPib45N6bZvMS3+g0lXir0NY1CDyWWJ3LOuBjgt8EbC5bngMszigWkSx400UpUoUXO7neJfj4HGRy702DjMQnKv/iOy/qgHcJPrYpsXxgJlGIZEPlX3znRR1QgncOyCQKkWxsAYply3tHfT2zswpGJANK8DnmxZcrUkk8XeeLidXayRWfeHGQ52uCTzZuSvDiG9UB8ZkX5d/XBO/F3pvIGNSLJT7zovwrwTu5/HJFxqA6ID4bVf7zeE8GXxO8F90zImPQOXjxVqGtawews2zVTNx9GXLF1wSvLnrxnY7gxXe5rwNK8E4uu2dExpD7xk1kHLmvA74m+G3A7rLlOcC8jGIRyULuGzeRceS+DniZ4OPpanUOUnym8i++y30d8DLBxzYnlnN5u0CRKlT+xXe5rwM+J/go6wBEMqTyL77LfR3wOcGLiIjklhK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA75nOBbEss2kyhEsqHyL77LfR3wOcHvn1jekkkUItlQ+Rff5b4OeJngo74ew+h7/ybvDSySZyr/4rvc1wEvEzywNzCzbHlnoa1rR1bBiGQg942byDhyXwd8TfC5/2JFxnFAYvnFTKIQyU7u64ASvKMEL75RHRBv+XKa1tcEn/s9N5Fx5L5xExnDPGBO2fJuYFtGsdSNrwlejZv4TnVAfDaq/BfaunSZXE6ocRPfqRdLfOZF+fc1wXvx5YqMQTu54jMvyr+vCd6LL1ekkqivpwC0JlZrJ1d84kUO8DXBJ4/gc/nlilSxHyPr/suFtq6BrIIRyYAXOcDXBL8wsayjF/GJyr/4zos64F2Cj69/PC6x+uksYhHJiMq/+M6LOuBdggcWAQvKlreT0y9XpIoTE8sPZRKFSHa8qAM+JviTEstrCm1dUSaRiGQjWQceziQKkQxEfT2zgGMSq9dkEUu9+ZjgvdhzExlDsg4owYtPjmXkveA3FNq6XskqmHpSglfjJh6Jx6BoJ1d85k359zHBq3tSfHY4bh7ukpeB5zOKRSQL3uQAHxO8N3tvIhWM6sHK4xzcImPwphfXqwQf9fXsBxxUtmoAeCKjcESykDx60Q6u+MabOuBVgmf0ntujhbauoUwiEcmGN0cvIklRX08LcHxidW7rgG8J3ptzLyJVqA6Iz44CZpctv1ho68rlLHbgX4LX+XfxlkbQi/hV/n1L8Dp6EZ8tBPYvW94JPJlRLCJZ8CoHeJPg41tknp1YvTqLWEQyck5i+WHN4iieSdaBXOcAbxI8cDLuNpklW8h594xIwusTy7dlEoVIBuKDvAsSq3NdB3xK8G2J5Vt19CKeSSb4lZlEIZKNE4HWsuWtwAMZxdIQPiV4NW7iraivZ2/g9MTqW7OIRSQjo3qwCm1dxUwiaRAvEnw8elgJXnz2OkbW9zWFtq6NWQUjkgHvcoAXCR5396BFZcvbgfsyikUkC8lTVH2ZRCGSgfggz7s64EuCT+653VFo6xrMJBKRbHh39CJS5mjg4LLlHcBvMoqlYXxN8GrcxBtRX89ewFmJ1Tr/Lj5J5oA7C21dA5lE0kC+JPhk14wSvPjkbGBm2fK6QlvX01kFI5IBLw/ycp/go76excARZat2A/dkFI5IFrxs3ETKeFkHcp/gGf3F3l1o69qVSSQi2fCycRMBiPp6DsfdZKZkALg7o3AayscEr8ZNvBH19cwCzk2szv3oYZEyydnr7im0de3MJJIGy3WCjy+N6Eis1uAi8cn5wNyy5eeAJzKKRSQLb04se5MDcp3ggdcAh5ct78SjL1cEeHtieUWhrctmEolIg0V9PTOAtyZWr8gilizkPcG/I7F8ky9dMyJxD9YlidU/ziIWkYycy8j5518Cbs8olobLbYKP+noWgfkEhfjqoMJMMIXj3XqRfIv6ehZhCndTmOkGFxVmAoUIWJVpYCINEteB747IAZgBRib8XDPW5q+3LurrWUTLzCdZsHi2OfI8mHcgbN+EXX87bHlyN8XBxYW2rheyjlOkHlT+xXeqA86MrANIQxiGrbibaRwPzDimZe6VsxYcMtucfCnGGPem+Yvg5Euxq6+fzeZ11wAXZhawSIrCMDS4sv86YOHRLfPeP3vBwSr/4o0wDPcCzgROA+Ye0zLvvbNUB5o7wYdheDbweVzDthM3WtjMtIMzzJHnDX+xMWMMLD4P+9KTyUvnRJpOGIazgCuAzwDzAQvMmWUHVP7FC2EYHg50A+8BdgFzgBkz7UCL6kCTJvgwDGcCXwDeC+wFGGBW6XUTDboumUrmHQDRoKn8okhzCMPwVbjRwAcD88pfU/kXH4Rh+B7g67g8NguYXXpNdcBpukF2cVfMSlxyn4tL7iPYwkzYvqnyBra/CIWZ+Rt4IN6Ie65W4Wbnmpd8XeVf8i4Mw3/EJfe5lB3clagOOE2V4ONzjctx17fPrfa+HS37YtffTnIAobUW++TtYCPNZidNKQzDo4Abgb2BlkrvGa/8W5V/aWJhGH4Y+BOUA8ZVU4I3xiwxxnzZGHOrMWarMcYaY75T7+Aq+CPcneH2GutNT88/hWjL09jV12NfeQEbFd3z6uuJtjxtI2svb0y4IumJd3Cvo8JRe7lxyj/r9z7rB42JWCRdYRieiBt3NaU6sHnWIX/cmIizVdNlcsaYVbij5m3ABuAE4LvW2nfXN7xhYRjuAzwF7FvL+wvRLg7f9gBzo62Y4gC2ZRY7CvuwYf4p24uFOb8fBIEm/JCmEobhZcB/MU7jBtXL/9PzTyEqzNkMHBEEwfa6By2SojAM+3DTL497cDpGDthVLMz5ZhAEf1b/iLNVa4J/Ay6xP447gr6Fxif4v8aNlhzz6L1GjwdBcGwK2xFpiPjo/UlGTr08WTuAq4Ig+JcUtiXSEGEYngXcTA07uDXYhdvJrXKiPh9q6qK31t5irX3MZjsrzgdIJ7kDHBKG4UkpbUukEU4F9k9pW3MBL7ooJVfeS9lI+SkqAr+T0ramraYYZBeG4bFAmlPMzgAuS3F7IvX2u6TXuAEcGYbh4hS3J1Jvv0t6l3bPA96X0ramraZI8LjZiYZS3N4s3HkckWZxPunOWzGA6xUQmfbCMFxIej24JSfGp75yq1kS/Cm4mbrSdHLK2xOppxNS3t5c4NUpb1OkXgLcefM0GeCQlLc5rTRLgj+c9GOtaTS+yDSxT8rbmwEcmvI2ReplIenngIF4u7nVLAm+HnE2y98uAhVmbExBxYlyRKahepR/S87zQLP8cZvrsM2dddimSL2k3T1pqU+9EqmHbUCU8jZbgFdS3ua00iwJ/kEg7Uk51qa8PZF6Wpfy9rYDq1Pepki9PEz6N0ebQ/r1alpplgQf4q5bTIsFfpXi9kTq7dcpb8/i6pVIM1gHzEx5mxuCIBhMeZvTSk17RMaYdwLvjBcPip/PNcZ8K/75RWvtJ1KOrdyvSfcczCvA9SluT6TefghcSnqD7YaAB1LalkhdBUFQjKepfVNKmxzE3R9AvAMAACAASURBVLgs12o9gj8VuCJ+dMTrXlW2bkn6oQ2L97KuId2j+JtT3JZIvf2c9AbFDQHfCYIgzfokUm//TnrnzAeBZSlta9qqdaraq621ZozHkXWOE+CfcJc1TNUO4F+CIEhz4hyRugqCYAD4CukMDh0EvpjCdkQa6X9IZyxWBDwYBMGDKWxrWmuWc/AEQbAG+C6we4qb2gn8w9QjEmm4zzD18r8L+M8gCDTIVJpKEAS7gQ8x9SS/G0/uxZD2qMR66wJ+Czcv/Zjn5AtDuzh8y73MHdoyfKvAGfsWN80/7s9fdUb7joZEK5KiIAi2hWF4JfA93Ex0VVUu/wvsMwtO3zw0Y+7fNSRgkfT9EDeH/Btwo+CrqpYDntv31G8dd9q59zck2ozVdLvY6SQMwxOBu3BT11bsgSgM7eK4/psp7L8Yc3QbzF8I2zZi1/ZB/7rdFAcWFzq6X2ho4CIpCcPwb4GrqJLkxyr/tn/dgCkOHKHyL80qDMP5uBxwNFWSvHKA0zRd9CVBEDwMnAk8RZXJPw7fcq/7Yk+9DLPPQZhCwT2fehm0HjUb03JNQ4MWSVEQBJ8FPow73TRqD32s8m9aj5ql8i/NLAiCbcC5wC1U6a5XDnCaLsEDBEHwKO5GGV/FDZob8SXPHdqCOboNY0b24htjMEe/HgqF1zcsWJE6CILgG8DrgFtxdWDPAFSVf8m7IAheAd4GfBTYhBtdv2dnV3XAacoED24vLgiCjwNH4I5mvg88BDxiigOuS6aS+QuhOJjrWwSKH4IgWBUEQRtwHnA1sBKVf/FEEAQ2CIL/wN00qRP4GnA/8KjqgNNsg+xGCYKgH/iv+AFA9Pz/RGzbaNjnoNG/sG0jtMxsroEHImMIgmAVsAr4HKj8i1/ieVJuiB+A6kBJ0x7BjymKVtq1fSQHEFprsWtXQhStzCgykfpT+RffqQ4AeU3wtng5/et22/uuxW59DhsV3fN917oRlLZ4edYhitRNqfyvSpb/a6B/3YDKv+Re1TrgVw5ousvkahWtWLoIU7iRQsspFAehZSYUh3aD9eLyCPGbK/8t11AotA2X/yIQfbHQ0f2RrOMTqbe4DiynUDg/UQfOK3R035F1fI2QzyN4oNDR/QI2OpvioBthXxwE7GzgkEwDE2mAQkf3C4WLr7qQ4uC7gbj8RwBvzDIukUaJ68AFFAdvAsrrwDlZxtVIuU3wAIWO7l3ALxKrvbg8QiT2M0ZeK//qaMXS/bMKRiQDP00se5MDcp3gY32JZW++XJFCR/dmIHlTjfOziEUkI8kccEG0YqkPuc/PBB+tWOrFNZAiMe3kis8eBF4uW94fOCmjWBrKhwR/PyPvIXwAcEJGsYhkIXlJkBK8eKPQ0V3EzfhYzos6kPsEH3+5tyVWt2URi0hGko3b6dGKpXtnEolINpI7uV7kgNwn+JiOYMRb8WWhj5StasHdrEPEF6NygA+nar1N8D58uSJltJMrPvsN7qZMJQcBx2QUS8P4kuDvxd1as+RQ4KiMYhHJghK8eKvQ0T0IJCe3yX0d8CLBFzq6B4A7E6tz/+WKlEmOpD87WrF0TiaRiGTDu51cLxJ8zLsvV6Sk0NH9NLC+bNUs4KxsohHJhHc5QAlexB+qA+Kze4CBsuUjoxVLj8gqmEbwKcHfDQyWLR8drVh6aFbBiGTAy0uFRAAKHd07cUm+XK53cr1J8IWO7h3ArxOrX5NFLCIZSc4HofIvvvGqDniT4GPJOblPzCQKkWw8DuwuWz4wWrH0gKyCEcmAVznAtwT/cGLZi/mIRWDPrI6PJFbnuoETSfAqB/ie4NW4iW8eSiyrDohPHmHk7ZOPjFYs3SurYOrNtwSfbNxO0ox24hmvjmBEysVjsdaXrTLA8dlEU3++Jfinge1ly/vipiwU8YWO4MV3ow70MomiAbxK8IWObou66cVvKv/iO2/qgFcJPqYuSvHZY0CxbPlw3TpWPKMEn2PefLkiSfF9GR5PrD4hi1hEMqIu+hzz5ssVqUK9WOKzZPk/NlqxdGYmkdSZjwleR/DiOw20E28VOrpfBp4tWzUDODqjcOrKxwT/BCNvOLAoWrF0/6yCEcmAjuDFd17UAe8SfKGjewg30KhcLvfeRKpINm4q/+IbL+qAdwk+9lxi+cBMohDJhsq/+M6LOuBrgt+UWNYNN8QnLyaWW6MVS31tC8RPyRygBJ8jyQYul1+uSCXxpXJby1cB+2UUjkgWkjkglwd5viZ4L/beRMagOiA+86L8K8E7udx7ExmDFw2cSBVelH8leCeXX67IGFQHxGdelH9fE7zOwYvvvDgHKVLFFkbek2HvaMXS2VkFUy++Jnh10YvvvDiCEamk0NEdAf2J1bnLA0rwjho38Y3qgPgu93XA1wS/GbBlywvyerMBkSpy37iJjCP3dcDLBF/o6C7ikny51ixiEcmIzsGL73JfB7xM8DGdhxefqfyL73JfB3xO8LsTyzMyiUIkGyr/4rvc1wGfE7yIiEhuKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JDPCd5kHYBIhlT+xXe5rwM+J/j5ieWdmUQhkg2Vf/Fd7uuAzwk+ee/f5J2FRPJM5V98l/s64GWCj1YsnQ3sXbaqCGzJKByRLOS+cRMZR+7rgJcJntH3/e0vdHRHmUQiko1kHXgxkyhEspP7OuBrgs/9npvIOFQHxHe5rwO+Jvjc77mJjCP3jZtINdGKpTOBBWWrLLA5o3DqxtcEr8ZNfJfcyVUdEJ+0Jpb7Cx3dxUwiqSNfE7waN/FdcidXvVjiEy/Kv68J3osvV2QM6sUSn3lR/pXgnVx+uSKVRCuWGlQHxG9elH9fE7y66MVnc4E5Zcu7gW0ZxSKSBS9ygK8JXl304rNR5b/Q0W0ziUQkG17kAF8T/OLE8guZRCGSDZV/8Z0XdcC7BB+tWDqXkV+uBR7PKByRLJyYWH4kkyhEsuNFHfAuwQPHM/I2gU8UOrpzdxchkTEkG7eHMolCJAPxIFMv6oCPCf6kxPLDmUQhkh3VAfHZIcA+ZcuvAM9kFEtd+ZjgvdhzExlDsg4owYtPkuV/TV4HmfqY4HX0It6KVizdFzi0bNUQGoMifknmgNwe5PmY4HUELz47IbH8eKGjeyCTSESy4U0PllcJPlqxdBZwbGL1mixiEcmIerDEd97UAa8SPHAM0FK2vKHQ0b01q2BEMqAeLPGdN3XAtwTvTdeMSBWqA+KtaMXSAxg5i91uYF1G4dSdbwnem64ZkSq8GWAkUsGoCW7yeB/4Et8SvDddMyJJ0YqlewFHla2y5HQGL5EqvOrB8i3Bn5ZYzvWXK5JwCiNncXyy0NG9I6tgRDLgVQ7wJsFHK5YuZOQlQkXgvozCEcnC6xPL92QShUh2vKoD3iR44ILE8m8KHd2vZBKJSDaSjdvKTKIQyUC0YumBjByDEgF3ZBROQ/iU4NsSy2rcxBvRiqUtjN7JVR0Qn5yfWF5V6Oh+OZNIGsSnBK+jF/FZAOxbtvwSsDqjWESy4F0O8CLBRyuW7ocbYFTutixiEclIsnG7tdDRHWUSiUg2lOBz6jxGjh5+sNDRvTmrYEQyoFNU4q34JkunJlbfmkUsjeRLgvduz02kJFqx1DC6DvRlEYtIRs5jZL5bXejofjGrYBplRtYBNIgSfIb6O9sN7h4ApYcBdrYu61UXcWMcz8jpObcBqzKKxTtx+S/Ej1IdKLYu692VaWB+8TIH5D7BRyuWzgfOSKzOfddMrfo722cCBwEHA4ckng+OX5vDyARdmOByJcX+zvZ+4MX4sanKz3uWW5f1alKWyUk2brcXOrqHMolkGurvbN+byuW/9NyKaysnWwcq9pT2d7bvpEpZr7Lc37qsV9/b5CjB59S5jEwyjxU6up/LKphG6e9sn4VLzuXJulIDdgAjxyc0SguwMH7UJG4QqzWCG3HTroaty3r7U4+2uXnXuMVHzXtTeac1uW5+RmHuBRwRP2rS39n+EtV3Bjbgrox4pHVZ7+7Uo21S0Yqlc4EzE6u9OMjzIcHntnHr72yfARyHGzxyKu5SqMNwjdYBGYZWL3sBh8ePqvo725/HNXRh/FgNrG5d1uvdrYHj8++5HWDX39m+D+4KmVIdOI7h5D03w9DqZb/4cewY7yn2d7Y/xnDZLz0/5mkPwDmMzHVrCx3dz2QVTCP5kODfmFhuysFFcTdieUNWSuhzsoxrAoq4maNKd26qZ9wHxY/28pX9ne1PMbLBC4GHc971fzRup69kF/CrjGKZtPiI/DBGlv/X4P6+ZmAZWQdmUr/2twU3LfcJwJKy9QP9ne1rGL3zuy7n42GSOSA3O7jjyXWCj6cmPDex+pcZhFKzuCE7lJEN2anUtyHbCDwHPBs/J3/exsjGqfwx7rpKjUd/Z/sc3LnNA8oeB46xfCCuUZyKUnfoW8rW2f7O9icYmfRD4NGcdHO+PbF8R6Gje1r/XfHppRMYXQf2q9NHDjCyzCfrwcb4PTWX+eS61mW9NvE3lk4hjFfuy5f3n+LfOQt3kJCcE2RHf2f7w4xM+iGwIRl3k0rWgVsyiSIDxto8fH+VRSuWXgl8s2zVqkJHd/JuQpmJB7hVasimWpHBHTFspHqjVXp+oXVZ72AKn1dXiQaxUkN4BHAy7naQs1L4yCKuobsF+AWwshm7+KMVS28BLixb9ZFCR/cXMwpnlP7O9gW4I/Hy8n8yU9+ZA9jN6PJfqR5sboZEFp+S24/KOwAH4rrtAyZwTn8cW4G7gV5cHVjVuqy3qe6dHq1YuhhYX74KWFjo6PZinE5uE3y0YukiKIS0tBxAcRBaZkJUfBIbnV3o6H4hi5j6O9vn4xrbN+HmBT+ZqSejjbi74q0C7gceZzhxe3e+LW4Ej8H9b4P4cTLu3Gy1Ef21KOLuPPULXIN313Q+wo9WLF2EabmOQuG8PeW/WASiswod3Zl10fd3th8JXAxcBJwFLJ7iJiPc4MpS+X8AeApXB7Y0Q+JOWzwu4SSGy36pHhw0xU2/BNyMK/+9uHP60/b/6+pA4TYKLccM14GhLWBPyCoHNFouE3y0YukiWmY9SetRs83RbTB/IWzbiF3bB/3rdlMcWNyIL7i/s70Fd//hi+PH65j8kYlluCErPe5vXdb7fAqh5l5/Z/ts3PXgycT/KiZ3FcFO3Lm80tHN/dPlPGbV8v94H2xuXPmHPcnmDbid2osZe3DYeLbjkviqsucw52MoUtPf2d7KcPkvrweT7TF8muHyf3Prst5pc3XSdMkBWctNgg/D0AALgBknPvuj5ebAY15vTr0MY4bbbmstdtW1sOnxvsLFV11Yjzj6O9uPYLgxu4jJVZ7tuCOR8mSuhqwO+jvb5+G69ZOJf8yR+pU2xXB3fi+wttrRTX9n+/HA/8X1KHymdVnvXZOLfqQwDGcB+57w3I+vLxxw9HkZlf8W3LwTpZ3a5GWqtXqGsh3Z+HntdNmJyov41NciRpb90vPeE9zcQwyX/77WZb1V79TW39n+PuC9uAGfn25d1rtl4tGPFobhfGBunAMuyKIOTCdNneDDMCwA7wD+FNeQzAaiE5//yZzCWVdi9hndI2W3Poe951u2cNHfpDJNb6Lb/WLcOfWJeJaRiVwN2TTQ39m+P+7ysotwo/GPn+AmnmS4K7O3dVnvnqOF/s72exi+LncAeHfrst4fTCbOMAwXAH8OXBbHWGxk+YcR3e4X4/5XCybw60XgYUb3TOV+GtHpLE78x+G+z4twvTAT/V5/xfAR/p2lU1r9ne0B8GDZe0Pgra3Lep+eTKxhGJ4GfBw3Wv5AYKjRdWC6atpR9GEYvha4Brf3OWKiClMccF0ylcxfCMXBSU/skkK3+2PAjcBNwB2ty3o3TTYWqZ/WZb2bgevjB/2d7YfhGrvS45BxNrEY+MP4QX9ne4hr6H7MyMl9ZgHX9ne2f6x1We8Xao0v7rH6BPAp3Expe8Uvzaxn+Ycpd7sPAbfj6kAvLplrytZpJu59eiR+fLWs3Svt8J7P2Je6tuCuPz8HuArY2d/Zfhvue9+QeG8A3Nnf2f6W1mW9D1KjMAwXAt+JY5nN8IyBM+pdB5pFUyb4MAyvBL7KcKM2gm2Zhdm2ESrsvbFtI7TMnFC3xRS73bfgGvabgJtal/Wum8hny/TQuqx3A/Bt4Nvx0c0JDCf7NzDyXuuVlLpAP4IbnZz0z3E5+/h4vTdhGM4DrsX1MMxLvl6H8j/Vbvc1DO/U9rUu631lIp8v2YtHz98bPz4fX+b6OoaP8M9g7JuX7YVrQ98UL7/CyFMAhwK39Xe2X9q6rPfm8eIJw/AM4OfxNkYNVE67DjSrpuuiD8PwD4B/Z4xZqha/eBvz9t2PqZx/6e9sPwnX7fk7uBGptRoC7sI1aDcC9zbbpSUyMfHI/dMZbuzOwx1RTMZy4D3VjmrDMJyBS5TnUOUIKqXyPwv3t1yGOw02kWvQN+N2am/E7dQ+NYHflSYUX+5YfkrrxEluahC4snVZ7/eqvSEMw5NwbWzVMQJp1IE8aKoEH4bhq3Ff7JhTUBaGdnFc/80U9l+MOfr1ZSMoV2L71xdNcfehlUZQ9ne2n4Br0C7DDTKpVanb/Ubgl814vbSkp7+zfS/c0U2psTuDiY3UfxB4Y6Xz0GEY/gPwIcaoA2OV/2jzkzw/78TzDzvn7bdXiHtmHO9lwKXUfs51ELiD4Tpwn3Zq/dbf2X4I7px4qQ4cNvZvjPJ54G+TA1XDMJwLPIo7RVa1To1ZB/rXW4qDB8/o+DuNogcwxrTiKvzbgFfjulMGcA3RN4FvWmvrOigsPud4F+N3BQHuCz58y73MHdqCKQ5gW2axY8YCnp1x3M7WZV+439jobHbvNsyebRkaXE+xuJPaj9RL3e6lI5T1k/yzxAP9ne374brxP4Br7Go5NbYdOLI8yYdheCxuRHnFU1PlqpX/Z2YcF+37k+9undX//L7D5X/oQYpDD+Jm+Kv19FOp2/1GXLf7thp/TzwTn9I6Fngz7hTVUTX+6k9bl/X+VvmKMAyX4gbUjXufgWp1YMOC124rztjr/UEQXDPBP6Xp1Jrg/wT4Gm7Wp1twE0ksAt6FO/d4HfA7to7dAWEYvhU3qG7Sd34qbNvKom/3MPP4U5h7aScthx1FccM6dlz/bQZX3we7d1b71SLuCOUm1O0ukxBfh/8UE7h7HvDV1mW9HywthGH4E6CDSY6dmUL5B9ftfhPDY0nU7S4T0t/Z/g7gRxP8tb1Kp6viQXXrSOcmQpuAQ4IgyPVkYLU2FI/izsP9tPxI3Rjzt7jZvX4bl+yvSz3CYR9hird1XPDT7zDz+FPY+y8+vee8zIzFx7D3XyzllS92M3j/3fFsX4BL6r24nYob4lHVIpNVYGLJHcqm2AzD8ABcd+ekB8ZOsPyDuwXpdbgBfSt9nBlRUnXwBN9fxE03XPJ7KcYyB9ebtiLFbU47NTUW1tqKoxqttc8bY74O/D3uWvC6JPj4Wt/kbV8nbPYLTzH3/R8bMegCwBjD3Hd28vLq30Cx+Atcg3a9rsWVtLQu693Z39l+NfBJKo9A34kboNmCm7XwFuCfyl5fEr8+2cF7tZb/zcAPcXXgFiV1SdF/405TnV7htQjYgSv7M+KfuxLn4P+Y9G4BPB94P0rw4yrdqKSeDcFZuNtcTrpxA2DXLloOq3z6p+Wwo2BgN63Let9U8Q0iU9S6rPfT/Z3tX8JNxrET14jtAHbVMKf3W6lwSdyE1FD+gYOa4eZD0nxal/Vu7e9sPwM3R0QLw+V/JzA4Vh0Iw3Av3MQ7aTG4Uf+5NqUEb4yZAXTGiz+fejhVnUwa9w+fM4fihnXMWHzMqJeKG9bBrDnNc0mBNKXWZb0v4W7aMVGvnvKH11D+W//jJ0ruUjdxEl8/iV89AbczMNHpc8eyXxiG84MgyO0A0alO1fd53OQd/2utrWdXx4lM9egd2L3oCHZc/22SYwGttey4YRkUB1dO9TNE6mSi5y9HUfmXJvYqXPd9mnZS+4j+pjTpBG+M+TDucoU1wHtSi6iyqXVNxra87d0MPvIgr3yxm6H1j2GHhhha/5gbYPTQfbsZGro8jc8RqYMp3x9d5V+a2Gwmd9fHsUSkcOA4nU2qi94Y80Hgi7i7B7Vba+s9wjyVuaqj+fvwwhVdLPjpdxj87Edh9y7XLV8cXMnQ0OXlNwQRmWaGqDAl50SMLP8fc5fFqfxLcxgk/SN4g5vPJbcmnOCNMR8BvoC7A1C7tXZj6lGNtgb3RUypgQPXyG2+/M8A1gZBMPpkpMj09AITv4XtKGXlfxfwV0EQfGmq2xRpgPV12OZeuLs+5taEuuiNMX+FS+6rgDc0KLkDrMadL0nT/SlvT6SeVqe8vd112KZIvTxMepfIlbwSBEHVe9bnQc0J3hjzSdygul/jjtwbeY34PaQxin7YdtxUsyLN4kbS3cmdg6vLItNePNI97aPtO1Pe3rRTUxe9MeYKYCluZqFbgQ8nJ8sA1ltrv5VqdLEgCF4Mw/Be3F260jAD+EFK2xJphGuAz6W4vduCINiS4vZE6u0bQDfpHOy9AvxnCtuZ1mo9B1+6lKAFN2VsJX3At6Ya0Bi+BLyGKU5Xi9tJWRkEgWapk6YRBMGzYRjeDZzP1C9v3Q58eepRiTTUd3AzQaYhAn6W0ramrZoaCmvt1dZaM87jwjrHuhx4OoXtDAB/kcJ2RBrtw4ycm3syLO72xj+eejgijRMEwdO4o+6pXlW1DegKgmCqdWnam+qRQMMEQRAB72Nq5yF3AN8MguDhdKISaZwgCO4HvsfU6sBO4P1BEGjWRmlG3bgeqMkq4g4Uv5FOONNb0yR4gCAI7gSuwiXqidqNu27/o6kGJdJYH8IdgU/m+t0dwCeCINDgOmlK8biRtzG5HGCBrcBbgiDw4nbfTZXgAYIg+ALu7nUTOYrZDtwHvCkIglxPbCD5FgTBLuCNwANMrJHbCXQHQfC1ugQm0iBBENwNvBPX1V5roh7A3QO+LQiCXF/7Xs4k56VuFmEYXozrrpxN9YF3A7jBFF8B/joIAt36UnIhDMOZwD/ibr/ZQvWpbLfhzlleHgRBxds+izSjMAyPBm4AjqR6DrC4nds7cXWgvzHRTQ9Nm+ABwjCcDfwh8EHcrQR34b7Q2cBm4EfAZ+PBGSK5E4bhYuBvgUuABbhTUQZ3KdEa3NUny9RzJXkUhqEBOoC/Ac7EHdAVcVeIFXFXd30mCIJ7MgsyQ02d4MvF9ws+Cnc080IQBI2aZU9kWgjDcBGwEDdv/bq4O1/EC2EYtuBywFzcufYnfR9MmpsELyIiIsOabpCdiIiIjE8JXkREJIeU4EVERHJICV5ERCSHlOBFRERySAleREQkh5TgRUREckgJXkREJIeU4EVERHJoRtYBiEgT2NVvwYK1uOdi2c/J52jPsy2t2/N6NPL9e95b4bU97ylWeD35c7RnWza53dLPUTTONsqf4xk+Sz+XxxMl46Pye7Ajf98ycl2U+LwoGrk+slAsVn3dluIovWfP6xW2ExWH445K/yMXt/v3WLe9yMZ/Rvxctuy+9rLfHfFe62aBtzZ+T9nvxJ9r48+1UYQtRu65/LUoil93f0NULL0exa9bbPz37NnWUHF4m1W2FRXLfh4RB3vWF8veU+k5Smwn+dqe7RD/G+Ln8p9Lz8UKryXfNzTONkrbAbjaWlOt2uoIXkREJIeU4EVERHJICV5ERCSHlOBFRERySAleREQkh5TgRUREckgJXkREJIeU4EVERHJICV5ERCSHlOBFRERySAleREQkh5TgRUREckgJXkREJIeU4EVERHJICV5ERCSHlOBFRERySAleREQkh5TgRUREckgJXkREJIeU4EVERHJICV5ERCSHlOBFRERySAleREQkh5TgRUREckgJXkREJIeMtTbrGERkmjPGfMBa++9Zx+ET/c8bK4//bx3Bi0gtPpB1AB7S/7yxcvf/VoIXERHJISV4ERGRHFKCF5Fa5OrcZJPQ/7yxcvf/1iA7ERGRHNIRvIiISA4pwYuIiOSQEryI7GGMebMx5hFjzOPGmL+u8PpsY8w18et3G2OObHyU+THe/7vsfUuMMdYYc0Yj48ubGsr3EcaYW4wx9xljHjDGvDWLONOiBC8iABhjWoCvAG8BTgJ+zxhzUuJt7wNestYeA3wB+IfGRpkfNf6/McbsDXwYuLuxEeZLjf/vvwOutdaeBvwu8NXGRpkuJXgRKTkLeNxa+4S1dgD4PnBJ4j2XAN+Of14OtBtjTANjzJNa/t8AnwF6gF2NDC6Havl/W2Cf+Od9gWcbGF/qlOBFpORQ4Omy5Q3xuorvsdYOAS8DrQ2JLn/G/X8bY04DDrfW/qSRgeVULeX7auDdxpgNwP8Cf96Y0OpDCV5ESiodiSevo63lPVKbMf+XxpgC7jTIxxsWUb7VUnZ/D/iWtfYw4K3A/4u/h6bUtIGLSOo2AIeXLR/G6C7KPe8xxszAdWNubkh0+TPe/3tvIAB+aYxZD5wD/FgD7SatlvL9PuBaAGvtncAc4ICGRFcHSvAiUvIr4FhjzFHGmFm4QUY/Trznx8AV8c9LgJutZsuarDH/39bal621B1hrj7TWHgncBbzDWntvNuE2vVrK91NAO4Ax5kRcgt/U0ChTpAQvIsCec+ofAlYAD+NGE682xiw1xrwjfts3gFZjzOPAx4Cql3bJ2Gr8f0tKavx/fxz4I2PM/cB/A1c28w6spqoVERHJIR3Bi4iI5JASvIiISA4pwYuI1MAY80tjTF3PaRpjjjPGDBhj/rKen5NnxphDjTE7jTGfyTqWrCnBi4hMH/8M9OOmVN3DGPOteC768sd2Y0xojPm8MWa/RgVojDk8/sxfG2NeMsYMGmM2GmN+YYz5C2PMvo2KpRJr7TPA14GPG2MObCS/GgAAB11JREFUH+/9eaZBdiIiNTDG/BJos9bWZWpeY8zrgNuBq6y1n0289i3c5Yk/AlbFqw8C3g4cAqwFzrLW1nVOAmPM+4F/BWYD9wN3AC/hZjM8HzgZ6LfWZnrtuDHmENwlb/9lrf1AlrFkaUbWAYiICAAfBCLg/43xnhustd8qLRhjPoG7Cc1JuGlVP12v4Iwxvw/8By6h/7a19qcV3nMeid6HLFhrnzXG3AT8gTHmL621L2cdUxbURS8i3jLGXGmMuc4Y80R83narMeZ2Y8y7x/id2caY/2OMWWeM2W2MWWuM+VQ8eUryvRcYY/7HGLMhfu/zxpi7jDGfSrxvH9zEQXdYa59Obqcaa+02hm/+c1bZ9l5rjPmiMeZ+Y8xmY8wuY8xjxph/qtSdH/8fbPz85ni8wculMQfxHe2+HL/9dysl9zie24GzK2y/3Rjz87JYHo27+Ud155fGOhhjZhljuuPbu+6OezHGjbXM94G5uAltvKQjeBHx2deAh4CVwHO4rubSHOTHW2s/WeF3rgXOxN1NbxB3R7KrgTOMMe8oTYxijHkz8FNgK27GtGeA/YETgT9j5NH264FZwG2T+BtKpwzKE9wfAZcCfcAvgBbgdNzkRG8xxpxtrX2lwraWAG8GfoY7j31k2fr9gbustTeOFYy1dveI4Iz5Y9z/eTvwA2AjcCHwV8DbjTHnWWu3VNjUdbj/88+AG+LfqyXWktvj5zcB/zZWzHmlBC8iPgustWvLV8RH4j8D/toY8/V40Fa5E4GTrbUvxe+/CrgF+C3g3Qx3sf8Rrpf0Qmvt/YnPSJ6jPj9+ntA0tMaY+QxPHVx+v/jPAR+01hYT738f8J+4HYx/qLDJtwJvtdb+vEp8vROMbzHwJWAbbozAmrLXvgr8Ke5WuJXOky/GfT8vVtl8tVgBsNY+bozZgtt58pK66EXEW8nkHq8bwJ1HnkE8L3nCZ0rJPX7/LuBv4sU/rPD+nRU+I5m0joifnxsn5HcaY66OH18DHsHtcKzFDX4rbf/JZHKP/ReuR6GjyvZ/VCVhHhw/bxgnvqR343om/rU8uceuAl4B3mOMmV3hdz85RnIfK9ZyzwMHGmPm1BxxjijBi4i3jDFHGGO+YoxZY4zZUboEDdc9DKPvFw6u2zvpVmAIOK1s3Xfj57uNMV83xlxujDmsSiit8fNLVV4vuQT4VPy4AngZ+L+4o+M9v2uMmWmM+ZAx5rb4vHcx/rsiYJ8qfxfAPVXWVzoNUIvT4+ebky/E8d6Hu6HLCROIpdbXYfhOh017R7ipUBe9iHjJGPMqXJLYD5egb8QlzCLufO4VuMvBkl5IrrDWFo0x/cDCsnU/NMb8Fu4GJn8I/HH8ub8G/sZae1PZJkpH+eMdab63fBT9GK7BnYN/Andp3fNA6dz4R6j8dxG/r5LSbVWr7aBUUxpEV61norR+wQRiqfV1gL3i51G9KD5QghcRX30Md+Q8KmkaY36P4XPbSYtw11iXv78l3tbW8vXxaPOfGmPm4UaX/xbuvPNPjDGnWWsfit9aGkDWyhQZd7/4S3GD695qrR0se60AdI3x69WO0G/D7aS0A5UGHlZTujztIGB1hdcPTrxvOJDxJ2mppTehFdezUtf5AaYrddGLiK+OiZ+vq/Ba2xi/V+m1C3AHTPdV+gVr7XZr7c3W2o8Bn8Wdl35L2VseiJ8rdVVPVOnv+nF5co+dxfBR7UQsxyXJc40xF431xsT59NL/48IK71sAnArswt2+NVXxTtWhwAPNfMvXqVCCFxFfrY+fLyxfaYzpAN4/xu99svxa8ngA1+fixW+WrW83xlRKpovi5x1l634ZP58zXtA1WB8/X1i+0hizkElOQhNfUvfhePGa+H80ijHmHODOslXfwV1K+OfGmGMSb/8MbjzAd5KX1qXkLNzlgbfUYdtNQV30IuKrrwLvBX5gjLkOd516gLu2+lrg8iq/9zCw2hhTfh380bhr3stnofsn4EjjprhdDwwArwXeCDyJm4gFAGttaIx5BGg3xrRUGQFfq1/hrgF/lzHmDlz3+iJcj8EjDJ9PnxBr7XfjHZZ/BX5ujFnFyKlqzwVeA7xY9jvrjTEfwe1Y/MYYcy2wCdcLci6wBnc9fD1cHD9X6qHxgo7gRcRL1toHgDfgktRbcefG9wHehZs4pZrLcJebvR34EK4dvRo3fWt5V/BncdfTn4zrEfgTXKL9LHBm+aj32Ndw56ovZgrinYN3xNs7BHfkfT7u+vcO3E7JZLf9n8CxuGvXLfAHuAS9BJfoP4rb2Sn/na/Gn3sX8Nu4sQ8LcaP/z63H/PnxWIN3A/dba+8c7/15pZvNiIhMA/F0tWtx09VeknU8zcwY83bc7IHvsdZ+J+t4sqIELyIyTRhj/gzXnX2mtXZCs9qJY4wxwK9xlzue5esAO9A5eBGR6eTfcNeELxrvjVLVQbij9xt8Tu6gI3gREZFc0iA7ERGRHFKCFxERySEleBERkRxSghcREckhJXgREZEcUoIXERHJof8PHDRacXe0J3cAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.03\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n",
"(2,-2) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.05\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n",
"(2,-2) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.08\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) --> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.10\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) o-> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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d3BkzbeEdf6fkLnVTWNbzLPA/k/jVY3HzOaTRRV9yaBiGs/J8yVytXd6lublfrPJ6aX2tXS8T9Qomd1nQXk8edj7RlvXYO67CvvAUNiq65zuugs3r9hAVL0spVpF6OGqqG9g5qxX74PUkB9bu3cmNijdP9TNE6uRE3OykadqFOxefW1M6Ki5TOiSu1wCcid78Y5RoxlweWfIWFj99G3P7vua6K2fMtETD18GnEahInUzmmt8RnjzsfDeK/o6rYOlFrlv+xWddctdOrkxv85j6LYWTIqZ2ud20V2uCLx2hV0u0+yXel7ZU9tyiGXPZeORFpcVdQRBMqVdApIGmvPOsnVxpYhHpH0Aa0u8VmFZqTfAPx8/VZiQ6Pn6udo5+qjbgvohaZ9WqhW67Ks3kBdyd4qakbCd3EOgOguBzU92mSAM8S/oJfna83dyqtcvjpvj5YmPMiN8xxuyLm4d4F+6OQfXwAG4qzDSFKW9PpJ4eSnl7O1EdkOaxhklOcjaGISDXvVY1JXhr7Trc7FVHAR9IvPwp3HmMFfW4Bj72a9IdPbkH0IAiaSa34I660zIbuDfF7YnUTRAEzwNp31I4zPtMdhMZtPCnwGbgi8aYHxhjPmuMuRE3i90juNux1kUQBOtxUxSmxQJXp7g9kXr7H9JN8I8GQfBUitsTqbdvM8X5UMpsB/4rpW1NWzUn+Pgo/kzgm8A5wMdw1yZ+ETivnvPQx76K61ZMw7ogCB5LaVsidRcEwRrc7HZp2IGbelqkmXwDSOua9RnAtSlta9qa0GUH1tonrbXvsdYeaq2dZa1dYq39c2vt1noFWOY/cOf5p2oH8OEUtiPSaB8hnbEo23GNpUjTCILgPtytYqd6FL8L+FIQBLkfaJ3WdfB1FwTBzjAMP4RL9ONeu1iIdrN4+/3MLb6IiQaxhZnsbNkvemruK+496bSzR91nWKQJ/ARYjbuL45hjUiqX//3ZNP/UHcXCnA8EQaAbJ0kz+iBu0PW4uataHXhq/qm7hgpzPl3/ULM3ofvBZy0MQ4M7D/N2xpjZrhDt5oSXbqOwYDHmqPNh3sGwYwt2w23YbU/sMcWBJYW2rlyPnpR8CsPwUFwDdyDDE0yNMHb5f7JoinsOV/mXZhWG4eW4U0yTzQEDpjhwpA91IO2ZgeoqHvH4btwI+KpdlYu33+++2FMuwcxfhDEF93zKJZgFR87GtGiAnTSlIAiewd2s4wWqDLobu/wvblH5l2YWBMFVwN8xxinbcXLALF/qQFMleID4xgBvAf4J9wWPOh8zt/gi5qjzK94W0yw5H0zhtY2IVaQegiB4AHcDpgdw59NHUPmXvAuC4LPApcDLVEj0qgNO0yV4gCAIoiAIrgROx927fQB3jeQOYKeJBl2XTCXzDoJosGLXpkiziI/kz8Ld0/phhuuAyr94IQiCH+PmZvk8bke39FAdiDXNILtKgiB4GLg0DMPZwKuApUCLLcz8mtmxxTC/wr2vdzwPhZnNM/BApIogCCLg+8D3wzBcCLwaWKjyL74IgmArcEUYhp/Atf9nAHNtYeZXVQeaPMGXxCOCb48fRDev+j274bY2Trlk9L3fN94GNtIsdpIrQRBsBn4AKv/in3hnd038ILp51e+qDjRpF/24bPEytm3cY9dch335OXfv95efw665DrZt3IPVbTElx1T+xXeqA0CTXSY3EVFfzyJM4XZMyzFEg1CYCdHQVrAn+3B5hPjNlf+WqzGFtuHyXwSi9xfauv4z6/hE6i3OAf+HaTmjrA4MQXR0oa1rU9bxNUI+j+CBQlvXc9jobUTxlUTRIGDnkf5d6USmnUJb13OF137sQqLBfwLi8h8BXJhhWCINE+eAs4gGNwOlOjADd67eC7lN8LEHgcfLlmcDr8koFpEs/CixfFHU1+PFCGKRQltXBPxvYvVFWcSShVwn+EJbl8Xd5racF9c/isTuYOS18gcDJ2YUi0gWvM0BuU7wsb7EsjdfrkihrWsIuC2xWnVAfJIcMf+qqK9nfiaRNJgPCf6WxPJZUV9P1TmMRXIo2cApwYs3Cm1dzwKPlK2aAZybUTgNlfsEX2jregpYV7ZqJp58uSKxZIJv03l48cyoOpBJFA2W+wQf0xGM+OyXQPntYY8AlmQUi0gWvMwBSvAiOVdo69oD3JlYrTogPknmgHOivp45mUTSQL4m+POivp5ZmUQikg3t5Iq3Cm1dG4EnylbNxt2sKdd8SfDrgfKZi+YAZ2YUi0gWdDWJ+M67nVwvEnx8Pbx3X65ImTuBobLl46O+nkOzCkYkA97lAC8SfMzLUZQiAIW2rh3APYnVuW/gRMokc8D5UV/PzEwiaRCfE/z5UV9PLm6XK1Ij745gRMo8AmwuW54HnJ5RLA3hU4JfC2wpW94XCDKKRSQLo3ZyM4lCJANVTtXmug54k+DjL/euxOqTs4hFJCPJS+VOivp6WjKJRCQbyTqQ6xzgTYKPPZRYzvWXK1Ku0NbVz8herNnA0RmFI5KFZA7I9a1jfU/wuf5yRSp4MLGsOiA+SZb/k/M8bbNvCV6Nm/hOO7nisyeAnWXLBwALM4ql7nxL8GsTy8fn/TIJkQSdphJvFdq6IuDhxOrc7uR6leALbV0vAk+XrZoBHJdROCJZUC+W+G5UN30mUTSAVwk+5s2XK1LBqC76PJ+DFKnAm9NUPiZ4b75ckQqeBl4qW94XODyjWESy4E0vlhK8juDFI/F8EKoD4jNvyr+PCd6bvTeRKlQHxGfrgMGy5UOjvp4FWQVTTz4m+OTem2bzEt/oNJV4q9DWNQg8mlidyzrgY4LfAmwtW54DLMkoFpEseNNFKVKFFzu53iX4+Bxkcu9Ng4zEJyr/4jsv6oB3CT62JbF8cCZRiGRD5V9850UdUIJ3DsokCpFsbAOKZcv7Rn09s7MKRiQDSvA55sWXK1JJPF3n84nV2skVn3hxkOdrgk82bkrw4hvVAfGZF+Xf1wTvxd6byBjUiyU+86L8K8E7ufxyRcagOiA+G1X+83hPBl8TvBfdMyJj0Dl48VahrWsnsKts1UzcfRlyxdcEry568Z2O4MV3ua8DSvBOLrtnRMaQ+8ZNZBy5rwO+JvjtwJ6y5TnAvIxiEclC7hs3kXHkvg54meDj6Wp1DlJ8pvIvvst9HfAywce2JpZzebtAkSpU/sV3ua8DPif4KOsARDKk8i++y30d8DnBi4iI5JYSvIiISA4pwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgO+ZzgWxLLNpMoRLKh8i++y30d8DnBH5hY3pZJFCLZUPkX3+W+DniZ4KO+HsPoe/8m7w0skmcq/+K73NcBLxM8sC8ws2x5V6Gta2dWwYhkIPeNm8g4cl8HfE3wuf9iRcZxUGL5+UyiEMlO7uuAEryjBC++UR0Qb/lymtbXBJ/7PTeRceS+cRMZwzxgTtnyHmB7RrHUja8JXo2b+E51QHw2qvwX2rp0mVxOqHET36kXS3zmRfn3NcF78eWKjEE7ueIzL8q/rwneiy9XpJKor6cAtCZWaydXfOJFDvA1wSeP4HP55YpUcQAj6/6LhbaugayCEcmAFznA1wS/MLGsoxfxicq/+M6LOuBdgo+vfzwhsfrJLGIRyYjKv/jOizrgXYIHFgELypZ3kNMvV6SKpYnlBzOJQiQ7XtQBHxP8yYnltYW2riiTSESykawDD2UShUgGor6eWcBxidVrs4il3nxM8F7suYmMIVkHlODFJ8cz8l7wmwptXS9nFUw9KcGrcROPxGNQtJMrPvOm/PuY4NU9KT5bjJuHu+RF4NmMYhHJgjc5wMcE783em0gFo3qw8jgHt8gYvOnF9SrBR309BwCHlK0aAB7PKByRLCSPXrSDK77xpg54leAZvef2SKGtayiTSESy4c3Ri0hS1NfTApyYWJ3bOuBbgvfm3ItIFaoD4rOjgdlly88X2rpyOYsd+Jfgdf5dvKUR9CJ+lX/fEryOXsRnC4EDy5Z3ARszikUkC17lAG8SfHyLzHMSq9dkEYtIRs5NLD+kWRzFM8k6kOsc4E2CB07B3SazZBs5754RSXhtYvnWTKIQyUB8kPeaxOpc1wGfEnxbYvkWHb2IZ5IJ/uZMohDJxlKgtWz5JeD+jGJpCJ8SvBo38VbU17MvcEZi9S1ZxCKSkVE9WIW2rmImkTSIFwk+Hj2sBC8+ezUj6/vaQlvX5qyCEcmAdznAiwSPu3vQorLlHcC9GcUikoXkKaq+TKIQyUB8kOddHfAlwSf33G4vtHUNZhKJSDa8O3oRKXMscGjZ8k7g1xnF0jC+Jng1buKNqK9nH+DsxGqdfxefJHPAHYW2roFMImkgXxJ8smtGCV58cg4ws2x5faGt68msghHJgJcHeblP8FFfzxLgyLJVe4C7MwpHJAteNm4iZbysA7lP8Iz+Yu8qtHXtziQSkWx42biJAER9PYtxN5kpGQDuyiichvIxwatxE29EfT2zgPMSq3M/elikTHL2ursLbV27MomkwXKd4ONLIzoSqzW4SHxyATC3bPkZ4PGMYhHJwhsTy97kgFwneOCVwOKy5V149OWKAG9NLK8qtHXZTCIRabCor2cG8ObE6lVZxJKFvCf4tyWWb/Cla0Yk7sF6e2L1j7KIRSQj5zFy/vkXgNsyiqXhcpvgo76eRWA+TiG+OqgwE0zhRLdeJN+ivp5FmMJdFGa6wUWFmUAhAlZnGphIg8R14NsjcgBmgJEJP9eMtfnrrYv6ehbRMnMjC5bMNkedD/MOhh1bsBtug20b91AcXFJo63ou6zhF6kHlX3ynOuDMyDqANIRh2Iq7mcaJwIzjWua+e9aCw2abUy7BGOPeNH8RnHIJds11s9m6/mrgwswCFklRGIYGV/ZfDSw8tmXe+2YvOFTlX7wRhuE+wFnA6cDc41rmvWeW6kBzJ/gwDM8BPodr2HbhRgubmXZwhjnq/OEvNmaMgSXnY1/YmLx0TqTphGE4C7gc+DQwH7DAnFl2QOVfvBCG4WKgG3gXsBuYA8yYaQdaVAeaNMGHYTgT+DzwHmAfwACzSq+baNB1yVQy7yCIBk3lF0WaQxiGx+BGAx8KzCt/TeVffBCG4buAr+Hy2Cxgduk11QGn6QbZxV0xN+OS+1xcch/BFmbCji2VN7DjeSjMzN/AA/FG3HO1Gjc717zk6yr/kndhGP4TLrnPpezgrkR1wGmqBB+fa1yJu759brX37WzZH7vhNpIDCK212I23gY00m500pTAMjwauB/YFWiq9Z7zyb1X+pYmFYfgh4I9RDhhXTQneGLPMGPMlY8wtxpiXjDHWGPOtegdXwR/i7gy3z1hvenL+qUTbnsSuuQ778nPYqOie11xHtO1JG1l7WWPCFUlPvIN7LRWO2suNU/7ZsO/Z32tMxCLpCsNwKW7c1ZTqwNZZh/1RYyLOVk2XyRljVuOOmrcDm4CTgG9ba99Z3/CGhWG4H/AEsH8t7y9Eu1m8/X7mRi9higPYllnsLOzHpvmn7igW5vxeEASa8EOaShiGlwL/zTiNG1Qv/0/OP5WoMGcrcGQQBDvqHrRIisIw7MNNvzzuwekYOWB3sTDnG0EQ/Gn9I85WrQn+dbjE/hjuCPomGp/g/wo3WnLMo/caPRYEwfEpbEekIeKj942MnHp5snYCVwRB8K8pbEukIcIwPBu4kRp2cGuwG7eTW+VEfT7U1EVvrb3JWvuozXZWnPeTTnIHOCwMw5NT2pZII5wGHJjStuYCXnRRSq68h7KR8lNUBH47pW1NW00xyC4Mw+OBNKeYnQFcmuL2ROrtd0ivcQM4KgzDJSluT6Tefof0Lu2eB7w3pW1NW02R4HGzEw2luL1ZuPM4Is3iAtKdt2IA1ysgMu2FYbiQ9HpwS5bGp75yq1kS/Km4mbrSdErK2xOpp5NS3t5c4BUpb1OkXgLcefM0GeCwlLc5rTRLgl9M+rHWNBpfZJrYL+XtzQAOT3mbIvWykPRzwEC83dxqlgRfjzib5W8XgQozNqag4kQ5ItNQPcq/Jed5oFn+uK112OauOmxTpF7S7p601KdeidTDdiBKeZstwMspb3NaaZYE/wCQ9qQc61Lenkg9rU95ezuANRcu2RMAACAASURBVClvU6ReHiL9m6PNIf16Na00S4IPcdctpsUCv0xxeyL19quUt2dx9UqkGawHZqa8zU1BEAymvM1ppaY9ImPMO4B3xIuHxM/nGWO+Gf/8vLX24ynHVu5XpHsO5mXguhS3J1Jv3wcuIb3BdkPA/SltS6SugiAoxtPUXpTSJgdxNy7LtVqP4E8DLo8fHfG6Y8rWLUs/tGHxXtbVpHsUf2OK2xKpt5+R3qC4IeBbQRCkWZ9E6u0/SO+c+SCwIqVtTVu1TlV7pbXWjPE4qs5xAvwz7rKGqdoJ/GsQBGlOnCNSV0EQDABfJp3BoYPAF1LYjkgj/S/pjMWKgAeCIHgghW1Na81yDp4gCNYC3wb2THFTu4B/mHpEIg33aaZe/ncD/xUEgQaZSlMJgmAP8EGmnuT34Mm9GNIelVhvXcBv4OalH/OcfGFoN4u33cPcoW3DtwqcsX9xy/wT/uyYM9t3NiRakRQFQbA9DMN3A9/BzURXVeXyv8A+teCMrUMz5v5tQwIWSd/3cXPIvw43Cr6qajngmf1P++YJp593X0OizVhNt4udTsIwXArciZu6tmIPRGFoNyf030jhwCWYY9tg/kLYvhm7rg/61++hOLCk0NH9XEMDF0lJGIZ/A1xBlSQ/Vvm3/esHTHHgSJV/aVZhGM7H5YBjqZLklQOcpumiLwmC4CHgLOAJqkz+sXjbPe6LPe1SzH6HYAoF93zapdB69GxMy9UNDVokRUEQfAb4EO5006g99LHKv2k9epbKvzSzIAi2A+cBN1Glu145wGm6BA8QBMEjuBtlfAU3aG7Elzx3aBvm2DaMGdmLb4zBHPtaKBRe27BgReogCIKvA68GbsHVgb0DUFX+Je+CIHgZeAvwEWALbnT93p1d1QGnKRM8uL24IAg+BhyJO5r5LvAg8LApDrgumUrmL4TiYK5vESh+CIJgdRAEbcD5wJXAzaj8iyeCILBBEPwn7qZJncBXgfuAR1QHnGYbZDdKEAT9wH/HDwCiZ/83Yvtmw36HjP6F7ZuhZWZzDTwQGUMQBKuB1cBnQeVf/BLPk/KD+AGoDpQ07RH8mKLoZruuj+QAQmstdt3NEEU3ZxSZSP2p/IvvVAeAvCZ4W7yM/vV77L3XYF96BhsV3fO917gRlLZ4WdYhitRNqfyvTpb/q6F//YDKv+Re1TrgVw5ousvkahWtWr4IU7ieQsupFAehZSYUh/aA9eLyCPGbK/8tV1MotA2X/yIQfaHQ0f3hrOMTqbe4DqykULggUQfOL3R03551fI2QzyN4oNDR/Rw2OofioBthXxwE7GzgsEwDE2mAQkf3c4WLr7iQ4uA7gbj8RwCvzzIukUaJ68BrKA7eAJTXgXOzjKuRcpvgAQod3buBnydWe3F5hEjsp4y8Vv4V0arlB2YVjEgGfpJY9iYH5DrBx/oSy958uSKFju6tQPKmGhdkEYtIRpI54DXRquU+5D4/E3y0arkX10CKxLSTKz57AHixbPlA4OSMYmkoHxL8fYy8h/BBwEkZxSKSheQlQUrw4o1CR3cRN+NjOS/qQO4TfPzl3ppY3ZZFLCIZSTZuZ0Srlu+bSSQi2Uju5HqRA3Kf4GM6ghFvxZeFPly2qgV3sw4RX4zKAT6cqvU2wfvw5YqU0U6u+OzXuJsylRwCHJdRLA3jS4K/B3drzZLDgaMzikUkC0rw4q1CR/cgkJzcJvd1wIsEX+joHgDuSKzO/ZcrUiY5kv6caNXyOZlEIpIN73ZyvUjwMe++XJGSQkf3k8CGslWzgLOziUYkE97lACV4EX+oDojP7gYGypaPilYtPzKrYBrBpwR/FzBYtnxstGr54VkFI5IBLy8VEgEodHTvwiX5crneyfUmwRc6uncCv0qsfmUWsYhkJDkfhMq/+MarOuBNgo8l5+RemkkUItl4DNhTtnxwtGr5QVkFI5IBr3KAbwn+ocSyF/MRi8DeWR0fTqzOdQMnkuBVDvA9watxE988mFhWHRCfPMzI2ycfFa1avk9WwdSbbwk+2bidrBntxDNeHcGIlIvHYm0oW2WAE7OJpv58S/BPAjvKlvfHTVko4gsdwYvvRh3oZRJFA3iV4Asd3RZ104vfVP7Fd97UAa8SfExdlOKzR4Fi2fJi3TpWPKMEn2PefLkiSfF9GR5LrD4pi1hEMqIu+hzz5ssVqUK9WOKzZPk/Plq1fGYmkdSZjwleR/DiOw20E28VOrpfBJ4uWzUDODajcOrKxwT/OCNvOLAoWrX8wKyCEcmAjuDFd17UAe8SfKGjewg30KhcLvfeRKpINm4q/+IbL+qAdwk+9kxi+eBMohDJhsq/+M6LOuBrgt+SWNYNN8QnzyeWW6NVy31tC8RPyRygBJ8jyQYul1+uSCXxpXIvla8CDsgoHJEsJHNALg/yfE3wXuy9iYxBdUB85kX5V4J3crn3JjIGLxo4kSq8KP9K8E4uv1yRMagOiM+8KP++JnidgxffeXEOUqSKbYy8J8O+0arls7MKpl58TfDqohffeXEEI1JJoaM7AvoTq3OXB5TgHTVu4hvVAfFd7uuArwl+K2DLlhfk9WYDIlXkvnETGUfu64CXCb7Q0V3EJflyrVnEIpIRnYMX3+W+DniZ4GM6Dy8+U/kX3+W+Dvic4PcklmdkEoVINlT+xXe5rwM+J3gREZHcUoIXERHJISV4ERGRHFKCFxERySEleBERkRxSghcREckhJXgREZEcUoIXERHJISV4ERGRHFKCFxERySGfE7zJOgCRDKn8i+9yXwd8TvDzE8u7MolCJBsq/+K73NcBnxN88t6/yTsLieSZyr/4Lvd1wMsEH61aPhvYt2xVEdiWUTgiWch94yYyjtzXAS8TPKPv+9tf6OiOMolEJBvJOvB8JlGIZCf3dcDXBJ/7PTeRcagOiO9yXwd8TfC533MTGUfuGzeRaqJVy2cCC8pWWWBrRuHUja8JXo2b+C65k6s6ID5pTSz3Fzq6i5lEUke+Jng1buK75E6uerHEJ16Uf18TvBdfrsgY1IslPvOi/CvBO7n8ckUqiVYtN6gOiN+8KP++Jnh10YvP5gJzypb3ANszikUkC17kAF8TvLroxWejyn+ho9tmEolINrzIAb4m+CWJ5ecyiUIkGyr/4jsv6oB3CT5atXwuI79cCzyWUTgiWViaWH44kyhEsuNFHfAuwQMnMvI2gY8XOrpzdxchkTEkG7cHM4lCJAPxIFMv6oCPCf7kxPJDmUQhkh3VAfHZYcB+ZcsvA09lFEtd+ZjgvdhzExlDsg4owYtPkuV/bV4HmfqY4HX0It6KVi3fHzi8bNUQGoMifknmgNwe5PmY4HUELz47KbH8WKGjeyCTSESy4U0PllcJPlq1fBZwfGL12ixiEcmIerDEd97UAa8SPHAc0FK2vKnQ0f1SVsGIZEA9WOI7b+qAbwnem64ZkSpUB8Rb0arlBzFyFrs9wPqMwqk73xK8N10zIlV4M8BIpIJRE9zk8T7wJb4leG+6ZkSSolXL9wGOLltlyekMXiJVeNWD5VuCPz2xnOsvVyThVEbO4rix0NG9M6tgRDLgVQ7wJsFHq5YvZOQlQkXg3ozCEcnCaxPLd2cShUh2vKoD3iR44DWJ5V8XOrpfziQSkWwkG7ebM4lCJAPRquUHM3IMSgTcnlE4DeFTgm9LLKtxE29Eq5a3MHonV3VAfHJBYnl1oaP7xUwiaRCfEryOXsRnAbB/2fILwJqMYhHJgnc5wIsEH61afgBugFG5W7OIRSQjycbtlkJHd5RJJCLZUILPqfMZOXr4gUJH99asghHJgE5Ribfimyydllh9SxaxNJIvCd67PTeRkmjVcsPoOtCXRSwiGTmfkfluTaGj+/msgmmUGVkH0CBK8Bnq72w3uHsAlB4G2NW6olddxI1xIiOn59wOrM4oFu/E5b8QP0p1oNi6ond3poH5xcsckPsEH61aPh84M7E6910ztervbJ8JHAIcChyWeD40fm0OIxN0YYLLlRT7O9v7gefjx5YqP+9dbl3Rq0lZJifZuN1W6OgeyiSSaai/s31fKpf/0nMrrq2cbB2o2FPa39m+iyplvcpyf+uKXn1vk6MEn1PnMTLJPFro6H4mq2Aapb+zfRYuOZcn60oN2EGMHJ/QKC3AwvhRk7hBrNYIbsZNuxq2rujtTz3a5uZd4xYfNe9L5Z3W5Lr5GYW5D3Bk/KhJf2f7C1TfGdiEuzLi4dYVvXtSj7ZJRauWzwXOSqz24iDPhwSf28atv7N9BnACbvDIabhLoY7ANVoHZRhavewDLI4fVfV3tj+La+jC+LEGWNO6ote7WwPH599zO8Cuv7N9P9wVMqU6cALDyXtuhqHVywHx4/gx3lPs72x/lOGyX3p+1NMegHMZmevWFTq6n8oqmEbyIcG/PrHclIOL4m7E8oaslNDnZBnXBBRxM0eV7txUz7gPiR/t5Sv7O9ufYGSDFwIP5bzr/1jcTl/JbuCXGcUyafER+RGMLP+vxP19zcAysg7MpH7tbwtuWu6TgGVl6wf6O9vXMnrnd33Ox8Mkc0BudnDHk+sEH09NeF5i9S8yCKVmcUN2OCMbstOob0O2GXgGeDp+Tv68nZGNU/lj3HWVGo/+zvY5uHObB5U9Dh5j+WBcozgVpe7QN5Wts/2d7Y8zMumHwCM56eZ8a2L59kJH97T+u+LTSycxug4cUKePHGBkmU/Wg83xe2ou88l1rSt6beJvLJ1CGK/cly8fOMW/cxbuICE5J8jO/s72hxiZ9ENgUzLuJpWsAzdlEkUGjLV5+P4qi1YtfzfwjbJVqwsd3cm7CWUmHuBWqSGbakUGd8SwmeqNVun5udYVvYMpfF5dJRrESg3hkcApuNtBzkrhI4u4hu4m4OfAzc3YxR+tWn4TcGHZqg8XOrq/kFE4o/R3ti/AHYmXl/9TmPrOHMAeRpf/SvVgazMksviU3AFU3gE4GNdtHzCBc/rjeAm4C+jF1YHVrSt6m+re6dGq5UuADeWrgIWFjm4vxunkNsFHq5YvgkJIS8tBFAehZSZExY3Y6JxCR/dzWcTU39k+H9fYXoSbF/wUpp6MNuPuircauA94jOHE7d35trgRPA73vw3ixym4c7PVRvTXooi789TPcQ3endP5CD9atXwRpuVaCoXz95b/YhGIzi50dGfWRd/f2X4UcDHwBuBsYMkUNxnhBleWyv/9wBO4OrCtGRJ32uJxCSczXPZL9eCQKW76BeBGXPnvxZ3Tn7b/X1cHCrdSaDluuA4MbQN7UlY5oNFymeCjVcsX0TJrI61HzzbHtsH8hbB9M3ZdH/Sv30NxYEkjvuD+zvYW3P2HL44fr2byRyaW4Yas9LivdUXvsymEmnv9ne2zcdeDJxP/MUzuKoJduHN5paOb+6bLecyq5f+xPtjauPIPe5PN63A7tRcz9uCw8ezAJfHVZc9hzsdQpKa/s72V4fJfXg8m22P4JMPl/8bWFb3T5uqk6ZIDspabBB+GoQEWADOWPv3Dlebg415rTrsUY4bbbmstdvU1sOWxvsLFV1xYjzj6O9uPZLgxewOTqzw7cEci5clcDVkd9He2z8N16ycT/5gj9SttiuHu/F5gXbWjm/7O9hOBf8T1KHy6dUXvnZOLfqQwDGcB+5/0zI+uKxx07PkZlf8W3LwTpZ3a5GWqtXqKsh3Z+HnddNmJyov41NciRpb90vO+E9zcgwyX/77WFb1V79TW39n+XuA9uAGfn2pd0btt4tGPFobhfGBunANek0UdmE6aOsGHYVgA3gb8Ca4hmQ1ES5/98ZzC2e/G7De6R8q+9Az27m/awhv+OpVpehPd7hfjzqlPxNOMTORqyKaB/s72A3GXl70BNxr/xAluYiPDXZm9rSt69x4t9He2383wdbkDwDtbV/R+bzJxhmG4APgz4NI4xmIjyz+M6Ha/GPe/WjCBXy8CDzG6Zyr304hOZ3HiPwH3fb4B1wsz0e/1lwwf4d9ROqXV39keAA+UvTcE3ty6ovfJycQahuHpwMdwo+UPBoYaXQemq6YdRR+G4auAq3F7nyMmqjDFAdclU8n8hVAcnPTELil0uz8KXA/cANzeuqJ3y2RjkfppXdG7FbguftDf2X4ErrErPQ4bZxNLgD+IH/R3toe4hu5HjJzcZxZwTX9n+0dbV/R+vtb44h6rjwOfxM2Utk/80sx6ln+Ycrf7EHAbrg704pK5pmydZuLep4fjx1fK2r3SDu8FjH2pawvu+vNzgSuAXf2d7bfivvdNifcGwB39ne1val3R+wA1CsNwIfCtOJbZDM8YOKPedaBZNGWCD8Pw3cBXGG7URrAtszDbN0OFvTe2b4aWmRPqtphit/s2XMN+A3BD64re9RP5bJkeWlf0bgKuAq6Kj25OYjjZv46R91qvpNQF+mHc6OSkf4nL2cfG670Jw3AecA2uh2Fe8vU6lP+pdruvZXintq91Re/LE/l8yV48ev6e+PG5+DLXVzN8hH8mY9+8bB9cG3pRvPwyI08BHA7c2t/Zfknrit4bx4snDMMzgZ/F2xg1UDntOtCsmq6LPgzD3wf+gzFmqVry/K3M2/8ApnL+pb+z/WRct+dv40ak1moIuBPXoF0P3NNsl5bIxMQj989guLE7H3dEMRkrgXdVO6oNw3AGLlGeS5UjqJTK/yzc33Ip7jTYRK5B34rbqb0et1P7xAR+V5pQfLlj+SmtpZPc1CDw7tYVvd+p9oYwDE/GtbFVxwikUQfyoKkSfBiGr8B9sWNOQVkY2s0J/TdSOHAJ5tjXlo2gvBnbv6FoinsOrzSCsr+z/SRcg3YpbpBJrUrd7tcDv2jG66UlPf2d7fvgjm5Kjd2ZTGyk/gPA6yudhw7D8B+ADzJGHRir/EdbN/LsvKUXHHHuW2+rEPfMON5LgUuo/ZzrIHA7w3XgXu3U+q2/s/0w3DnxUh04YuzfGOVzwN8kB6qGYTgXeAR3iqxqnRqzDvRvsBQHD53R8bcaRQ9gjGnFVfi3AK/AdacM4BqibwDfsNbWdVBYfM7xTsbvCgLcF7x42z3MHdqGKQ5gW2axc8YCnp5xwq7WFZ+/z9joHPbsMcyebRka3ECxuIvaj9RL3e6lI5QNk/yzxAP9ne0H4Lrx349r7Go5NbYDOKo8yYdheDxuRHnFU1PlqpX/p2acEO3/42+/NKv/2f2Hy//QAxSHHsDN8Ffr6adSt/v1uG737TX+nngmPqV1PPBG3Cmqo2v81Z+0ruj9jfIVYRguxw2oG/c+A9XqwKYFr9penLHP+4IguHqCf0rTqTXB/zHwVdysTzfhJpJYBPwm7tzjtcBv2zp2B4Rh+GbcoLpJ3/mpsP0lFl3Vw8wTT2XuJZ20HHE0xU3r2XndVQyuuRf27Kr2q0XcEcoNqNtdJiG+Dv8JJnD3POArrSt6P1BaCMPwx0AHkxw7M4XyD67b/QaGx5Ko210mpL+z/W3ADyf4a/uUTlfFg+rWk85NhLYAhwVBkOvJwGptKB7BnYf7SfmRujHmb3Cze/0WLtlfm3qEwz7MFG/ruOAn32Lmiaey759/au95mRlLjmPfP1/Oy1/oZvC+u+LZvgCX1HtxOxU/iEdVi0xWgYkldyibYjMMw4Nw3Z2THhg7wfIP7hak1+IG9N3s48yIkqpDJ/j+Im664ZLfTTGWObjetFUpbnPaqamxsNZWHNVorX3WGPM14O9x14LXJcHH1/omb/s6YbOfe4K57/voiEEXAMYY5r6jkxfX/BqKxZ/jGrTrdC2upKV1Re+u/s72K4FPUHkE+i7cAM0W3KyFNwH/XPb6svj1yQ7eq7X8bwW+j6sDNympS4r+B3ea6owKr0XATlzZnxH/3JU4B/9HpHcL4PnA+1CCH1fpRiX1bAjOxt3mctKNGwC7d9NyROXTPy1HHA0De2hd0XtRxTeITFHrit5P9Xe2fxE3GccuXCO2E9hdw5zeb6bCJXETUkP5Bw5phpsPSfNpXdH7Un9n+5m4OSJaGC7/u4DBsepAGIb74CbeSYvBjfrPtSkleGPMDKAzXvzZ1MOp6hTSuH/4nDkUN61nxpLjRr1U3LQeZs1pnksKpCm1ruh9AXfTjol6xZQ/vIby3/qfP1Zyl7qJk/iGSfzqSbidgYlOnzuWA8IwnB8EQW4HiE51qr7P4Sbv+D9rbT27OpYy1aN3YM+iI9l53VUkxwJaa9n5gxVQHLx5qp8hUicTPX85isq/NLFjcN33adpF7SP6m9KkE7wx5kO4yxXWAu9KLaLKptY1Gdv2lncy+PADvPyFboY2PIodGmJow6NugNGD9+5haOiyND5HpA6mfH90lX9pYrOZ3F0fxxKRwoHjdDapLnpjzAeAL+DuHtRura33CPNU5qqO5u/Hc5d3seAn32LwMx+BPbtdt3xx8GaGhi4rvyGIyDQzRIUpOSdiZPn/qLssTuVfmsMg6R/BG9x8Lrk14QRvjPkw8HncHYDarbWbU49qtLW4L2JKDRy4Rm7rZX8KsC4IgtEnI0Wmp+eY+C1sRykr/7uBvwyC4ItT3aZIA2yowzb3wd31Mbcm1EVvjPlLXHJfDbyuQckdYA3ufEma7kt5eyL1tCbl7e2pwzZF6uUh0rtEruTlIAiq3rM+D2pO8MaYT+AG1f0Kd+TeyGvE7yaNUfTDduCmmhVpFteT7k7uHFxdFpn24pHuaR9t35Hy9qadmrrojTGXA8txMwvdAnwoOVkGsMFa+81Uo4sFQfB8GIb34O7SlYYZwPdS2pZII1wNfDbF7d0aBMG2FLcnUm9fB7pJ52DvZeC/UtjOtFbrOfjSpQQtuCljK+kDvjnVgMbwReCVTHG6WtxOys1BEGiWOmkaQRA8HYbhXcAFTP3y1h3Al6YelUhDfQs3E2QaIuCnKW1r2qqpobDWXmmtNeM8LqxzrCuBJ1PYzgDw5ylsR6TRPsTIubknw+Jub/yjqYcj0jhBEDyJO+qe6lVV24GuIAimWpemvakeCTRMEAQR8F6mdh5yJ/CNIAgeSicqkcYJguA+4DtMrQ7sAt4XBIFmbZRm1I3rgZqsIu5A8evphDO9NU2CBwiC4A7gClyinqg9uOv2P5JqUCKN9UHcEfhkrt/dCXw8CAINrpOmFI8beQuTywEWeAl4UxAEXtzuu6kSPEAQBJ/H3b1uIkcxO4B7gYuCIMj1xAaSb0EQ7AZeD9zPxBq5XUB3EARfrUtgIg0SBMFdwDtwXe21JuoB3D3g24IgyPW17+VMcl7qZhGG4cW47srZVB94N4AbTPFl4K+CINCtLyUXwjCcCfwT7vabLVSfynY77pzlZUEQVLzts0gzCsPwWOAHwFFUzwEWt3N7B64O9DcmuumhaRM8QBiGs4E/AD6Au5XgbtwXOhvYCvwQ+Ew8OEMkd8IwXAL8DfB2YAHuVJTBXUq0Fnf1yQr1XEkehWFogA7gr4GzcAd0RdwVYkXc1V2fDoLg7syCzFBTJ/hy8f2Cj8YdzTwXBEGjZtkTmRbCMFwELMTNW78+7s4X8UIYhi24HDAXd659o++DSXOT4EVERGRY0w2yExERkfEpwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgOzcg6ABFpArv7LViwFvdcLPs5+RztfbaldXtfj0a+f+97K7y29z3FCq8nf472bssmt1v6OYrG2Ub5czzDZ+nn8niiZHxUfg925O9bRq6LEp8XRSPXRxaKxaqv21Icpffsfb3CdqLicNxR6X/k4nb/Huu2F9n4z4ify5bd1172uyPea90s8NbG7yn7nfhzbfy5Noqwxcg9l78WRfHr7m+IiqXXo/h1i43/nr3bGioOb7PKtqJi2c8j4mDv+mLZeyo9R4ntJF/bux3if0P8XP5z6blY4bXk+4bG2UZpOwBXWmuqVVsdwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQEryIiEgOKcGLiIjkkBK8iIhIDinBi4iI5JASvIiISA4pwYuIiOSQsdZmHYOITHPGmPdba/8j6zh8ov95Y+Xx/60jeBGpxfuzDsBD+p83Vu7+30rwIiIiOaQELyIikkNK8CJSi1ydm2wS+p83Vu7+3xpkJyIikkM6ghcREckhJXgREZEcUoIXkb2MMW80xjxsjHnMGPNXFV6fbYy5On79LmPMUY2PMj/G+3+XvW+ZMcYaY85sZHx5U0P5PtIYc5Mx5l5jzP3GmDdnEWdalOBFBABjTAvwZeBNwMnA7xpjTk687b3AC9ba44DPA//Q2Cjzo8b/N8aYfYEPAXc1NsJ8qfH//bfANdba04HfAb7S2CjTpQQvIiVnA49Zax+31g4A3wXennjP24Gr4p9XAu3GGNPAGPOklv83wKeBHmB3I4PLoVr+3xbYL/55f+DpBsaXOiV4ESk5HHiybHlTvK7ie6y1Q8CLQGtDosufcf/fxpjTgcXW2h83MrCcqqV8Xwm80xizCfg/4M8aE1p9KMGLSEmlI/HkdbS1vEdqM+b/0hhTwJ0G+VjDIsq3Wsru7wLftNYeAbwZ+H/x99CUmjZwEUndJmBx2fIRjO6i3PseY8wMXDfm1oZElz/j/b/3BQLgF8aYDcC5wI800G7Sainf7wWuAbDW3gHMAQ5qSHR1oAQvIiW/BI43xhxtjJmFG2T0o8R7fgRcHv+8DLjRarasyRrz/22tfdFae5C19ihr7VHAncDbrLX3ZBNu06ulfD8BtAMYY5biEvyWhkaZIiV4EQH2nlP/ILAKeAg3mniNMWa5MeZt8du+DrQaYx4DPgpUvbRLxlbj/1tSUuP/+2PAHxpj7gP+B3h3M+/AaqpaERGRHNIRJ9oEzQAAB9xJREFUvIiISA4pwYuIiOSQEryISA2MMb8wxtT1nKYx5gRjzIAx5i/q+Tl5Zow53Bizyxjz6axjyZoSvIjI9PEvQD9uStW9jDHfjOeiL3/sMMaExpjPGWMOaFSAxpjF8Wf+yhjzgjFm0Biz2Rjzc2PMnxtj9m9ULJVYa58CvgZ8zBizeLz355kG2YmI1MAY8wugzVpbl6l5jTGvBm4DrrDWfibx2jdxlyf+EFgdrz4EeCtwGLAOONtaW9c5CYwx7wP+DZgN3AfcDryAm83wAuAUoN9am+m148aYw3CXvP23tfb9WcaSpRlZByAiIgB8AIiA/zfGe35grf1macEY83HcTWhOxk2r+ql6BWeM+T3gP3EJ/bestT+p8J7zSfQ+ZMFa+7Qx5gbg940xf2GtfTHrmLKgLnoR8ZYx5t3GmGuNMY/H521fMsbcZox55xi/M9sY83fGmPXGmD3GmHXGmE/Gk6ck3/saY8z/GmM2xe991hhzpzHmk4n37YebOOh2a+2Tye1UY63dzvDNf84u296rjDFfMMbcZ4zZaozZbYx51Bjzz5W68+P/g42f3xiPN3ixNOYgvqPdl+K3/06l5B7HcxtwToXttxtjflYWyyNxN/+o7vzSWAdjzCxjTHd8e9c9cS/GuLGW+S4wFzehjZd0BC8iPvsq8CBwM/AMrqu5NAf5idbaT1T4nWuAs3B30xvE3ZHsSuBMY8zbShOjGGPeCPwEeAk3Y9pTwIHAUuBPGXm0/VpgFnDrJP6G0imD8gT3h8AlQB/wc6AFOAM3OdGbjDHnWGtfrrCtZcAbgZ/izmMfVbb+QOBOa+31YwVjrd0zIjhj/gj3f94BfA/YDFwI/CXwVmPM+dbabRU2dS3u//xT4Afx79USa8lt8fNFwL+PFXNeKcGLiM8Ca+268hXxkfhPgb8yxnwtHrRVbilwirX2hfj9VwA3Ab8BvJPhLvY/xPWSXmitvS/xGclz1BfEzxOahtYYM5/hqYPL7xf/WeAD1tpi4v3vBf4Lt4PxDxU2+Wbgzdban1WJr3eC8S0Bvghsx40RWFv22leAP8HdCrfSefIluO/n+SqbrxYrANbax4wx23A7T15SF72IeCuZ3ON1A7jzyDOI5yVP+HQpucfv3w38dbz4BxXev6vCZyST1pHx8zPjhPwOY8yV8eOrwMO4HY51uMFvpe1vTCb32H/jehQ6qmz/h1US5qHx86Zx4kt6J65n4t/Kk3vsCuBl4F3GmNkVfvcTYyT3sWIt9yxwsDFmTs0R54gSvIh4yxhzpDHmy8aYtcaYnaVL0HDdwzD6fuHgur2TbgGGgNPL1n07fr7LGPM1Y8xlxpgjqoTSGj+/UOX1krcDn4wflwMvAv+IOzre+7vGmJnGmA8aY26Nz3sX478rAvar8ncB3F1lfaXTALU4I36+MflCHO+9uBu6nDSBWGp9HYbvdNi0d4SbCnXRi4iXjDHH4JLEAbgEfT0uYRZx53Mvx10OlvRccoW1tmiM6QcWlq37vjHmN3A3MPkD4I/iz/0V8NfW2hvKNlE6yh/vSPM95aPox3A17hz847hL654FSufGP0zlv4v4fZWUbqtabQelmtIgumo9E6X1CyYQS62vA+wTP4/qRfGBEryI+OqjuCPnUUnTGPO7DJ/bTlqEu8a6/P0t8bZeKl8fjzb/iTFmHm50+W/gzjv/2BhzurX2wfitpQFkrUyRcfeLvwQ3uO7N1trBstcKQNcYv17tCP1W3E5KO1Bp4GE1pcvTDgHWVHj90MT7hgMZf5KWWnoTWnE9K3WdH2C6Uhe9iPjquPj52gqvtY3xe5Veew3ugOneSr9grd1hrb3RWvtR4DO489JvKnvL/fFzpa7qiSr9XT8qT+6xsxk+qp2IlbgkeZ4x5g1jvTFxPr30/7iwwvsWAKcBu3G3b01VvFN1OHB/M9/ydSqU4EXEVxvi5wvLVxpjOoD3jfF7nyi/ljwewPXZePEbZevbjTGVkumi+Hln2bpfxM/njhd0DTbEzxeWrzTGLGSSk9DEl9R9KF68Ov4fjWKMORe4o2zVt3CXEv6ZMea4xNs/jRsP8K3kpXUpORt3eeBNddh2U1AXvYj46ivAe4DvGWOuxV2nHuCurb4GuKzK7z0ErDHGlF8HfyzumvfyWej+GTjKuCluNwADwKuA1wMbcROxAGCtDY0xDwPtxpiWKiPga/VL3DXgv2mMuR3Xvb4I12PwMMPn0yfEWvvteIfl34CfGWNWM3Kq2vOAVwLPl/3OBmPMh3E7Fr82xlwDbMH1gpwHrMVdD18PF8fPlXpovKAjeBHxkrX2fuB1uCT1Zty58f2A38RNnFLNpbjLzd4KfBDXjl6Jm761vCv4M7jr6U/B9Qj8MS7RfgY4q3zUe+yruHPVFzMF8c7B2+LtHYY78r4Ad/17B26nZLLb/i/geNy16xb4fVyCXoZL9B/B7eyU/85X4s+9E/gt3NiHhbjR/+fVY/78eKzBO4H7rLV3jPf+vNLNZkREpoF4utp1uOlq3551PM3MGPNW3OyB77LWfivreLKiBC8iMk0YY/4U1519lrV2QrPaiWOMMcCvcJc7nu3rADvQOXgRkenk33HXhC8a741S1SG4o/cf+JzcQUfwIiIiuaRBdiIiIjmkBC8iIpJDSvAiIiI5pAQvIiKSQ0rwIiIiOaQELyIikkP/Hzkz5Vji/I6SAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.30\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) o-> (0, 0)\n",
"(0, 0) o-o (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.50\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0,-1) o-> (0, 0)\n",
"(1,-2) o-> (0, 0)\n",
"(0, 0) <-o (1, 0)\n",
"(1,-1) o-> (1, 0)\n",
"(2,-1) o-> (1, 0)\n",
"(1, 0) o-o (2, 0)\n",
"(2,-1) o-> (2, 0)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Parameter settings\n",
"tau_max = 2\n",
"pc_alpha = 0.80\n",
"\n",
"# Run LPCMCI with these settings and the ParCorr CI test\n",
"svarfci = SVARFCI(dataframe = data, cond_ind_test = ParCorr())\n",
"link_matrix = svarfci.run_svarfci(tau_max = tau_max,\n",
" pc_alpha = pc_alpha,\n",
" verbosity = 0)\n",
"\n",
"# Plot results\n",
"svarfci._print_graph_dict()\n",
"tp.plot_time_series_graph(link_matrix = link_matrix,\n",
" val_matrix = svarfci.val_min_matrix,\n",
" figsize = (8, 6),\n",
" link_colorbar_label = 'abs(ParCorr)',\n",
" vmin_edges = 0,\n",
" cmap_edges = plt.cm.get_cmap('OrRd'),\n",
" label_fontsize = 20)\n",
"plt.show()"
]
},
{
"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": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| gpl-3.0 |
mkudija/Map-Tools | (1)_get_lat-lng.ipynb | 1 | 22180 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name_Orig</th>\n",
" <th>Lat_Orig</th>\n",
" <th>Lng_Orig</th>\n",
" <th>Name_Des</th>\n",
" <th>Lat_Des</th>\n",
" <th>Lng_Des</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Columbus, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Fort Worth, TX</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Columbus, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cincinnati, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Columbus, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Dublin, Ireland</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Paso Robles, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Palm Springs, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Paso Robles, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Monterey, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name_Orig Lat_Orig Lng_Orig Name_Des Lat_Des Lng_Des\n",
"15 Columbus, OH NaN NaN Fort Worth, TX NaN NaN\n",
"16 Columbus, OH NaN NaN Cincinnati, OH NaN NaN\n",
"17 Columbus, OH NaN NaN Dublin, Ireland NaN NaN\n",
"18 Paso Robles, CA NaN NaN Palm Springs, CA NaN NaN\n",
"19 Paso Robles, CA NaN NaN Monterey, CA NaN NaN"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from map_functions import *\n",
"\n",
"df = pd.read_csv('data/locations (no_lat-lng)_2017.csv')\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name_Orig</th>\n",
" <th>Lat_Orig</th>\n",
" <th>Lng_Orig</th>\n",
" <th>Name_Des</th>\n",
" <th>Lat_Des</th>\n",
" <th>Lng_Des</th>\n",
" <th>Payload_Orig</th>\n",
" <th>Payload_Des</th>\n",
" <th>Distance (nm)</th>\n",
" <th>Distance (mi)</th>\n",
" <th>Distance (km)</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Columbus, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Fort Worth, TX</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Columbus, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Cincinnati, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Columbus, OH</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Dublin, Ireland</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Paso Robles, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Palm Springs, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Paso Robles, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Monterey, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name_Orig Lat_Orig Lng_Orig Name_Des Lat_Des Lng_Des \\\n",
"15 Columbus, OH NaN NaN Fort Worth, TX NaN NaN \n",
"16 Columbus, OH NaN NaN Cincinnati, OH NaN NaN \n",
"17 Columbus, OH NaN NaN Dublin, Ireland NaN NaN \n",
"18 Paso Robles, CA NaN NaN Palm Springs, CA NaN NaN \n",
"19 Paso Robles, CA NaN NaN Monterey, CA NaN NaN \n",
"\n",
" Payload_Orig \\\n",
"15 https://maps.googleapis.com/maps/api/geocode/j... \n",
"16 https://maps.googleapis.com/maps/api/geocode/j... \n",
"17 https://maps.googleapis.com/maps/api/geocode/j... \n",
"18 https://maps.googleapis.com/maps/api/geocode/j... \n",
"19 https://maps.googleapis.com/maps/api/geocode/j... \n",
"\n",
" Payload_Des Distance (nm) \\\n",
"15 https://maps.googleapis.com/maps/api/geocode/j... 0 \n",
"16 https://maps.googleapis.com/maps/api/geocode/j... 0 \n",
"17 https://maps.googleapis.com/maps/api/geocode/j... 0 \n",
"18 https://maps.googleapis.com/maps/api/geocode/j... 0 \n",
"19 https://maps.googleapis.com/maps/api/geocode/j... 0 \n",
"\n",
" Distance (mi) Distance (km) \n",
"15 0 0 \n",
"16 0 0 \n",
"17 0 0 \n",
"18 0 0 \n",
"19 0 0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Add columns for origin and destination payloads (for Google API) \n",
"payload_pre = 'https://maps.googleapis.com/maps/api/geocode/json?address='\n",
"df['Payload_Orig'] = df['Name_Orig'].str.replace(', ','+').str.replace(' ','+')\n",
"df['Payload_Des'] = df['Name_Des'].str.replace(', ','+').str.replace(' ','+')\n",
"df['Payload_Orig'] = payload_pre + df['Payload_Orig']\n",
"df['Payload_Des'] = payload_pre + df['Payload_Des']\n",
"df['Distance (nm)'] = 0\n",
"df['Distance (mi)'] = 0\n",
"df['Distance (km)'] = 0\n",
"df.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I round the decimal degrees and distance measurements for readability. Pick what to round decimal degrees to based on the accuracy required. For our purposes, 3 decimal places should suffice. \n",
"\n",
"This table [from Wikipedia](https://en.wikipedia.org/wiki/Decimal_degrees#Precision) gives the precision of decimal degrees. \n",
"\n",
"| Decimal Degrees\t| Identifiable\t| Distance At Equator | \n",
"|------|------|------|\n",
"|1\t| country or large region\t| 111.32 km |\n",
"|0.1\t| large city or district\t| 11.132 km |\n",
"|0.01\t| town or village\t| 1.1132 km |\n",
"|0.001\t| neighborhood, street\t| 111.32 m |\n",
"|0.0001\t| individual street, land parcel\t| 11.132 m |\n",
"|0.00001\t| individual trees\t| 1.1132 m |\n",
"|0.000001\t| individual humans\t| 111.32 mm |\n",
"|0.0000001\t| practical limit of commercial surveying\t| 11.132 mm |\n",
"|0.00000001\t| specialized surveying (e.g. tectonic plate mapping)\t| 1.1132 mm |"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Completed...\n",
"\tSKIPPING: Columbus, OH to Hotel de Rome, Berlin, Germany\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Columbus+OH\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Hotel+de+Rome+Berlin+Germany\n",
"\tSKIPPING: Berlin, Germany to Cathedrale Notre-Dame de Paris, Paris, France\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Berlin+Germany\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Cathedrale+Notre-Dame+de+Paris+Paris+France\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mkudija/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:15: DeprecationWarning: \n",
".ix is deprecated. Please use\n",
".loc for label based indexing or\n",
".iloc for positional indexing\n",
"\n",
"See the documentation here:\n",
"http://pandas.pydata.org/pandas-docs/stable/indexing.html#deprecate_ix\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" - 42.3 nm Paris, France to Cathedrale Notre-Dame de Chartres\n",
"\tSKIPPING: Paris, France to Columbus, OH\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Paris+France\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Columbus+OH\n",
" - 1795.9 nm Columbus, OH to Paso Robles, CA\n",
"\tSKIPPING: Paso Robles, CA to Dallas, TX\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Paso+Robles+CA\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Dallas+TX\n",
" - 793.8 nm Dallas, TX to Columbus, OH\n",
" - 927.3 nm Columbus, OH to Austin, TX\n",
" - 1691.7 nm Columbus, OH to Reno, NV\n",
"\tSKIPPING: Reno, NV to Incline Village, NV\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Reno+NV\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Incline+Village+NV\n",
" - 30.1 nm Columbus, OH to Utica, OH\n",
" - 129.9 nm Oakland, CA to Yosemite National Park, California\n",
"\tSKIPPING: Columbus, OH to South Bend, IN\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Columbus+OH\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=South+Bend+IN\n",
" - 424.1 nm Columbus, OH to Ossining, NY\n",
" - 70.3 nm Columbus, OH to Wooster, OH\n",
" - 816.3 nm Columbus, OH to Fort Worth, TX\n",
" - 87.1 nm Columbus, OH to Cincinnati, OH\n",
" - 3110.3 nm Columbus, OH to Dublin, Ireland\n",
"\tSKIPPING: Paso Robles, CA to Palm Springs, CA\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Paso+Robles+CA\n",
"\thttps://maps.googleapis.com/maps/api/geocode/json?address=Palm+Springs+CA\n",
" - 83.5 nm Paso Robles, CA to Monterey, CA\n"
]
},
{
"data": {
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"<div>\n",
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" .dataframe thead tr:only-child th {\n",
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" }\n",
"\n",
" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name_Orig</th>\n",
" <th>Lat_Orig</th>\n",
" <th>Lng_Orig</th>\n",
" <th>Name_Des</th>\n",
" <th>Lat_Des</th>\n",
" <th>Lng_Des</th>\n",
" <th>Payload_Orig</th>\n",
" <th>Payload_Des</th>\n",
" <th>Distance (nm)</th>\n",
" <th>Distance (mi)</th>\n",
" <th>Distance (km)</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>15</th>\n",
" <td>Columbus, OH</td>\n",
" <td>39.961</td>\n",
" <td>-82.999</td>\n",
" <td>Fort Worth, TX</td>\n",
" <td>32.755</td>\n",
" <td>-97.331</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>816.3</td>\n",
" <td>939.3</td>\n",
" <td>1511.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Columbus, OH</td>\n",
" <td>39.961</td>\n",
" <td>-82.999</td>\n",
" <td>Cincinnati, OH</td>\n",
" <td>39.103</td>\n",
" <td>-84.512</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>87.1</td>\n",
" <td>100.2</td>\n",
" <td>161.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Columbus, OH</td>\n",
" <td>39.961</td>\n",
" <td>-82.999</td>\n",
" <td>Dublin, Ireland</td>\n",
" <td>53.350</td>\n",
" <td>-6.260</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>3110.3</td>\n",
" <td>3579.3</td>\n",
" <td>5760.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Paso Robles, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Palm Springs, CA</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Paso Robles, CA</td>\n",
" <td>35.637</td>\n",
" <td>-120.655</td>\n",
" <td>Monterey, CA</td>\n",
" <td>36.600</td>\n",
" <td>-121.895</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>https://maps.googleapis.com/maps/api/geocode/j...</td>\n",
" <td>83.5</td>\n",
" <td>96.0</td>\n",
" <td>154.6</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name_Orig Lat_Orig Lng_Orig Name_Des Lat_Des Lng_Des \\\n",
"15 Columbus, OH 39.961 -82.999 Fort Worth, TX 32.755 -97.331 \n",
"16 Columbus, OH 39.961 -82.999 Cincinnati, OH 39.103 -84.512 \n",
"17 Columbus, OH 39.961 -82.999 Dublin, Ireland 53.350 -6.260 \n",
"18 Paso Robles, CA NaN NaN Palm Springs, CA NaN NaN \n",
"19 Paso Robles, CA 35.637 -120.655 Monterey, CA 36.600 -121.895 \n",
"\n",
" Payload_Orig \\\n",
"15 https://maps.googleapis.com/maps/api/geocode/j... \n",
"16 https://maps.googleapis.com/maps/api/geocode/j... \n",
"17 https://maps.googleapis.com/maps/api/geocode/j... \n",
"18 https://maps.googleapis.com/maps/api/geocode/j... \n",
"19 https://maps.googleapis.com/maps/api/geocode/j... \n",
"\n",
" Payload_Des Distance (nm) \\\n",
"15 https://maps.googleapis.com/maps/api/geocode/j... 816.3 \n",
"16 https://maps.googleapis.com/maps/api/geocode/j... 87.1 \n",
"17 https://maps.googleapis.com/maps/api/geocode/j... 3110.3 \n",
"18 https://maps.googleapis.com/maps/api/geocode/j... 0.0 \n",
"19 https://maps.googleapis.com/maps/api/geocode/j... 83.5 \n",
"\n",
" Distance (mi) Distance (km) \n",
"15 939.3 1511.7 \n",
"16 100.2 161.2 \n",
"17 3579.3 5760.3 \n",
"18 0.0 0.0 \n",
"19 96.0 154.6 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Find latitude and longitude of each location, and distance between\n",
"round_coord = 3\n",
"round_dist = 1\n",
"\n",
"print('Completed...')\n",
"for row in range(0,df.shape[0]):\n",
" try: \n",
" # Read payloads to pass to Google API\n",
" payload_orig = df.iloc[row,6]\n",
" payload_des = df.iloc[row,7]\n",
" \n",
" # Request and write latitude and longitude\n",
" lat_orig, lng_orig = get_lat_lng(payload_orig)\n",
" lat_des, lng_des = get_lat_lng(payload_des)\n",
" df.ix[row,'Lat_Orig'] = round(lat_orig,round_coord)\n",
" df.ix[row,'Lng_Orig'] = round(lng_orig,round_coord)\n",
" df.ix[row,'Lat_Des'] = round(lat_des,round_coord)\n",
" df.ix[row,'Lng_Des'] = round(lng_des,round_coord)\n",
" \n",
" # Calculate and write distance\n",
" nm, mi, km = get_distance(lat_orig, lng_orig, lat_des, lng_des)\n",
" df.ix[row,'Distance (nm)'] = round(nm,round_dist)\n",
" df.ix[row,'Distance (mi)'] = round(mi,round_dist)\n",
" df.ix[row,'Distance (km)'] = round(km,round_dist)\n",
" \n",
" print(' - '+ str(round(nm,round_dist))+' nm '+df.iloc[row,0]+' to '+df.iloc[row,3])\n",
" \n",
" except:\n",
" print('\\tSKIPPING: '+df.iloc[row,0]+' to '+df.iloc[row,3])\n",
" print('\\t'+payload_orig)\n",
" print('\\t'+payload_des)\n",
"\n",
"df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Save df to csv\n",
"header = ['Name_Orig','Lat_Orig','Lng_Orig','Name_Des','Lat_Des','Lng_Des',\n",
" 'Distance (nm)','Distance (mi)','Distance (km)']\n",
"df.to_csv('data/locations.csv', columns=header, index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
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},
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},
"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 |
datahac/jup | v01/user_flow_JSON.ipynb | 2 | 23055 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# User flow"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#File for goals (goals_dict)\n",
"\n",
"request1 = \"POST https://analyticsreporting.googleapis.com/v4/reports:batchGet?fields=reports(columnHeader%2Cdata(rows%2Ctotals))&key={YOUR_API_KEY}\"\n",
"request1 = {\n",
" \"reportRequests\": [\n",
" {\n",
" \"viewId\": \"123303369\",\n",
" \"dateRanges\": [\n",
" {\n",
" \"startDate\": \"2017-01-01\",\n",
" \"endDate\": \"2017-04-30\"\n",
" }\n",
" ],\n",
" \"metrics\": [\n",
" {\n",
" \"expression\": \"ga:goal1Completions\" #instead of \"ga:goal1Completions\" use \"goal_to_use_in_request\" variable from tracking-tags code\n",
" }\n",
" ],\n",
" \"dimensions\": [\n",
" {\n",
" \"name\": \"ga:goalCompletionLocation\"\n",
" }\n",
" ]\n",
" }\n",
" ]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#File for sessions (sessions_dict)\n",
"\n",
"request2 = \"POST https://analyticsreporting.googleapis.com/v4/reports:batchGet?fields=reports(columnHeader%2Cdata(rows%2Ctotals))&key={YOUR_API_KEY}\"\n",
" \n",
"request2 = {\n",
" \"reportRequests\": [\n",
" {\n",
" \"viewId\": \"123303369\",\n",
" \"dateRanges\": [\n",
" {\n",
" \"startDate\": \"2017-01-01\",\n",
" \"endDate\": \"2017-04-30\"\n",
" }\n",
" ],\n",
" \"metrics\": [\n",
" {\n",
" \"expression\": \"ga:sessions\"\n",
" }\n",
" ],\n",
" \"dimensions\": [\n",
" {\n",
" \"name\": \"ga:pagePath\"\n",
" }\n",
" ]\n",
" }\n",
" ]\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import json\n",
"\n",
"with open('files/TMRW_goal1_goalloc.json') as file: \n",
" input_goals = json.load(file)\n",
"\n",
"goals = input_goals[\"reports\"][0]['data']['rows']\n",
"#goals"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"with open('files/TMRW_sess_page.json') as file: \n",
" input_sess = json.load(file)\n",
"\n",
"sessions = input_sess[\"reports\"][0]['data']['rows']\n",
"#sessions"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'(entrance)': 6,\n",
" '/': 85,\n",
" '/TMRW_Byte_Cafe.php': 22,\n",
" '/TMRW_FAQs.php': 24,\n",
" '/TMRW_Get_in_touch.php': 3,\n",
" '/TMRW_the_team.php': 5,\n",
" '/portfolio-single-gallery.html': 1}"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def create_dict(x):\n",
" goals_dict = {}\n",
"\n",
" for s in x:\n",
" \n",
" #print (str((s['dimensions'])))\n",
" #print (s['metrics'][0]['values'])\n",
" #session_dict[] = 0\n",
" goals_dict[s['dimensions'][0]] = int(s['metrics'][0]['values'][0])\n",
" #session_dict[s['dimensions']] = s['metrics'][0]['values']\n",
" \n",
" return goals_dict\n",
" \n",
"\n",
"goals_dict = (create_dict(goals))\n",
"\n",
"goals_dict"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6893"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get totals\n",
"\n",
"total_conv = int(input_goals['reports'][0]['data']['totals'][0]['values'][0])\n",
"\n",
"total_sess = int(input_sess['reports'][0]['data']['totals'][0]['values'][0])\n",
"\n",
"total_sess"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"def create_dict(x):\n",
" sessions_dict = {}\n",
"\n",
" for s in x:\n",
" \n",
" #print (str((s['dimensions'])))\n",
" #print (s['metrics'][0]['values'])\n",
" #session_dict[] = 0\n",
" sessions_dict[s['dimensions'][0]] = int(s['metrics'][0]['values'][0])\n",
" #session_dict[s['dimensions']] = s['metrics'][0]['values']\n",
" \n",
" return sessions_dict\n",
" \n",
"\n",
"sessions_dict = (create_dict(sessions))\n",
"\n",
"#sessions_dict"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'/': 5925,\n",
" '/TMRW_Byte_Cafe.php': 270,\n",
" '/TMRW_FAQs.php': 129,\n",
" '/TMRW_the_team.php': 140,\n",
" '/trainstrikes.php': 231,\n",
" '/voteforbyte.php': 79}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter page with less than 3% of conversions and less than 1% of sessions\n",
"\n",
"# define thresholds for filter\n",
"threshold_conv = round(total_conv * 0.03,0)\n",
"threshold_sess = round(total_sess * 0.01,0)\n",
"\n",
"def filter_data(data,thr):\n",
" result = {}\n",
" for page in data:\n",
" if data[page] > thr:\n",
" #print (data[page])\n",
" result[page] = 0\n",
" result[page] = data[page]\n",
" \n",
" return result\n",
"\n",
"goals_dict = filter_data(goals_dict,threshold_conv)\n",
"sessions_dict = filter_data(sessions_dict,threshold_sess)\n",
"sessions_dict"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'(entrance)',\n",
" '/',\n",
" '/TMRW_Byte_Cafe.php',\n",
" '/TMRW_FAQs.php',\n",
" '/TMRW_the_team.php'}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"goals_dict_keys = set(goals_dict.keys())\n",
"goals_dict_keys"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'/', '/TMRW_Byte_Cafe.php', '/TMRW_FAQs.php', '/TMRW_the_team.php'}"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def dict_compare(goals_dict, sessions_dict):\n",
" \n",
" goals_dict_keys = set(goals_dict.keys())\n",
" \n",
" sessions_dict_keys = set(sessions_dict.keys())\n",
" \n",
" intersect_keys = goals_dict_keys.intersection(sessions_dict_keys)\n",
" \n",
" return intersect_keys\n",
"\n",
"\n",
"correct_pages = dict_compare(goals_dict, sessions_dict)\n",
"correct_pages"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'/': 1.4345991561181435,\n",
" '/TMRW_Byte_Cafe.php': 8.148148148148149,\n",
" '/TMRW_FAQs.php': 18.6046511627907,\n",
" '/TMRW_the_team.php': 3.571428571428571}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conv_rate_dict = {}\n",
"\n",
"for page in correct_pages:\n",
" for page2 in goals_dict:\n",
" if page == page2:\n",
" #print(page2)\n",
" \n",
" \n",
" conv_rate = (goals_dict[page]/(sessions_dict[page])*100)\n",
" conv_rate_dict[page2] = conv_rate \n",
" \n",
"\n",
"conv_rate_dict"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[18.6046511627907, 8.148148148148149, 3.571428571428571, 1.4345991561181435]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dictValues = []\n",
"\n",
"# For each key in the dict's Values,\n",
"for x in conv_rate_dict.values():\n",
" # add the key to dictValues\n",
" dictValues.append(x)\n",
"\n",
"# View the dictionaryValues list\n",
"dictValues"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'18.60%'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_value = max(dictValues)\n",
"max_value = \"{0:.2f}%\".format(round(max_value,2))\n",
"max_value"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"'1.43%'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min_value = min(dictValues)\n",
"min_value = \"{0:.2f}%\".format(round(min_value,2))\n",
"min_value"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'/TMRW_FAQs.php'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_page = max(conv_rate_dict, key=conv_rate_dict.get)\n",
"\n",
"if max_page == '/':\n",
" max_page = \"homepage\"\n",
"else:\n",
" max_page = max_page\n",
"\n",
"max_page"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'homepage'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"min_page = min(conv_rate_dict, key=conv_rate_dict.get)\n",
"\n",
"if min_page == '/':\n",
" min_page = \"homepage\"\n",
"else:\n",
" min_page = min_page\n",
"\n",
"min_page\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Print"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Page '/TMRW_FAQs.php' has the highest Conversion Rate - 18.60%.\n",
"Page 'Homepage' has the lowest Conversion Rate - 1.43%.\n"
]
}
],
"source": [
"print('Page \\'%s\\' has the highest Conversion Rate - %s.' % (max_page, max_value))\n",
"\n",
"print('Page \\'%s\\' has the lowest Conversion Rate - %s.' % (min_page.title(), min_value))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g = {'/': 1.4345991561181435,\n",
" '/TMRW_Byte_Cafe.php': 8.148148148148149,\n",
" '/TMRW_FAQs.php': 18.6046511627907,\n",
" '/TMRW_the_team.php': 3.571428571428571}"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"### Visualisation"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import plotly\n",
"\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"\n",
"py.sign_in('m-nudha', 'D8jC4ovl5yHKcvRy0nWk')"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"ename": "PlotlyRequestError",
"evalue": "Hey there! You've hit one of our API request limits. \n\nTo get unlimited API calls(10,000/day), please upgrade to a paid plan. \n\nUPGRADE HERE: https://goo.gl/i7glmM \n\nThanks for using Plotly! Happy Plotting!",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mPlotlyRequestError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-192-90aa7278265f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'life-expectancy-per-GDP-2007'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bubblechart-color'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32mC:\\Users\\Analytics\\Anaconda3\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36miplot\u001b[0;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 135\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 136\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Analytics\\Anaconda3\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data'\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 226\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'layout'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'layout'\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--> 227\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mv1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclientresp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Analytics\\Anaconda3\\lib\\site-packages\\plotly\\api\\v1\\clientresp.py\u001b[0m in \u001b[0;36mclientresp\u001b[0;34m(data, **kwargs)\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'{plotly_domain}/clientresp'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mcfg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'post'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpayload\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[1;31m# Old functionality, just keeping it around.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Analytics\\Anaconda3\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mresponse\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 85\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m \u001b[0mvalidate_response\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 87\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;32mC:\\Users\\Analytics\\Anaconda3\\lib\\site-packages\\plotly\\api\\v1\\utils.py\u001b[0m in \u001b[0;36mvalidate_response\u001b[0;34m(response)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcontent\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'No Content'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyRequestError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus_code\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontent\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[0;31mPlotlyRequestError\u001b[0m: Hey there! You've hit one of our API request limits. \n\nTo get unlimited API calls(10,000/day), please upgrade to a paid plan. \n\nUPGRADE HERE: https://goo.gl/i7glmM \n\nThanks for using Plotly! Happy Plotting!"
]
}
],
"source": [
"trace0 = go.Scatter(\n",
" \n",
" x = [5925, 270, 129, 140, 231, 79], #[85.96, 0.039, 0.019, 0.02, 3.35, 1.146],\n",
" #[85, 24, 22, 5, 1, 1], \n",
" y = [1.4345991561181435, 8.148148148148149, 18.6046511627907, 3.571428571428571, 0, 0],\n",
" text = ['/', '/TMRW_Byte_Cafe.php', '/TMRW_FAQs.php', '/TMRW_the_team.php','/trainstrikes.php','/voteforbyte.php'],\n",
" mode = 'markers',\n",
" marker = dict(\n",
" color = ['rgb(93, 164, 214)', 'rgb(255, 144, 14)',\n",
" 'rgb(44, 160, 101)', 'rgb(255, 65, 54)',\n",
" 'rgb(44, 160, 101)', 'rgb(255, 144, 14)'],\n",
" opacity=[1, 0.9, 0.8, 0.7, 0.6, 0.5],\n",
" size= [85, 24, 22, 5, 1, 1], #[85.96, 0.039, 0.019, 0.02, 3.35, 1.146], # [5925, 270, 129, 140, 231, 79], \n",
" \n",
" )\n",
")\n",
"\n",
"data = [trace0]\n",
"\n",
"\n",
"layout = go.Layout(\n",
" title='Conversions vs Sessions',\n",
" xaxis=dict(\n",
" title='Conversions',\n",
" gridcolor='rgb(255, 255, 255)',\n",
" range=[-10, 500],\n",
" type='log',\n",
" zerolinewidth=1,\n",
" ticklen=5,\n",
" gridwidth=2,\n",
" ),\n",
" yaxis=dict(\n",
" title='Conversion Rate',\n",
" gridcolor='rgb(255, 255, 255)',\n",
" range=[-5, 25],\n",
" zerolinewidth=1,\n",
" ticklen=5,\n",
" gridwidth=2,\n",
" ),\n",
" paper_bgcolor='rgb(243, 243, 243)',\n",
" plot_bgcolor='rgb(243, 243, 243)',\n",
")\n",
"\n",
"\n",
"fig = go.Figure(data=data, layout=layout)\n",
"py.iplot(fig, filename='life-expectancy-per-GDP-2007')\n",
"py.iplot(data, filename='bubblechart-color')"
]
},
{
"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
}
| apache-2.0 |
evandrake20/phys202-project | schelling_project_work.ipynb | 1 | 65508 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Original Prompt"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"from ipythonblocks import BlockGrid as bg\n",
"from IPython.html.widgets import interact, interactive, fixed\n",
"from IPython.html import widgets\n",
"from IPython.display import display\n",
"import timeit"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Creating interacts that allow the user to choose the percent of each color, \n",
"#size of the grid, the individual box size, satisfaction percentage.\n",
"def interacts(size1):\n",
" return size1\n",
"def interacts1(satisfaction_1):\n",
" return satisfaction_1\n",
"def interacts2(orange_1):\n",
" return orange_1\n",
"def interacts3(blue_1, ):\n",
" return blue_1\n",
"def interacts4(block_size1):\n",
" return block_size1\n",
"\n",
"j = interactive(interacts, size1 = (2,12))\n",
"p = interactive(interacts1, satisfaction_1 = (0,1,0.01))\n",
"o = interactive(interacts2, orange_1 = (0,1,0.01))\n",
"b = interactive(interacts3, blue_1 = (0,1,0.01))\n",
"bs = interactive(interacts4, block_size1 = (0,30,0.1))\n",
"display(bs)\n",
"display(j)\n",
"display(b)\n",
"display(o)\n",
"display(p)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[1 0 2 0 1 0 0 2 1 1 2 2]\n",
" [2 1 2 2 2 2 1 2 1 1 2 1]\n",
" [0 1 1 0 1 2 0 1 2 1 1 1]\n",
" [0 0 1 0 2 1 0 0 2 0 1 0]\n",
" [1 0 0 1 0 2 0 0 2 2 1 1]\n",
" [0 0 1 1 1 1 2 1 1 2 0 2]\n",
" [1 1 1 1 1 1 1 1 1 1 1 1]\n",
" [2 2 2 1 0 1 1 0 2 1 1 0]\n",
" [1 0 0 0 2 1 2 0 1 0 1 0]\n",
" [1 0 0 0 1 2 0 1 0 2 0 0]\n",
" [1 0 0 1 1 1 0 1 2 1 1 0]\n",
" [2 1 1 0 0 2 0 2 1 0 1 2]]\n",
"[1 0 2 0 1 0 0 2 1 1 2 2 2 1 2 2 2 2 1 2 1 1 2 1 0 1 1 0 1 2 0 1 2 1 1 1 0\n",
" 0 1 0 2 1 0 0 2 0 1 0 1 0 0 1 0 2 0 0 2 2 1 1 0 0 1 1 1 1 2 1 1 2 0 2 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 2 2 2 1 0 1 1 0 2 1 1 0 1 0 0 0 2 1 2 0 1 0 1 0 1 0 0\n",
" 0 1 2 0 1 0 2 0 0 1 0 0 1 1 1 0 1 2 1 1 0 2 1 1 0 0 2 0 2 1 0 1 2]\n"
]
}
],
"source": [
"#Creates an nxn numpy grid with a chosen percent of 1's and 2's which correspond to orange and blue blocks.\n",
"k = j.result\n",
"size_of_block = bs.result\n",
"satisfaction_percentage = p.result\n",
"blue = b.result\n",
"orange = o.result\n",
"black = 1 - blue - orange\n",
"y = k - 1\n",
"grid = np.random.choice([0,1,2],size=(k,k),p = [black,orange,blue])\n",
"print (grid)\n",
"grid2 = np.hstack(grid)\n",
"print (grid2)\n",
"grid1 = bg(k,k, block_size=size_of_block)\n",
"grid3 = bg(k,k, block_size=size_of_block)\n",
"grid4 = bg(k,k, block_size=size_of_block)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Orange 64\n",
"Blue 34\n",
"Black 46\n"
]
},
{
"data": {
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height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [7, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [7, 4] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [7, 6] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [7, 7] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 8] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [7, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [7, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; 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height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [8, 7] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [8, 9] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [8, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [9, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [9, 1] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 2] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 3] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [9, 5] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [9, 8] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 9] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 10] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [10, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 1] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [10, 2] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [10, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [10, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 8] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [10, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [11, 0] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [11, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [11, 3] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [11, 4] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [11, 5] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [11, 7] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [11, 9] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [11, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [11, 11] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td></tr></tbody></table>"
],
"text/plain": [
"<ipythonblocks.ipythonblocks.BlockGrid at 0x7f72c5df6860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"10 loops, best of 2: 1.53 ms per loop\n"
]
}
],
"source": [
"#Creates an nxn IPythonBlocks grid. \n",
"def make_grid():\n",
" x = 0\n",
" orange2 = 0\n",
" blue2 = 0\n",
" black2 = 0\n",
" for i in grid4: \n",
" if grid2[x]==1:\n",
" orange2 += 1\n",
" i.set_colors(300, 178, 34)\n",
" elif grid2[x]==2:\n",
" blue2 += 1\n",
" i.set_colors(90, 300, 420)\n",
" else:\n",
" black2+=1\n",
" x += 1\n",
" return orange2,blue2,black2\n",
"orange3, blue3, black3 = make_grid()\n",
"print ('Orange', orange3) \n",
"print ('Blue', blue3)\n",
"print ('Black', black3)\n",
"display(grid4)\n",
"%timeit -n10 -r2 make_grid()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Finds the neighbors of the grid and checks to see if the neighbors\n",
"#have the same value and then calculates the satisfaction of that block.\n",
"def satisfaction_percent():\n",
" same_c1 = 0\n",
" same_c2 = 0\n",
" same_c3 = 0\n",
" same_c4 = 0\n",
" same_col_1 = 0\n",
" same_col_2 = 0\n",
" same_row_1 = 0\n",
" same_row_2 = 0\n",
" satisfaction_c1 = 0\n",
" satisfaction_c2 = 0\n",
" satisfaction_c3 = 0\n",
" satisfaction_c4 = 0\n",
" satisfaction_col_1 = 0\n",
" satisfaction_col_2 = 0\n",
" satisfaction_row_1 = 0\n",
" satisfaction_row_2 = 0 \n",
" sat_col_1=[] \n",
" sat_col_2=[] \n",
" sat_row_1=[] \n",
" sat_row_2=[] \n",
" sat_1=[] \n",
" same = 0\n",
" row = 0\n",
" col = 0\n",
" i_5 = 0\n",
" i_6 = 0\n",
" i_7 = 0\n",
" i_8 = 0\n",
" i_9 = 0\n",
" for n in grid:\n",
" if row==0 and col==0:\n",
" if grid[0,0]==0:\n",
" same_c1+=3\n",
" else:\n",
" if grid[0,0]==grid[1,0]:\n",
" same_c1+=1\n",
" if grid[0,0]==grid[0,1]:\n",
" same_c1+=1\n",
" if grid[0,0]==grid[1,1]:\n",
" same_c1+=1\n",
" satisfaction_c1 = same_c1/3\n",
" print ('satisfaction_c1', satisfaction_c1)\n",
" if row==0 and col==y:\n",
" if grid[0,y]==0:\n",
" same_c2+=3\n",
" else:\n",
" if grid[0,y]==grid[0,y-1]:\n",
" same_c2+=1\n",
" if grid[0,y]==grid[1,y-1]:\n",
" same_c2+=1\n",
" if grid[0,y]==grid[1,y]:\n",
" same_c2+=1\n",
" satisfaction_c2 = same_c2/3\n",
" print ('satisfaction_c2',satisfaction_c2)\n",
" if row==y and col==0:\n",
" if grid[y,0]==0:\n",
" same_c3+=3\n",
" else:\n",
" if grid[y,0]==grid[y-1,0]:\n",
" same_c3+=1\n",
" if grid[y,0]==grid[y-1,1]:\n",
" same_c3+=1\n",
" if grid[y,0]==grid[y,1]:\n",
" same_c3+=1\n",
" satisfaction_c3 = same_c3/3\n",
" print ('satisfaction_c3',satisfaction_c3)\n",
" if row==y and col==y:\n",
" if grid[y,y]==0:\n",
" same_c4+=3\n",
" else:\n",
" if grid[y,y]==grid[y-1,y]:\n",
" same_c4+=1\n",
" if grid[y,y]==grid[y,y-1]:\n",
" same_c4+=1\n",
" if grid[y,y]==grid[y-1,y-1]:\n",
" same_c4+=1\n",
" satisfaction_c4 = same_c4/3\n",
" print ('satisfaction_c4', satisfaction_c4)\n",
" if row==0 and col!=(0 or y):\n",
" i_5+=1\n",
" if grid[row,col]==0:\n",
" same_col_1+=5\n",
" else:\n",
" if grid[row,col]==grid[row,col-1]:\n",
" same_col_1+=1\n",
" if grid[row,col]==grid[row,col+1]:\n",
" same_col_1+=1\n",
" if grid[row,col]==grid[row+1,col-1]:\n",
" same_col_1+=1\n",
" if grid[row,col]==grid[row+1,col]:\n",
" same_col_1+=1\n",
" if grid[row,col]==grid[row+1,col+1]:\n",
" same_col_1+=1\n",
" satisfaction_col_1 = same_col_1/5\n",
" sat_col_1.append(satisfaction_col_1)\n",
" true_satisfaction_col_1 = np.hstack(sat_col_1) \n",
" if i_5>y-1:\n",
" sats_col_1 = true_satisfaction_col_1\n",
" print ('sats_col_1',sats_col_1)\n",
"\n",
" elif row==y and col!=(0 or y):\n",
" i_6+=1\n",
" if grid[row,col]==0:\n",
" same_col_2+=5\n",
" else:\n",
" if grid[row,col]==grid[row,col-1]:\n",
" same_col_2+=1\n",
" if grid[row,col]==grid[row,col+1]:\n",
" same_col_2+=1\n",
" if grid[row,col]==grid[row-1,col-1]:\n",
" same_col_2+=1\n",
" if grid[row,col]==grid[row-1,col]:\n",
" same_col_2+=1\n",
" if grid[row,col]==grid[row-1,col+1]:\n",
" same_col_2+=1\n",
" satisfaction_col_2 = same_col_2/5\n",
" sat_col_2.append(satisfaction_col_2)\n",
" true_satisfaction_col_2 = np.hstack(sat_col_2) \n",
" if i_6>y-1:\n",
" sats_col_2 = true_satisfaction_col_2\n",
" print ('sats_col_2',sats_col_2)\n",
" elif row!=(0 or y) and col==0:\n",
" i_7+=1\n",
" if grid[row,col]==0:\n",
" same_row_1+=5\n",
" else:\n",
" if grid[row,col]==grid[row-1,col]:\n",
" same_row_1+=1\n",
" if grid[row,col]==grid[row+1,col]:\n",
" same_row_1+=1\n",
" if grid[row,col]==grid[row-1,col+1]:\n",
" same_row_1+=1\n",
" if grid[row,col]==grid[row,col+1]:\n",
" same_row_1+=1\n",
" if grid[row,col]==grid[row+1,col+1]:\n",
" same_row_1+=1\n",
" satisfaction_row_1 = same_row_1/5\n",
" sat_row_1.append(satisfaction_row_1)\n",
" true_satisfaction_row_1 = np.hstack(sat_row_1) \n",
" if i_7>y-1:\n",
" sats_row_1 = true_satisfaction_row_1\n",
" print ('sats_row_1',sats_row_1)\n",
" elif row!=(0 or y) and col==y:\n",
" i_8+=1\n",
" if grid[row,col]==0:\n",
" same_row_2+=5\n",
" else:\n",
" if grid[row,col]==grid[row-1,col]:\n",
" same_row_2+=1\n",
" if grid[row,col]==grid[row+1,col]:\n",
" same_row_2+=1\n",
" if grid[row,col]==grid[row-1,col-1]:\n",
" same_row_2+=1\n",
" if grid[row,col]==grid[row,col-1]:\n",
" same_row_2+=1\n",
" if grid[row,col]==grid[row+1,col-1]:\n",
" same_row_2+=1\n",
" satisfaction_row_2 = same_row_2/5\n",
" sat_row_2.append(satisfaction_row_2)\n",
" true_satisfaction_row_2 = np.hstack(sat_row_2) \n",
" if i_8>y-1:\n",
" sats_row_2 = true_satisfaction_row_2\n",
" print ('sats_row_2',sats_row_2)\n",
" else:\n",
" i_9+=1\n",
" if grid[row,col]==0:\n",
" same+=8\n",
" else:\n",
" if grid[row,col]==grid[row-1,col]:\n",
" same+=1\n",
" if grid[row,col]==grid[row,col-1]:\n",
" same+=1\n",
" if grid[row,col]==grid[row-1,col-1]:\n",
" same+=1\n",
" if grid[row,col]==grid[row+1,col]:\n",
" same+=1\n",
" if grid[row,col]==grid[row,col+1]:\n",
" same+=1\n",
" if grid[row,col]==grid[row+1,col+1]:\n",
" same+=1\n",
" if grid[row,col]==grid[row-1,col+1]:\n",
" same+=1\n",
" if grid[row,col]==grid[row+1,col-1]:\n",
" same+=1\n",
" satisfaction = same/8\n",
" sat_1.append(satisfaction)\n",
" true_satisfaction_1 = np.hstack(sat_1) \n",
" if i_9>y-2:\n",
" sats_1 = true_satisfaction_1\n",
" print ('sats_1',sats_1)\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" \n",
" sat_c_1_1 = []\n",
" sat_c_2_1 = []\n",
" sat_c_3_1 = []\n",
" sat_c_4_1 = []\n",
" sat_col_1_1 = []\n",
" sat_col_2_1 = []\n",
" sat_row_1_1 = []\n",
" sat_row_2_1 = []\n",
" sat_1_1 = []\n",
" m_1=0\n",
" m_2=0\n",
" m_3=0\n",
" m_4=0\n",
" m_5=0\n",
" m_6=0\n",
" m_7=0\n",
" m_8=0\n",
" m_9=0\n",
" \n",
" for n in sat_col_1:\n",
" if m_5==0:\n",
" new_value_5 = sat_col_1[0]\n",
" sat_col_1_1.append(new_value_5)\n",
" s_col_1 = np.hstack(sat_col_1_1)\n",
" m_5+=1\n",
" else:\n",
" new_value_5 = sat_col_1[m_5] - sat_col_1[m_5-1]\n",
" sat_col_1_1.append(new_value_5)\n",
" m_5+=1 \n",
" \n",
" for n in sat_col_2:\n",
" if m_6==0:\n",
" new_value_6 = sat_col_2[0]\n",
" sat_col_2_1.append(new_value_6)\n",
" s_col_2 = np.hstack(sat_col_2_1)\n",
" m_6+=1 \n",
"\n",
"\n",
" else:\n",
" new_value_6 = sat_col_2[m_6] - sat_col_2[m_6-1]\n",
" sat_col_2_1.append(new_value_6)\n",
" m_6+=1 \n",
" \n",
" for n in sat_row_1:\n",
" if m_7==0:\n",
" new_value_7 = sat_row_1[0]\n",
" sat_row_1_1.append(new_value_7)\n",
" sat_row_1 = np.hstack(sat_row_1_1)\n",
" m_7+=1\n",
" else:\n",
" new_value_7 = sat_row_1[m_7] - sat_row_1[m_7-1]\n",
" sat_row_1_1.append(new_value_7)\n",
" m_7+=1 \n",
" for n in sat_row_2:\n",
" if m_8==0:\n",
" new_value_8 = sat_row_2[0]\n",
" sat_row_2_1.append(new_value_8)\n",
" s_row_2 = np.hstack(sat_row_2_1)\n",
" m_8+=1\n",
" else:\n",
" new_value_8 = sat_row_2[m_8] - sat_row_2[m_8-1]\n",
" sat_row_2_1.append(new_value_8)\n",
" m_8+=1 \n",
" \n",
" for n in sat_1:\n",
" if m_9==0:\n",
" new_value_9 = sat_1[0]\n",
" sat_1_1.append(new_value_9)\n",
" sat_1 = np.hstack(sat_1_1)\n",
" m_9+=1\n",
" else:\n",
" new_value_9 = sat_1[m_9] - sat_1[m_9-1]\n",
" sat_1_1.append(new_value_9)\n",
" m_9+=1 \n",
" \n",
" \n",
" s_col_1 = np.hstack(sat_col_1_1)\n",
" \n",
" print (\"satisfaction of 1st row\", s_col_1)"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"satisfaction_c1 0.3333333333333333\n",
"satisfaction of 1st row [ 0.4 1. 0.4 1. 0. 1. 1. 0.2 0.6 0.6]\n",
"1 loops, best of 1: 1.32 ms per loop\n"
]
}
],
"source": [
"#Times how long it takes to find the satisfaction of each block\n",
"%timeit -n1 -r1 satisfaction_percent()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#If the block's satisfaction is below the satisfaction percentage chosen by the user\n",
"#then the blocks moves to another position in the nxn grid.\n",
"def move_unsatisfied():\n",
" row = 0\n",
" col = 0\n",
" ii = 0\n",
" for n in grid:\n",
" if sat_c_1_1 < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[0,0]==1:\n",
" if grid[row,col]==0: \n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[0,0]==2:\n",
" if grid[row,col]==0: \n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_c_2_1 < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[0,y]==1:\n",
" if grid[row,col]==0: \n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[0,y]==2:\n",
" if grid[row,col]==0: \n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_c_3_1 < satisfaction_percentage: \n",
" n.set_color(0,0,0)\n",
" if grid[y,0]==1:\n",
" if grid[row,col]==0: \n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[y,0]==2:\n",
" if grid[row,col]==0: \n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_c_4_1 < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[y,y]==1:\n",
" if grid[row,col]==0: \n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[y,y]==2:\n",
" if grid[row,col]==0: \n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_col_1_1[ii] < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[row,col]==1:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[row,col]==2:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_col_2_1[ii] < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[row,col]==1:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[row,col]==2:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_row_1_1[ii] < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[row,col]==1:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[row,col]==2:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_row_2_1[ii] < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[row,col]==1:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[row,col]==2:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if sat_1_1[ii] < satisfaction_percentage:\n",
" n.set_color(0,0,0)\n",
" if grid[row,col]==1:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(90, 300, 420)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" if grid[row,col]==2:\n",
" if grid[row+ii,col+ii]==0:\n",
" n.set_colors(300, 178, 34)\n",
" else:\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" ii+=1\n",
" col+=1\n",
" if col>=10:\n",
" col = col - 10\n",
" row+=1\n",
" return grid\n",
" %timeit -n1 -r1 move_unsatisfied()"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Orange 64\n",
"Blue 34\n",
"Black 46\n"
]
},
{
"data": {
"text/html": [
"<style type=\"text/css\">table.blockgrid {border: none;} .blockgrid tr {border: none;} .blockgrid td {padding: 0px;} #blocks9d366fd8-8277-4970-8136-f63296e17ab0 td {border: 1px solid white;}</style><table id=\"blocks9d366fd8-8277-4970-8136-f63296e17ab0\" class=\"blockgrid\"><tbody><tr><td title=\"Index: [0, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 6] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [0, 11] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td></tr><tr><td title=\"Index: [1, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 6] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [1, 11] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td></tr><tr><td title=\"Index: [2, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 6] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [2, 11] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td></tr><tr><td title=\"Index: [3, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 6] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [3, 11] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td></tr><tr><td title=\"Index: [4, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 4] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 5] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 6] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 7] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 8] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 9] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 10] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [4, 11] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td></tr><tr><td title=\"Index: [5, 0] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [5, 1] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [5, 2] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [5, 3] Color: (255, 178, 34)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(255, 178, 34);\"></td><td title=\"Index: [5, 4] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 5] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 7] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 8] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 9] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 10] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [5, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [6, 0] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 1] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 2] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 3] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 4] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 5] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 7] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 8] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 9] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 10] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [6, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [7, 0] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 1] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 2] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 3] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 4] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 5] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 7] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 8] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 9] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 10] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [7, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [8, 0] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 1] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 2] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 3] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 4] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 5] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 6] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 7] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 8] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 9] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 10] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [8, 11] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td></tr><tr><td title=\"Index: [9, 0] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 1] Color: (0, 0, 0)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(0, 0, 0);\"></td><td title=\"Index: [9, 2] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 3] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 4] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 5] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 6] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 7] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 8] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 9] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 10] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [9, 11] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td></tr><tr><td title=\"Index: [10, 0] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 1] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 2] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 3] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 4] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 5] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 6] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 7] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 8] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 9] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 10] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [10, 11] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td></tr><tr><td title=\"Index: [11, 0] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 1] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 2] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 3] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 4] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 5] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 6] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 7] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 8] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 9] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 10] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td><td title=\"Index: [11, 11] Color: (90, 255, 255)\" style=\"width: 19.8px; height: 19.8px;background-color: rgb(90, 255, 255);\"></td></tr></tbody></table>"
],
"text/plain": [
"<ipythonblocks.ipythonblocks.BlockGrid at 0x7f72c42b4e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Displays the final grid after all the blocks are at their max satisfaction.\n",
"def final_grid():\n",
" x = 0\n",
" orange4 = 0\n",
" blue4 = 0\n",
" black4 = 0\n",
" for i in grid3:\n",
" if orange3 > orange4:\n",
" i.set_colors(300, 178, 34)\n",
" orange4+=1\n",
" elif black3 > black4: \n",
" i.set_colors(0,0,0)\n",
" black4+=1\n",
" else:\n",
" i.set_colors(90, 300, 420)\n",
" print ('Orange', orange3)\n",
" print ('Blue', blue3)\n",
" print ('Black', black3)\n",
" display(grid3)\n",
" \n",
" \n",
"final_grid()"
]
},
{
"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 |
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